Comparative phylogeography and population genetics of ... · Conservation Genetics Resources, 5, 533-535. (Appendix D) Chapter 3 Nistelberger HM, Byrne M, Coates DJ and Roberts JD

1

Comparative phylogeography and

population genetics of three Banded

Iron Formation endemics

Heidi M. Nistelberger

B. Biol. Sc. (Hons)

Supervisors: Prof. J. Dale Roberts

Dr Margaret Byrne

Dr David Coates

This thesis is presented for the degree of Doctor of Philosophy of The University of Western

Australia, School of Animal Biology 2014

2014

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3

Thesis Declaration

This thesis contains published work and work prepared for publication, some of which has

been co-authored. The bibliographic details of the works and where they appear in the thesis

are set out below.

Chapter 2

Nistelberger HM, Byrne M, Coates DJ and Roberts JD (2014) Strong phylogeographic structure

in a millipede indicates Pleistocene vicariance between populations on banded iron formations

in semi-arid Australia. PLoS ONE, 9, e93038.

Nistelberger HM, Byrne M, Roberts JD and Coates DJ (2013) Isolation and characterisation of

11 microsatellite loci from the Western Australian Spirostreptid millipede Atelomastix

bamfordi. Conservation Genetics Resources, 5, 533-535. (Appendix D)

Chapter 3

Nistelberger HM, Byrne M, Coates DJ and Roberts JD (2014) Terrestrial islands in an ancient

landscape: Evidence of both historical isolation and connectivity of populations of Grevillea

georgeana, a banded iron formation endemic. Submitted: Journal of Biogeography.

Nistelberger HM, Coates DJ, Byrne M and Roberts, JD (2013) Isolation and characterisation of

11 microsatellite loci in the short-range endemic shrub Grevillea georgeana McGill

(Proteaceae). Conservation Genetics Resources, 6, 221-222. (Appendix D)

Chapter 4

Nistelberger HM, Byrne M, Coates DJ and Roberts JD (2014) Patterns of isolation and

connectivity in terrestrial island populations of Banksia arborea (Proteaceae). Submitted:

Biological Journal of the Linnean Society.

Nistelberger HM, Roberts JD, Coates DJ and Byrne M (2013) Isolation and characterisation of

14 microsatellite loci from a short-range endemic, Western Australian tree, Banksia arborea

(C.A. Gardner). Conservation Genetics Resources, 5, 1143-1145. (Appendix D)

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All published work was primarily conducted by HMN (approx. 90%). JDR, MB and DC assisted

with project design, provided analysis advice and editing. One more paper, Chapter 5 -

Conservation of the Yilgarn BIFs, is in preparation for publication.

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Summary

The Yilgarn Banded Iron Formations (BIFs) of south-western Australia are terrestrial islands

embedded within the largest remaining stand of Mediterranean woodland on Earth. These

ancient geological features are centres of high species richness, turnover and endemism -

characteristics attributed to the diversity of niche habitats provided by their topographic

complexity as well as historical factors, including an absence of glaciation since the Permian

(250 million years ago (Ma)). The BIFs to the south west of the Yilgarn province, along the

boundary of the South Western Australian Floristic Region, are particularly notable for their

high endemism and many species here occur across several formations, reminiscent of an

oceanic island archipelago. It is not known to what extent these disjunct BIF populations have

been historically connected by population expansion and contraction, or by long distance

dispersal events, or whether they have been isolated for long periods of time as found in taxa

endemic to granitic terrestrial islands. Regional BIF endemics provide an excellent opportunity

to assess population genetic connectivity, or lack thereof, using phylogeographic and

population genetic approaches.

I selected three taxa for phylogeographic and population genetic analysis that a) appeared to

show limited dispersal capability, b) occurred across five or more Yilgarn BIFs within the same

region and did not occur in the intervening landscape and c) were unrelated, so any

congruence in phylogeographic patterns between the taxa could not be ascribed to

phylogenetic relatedness. These taxa are:

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1 ) Atelomastix bamfordi - a spirostreptid millipede that is restricted to pockets of moist

habitat provided by the shady gullies and areas of dense leaf litter, present on five of the

Yilgarn BIFs. Unlike its congeners found in the wetter south-western corner of the continent, A.

bamfordi was often difficult to find.

2 ) Grevillea georgeana - a proteaceous perennial shrub that is restricted to banded ironstone

and was sampled from seven Yilgarn BIFs for the purposes of this study. The showy, red

flowers of this attractive plant form a dominant food source for several species of honeyeater

pollinators. The species can be common where it occurs but on some BIFs persists only as very

small populations.

3 ) Banksia arborea - formerly known as Dryandra arborea and commonly known as the Yilgarn

tree, is a conspicuous element of the BIF flora and was sampled from eight Yilgarn BIFs. This

insect and bird pollinated species has a broader habitat requirement than either A. bamfordi

or G. georgeana and occurs anywhere from the exposed summits of outcrops to the shale and

sandy slopes lower down.

I used a combination of nuclear and cytoplasmic markers to investigate patterns of genetic

diversity and structure in these BIF endemics. The three species showed different patterns of

isolation and connectivity amongst the BIF populations, but all highlighted the Pleistocene as

an important period for lineage divergence in this landscape.

Atelomastix bamfordi: Strong phylogeographic structure indicated that the five BIF populations

have experienced long-term isolation following their separation during the Pleistocene. The

timing coincides with increasing aridity in this landscape, a process which likely marooned

millipedes in the remaining moist pockets of habitat provided by these topographical

formations, where they persist today.

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Grevillea georgeana: The seven BIF populations of this species showed more complex

phylogeographic patterning. Four populations were behaving as isolated islands, with genetic

drift driving genetic differentiation, similar to patterns in A. bamfordi. In contrast three

populations showed evidence of more recent connectivity, possibly via inaccessible, extant

populations on low lying banded ironstone outcrops between sampled populations. Pollen

movement was restricted, although a pattern of isolation by distance across the landscape

suggests some level of connectivity between neighbouring BIFs. Again, lineage and haplotype

divergence occurred during the Pleistocene.

Banksia arborea: The eight BIF populations of this species showed complex phylogeographic

patterns with genetic divergence of some populations, reflecting isolation and genetic drift but

an absence of differentiation amongst others, suggesting more recent connectivity. Pollen

dispersal was limited and did not appear to occur between neighbouring BIF populations.

Divergence of the differentiated populations occurred during the Pleistocene.

Several common patterns of diversity and divergence emerged from the data that has allowed

identification of BIFs that may warrant conservation priority. Large, topographically complex

BIFs were found, in general, to act as reservoirs of genetic diversity, whilst small and/or

spatially isolated populations were often the most genetically divergent. These findings have

significant implications for the management of these BIFs and potentially, for other terrestrial

islands throughout the world.

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9

Table of Contents

Summary 5

Table of Contents 9

List of Tables 13

List of Figures 14

Acknowledgements 15

Chapter 1: Introduction 19

1.1 Comparative phylogeography 19

1.1.1 Genetic markers for phylogeography and population genetic analysis 20

1.1.2 Molecular clocks 21

1.2 Genetic studies for conservation outcomes 22

1.3 Island biogeography and terrestrial islands 22

1.4 Phylogeography and population genetics of terrestrial island endemics – a global

perspective 23

1.5 Banded Iron Formations 24

1.6 The Yilgarn Banded Iron Formations 24

1.7 History of the Yilgarn 25

1.8 Yilgarn BIF endemics 26

1.9 Conservation of the Yilgarn BIFs 26

1.10 Study species and rationale 27

1.11 Aims and objectives 29

Chapter 2: Strong phylogeographic structure in a millipede indicates Pleistocene vicariance

between populations on banded iron formations in semi-arid Australia

2.1 Abstract 33

2.2 Introduction 34

2.3 Materials and Methods 39

2.3.1 Sampling and DNA extraction 39

2.3.2 Mitochondrial DNA amplification and sequencing 40

2.3.3 Mitochondrial DNA analysis 41

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2.3.4 Nuclear microsatellite genotyping and data analysis 43

2.4 Results 45

2.4.1 Genetic variation 45

2.4.1.1 Mitochondrial DNA data 45

2.4.1.2 Nuclear microsatellite data 46

2.4.2 Phylogeographic structure 49

2.4.3 Population genetic structure 50

2.5 Discussion 53

2.5.1 Genetic differentiation of BIF populations 53

2.5.1.1 Spatial patterns 53

2.5.1.2 Temporal patterns 55

2.5.2 BIF population dynamics-historical and contemporary patterns 55

2.5.2.1 Historical patterns 55

2.5.2.2 Contemporary patterns 56

2.5.3 Conservation implications 57

2.5.4 Conclusion 58

Chapter 3: Terrestrial islands in an ancient landscape: Evidence of both historical isolation

and connectivity of populations of Grevillea georgeana, a Banded Iron Formation endemic

3.1 Abstract 61

3.2 Introduction 62



3.3.2 cpDNA sequencing 66

3.3.3 Fragment analysis 66

3.3.4 cpDNA data analysis 66

3.3.5 Microsatellite data analysis 68

3.4 Results 69

3.4.1 cpDNA diversity 69

3.4.2 Microsatellite data 70

3.5 Discussion 79

3.5.1 Genetic differentiation among populations 80

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3.5.2 Genetic variation within populations 82


Chapter 4: Patterns of genetic isolation and connectivity in terrestrial island populations of

Banksia arborea (Proteaceae)

4.1 Abstract 87

4.2 Introduction 88



4.3.2 cpDNA amplification 91

4.3.3 Microsatellite amplification 92

4.3.4 cpDNA analysis 92

4.3.5 Microsatellite analysis 93

4.4 Results 95

4.4.1 cpDNA variation 95

4.4.2 Microsatellite variation 97

4.5 Discussion 105

4.5.1 Genetic differentiation among BIF populations 105

4.5.2 Genetic variation and differentiation within a BIF population 108


Chapter 5: Hotspots of genetic diversity identify terrestrial islands of conservation value in a

semi-arid Australian landscape

5.1 Abstract 113

5.2 Introduction 114


5.3.1 Study species and population sampling 117

5.3.1.1 Atelomastix bamfordi 118

5.3.1.2 Grevillea georgeana 120

5.3.1.3 Banksia arborea 120

5.3.2 Diversity and differentiation analysis 120

5.4 Results 121

5.4.1 Contribution to population diversity (CSR an CS) 121

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5.4.2 Contribution to divergence (CDR and CD) 122




5.4.3 Contribution to overall diversity (CTR and CT) 123




5.5 Discussion 128

5.5.1 Large, topographically complex BIFs support populations with higher genetic

diversity 128

5.5.2 Small and spatially isolated BIFs harbour populations with greater genetic

divergence 130

5.5.3 Prioritisation of BIFs for conservation 130

Concluding remarks 135

References 139

Appendix

Appendix A 157

Appendix B 160

Appendix C 162

Appendix D 163

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List of Tables

Chapter 2:

2.1 Atelomastix bamfordi mitochondrial and nuclear data diversity statistics 47

2.2 Population pairwise nucleotide divergence estimates of A. bamfordi 48

Chapter 3:

3.1 Grevillea georgeana chloroplast data diversity statistics 71

3.2 Population pairwise nucleotide divergence estimates of G. georgeana 71

3.3 Microsatellite genetic diversity estimates for nine G. georgeana populations 77

3.4 Population pairwise FST values for G. georgeana 78

Chapter 4:

4.1 Banksia arborea chloroplast data diversity statistics 99

4.2 Population pairwise nucleotide divergence estimates of Banksia arborea 99

4.3 Microsatellite genetic diversity estimates for 10 B. arborea populations 100

4.4 Population pairwise FST and DEST values for B. arborea 103

4.5 Population pairwise indirect gene flow estimates of B. arborea 104

Chapter 5:

5.1 Details of the 10 Yilgarn BIFs sampled for this study 119

5.2a Contributions of BIF populations to genetic diversity (cytoplasmic DNA data) 127

5.2b Contributions of BIF populations to genetic diversity (nuclear microsatellite data) 127

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List of Figures

Chapter 1:

1.1 Map of the 10 Yilgarn BIFs sampled for this study 29

Chapter 2:

2.1 Map of the five BIF populations of Atelomastix bamfordi sampled, coloured according to

mtDNA genetic clade 49

2.2 Bayesian cladogram of A. bamfordi individuals based on mtDNA data 51

2.3 Bayesian population assignment of A. bamfordi individuals (microsatellite data) 52

2.4 Unrooted neighbour joining tree of Cavalli Sforza chord distances between the five BIF

populations of A. bamfordi 53

Chapter 3:

3.1 Map of Grevillea georgeana populations and cpDNA haplotypes identified 72

3.2 Bayesian tree of G. georgeana haplotypes based on cpDNA data 73

3.3 Maximum parsimony network for all G. georgeana haplotypes identified 74

3.4 Map of G. georgeana populations with pie charts showing proportion of assignment of

individuals to genetic clusters identified based on microsatellite data 75

3.5 Principal coordinates analysis of G. georgeana populations 79

Chapter 4:

4.1 Map of Banksia arborea populations and cpDNA haplotypes identified 96

4.2 Maximum parsimony network of all B. arborea haplotypes identified 97

4.3 Bayesian cladogram of B. arborea haplotypes based on cpDNA data 98

4.4 Principal coordinates analysis of B. arborea populations 102

Chapter 5:

5.1 Map of the 10 Yilgarn BIFs sampled for this study 118

5.2 Contributions of Atelomastix bamfordi populations to total genetic diversity 124

5.3 Contributions of Grevillea georgeana populations to total genetic diversity 125

5.4 Contributions of Banksia arborea populations to total genetic diversity 126

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Acknowledgements

You know how in some theses the student acknowledges the supervisors, but you can tell it’s out of

duty and not because they really mean it? This is not one of those theses. The last four years have been

a wonderful experience for me and I have three awesome supervisors to thank for it.

Dale, I couldn’t have asked for a better primary supervisor. You never suffocated or hovered over me,

neither did I ever feel abandoned or without support. Even when you moved to Albany you were always

a phone call away…never too busy to discuss my ‘conceptual crises’ and run through them with me on

the whiteboard. I loved Friday student coffee sessions, chatting about life, science and even occasionally

our research. Your dedication towards me (and all of your students) went above and beyond what I

could ever have hoped for - I will always remember this as an amazing period of my life…and I have you

to thank for it.

Margaret, you have been my biggest critic and therefore biggest supporter for years now. You

continually push me to improve my understanding in all areas of science and your unwavering

commitment to me, despite being one of the busiest women on earth, leaves me grateful beyond

words. That you managed to return writing to me within the week despite all your other commitments

was always noticed and shows how seriously you take on the role of supervisor. Thankyou thankyou

thankyou.

Dave, you were always the person I turned to when I experienced occasional episodes of self-doubt.

That you could recognise when I was struggling and would make the effort to ring me up to make me

laugh, and make sure I was confident and on-track shows how much you cared…and went a long way

toward helping me through the really busy times. Whenever I need to be reminded of why I’m working

in science I go and park myself in your office. Your passion and enthusiasm for research are infectious

and always re-ignite my love of science. I look forward to many more discussions over a beer in the

future. Thankyou for all your support.

My family. Mum, Dad and Alan, you’ve always been my biggest supporters, encouraging me along the

way, getting as excited about my results as I was. Mum, I’ll never forget the day you pointed out that

although you weren’t sure what the term meant you thought I had spelled “heterozygote”

incorrectly…and you were right, of course. You have all inspired me throughout my whole life and

continue to do so. I wouldn’t be where I am today without your unwavering love and support. I’m the

luckiest girl in the world to be able to call you all my own.

Brad, you came into my life during what some describe as the most stressful times of one’s life. Yet

straight away you jumped right in, reassuring me when I panicked and broke down in tears because I

realised I only had 6 months left and didn’t want it all to end! You never complained about my lack of

time, money and occasionally, ability to talk of anything but my Ph D . You have been my rock this last

year and I couldn’t have finished in the semi-sane state I did without your loving influence and support.

Thanks Handsome.

Uni crew. Ahh I’ve had such fun here. This place is buzzing with interesting, passionate people, so many

of whom have made my time here. The zoology building feels like an extension of my home and there

are too many people to thank to list here. In particular I’m going to miss the Friday sessions at the pub,

summer drinks on Kath and Emile’s balcony. Renee, we’ve had some ridiculous adventures tapering to

the more sensible ‘swim sessions’ as I got ready to submit. Sam, Steve and Georgina it’s been a blast to

share an office with you all, always ready to discuss the finer horrors of ms word, or to solve the world’s

problems over a packet of stale ginger nuts. Danilo, we were the lone phylogeographers, thank

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goodness we had each other to support, to bitch about analyses that took weeks on end (only to crash

days before completion due to power outages). Thanks for all your feedback and support.

DPaW ladies. My other home away from home. Bron and Shell you have always been so supportive of

me, both in and out of the lab. I love that we can laugh over the minor disasters… the smiley gels and

snotty DNA extracts and enjoy a good session of tip packing when there was some need of therapy time.

You made my time of crazy lab work bearable and even enjoyable, thankyou. All the other ladies down

the corridor, you all contributed to making my visits something I looked forward to and in particular

Kym, Sarzie, Binksie, Hevs, Donna and Tan L …you made me laugh till my tummy hurt. I’ll always

remember hoopla mornings with fondness.

Emily, thanks for all the amazing help in the field, your keen ‘milli’ eyes, endless enthusiasm and sense

of humour made our long days of fossicking a blast. I absolutely could not have done this project

without you.

My school gals. Even though it was a world removed from yours you still supported me these last 4

years. I’m so lucky to have friends that span the length of my life. I always look forward to our coffee

catch ups where I no doubt bore you senseless, gabbing on about my research. Thanks for listening, now

and always.

Outdoor crew. For introducing me to a world away from academia, thanks Bruno, Steve, Jerome, Robin

and all the UWA ODC crew for keeping me sane on the weekends and my hands nicely calloused for all

those climbing adventures.

Funding. I’d like to thank Cliffs Natural Resources for their generous contribution. This research would

not have been possible without their support. The school of Animal Biology (UWA), for annual funding

and the Holsworth Wildlife Research Grant, for making the microsatellite libraries possible. Finally,

Department of Parks and Wildlife for help with lab consumables and other unforeseen costs.

Specific chapters

Chapter 2: Thanks to Mark Harvey, Mike Rix (Western Australian Museum), Roy Teale (Biota) and Karl

Brennan (DPaW) for millipede discussions and specimens. Thanks to Janine Rix, Maxine Lovegrove and

Yvette Hitchen for extraction advice. Nicole Harry, Jeremy Sheperdson (Cliffs) and Scott Reiffer for

arranging site access, fuel and accommodation when required and Emily Ager for fieldwork.

Chapter 3: Thanks to Kym Ottewell for advice regarding pcr multiplexing…saved me many swears and

Neil Gibson (DPaW) for BIF chats and Emily Ager for fieldwork.

Chapter 4: Thanks to Marcel Cardillo for providing Banksia node date estimates and to Neil Gibson for all

the helpful BIF discussions and Emily Ager for fieldwork.

How else could I end but to say thanks to the Yilgarn BIFs, what an amazing place…

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A tale of three species,

one landscape

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Chapter 1

Introduction

1.1 Comparative phylogeography

Phylogeography ties together the disciplines of biogeography, cladistics and population

genetics. By definition, it is the study of historical phylogenetic components that result in the

spatial distributions of gene lineages (Avise, 2000). In comparative phylogeography, these

lineages are compared in order to infer common historical events that may have led to

unrelated taxa sharing similar distributions (Carstens et al., 2005). Comparative

phylogeography studies generally focus on two processes affecting species distributions and

division: vicariance and dispersal and their role in speciation (Cox & Moore, 2000; Raxworthy

et al., 2002; Yoder & Nowak, 2006; Gamble et al., 2008).

By studying the historical patterns present in a suite of co-occurring taxa, we can infer a great

deal about the important evolutionary processes acting in landscapes. For example, in a study

of five widespread, south-west Australian tree species, three were identified as showing

concordant patterns of lineage divergence, the timing of which suggested a generalised

response to climatic fluctuations during the Pleistocene (Byrne, 2007). Another study of two

plant and four animal species from the Pacific Northwest, U.S.A., was used to test three

hypotheses, one of vicariance and two of dispersal, that would explain disjunctions in the

mesic forest ecosystem of the region. Three amphibian lineages indicated a generalised

response to an ancient vicariance event, whilst the other three species showed recent

dispersal in a northern direction, suggesting that a combination of historical events had led to

the congruent, disjunct distributions of these species (Carstens et al., 2005). Comparative

studies may also be used to uncover regions within a landscape that commonly exhibit high

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genetic diversity or distinctiveness, characteristics that may warrant conservation of these

areas (Moritz & Faith, 1998).

1.1.1 Genetic markers for phylogeographic and population genetic analysis

Mitochondrial DNA (mtDNA) and chloroplast DNA (cpDNA) sequences have been popular

markers for inferring phylogeographic history because of their beneficial characteristics

including a lack of recombination, effective haploidy (Harris & Ingram, 1991; Zhang & Hewitt,

1996; Avise, 2000) and a maternal mode of inheritance, allowing observation of lineages not

confounded by recombination during sexual reproduction (but see Hipkins et al., 1994;

Testolin & Cipriani, 1997; McCauley et al., 2007 for exceptions in plants, and Kondo et al.,

1990; Gyllensten et al., 1991 for exceptions in animals). Maternal inheritance of cpDNA is

particularly useful in population studies of plants as contrasting cpDNA polymorphism with

biparentally inherited marker polymorphism can allow evaluation of the relative influences of

seed and pollen on gene flow (McCauley, 1995). Several criticisms have been raised regarding

the exclusive use of organelle DNA markers in phylogeographic studies. Their asexual mode of

inheritance effectively means that these genomes can be thought of as a single locus (Zhang &

Hewitt, 2003) where all character states are linked (Avise, 2000), and therefore, potentially

subject to selective sweeps (Avise et al., 1987; Zhang & Hewitt, 1996; Gerber et al., 2001). The

majority of mutations in mtDNA have been observed in silent positions of protein-coding

genes and in the non-transcribed D-loop region. These positions are not expected to be linked

to organismal fitness and so are considered useful for phylogeography (Brown, 1983; Avise et

al., 1987), likewise mutations in the cpDNA typically occur in non-coding regions (Palmer et al.,

1988; Taberlet et al., 1991). Another potential problem with mtDNA and cpDNA genealogies is

that they allow only one, matrilineal perspective on evolutionary history, and, as the structure

and history of any one particular gene or DNA sequence is likely to differ from that of the

population or species, it is necessary to take a multi-locus approach to phylogeographic studies

(Hare, 2001; Zhang & Hewitt, 2003; Brito & Edwards, 2009). The use of mtDNA and cpDNA

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markers needs to be combined with nuclear DNA (nrDNA) markers where possible.

