Juanita Renwick B. App. Sci. (Hons) - eprints.qut.edu.aueprints.qut.edu.au › 16181 › 1 ›...

POPULATION STRUCTURE AND GENETIC DIVERSITY OF

SOUTHEAST QUEENSLAND POPULATIONS OF THE

WALLUM FROGLET, CRINIA TINNULA (TSCHUDI).

Juanita Renwick

B. App. Sci. (Hons)

School of Natural Resource Sciences

Queensland University of Technology

Brisbane, Australia

This dissertation is submitted as a requirement of the

Doctor of Philosophy Degree

2006

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KEYWORDS

Crinia tinnula; population genetic structure; phylogeography; Pliocene; mitochondrial DNA;

12S; COI; wallum; southeast Queensland.

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ABSTRACT

Genetic diversity is a fundamental attribute that contributes to a species evolutionary

survival. In recent times, conservation managers have recognized the need to preserve

genetic diversity of declining species, and have also acknowledged the utility of genetic

markers for describing genetic and ecological relationships within and among populations.

Information obtained from genetic studies can be used in conjunction with information on

population demography, land use patterns and habitat distribution to develop effective

management strategies for the conservation of species in decline.

The wallum froglet, Crinia tinnula, is one of Australia’s smallest habitat specialist anurans.

In recent years there has been a dramatic decrease in population numbers of this species.

The habitat to which C.tinnula is endemic (‘wallum’ habitat) is restricted to low coastal

plains along the southeast Queensland and northern New South Wales coastline. As human

populations in this region expanded, the coastal areas have undergone significant

development and large areas of wallum habitat have been cleared. The effect has been to

convert once largely continuous patches of coastal heathland in to a matrix of small habitat

patches within an area undergoing rapid urban expansion.

This study aimed to document levels and patterns of genetic diversity and to define the

population structure of C.tinnula populations within southeast Queensland, with the

objective of defining possible conservation management units for this species. Results from

12S and COI mitochondrial markers clearly showed that two distinct evolutionary lineages

of C.tinnula are present within southeast Queensland. The high level of divergence between

lineages and strict geographic partitioning suggests long term isolation of C.tinnula

populations. It is hypothesized that ancestral C.tinnula populations were once confined to

wallum habitat refugia during the Pliocene resulting in phylogeographic delineation of

‘northern’ and ‘southern’ C.tinnula clades.

Populations within each geographic region show evidence of range contraction and

expansion, with subsequent restricted gene flow. Levels of genetic diversity appear, largely,

to be the product of historical associations rather than contemporary gene flow. A revision of

the current systematics of C.tinnula is required to ensure that discrete population groups are

recognized as distinct evolutionary lineages and will therefore be protected accordingly.

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TABLE OF CONTENTS

KEYWORDS...................................................................................................................................ii

ABSTRACT....................................................................................................................................iii

TABLE OF CONTENTS................................................................................................................ iv

LIST OF FIGURES ......................................................................................................................viii

LIST OF TABLES.......................................................................................................................... ix

LIST OF APPENDICES.................................................................................................................xi

STATEMENT OF ORIGINAL AUTHORSHIP ...........................................................................xii

Chapter One: General Introduction .................................................................................................1

1.1 Relevance of genetics to conservation biology .......................................................... 1

1.2 Population genetic structure ....................................................................................... 2

1.2.1 Earth history events that shape population structure ........................................... 6

1.3 The use of molecular markers to describe genetic variation and population

structure.................................................................................................................................. 7

1.4 Declining frog populations......................................................................................... 9

1.5 Case Study: The Wallum Froglet (Crinia tinnula).................................................. 11

1.6 Thesis structure and Aims ........................................................................................ 13

Chapter Two: General Methods.....................................................................................................15

2.1 The study area .......................................................................................................... 15

2.1.1 Biogeography of the wallum.............................................................................. 15

2.1.2 Biogeography of the coastal sand islands of southeast Queensland .................. 19

2.2 The study species; Crinia tinnula............................................................................. 21

2.2.1 Systematics ........................................................................................................ 21

2.2.2 Morphology ....................................................................................................... 22

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2.3 Sampling design and sample collection ................................................................... 24

2.4 Laboratory methods: Mitochondrial DNA techniques ............................................ 28

2.4.1 Outgroup species................................................................................................ 29

2.4.2 DNA extraction.................................................................................................. 29

2.4.3 Polymerase chain reaction (PCR) ...................................................................... 29

2.4.4 Temperature gradient gel electrophoresis (TGGE)............................................ 30

2.4.5 Sequencing......................................................................................................... 34

2.5 Laboratory methods: Nuclear DNA techniques ...................................................... 35

2.5.1 Development of microsatellite genomic library................................................. 35

2.5.2 Primer design and optimisation of PCR............................................................. 36

2.5.3 Amplification of F2.5 ........................................................................................ 37

2.5.4 Amplified fragment length polymorphism (AFLP) ........................................... 38

2.5.5 Data analyses ..................................................................................................... 40

Chapter Three: Historical Population Structure Inferred from Mitochondrial 12S rRNA.............44

3.1 Introduction .............................................................................................................. 44

3.2 Materials and methods.............................................................................................. 47

3.2.1 Sampling localities and sample numbers ........................................................... 47

3.2.2 DNA extraction and amplification of 12S rRNA mitochondrial DNA fragment .......................................................................................................................... 48

3.2.3 Temperature gradient gel electrophoresis (TGGE), Heteroduplex analysis (HA) and Sequencing..................................................................................................... 49

3.2.4 Data analysis ...................................................................................................... 50

3.3 Results ...................................................................................................................... 52

3.3.1 Mitochondrial DNA sequences.......................................................................... 52

3.3.2 Sequence variation............................................................................................. 53

3.3.3 Neutrality tests ................................................................................................... 54

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3.3.4 Test for clock-like evolution.............................................................................. 54

3.3.5 Population genetic diversity and structure ......................................................... 56

3.3.6 Population structure across the natural distribution of C.tinnula....................... 61

3.3.7 Genetic comparisons within Crinia genus ......................................................... 64

3.3.8 Phylogenetic analyses ........................................................................................ 65

3.3.9 Genetic structure within regions ........................................................................ 67

3.4 Discussion ................................................................................................................ 73

3.4.1 Broad scale population structure........................................................................ 73

3.4.2 Population structure within regions ................................................................... 77

3.4.3 Evolution of C.tinnula ....................................................................................... 80

Chapter Four: Local Scale Population Structure and Gene Flow Inferred from Mitochondrial Cytochrome oxidase subunit I (COI) Sequence Data.................................................82

4.1 Introduction .............................................................................................................. 82

4.2 Materials and methods.............................................................................................. 85

4.2.1 Sample localities and sample numbers .............................................................. 85

4.2.2 DNA extraction and amplification of COI mitochondrial DNA fragment ........ 86

4.2.3 Temperature gradient gel electrophoresis (TGGE), Heteroduplex analysis (HA) and Sequencing...................................................................................................... 87

4.2.4 Data analysis ...................................................................................................... 89