Unfortunately, sequence markers commonly available for nuclear genes may lack sufficient

variation to be useful at the population level (Zhang & Hewitt, 2003). Other, more rapidly-

evolving nuclear markers such as microsatellites, can provide a useful alternative and have

been used in conjunction with traditional mtDNA and cpDNA markers in many studies (Bech et

al., 2009; Beatty & Provan, 2011; Keir et al., 2011; Sakaguchi et al., 2012). The high variability

of these markers and their co-dominant, Mendelian inheritance patterns, provides important

population level information on genetic diversity and structure that can be used to detect

more contemporary evolutionary processes influencing populations, such as recent

bottlenecks (Luikart et al., 1998) and contemporary gene flow (Sork et al., 1999). Using both

rapidly evolving markers and the slower mtDNA and cpDNA markers is advantageous in

situations where phylogenetic splits may be relatively recent, which may occur in population-

level studies. In these cases, a lack of variation at the cytoplasmic level may be compensated

for by the highly informative microsatellite markers. This is particularly important in plants,

where cpDNA variation may often be very low (Keir et al., 2011).

1.1.2 Molecular clocks

Molecular clocks have become an important component of phylogeographic analysis, allowing

for the timing of divergence of major clades and lineages at the intra and interspecific level to

be estimated. The molecular clock hypothesis is based on the finding that DNA sequences

accrue mutations at a relatively constant rate (Margoliash, 1963; Zuckerkandl & Pauling, 1965).

In some species, this rate can be determined by calibrating a phylogenetic tree with a known

divergence date from fossil or geological data (Weir & Schluter, 2008). When this information

is not available, a generalised mutation rate inferred from other, closely related species can be

used (Byrne, 2007; Edwards et al., 2007). Ideal phylogeographic markers possess a rate of

evolution fast enough to reveal adequate variation but not so fast as to incur the detrimental

effects of homoplasy and saturation (Goldman, 1998). This has commonly involved the use of

mtDNA sequence markers such as CO1 in animals (Hebert et al., 2003) and non-coding

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intergenic cpDNA markers in plants (Shaw et al., 2007) because of their optimal rates of

mutation at intra-specific scales.

1.2 Genetic studies for conservation outcomes

Genetic analysis has become an increasingly important component of species’ management

plans (Hedrick, 2001; Allendorf et al., 2013). In the USA, agencies administering the

Endangered Species Act are now turning to genetic data to target populations for conservation

(Kelly, 2010). Preservation of genetic diversity is at the forefront of conservation decisions, as

higher diversity levels are thought to enable continual adaptation to changing environmental

conditions (Soulé, 1980). The degree of genetic divergence present within a species is also

important. Populations that have been isolated for extensive periods of time may show genetic

divergence that may result in future allopatric speciation if isolation is prolonged (Dobzhansky,

1970; Mayr, 1970). Conservation efforts will ideally encourage preservation of populations or

regions of both high genetic diversity, as well as genetic divergence (Faith, 1992; Petit et al.,

1998).

1.3 Island biogeography and terrestrial islands

Islands provide a unique context for studying the evolutionary processes that drive species

distribution. They are discrete and quantifiable entities that provide a natural laboratory from

which to examine processes of speciation, and patterns of species colonisation, persistence

and extinction (Whittaker, 1998). Terrestrial islands, or habitat islands, are discrete habitat

patches embedded in the surrounding landscape, and include anything from large and

conspicuous geological formations, such as mountain tops, to dung piles in a grassland

landscape (Whittaker, 1998). Large and temporally stable terrestrial islands such as granite

outcrops and inselbergs, may be characterised by high species endemism in comparison to the

surrounding environment, a result of the different habitats provided by these topographical

23

features (Burke, 2003; Jacobi et al., 2007; Gibson et al., 2010). Species endemic to these

terrestrial islands are of particular interest to evolutionary biologists, as they provide a model

for studying patterns of species dispersal and colonisation in a fragmented landscape.

1.4 Phylogeography and population genetics of terrestrial island endemics - a global

perspective

Sky islands are amongst the most well-studied terrestrial island systems (Knowles, 2000;

DeChaine & Martin, 2005; Smith & Farrell, 2005; Assefa et al., 2007; Bech et al., 2009). These

are mountains or mountain-tops surrounded by low-lying areas of starkly contrasting

environmental habitat. Flora and fauna may be restricted to these mountain top ‘islands’ or to

the habitat ‘islands’ between the mountain tops. Phylogeography and population genetic

studies have been used to show whether disjunct sky island populations have maintained

genetic connectivity, either via dispersal or repeated expansion dynamics during favourable

climatic periods (Floyd et al., 2005), or whether they have been isolated for long periods of

time leading to substantial genetic divergence (Masta, 2000). When examined from a

landscape context, studies can uncover common patterns relating to the history of a region,

for example, research from the northern hemisphere has highlighted the influence of

Quaternary glacial cycles on the geographic distributions of population genomes, with several

species showing rapid northward expansion of populations into newly inhabitable landscapes

during more moderate climates of the inter-glacial periods (Hewitt, 2000).

More recently, phylogeographic studies have been undertaken on the more low-lying granites,

inselbergs and other rock outcrops as terrestrial islands (Levy & Neal, 1999; Byrne & Hopper,

2008; Duputié et al., 2009; Levy et al., 2012; Tapper et al., 2014a, 2014b). These have

identified contrasting patterns of connectivity and isolation reflecting different evolutionary

responses of species to historical events.

24

1.5 Banded Iron Formations

Banded Iron Formations, or BIFs, are ancient geological features associated with Archaean

Cratons throughout the world that behave as terrestrial islands. Often prospective for resource

development (Jacobi & Carmo, 2008; Gibson et al., 2012), BIFs are surprisingly under-studied

in comparison to other terrestrial islands systems, a concerning fact given that flora surveys of

BIFs in south-eastern Brazil and south-western Australia have both identified high species

richness and endemism (Jacobi et al., 2007; Meissner & Caruso, 2008a; Gibson et al., 2010). To

date there has been just one population-level genetic study conducted on a BIF endemic;

genetic diversity and differentiation was examined across populations of Acacia

woodmaniorum, found on four BIF islands in Western Australia. The study identified sufficient

connectivity amongst BIF islands to counteract the potentially negative influences of genetic

drift and inbreeding on populations, an outcome which has significant implications for

management and future restoration of this species (Millar et al., 2013).

1.6 The Yilgarn Banded Iron Formations

The Yilgarn BIFs are part of the Yilgarn Craton of Western Australia, one of the oldest regoliths

on Earth that covers an area of 657 000 km2 (Freeman, 2001; Chivas & Atlhopheng, 2010). The

BIFs are comprised of banded iron talus slopes and areas of weathered duricrust (Hocking et

al., 2007), and are associated with greenstones and granites that formed 2.6 to 3 billion years

ago (Myers, 1993). These island-like formations occur along the boundary of the Southwest

Australian Floristic Region (SWAFR) and extend inland to the arid zone for over 750 km (Gibson

et al., 2012). BIFs vary widely in size, shape and distance from one another. Those bordering

the SWAFR show higher endemism and species turnover than more arid BIFs (Gibson et al.,

2010, 2012), characteristics attributed to their positioning along the boundary of the wetter

south-western environment and the arid interior (Butcher et al., 2007). The landscape

25

surrounding the BIFs is a mosaic of alluvial clay soils dominated by eucalypt woodland,

interspersed with sandplains and occasional granite outcropping.

1.7 History of the Yilgarn

The Yilgarn region of Western Australia has been geologically stable, un-glaciated and above

sea level since the Permian (250 Ma) (Hopper, 2000; McLoughlin, 2001). Nonetheless there

have been substantial changes in climate and vegetation communities over time. At the

beginning of the Cenozoic (65 Ma) the region was warm, humid and dominated by temperate

rainforest (Martin, 2006). Over time, the landscape became increasingly arid, beginning

approximately 11 Ma in the mid-Miocene, as evidenced by the absence of regular flows in

paleo-drainage systems (Martin, 2006). By the early Pleistocene (approx. 2 Ma) the modern

climate was established but mesic-arid fluctuations continued throughout this period (Zheng et

al., 1998).

The complex topography of BIFs provide an abundance of niche habitats that are believed to

have allowed for the persistence of species that were progressively extirpated in the

increasingly arid environments surrounding these BIFs (Byrne et al., 2008). The current

biodiversity is thought to be a mixture of these relictual species and more recently derived,

arid-adapted species (Byrne et al., 2008). Today the region is considered semi-arid, with

rainfall occurring throughout the year but predominantly in winter months (200-300 mm

annually) (Gibson et al., 2012).

26

1.8 Yilgarn BIF endemics

Yilgarn BIF endemics can be broadly categorised into those that are localised, occurring on just

one BIF and those that are regional, occurring across multiple formations. Flora surveys of the

Yilgarn BIFs have identified many regionally endemic plant species with distributions restricted

to, or centred on BIFs (Gibson et al., 2007). Less is known about the fauna of the BIFs, but it is

expected that species with limited dispersal capabilities (short-range endemics), may exhibit

regional endemism, particularly relictual invertebrates such as millipedes, land snails and

troglofauna that persist in the mesic micro-habitats provided by BIFs (Harvey, 2002;

Bennelongia, 2008; Biota, 2009). It is not known whether individual BIF populations of regional

endemics are genetically isolated, or whether they show evidence of population connectivity.

In plant populations connectivity will be maintained by pollen or seed dispersal. In fauna,

connectivity must occur via migration, which in less mobile species may be facilitated by

animal vectors or flood events.

1.9 Conservation of the Yilgarn BIFs

The Yilgarn BIFs and their surrounds are unique in that until now, they have remained in a

relatively pristine condition in comparison to other BIFs throughout the world (Burke, 2003).

Mining for iron-ore and gold in the region has led to identification of the diversity and

endemism present on these formations (Gibson et al., 2012) and there has been recent

political interest as to whether the balance between conservation and resource development

in this landscape is being met (Environmental Protection Authority, 2013). The current speed

of development has hastened the importance of understanding the processes resulting in high

species endemism on the BIFs, in order to mitigate the potential impact on these restricted

species.

27

1.10 Study species and rationale

I investigated the patterns of genetic diversity and differentiation present in regional endemics

of the Yilgarn BIFs, in order to understand the evolutionary forces shaping distributions in this

landscape. To select my target taxa I worked with four major criteria:

(1) Taxa showed evidence of restricted dispersal capabilities. In plants this meant selecting species

with seed that showed no active dispersal mechanisms and were not wind-pollinated, as wind-

pollination generally maintains population connectivity (Loveless & Hamrick, 1984).

(2) Taxa were each found on more than three Yilgarn BIFs.

(3) Taxa were taxonomically resolved, with vouchered specimens from multiple locations.

(4) Taxa were unrelated. The more dissimilar the species selected, the greater the strength of the

study, as congruent patterns between taxonomically diverse species are more likely to suggest

a wide-spread response to an historical event (Zink, 1996).

Based on these criteria I selected three species, a millipede and two plants.

(a) Atelomastix bamfordi (Edward and Harvey) is a recently described Spirostreptid millipede,

originally recorded from five Yilgarn BIFs (Edward & Harvey, 2010) that is now known to occur

on six; the Die Hardy Range, Helena Aurora Range, Windarling Range, Mt Jackson,

Koolyanobbing Range and Marvel Loch. Windarling, Koolyanobbing and Mt Jackson are all

actively mined for iron-ore and for Windarling and Koolyanobbing, specimens were sourced

from the Western Australian Museum (WAM) where they had been stored in 100 % ethanol.

Specimens from Helena Aurora, Die Hardy and Mt Jackson were collected by hand. Samples

could not be collected from Marvel Loch due to mining operations. In contrast to its congeners

found around the wetter regions of the south-western Australian coastline, A. bamfordi was

28

often difficult to find. Sampling success was greatest in the cold winter months or following

rain events that occur sporadically throughout the year. The majority of millipedes were

associated with deep leaf litter at the base of rocky slopes and under rocks in shaded gullies.

Several were found behind bark at the base of large, shaded trees. At the Helena Aurora

Range, millipedes were found under logs and leaf litter on flat clay soils surrounding the BIF at

the base of drainage lines that were prone to flooding. To ensure the species was restricted to

BIF, other flood-prone sites in the intervening flats were also searched and, although other

millipedes from the genus Antichiropus were recovered, A. bamfordi was never found away

from a BIF.

(b) Grevillea georgeana (McGill) is a member of the family Proteaceae. This bird-pollinated,

perennial woody shrub has attractive pink to red flowers that provide a major food source for

a number of local honeyeater species (Olde & Marriott, 1995). It is an obligate seeder and was

noted to respond well to disturbance at one site. Strongly associated with the dark,

mineralised rocky outcrops of the BIF, G. georgeana is capable of tolerating extremes in heat

(Olde & Marriott, 1995). The species tended to be patchily distributed across most of the BIFs

but was common where it did occur. I sampled G. georgeana from seven Yilgarn BIFs; the

Helena Aurora Range, Die Hardy Range, Hunt Range, Mt Correll, Mt Manning, Mt Dimer and

Mt Finnerty.

(c) Banksia arborea (C. A. Gardner), formerly Dryandra arborea, is another member of the

Proteaceae but the genus is significantly removed from Grevillea, having diverged

approximately 77 Ma (Sauquet et al., 2009; Cardillo & Pratt, 2013). This long-lived tree forms a

conspicuous component of the BIF vegetation, producing an abundance of yellow flowers that

are visited by a range of birds and invertebrates. The woody fruits are serotinous, releasing

their seed following fire (Wrigley & Fagg, 1989). Banksia arborea tends to be common where it

occurs and is not as habitat specific as G. georgeana, with some populations occurring at the

base of BIFs and one population noted at the base of a granite outcrop (pers. obs. N. Gibson),

suggesting the species prefers the well-drained habitats of upland areas. I sampled B. arborea

29

from eight Yilgarn BIFs; the Koolyanobbing Range, Mt Jackson, Windarling Range, Die Hardy

Range, Mt Manning, Taipan Hill, Hunt Range and the Helena Aurora Range.

Figure 1.1 Map of the 10 Yilgarn BIFs in Western Australia sampled for Atelomastix bamfordi, Grevillea georgeana and Banksia arborea. Image adapted from Google Earth.

1.11 Aims and objectives

The research undertaken in this thesis had three primary objectives:

1) Determine the evolutionary processes shaping species distributions by assessing patterns in

phylogeography and population genetic structure. This is composed of two parts to be

addressed separately for each species:

i) Quantify genetic diversity within and between island populations and determine whether

diversity is geographically structured.

ii) If genetically divergent, to estimate the time of divergence of island populations.

30

2) Determine whether there are any congruent phylogeographic patterns between taxa that

could indicate shared responses to historical events. For example, has population divergence

coincided with a known period of increasing aridity?

3) Propose conservation strategies to ensure the long-term persistence of these BIF endemics

based on collective data.

31

Oh shining

millipede…Where art

thou?

32

Work in this chapter has been published as:

Nistelberger HM, Byrne M, Coates DJ and Roberts JD (2014) Strong phylogeographic structure

in a millipede indicates Pleistocene vicariance between populations on banded iron formations

in semi-arid Australia. PLoS ONE, 9, e93038.

Nistelberger HM, Byrne M, Roberts JD and Coates DJ (2013) Isolation and characterisation of

11 microsatellite loci from the Western Australian Spirostreptid millipede Atelomastix

bamfordi. Conservation Genetics Resources, 5, 533-535. (Appendix D)

33

Chapter 2

Strong phylogeographic structure in a millipede indicates Pleistocene

vicariance between populations on Banded Iron Formations in semi-arid

Australia.

2.1 ABSTRACT

The Yilgarn Banded Iron Formations of Western Australia are topographical features that

behave as terrestrial islands within the otherwise flat, semi-arid landscape. The formations are

characterised by a high number of endemic species, some of which are distributed across

multiple formations without inhabiting the intervening landscape. These species provide an

ideal context for phylogeographic analysis, to investigate patterns of genetic variation at both

spatial and temporal scales. We examined genetic variation in the spirostreptid millipede,

Atelomastix bamfordi, found on five of these Banded Iron Formations at two mitochondrial loci

and 11 microsatellite loci. Strong phylogeographic structuring indicated the five populations

became isolated during the Pleistocene, a period of intensifying aridity in this landscape, when

it appears populations have been restricted to pockets of moist habitat provided by the

formations. The pattern of reciprocal monophyly identified within the mtDNA and strong

differentiation within the nuclear microsatellite data, highlight the evolutionary significance of

these divergent populations and we suggest the degree of differentiation warrants designation

of each as a conservation unit.

34

2.2 INTRODUCTION

Islands provide a unique context for studying the evolutionary processes that drive species

distributions. They are natural laboratories, allowing examination of processes of speciation,

and patterns of colonisation, persistence, dispersal and extinction (Whittaker, 1998). The

genetic consequences of island distributions are well documented by phylogeographic and

population genetic studies that infer historical patterns of natural and sexual selection, drift

and their interactions with historical and ongoing dispersal (Raxworthy et al., 2002;

Beheregaray et al., 2004; Jones & Kennedy, 2008; Wesener et al., 2010). The study of

terrestrial or ‘habitat’ islands appears to offer a different challenge to oceanic islands, as the

matrix between islands can be more permeable than water barriers, resulting in potentially

complex spatial and temporal patterns of genetic structure (Masta, 2000; Robin et al., 2010).

The most conspicuous and well-studied terrestrial islands include mountain ranges that form

‘sky islands’, such as the tepui summits of South America, the Western Ghats of southern India

and the Madrean and Rocky Mountain sky island systems of central and northern America

(DeChaine & Martin, 2004, 2005; Smith & Farrell, 2005; Storfer et al., 2007; Robin et al., 2010;

Salerno et al., 2012). Here, ‘island species’ form disjunct populations on either mountain tops

or the lowland valleys separating mountains. Other, less conspicuous topographic habitat

islands are found throughout the world and include granite outcrops, inselbergs and Banded

Iron Formations (BIFs), many of which display high endemism. Although topographically less

dramatic, these provide equally compelling bases for phylogeographic study (Baskin & Baskin,

1988; Burke, 2003; Jacobi et al., 2007; Byrne & Hopper, 2008; Gibson et al., 2010).

Inferring the evolutionary history of terrestrial island endemics can be difficult given that

isolation is not always absolute, either spatially or temporally. Dispersal events can occur

across valleys and lowlands for mountain-top endemics or over mountains for valley floor

endemics, depending on the vagility of the organism and their environmental requirements

35

(Masta, 2000; Robin et al., 2010). Despite this, phylogeographic patterns, although potentially

complex, may reveal the degree of connectivity or isolation experienced by island populations.

Restricted gene flow between island populations over long periods of time will result in

substantial genetic divergence due to genetic drift and/or, local adaptation (Dobzhansky,

1970). Given sufficient time, populations may show patterns of reciprocal monophyly, where

all individuals can be traced to a common ancestor within that population (Avise et al., 1983;

Moritz, 1994). Recurrent migration and gene flow between islands on the other hand, has a

homogenising effect on genetic structure, unless there is strong selection favouring local

genotypes on each island. For example, populations of yellow bellied marmots, restricted to

the Great Basin mountain-tops of North America, were believed to have been marooned on

the rocky outcrops of mountain tops following habitat contraction during the Pleistocene

(Floyd et al., 2005). A pattern of isolation by distance and high genetic diversity showed that

occasional dispersal events across the arid scrublands of the Basin have been an important

process connecting mountain-top populations (Floyd et al., 2005). A contrasting pattern was

found in the tree Eucalyptus caesia, endemic to granite outcrop ‘islands’ of Western Australia.

This species has high levels of genetic divergence with little geographic pattern to the

distribution of chloroplast haplotypes, reflecting long-term isolation and persistence without

connectivity and emphasising the role of genetic drift in this landscape (Moran & Hopper,

1983).

The Yilgarn Banded Iron Formations (BIFs) of south-western Australia are ancient, island-like

rock outcrops that provide an ideal system to investigate island population dynamics. They

border the transitional rainfall zone, a biogeographic region separating the high rainfall forest

region from the arid interior and are characterised by high species endemism and richness

(Gibson et al., 2007, 2010). BIFs vary widely in height (highest point approx. 700 m above sea

36

level), shape and distance from one another and are comprised of banded iron talus slopes

and areas of weathered duricrust (Hocking et al., 2007). These topographically complex

formations occur in an otherwise flat landscape that is dominated by clay, silt and sand plains.

The region has been geologically stable, un-glaciated and above sea level since the Permian

(250 Ma) (Hopper, 2000; McLoughlin, 2001; Anand & Paine, 2002). Nonetheless, there have

been substantial changes in climate and vegetation communities over this time. At the

beginning of the Cenozoic (65 Ma), the region was warm, humid and dominated by temperate

rainforest (Martin, 2006). Over time, the landscape became increasingly arid, beginning

approximately 11 Ma in the mid-Miocene, shown by the absence of regular flows in paleo-

drainage systems (Martin, 2006). Another aridification event occurred 7 Ma in the late

Miocene with several more throughout the Pliocene, shown by changes in vegetation structure

in palynological deposits to more arid-adapted forms (Dodson & Macphail, 2004; Martin,

2006). By the early Pleistocene (approx. 2.6 Ma), the modern climate had more or less become

established but fluctuations throughout this period continued, associated with successive

glacial and interglacial periods, the latter with intensifying arid conditions (Martin, 2006).

Historically, the altitudinal relief of BIFs and their complex structure, including deep valleys and

shaded gullies as well as exposed outcrops and talus slopes (Hocking et al., 2007), would have

provided an abundance of niche habitats that allowed for the persistence of both mesic and

xeric-adapted species during changing climates (Hopper et al., 1996). Whilst this may account

for the high species diversity found on the BIFs today, the high endemism (Gibson et al., 2007,

2010; Yates et al., 2011) also suggests long periods of isolation, allowing for speciation and the

development and persistence of a unique flora and fauna. The Yilgarn BIF endemics consist of

species that are either localised (found on one BIF), or regional (located on several BIFs but not

in the intervening landscape) (Gibson et al., 2007). Regional endemics provide an opportunity

to study phylogeographic patterns, to determine what processes have influenced current

37

distributions, and historically, how diversity has evolved and accumulated. The floral

assemblages of the Yilgarn BIFs are well known, with many examples of regional endemics

(Gibson et al., 2007). Although less is known of the faunal assemblages, taxa that exhibit short-

range endemism consistent with limited dispersal capacity, such as millipedes, mygalomorph

spiders, land snails and troglofauna (Harvey, 2002; Bennelongia, 2008; Biota, 2009), may also

exhibit patterns of regional endemism, persisting in micro-habitats provided by the BIF.

We investigated phylogeographic pattern in the spirostreptid millipede Atelomastix bamfordi,

known to occur on five BIFs on the Yilgarn Plateau in Western Australia. Millipedes lack the

waxy cuticle that protects other arthropods from desiccation and are reliant on moist

environments for survival (Mesibov, 1994; Harvey, 2002). Atelomastix, from the family

Iulomorphidae, is a predominantly south-west Australian genus, with many species distributed

across the wetter parts of the southern and south-western coastline and mesic inland Ranges,

including the Stirling and Porongorup Ranges (Moir et al., 2009; Edward & Harvey, 2010).