4.3 Results ...................................................................................................................... 89

4.3.1 Mitochondrial DNA sequences.......................................................................... 89

4.3.2 Sequence variation............................................................................................. 90

4.3.3 Neutrality tests ................................................................................................... 95

4.3.4 Test for clock-like evolution.............................................................................. 95

4.3.5 Broad-scale population structure ....................................................................... 95

4.3.6 Local-scale diversity and population structure .................................................. 98

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4.3.7 Phylogenetic analysis....................................................................................... 107

4.4 Discussion .............................................................................................................. 109

4.4.1 Broad scale population structure: concordance of markers ............................. 109

4.4.2 Population structure within regions ................................................................. 109

4.4.3 Genetic variation within populations ............................................................... 114

Chapter Five: General Discussion ...............................................................................................118

5.1 Model for the past evolutionary history of C.tinnula populations in southeast

Queensland......................................................................................................................... 118

5.2 Comparative studies ............................................................................................... 122

5.3 Conservation Implications...................................................................................... 123

5.3.1 Future Climate Change .................................................................................... 126

5.4 Conclusion.............................................................................................................. 127

References....................................................................................................................................145

FOLD OUT REFERENCE MAP OF SOUTHEAST QUEENSLAND C.TINNULA

POPULATIONS INCLUDED ON LAST PAGE

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LIST OF FIGURES

Figure 2.1. Habitat structure of wallum heathlands in southeast Queensland ..............................16

Figure 2.2. Habitat characteristics of wallum heathland in southeast Queensland .......................18

Figure 2.3. Conservative summary of the phylogenetic relationships among Crinia species’ based on combined ND2 and 12S sequence data .......................................................22

Figure 2.4. Wallum froglet, Crinia tinnula. . ...............................................................................23

Figure 2.5. Southeast Queensland sampling sites for C.tinnula. ................................................26

Figure 2.6. Example of a parallel TGGE .....................................................................................34

Figure 3.1. Alignment of variable sites from the 362bp of mitochondrial 12S sequenced for C.tinnula .....................................................................................................................55

Figure 3.2. Locations of C.tinnula samples obtained from the South Australian museum...........62

Figure 3.3. Neighbour-joining (NJ) tree showing inferred phylogenetic relationships among C.tinnula 12S mtDNA haplotypes..............................................................................66

Figure 3.4. Nested cladogram of southeast Queensland and northern New South Wales C.tinnula 12S mtDNA haplotypes..............................................................................69

Figure 3.5. 12S mtDNA mismatch distribution for southern mainland and Bribie Island populations .................................................................................................................72

Figure 3.6. mtDNA haplotype frequencies of Oxleyan Pygmy Perch populations from southeast Queensland .................................................................................................75

Figure 4.1. Alignment of variable sites from the 543bp of mitochondrial COI sequenced for C.tinnula .....................................................................................................................92

Figure 4.2. Alignment of amino acid sequence for 543bp mitochondrial COI sequenced for C.tinnula .....................................................................................................................93

Figure 4.3. Nested Cladogram for northern C.tinnula COI mtDNA haplotypes ........................102

Figure 4.4. Nested Cladogram for southern C.tinnula COI mtDNA haplotypes ........................105

Figure 4.5. COI mtDNA mismatch distribution for southern mainland and Bribie Island populations ...............................................................................................................106

Figure 4.6. Neighbour-joining tree showing inferred phylogenetic relationships among C.tinnula COI mtDNA haplotypes ...........................................................................108

Figure 4.7. Channels of Brisbane and Pine Rivers across the Moreton Bay plain......................113

Figure 4.8. Sea level fluctuations over the last 200 000 years. ...................................................113

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LIST OF TABLES

Table 2.1. Collection sites and sample size for C.tinnula populations .........................................28

Table 3.1. C.tinnula populations and sample sizes for 12S mtDNA analyses..............................47

Table 3.2. Pairwise nucleotide differences among C.tinnula and C.parinsignifera TGGE reference samples .......................................................................................................50

Table 3.3. Average nucleotide frequencies for myobatrachid 12S mtDNA sequences ................53

Table 3.4. Distribution of 12S mtDNA haplotypes for southeast Queensland populations of C.tinnula .....................................................................................................................57

Table 3.5. 12S mtDNA haplotype diversity and nucleotide diversity ...........................................58

Table 3.6. Pairwise genetic distances for C.tinnula 12S mtDNA haplotypes................................59

Table 3.7. AMOVA showing partitioning of 12S mtDNA variation within and among regions of southeast Queensland populations of C.tinnula ........................................61

Table 3.8. Range of pairwise genetic distances for New South Wales C.tinnula 12S mtDNA haplotypes...................................................................................................................63

Table 3.9. Average genetic distances between C.tinnula population groups.................................64

Table 3.10. Pairwise genetic distances for C.parinsignifera and C.signifera 12S mtDNA haplotypes...................................................................................................................65

Table 3.11. Permutational chi-squared probabilities for geographical structure of clades ...........70

Table 4.1. C.tinnula populations and sample sizes for COI mtDNA analyses ..............................85

Table 4.2. Average pairwise differences within and between C.tinnula population groups..........88

Table 4.3. Average nucleotide frequencies for myobatrachid COI mtDNA sequences................90

Table 4.4. Average genetic distances between C.tinnula population groups.................................95

Table 4.5. AMOVA showing partitioning of variation within and among regions of southeast Queensland populations of C.tinnula. ........................................................................96

Table 4.6. Distribution of COI mtDNA haplotypes for C.tinnula populations..............................97

Table 4.7. COI mtDNA haplotype diversity and nucleotide diversity..........................................98

Table 4.8. Pairwise genetic distances for C.tinnula COI mtDNA haplotypes .............................100

Table 4.9. AMOVA showing the partitioning of variation within and among southern population groups of C.tinnula.................................................................................104

Table 4.10. Permuational chi-squared probabilities for geographical structure of southern clades ........................................................................................................................106

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Table 4.11. Haplotype diversity and nucleotide diversity for 12S and COI mtDNA haplotypes.................................................................................................................115

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LIST OF APPENDICES

Appendix 1: Microsatellite Chapter: Fine Scale Population Structure and Contemporary Gene Flow Among Wallum Froglet Populations .....................................................129

Appendix 2: Alignment of myobatrachid frogs to check the mitochondrial 12S sequenced for C.tinnula is not a nuclear insert ................................................................................137

Appendix 3: Alignment of variable sites from 12S mitochondrial sequence data for southeast Queensland and New South Wales C.tinnula samples.............................................139

Appendix 3.1: Pairwise genetic distances for 12S mtDNA southeast Queensland and New South Wales haplotypes ...........................................................................................140

Appendix 4: Permuational chi-squared probabilities for geographical structure of the clades identified in Figure 3, Chapter 4...............................................................................144

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STATEMENT OF ORIGINAL AUTHORSHIP

This work has not been previously submitted for a degree or diploma at any other

educational institution. To the best of my knowledge, this thesis contains no material from

another source except where due reference is made.

___________________

Juanita Renwick

January 2006

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ACKNOWLEDGEMENTS

I would like to thank my supervisor Associate Professor Peter Mather for all his time,

patience and words of encouragement throughout my PhD candidature. To all those in the

Ecology department and the genetics lab, a huge thank you for all the support and guidance,

especially Nat Baker for all the endless hours of listening and more than welcome advice.

To crazy ADD and Ang Duffy – thanks for keeping me sane. To Shaun Meredith, thanks for

all your support and for helping me to believe that one person can change the world. A big

thank you to all the willing (and not so willing) people that helped out in the field; Amanda,

Danny, Dave, Pete, Shaun, Paul, Traci Jo, Adam, Ed Meyer, Jo, Craig, Geoff, Grant, Simon,

….I apologise, once again, for the leeches and the sandflies, and I assure you that Crinia

tinnula are not just a figment of my imagination. Thank you to Rod Hobson, Omar and all

the rangers on Fraser Island that always made us feel welcome, you guys do a fantastic job!

Thank you to Harry Hines at the EPA for helping with permits and to Dr. Mike Mahony for

allowing me to use the SA museum C.tinnula samples. Thank you to my Mildura family and

friends, who gave me love, support and encouragement when I needed it the most. Thank

you to my family who have endured many of the long hours and late nights, and also the

highs and the lows of this journey. A very special thank you to my Nana Beck, who has

always been an inspiration, and who showed me that no mountain is too high or too steep to

climb. This is for Kahlua.

Chapter One: General Introduction

1

CHAPTER ONE

1 GENERAL INTRODUCTION

1.1 RELEVANCE OF GENETICS TO CONSERVATION BIOLOGY

Understanding genetic and ecological relationships among populations is important for

effective management of natural systems and the development of appropriate conservation

strategies for declining species. In the past, management strategies for threatened species

have focused primarily on protecting declining populations and on maintaining areas of

natural habitat in an attempt to alleviate decreases in population numbers due to

demographic and/or environmental stochasticity (Marcot 1992; Richards et al. 1993). At

this time, it was believed that demographic and environmental factors were likely to have a

greater influence on extinction probability of natural populations before genetic deterioration

imposed a serious threat (Lande 1988). There is now compelling evidence, however, to

support the argument that genetic changes in small populations can play a significant role in

determining population survival (Frankham 1995; Saccheri et al. 1998). Consequently, over

the last decade substantial efforts have been directed towards conserving the genetic

diversity of species and using genetic information to make more informed decisions about

how threatened species should be managed (McNeely et al. 1990; Driscoll 1998; Gaudeul et

al. 2000).

Genetic diversity is a fundamental attribute that contributes to a species evolutionary

longevity (Frankham et al. 2002; Hansson and Westerberg 2002; Reed and Frankham 2003).

Genetic variation is important for maintaining high levels of fitness and allows populations

to adapt to changing environmental conditions (Mitton and Grant 1984; Frankham 1995,

1996). Studies have shown that in small, relatively isolated populations, loss of genetic

diversity and genetic factors associated with small population size can contribute to a

population’s risk of extinction via factors such as susceptibility to disease and a decline in

fitness associated with increased homozygosity (Frankel and Soule 1981; Quattro &

Vrijenhoek 1989; Newman and Pilson 1997; Saccheri et al. 1998; Eldridge et al. 1999). A

primary aim of present-day conservation management strategies therefore, is to preserve

genetic diversity to increase a species chance of long term survival.

Conservation genetic studies use genetic markers to describe levels and patterns of diversity,

which in turn can identify populations with greater levels of genetic variation and also

identify populations of concern (with respect to inbreeding and loss of diversity). Genetic

markers can also be useful for describing population structure and to identify appropriate


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genetic management units for conservation, and also to provide information on a species’

biology. The availability of a wide range of markers which differ in mode of inheritance,

rate of evolution, selective neutrality and applicability for use at various levels of scale (e.g.

population level versus species level) means that many questions relating to a species

biology and ecology can be answered using genetic data. Information obtained from genetic

data can then be used in conjunction with information on population demographics, land use

patterns and habitat distribution to develop effective management strategies for the

conservation of species in decline (Kretzmann et al. 1997; Morales et al. 1997; Shaffer et al.

2000).

1.2 POPULATION GENETIC STRUCTURE

One of the more important advances in the field of conservation genetics has been the move

away from quantifying genetic diversity at the species level to recognising that genetic

diversity also needs to be documented at the level of the population. Populations often show

some degree of spatial structure (discontinuity) across their natural range (Andrewartha and

Birch 1954; Harrison 1989; Bos and Sites 2001). Spatial structure in conjunction with

natural heterogeneity of landscapes and inherent population dynamics (genetic drift,

selection, inbreeding), interact to produce varying levels and patterns of genetic diversity

among populations (Frankham et al. 2002).

For populations which have been affected by habitat fragmentation, the variability in levels

and patterns of genetic diversity among populations is often increased. Fragmentation of

natural habitat commonly results in the formation of remnant patches of habitat surrounded

by human altered environments (McKenzie and Cooper 1995; Briggs 1996). The change in

habitat and population spatial dynamics is often associated with increased isolation among

populations and reductions in population size; both of which can contribute to a loss of

genetic diversity. In conjunction with loss of diversity, fragmentation may also cause a

change in how genetic variation is partitioned. Allele frequencies within populations can

change via processes such as population bottlenecks (Allendorf 1986) and different

populations consequently may retain different alleles and haplotypes. The spatial isolation

of populations caused by fragmentation can also lead to genetic differentiation due to

fixation of different alleles in different populations via the process of genetic drift (Wright

1931; Avise 1994).


3

Recent studies have identified that levels and patterns of genetic variation can also vary

naturally depending on the geographic positioning of populations in relation to the species

natural range. Hoffman and Blouin (2004) showed that peripheral populations of the

Northern leopard frog, Rana pipiens, tended to posses lower diversity than did interior

populations owing to isolation, founder effects and chronically smaller population sizes.

In many cases it is not possible (or cost effective) to conserve all populations of a threatened

species and therefore, it is necessary to determine where management would be most

effective for ensuring the continued survival of a species. Describing genetic variation at the

population level allows a better understanding of the levels of diversity within a species and

a comprehensive understanding of how that diversity is distributed spatially across a species

range (Moritz 1994).

Describing genetic variation at the population level has also been beneficial in studies where

there may be taxonomic uncertainty. Since genetic analysis has become more common in

ecological research there have been many studies which have shown that designation of

species based on morphological or biogeographical data do not always correspond to

observed patterns of genetic differentiation (e.g. the existence of cryptic species; Green et al.

1996, 1997; Gleeson et al. 1999; Burbidge et al. 2003). If taxonomic relationships are

questionable, it is possible that subspecies or populations of evolutionary significance may

go undetected and result in inadequate management and loss of diversity that cannot be

replaced. Sampling at the population level is more likely to highlight genetic anomalies

which may identify cryptic species, particularly for species which exist in sympatry. It is

necessary for effective conservation management that we understand what it is we are

attempting to conserve.