Almost all species have restricted distributions and have been characterised as short-range

endemics (geographic range < 10000 km2) (Harvey, 2002). Atelomastix bamfordi is the most

easterly occurring member of this genus and inhabits moist habitats provided by BIFs, such as

the base of rocky slopes, under large rocks and under dense leaf litter. The life cycle is likely

annual with adult millipedes dying prior to, or during the harsh summer months (pers. comm.

M Harvey). The species has been reported as abundant on the large Koolyanobbing Range

(Biota, 2009) but appears scarcer on the drier BIFs to the north and east. Increased exploration

and mining of iron ore within the region (Gibson et al., 2007) has resulted in the need for a

delicate balance between mining activities and conservation of BIF endemic species.

Understanding the patterns and evolution of diversity in these sites may guide two key

conservation issues: i) the viability and conservation of highly endemic species ii) the

conservation of landscape features which maximise the chances of maintaining patterns of

38

dispersal, gene flow and local adaptation that have shaped the history of extant diversity and

continue to be major factors in the persistence of this diversity.

Although unconnected today, BIF populations of A. bamfordi may have repeatedly come into

contact through population expansion associated with historical climate change in this

landscape. If this were the case, we would expect periods of gene exchange between

neighbouring BIF populations to result in a pattern of isolation-by-distance, where proximate

populations are more genetically related, or, if gene exchange has been extensive, to result in

genetic homogeneity of populations (Kimura & Weiss, 1964a). Alternately, the current

distribution may be due to contraction of a formerly widespread ancestor, followed by long-

term isolation and persistence on BIF islands. If this were the case, we would expect to see

signatures of deep genetic divergence amongst BIF populations but no clear pattern of

isolation by distance. Haplotypes would be conserved and there may be evidence of reciprocal

monophyly, if sufficient time for lineage sorting has occurred (Avise, 1989). This is the first

phylogeographic study to examine patterns of population level divergence in a regional BIF

endemic species from this biodiverse region.

We evaluated the genetic variation present in A. bamfordi to investigate evolutionary

relationships and patterns of genetic diversity and structure across BIF populations. Our goals

were:

1. To determine patterns of genetic differentiation among the five known BIF populations

and to use those data to infer the impact of historical processes on the distribution of

millipedes across the landscape.

2. To assess genetic diversity and structure to determine how limited dispersal

capabilities affect structure within BIF millipede populations.

39

3. To use patterns of genetic differentiation, both within and among BIFs, to inform

conservation and management of this species.

2.3 MATERIALS AND METHODS

2.3.1 Sampling and DNA extraction

A. bamfordi had been recorded from four of the Yilgarn BIFs; Koolyanobbing (KOO), Windarling

(WIN), Mt Jackson (MTJ) and the Die Hardy Range (DH) (pers. comm. R. Teale) (Biota, 2009).

This study extended the known distribution to include the Helena Aurora (HA) Range. Four

more BIFs in the region (Mt Dimer, Mt Correll, Hunt Range and Mt Manning) were also

searched but no specimens were found. Specimens from the Windarling and Koolyanobbing

Ranges, preserved in 100% ethanol were available from the Western Australian Museum’s

Department of Terrestrial Zoology collection (Appendix Table A1). For the remaining BIFs (DH,

MTJ and HA) sampling was spread across the geographic extent of each as far as was practical

to avoid sampling closely related individuals. For some BIFs this was not possible, as millipedes

were either not found and/or there was no suitable habitat. The same search procedure was

applied to all BIFs, so we believe it is reasonable to assume that the specimens collected reflect

the genetic diversity contained within each site. The five BIFs vary in size, topographic

complexity and orientation. Koolyanobbing is large, approximately 12 km in length and

reaching heights of 500 m. This BIF is topographically complex and contains many sites with

habitat suitable for millipedes. Helena Aurora is also large (~ 12 km) and elevated (600-700 m

height), but is comprised of more rounded hills with gentle slopes. Millipedes here were

associated with drainage lines coming off the peaks. The Die Hardy Range is large and X-

shaped, a topology providing many shaded slopes that supported habitat suitable for

millipedes. The two smaller Ranges, Windarling and Mt Jackson, have a narrow east to west

orientation and are therefore subject to an abundance of northern sunlight. These smaller

Ranges did not possess as many water-gaining sites and millipedes were scarcer. Invertebrate

40

surveys conducted on the intervening ‘flats’ between the Yilgarn BIF have not recorded A.

bamfordi (pers. comm. R. Teale). We also searched any water-gaining or densely vegetated

sites between BIFs and although these recovered other millipedes from the genus

Antichiropus, A. bamfordi was not found. As such, to the best of our knowledge, A. bamfordi

populations are restricted to BIF habitat and are not presently connected by populations in

intervening habitat. The field study did not involve any endangered or protected species and

none of the field sites from which we sampled (i.e. excluding KOO and WIN) required access

permission, with the exception of Mt Jackson (permission required from Cliffs Natural

Resources) and Mt Manning (Regulation 4 permit required from the Western Australian

Department of Parks and Wildlife (DPaW)). A license to take fauna for scientific purposes was

obtained prior to sampling from DPaW. GPS coordinates of the sampling locations are

provided in the supporting information (Appendix Table A1).

In total, 77 individuals from five BIFs were sequenced at two mitochondrial regions (77 at 16S,

75 at CO1) and genotyped at 11 nuclear microsatellite loci. DNA was extracted from the legs or

from the crushed heads of juvenile specimens stored in 100 % ethanol using a Qiagen DNeasy

Kit or a salt-based DNA extraction protocol (Beveridge & Simmons, 2004). Elutions were

performed in 100 µl or 50 µl of TE buffer respectively. Legs and head tissue were homogenized

in the lysis buffer prior to addition of proteinase K using mini micropestles (Geneworks).

2.3.2 Mitochondrial DNA amplification and sequencing

We sequenced two partial regions of the mitochondria, the Cytochrome Oxidase Subunit 1

(CO1) and the large Ribosomal Subunit 16S. For amplification of CO1, we used the Folmer

primers LCO1490 (5’GGTCAACAAATCATAAAGATATTGG3’) and HCO2198

(5’TAAACTTCAGGGTGACCAAAAAATCA3’) (Folmer et al., 1994) and for 16S, the ‘universal’

41

primers 16Sar (5’CGCCTGTTTTTCAAAAACAT3’) and 16Sbr (5’CCGGTTTGAACTCAGATCATGT3’)

(Simon et al., 1994). We also amplified two nuclear gene regions, the Internal Transcribed

Spacer 1 (ITS-1) (Ji et al., 2003) and the 18S rRNA gene (Hillis & Dixon, 1991). ITS-1 sequenced

products showed evidence of site heterogeneity and were excluded from use, and 18S

sequences were monomorphic. Amplifications were performed in 25 µl reactions containing 1x

PCR buffer, 3 mM MgCl2, 200 µM dNTP’s, 0.4 µM of each forward and reverse primer, 0.25 µg

of Bovine Serum Albumin (Fisher Biotech), 0.3 U of Platinum Taq DNA Polymerase (Invitrogen)

and approximately 20 ng/µl DNA. The thermal cycler profile for both CO1 and 16S involved an

initial denaturation at 94°C for 3 min, followed by 35 cycles of 94°C for 30 sec, 46°C for 30 sec

and 72°C for 30 sec followed by a final extension at 72°C for 2 min. PCR products were purified

using an UltraClean PCR Cleanup kit (Mo-Bio technologies, Geneworks) and the cleaned

product was quantified using a Nanodrop (Nanodrop Technologies). Sequencing reactions

(1/8) were carried out in 10 µl volumes containing 1 µl of Big Dye Terminator, 1.5 µl of 5 x

sequencing buffer, 1 µl of 3.2 pmol primer and approximately 40 ng of purified PCR product.

Thermal cycling conditions for sequencing reactions involved an initial denaturation at 96°C for

2 min, followed by 25 cycles of 96°C for 10 sec, 46°C for 5 sec and 60°C for 4 min. Sequenced

products were cleaned prior to electrophoresis using a standard ethanol precipitation method

(Applied Biosystems). Products were sequenced on an Applied Biosystems 3730 capillary

sequencer (Murdoch University).

2.3.3 Mitochondrial DNA analysis

Sequence data were aligned using Clustal W in the program BioEdit [26] and finalised by eye

with all mutations checked against raw sequence data. We used AMOVA, implemented in

Arlequin v. 3.5 (Excoffier & Lischer, 2010) to determine the structuring of mitochondrial DNA

diversity amongst BIF populations using the number of pairwise base-pair differences and

tested for significance with 10000 permutations. Bayesian analysis was used to visualise

42

genetic relationships and to estimate coalescence times of the populations in BEAST v. 1.7.5

(Drummond et al., 2012). Data were partitioned into the CO1 and 16S datasets, enforcing

monophyly for all A. bamfordi samples, with a SRD06 codon-partitioned model of sequence

evolution applied to the CO1 dataset and an HKY+G model to the 16S partition. Due to an

absence of fossil data and geological calibration points, estimates of population coalescence

times were generated by applying the average arthropod mutation rate of 2.3% per million

years to the partitioned data set (Brower, 1994). We applied a strict clock to both partitions

and used a Coalescent-Constant Size tree prior for four independent runs of 40 million

generations, sampling every 1000 generations. Log files were combined in Log Combiner and

the first 25% discarded as burnin. Visual inspection of the traces and high ESS values (>200)

confirmed stationarity had been reached in Tracer (BEAST package). Tree files were combined

in LogCombiner and the final file annotated in TreeAnnotater (BEAST package).

To distinguish between incomplete lineage sorting and migration for the two populations that

were paraphyletic (see results), we performed nested model likelihood ratio tests of various

isolation versus migration models in ‘L mode’ in the program IMa2 (Hey, 2010). IMa2 uses

Markov chain Monte Carlo simulations of gene genealogies to estimate divergence times of

populations and effective population sizes of extant and ancestral populations (Hey & Nielsen,

2004). To assess whether migration or drift was responsible for the paraphyly observed in the

two populations we performed a run on the two populations in ‘M mode’ using the following

command line priors: q200, t10, m1, b100000, l1000000, hfg, hn6, ha0.96, hb0.9. Convergence

was assessed by monitoring ESS values (all >20 000), chain swap rates (>80%) and running the

same command line four times independently using different seed values to ensure

consistency of results. The output was then used to test the likelihood of 24 different

migration/isolation models outlined in Hey and Nielsen (Hey & Nielsen, 2007) using the

following command line: q200, t10, m1, r0, c2 b100000 l1000000. Likelihood ratio tests were

43

used to reject models that were significantly worse than the full model, and the remaining

models were compared using the AIC criterion (AIC = -2(log(P) + 2K)) to identify the model of

best fit, where K is the number of parameters in the statistical model and L is the maximised

value of the likelihood function (Akaike, 1974).

To assess the historical stability of BIF population sizes we performed tests of neutrality that

can also serve as a measure of demographic expansion or contraction. Estimates of Tajima’s D

were calculated in Arlequin and diversity statistics including haplotype diversity, nucleotide

diversity (Pi), and nucleotide divergence (Jukes and Cantor) between populations were

calculated in DnaSP v. 5.10 (Librado & Rozas, 2009).

To test for isolation by distance within BIF populations using the combined sequence data, we

conducted mantel tests in R using the Vegan package (R Core Team, 2013). Permutations were

performed 10000 times on matrices of the number of pairwise sequence differences and

geographic distance. The R statistic is based on Pearson’s product-moment correlation

coefficient. We plotted the number of pairwise differences on geographic distance and

established a linear relationship, meaning no transformations of the data were required

2.3.4 Nuclear microsatellite genotyping and data analysis

Genotyping of the 11 microsatellite markers was carried out according to Nistelberger et al.,

(2013a). We checked all pairwise combinations of loci for linkage disequilibrium by performing

exact tests in Genepop v.1.2 (Raymond & Rousset, 1995) and applying a sequential Bonferroni

correction to determine significance (Rice, 1989). There was no significant association among

loci following sequential Bonferroni correction. Traditional F statistics were of limited use in

our study due to the likelihood that our BIF ‘populations’ do not represent true panmictic

44

populations given the distances over which individuals were sampled and the limited dispersal

capabilities of A. bamfordi. This was confirmed, with all populations showing significant

deviation from Hardy-Weinberg equilibrium, with the exception of Windarling, where

individuals had been sampled in close proximity to one another. Rarefied allelic richness

provided a better estimator of genetic diversity, given the sampling, and was calculated in HP-

Rare v 1.0 (Kalinowski, 2005). The partitioning of genetic diversity amongst BIF populations

was assessed using an Analysis of Molecular Variance (AMOVA) implemented in Arlequin, with

significance determined with 10000 permutations. An analysis of genetic structure across the

five BIF populations was assessed using a Bayesian clustering algorithm implemented in TESS

ver. 2.3 (Chen et al., 2007). This program assumes proximate interactions between individuals,

with spatial information prescribed at the individual level. We ran TESS with no admixture,

owing to the limited dispersal capabilities of millipedes and performed 100 iterations of K

values between 1 and 7, with a burnin length of 20000, 100000 sweeps and a spatial

interaction parameter of 0.8. Lowest DIC values for each K value were plotted in order to

determine the optimal K value. The results were then used in the downstream program

CLUMPP (Jakobsson & Rosenberg, 2007), which aligns cluster assignment across the replicate

analyses and ensures the correct assignment of K. Structure output graphics were created

using DISTRUCT (Rosenberg, 2004).

To further reconstruct the phylogenetic relationships of populations using microsatellite data,

we generated Cavalli-Sforza chord distances (DC) (Cavalli-Sforza & Edwards, 1967) between

populations in the program Populations 1.2.30 (Langella, 2000) and drew a neighbour-joining

tree of populations after 100 bootstrap replications in TreeView (Page, 1996). This geometric

distance measure does not make any biological assumptions about the data and can be more

useful for phylogenetic inference than estimates that use stepwise mutation models when

small population sizes are involved (Cavalli-Sforza & Edwards, 1967; Goldstein & Pollock,

45

1997). To assess isolation by distance within BIF populations using microsatellite data, we

correlated pairwise population matrices of genetic distance and geographic distance and

tested for significance using a Mantel test with 9999 permutations in the program IBDWS v

3.23 (Jensen et al., 2005).

2.4 RESULTS

2.4.1 Genetic variation

2.4.1.1 Mitochondrial DNA data

Of the two mitochondrial regions amplified, CO1 was the most variable, with 45 haplotypes

identified from the 75 individuals sequenced (two WIN samples did not amplify at CO1). The

variation was composed of 54 transitions, eight transversions and two multistate substitutions.

The 16S region yielded 24 haplotypes from the 77 individuals sequenced and included 21

transitions, one transversion and one single base pair indel. Combining the two regions

resulted in a total aligned sequence length of 1114 bp and 48 haplotypes. The most prevalent

haplotype (Hap 47 WIN) had a frequency of 23 %, followed by Hap 35 (MTJ) and Hap 39 (HA)

with frequencies of 8%, and Hap 33 (MTJ) and Hap 40 (HA) with 6%. The remaining haplotypes

were only found in one or two individuals. Measures of genetic diversity were consistently

lowest in the Windarling population and highest in Koolyanobbing. Haplotype diversity ranged

from 0.295 (WIN) to 0.986 (KOO), with an average of 0.794. Nucleotide diversity ranged from

0.06 % (WIN) to 8.5 % (KOO), with an average of 4%. No populations departed from the neutral

model of evolution except for Windarling (D = -1.775) (Table 2.1). Measures of diversity in

Windarling are likely to be biased due to restricted sampling. Millipedes had been previously

collected from two closely located sites on this BIF (<260 m apart), and although noted to be

rare along the length of the Range (pers. comm. R. Teale), further sampling was not possible

due to restricted access associated with mining activity.

46

There was a large and highly significant degree of genetic structuring of the mitochondrial

genetic diversity, with most variation (AMOVA) occurring among BIF populations (71.56%)

rather than within (28.4%). The greatest genetic divergence occurred between the Mt Jackson

and Helena Aurora populations (approx. 40 km apart) and the least between the two most

spatially isolated populations, Koolyanobbing and Die Hardy (approx. 100 km apart) (Table 2.2,

Fig.2.1).

2.4.1.2 Nuclear microsatellite data

Microsatellite genetic diversity was low, both within and amongst BIF populations. Rarefied

allelic richness ranged from 1.89 (HA) to 3.28 (KOO), with an average of 2.47. There was a

highly significant degree of structuring of the microsatellite diversity, with the majority of

variation (68.11%) maintained within populations, compared to (31.89%) among populations.

47

Table 2.1 Diversity statistics for the five Atelomastix bamfordi BIF populations for mitochondrial DNA data and nuclear microsatellite data (bold), standard

error in parentheses: N, number of individuals; # haps, number of haplotypes; HD, haplotype diversity; Pi, nucleotide diversity; D, Tajima’s D; n, average

number of individuals genotyped; AR^, rarefied allelic richness; * P value<0.05

Population # Haplotype Genbank accession #

Population abbrev. N haps HD Pi D numbers CO1 16S n AR^

Die Hardy Range DH 16 14 0.983 (0.002) 0.0050 (0.000) -0.569 19-32 KC689871-KC689883 KC689837-KC689841 13.7 2.62 (0.34)

Helena Aurora Range HA 14 8 0.890 (0.004) 0.0042 (0.000) -0.733 38-45 KC689889-KC689894 KC689845-KC689851 13.9 1.89 (0.28)

Koolyanobbing Range KOO 21 18 0.986 (0.001) 0.0085 (0.000) -0.316 1-18 KC689853-KC689870 KC689829-KC689836 21.7 3.28 (0.40)

Mt Jackson MTJ 11 5 0.818 (0.008) 0.0017 (0.000) -0.234 33-37 KC689884-KC689888 KC689842-KC689844 9.6 2.57 (0.36)

Windarling Range WIN 13 3 0.295 (0.012) 0.0006 (0.000) -1.775* 46-48 KC689895-KC689897 KC689852 14 1.97 (0.27)

48

Table 2.2 Pairwise population nucleotide divergence based on Jukes and Cantor of five Atelomastix bamfordi populations using mtDNA data. DH, Die Hardy; HA, Helena Aurora;

MTJ, Mt Jackson; Win, Windarling; KOO, Koolyanobbing.

DH HA MTJ WIN KOO

DH 0

HA 0.016 0

MTJ 0.017 0.022 0

WIN 0.013 0.018 0.019 0

KOO 0.013 0.014 0.018 0.017 0

49

Figure 2.1 Schematic representation of the five Banded Iron Formation populations, coloured according to the five genetic clades identified with mtDNA analysis. Black circles indicate sample sites. Study area shown in inset.

2.4.2 Phylogeographic structure

There was evidence of strong phylogeographic structure within A. bamfordi, with minimal

sharing of CO1 and 16S haplotypes amongst individuals within BIF populations and no sharing

of haplotypes between populations. Bayesian analysis identified five genetic clades in which

populations were reciprocally monophyletic, with the exception of Koolyanobbing and Helena

Aurora, for which some samples were paraphyletic (Fig. 2.2). This was due to a subset of six

individuals from the north of the Koolyanobbing Range being grouped with samples from

Helena Aurora (Fig.2.1). Support for the five genetic clades was high (posterior probabilities >

0.85) although the relationships between the clades was poorly supported. Dating estimates

50

showed coalescence during the Pleistocene, between 0.729 Ma [0.532-0.952] and 1.078 Ma

[0.792-1.433] (Fig.2.2).

Isolation and migration analysis in the program IMa2 was used to determine whether the

paraphyly between Koolyanobbing and Helena Aurora was due to incomplete lineage sorting

(isolation) or gene flow (migration). The three models with the highest AIC support all

indicated there was no historical migration between the two populations (Appendix Table A2).

Mantel tests of pairwise sequence differences against geographic distance indicated significant

isolation by distance within the Koolyanobbing (r=0.740 p=0.0001), Mt Jackson (r=0.225

p=0.0375) and Helena Aurora (r=0.399 p=0.0025) populations.

2.4.3 Population genetic structure

Bayesian cluster analysis identified four genetic clusters in the microsatellite data. The first

corresponded to the Die Hardy and Mt Jackson populations, and the remaining clusters to each

of the three other populations (HA, WIN, KOO) (Fig. 2.3). Further investigation of structure in

the first cluster revealed two more groupings corresponding to the separate BIF populations

(DH, MTJ).

51

Figure 2.2 Bayesian cladogram of 75 Atelomastix bamfordi individuals based on combined CO1 and 16S mtDNA data. Clades are designated separate colours as per Fig. 2.1. Numbers on terminals indicate haplotype number. The * denotes major nodes with high support (posterior probability > 0.85) and the numbered node indicates divergence estimate in millions of years, with confidence intervals in brackets. NB the A. nigrescens branch has been truncated (//) for viewing purposes.

52

There was some evidence of admixture in the Mt Jackson and Koolyanobbing populations (Fig.

2.3). Phylogenetic analysis of relationships between populations using Cavalli-Sforza chord

distances, showed distinct genetic structure amongst populations using microsatellite data,

but with some clustering of Koolyanobbing and Helena Aurora (97% bootstrap support) and

Die Hardy and Mt Jackson (62%) (Fig. 2.4). Two of the five populations, Helena Aurora (r=0.30

p=0.004) and Die Hardy (r=0.32 p=0.009), showed weak but significant patterns of isolation-by-

distance at microsatellite loci following Mantel tests.

Figure 2.3 Bayesian population assignment of Atelomastix bamfordi individuals based on nuclear microsatellite data generated in TESS for the optimal grouping of K=4. Each vertical line represents an individual and the colour displays the probability of belonging to each genetic cluster. DH, Die Hardy; MTJ, Mt Jackson; HA, Helena Aurora; WIN, Windarling; KOO, Koolyanobbing.

53

Figure 2.4 Unrooted neighbour-joining tree of Cavalli-Sforza chord distances between the five BIF populations of Atelomastix bamfordi. Distances are based on 11 microsatellite loci with bootstrap support shown at nodes. DH, Die Hardy; MTJ, Mt Jackson; HA, Helena Aurora; WIN, Windarling; KOO, Koolyanobbing.

2.5 DISCUSSION

2.5.1 Genetic differentiation of BIF populations

2.5.1.1 Spatial patterns

Phylogeographic analysis of A. bamfordi revealed strong genetic differentiation between BIF

populations, with mtDNA genetic lineages corresponding to each of the five BIF populations,

except for six individuals from Koolyanobbing that were paraphyletic with the Helena Aurora

Range due to incomplete lineage sorting. Microsatellite data were broadly consistent with this

result, identifying four genetic clusters, the first of which was further broken down into

individual populations.

The lack of correlation between genetic divergence and geographic distance, with the two

most spatially isolated populations showing the least genetic divergence, suggests there has

54

been little, to no connectivity of BIF populations following their separation. This result was

emphasized in the Bayesian analysis which showed high support for genetic clades but poor

support for the relationships between clades (Fig. 2.2). It is also consistent with estimates of

sequence neutrality (D) that indicated a lack of demographic expansion and contraction

events, with the exception of Windarling, where the result can be explained by close sample

proximity. This pattern of differentiation has been observed in other taxa with restricted

dispersal capabilities, where populations have become isolated as a response to climate

change and habitat loss. Like A. bamfordi, these studies emphasise the role of genetic drift in

driving population differentiation. For example, mitochondrial and nuclear sequence data

revealed extraordinarily strong phylogeographic structure in populations of trapdoor spiders

(Moggridgea tingle) in south-western Australia, often situated just kilometres apart.

Molecular dating suggested these populations became isolated in the mesic valleys and

uplands of the Porongorup and Stirling Ranges following the onset of aridity in the late

Miocene (Cooper et al., 2011). Like A. bamfordi, reciprocally monophyletic lineages across

three geographic locations highlighted the reproductive isolation of M. tingle populations, but

the pattern emphasized a more ancient division, with monophyly observed in both mtDNA and

the more slowly evolving nuclear genome (Cooper et al., 2011). Studies of genetic structure in

two frog species in south-western Australia, Geocrinia lutea and G. rosea, also described

patterns of extremely low dispersal and long-term isolation between habitat ‘islands’, with

divergence attributed to isolation via range changes resulting from climate change rather than

geographical barriers (Driscoll, 1998). Another striking example of genetic divergence across

terrestrial islands occurred in the sky islands of south-eastern Arizona - forested mountain

Ranges, separated by low-lying grassland and desert. Here, the jumping spider Habronattus

pugillis occupies woodland habitat at altitudes above 1400 m but is absent from the lowlands.

Using mtDNA, Masta (Masta, 2000) showed that like A. bamfordi, smaller populations of H.

pugillis formed monophyletic clades, whereas larger Ranges showed evidence of paraphyly.

Like A. bamfordi, this was ascribed to incomplete lineage sorting rather than resulting from

55

migration (Masta, 2000). The isolation of populations was attributed to climate-induced

contraction of woodland populations onto disjunct mountain Ranges.

2.5.1.2 Temporal patterns

Molecular dating with the arthropod clock rate has placed the separation of A. bamfordi clades

at 0.7 to 1.1 million years ago during the Pleistocene. The timing coincides with known

increases in aridity in the transitional rainfall zone (TRZ) due to climatic fluctuations associated

with Milankovitch cycles during this period (Bowler, 1982). In particular, there was a major arid

shift in southern Australia corresponding to the middle Pleistocene transition (1250 ka - 700

ka), a period of increased severity of glaciation, associated with greater ice volumes, lowered

temperatures and increased aridity (Clark et al., 2006). Although the TRZ remained un-

glaciated, this period of intense climatic instability is likely to have caused the fragmentation of

species distributions due to changes in suitable habitat within this region (Byrne, 2007).

Although the divergence estimates presented here must be treated with caution given the use

of a generalised arthropod mutation rate, the errors associated with this method are lessened

given we are working at shallow phylogenetic levels (Buckley et al., 2001; Pons et al., 2010).

2.5.2 BIF population dynamics – historical and contemporary patterns

2.5.2.1 Historical patterns

The process of genetic drift acting independently on each BIF population, is reflected in

different patterns of genetic divergence and diversity seen in the mtDNA data. The smaller

populations, Mt Jackson and Windarling, and the larger population Helena Aurora, which had

less suitable millipede habitat, showed lower genetic diversity and higher levels of genetic

divergence than the larger populations (KOO and DH). These three BIFs have probably

harboured historically small effective population sizes, owing to the limited availability of

56

moist sites, increasing their susceptibility to forces of genetic drift driving divergence (Wright,

1929). They may also be influenced by different selective forces associated with the more arid

conditions present on these BIFs. On the other hand, the two large BIF populations

characterised by a greater availability of suitable habitat (KOO and DH), retained genetic

diversity and had less genetic divergence, consistent with historically large population sizes

(Wright, 1929). The paraphyly of individuals from Koolyanobbing with those of Helena Aurora

further supports the likelihood of a large historical population size at Koolyanobbing, as larger

populations take longer to achieve reciprocal monophyly through lineage sorting. Over time

this signature is likely to erode, leaving the five BIFs reciprocally monophyletic.

2.5.2.2 Contemporary patterns

Rapidly evolving microsatellite markers can provide a more contemporary view of BIF

population dynamics. The information we gained from these markers was limited by a lack of

Hardy-Weinberg equilibrium within our BIF populations, yet we still identified patterns in

genetic diversity that appear to support the impact of genetic drift on populations: lower

genetic diversity in BIFs likely to have had small effective population sizes (WIN, MTJ and HA).

Signals of isolation by distance (IBD) were seen in some of the BIF populations (HA, DH), but

these were weak and likely to be heavily influenced by limited sample sizes. The small pairwise

distances between samples at Windarling explains the lack of IBD seen in this BIF, but we were

surprised to see evidence of IBD in the Helena Aurora and Mt Jackson populations. Here, we

expected limited gene flow to result in a pattern of ‘islands within islands’, where significant

isolation has resulted in genetically divergent populations within BIFs that do not correlate

with geographic distance. An increased number of samples from more locations would be

required to determine whether IBD is really occurring at these spatial scales.

57

2.5.3 Conservation implications

The evolutionary significance of geographically isolated genetic lineages has long been

recognised, and conservation biologists have attempted to formalise this by delineating

conservation units within a species, such as Evolutionary Significant Units (ESUs) and

Management Units (MUs) (Ryder, 1986). Conservation units have been defined at various

levels of genetic differentiation within species and are likely to experience limited gene flow or

even total reproductive isolation. For example, ESUs have been defined as a population or a

group of populations that warrant separate management for conservation because of high

genetic and ecological distinctiveness (Allendorf et al., 2013), with Moritz (1994) suggesting

that ESUs might be characterised by a pattern of reciprocal monophyly amongst populations.

Applying this criterion to our mtDNA data, results in the recognition of three BIF populations

(DH, MTJ, WIN) as ESUs. However, Paetkau (1999) and Crandall et al. (2000) highlight the

problems inherent in ignoring paraphyletic lineages, such as Koolyanobbing and Helena

Aurora, which are often ancestral and maintain high levels of diversity and divergence within

themselves. In our case, the paraphyly of Koolyanobbing with Helena Aurora is a result of a

slower progression towards monophyly due to ongoing lineage sorting, probably owing to

large population sizes. Given the clear evidence for restricted gene flow between these two

populations, both could readily be recognised as MUs. Therefore we consider that all five BIF

populations warrant designation as conservation units.

Although it is difficult to predict future climate change at such fine scales, broad-scale models

for this region over the 21st century indicate increasing temperature, decreasing rainfall and a

greater incidence of fire (Hughes, 2003). If these predictions are correct, populations are

unlikely to re-connect via suitable intervening habitat in the future, further consolidating their

isolation to the remaining moist pockets provided by BIF, where they will continue to be

58

influenced by forces of genetic drift and local selection. Given their dependence on moisture,

conservation efforts should focus on protecting areas of dense leaf litter, large outcrops and

shaded gullies on each BIF in order to preserve the five genetic lineages identified here. To

achieve this, preservation of the height and topographical complexity of BIFs is essential, in

order to maintain drainage lines and water-collecting sites. Conservation of these areas is also

likely to support populations of other relict fauna and flora that are adapted to moist

environments.

2.5.4 Conclusion

This is the first phylogeographic study conducted on a species inhabiting multiple Banded Iron

Formations in this landscape and provides evidence that the disjunct populations have

experienced long-term isolation following their separation during the Pleistocene. The

divergence of populations during this period coincides with increases in aridity and emphasizes

the role of climatic instability, along with topographic features in driving differentiation and,

ultimately speciation in this landscape. We consider the strong phylogeographic structure and

evidence of restricted gene flow between BIF populations warrant the designation of all five

populations as distinct conservation units.

59

Attractive,

interesting…what more

could a biologist ask for?

60

Work in this chapter has been submitted for publication and published as:

Nistelberger HM, Byrne M, Coates DJ and Roberts JD (2014) Terrestrial islands in an ancient

landscape: Evidence of both historical isolation and connectivity of populations of Grevillea

georgeana, a banded iron formation endemic. Submitted: Journal of Biogeography.

Nistelberger HM, Coates DJ, Byrne M and Roberts, J D (2013) Isolation and characterisation of

11 microsatellite loci in the short-range endemic shrub Grevillea georgeana McGill

(Proteaceae). Conservation Genetics Resources, 6, 221-222. (Appendix D)

61

Chapter 3

Terrestrial islands in an ancient landscape: Evidence of both historical

isolation and connectivity of populations of Grevillea georgeana, a

Banded Iron Formation endemic.

3.1 ABSTRACT

Plant species with island distributions might be expected to have restricted seed and pollen

dispersal with significant genetic divergence among populations. In this study we assessed

population genetic connectivity through patterns of genetic diversity and structure in seven

terrestrial island populations of Grevillea georgeana. Phylogeographic pattern and population

genetics of seven Yilgarn Banded Iron Formation (BIF) populations were examined using

sequence data from two chloroplast intergenic spacers and 10 nuclear microsatellite loci.

Phylogenetic relationships of 67 individuals were assessed using both Bayesian and statistical

parsimony methods, and genetic divergence of haplotypes was dated using two angiosperm

chloroplast substitution rates. Microsatellite variation was assessed in geographically clustered

groups of 24 individuals from each BIF, with the exception of Die Hardy Range where three

groups of 24 individuals were sampled to infer patterns within a large range. Pollen movement

was assessed using indirect methods based on the average frequency of private alleles at

microsatellite loci. The results show that different historical processes have shaped the

evolution of Grevillea georgeana populations. Some populations are behaving as isolated

islands, with genetic drift driving patterns of differentiation, whilst others show patterns of

more recent connectivity. Pollen movement, although generally restricted, appears to be an

important process connecting neighbouring populations that should be considered in the face

of future habitat disturbance.

62

3.2 INTRODUCTION

The evolutionary consequences of island distributions have long been of interest to biologists.

Species with disjunct, island populations may experience restricted gene flow that over time,

leads to genetic divergence as population specific processes, such as genetic drift, mutation,

bottlenecks and natural selection, alter genetic structure (Wilson & MacArthur, 1967;

Dobzhansky, 1970; Mayr, 1970; Whittaker, 1998). If genetic isolation is prolonged, populations

follow independent evolutionary trajectories that can result in reproductive barriers and

allopatric speciation (Dobzhansky, 1970; Mayr, 1970).

Oceanic and continental islands generally have well defined boundaries to dispersal, with

pronounced genetic isolation leading to patterns of deep genetic divergence (Beheregaray et

al., 2003; Juan et al., 2004; Measey et al., 2007; Zhai et al., 2012). In comparison, terrestrial or

habitat islands have less obvious barriers to dispersal and the degree of genetic isolation

among populations may be difficult to predict (Masta, 2000; Robin et al., 2010). In these

situations, evidence of long-term isolation or connectivity between habitat islands can be

inferred from phylogeographic and population genetic studies (Masta, 2000; Floyd et al., 2005;

Byrne & Hopper, 2008; Cooper et al., 2011).

The degree of connectivity or isolation of island populations is dependent on dispersal

mechanisms that in plants, includes movement of both seed and pollen (Epperson, 1993).

Distinguishing between these two modes of dispersal requires the use of both nuclear (nrDNA)

and chloroplast DNA (cpDNA) markers. CpDNA is generally maternally inherited in

angiosperms, reflecting patterns of seed dispersal (McCauley et al., 2007) and provides a good

indicator of historical relationships owing to its uni-parental inheritance, low mutation rate

and lack of recombination (Avise, 2000). Nuclear markers are bi-parentally inherited, providing

63

a means of estimating gene flow through both pollen and seed dispersal (Ennos, 1994). At the

population level, nrDNA sequence markers may lack sufficient variation to provide

phylogeographic information (Zhang & Hewitt, 2003), but rapidly mutating neutral markers,

such as microsatellites, can be used to provide a nuclear perspective on patterns of population

diversity and structure caused by more recent evolutionary processes (Zhang & Hewitt, 2003).

When used together, both cpDNA and nrDNA genetic markers allow for inference of historical

phylogeographic patterns relating to seed movement, as well as more recent population

processes, including pollen dispersal between island populations of plant species.

The Yilgarn Banded Iron Formations (BIFs) of Western Australia are topographical features

characterised by high species richness and endemism that behave as terrestrial islands in a

semi-arid landscape (Gibson et al., 2012). Several endemic species occur across multiple

formations without inhabiting the intervening landscape and these provide an excellent

system for phylogeographic and population genetic analysis to assess patterns of connectivity

among these terrestrial island formations (Gibson et al., 2007). Grevillea georgeana McGill is a

bird-pollinated perennial shrub (2 - 2.5 m height) that occurs across multiple BIFs, but not in

the surrounding sand and alluvial plains (McGillivray & Makinson, 1993; Gibson et al., 2007).

The geographic distance between the island populations ranges from approximately 7 km to

115 km and it is not known whether populations are connected via gene flow or if they are

reproductively isolated. Seeds in G.georgeana have no active mechanism of dispersal and the

species regenerates from seed following fire (Olde & Marriott, 1995). Given the lack of

dispersal mechanism we hypothesise little connectivity of BIF populations via seed. If disjunct

populations have been separated for long periods, this may result in population differentiation

of cpDNA that is not geographically structured, with haplotypes restricted to particular

populations (Lee, 2000). If there has been connectivity, either via movement of seed between

BIFs or via the connection of current populations through now extinct populations, we would

64

expect shallow genetic differentiation that is potentially geographically structured, with

sharing of haplotypes between adjacent BIF populations (Kimura & Weiss, 1964b).

Patterns in nuclear microsatellite data will reflect contemporary pollen movement across the

landscape as well as seed dispersal (Sork et al., 1999). Bird pollination can promote or diminish

genetic structure depending on the species and their foraging habits, leading to difficulties in

predicting pollen flow and nuclear genetic structure (Degen et al., 2004; Llorens, 2004; Byrne

et al., 2007). Depending on the movement of pollinators, we can expect one of three potential

outcomes. An absence of pollen flow between BIFs would lead to a pattern of genetic

differentiation that is not geographically structured, with genetic drift driving patterns of

random genetic structure. If pollen movement is restricted to neighbouring BIFs then a pattern

of isolation by distance (IBD) will develop (Wright, 1943), with less genetic divergence among

adjacent BIFs. Finally, if pollen movement is unrestricted across all BIF populations, we would

expect a lack of genetic structure. In evaluating the influence of historical and contemporary

processes on relationships among G. georgeana populations, we expect to see genetic

signatures of differentiation in cpDNA arising from limited seed dispersal, and some structure

amongst BIF populations in the nrDNA, depending on the level of pollen dispersal.

Understanding the patterns of genetic differentiation in terrestrial island species, such as those

endemic to BIF, not only provides valuable information on the evolution and consequences of

species inhabiting island distributions, but also informs conservation of these terrestrial island

habitats. Species with small populations and disjunct distributions may be at heightened risk of

extinction, either from demographic effects associated with the increased impact of

disturbance or habitat loss, or from reduced genetic diversity and inbreeding depression

(Frankham et al., 2002). Understanding levels of connectivity via gene flow will be critical in

assessing the impact of reduced genetic diversity and potential inbreeding depression.

65

Evaluating the extent of genetic relationships among populations or groups of populations is

also important, as high genetic differentiation could require the recognition of distinct units for

conservation (Allendorf et al 2013). This is particularly important in the case of G. georgeana

as many of the Yilgarn BIFs are prospective for resource development, with potential for

extensive habitat removal and disturbance.

We used a combination of non-coding cpDNA sequences and nuclear microsatellite markers

specifically developed for G. georgeana in order to test our hypotheses through (i) assessment

of historical connectivity of BIF populations via seed and pollen dispersal, and (ii) assessment

of patterns of genetic variation within BIF populations to infer population specific dynamics

and evolutionary history.



Samples were collected from seven Yilgarn BIFs, and voucher specimens collected from each

population are lodged at the Western Australian Herbarium. The BIFs vary in size and

accessibility, so distances between individuals sampled varied considerably. Sampling for the

phylogeographic (cpDNA) component of the study (10 individuals) was spaced out along each

BIF where possible, in order to capture the greatest possible spatial haplotype diversity. To

determine population differentiation using microsatellite markers, a geographically clustered

group of 24 individuals was collected from a site where the species was common. For the large

Die Hardy Range, we collected from three sites (north, central and south) to allow assessment

of within BIF genetic structure. DNA was extracted from freeze-dried (Labconco) leaves using a

modified 2% CTAB method as outlined in Nistelberger et al. (2013).

66

3.3.2 cpDNA Sequencing

We screened six non-coding cpDNA regions known to detect phylogeographic structure in

Australian plants (Byrne & Hankinson, 2012) and selected the two most variable, trnQ-rps16

(primers trnQ, rps16) and psbD-trnT (primers psbD, trnT)(Shaw et al., 2007). PCR reactions (50

µl) contained 50 mM KCl, 20 mM Tris-HCl (pH 8.4), 0.2 mM each dNTP, 0.1 µM primers, 1.5 and

2 mM MgCl2 (psbD-trnT, trnQ-rps16 respectively) and 0.1 U of Taq polymerase (Invitrogen).

The amplification protocol included a hot start of 5 min at 80°C, followed by 30 cycles of 95°C

for 1 min, 50°C for 1 min and 65°C for 4 min, with a final extension of 65°C for 5 min. PCR

products were cleaned and sequenced by AGRF-Perth (Australian Genome Research Facility)

on an Applied Biosystems 3730x1 DNA Analyzer platform. DNA sequence data were aligned

and edited using ClustalW in BioEdit (Hall, 1999) and checked by eye. The two sequences were

combined to form a total aligned sequence length of 2777 base pairs.

3.3.3 Fragment Analysis

For microsatellite analysis, multiplexed PCR reactions were performed in a total volume of 5µl

as outlined in Nistelberger et al. (2013). Amplified products were run on a 3% agarose gel and

eleven primer pairs were selected based on clear, interpretable banding pattern. PCR products

were visualised on an Applied Biosystems 3730 capillary sequencer (Murdoch University) and

scored using GeneMapper v 4.0 (Applied Biosystems).

3.3.4 cpDNA data analysis

Descriptive statistics, including the number of haplotypes, haplotype diversity, nucleotide

diversity and population divergence (Jukes & Cantor, 1969), were calculated in DNAsp v5.10

(Librado & Rozas, 2009). Partitioning of chloroplast diversity was determined with an analysis

of molecular variance (AMOVA) using the number of pairwise base-pair differences and testing

67

for significance with 10,000 permutations in Arlequin v.3.5 (Excoffier & Lischer, 2010).

Historical stability of populations was assessed with tests of neutrality (Tajima’s D), that also

serve as indicators of population expansion and contraction in Arlequin, with significance

tested with 10,000 permutations (Tajima, 1989). These analyses were performed using data

with indels manually encoded as transitions. We inferred the evolutionary relationships of

haplotypes and populations using a Bayesian analysis implemented in MrBayes v3.1

(Huelsenbeck & Ronquist, 2001). Data were partitioned with indels coded according to

Simmons & Ochoterena (2000) as implemented in Seqstate v.1.4.1 (Muller, 2005). An HKY+I+G

nucleotide substitution model was used as determined by Jmodeltest v0.1.1 (Posada, 2008)

and the indel partition was set to nst=1. We ran the Markov Chain Monte Carlo method for

four million generations, sampling from the posterior distribution every 1000 generations. The

first 25% of trees were discarded as burnin. The time to most recent common ancestor

(TMRCA) of haplotypes was estimated using two nucleotide substitution rates commonly

reported for angiosperms, 1.2x10-9 (Graur & Li, 2000) and 2.0x10-9 substitutions per site per

year (Wolfe et al., 1987) in BEAST v.1.7 (Drummond et al., 2012). We used an uncorrelated,

log-normal clock with a coalescent-constant size tree prior to estimate divergence times. Two

independent runs of 10 million generations were conducted, sampling every 1000 generations.

Stationarity was assessed by viewing log files in TRACER and ensuring effective samples sizes

for all parameters exceeded 200. Tree files were combined using LOGCOMBINER and

annotated in TREEANNOTATER (available BEAST package) with the first 25% of trees discarded

as burnin. Haplotype relationships were also visualised with an unrooted haplotype network

based on statistical parsimony analysis, implemented in TCS v.1.21 (Clement et al., 2000).

To assess whether haplotypes show IBD across the landscape we conducted Mantel tests in R

v. 3 (Vegan package) on the number of pairwise sequence differences and geographic distance,

permuted 10,000 times (R Core Team, 2013).

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3.3.5 Microsatellite data analysis

We tested for the presence of null alleles using a maximum likelihood method implemented in

ML-Null (Kalinowski & Taper, 2006). Tests for linkage disequilibrium and Hardy-Weinberg

equilibrium were conducted using FSTAT v.2.9.3 (Goudet, 2001). Measures of population

genetic diversity and structure, as well as an AMOVA, were calculated using Arlequin.

Differences in expected heterozygosity and the number of alleles per population were tested

with a Friedman ANOVA (for multiple paired comparisons), with samples paired by locus.

We used the Bayesian assignment approach of TESS v.2.3 to determine the number of distinct

genetic units (K) present in the dataset (Chen et al., 2007). We performed a total of 50,000

sweeps, with 20,000 burnin and a spatial interaction prior of 0.8, and both admixture and no-

admixture models. The optimal value of K was determined by plotting the lowest DIC values of

each iteration and convergence was assessed using CLUMPP (Jakobsson & Rosenberg, 2007).

We also visualised the population relationships with principal co-ordinates analysis (PCoA) of

pairwise population estimates of Nei’s unbiased genetic distance (Nei, 1978), implemented in

Genalex v.6 (Peakall & Smouse, 2006). IBD across populations was tested for significance

(10,000 permutations) with a Mantel test Implemented in R (Vegan package). IBD within a BIF

range was also tested among individuals from the three Die Hardy Range populations.

Indirect measures of gene flow were calculated to provide an indication of the degree of

population connectivity or isolation (Slatkin & Barton, 1989) based on nine populations (ie.

including all three DH populations). The effective number of immigrants per generation (Nm)

was calculated according to Slatkin (1985), as implemented in Genepop v.1.2 (Raymond &

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Rousset, 1995). This estimate must always be treated cautiously as it also assumes that

populations are large and stable, and that gene frequencies are not influenced by mutation or

selection (Slatkin, 1985).