For conservation purposes it is important to document not only the levels of variation within

a species but also to understand how genetic variation is partitioned spatially among

populations, i.e. to determine population genetic structure. Describing population genetic

structure enables us to make inferences about levels and patterns of dispersal among

populations, the potential for diversification and differentiation among populations and the

evolutionary history of populations (Avise 1992; Bossart and Prowell 1998). It is also

important to understand the ecological and evolutionary processes which may have shaped

the patterns of population structure (Avise 1989; Moritz 1994b, 1995).

Population genetic structure is defined by the partitioning of genetic variation among

populations (often defined by geographic boundaries) and results from the product of both


4

contemporary and historical gene flow. The level and pattern of gene flow among

populations influences the potential for differentiation (Avise et al. 1987) and can also affect

local population persistence (Harrison et al. 1988). Populations in different habitat patches

may be completely isolated, partially isolated, effectively a single population, or a matrix of

interconnected populations (e.g. metapopulation), depending on the extent of gene flow and

population extinction rates (Lavery et al. 1995; Boulton et al. 1998; Frankham et al. 2002)

The amount of gene flow among populations is primarily determined by the inherent

dispersal capability of a species in conjunction with geographic, ecological and geological

impediments to movement. These factors can have varying affects across a species range

and consequently population structure across a species distribution can vary in terms of the

level of structuring e.g. broad scale versus fine scale (Barber 1999a, 1999b) and the pattern

of structuring among populations i.e. because levels and patterns of dispersal are not always

uniform across a species distribution, populations may exhibit panmixia in one area of their

range (high levels of gene flow) and isolation by distance in another area (via restricted gene

flow) (Lavery et al. 1995). Quantifying levels of genetic variation among populations at

different spatial scales permits inferences to be made about patterns of population structure

and gene flow across the species distribution (Slatkin 1985a; 1987).

Traditionally, spatial analysis of variation in gene frequencies has been the approach adopted

for estimating population genetic structure and there has been a long history of model

development to infer the extent of gene flow from gene frequency data (e.g. FST and Nm

values which were derived to quantify levels of gene flow among populations; Wright 1931,

Slatkin 1987). The best known model is Wright’s (1931) Island model of population

structure which is based on the assumption that dispersal is equal among equal sized

‘islands’ or populations. Few natural population systems adhere, however, to the

assumptions of the Island model so variations on this model and alternate models of gene

flow have been proposed which relax and/or modify certain parameters to better fit the

dynamics of wild populations.

Alternative models of gene flow include Source-sink models (a source population provides

migrants to a number of smaller sink populations, Gyllenberg and Hanski 1992; Gaggiotti

1996); the Stepping Stone model (exchange of individuals is limited to adjacent populations

either in a one-, two- or three-dimensional pattern; Kimura and Weiss 1964); Isolation by

Distance models (describe a model of population structure in which populations are

distributed relatively continuously over a large area and individuals living nearby tend to be

more alike than those living far apart; Wright 1943; Slatkin 1993) and Metapopulation


5

models that describe dispersal among sets of conspecific populations existing in a balance

between extinction and recolonisation (Levins 1970; Hanski and Gilpin 1991).

Identifying population structure and relative gene flow gives an indication as to how

populations interact through dispersal and also identifies ecological barriers to dispersal and

inherent limitations to dispersal. This information allows managers to plan how to

effectively manage population systems and potentially preserve areas which may maintain

higher levels of diversity (NSW National Parks and Wildlife Service 2003; Department of

Environment and Conservation [NSW] 2005). This is important also for assessing how

populations will be affected by changes to their surrounding environments e.g. habitat loss or

fragementation. For example, populations which exhibit traditional metapopulation models

may be more at risk from processes such as habitat fragmentation because patches within a

metapopulation rely on colonisation from other populations. Barriers to dispersal among

patches and increased isolation among populations may limit the potential for recolonisation

particularly for those species which have small dispersal ranges or limited dispersal ability

(e.g. pool frog Rana lessonae; Sjogren 1991a, 1991b).

The effect of fragmentation on population structure will inevitably depend on the dispersal

capability of a species (Frankham et al. 2002). Small terrestrial species, such as amphibians,

are particularly vulnerable to habitat fragmentation because they often show poor vagility

and may require highly specialised habitats (Hitchings and Beebee 1998; Vos et al. 2001).

Species are also likely to be impacted differently by habitat fragmentation subject to how

dependent they are on a particular type of habitat as a corridor for dispersal. Overall,

generalist species tend to be opportunistic and can therefore potentially overcome habitat

changes (loss and fragmentation of patches) given a wider range of available suitable

breeding habitat (Dynesius and Jansson 2000). Specialists in contrast, are often endemic to a

particular type of habitat and are therefore habitat restricted. The requirement for specific

habitat attributes commonly means that populations are already isolated to a degree and may,

as a result be small in size. Populations with these characteristics may be particularly

sensitive to further loss of habitat and to habitat change. Increased habitat fragmentation can

lead to the complete isolation of populations and to significant reductions in population size

(Frankham 1998).


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1.2.1 EARTH HISTORY EVENTS THAT SHAPE POPULATION STRUCTURE

Since the late 1980’s, studies have demonstrated the importance of identifying historical

barriers to gene flow that may have influenced population structure (Avise 1992). Studying

patterns of genetic variation in a geographical context via gene trees (i.e phylogeography)

has contributed considerably to the understanding of potential factors that may have

influenced population structure and species divergence (e.g. Avise 1994).

Genetic differentiation among populations may be initiated by geographical isolation related

to physical or ecological barriers. Much of the genetic variation present within a widespread

species may be a consequence of vicariant isolation and subsequent divergence resulting

from large-scale climatic cycles or geological events (Printzen et al. 2003; Veith et al. 2003).

A number of studies have shown that past glacial periods and related eustatic oscillations

have had a significant effect on the current population structures of a range of animal and

plant species (Avise 1992; Hewitt 1996; Wong et al. 2004). Climate oscillations in the past

have repeatedly confined many animal and plant species to habitat refugia. During glacial

periods many species as a consequence, evolved distinct phylogeographical lineages

(Taberlet et al. 1998; Hewitt 1999).

The effect of climatic fluctuations on population genetic structure has been studied

extensively in a wide range of species, particularly across the European continent. Studies

have shown that conspecific populations which were restricted to separate habitat isolates

during glacial periods experienced significant genetic divergence (Bowen and Avise 1990;

Avise 1992; Hewitt 2000; Hewitt 2001). Interglacial periods and the onset of more stable

climatic conditions during the Holocene resulted in many plant and animal species

expanding their ranges into areas which were previously unoccupied during glacial periods.

These range expansions had a number of genetic consequences. In some species narrow

hybrid zones produced by the meeting of two divergent genomes have formed subsequently

as populations have expanded their ranges from separate habitat refugia (e.g. European

meadow grasshopper; Cooper et al. 1995). In other species postglacial expansion resulted in

parapatric distributions of divergent mtDNA and allozymes (e.g. European hedgehogs;

Santucci et al. 1998) and for some species, e.g. the Natterjack toad, Bufo calamita, genetic

analysis revealed that range expansions caused a loss of genetic diversity and an increase in

homozygosity in colonising populations as a result of rapid long distance range expansion

and founder events (Ibrahim et al. 1996; Beebee & Rowe 2000). Genetic structures that

developed due to glacial isolation and post-glacial range changes are thus important factors


7

that have contributed to the broad-scale distribution of genetic diversity (Comes and

Kadereit 1998; Hewitt 2001).

In recent years, population genetic models based on coalescent theory (Kingman 1982a,

1982b) have provided a statistical framework for estimating demographic parameters, such

as migration rates, population expansion and divergence times. Coalescent theory describes

the genealogical process of a sample of selectively neutral genes from a population looking

backward in time. The rapidly growing field of ‘statistical phylogeography’ (Knowles and

Maddison 2002) has produced a number of models which can explicitly test particular

phylogeographic hypotheses for simple population structures (Takahata et al.. 1995; Beerli

and Felsenstein 1999; Wakeley 2001; Excoffier 2004).

Using coalescent theory, methods such as Nested Clade Analysis and analysis programs such

as Geodis (Templeton et al. 1995; Posada et al. 2000) aim to distinguish among a diverse

array of historical processes to describe how current population structure may have formed.

This approach is based on the findings that different patterns of population growth, dispersal

and biogeographical history leave distinct signatures in current spatial patterns of neutral

genetic variation (Hutchison and Templeton 1999).

Describing historical population structure and determining factors that affect dispersal

capacity can provide an indication as to how contemporary dispersal may be affected by

current barriers and impediments to movement, in particular how species and populations

may react to future fragmentation or loss of habitat. It also allows us to understand, (1) how

the observed population structure of a species evolved and (2) the evolutionary relationships

among constituent populations. It is important to identify evolutionary lineages in order to

retain maximum genetic diversity.

1.3 THE USE OF MOLECULAR MARKERS TO DESCRIBE GENETIC VARIATION AND

POPULATION STRUCTURE

Neutral genetic markers have been used in many studies over the last 40 years to describe the

population structures of many species and to characterise levels and patterns of genetic

diversity. Although a variety of genetic markers have been developed, mitochondrial (mt)

DNA has proven invaluable for use in many population systems (Neigel 1997). MtDNA is a

circular, haploid, molecule which is inherited maternally in most animal species (Wilson et

al. 1985, Avise 1992). MtDNA has a comparatively higher net mutation rate than nuclear


8

DNA, does not undergo recombination and has ¼ the effective population size1 (as it is

haploid and maternally inherited). Consequently, it is far more sensitive than nuclear DNA

to reductions in population size due to processes such as founder effects and population

bottlenecks, making it a suitable marker for detecting the effects of stochastic processes

(Wilson et al. 1985; Harrison 1989; Brookes et al. 1997). The higher net mutation rate also

means that differentiation should be greater at mtDNA loci than at equivalent nuclear loci

(Birky et al. 1989).

MtDNA markers have been used successfully in a wide range of organisms to describe broad

scale historical population genetic structure and, even at the relatively fine spatial scales of

gene flow mtDNA markers have allowed greater understanding of interactions among local

populations (Nagata et al. 1998; Barber 1999b). Phylogenies derived from mtDNA

sequence data have proved to be invaluable for exploring evolutionary process and

demographic events in a species past (Avise et al. 1987; 1992).

Nuclear markers such as microsatellites and amplified fragment length polymorphisms

(AFLP) have also become increasingly popular in genetic studies because of the high level

of variability commonly observed at these loci. Microsatellites have advantages over other

DNA markers as they combine high variability with co-dominant inheritance and they can be

typed following non-invasive sampling. Microsatellites are relatively short, tandomly

repeated (1-4bp) stretches of DNA that occur ubiquitously throughout the genome of most

organisms (Scribner and Pearce 2000). Microsatellite loci often have larger number of

alleles and higher heterozygosity than other equivalent nuclear loci such as allozymes

(Reusch et al. 1999) and as such they have been used in a large number of studies to assess

genetic diversity. Microsatellite markers are particularly valuable for examining fine scale

population structure and for estimating the extent of contemporary gene flow.

Both mitochondrial and nuclear markers have been used to address conservation genetic

questions in a wide variety of organisms (Jones et al. 1996; Gonzales et al. 1998; Gaudel et

al. 2000; Bos and Sites 2001; Burns et al. 2004) and when they are used together they can be

invaluable for examining contemporary and historical patterns of genetic diversity and

population genetic structure (Monsen and Blouin 2003). Concordant results among markers

can provide strong support for hypotheses on a species evolutionary history (Cummings et

1 Note: This holds true as long as there is equal effective population size for each sex.


9

al. 1995), alternatively, findings of incongruent patterns can also be valuable because they

may provide a better understanding of important evolutionary processes such as introgressive

hybridization, sex-biased dispersal or the effects of selection (Fitzsimmons et al. 1997;

Rieseberg 1998; Sumida et al. 2000). Independent markers which differ in their rates of

mutation and heritability can also be useful for describing population structure over different

temporal and spatial scales (e.g. Ryan et al. 1996; Rafinski and Babik 2000; Lampert et al.

2003; Babik et al. 2004).

1.4 DECLINING FROG POPULATIONS

Anuran populations have been the subject of much discussion over the last decade as a result

of concerns about apparent worldwide declines in many species. While general hypotheses

including climate change, microbial pathogens and natural long term population fluctuations

(Blaustein and Wake 1990; Phillips 1990; Lips 1999; Pounds et al. 1999) have been

proposed as likely causes, of primary importance in many anuran population declines has

been loss and/or fragmentation of natural habitat (Ferraro and Burgin 1993a, 1993b; Bell and

Bell 1994; Brown 1994; Green 1994). Factors associated with habitat changes, e.g.

infrastructure (roads, railways) and changes in habitat quality and structure, have also been

demonstrated to cause declines in frog populations (Marsh and Pearman 1997).

Most frog species are ground dwelling and have relatively low individual dispersal capability

(Beshkov and Jameson 1980; Sinsch 1990). Gene flow among populations will depend on

the distance between suitable habitat patches and on the relative resistance of the intervening

landscape to dispersal among patches (Hitchings and Beebee 1997, 1998). Hitchings and

Beebee (1998) observed that measures of genetic diversity and survival of populations were

significantly lower in small, urban populations of the Common Toad, Bufo bufo than in

larger, rural populations in the same region. Genetic analysis and autecology of this species

indicated that the causal mechanism was random genetic drift arising from barriers to

dispersal among habitat patches as a result of urban development.

Because anurans often show limited dispersal capabilities and can exhibit site fidelity

(Berven and Grudzien 1990; Semlitsch and Bodie 2002) even a relatively small degree of

habitat fragmentation can effectively isolate populations. Most studies of genetic population

structure in anurans support the hypothesis that populations tend to be relatively isolated

from other populations (Shaffer et al. 2000) and exhibit significant differentiation even at

fine spatial scales (Waldman et al. 1992; Driscoll 1998; Shaffer et al. 2000). Vos et al.