We tested for bottleneck events using a Wilcoxon signed rank test for excess of observed

heterozygotes using BOTTLENECK v1.2.02 (Piry et al., 1999). Heterozygote excess was tested

under the Infinite Allele Model, the Stepwise Mutation Model and the Two Phase Model. For

the Two Phase Model we used settings typical for microsatellite markers with the frequency of

single step mutations set to 90% and the variance of those mutations as 12% (Busch et al.,

2007).

3.4 RESULTS

3.4.1 cpDNA diversity

The total combined cpDNA sequence alignment (2777 bp) generated 20 haplotypes from 67

individuals. Variation was comprised of seven transitions, 15 transversions and nine variable

length indels. The Die Hardy and Mt Manning populations were significantly more diverse than

other populations (Table 3.1). Mt Correll showed the least genetic diversity with just one

haplotype identified. Two of the haplotypes, H11 and H12, were shared across three

populations (FIN, HUN, TH) but all other haplotypes were specific to individual populations

(Fig. 3.1). The majority of haplotype diversity occurred between populations, but a relatively

large amount (32.65%) was maintained within populations. Genetic divergence of populations

was high, with the exception of the three populations sharing haplotypes. The spatially

isolated Mt Correll showed the greatest average genetic divergence from all other populations

(0.25%) (Table 3.2). No population departed from the neutral model of evolution (Table 3.1). A

Mantel test showed no pattern of isolation by distance of haplotypes across the landscape

(R=0.034, P=0.37).

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Bayesian analysis of haplotypes resulted in a tree with a mostly unresolved polytomy (Fig. 3.2).

There was some geographic clustering of related haplotypes within populations, eg. H3, H8,

H10 (DH), and H13, H14 (HA), but many haplotypes from the Die Hardy and Mt Manning

ranges were distributed throughout the tree, indicating differentiated haplotypes present

within these populations, a pattern confirmed in the parsimony network (Fig. 3.3).

Interestingly, the haplotype present in the geographically isolated population of Mt Correll was

most closely related to a subset of haplotypes at Mt Manning over 90 km away to the east (Fig.

3.2 and 3.3).

The TMRCA of haplotypes ranged from 0.99 [0.56-1.57] to 1.6 [0.96-2.64] million years ago

respectively, indicating divergence during the Mid Pleistocene. The divergence of haplotypes

within populations was estimated to be 0.08-0.14 to 0.989-1.6 million years ago (Fig. 3.2).

3.4.2 Microsatellite data

One microsatellite marker, Gg16, showed a high frequency of null alleles (40%) and was

discarded. Three other markers (Gg5, Gg18, Gg29) showed null alleles at frequencies between

10 and 16 % and initial analyses with removal of these markers did not significantly alter

results so all 10 loci were retained for analyses. The 10 loci were in Hardy-Weinberg

equilibrium and there was no evidence of linkage disequilibrium following Bonferroni

correction. Genetic diversity was low to moderate (Table 3.3) and a Friedman test indicated a

highly significant difference amongst expected heterozygosity values (Friedman stat = 36.4,

p=0.0001) but no significant difference in the average number of alleles per locus.

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Table 3.1 Diversity statistics for the seven Grevillea georgeana BIF populations assayed for chloroplast DNA variation: N, number of individuals; # Haps, number of

haplotypes; HD, haplotype diversity (std dvn); Pi, nucleotide diversity (std dvn).

Population Pop abbrev.

N # Haps HD Pi Tajima’s D

Mt Correll COR 10 1 0 0 na

Die Hardy Range DH 10 9 0.978 (0.05) 0.00188 (0.00028) -0.573

Mt Finnerty FIN 10 2 0.356 (0.16) 0.00013 (0.00006) 0.015

Helena Aurora Range HA 9 2 0.222 (0.17) 0.00017 (0.00013) -1.362

Hunt Range HUN 10 3 0.378 (0.18) 0.00044 (0.00028) -1.388

Mt Manning MM 9 5 0.833 (0.10) 0.00215 (0.00030) 1.343

Taipan Hill TH 9 2 0.500 (0.13) 0.00019 (0.00005) 0.987

Table 3.2 Estimates of pairwise divergence % (JC) based on chloroplast DNA variation amongst populations of Grevillea georgeana. The distances represent an average among individuals from each population.

COR DH FIN HA HUN MM

DH 0.29 FIN 0.25 0.12

HA 0.29 0.15 0.11 HUN 0.24 0.13 0.02 0.12

MM 0.19 0.21 0.17 0.20 0.17 TH 0.25 0.13 0.02 0.12 0.03 0.17

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Figure 3.1 Map of the seven BIF Grevillea georgeana populations with pie charts showing the frequency and occurence of each cpDNA haplotype (legend). Only two haplotypes were shared amongst populations (H11 and H12). Image modified from Google Earth.

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Figure 3.2 Bayesian tree of 20 Grevillea georgeana haplotypes based on combined trnQ-rps16 and psbD-trnT cpDNA data. Numbers in red reflect divergence estimates for the conservative mutation rate in millions of years. Numbers in black are posterior probabilities. COR, Mt Correll; DH, Die Hardy; FIN, Mt Finnerty; HA, Helena Aurora; HUN, Hunt; MM, Mt Manning; TH, Taipan Hill.

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Figure 3.3 Maximum parsimony network for all Grevillea georgeana haplotypes (numbered) based on combined cpDNA data. Node size is proportional to the frequency of haplotypes with the high frequency Hap 11 representing 34% of individuals. Black dots indicate mutational steps. Colours identify the haplotype according to the key in Fig. 3.1.

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Figure 3.4 Map of the seven Grevillea georgeana BIF populations with pie charts indicating the proportion of assignment of sampled populations to each of the six genetic clusters (colours) identified in the nrDNA microsatellites using TESS.

Estimates of population differentiation using FST were moderate to high, with an average value

of 0.174. All pairwise combinations were significant following Bonferroni correction, with the

exception of Die Hardy south and Hunt Range (FST = 0.026), and Die Hardy south and Die Hardy

central (FST = 0.031). The spatially isolated population at Mt Correll showed the highest

average pairwise FST estimate (0.247), and Hunt Range the least (0.119) (Table 3.4). Bayesian

analysis with TESS resulted in the identification of six genetic clusters corresponding to each

population, with the exception of Die Hardy and Hunt Range that clustered together (Fig. 3.4).

In contrast, PCoA identified two major genetic clusters, the first corresponded to Mt Finnerty,

Taipan Hill and Helena Aurora, and the second to all other populations, with further

subdivision of Mt Manning and Mt Correll separate from Die Hardy and Hunt Range. The first

two axes explained 36 % and 22.5% of the variation present (Fig. 3.5). The Mantel test showed

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weak but significant IBD across all populations (r=0.21, p=0.0001) and also within the Die

Hardy Range (r=0.24, p=0.0001).

Indirect estimates of gene flow indicated low levels of migration between populations

(average Nm=0.75), and within a BIF (average Nm=1.31) (Table 3.4). The greatest gene flow

occurred between the Die Hardy south and Hunt Range populations (Nm = 1.458), and the

least between two of the most spatially distant, Mt Correll and Mt Finnerty (Nm = 0.182). The

centrally located Helena Aurora population received the highest gene flow with an average Nm

of 0.991.

Four of the nine populations (TH, DHN, DHS and HUN) showed significant departure from

mutation-drift equilibrium under the infinite allele mutation model using the Wilcoxon test

(P=<0.05). None of the populations deviated from mutation-drift equilibrium under the

stepwise mutation or two phase mutation model.

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Table 3.3 Microsatellite genetic diversity estimates for nine populations of Grevillea georgeana genotyped using 10 loci. N, number of individuals genotyped; #

alleles, average number of alleles per locus; HO, observed heterozygosity; HE, expected heterozygosity; FIS, inbreeding coefficient. Standard deviations in

parentheses.

Population Pop abb. Latitude Longitude N # alleles HO HE FIS

Mt Correll COR -30.53937449 118.9105704 24 2.600(0.84) 0.390(0.28) 0.395(0.16) 0.012

Die Hardy north DHN -29.86778772 119.4049258 24 3.100(0.99) 0.406(0.26) 0.481(0.26) 0.158

Die Hardy central DHC -29.89930478 119.3565458 24 3.444(1.88) 0.505(0.35) 0.431(0.25) -0.176

Die Hardy south DHS -29.94275564 119.3747744 23 3.778(1.72) 0.518(0.23) 0.541(0.16) 0.043

Mt Finnerty FIN -30.67567776 120.1341005 24 3.700(1.64) 0.407(0.26) 0.451(0.25) 0.1

Helena Aurora HA -30.35353016 119.7039402 24 4.100(2.77) 0.491(0.30) 0.457(0.21) -0.076

Hunt Range HUN -29.94269066 119.3748764 17 4.000(1.80) 0.609(0.29) 0.580(0.15) -0.045

Mt Manning MM -29.81221491 119.6053819 24 3.143(1.46) 0.505(0.29) 0.436(0.25) -0.164

Taipan hill TH -30.38923472 119.9079597 24 3.900 (1.66) 0.497(0.22) 0.530(0.22) 0.062

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Table 3.4 Pairwise FST values based on 10 microsatellite loci (upper diagonal) and Nm estimates (Slatkin, 1985) (lower diagonal) *P < 0.005. COR, Mt Correll; DH,

Die Hardy north, central, south; FIN, Mt Finnerty; HA, Helena Aurora; HUN, Hunt; MM, Mt Manning; TH, Taipan Hill.

COR DHN DHC DHS FIN HA HUN MM TH

COR 0 0.284* 0.193* 0.232* 0.382* 0.287* 0.146* 0.169* 0.284*

DHN 0.197 0 0.108* 0.046* 0.189* 0.189* 0.091* 0.232* 0.108*

DHC 0.322 0.959 0 0.031 0.198* 0.203* 0.050* 0.115* 0.162*

DHS 0.431 0.792 1.752 0 0.239* 0.209* 0.026 0.163* 0.117*

FIN 0.182 0.438 0.635 0.635 0 0.144* 0.202* 0.330* 0.087*

HA 0.399 0.986 1.420 1.266 1.178 0 0.192* 0.261* 0.098*

HUN 0.360 0.697 0.830 1.458 0.613 1.300 0 0.130* 0.119*

MM 0.226 0.254 0.532 0.607 0.315 0.654 0.391 0 0.247*

TH 0.278 0.308 0.385 0.510 0.603 0.726 0.587 0.316 0

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Figure 3.5 Principal coordinates analysis of all Grevillea georgeana populations based on Nei’s genetic distance, calculated across 10 microsatellite loci. Primary and secondary axes shown, with the percentage of total variation explained by each. COR, Mt Correll; DH, Die Hardy north, central, south; FIN, Mt Finnerty; HA, Helena Aurora; HUN, Hunt; MM, Mt Manning; TH, Taipan Hill.

3.5 DISCUSSION

Phylogeographic analysis of G. georgeana revealed patterns of both long-term isolation and

connectivity, indicating different historical processes shaping evolution of these terrestrial

island populations. Some are behaving as isolated islands, with genetic drift driving patterns of

differentiation without geographic structure, whilst others show patterns of more recent

connectivity. The patterns of isolation are consistent with expectations from island

biogeography, but evidence of some pollen dispersal demonstrates that populations on

terrestrial habitat islands may have opportunities for connectivity. The potential for some

pollen movement between neighbouring BIFs is an important process that should be taken

into consideration when making decisions regarding land use management in this region.

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3.5.1 Genetic differentiation among BIF populations

Analysis of cpDNA variation revealed complex patterns of genetic structure amongst

populations, with some geographic structuring of haplotypes, such as those specific to Helena

Aurora and Mt Correll, and others shared among populations, such as H11 and H12 from Mt

Finnerty, Taipan Hill and Hunt Range. The high diversity and divergence of haplotypes at Die

Hardy and Mt Manning is in contrast to specific haplotypes in other populations, such as

Helena Aurora and Mt Correll. The high level of genetic divergence among many populations

suggests long-term isolation of these populations, allowing the independent processes of

mutation and genetic drift to shape genetic variation (Byrne & Hopper, 2008). In particular, the

unresolved relationship of Die Hardy and Mt Manning haplotypes may reflect long periods of

isolation and persistence of G. georgeana on these large, northern populations (Lee, 2000).

Divergence of haplotypes both within and between BIFs, suggests isolation since the Mid

Pleistocene. The Pleistocene was a period of intensifying aridity in this landscape that

impacted the distribution of many species, causing localised extinctions and range shifts

(Bowler, 1982; Dodson & Macphail, 2004; Byrne, 2007; Fujioka et al., 2009), and this is likely to

have led to increased isolation in populations of G. georgeana that may have been restricted

to BIF islands prior to the Pleistocene. Similar high differentiation arising from historical

isolation from the early-mid Pleistocene has been observed in a millipede endemic to the

Yilgarn BIFs (Nistelberger et al., 2014a), and in other rare and common species that are

endemic to granite outcrops in the south western Australian landscape that also act as

terrestrial islands (Byrne & Hopper, 2008; Tapper et al., 2014a, 2014b). This pattern has also

been observed in intra-specific studies from other parts of the world on populations endemic

to terrestrial islands, such as the sky islands of Southern India (Robin et al., 2010), the Afro-

Alpine sky islands of East Africa (Assefa et al., 2007) and the Madrean sky islands of south

81

western USA (Smith & Farrell, 2005), highlighting the global influence of Pleistocene climatic

oscillations on driving species biogeography (Hewitt, 1996, 2000).

The general pattern of population divergence is consistent with our hypothesis of historical

isolation of populations, but we were surprised to see some evidence of connectivity between

the three south-eastern populations. This pattern may be explained by the underlying geology

of this part of the landscape. Mt Finnerty, Taipan Hill and Hunt Range, although separate

regions of uplift in the Yilgarn craton, are all part of the large Yendilberin/Watt Hills

greenstone belt that extends for approx. 90 km in a north-west to south-east direction in the

southern part of this species’ distribution. The results suggest populations of G. georgeana

along these hills may have been connected relatively recently, or indeed may still be

connected to some extent across portions of the belt that were difficult to access. The

widespread nature of the two haplotypes present in these populations and their position in

the haplotype network suggested these may be ancestral (Crandall & Templeton, 1993).

However, they form part of the unresolved polytomy in the Bayesian tree, making their

position relevant to other haplotypes difficult to determine.

Significant differentiation among many populations was also evident in the nrDNA, along with

isolation by distance, suggesting historical pollen flow between neighbouring BIF populations

was not frequent enough to override forces of genetic drift (Slatkin, 1985; Lowe & Allendorf,

2010). Pollen flow between neighbouring BIF populations was observed in a study of the

insect-pollinated Acacia woodmaniorum, and this was attributed to the long-distance dispersal

of pollinators by prevailing winds (Millar et al., 2013), although the geographic distances

between these BIFs were substantially less than those in our study. Grevillea georgeana is

visited by a range of honeyeaters that probably facilitate the majority of pollination, although

some insect pollination may occur (McGillivray & Makinson, 1993). Foraging behaviours are

82

variable amongst honeyeaters, ranging from within-tree and nearest-neighbour flower

visitation to long-distance dispersal events, and can be highly influenced by other species of

birds occupying the area (Ford, 1981; Hopper & Moran, 1981; Ford et al., 2000; Southerton et

al., 2004). The genetic results identified here suggest inter-population pollinator movements

are restricted, as would be expected given the large distances and dramatic change in

vegetation composition between BIFs that are highly likely to act as barriers to dispersal for

pollinators. Higher estimates of gene flow for the centrally located Helena Aurora population

suggest it may provide a stepping stone for birds moving through the broader landscape.

By sampling three populations across the large Die Hardy Range we were able to assess the

presence of genetic structure across a BIF that supports a large but discontinuous population.

The differentiation and pattern of isolation by distance between the Die Hardy sub-

populations suggests pollen movement is also restricted within a BIF, allowing differentiation

to develop at localised scales (approximately 4 km between sub-populations).

3.5.2 Genetic variation within BIF populations

The slow mutation rate of cpDNA can make it difficult to detect genetic variation at the

population level in angiosperms (McCauley 1995), yet we found high cpDNA variation,

particularly within the Mt Manning and Die Hardy populations. This high diversity suggests

historically large effective population size of G. georgeana on these BIFs (Frankham et al.,

2002). As expected, smaller populations, such as Mt Correll and Mt Finnerty, showed less

genetic variation, most likely due to limited gene flow, population bottlenecks and genetic drift

(Hamrick & Godt, 1989; Barrett & Kohn, 1991; Gitzendanner & Soltis, 2000). The population at

Mt Correll, a small and isolated BIF, was identified by both data sets as the least genetically

diverse and the most divergent. The overall microsatellite diversity in G. georgeana was similar

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to that identified using microsatellite data in other Grevillea species with naturally restricted

distributions, including G. caleyi, G. longifolia and G. macleayana, indicating that long-term

isolation on BIF islands has not necessarily driven lower diversity in this species compared to

its congeners (England et al., 2003; Llorens et al., 2004).

The identification of recent bottlenecks under the IAM model in four of the populations

suggests fluctuations in population size may be a common occurrence in G. georgeana, due to

stochastic influences of fire, competition or climatic fluctuations on populations. Many

grevilleas, including G. georgeana, are killed by fire, with re-establishment occurring from the

seed bank (Gill, 1981; Olde & Marriott, 1995; Burne et al., 2003). England et al (2002)

suggested that fluctuations in the size of the accumulated seed bank due to varying fire

intervals may be responsible for population bottlenecks in some of the naturally fragmented G.

macleayana populations. Fire is likely to have played a large role in generating population

structure in this semi-arid landscape and may have caused periodic bottleneck events.


This study has highlighted the importance of understanding genetic patterns in species

endemic to terrestrial islands, given the possibility for complex genetic structure that has

implications for conservation. A key goal of conservation biology is to preserve a range of

genetic diversity to ensure the potential for future adaptation and prevent inbreeding

depression (Soulé, 1980), and also to protect divergent lineages that may represent cryptic

species (Hebert et al., 2004) or progress towards speciation (Faith, 1992). In the case of G.

georgeana, the protection of Die Hardy Range and Mt Manning would capture both large

amounts of cpDNA genetic variation and divergent haplotypes. Mt Correll may also be

considered important, given the data indicates long periods of genetic isolation, which is likely

84

to continue into the future given this population’s spatial isolation. Perhaps most importantly,

the microsatellite data shows that whilst pollen flow is generally restricted, it has occurred to

some extent between neighbouring populations. Maintenance of connectivity between these

neighbouring populations is likely to be an integral process contributing to the persistence of

these otherwise isolated BIF populations.

85

Majestic, grand…and

incredibly prickly

86

Work in this chapter has been submitted for publication and published as:

Nistelberger HM, Byrne M, Coates DJ and Roberts JD (2014) Patterns of isolation and

connectivity in terrestrial island populations of Banksia arborea (Proteaceae). Submitted

Biological Journal of the Linnean Society.

Nistelberger HM, Roberts JD, Coates DJ and Byrne M (2013) Isolation and characterisation of

14 microsatellite loci from a short-range endemic, Western Australian tree, Banksia arborea

(C.A. Gardner). Conservation Genetics Resources, 5, 1143-1145. (Appendix D)

87

Chapter 4

Patterns of genetic isolation and connectivity in terrestrial island

populations of Banksia arborea.

4.1 ABSTRACT

The Banded Iron Formations (BIFs) of south-western Australia are terrestrial islands

characterised by high species richness and endemism. Regional endemics occur across multiple

formations without inhabiting the intervening landscape matrix. We asked whether eight BIF

populations of the regional endemic Banksia arborea, showed evidence of genetic connectivity

or isolation. Connectivity was assessed using three chloroplast DNA sequence markers and 11

nuclear microsatellite loci. Phylogenetic relationships were assessed with statistical parsimony

and Bayesian methods. Dating estimates of haplotype divergence were estimated using the

time to most recent common ancestor of B. arborea and B. purdieana, as well as a

conservative angiosperm chloroplast DNA mutation rate. Population genetic diversity and

structure was assessed amongst and within populations by genotyping 24 geographically

clustered individuals from each BIF and three sub-populations within the Die Hardy Range BIF.

Indirect gene flow estimates were determined using a method based on the frequency of

private alleles. Banksia arborea showed a complex evolutionary history with patterns of

genetic divergence amongst some BIF populations, reflecting processes of long-term isolation

and genetic drift, and an absence of differentiation amongst others. There was little evidence

of pollen dispersal among BIFs and within the large Die Hardy Range.

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4.2 INTRODUCTION

Terrestrial or habitat islands are discrete habitat patches that are embedded within the

surrounding landscape. Depending on an organism’s habitat requirements, they can range

from large and conspicuous geological formations, such as mountain tops, to dung piles in a

grassland landscape (Whittaker, 1998). Terrestrial islands generally harbour different species

from the surrounding landscape due to the different habitats they provide (Rosenzweig, 2002).

Banded iron formations (BIFs) are ancient, geological features associated with Archean cratons

throughout the world and form some of the oldest structures known on Earth (Klein, 2005).

The most well-studied BIF terrestrial islands are those found in south-eastern Brazil and south-

western Australia. These are characterised by high species richness and endemism as a result

of the diversity of habitats, including changes in elevation, substrate, soil accumulation,

temperature and moisture (Jacobi et al., 2007; Jacobi & Carmo, 2008; Markey & Dillon, 2008;

Meissner & Caruso, 2008a, 2008b; Gibson et al., 2010, 2012), and also of the relative stability

of these landscapes that remained un-glaciated during the Pleistocene, allowing for the

development and persistence of diversity over time (Haffer, 1969; Gibson et al., 2010).

The BIFs of south-western Australia are features of the Yilgarn craton, one of the oldest

geological shields on earth (Freeman, 2001). They occur along the boundary of the Southwest

Australian Floristic Region (SWAFR) and extend inland to the arid zone for over 750 km (Gibson

et al., 2012). Floristic analysis of BIFs bordering the SWAFR and adjacent semi-arid zone

identified higher species turnover and endemism than those further inland in the arid zone

(Gibson et al., 2010, 2012). BIF endemics may be restricted to just one formation, or may be

regional, occurring across several BIFs, displaying an island-archipelago distribution. These

regional island endemics provide an excellent opportunity to examine patterns of population

connectivity or isolation using a combined phylogeographic and population genetic approach.

Compared to other terrestrial island systems, such as the sky islands of northern America

89

(DeChaine & Martin, 2004, 2005) and southern India (Robin et al., 2010), and rocky inselberg

and serpentine soils (Barbará et al., 2007; Byrne & Hopper, 2008; Boisselier-Dubayle et al.,

2010; Moore et al., 2013; Tapper et al., 2014a, 2014b), there has been little population-level

analysis of the patterns of genetic diversity and structure present in regional BIF endemics

worldwide. Recent studies on south-western Australian BIF flora and fauna have shown

organisms endemic to BIFs have complex phylogeographic histories, with some populations

showing evidence of isolation since the Pleistocene and others showing more recent

connectivity. For example, the millipede Atelomastix bamfordi occurs on five BIFs, where it

inhabits mesic microhabitats provided by shaded gullies containing accumulated leaf litter.

Analysis of populations using both mtDNA sequencing and nuclear microsatellite markers

indicated vicariance of a formerly widespread ancestor during the Pleistocene with subsequent

isolation of populations, resulting in patterns of genetic differentiation that were not

geographically structured (Nistelberger et al., 2014a). This pattern of long-term isolation on

terrestrial islands is similar to those observed in plant species restricted to granite outcrops of

Western Australia (Byrne & Hopper, 2008; Tapper et al., 2014a, 2014b) and to invertebrates

restricted to sky islands (Masta, 2000). In contrast, the BIF plant endemic Grevillea georgeana

has mixed phylogeographic patterns, with some populations showing evidence of genetic

isolation in cpDNA, and others showing evidence of recent population connectivity via both

seed and pollen flow (Nistelberger et al., 2014b). The insect-pollinated Acacia woodmaniorum

showed even greater evidence of population connectivity amongst disjunct BIF formations, a

pattern attributed to effective long-distance pollen dispersal (Millar et al., 2013). These studies

show different responses of species to isolation on BIF islands and highlight the need for

research on a range of species in order to identify whether common patterns exist. Major BIF

formations in Brazil and Western Australia are prospective for mining, placing populations at

risk from habitat removal and disruption of gene flow events. Understanding patterns of

genetic connectivity or isolation is important as it provides a basis for assessing the impact of

90

population loss and reduced connectivity on genetic diversity, inbreeding in small isolated

populations and species persistence.

To further the understanding of factors influencing population connectivity in BIFs, we

investigated phylogeographic pattern in a locally common tree species. Banksia arborea (C.A.

Gardner: A.R. Mast & K.R. Thiele) is restricted to the south–western, species rich BIFs that

occur close to the SWAFR (Gibson et al., 2007) and forms a conspicuous component of the BIF

vegetation community. It is a long-lived tree, up to eight metres high, with yellow flowers that

appear throughout the year (Wrigley & Fagg, 1989). Although little is known of the species’

ecology, it is presumed that insects, and potentially birds, are responsible for pollination (Ladd

et al., 1996). The woody fruits are serotinous and contain seeds that have no active

mechanism of dispersal (Wrigley & Fagg, 1989). We used cpDNA sequence data to assess

historical patterns of population connectivity, and nuclear microsatellite data to assess

contemporary patterns reflecting both pollen and seed dispersal within and between BIF

populations. Given the lack of active seed dispersal mechanisms in B. arborea we hypothesised

limited population connectivity via seed, resulting in strong signals of genetic differentiation in

the cpDNA data. Contemporary patterns of connectivity may be dependent on the mobility of

pollinators. If pollinator movement is limited we would expect significant population

differentiation that lacks geographic structure as a result of genetic drift driving genetic

structure, or, if pollen movement occurs between neighbouring BIFs, a pattern of isolation by

distance would be predicted.

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Banksia arborea was collected from eight Yilgarn BIFs; Koolyanobbing Range (KOO), Mt

Jackson (MTJ), Windarling Range (WIN), Die Hardy Range (DH), Mt Manning (MM), Taipan Hill

(TH), Hunt Range (HUN) and the Helena Aurora Range (HA). Voucher specimens from each

population were lodged at the Western Australian Herbarium. The BIFs vary in size, shape,

height and distance from one another. All form part of the Yilgarn Province, a region under

active exploration and resource development. For the phylogeographic component of the

study, 10 individuals were sampled across the geographic extent of each BIF where possible, in

order to avoid sampling closely related individuals and to maximise the genetic diversity

captured. Population genetic structure amongst the different BIF populations was analysed

using 11 specifically designed microsatellite markers on a geographically clustered group of 24

individuals (Nistelberger et al., 2013c). For the Die Hardy Range, three sub-populations of 24

individuals were collected, in the north, central and south of the range to allow assessment of

patterns of structure within this large BIF. Adult leaves from each tree were collected in paper

envelopes and placed immediately on silica gel, then desiccated in a freeze-drier (Labconco)

for 48 hours. DNA was extracted using a modified 2 % CTAB method outlined in Nistelberger et

al. (2013).

4.3.2 cpDNA amplification

Non-coding cpDNA regions that have been found useful for phylogeographical studies in

Australian plants (Byrne & Hankinson, 2012) were trialled in B. arborea, and the three regions

that produced the highest sequence quality and nucleotide diversity, the intergenic spacers

psbD-trnT and psbA-trnH and the D loop intron PetB, were selected for further analysis. PCR

reactions were carried out in 50 µl individual, non-multiplexed volumes containing 50 mM KCl,

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20 mM Tris-HCl (pH8.4), 0.2 mM of each dNTP, 0.1 µM (psbD-trnT and psbA-trnH) or 0.08 µM

(PetB) primers, 1.5 mM (psbD-trnT and PetB) or 2.5 mM (psbA-trnH) MgCl2 and 0.1 U of Taq

polymerase (Invitrogen). The amplification protocol involved an initial hot start of 5 min at

80˚C, followed by 30 cycles of 95˚C for 1 min, 50˚C for 1 min and 65˚C for 4 min, with a final

extension step of 65˚C for 5 min. PCR products were cleaned and sequenced at the AGRF-Perth

(Australian Genome Research Facility) on an Applied Biosystems 3730 x 1 DNA Analyser

platform. Sequence data were aligned and edited in BioEdit (Hall, 1999) and finalised by eye.

The three regions were combined to form a total aligned sequence length of 1914 base pairs.

4.3.3 Microsatellilte amplification

PCR reactions (non-multiplexed) were performed in a total volume of 15 µl as outlined in

Nistelberger et al. (2013). Amplified products were run on 3% agarose gel with gel red nucleic

acid stain (Biotium) to assess amplification and polymorphism. Fourteen M13 labelled primer

pairs were initially used to amplify DNA; three of these were subsequently discarded, two

(Ba18 and Ba28) due to detection of high frequencies of null alleles, and the third (Ba04) due

to inconsistent banding patterns. PCR products were visualised on an Applied Biosystems 3730

capillary sequencer (Murdoch University) and scored using GeneMapper version 4.0 (Applied

Biosystems).

4.3.4 cpDNA analysis

Descriptive statistics, including number of haplotypes, nucleotide diversity and population

divergence (Jukes & Cantor, 1969) were calculated using DNAsp v5.10 (Librado & Rozas, 2009).

The structuring of cpDNA diversity amongst BIF populations was determined with an analysis

of molecular variance (AMOVA), based on the number of pairwise, base-pair differences, as

implemented in Arlequin v 3.5 (Excoffier & Lischer, 2010).

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We used a Statistical Parsimony method to create an un-rooted haplotype network in the

program TCS (Clement et al., 2000) and estimated divergence times of the different haplotypes

using a Bayesian analysis in BEAST (Drummond et al., 2012). The best substitution model for

the dataset was identified as F81 based on AIC log likelihoods (Posada, 2008), so we

implemented the simplest substitution model available in the BEAST package (HKY) and

applied a Strict Clock model, owing to the low diversity of the cpDNA data, with a

coalescent:constant size tree prior. We used the time to most recent common ancestor

(TMRCA) of B. arborea and B. purdieana (Diels) A.R. Mast & K. R. Thiele, taken from Cardillo

and Pratt (2013) to calibrate the tree for molecular dating. For the root tree node we used a

normal prior (mean = 20.39, sd = 1) and initiated two independent MCMC runs of 10 million

generations from starting trees, sampling every 1000 generations. Stationarity was assessed by

inspecting log files for high ESS values in Tracer. Tree files were combined in LogCombiner and

annotated in TreeAnnotater discarding the first 25% of trees as burnin (available BEAST

package). As a further test we compared these dates to those obtained using the conservative

mutation rate commonly reported for angiosperms 1.2 x 10-9 (Graur & Li, 2000).

4.3.5 Microsatellite analysis

Tests for linkage disequilibrium and Hardy-Weinberg equilibrium along with estimates of FIS

were calculated using FSTAT v 2.9.3 (Goudet, 2001). Estimates of population genetic diversity

and structure parameters, including the average number of alleles per locus, HO and HE,

pairwise FST as well as AMOVA were calculated in Arlequin v.3.5 (Excoffier & Lischer, 2010).

Jost’s D statistic, based on the effective number of alleles rather than heterozygosity (Jost,

2008) was also used to measure population differentiation, calculated in GenAlEx v. 6.5

(Peakall & Smouse, 2006). Differences in population heterozygosity and the number of alleles

per population were tested with a Friedman ANOVA (for multiple paired comparisons) with

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samples paired by locus. The presence and frequency of null alleles were estimated using a

maximum likelihood method in the program ML-null (Kalinowski & Taper, 2006).

Genetic structure amongst BIF populations was also assessed using Bayesian clustering

algorithms implemented in the programs STRUCTURE v.2.3.3 (Pritchard et al., 2000) and TESS

v.2.3.1 (Chen et al., 2007). STRUCTURE assigns individuals to the most likely number of groups

(K) without prior knowledge of spatial location by maximising within-K Hardy-Weinberg and

linkage equilibria (Pritchard & Wen, 2004). We ran STRUCTURE for 1 million MCMC repetitions

with a burnin length of 100,000. Simulations for proposed K values between 1 and 11 were

iterated 10 times and the posterior mean recorded. We used a run model that assumed

independence of allele frequencies and admixed ancestry. The output was run through

STRUCTURE HARVESTER (http://taylor0.biology.ucla.edu/structureHarvester/) that uses the

Evanno method (Evanno et al., 2005) to determine the optimal value of K. These results were

then used in the program CLUMPP (Jakobsson & Rosenberg, 2007) that aligns cluster

assignment across the replicate analyses and ensures the correct assignment of K. In contrast

to STRUCTURE, TESS assumes proximate interactions between individuals, with spatial

information prescribed at the individual level. TESS has been shown to perform better under

scenarios with nil to moderate admixture, whilst STRUCTURE is better at assigning K under

high levels of admixture (Chen et al., 2007). We tested for structure across the BIF populations

with TESS under both admixture and no-admixture models. For the admixture model,

simulations for K values of one to 11 were iterated 100 times each, with a total of 100,000

sweeps run and a burnin of 20,000. Under the no-admixture model we conducted the same

number of runs and iterations and set the spatial interaction parameter to 0.8. The optimal

value of K was determined by plotting the lowest DIC values of each K iteration and

convergence was assessed using the downstream program CLUMPP. The genetic relationships

among populations were further examined using principal coordinates analysis (PCoA) of

http://taylor0.biology.ucla.edu/structureHarvester/

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pairwise population estimates of Nei’s unbiased genetic distance (1978), implemented in

Genalex6.5 (Peakall & Smouse, 2006).

We tested for a pattern of isolation by distance (IBD) across all eight BIF populations and

across the three Die Hardy sub-populations by performing a Mantel test on matrices of genetic

distance (Nei, 1978) and log geographic distance in the program IBDWS. A Wilcoxon signed

rank test was used to test for an excess of observed heterozygotes that can indicate a

bottleneck event using the program BOTTLENECK v 1.2.02 (Piry et al., 1999). Heterozygote

excess was tested under the Infinite Allele Model, the Stepwise Mutation Model and the Two

Phase Model. For the Two Phase Model, we used settings typical for microsatellite markers

with the frequency of single step mutations set to 90% and the variance of those mutations as

12% (Busch et al., 2007). Indirect measures of gene flow (Nm) between all BIF populations and

the Die Hardy sub-populations were calculated using a method based on the frequency of

private alleles (Slatkin, 1985), in the program Genepop v 1.2 (Raymond & Rousset, 1995).

4.4 RESULTS

4.4.1 cpDNA variation

Amplification of the three cpDNA regions across 79 individuals (one individual from Helena

Aurora failed to amplify) revealed five haplotypes comprised of three transitions and three

transversions. One haplotype was specific to two individuals from the Die Hardy population

(H1), one haplotype was restricted to Taipan Hill (H4) and one to Koolyanobbing (H5). Two

were specific to multiple populations (MTJ, HA, HUN) (H2) and (WIN, DH, MM) (H3) (Table 4.1)

(Fig. 4.1). The majority of cpDNA variation (98%) occurred amongst populations, with only the

Die Hardy population possessing more than one haplotype.

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The parsimony network showed some structure among haplotypes, with the two common

haplotypes and the haplotype restricted to Die Hardy being more closely related, and

divergence of two haplotypes present at Koolyanobbing and Taipan Hill (Fig. 4.2, Table 4.2).

The divergence times amongst haplotypes based on the calibrated BEAST tree ranged from 1.2

Ma (95% hpd levels 0.6-2.0 Ma) and 1.4 Ma (95% hpd levels 0.5-2.6 Ma) between haplotypes 2

and 3 and haplotypes 4 and 5 respectively, to 2.7 Ma (95% hpd levels 1.2-4.7 Ma) for the

divergence of haplotypes 4 and 5 from haplotypes 1, 2 and 3 (Fig. 4.3). These estimates were

older than those generated using the conservative, angiosperm mutation rate that indicated

divergence of 0.3 Ma (95% hpd levels 0.1-0.5 Ma) between haplotypes 2 and 3 and 0.74 (95%

hpd levels 0.3-1.2 Ma) for the divergence of haplotypes 4 and 5 from haplotypes 1, 2 and 3.

Figure 4.1 Map of the eight Banksia arborea BIF population locations with haplotypes represented by different colours. Inset shows study location. Image adapted from Google Earth.

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Figure 4.2 Maximum parsimony haplotype network for Banksia arborea based on combined cpDNA data. Node size is proportional to the frequency of haplotypes with H1 representing 2 individuals and H2 29 individuals. The inferred ancestral haplotype is shown as a rectangle and black dots indicate un-sampled or extinct haplotypes. Colours identify sequence haplotypes as shown in Fig. 4.1. DH, Die Hardy; HA, Helena Aurora; HUN, Hunt; KOO, Koolyanobbing; MM, Mt Manning; MTJ, Mt Jackson; TH, Taipan Hill; WIN, Windarling.

4.4.2 Microsatellite variation

There was no evidence of linkage disequilibrium between any pairwise combinations of loci.

Null alleles were estimated to occur at frequencies between 10% and 20% in Barb 10, 16 and

36. To examine the influence of these three loci on the results we also analysed our data

without these markers, but this did not significantly alter the analysis outcomes and the data

for all 11 loci is presented. Genetic variation within populations was low, with an average of

4.18 alleles per locus across all populations. Observed and expected heterozygosity was also

low, ranging from 0.39 - 0.56 and 0.46 - 0.55 respectively. Seven out of 10 populations had

private alleles, with Die Hardy north showing significantly higher numbers than all other

populations (> 2SD from the mean) (Table 4.3). All populations were in Hardy-Weinberg

equilibrium following Bonferroni correction (Table 4.3).

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Figure 4.3 Bayesian cladogram of five Banksia arborea haplotypes based on combined cpDNA data using B. densa and B. purdieana as outgroups. Numbers on the left-hand side of nodes indicate posterior probabilities. Numbers on the right-hand side of nodes indicate divergence estimates in millions of years with confidence intervals shown in brackets. Age estimates shown were based on the TMRCA of B. arborea and B. purdieana. NB. the branch to B. purdieana was truncated to improve tree appearance. DH, Die Hardy; HA, Helena Aurora; HUN, Hunt; KOO, Koolyanobbing; MM, Mt Manning; MTJ, Mt Jackson; TH, Taipan Hill; WIN, Windarling.

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Table 4.1 Diversity statistics for chloroplast DNA data for eight Banksia arborea populations: N, number of individuals; # Haps, number of haplotypes; HD, haplotype diversity; Pi, nucleotide diversity. DH, Die Hardy; HA, Helena Aurora; HUN, Hunt; KOO, Koolyanobbing; MM, Mt Manning; MTJ, Mt Jackson; TH, Taipan Hill; WIN, Windarling.

Genbank numbers

Population N # HAPS HD PI (%) PetB PsbA PsbD

DH 10 2 0.356 0.019 KF668088 KF668091 KF668094 KF668095

HA 9 1 0 0 KF668089 KF668091 KF668094

HUN 10 1 0 0 KF668089 KF668091 KF668094

KOO 10 1 0 0 KF668090 KF668092 KF668095

MM 10 1 0 0 KF668088 KF668091 KF668094

MTJ 10 1 0 0 KF668089 KF668091 KF668094

TH 10 1 0 0 KF668090 KF668093 KF668095

WIN 10 1 0 0 KF668088 KF668091 KF668094

Table 4.2 Estimates of cpDNA divergence (JC) (%) between populations of Banksia arborea

DH HA HUN KOO MM MTJ TH

HA 0.063 HUN 0.063 0

KOO 0.304 0.262 0.262 MM 0.010 0.052 0.052 0.314

MTJ 0.063 0 0 0.262 0.052 TH 0.251 0.209 0.209 0.052 0.262 0.209

WIN 0.010 0.052 0.052 0.314 0 0.052 0.262

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Table 4.3 Microsatellite genetic diversity estimates for 10 populations of Banksia arborea genotyped at 11 loci. N, number of individuals genotyped; # alleles,

average number of alleles per locus; HO, observed heterozygosity; HE, expected heterozygosity; FIS, inbreeding coefficient. Standard error in parentheses. *

Significantly higher than the mean number of private alleles.

Population pop abb.

N # alleles

# Private HO HE FIS alleles

Die Hardy north DHN 24 4.80(0.50) 7* 0.43(0.06) 0.50(0.05) 0.147

Die Hardy central DHC 23 4.10(0.53) 0 0.43(0.06) 0.53(0.04) 0.186

Die Hardy south DHS 24 4.60(0.58) 1 0.52(0.05) 0.55(0.05) 0.057

Helena Aurora HA 24 4.60(0.47) 2 0.52(0.07) 0.55(0.06) 0.063

Hunt Range HUN 11 3.46(0.46) 1 0.42(0.08) 0.51(0.06) 0.170

Koolyanobbing KOO 24 3.82(0.55) 3 0.43(0.05) 0.46(0.04) 0.068

Mt Jackson MTJ 24 4.46(0.44) 0 0.39(0.05) 0.52(0.05) 0.248

Mt Manning MM 24 3.46(0.36) 0 0.47(0.05) 0.54(0.04) 0.139

Taipan Hill TH 24 4.18(0.53) 3 0.44(0.07) 0.49(0.06) 0.115

Windarling WIN 20 4.27(0.61) 2 0.56(0.06) 0.51(0.05) -0.108

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Analysis of molecular variance indicated significant partitioning of the overall genetic diversity

with 12.1% of the variation found amongst populations. All pairwise population FST values were

significant, with the exception of the values for Die Hardy central and Die Hardy south, Die

Hardy south and Die Hardy north, and Mt Manning and Hunt Range (Table 4.4). The average

FST across all BIF population pairs was 0.14 (excluding DHN and DHS). All pairwise population

estimates of DEST were significant (Table 4.4). The lack of significant differentiation obtained in

FST values between Mt Manning and Hunt Range was not reflected in DEST values or in a higher

Nm estimate between these two populations, and is likely an artefact of lower sample size

(n=11) taken from Hunt Range.

The two Bayesian analyses of genetic structure produced conflicting results. The program

STRUCTURE resolved two genetic clusters, both showing high levels of admixture. TESS analysis

showed K = 10 under a no-admixture model and K = 11 under an admixture model but none of

the runs from STRUCTURE or TESS indicated convergence following analysis in the downstream

program CLUMPP (all H’ values <0.8). This failure to detect genetic clusters, despite significant

population differentiation (FST and DEST) is most likely due to a lack of sufficient genetic

variation within the data (Rosenberg et al., 2001). Principal coordinates analysis showed

differentiation amongst all populations, but with some clustering of the Die Hardy sub-

populations and greatest divergence of the Windarling population. The first two principal

component (PC) axes explained 27% and 20% of the genetic variation among populations (Fig.

4.4).

There was no signal of IBD across all BIF populations (p = 0.88), although there was a weak, but

significant relationship across the three sub-populations sampled from the Die Hardy Range (r=

0.12, p=< 0.001) (appendix Fig. C1). Three populations (DHC, DHS and MM) showed significant

heterozygote excess, indicating potential bottleneck events using the Wilcoxon sign rank test

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under the Infinite allele method. No bottlenecks were evident under the stepwise mutation

model or two-phase mutation model.

Estimates of gene flow indicated low pollen movement both within and between BIFs, with an

average Nm of 0.94 across all Die Hardy sub-populations and 0.78 between all BIF populations

(Table 4.5). The highest level of gene flow between BIF populations occurred between Die

Hardy north and Taipan Hill (Nm = 1.81), and the least between Hunt Range and Mt Manning

(Nm = 0.32) (Table 4.5). This latter result was interesting given Hunt Range and Mt Manning

were not significantly differentiated according to FST estimates. Die Hardy north showed the

highest average Nm (1.16) estimate across all populations (when excluding DHC and DHS) and

Mt Manning the least (0.06).

Figure 4.4 Principal coordinate analysis of Banksia arborea populations based on pairwise population comparisons of Nei’s unbiased genetic distance calculated across 11 microsatellite loci. Primary and secondary axes shown, with the percentage of total variation explained by each. DH, Die Hardy north, central, south; HA, Helena Aurora; HUN, Hunt; KOO, Koolyanobbing; MM, Mt Manning; MTJ, Mt Jackson; TH, Taipan Hill; WIN, Windarling.

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Table 4.4 Banksia arborea population pairwise FST values (lower diagonal) and DEST values (upper diagonal).* P < 0.05. DH, Die Hardy north, central, south; HA,

Helena Aurora; HUN, Hunt; KOO, Koolyanobbing; MTJ, Mt Jackson; MM, Mt Manning; TH, Taipan Hill; WIN, Windarling.

DHN DHC DHS HA HUN KOO MTJ MM TH WIN

DHN 0 0.062* 0.036* 0.079* 0.042* 0.103* 0.184* 0.091* 0.039* 0.158*

DHC 0.043* 0 0.022* 0.062* 0.086* 0.119* 0.127* 0.063* 0.108* 0.088*

DHS 0.027 0.020 0 0.043* 0.024* 0.099* 0.112* 0.065* 0.081* 0.100*

HA 0.074* 0.126* 0.067* 0 0.051* 0.057* 0.036* 0.110* 0.055* 0.084*

HUN 0.101* 0.164* 0.054* 0.119* 0 0.056* 0.074* 0.063* 0.075* 0.178*

KOO 0.148* 0.193* 0.129* 0.120* 0.086* 0 0.113* 0.102* 0.126* 0.169*

MTJ 0.213* 0.210* 0.121* 0.148* 0.110* 0.211* 0 0.099* 0.107* 0.067*

MM 0.108* 0.141* 0.074* 0.144* 0.047 0.112* 0.158* 0 0.135* 0.132*

TH 0.126* 0.150* 0.072* 0.058* 0.079* 0.172* 0.123* 0.130* 0 0.139*

WIN 0.054* 0.060* 0.068* 0.088* 0.185* 0.138* 0.282* 0.110* 0.157* 0

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Table 4.5 Pairwise indirect gene flow estimates (Nm) between populations of Banksia arborea based on the private allele method of Slatkin (1985). DH, Die Hardy north, central, south; HA, Helena Aurora; HUN, Hunt; KOO, Koolyanobbing; MTJ, Mt Jackson; MM, Mt Manning; TH, Taipan Hill; WIN, Windarling.