10

(2001) examined the correlation between genetic distance and geographical distance in the

moor frog, Rana arvalis and found a significant positive association. Dispersal rates among

populations decreased with distance and barriers to dispersal such as roads and railways

affected the dispersal rate to a much greater extent than did geographic distance alone. Other

studies have shown similar isolation-by-distance effects on dispersal among frog populations

and that anthropogenic modification of landscapes can have a negative impact on dispersal

(Reh and Seitz 1990; Hitchings and Beebee 1997, 1998; Vos and Chardon 1998; Rowe et al.

2002;).

Studies of a wide range of frog species have shown that patterns of genetic diversity in

current populations are often determined by past geological and glacial events (Barber

1999a; Crawford 2003; Masta et al. 2003). Frogs, like many other animal and plant species,

exhibit patterns of range contraction and expansion from glacial refugia, hybridisation due to

postglacial range expansions and distinct phyogeographic lineages associated with

geological and ecological earth history events (McGuigan et al. 1998; Schneider et al. 1998,

Beebee and Rowe 2000, James and Moritz 2000; Pagano et al. 2001; Crawford 2003).

Many of Australia’s anuran populations, like anuran populations around the world, have

experienced recent declines in population numbers. Many of these declines have been

attributed to dramatic environmental change including deforestation and reclamation of low

lying land by humans (Tyler, 1979). In Australia many frogs are confined to areas of

sufficient, reliable precipitation (Woinarske et al. 1999). These environments are generally

restricted to the edges of the Australian continent, which is also the area of greatest human

occupation. This means that many of Australia’s native anurans are potentially very

vulnerable to deleterious effects associated with the results of human-mediated habitat

modification.

The frog fauna of Australia consists of five families; Hylidae (tree frogs), Ranidae (true

frogs), Microhylidae (narrow-mouthed frogs); Myobatrachidae (southern frogs) and

Bufonidae (true toads, introduced species). The Myobatrachidae are the only family that is

restricted solely to Australia and Papua New Guinea and they represent 57 percent of

Australian frog species. Members of the family display considerable diversity in

morphology, life cycles and ecology. Many species within the Family Myobatrachidae are

listed as endangered or vulnerable and three species are recognised as extinct.


11

1.5 CASE STUDY: THE WALLUM FROGLET (CRINIA TINNULA)

The wallum froglet, Crinia tinnula, is one of Australia’s habitat-specialist myobatrachid

anurans that in recent years, has suffered a dramatic decline in local population numbers

(Ehmann 1997). It is one of fourteen species in the endemic genus Crinia (Straughan and

Main 1966; Cogger 1996; Read et al. 2001). C.tinnula is restricted to coastal wallum

heathland and associated Melaleuca swamps in southeast Queensland and north-eastern New

South Wales. Along with three Litoria species (L.cooloolensis, L.olongburensis, and

L.freycineti), C.tinnula is commonly referred to as an ‘acid’ frog, because it is found in

association with acidic waters (pH <5) of lake, creek and swamp systems of the wallum

heath.

C.tinnula was first recognised as a distinct species in 1966 by Robert Straughan and Ian

Main. The species is very similar morphologically to other Crinia species, in particular

C.parinsignifera and C.signifera, and exhibits polymorphism for back colour and patterns

characteristic of the Crinia genus. C.tinnula produce relatively small clutches of eggs

(approximately 80 per clutch, range of 33 – 118; Straughan and Main 1966), and breeding

follows the passage of cold fronts bearing rain during the winter months. C.tinnula is the

only species of acid frog to breed predominantly in winter.

The patchy distribution of populations, winter breeding activity and morphological similarity

to other Crinia species has resulted in some populations only being discovered recently

(Ehmann 1997; Hero et al. 2000). In a report in 1997, the species was suggested to be absent

from Fraser Island, however, several populations have since been found on the island.

Although ‘new’ populations have been found relatively recently there are many sites which

in the past were known to support wallum froglet populations and now apparently no longer

do so (Ehmann 1997).

The species is not generally associated with disturbed areas. Ehmann (1997) noted that

C.tinnula was absent from areas of habitat that had been disturbed by sandmining, pasture

improvement, cane farming and landfill activities. The habitat to which C.tinnula are

endemic, (“wallum” habitat) is restricted to low coastal plains behind sand dunes in

southeast Queensland and northern New South Wales. As human populations in this region

have grown, coastal areas have become prime areas for development for agriculture,

residential property and large commercial pine plantations. Throughout the greater Brisbane

region, the Sunshine Coast and the Gold Coast areas wallum habitat has been significantly

reduced, modified or subject to disturbance from anthropogenic activities (Coaldrake 1962;


12

Hero et al. 2000).

Reduction of natural habitat is considered to be the major cause of declines in the acid frog

populations. Local extinctions and reductions in population numbers has resulted in the

listing of all four acid frog species in the Queensland Nature Conservation (Wildlife)

Regulation (1994, 2004) as either Vulnerable (L.freycineti, L.olongburensis, C.tinnula) or

rare (L.cooloolensis). C.tinnula is also listed as Vulnerable under the New South Wales

Threatened Species Conservation Act (1995, 2002). All four species are protected under the

Federal Environment Protection and Biodiversity Conservation Act (1999).

A landuse study carried out in the early 1970’s described wallum as largely ‘useless’ and

suggested that modification of wallum habitat would allow for expansion of beef cattle

production in the south east Queensland region (Bullen, 1970). Between 1974 and 1989,

over 50% of Melaleuca forest and 34% of the heathland that was present in south-eastern

Queensland were cleared (Catterall and Kingston 1993) and over the last fifteen years, large

areas of coastal heathland have been destroyed for agriculture, mining and residential

development (Hines et al. 1999). The effect has been to convert a once largely continuous

patch of coastal heathland and Melaleuca swamps into a matrix of small patches, within an

area undergoing rapid urban expansion.

Much of the wallum is now recognised as both of evolutionary and ecological significance

and this habitat type has been protected on some offshore sand islands in the region (Fraser

and Moreton Islands), however, mainland areas and populations on other sand islands (e.g.

Bribie Island and Stradbroke Island) are still under threat, particularly from urban

development (Hero et al. 2000). Long term survival of the wallum froglet will require

implementation of conservation management plans to ensure persistence of these species in a

region subject to ongoing rapid environmental change.

To plan effective management strategies for the conservation of C.tinnula populations it is

first necessary to understand the species population structure and levels and patterns of

genetic diversity within and among extant populations. Relative conservation status of each

population can then be determined and this information can be used to develop appropriate

management plans for the species.


13

1.6 THESIS STRUCTURE AND AIMS

Existing information on ecology of C.tinnula populations is very limited and is restricted

largely to basic distributional data. Nothing is known about movement patterns or the extent

of interactions among natural populations across the species distribution. Neither is anything

known about genetic diversity or population genetic structure of this species. While

populations are protected under the Nature Conservation Act (1992) currently there are no

specific conservation management plans for this species. Given the rapid rate of clearing

and fragmentation of wallum habitat in southeast Queensland due to human population

expansion, it is likely that conservation management plans will be necessary for C.tinnula

populations in the near future.

This study aimed to document levels and patterns of genetic diversity and to define the

population structure for C.tinnula populations across the natural distribution in southeast

Queensland. Specific aims of the project were; to use mitochondrial markers to describe

patterns of historical population structure, to determine how current patterns of genetic

diversity evolved and what processes may have influenced the evolution of C.tinnula

populations. It is hypothesized that sea level fluctuations during the Pleistocene may have

influenced the distribution and connectivity of wallum habitat in eastern Australia and this

may have consequently influenced the dispersal patterns and population structure of

C.tinnula populations. Describing patterns of historical population structure will be useful

for defining evolutionarily significant units for conservation of C.tinnula, and historical

patterns of gene flow may also give an indication as to the dispersal capacity of C.tinnula.

Historical population structure may also provide insight into patterns of colonisation of the

major sand islands in the region.

The project also aimed to describe contemporary levels and patterns of gene flow and

genetic diversity using microsatellite markers. In particular, to look at local scale dispersal

patterns among populations to infer potential impacts that habitat fragmentation could have

on modern population structures and levels of genetic diversity.

The genetic information obtained in this study can be used to assist in assigning relative

conservation status to populations or groups of populations based on levels and patterns of

diversity, genetic structure and evolutionary significance. In conjunction with genetic data,

information on land use patterns, distribution of remaining wallum habitat and current

information on population distribution data can then be used to develop effective

management strategies for C.tinnula in southeast Queensland.


14

General methods used in the present study are described in Chapter 2, where information

regarding the sampling design, location of collection sites, laboratory methods and statistical

analysis are given. Specific methodological information is also provided in Chapters 3 and

4. Chapter 3 describes the broad-scale population structure of C.tinnula. Chapter 4

describes local population structure and genetic diversity within regions. Chapter 6

examines the patterns of genetic diversity and population structure and the implications for

C.tinnula conservation management.

Chapter Two: General Methods

15

CHAPTER TWO.

2 GENERAL METHODS

2.1 THE STUDY AREA

2.1.1 BIOGEOGRAPHY OF THE WALLUM

Sites sampled for this study are located in southeast Queensland within the biogeographic

region known as the Coastal Lowlands (Coaldrake 1961). The coastal lowlands are a natural

system extending from Gladstone in Queensland to Coffs Harbour in NSW and form part of

a discontinuous belt of lowland country extending along the eastern and southern coasts of

Australia (Coaldrake 1961).

Within southeast Queensland and northern NSW, coastal lowlands are also known as

“wallum”. The word ‘wallum’ is an aboriginal word which was used to describe the small

woody tree, Banksia aemula (Harrold 1994). Over time, the use of the term has been

extended to describe other plant communities found in the coastal lowlands in the

Queensland region, in particular heathlands, which tend to be dominated by Banksia aemula

and other similar Banksia species.

The coastal lowlands are distributed across low lying undulating alluvial plains

(approximately 1 to 10 metres above sea level) found in behind coastal dune systems. The

lowlands have a mild subtropical climate with a marked dominance of summer rainfall and a

small but significant winter rainfall. The winter rainfall provides the temporary water bodies

that C.tinnula utilise for breeding. The sandy soils of the wallum are low in fertility except

in areas where volcanic influences have added nutrients to the soil. Typical plant

communities of the wallum include open woodland forests of Melaleuca quinquenervia

associated with heath understory (Banksia alliances) and wet and dry heath (Southeast QLD

Bioregion - Regional Ecosystems 12.2.9; 12.2.15; 12.3.5; Sattler and Williams 1999).

Wallum habitat throughout southeast Queensland is similar floristically but can differ quite

markedly in structure (see Figure 1 and 2). Wallum habitat associated with the perched lake

systems of the Fraser Island-Cooloola sand masses and those of Moreton and North

Stradbroke Islands generally consists of extensive, dense reed beds in shallow areas of the

lakes and the fringing areas of the lake support stands of M.quiquenervia.


16

Figure 1. Habitat structure of wallum heathlands in southeast Queensland. A Wallum

heathland (Ungowa Fens) on Fraser Island. Wet heathland consisting mainly of sedges,

outer edges of the heath are composed of Melaleuca and Eucalypt woodland. B. Honeyeater

Lake, Moreton Island. Perched lake surrounded by dry heath. Dense stand of reeds form in

the shallow areas of the lake. C. Amity Point, Stradbroke Island. Wallum Freshwater

swamp (recently burnt out by fires).

A.

B.

C.


17

Wallum habitat also includes freshwater swamps and wallum plains which comprise wet and

dry heaths. The dry heaths are a prominent feature of the older dune systems of the coastal

sand masses. In areas exposed to the wind, vegetation is generally less than one metre in

height and consists of small woody shrubs and sedges but where vegetation is more

protected small trees are present. Temporary water bodies, formed from winter rains,

provide the favoured breeding habitat for C.tinnula. These areas may also be associated with

creek catchments and lake systems, with the dry heaths forming on elevated soils.

The wet heaths (varying degrees of ‘wet’) are generally very simple in plant structure,

usually devoid of tree and shrub species (small stands of paperbarks may be found on the

outer edges of the heathland). They form in the catchments of creeks and drain water from

the neighbouring dunes and usually support dense sedge-like vegetation. During periods of

high rainfall these areas are inundated with water and can support large breeding populations

of C.tinnula.

One of the most distinctive features of wallum is the tea-like colour and low pH of the water

bodies associated with this habitat (Figure 2). Water colour is due in part to the amount of

decaying organic matter in the water. Acidity of the water is affected by input from

vegetation, the age of the soils and the nature of the organic layer on which the water body

forms (Bayly 1964; Ingram and Corben 1975). pH levels can range from as low as 2.8 to

5.5. It is the adaptation to low pH levels and the ability of the larvae of acid frogs to develop

in these relatively acidic environments that has led to the recognition of the acid frogs as a

specialist ecological group.

Coaldrake (1962) suggests that the development of the present wallum ecological pattern

dates from varying periods during the Pliocene. It is certain that much of the wallum has

been within the range of eustatic oscillations of the Pleistocene (Coaldrake 1961, 1962;

Thom et al. 1994). A drop of about 28 metres would move the present south eastern

Queensland coastline east of Moreton and Stradbroke Islands (approximately 40km) and link

the major areas of now disjunct wallum existing across the mainland and the coastal sand

islands (Willmott and Stephens 1992). It is unknown, however, whether wallum formed a

semi-continuous distribution from the mainland to the island wallum areas during lower sea

levels or whether wallum habitat on the islands has formed as disjunct isolated patches.


18

Figure 2. Habitat characteristics of wallum heathland in southeast Queensland. A & B.