DHN DHC DHS HA HUN KOO MTJ MM TH WIN

DHN 0 DHC 1.42 0

DHS 1.65 1.15 0 HA 1.06 0.61 0.75 0

HUN 1.49 0.45 1.00 1.09 0 KOO 0.97 0.50 0.49 0.74 0.57 0

MTJ 1.13 1.26 0.90 1.08 0.92 1.06 0 MM 0.81 0.76 0.50 0.71 0.32 0.47 0.84 0

TH 1.81 0.66 0.83 1.30 0.69 0.90 0.71 0.56 0 WIN 0.84 0.78 0.83 0.95 0.52 0.56 1.02 0.45 1.06 0

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4.5 DISCUSSION

Phylogeographic studies of species with island distributions can aid in our understanding of

factors influencing connectivity and isolation through their effect on genetic structure.

Phylogeography of B. arborea populations has shown contrasting patterns of both isolation

and connectivity amongst BIF populations reflecting, different historical processes operating at

different scales throughout this landscape. The northern populations showed evidence of

connectivity in the cpDNA, although appear to have limited connectivity through pollen

dispersal, whereas divergence is evident in the geographically isolated and demographically

small populations of Koolyanobbing and Taipan Hill respectively.

4.5.1 Genetic differentiation among BIF populations

Variation in the cpDNA showed genetic differentiation of some BIF populations, such as Taipan

Hill and Koolyanobbing that both possessed population specific haplotypes. In contrast the

most northern populations were genetically similar with shared haplotypes across several BIFs.

The sharing of haplotypes between Die Hardy, Windarling and Mt Manning, and between Mt

Jackson, Helena Aurora and Hunt Range, may be due to ongoing connectivity among these BIF

populations via dispersal of seed, or may indicate a more widespread historical distribution

that covered both BIFs and the intervening landscape. Banksia arborea has less restrictive

habitat requirements than some other endemic BIF plants, such as G. georgeana and A.

woodmaniorum, and although it is typically confined to BIF, it has also been identified on the

edge of BIF in sand habitat and one population has been sighted at the base of a granite

outcrop (pers. comm. N. Gibson). The landscape between the BIFs consists of alluvial soils

dominated by eucalypt woodland, interspersed with patches of sandplain generated by the

weathering of surrounding granite outcrops (Payne et al., 1998). Ancestral populations of B.

arborea may have occurred on these sandplains in the past, connecting the now disjunct

northern populations. The other alternative of long-distance seed dispersal connecting

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populations is also plausible, as long-distance seed dispersal has been shown to occur in other

Banksia species. Studies by He et al. (2004, 2009) revealed instances of remarkably long-

distance seed dispersal in Banksia hookeriana (Meisn) and B. attenuata (R. Br.), species with

patchy distributions endemic to the Eneabba sand plains in south-west Australia. Those studies

showed seed dispersal over several kilometres, and attributed this to transportation of cones

by birds or via wind vortices that can occur following fire. The dispersal of B. arborea seed

across the large distances separating the northern Yilgarn BIFs may be a rare phenomenon, but

even rare events, over the course of millions of years, may be sufficient to maintain genetic

connectivity among populations. Unfortunately, due to the lack of variation within

populations, it was not possible to distinguish between the contraction of a widespread

ancestor to BIF or connectivity via seed dispersal. The greater genetic divergence of the

demographically smaller Taipan Hill population and the geographically isolated population at

Koolyanobbing, suggests either longer periods of isolation of these populations or the greater

influence of genetic drift on historically small effective population size in comparison to the

larger northern populations. Contrasting patterns of isolation and connectivity were also

observed in the Yilgarn BIF endemic Grevillea georgeana, where the majority of populations

exhibited population specific haplotypes reflecting long-term isolation with the exception of

three southern populations which demonstrated evidence of connectivity, sharing haplotypes

and showing little genetic differentiation (Nistelberger et al., 2014b).

Molecular dating showed the divergence of all haplotypes occurred during the Pleistocene.

This was a period of intensifying aridity, associated with climatic fluctuations in this landscape

that saw the transition of vegetation communities to dominance by more arid-adapted species

(Bowler, 1982; Crisp et al., 2004; Dodson & Macphail, 2004; Martin, 2006). Populations of B.

arborea may have already been restricted to BIF outcrops by this period, yet climatic

fluctuations would still have shaped population distributions in this landscape, driving localised

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extinction as well as contraction and expansion of population boundaries. The broad influence

of these processes is evident in similar genetic patterns seen in other taxa endemic to the

Yilgarn BIFs, including the millipede Atelomastix bamfordi (Nistelberger et al., 2014a) and the

plant Grevillea georgeana (Nistelberger et al., 2014b), as well as in other more widely

distributed species from south-western Australia (Byrne et al., 2002, 2003; Byrne & Hines,

2004; Hopper & Gioia, 2004; Byrne, 2007).

Patterns of genetic structure in the microsatellite data did not reflect those in the cpDNA and

showed all populations, with the exception of Mt Manning and Hunt Range, to be significantly

differentiated but without a geographical pattern to the structure. Discordance between the

two marker data sets can occur given the different modes of inheritance and mutation rates of

cpDNA and nuclear DNA (Avise, 2000). The pattern of differentiation seen in the microsatellite

data is consistent with processes of population isolation and genetic drift driving

differentiation and the derived migration rates suggest very limited pollen movement between

populations (Mills & Allendorf, 1996; Lowe & Allendorf, 2010). This was in contrast to the

patterns observed in other BIF endemics Grevillea georgeana and Acacia woodmaniorum,

where isolation by distance amongst populations indicated some pollen flow between

adjacent BIFs (Millar et al., 2013; Nistelberger et al., 2014b). This contrast may reflect different

behaviours of pollinators. The bright, yellow flowers of B. arborea attract a range of insects

and birds (Pers. obs. H. Nistelberger). The dominant pollinators are not known, but insect

pollinators often exhibit restricted movement patterns (Heinrich & Raven, 1972; Loveless &

Hamrick, 1984), but many bird species can also show limited movement due to territorial and

nearest-neighbour foraging behaviours (Ford, 1981; Turner et al., 1982).

By sampling three sub-populations within the Die Hardy Range we were able to assess patterns

of genetic structure within a BIF. Sub-population differentiation was lower than that between

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BIFs, but although there was a weak pattern of isolation by distance, migration estimates still

indicated limited pollen flow within a BIF. This is surprising, as habitat conditions within a BIF

would be expected to promote pollinator movement, compared to the differing habitat of the

surrounding landscape.

4.5.2 Genetic variation and differentiation within a BIF population

Both cpDNA and nrDNA markers used in this study showed low levels of genetic diversity

within populations. The microsatellite data in particular showed low allelic richness and

expected heterozygosity in comparison to other Banksia studies that have used microsatellites,

including two species from SWAFR, Banksia sphaerocarpa (R. Br.) (Llorens et al., 2012) and B.

hookeriana (Krauss et al., 2006). Both of these species have more widespread distributions

that typically maintain higher levels of genetic diversity owing to greater gene flow, less

genetic drift and fewer population bottlenecks (Hamrick & Godt, 1989). Low diversity detected

here in B. arborea suggests populations have been through bottlenecks, perhaps both

historically and more recently. Indeed, evidence for recent bottleneck events were detected in

some populations. Larger BIFs might be expected to maintain larger populations that are more

resistant to loss of diversity caused by genetic drift and population bottlenecks (Wright, 1929).

The populations on the large Die Hardy Range did show higher genetic diversity in terms of

cpDNA haplotypes and private alleles in the nuclear data in Die Hardy north, although there

was also evidence of bottlenecks in two populations. Persistence of low frequency alleles in

the Die Hardy north population suggests maintenance of large effective population size, as

bottleneck events and genetic drift remove novel, low-frequency alleles leaving dominant,

high-frequency alleles (Luikart et al., 1998).

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This study highlights that species-specific patterns of population genetic differentiation can

occur in regional BIF endemics. Genetic divergence of the small Taipan Hill and geographically

isolated Koolyanobbing populations in B. arborea is consistent with population genetic theory

and with results from other studies on terrestrial island habitats (Byrne & Hopper, 2008; Millar

et al., 2013; Nistelberger et al., 2014a, 2014b; Tapper et al., 2014a, 2014b). However, the

extent of apparent connectivity in the cpDNA among many of the populations is in contrast to

the general expectations of island biogeography, and in contrast to the level of genetic

structure and isolation evident in a previous study of the shrub Grevillea georgeana that also

occurs on these BIFs (Nistelberger et al., 2014b). These differences in connectivity may relate

to different seed dispersal mechanisms or the possibility that B. arborea has been connected

through populations in the intervening matrix in the past.

Understanding these patterns of genetic connectivity and isolation is important if managers

need to identify target regions or particular BIFs as priorities for conservation or reservation.

Those areas with habitats that facilitate maintenance of high genetic diversity, and/or Ranges

where isolation has driven genetic divergence, would have high priority for conservation.

While other factors such as species richness and endemism are often considered critical we

suggest that these zones of high genetic diversity should also be taken into account.

Knowledge of population dynamics is also important in order to assess the impact of

population loss and reduced connectivity on genetic diversity loss and potential inbreeding in

small isolated populations. In this study, the Die Hardy Range emerged as a region of higher

genetic diversity. This large BIF is topographically complex, comprised of a mixture of sheer

cliff faces, gentle slopes and shaded valleys. This has resulted in localised groups of individuals,

a feature that may allow for the retention of diversity over time, with some sub-populations

influenced by bottlenecks, such as Die Hardy central and Die Hardy south, whilst others, like

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Die Hardy north remaining stable. Populations that are highly divergent, such as those at

Koolyanobbing and Taipan Hill may also be considered significant for conservation purposes

given the potential for prolonged isolation of divergent lineages to result in reproductive

isolation and allopatric speciation.

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Striking the balance:

Conservation and the

Yilgarn BIFs

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Chapter 5

Hotspots of genetic diversity identify terrestrial islands of conservation

value in a semi-arid Australian landscape.

5.1 ABSTRACT

The Yilgarn Banded Iron Formations (BIFs) of Western Australia are terrestrial islands

characterised by high species richness and endemism that are subject to increasing land-use

pressures. BIFs vary in size, complexity and isolation, and the distribution of genetic diversity

and divergence in species endemic to these BIFs may also vary accordingly. We used genetic

data from three regional BIF endemics, which occur across multiple ‘islands’ but not in the

intervening landscape, to determine whether patterns of high genetic diversity and/or

divergence were similar across all three taxa. The relative contribution of BIF populations to

total species genetic diversity was determined using measures of allelic richness and gene

diversity. Large BIFs, including the Die Hardy Range and Koolyanobbing, consistently showed

higher contributions to within population diversity, suggesting a role of these BIFs as genetic

reservoirs in this landscape. Smaller or more spatially isolated BIFs, such as Windarling and

Koolyanobbing, showed high contributions to population divergence. Four BIFs,

Koolyanobbing, Die Hardy Range, Taipan Hill and Windarling Range emerged as major

contributors to overall landscape genetic diversity in these species and warrant consideration

as priorities for conservation.

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5.2 INTRODUCTION

Global human population growth and associated changes in land-use, are placing increasing

pressure on remaining natural environments, driving the need for informed decision-making to

determine what should receive conservation priority. Studies of genetic diversity complement

assessment of species richness and endemism, and provide a means of including an

evolutionary perspective in identification of populations, species or regions that may have

higher priority for conservation (Allendorf et al., 2013).

There are several approaches based on the identification of high genetic diversity and

divergence that have been designed to assist in determining priorities for conservation.

Preservation of genetic diversity is important for maximising persistence and capacity to

respond to changing environmental conditions (Frankel & Soule, 1981; Shaffer, 1981), whilst

maximising genetic divergence, or ‘distinctiveness’ (Faith, 1992; Moritz & Faith, 1998; Petit et

al., 1998) is important as divergent lineages can indicate historical isolation, and may result in

allopatric speciation with prolonged isolation (Ehrlich, 1988; Crozier, 1992; Faith, 1992). The

Phylogenetic Diversity (PD) method of Faith (1992) uses cladistic relations among taxa to

determine the subset that displays the maximum underlying feature (phylogenetic) diversity

and is calculated by summing the tree branch lengths in the minimum spanning path between

taxa (Faith, 1992). This measure has been implemented in a number of studies, typically at

higher systematic levels, that have identified regions of evolutionary potential in the Cape flora

of South Africa (Forest et al., 2007), mammal speciation in California (Davis et al., 2008) and

phylogenetic diversity of bird genera in north West Africa (Rodrigues & Gaston, 2002). Another

approach is to identify Evolutionarily Significant Units (ESUs), groups that show either genetic

or ecological distinctiveness, as a means to identify lineages that are a priority for conservation

effort (Ryder, 1986; Allendorf et al., 2013). Moritz (1994) provided guidelines to enable clear

identification of ESUs based on genetic data, such as evidence of reciprocal monophyly in the

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faster evolving mitochondrial DNA genome and significant divergence of allele frequencies in

the nuclear genome (Moritz, 1994).

These methods provide effective means of identifying evolutionary units and geographical

areas that are a focus for conservation, but a downfall is their reliance on resolved

phylogenetic trees (Faith, 1992). When dealing with population-level data, particularly

populations that are heavily influenced by genetic drift, obtaining well-resolved trees can be

problematic (Purvis & Garland, 1993; Kim et al., 1998; Lee, 2000). These phylogenies may

contain unresolved branches (polytomy), hampering efforts to identify monophyly or to

calculate the minimum spanning path between a subset of populations (Swenson, 2009).

An alternative method developed by Petit et al (1998), allows for the relative contribution of

populations to total species diversity and divergence to be determined when relationships

amongst populations are unresolved. The method involves calculating total genetic diversity -

in the form of either allelic richness or gene diversity, both of which can be broken down into

their two components: variation within populations and divergence among populations. This

method is useful as estimates of allelic richness and gene diversity are widely used in

conservation genetic studies (Petit et al., 1998), and allelic richness in particular has been

widely argued to provide one of the best measures of genetic diversity in the context of

conservation genetics (Schoen & Brown, 1993; Bataillon et al., 1996; El Mousadik & Petit,

1996) as it is a strong indicator of past demographic changes within populations (Petit et al.,

1998). A drawback of allelic richness is its reliance on sample size, and this problem has been

dealt with in the software CONTRIB (Petit et al. 1998) by using rarefaction, a statistical

technique that compensates for variation in sample size (Petit et al., 1998; Kalinowski, 2005).

The CONTRIB method has been used to estimate the relative contribution of populations to

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total species diversity in tree species and horse and sheep breeds (Petit et al., 1998; Comps et

al., 2001) (Solis et al., 2005; Tapio et al., 2005).

Population-level conservation genetic studies are often designed around single species that

are likely to be heavily impacted by some form of disturbance; either because they are

charismatic, rare and/or have restricted distributions (Soulé & Mills, 1992; Ueno et al., 2005;

Marshall et al., 2008; Butcher et al., 2009; Allendorf et al., 2013; Millar et al., 2013). However,

the same data can be used in a multi-species approach to identify specific areas or habitats

where protection may result in more effective net conservation outcomes at a landscape level

(Mace et al., 2003). This approach may be particularly useful in analysis of sympatric species

that exhibit disjunct distributions, such as found in terrestrial island systems, to identify

landscape genetic hotspots that require conservation priority, rather than relying on

population analyses of single species.

The Yilgarn Banded Iron Formations (BIFs) of Western Australia are ancient topographical

features that behave as terrestrial islands in a flat, semi-arid landscape. They are centres of

species richness and endemism and are therefore of high conservation value (Gibson et al.,

2007, 2010, 2012). The southern Yilgarn BIFs that border the South West Australian Floristic

Region are in a relatively pristine state in comparison to other BIFs throughout the world

(Burke, 2003); although many are prospective for resource development (Gibson et al., 2012)

and are subject to pastoral activities, and there is a need to find a balance between land-use

activities and conservation in this region (Environmental Protection Authority, 2013). The BIFs

vary in size, shape and degree of isolation, and there is a high turnover in plant species

diversity both within and between BIFs (Gibson et al., 2010). There is also variation in the

number of endemic species present on each BIF (Gibson et al., 2010), but it is not known

whether these differences in species richness and endemism amongst BIFs are also reflected in

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differences in genetic diversity within and divergence between populations in plant and animal

species found on the formations. Regional endemic species provide an excellent opportunity

to investigate whether common patterns of genetic diversity and structure exist within the

landscape, as this might enable the identification of BIFs that can be considered a higher

priority for conservation.

Recent studies (Nistelberger et al., 2014; Chapter 3; Chapter 4) have evaluated the

phylogeography and population genetics of three regional endemics found on the most

southerly Yilgarn BIFs in south-western Australia. Here, we use these data to answer three key

questions. First, is genetic diversity across species partitioned equally amongst BIF islands or

are there particular BIFs with higher diversity? Second, are there any BIFs which exhibit higher

levels of genetic divergence than others? Finally, are there BIFs that encompass both of these

attributes that may be prioritised for conservation? The results will not only aid in our

understanding of the evolutionary history of the Yilgarn but will contribute to informed

decisions that consider the underlying genetic diversity that is important for persistence of

species endemic to this region.


5.3.1 Study species and population sampling

A total of 10 south-western Yilgarn BIFs were included in this study (Fig. 5.1). We used

cytoplasmic and nuclear DNA data from three species endemic to the southern Yilgarn BIFs,

Atelomastix bamfordi, Grevillea georgeana and Banksia arborea (Table 5.1), to assess the

relative contribution of BIF populations to genetic diversity and divergence.

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Figure 5.1 Map of the 10 Yilgarn BIFs sampled for Atelomastix bamfordi, Grevillea georgeana and Banksia arborea. Image adapted from Google Earth.

5.3.1.1 Atelomastix bamfordi

The spirostreptid millipede, A. bamfordi, is found on five of the Yilgarn BIFs; the Die Hardy,

Windarling, Helena Aurora and Koolyanobbing Ranges, and Mt Jackson. It occurs in mesic

habitat provided by shaded gullies and areas of dense leaf litter on BIFs but has not been

found on the flats between BIFs. The analysis used data from two sequenced regions of the

mtDNA genome (16S and CO1) and genotype data from 11, specifically developed, nuclear

microsatellite markers as outlined in Nistelberger et al. (2013, 2014). Millipedes were acquired

by searching in litter, under rocks etc. or tissue samples were taken from specimens lodged at

the Western Australian Museum. All available specimens were sequenced and genotyped as

previously described in Nistelberger et al. (2014).

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Table 5.1. Details of the 10 Yilgarn banded iron formations (BIFs) sampled for this study and the presence (+) of the three species on each.

BIF Pop

code Latitude Longitude Atelomastix bamfordi Grevillea georgeana Banksia arborea

Die Hardy Range DH -29.89575 119.36179 + + +

Helena Aurora Range HA -30.34780 119.70820 + + +

Hunt Range HUN -30.20180 119.86395

+ +

Koolyanobbing Range KOO -30.84039 119.54244 +

+

Mt Correll COR -30.53926 118.90991

+

Mt Finnerty FIN -30.67577 120.13417

+

Mt Jackson MTJ -30.25508 119.27631 +

+

Mt Manning MM -29.99582 119.64289

+ +

Taipan Hill TH -30.39248 119.91232

+ +

Windarling Range WIN -30.01100 119.31392 + +

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5.3.1.2 Grevillea georgeana

The bird-pollinated, proteaceous shrub, G. georgeana, was sampled from seven Yilgarn BIFs

the Helena Aurora, Die Hardy Range and Hunt Ranges, Mt Correll, Mt Manning, Mt Dimer and

Mt Finnerty. Analysis was based on sequence data from two intergenic chloroplast DNA

spacers (trnQ-rps16 and psbD-trnT) and 10 specifically developed nuclear microsatellite loci as

previously described in Nistelberger et al. (2013b; Chapter 3). Samples for the sequence data

were taken from 10 plants spaced out across the BIF as far as practicably possible, whereas the

samples for microsatellite genotyping were taken from a geographically clustered subset of 24

individuals from each BIF. The previous study (Chapter 3) analysed three subpopulations from

the Die Hardy Range, but only one subpopulation (northern) was used in this analysis to avoid

bias when estimating the contribution of a BIF population to overall genetic diversity.

5.3.1.3 Banksia arborea

Banksia arborea, a proteaceous, perennial tree that is likely pollinated by both birds and

insects, was sampled from eight Yilgarn BIFs, the Koolyanobbing, Mt Jackson, Windarling, Die

Hardy, Hunt and Helena Aurora Ranges, Mt Manning and Taipan Hill. Analysis was based on

sequence data from three chloroplast DNA regions (psbD-trnT, psbA-trnH, PetB) and genotype

data from 11 specifically developed nuclear microsatellite loci as previously described in

Nistelberger et al. (2013c; Chapter 4). Sampling was the same as for G. georgeana.

5.3.2 Diversity and differentiation analysis

The contributions of populations to total species allelic richness (CTR) and gene diversity (CT)

were calculated for both cytoplasmic DNA (mitochondrial (mtDNA) and chloroplast (cpDNA))

and nuclear DNA (nrDNA) datasets using the software CONTRIB (available at

http://www.pierroton.inra.fr/genetics/labo/Software/Contrib/). Total allelic richness and total

http://www.pierroton.inra.fr/genetics/labo/Software/Contrib/

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gene diversity measures are comprised of two components; within population diversity (allelic

richness = CSR, gene diversity = CS) and between population differentiation (allelic richness =

CDR, gene diversity = CD) respectively. The joint summation of the diversity (CSR, CS) and the

differentiation (CDR, CD) components determines if the overall contribution of a particular

population to diversity (allelic richness = CRT, gene diversity = CT) within the species is positive

or negative. These measures allow for the comparative assessment of the relative

contributions of populations to total genetic diversity. Populations that contributed the most

to each of these three measures (within population diversity, among population divergence

and total genetic diversity) in each species were identified (Fig. 5.2, 5.3, 5.4). Comparisons

across BIFs for all three species were then used to determine those BIFs that harbour

populations that contribute the most to total genetic diversity (i.e. diversity and

differentiation) of these species (Table 5.2).

5.4 RESULTS

5.4.1 Contribution to population diversity (CSR and CS)


The large Koolyanobbing BIF population made the greatest contribution to genetic diversity

across both chloroplast and nuclear data sets, based on both allelic richness and gene diversity

estimates (Fig. 5.2).


The large Die Hardy BIF population made the greatest contribution to genetic diversity based

on both allelic richness and gene diversity estimates in the cpDNA data and the Taipan Hill

population showed greatest diversity in the nrDNA data (Fig. 5.3).

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Again, Die Hardy showed high genetic diversity in the cpDNA data based on both allelic

richness and gene diversity estimates but there were contrasting patterns in the nrDNA, with

the Mt Manning population contributing the most to gene diversity and the Mt Jackson

population contributing the most to allelic richness (Fig. 5.4).

5.4.2 Contribution to divergence (CDR and CD)


The small Windarling BIF population was the most genetically divergent from all other

populations in both chloroplast and nuclear data sets and across both diversity measures (Fig.

5.2).


Mt Correll, the small and spatially isolated BIF at the western boundary of the species’

distribution, was the most genetically divergent population based on both allelic richness and

gene diversity estimates, with the exception of the Mt Manning population in the nrDNA data

that showed more divergence using allelic richness (Fig. 5.3).


The cpDNA data showed both the Koolyanobbing and Taipan Hill populations to be the most

divergent in this species. Results from the nrDNA showed the population at the small

Windarling BIF to be the most divergent (Fig. 5.4).

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5.4.3 Contribution to total genetic diversity (CTR and CT)

Total genetic diversity is the sum of a population’s contribution to within population diversity

(CSR, CS) and between population divergence (CDR, CD).


Contribution to both population diversity and divergence varied across the mitochondrial and

nuclear datasets and two estimates (gene diversity and allelic richness). In the mtDNA data,

the Die Hardy Range population was a high overall contributor using allelic richness as a

measure of diversity. In the gene diversity estimate all populations were equal overall

contributors owing to the population specificity of all haplotypes in this species. Populations at

Koolyanobbing and Windarling showed highest contributions in the nrDNA dataset using allelic

richness and gene diversity measures respectively (Fig. 5.2) (Table 5.2).


The Taipan Hill population showed greatest contribution to overall diversity in the nrDNA data

and the Die Hardy population in the cpDNA data, but populations at Mt Correll, Helena Aurora

and Mt Manning also made a high contribution based on the cpDNA gene diversity measure

(Fig. 5.3) (Table 5.2).


Both Koolyanobbing and Taipan Hill populations showed high overall contribution in the

cpDNA data but the nrDNA data showed the Windarling population contributes the most to

total genetic diversity based on allelic richness and gene diversity, while the Mt Manning

population also made a high contribution based on gene diversity (Fig. 5.4) (Table 5.2).

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Figure 5.2 a-d Contribution of Atelomastix bamfordi populations to total diversity in terms of allelic richness (CTR) (a,b) and gene diversity (CT)(c,d) at both

mitochondrial (mtDNA)(a,c) and nuclear microsatellite (nrDNA)(b,d) datasets. NB. In the gene diversity mtDNA estimate all populations were equal overall

contributors owing to the population specificity of all haplotypes in this species. DH, Die Hardy; HA, Helena Aurora; KOO, Koolyanobbing; MTJ, Mt Jackson;

WIN, Windarling.

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Figure 5.3 a-d Contribution of Grevillea georgeana populations to total diversity in terms of allelic richness (CTR) (a,b) and gene diversity (CT)(c,d) at both

chloroplast (cpDNA)(a,c) and nuclear microsatellite (nrDNA)(b,d) datasets. COR, Mt Correll; DH, Die Hardy; FIN, Mt Finnerty; HA, Helena Aurora; HUN, Hunt;

MM, Mt Manning; TH, Taipan Hill.

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Figure 5.4 a-d Contribution of Banksia arborea populations to total diversity in terms of allelic richness (CTR) (a,b) and gene diversity (CT)(c,d) at both

chloroplast (cpDNA)(a,c) and nuclear microsatellite (nrDNA)(b,d) datasets. DH, Die Hardy; HA, Helena Aurora; HUN, Hunt; KOO, Koolyanobbing; MM, Mt

Manning; MTJ, Mt Jackson; TH, Taipan Hill; WIN, Windarling.

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Table 5.2a Contributions (%) of populations of Atelomastix bamfordi, Grevillea georgeana and Banksia arborea to total genetic diversity, comprised of within population diversity and between population divergence using cytoplasmic DNA data. COR, Mt Correll; DH, Die Hardy; FIN, Mt Finnerty; HA, Helena Aurora; HUN, Hunt; KOO, Koolyanobbing; MM, Mt Manning; MTJ, Mt Jackson; TH, Taipan Hill; WIN, Windarling.

cytoplasmic DNA Atelomastix bamfordi Grevillea georgeana Banksia arborea contribution to total genetic diversity

Gene Diversity (CT)

Allelic Richness (CTR)

Gene Diversity (CT)


Gene Diversity (CT)


>30% - DH - DH KOO, TH KOO, TH

21-30% - KOO COR, DH, HA, MM HA, MM - -

11-20% - HA, MTJ - COR - DH

0-10% - WIN FIN, HUN, TH FIN, HUN, TH DH, HA, HUN, MM, MTJ, WIN HA, HUN, MM, MTJ, WIN

Table 5.2b. Contributions (%) of populations of Atelomastix bamfordi, Grevillea georgeana and Banksia arborea to total genetic diversity, comprised of within population diversity and between population divergence using microsatellite DNA data. COR, Mt Correll; DH, Die Hardy; FIN, Mt Finnerty; HA, Helena Aurora; HUN, Hunt; KOO, Koolyanobbing; MM, Mt Manning; MTJ, Mt Jackson; TH, Taipan Hill; WIN, Windarling.

microsatellite DNA Atelomastix bamfordi Grevillea georgeana Banksia arborea contribution to total genetic diversity

Gene Diversity (CT)


Gene diversity (CT)


Gene diversity (CT)


>30% WIN KOO - TH - WIN

21-30% DH WIN TH - MM, WIN -

11-20% HA DH, MTJ COR, DH, FIN, HA, HUN DH, FIN, HUN MTJ MM, MTJ, TH

0-10% KOO, MTJ HA MM COR, HA, MM DH, HA, HUN, KOO, TH DH, HA, HUN, KOO

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5.5 DISCUSSION

Genetic diversity and divergence are important for ongoing persistence and evolutionary

potential (Frankel & Soule, 1981; Faith, 1992). Our analysis of diversity in three endemic BIF

species revealed patterns that were largely congruent and consistent with expectations based

on size and isolation of BIF populations, allowing for the identification of key BIFs that can be

prioritised for conservation. Although the three species used in this study are not sympatric

across all 10 BIFs, the relative contributions of each BIF population to diversity and divergence

within each species showed similar trends with large, topographically complex BIFs supporting

populations with higher genetic diversity and small and/or isolated BIFs harbouring

populations with greater divergence.

5.5.1 Large, topographically complex BIFs support populations with higher genetic diversity

The large and topographically complex BIFs at Die Hardy Range and Koolyanobbing were

consistent in harbouring populations with high genetic variation in all three species. Genetic

theory and practical examples have reinforced the importance of large effective population

size in maintaining diversity by conferring resilience to bottlenecks, genetic drift, and

inbreeding (Wright, 1929; Ellstrand & Elam, 1993; Frankham, 1996; Allendorf et al., 2013).

Consequently, large populations, such as those at Die Hardy and Koolyanobbing, may play an

important role as genetic reservoirs within this landscape. High genetic diversity has also been

connected with high species diversity (Vellend & Geber, 2005). Whilst some surveys of species

diversity have been carried out in this landscape (Gibson et al., 2010), not all the BIFs we

evaluated have been assessed for species diversity and those that have were not surveyed

equally, so we were unable to correlate BIF species richness with genetic diversity in the

species we investigated.

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We did not observe a high contribution of the populations from the large Helena Aurora Range

to genetic diversity, which is surprising since it is characterised by a high number of localised

and regional plant endemics making it of conservation significance in that context (Gibson et

al., 1997, 2007, 2010). However, large BIFs may not always support large populations of

particular species, and a similar study of two common plant species restricted to granite

outcrop islands in this region showed no relationship between outcrop area and population

diversity (Tapper et al. 2014a,b). Although large (approx. 12 km long) and elevated (600-700 m

height), the topology of the Helena Aurora Range with its gentle, rounded hills, creates a

different environment to the other large BIFs. Individuals of A. bamfordi were difficult to find

here in comparison to the Die Hardy and Koolyanobbing Ranges (Biota, 2009), and G.

georgeana was also less common in comparison to the diverse Die Hardy Range (it does not

occur on Koolyanobbing). Personal observations of the three species and their distributions,

suggest that it is not only size but complexity of habitats that are important for ensuring

persistence of large populations and associated genetic diversity in these species. Habitat

complexity has often been shown to be associated with species diversity (Kohn & Leviten,

1976; Cowling & Lombard, 2002), and has been associated with genetic diversity (Nistelberger

et al., 2014a) and divergence (Byrne et al., 2001; Byrne & Hopper, 2008) in some species.

Quantifying topographic and habitat complexity of BIFs is currently a difficult and expensive

exercise, yet increasingly this is being seen as an important factor in identifying areas that may

support higher biological diversity (Ashcroft et al., 2012; Schut et al., 2014). Recently

developed techniques based on the use of fine-scale topo-climatic grids, should enable more

precise quantification of these traits in the future (Ashcroft et al., 2012) and this would allow

the correlation between genetic diversity and habitat complexity to be investigated.

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5.5.2 Small and spatially isolated BIFs harbour populations with greater genetic divergence

Populations on the small Windarling Range and at Mt Correll, along with those on the spatially

isolated (but diverse) Koolyanobbing Range, frequently made the greatest contributions to

genetic divergence within the species. In comparison to large populations, small populations

suffer increased susceptibility to population bottlenecks and genetic drift, generally resulting

in genetic divergence (Ellstrand & Elam, 1993). Peripheral or spatially isolated populations may

also show genetic divergence as a result of genetic isolation, genetic drift and natural selection

(Lesica & Allendorf, 1995). Peripheral populations can also show reduced genetic diversity as a

result of these processes (Lesica & Allendorf, 1995) yet the spatially isolated Koolyanobbing

Range had populations that contributed to divergence, as expected, but also maintained high

genetic diversity. This high genetic diversity is likely due to the large and complex nature of this

Range that would facilitate maintenance of larger population size over time. However, the

spatial isolation of this BIF has limited gene flow and driven divergence of populations, as

expected from genetic theory (Lesica & Allendorf, 1995).

5.5.3 Prioritisation of BIFs for conservation

While the comparisons across the three species show the pattern of distribution of genetic

diversity is not fully consistent, there is sufficient similarity to enable an assessment of overall

contribution to genetic diversity. From a landscape perspective, evaluation of genetic diversity

in populations of three species endemic to the BIFs in this region suggests that conservation of

the large Die Hardy and Koolyanobbing Ranges, along with the smaller Taipan Hill and

Windarling Range would capture maximum genetic diversity and distinctiveness of these three

species in this region, with populations in these four Ranges frequently contributing more than

30% of the total genetic diversity within a species. Thus, these four BIFs would rate higher in

terms of priority for conservation when evaluating competing land use practices. Identifying

regions of genetic diversity and distinctiveness can complement species-based approaches to

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conservation through inclusion of an evolutionary perspective (Mace et al., 2003). This analysis

has revealed important patterns of genetic diversity present in this landscape and has

highlighted the characteristics of BIFs that might be expected to harbour high genetic diversity

(large and complex) and high genetic divergence (small and/or isolated). These patterns are

consistent with genetic theory and are likely to apply to other island landscapes.

There is always a degree of caution required when using this type of analysis as a basis for

prioritising populations and geographical areas for conservation (Petit et al., 1998). The use of

this approach is based on three major assumptions; the first is that the diversity observed at

neutral loci reflects functional diversity, the second is that diversity at the selected loci reflects

genome-wide diversity, and the third is that patterns in these three taxa reflect genetic

patterns in other taxa present in these locations. These issues have been discussed at length

by Petit et al. (1998) but despite the drawbacks, assessment of patterns of neutral genetic

diversity still provides one of the most accessible methods available to biologists for identifying

populations of conservation significance (Petit et al., 1998; Reed & Frankham, 2003). The data

obtained here conforms to theoretical expectations of the relationship between diversity and

size, and isolation and divergence; and identification of concordant patterns across different

species provides a basis for generalisations about patterns of genetic diversity in this

landscape. This information can be combined with evaluation of endemism and species

richness to provide a more comprehensive basis for decisions aiming to find a balance

between competing land uses in this region.

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Where to from here…?

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Concluding Remarks

“The one process now going on that will take millions of years to correct is

the loss of genetic and species diversity by the destruction of natural

habitats. This is the folly our descendants are least likely to forgive us.”

E. O. Wilson

Our natural landscapes are facing unprecedented levels of degradation, with approximately

two thirds of the planet’s ecosystems already considered degraded (Nellemann & Corcoran,

2010). As a consequence, there is increasing pressure for areas of conservation priority to be

identified (Riggs, 1990). The southern BIFs of the Yilgarn, with their high species richness and

endemism (Gibson et al., 2010, 2012) and presence of aboriginal sites (Department of

Aboriginal Affairs), are of great natural and indigenous value. There has been a long history of

prospecting, resource development, and pastoralism in this landscape, with gold mining

commencing in the 19th century and iron-ore production at the Koolyanobbing Range and

pastoralism commencing in the 1960’s (Lord & Trendall, 1976). Conservation in this region has

focussed on protecting threatened flora, referred to as Declared Rare Flora (DRF) under the

Wildlife Conservation Act 1953, and Priority Flora (Smith, 2013), with resource companies

required to satisfy a range of conditions that afford these species protection and continued

preservation (Department of Mines and Petroleum, 2006). This requirement has resulted in

the protection of threatened flora, such as Tetratheca paynterae and Ricinocarpos brevis

endemic to Windarling Range (Butcher et al., 2009; Environmental Protection Authority, 2012)

and a number of Priority Flora (Smith, 2013). The establishment of conservation parks and

reserves in the area, such as the Mt Manning Nature Reserve, has also contributed to

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conservation efforts; however, these areas are not precluded from development activities

(Environmental Protection Authority, 2007).

While the protection of DRF and Priority Flora is an important aspect of conservation in this

landscape, to ensure the preservation of landscape ecosystem functioning, we need to

understand the wider evolutionary dynamics that drive patterns of genetic diversity and

structure in more common species. These are the species that form the backbone of the

unique BIF flora communities and those that may be required for restoration practices in the

future.

As discussed in Chapter 5, the best way to ensure the persistence and evolutionary potential of

species is to preserve the maximum amount of genetic diversity and genetic ‘distinctiveness’.

The common patterns of genetic diversity in the three taxa studied for my thesis have

highlighted the importance of maintaining large effective population sizes and topographic

complexity to enable preservation of maximum genetic diversity in BIF endemics. We have also

seen the importance of small or spatially isolated BIF populations in contributing towards

genetic divergence or ‘distinctiveness’ (Chapter 5). Ideally, these large and isolated BIFs would

receive conservation protection, yet the BIFs identified as high conservation value in this study

are also of high resource value, posing immense challenges for conservation (Gibson et al.,

2012).

Mining in the Yilgarn landscape often involves the partial removal of a BIF and may be

structured around requirements to protect DRF, such as T. paynterae. I suggest that the

conservation of disturbed BIFs should also focus on leaving intact portions of BIF that capture

maximum habitat complexity. Many of the endemic species found on these BIFs are reliant on

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the multitude of habitats provided by topographic complexity including tors, gullies and

shaded sites. For example, the millipede Atelomastix bamfordi requires the moist pockets of

habitat provided by the shaded gullies and leaf litter that accumulates at the base of rocky BIF

slopes (Nistelberger et al., 2014a). Preservation of these habitats is likely to protect other

groups of short-range endemics that also depend upon these moist environments (e.g.

isopods, snails, mygalomorph spiders)(Harvey, 2002).

Post-disturbance

Prevention of habitat degradation in the first instance is better than attempting to restore

degraded environments (Menz et al 2013). Yet the reality remains that large tracts of land

worldwide currently require rehabilitation as a result of human disturbance (Merritt and Dixon

2011). Understanding how to restore the Yilgarn landscape post-disturbance, is another crucial

component to BIF biodiversity conservation. Restoration attempts in dryland regions, such as

Western Australia’s Pilbara region, have been problematic, with less than 15% of species

biodiversity being returned and, in some cases, failure to restore even the most common of

native plant species (Merritt & Dixon, 2011; Environmental Protection Authority, 2013). There

is a need for research and innovation into restoration ecology practices in these semi-arid

landscapes, a challenge that will require a bottom-up approach to rebuilding the landscape.

This will require getting the foundations of the rehabilitation site as close to its original state as

possible, in terms of substrate, aspect and drainage, before species are returned to the site.

Essentially, the foundation requires the return of topographic complexity. Undoubtedly, this is

a challenging task that requires financial investment and a period of trial and error, but it also

presents an exciting opportunity to think more creatively about restoration practices. Rather

than asking how can we improve the survival rate of replanted species, a current problem in

dryland rehabilitation (Merritt & Dixon, 2011), what about asking; how can we return large,

complex physical structure back to these restored hills? How can we re-create deep rock

138

fissures and cracks in which species such as the BIF specialist Tetratheca species can re-

germinate? And how can we ensure there are pockets of mesic habitat available for moisture-

dependent species such as A. bamfordi; habitat that receives higher shade and is covered in

dense leaf litter, or an appropriate analogue? These questions require a creative range of

engineering, biological and geological solutions that, whilst initially expensive, should result in

a higher rehabilitation success rate in terms of biodiversity return for these landscapes.

139

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APPENDIX A

Table A1 Specimen information, including spatial coordinates from UTM zone 50J and haplotype number based on combined CO1 and 16S mtDNA data. Samples from Windarling and Kooylanobbing (bold) are from specimens lodged at the Western Australian Museum, registration numbers shown (WAM_T).

Sample Population Easting Northing Haplotype no.

DH23 Die Hardy 729291 6685360 Hap21

DH2 Die Hardy 725805 6684839 Hap19

DH32 Die Hardy 727525 6687269 Hap24

DH33 Die Hardy 727525 6687269 Hap25

DH36 Die Hardy 727458 6687278 Hap26

DH37 Die Hardy 727458 6687278 Hap27

DH40 Die Hardy 727411 6687282 Hap28

DH41 Die Hardy 727347 6687287 Hap29

DH43 Die Hardy 727257 6687301 Hap24

DH46 Die Hardy 728060 6690423 Hap30

DH47 Die Hardy 728411 6690546 Hap31

DH48 Die Hardy 728411 6690546 Hap32

DH6 Die Hardy 729215 6685314 Hap20

DH7 Die Hardy 729215 6685314 Hap21

DH9 Die Hardy 729215 6685314 Hap23

HA36 Helena Aurora 760332 6639565 Hap42





HNAF11 Helena Aurora 752715 6637738 Hap39









MTJ39 Mt Jackson 718999 6650761 Hap35









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WAM_T98784 Windarling 723016 6677816 Hap46






WAM_T98796 Windarling 723261 6677891 na








WAM_T98805 Koolyanobbing 748961 6581863 Hap1



WAM_T98814 Koolyanobbing 749410 6581553 na



















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Table A2 The three, best-fit models determined in IMa2 for tests of migration versus isolation between the paraphyletic populations Koolyanobbing and Helena

Aurora. Results are based on mtDNA variation.

Model log(p) terms df 2LLR AIC Delta AIC

Population size KOO equal to population size HA, migration rates zero -6.091 2 3 1.44 16.182 0

Migration rates zero -5.371 3 2 0 16.742 0.56

Population size KOO equal to population size 2 (ancestral population size), migration rates zero -6.708 2 3 2.675 17.416 1.234

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APPENDIX B

Table B1 Specimen information, including spatial coordinates from UTM zone 50J and 51J (Mt Correll) and haplotype number based on combined trnQ-rps16 and psbD-trnT cpDNA data.

Sample Population Easting Northing Haplotype no.

corpop1 Mt Correll 683219 6619906 1










MGs1 Die Hardy 731211 6683058 2

DHs1 Die Hardy 728545 6692270 3

DH2 Die Hardy 731078 6690882 4

dhs3 Die Hardy 727987 6687485 5

DHs4 Die Hardy 725814 6684856 6

dhs5 Die Hardy 727132 6685587 7

dhs6 Die Hardy 728074 6690048 8

dhs7 Die Hardy 731255 6692878 9

dhs9 Die Hardy 729291 6685360 10

dhs10 Die Hardy 728574 6680010 10

finpop1 Mt Finnerty 225443 6602805 11










HAs2 Helena Aurora 759972 6638979 13



hapop8 Helena Aurora 759985 6638854 13




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HUs1 Hunt Range 775758 6655357 11

hupop1 Hunt Range 775718 6655385 11

HUs2 Hunt Range 778002 6645231 11






hupop13 Hunt Range 775580 6655114 11

hupop16 Hunt Range 775751 6655273 15

mmpop1 Mt Manning 751789 6699172 16

MM2 Mt Manning 753012 6678501 17

mms3 Mt Manning 753299 6678801 17

mms5 Mt Manning 753299 6678801 16

mms6 Mt Manning 754957 6678737 16

MMs7 Mt Manning 754957 6678737 18

MMs8 Mt Manning 754731 6680426 19

MMs9 Mt Manning 754526 6679481 17

mmpop17 Mt Manning 751811 6699176 20

ths1 Taipan Hill 779913 6634137 12

thpop1 Taipan Hill 779424 6634494 12

TH2 Taipan Hill 779834 6634123 12

ths3 Taipan Hill 779566 6634455 11

thpop3 Taipan Hill 779424 6634494 11

ths5 Taipan Hill 779278 6634562 11

ths6 Taipan Hill 779144 6634628 11

ths8 Taipan Hill 778490 6634412 11

ths9 Taipan Hill 778520 6634997 11

ths10 Taipan Hill 778618 6635295 11

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APPENDIX C

Table C1 Diversity statistics for the eight Banksia arborea BIF populations for chloroplast DNA data: N, number of individuals; # Haps, number of haplotypes; HD, haplotype diversity; Pi, nucleotide diversity.

Pop N # Haps HD PI (%) Genbank

petB psbA psbD

DH 10 2 0.356 0.019 KF668088 KF668091 KF668094 KF668095

HA 9 1 0 0 KF668089 KF668091 KF668094 HUN 10 1 0 0 KF668089 KF668091 KF668094 KOO 10 1 0 0 KF668090 KF668092 KF668095 MM 10 1 0 0 KF668088 KF668091 KF668094 MTJ 10 1 0 0 KF668089 KF668091 KF668094 TH 10 1 0 0 KF668090 KF668093 KF668095 WIN 10 1 0 0 KF668088 KF668091 KF668094

Figure C1 Mantel correlograms of pairwise genetic distance (Nei, 1978) against log of geographic distance

for all individuals from Die Hardy north, central and south sub-populations.

Appendix D removed due to copyright issues.

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Comparative phylogeography and population genetics of ... · Conservation Genetics Resources, 5, 533-535. (Appendix D) Chapter 3 Nistelberger HM, Byrne M, Coates DJ and Roberts JD