Dense reed beds associated with the perched lakes and wet heath systems. C. Characteristic

tea colour water of wallum habitat.

A.

B.

C.


19

Human population growth in southeast Queensland is causing rapid changes to established

land use patterns. Town planning for the region aims to accommodate a total population of

2.5 million by 2011 (BCC1990). It is estimated that 29% of Australia’s population growth

between 1991 and 2011 will occur within this area (Kordas et al. 1993). Large tracts of land

have already been drained and cleared on the Sunshine Coast for pine plantations (Pinus

spp.) and housing developments (Batianoff and Elsol 1989).

In the Brisbane region, much of the pre-European wetland habitat has been cleared or is

currently under threat from rural or residential development. Within the area under Brisbane

City Council authority, it has been estimated that more than 95% of wetland habitat has

already been cleared (ES&S 1989). In particular, very few areas of wet heathland remain

within the Brisbane region. Most of the remaining wet heathland habitat is restricted to the

sand islands and the Cooloola region.

One of the most significant patches of remaining wallum habitat in the Brisbane region is

found in Karawatha Forest. Within Karawatha, a small patch of wallum heathland exists in

the seasonally wet floodplain of the Scrubby Creek catchment system. This small area is of

high local and regional significance, containing both a high diversity of herbaceous species

and sedges and a number of relatively uncommon species (Kordas et al. 1993). Karawatha

forms an important core habitat area with links to significant areas of bushland remnants in

the Brisbane area (Kordas et al. 1993).

2.1.2 BIOGEOGRAPHY OF THE COASTAL SAND ISLANDS OF SOUTHEAST QUEENSLAND

At present, many of the protected areas of wallum habitat occur on the sand islands adjacent

to southeast Queensland. These islands are relatively young in geological time and going by

Coaldrake’s (1962) estimates of how old wallum habitat is, the sand deposits (from which

the islands developed), were established after the appearance of wallum habitat on the

mainland. Populations of wallum froglets have been found on all of the major sand islands

(Fraser Island, Bribie Island, Moreton Island, North and South Stradbroke Islands).

The larger sand islands (Fraser Island, Moreton Island and North and South Stradbroke

Islands) are believed to have been formed from a series of parabolic dunes constructed

episodically during the period of fluctuating sea levels in the late-Quaternary (Ward 1977;

Clifford & Specht 1979). Fraser Island is situated approximately 200km north of Brisbane

and is currently separated from the mainland (at its most southern point) by a distance of


20

approximately 1.5km. It is the world’s largest sand island, stretching 123km along the

southern coast of Queensland. Geological evidence suggests that the dunes of Fraser Island

formed synchronously with the Cooloola sandmass during the last million years (Thompson

1992; Longmore 1997). The oldest dune deposits of Cooloola date back to 700 000 years

before present (Tejan-Kella et al. 1990) and sedimentary sequences from Lake Coomboo, a

relic perched lake in the oldest dune system on the western side of Fraser Island, date back to

600 000 years before present (Longmore and Heijnis 1997). Geological studies suggest

Fraser Island would have been linked to the mainland for the majority of the last one million

years except for relatively brief interglacial periods (Longmore 1997).

Wallum heaths and swamps are associated with the oldest dune systems of Fraser Island and

the Cooloola sandmass (Walker et al. 1981) and represent a retrogressive stage of vegetation

development (vegetation succession reaches a climax ‘high nutrient and biomass’ stage

followed by a nutrient deficient, low biomass stage characterised by dwarf woodland

communities adapted to fire and low nutrient status; wallum habitat forms a major part of

this retrogressive vegetation). The majority of wallum habitat is distributed along the

western side of Fraser Island, with small patches associated with the freshwater lakes found

on the central dune ridge and some patches of wallum found on the eastern side of the island.

Moreton Island is Queensland’s second largest sand island and is situated approximately

30km east of the mainland. The island can be divided up into 3 main sections based on

regional topographic differences; northern, central and southern. The northern part of the

island supports large expanses of wallum heath and swamp and contains many of the

freshwater lakes on the island. The central part of Moreton Island is composed of large dune

ridges and there is very little wallum habitat through this part of the island. The southern

area is a low undulating coastal sand plain which is quite exposed. There are extensive

Melalueca quiquenervia swamps in this area with little to no heath or sedge understory.

North Stradbroke Island and Moreton Island are thought to have evolved synchronously with

the Fraser-Cooloola sand mass (Tejan-Kella et al. 1990; Jones 1992), however, geological

data for North Stradbroke Island suggests that this is a younger island. Clifford and Specht

(1979) proposed that the formation of North Stradbroke Island began during a glacial period

approximately 400 000 – 500 000 years ago. The formation of Moreton Island may also

have begun around this time (Jones 1992).

Moreton Island and North Stradbroke Island formed around small rocky pinnacles of what

are currently Dunwich, Point Lookout and Cape Moreton. These pinnacles acted as groynes


21

on the continental shelf to anchor the build up of sand spits. At times of highest sea levels,

Moreton Bay spilled around behind these growing spits to convert them into islands

(ancestors of North Stradbroke and Moreton Islands) (Jones 1992). Geological evidence

suggests that sea levels would have reached their present height approximately 6 000 years

ago.

The smaller sand island, Bribie Island, was also formed during the Pleistocene but is most

likely younger than the other sand islands. Geological evidence suggests that the older dunes

of Bribie Island were formed approximately 100 000 years ago from sand barriers (dune

systems) developed during the Pleistocene and the younger dunes on the island were formed

approximately 6 000 to 12 000 years ago from sand barriers developed during the Holocene

(Batianoff and Elsol 1989).

2.2 THE STUDY SPECIES; CRINIA TINNULA

2.2.1 SYSTEMATICS

The high level of morphological similarity among a number of Crinia species resulted in

delineation of species being based on male calls and/or experiments that tested reproductive

compatibility between different ‘populations’. The recognition of C.tinnula as a distinct

species was based on morphology and call discrimination tests among C.signifera,

C.parinsignifera and Crinia sp. nov [C.tinnula] individuals that were collected in the same

creek system (Straughan and Main, 1966). C.tinnula females were found to discriminate in

favour of conspecific calls against C.parinsignifera or C.signifera calls and in vitro crosses

of C.tinnula with C.parinsignifera and C.signifera resulted in both abnormalities of tadpoles

and death of tadpoles shortly after hatching (Straughan and Main 1966). The designation of

C.tinnula as a distinct species was therefore based on results of reproductive incompatibility

and male call structure.

The Crinia genus has been subject to a number of taxonomic revisions since the late 1950’s

based on information relating to male call structure, hybridization experiments, morphology,

biogeography and most recently molecular systematics (Main 1957; Blake 1973; Heyer and

Liem 1976; Thompson 1981; Heyer et al. 1982; Read et al. 2001).

Apart from a temporary name change (which saw all but two of the Crinia species

reassigned to the subgenus Ranidella), C.tinnula has not experienced any major taxonomic


22

‘reshuffling’ during the revisions, its taxonomic position as a distinct sister taxon to other

Crinia species has remained relatively consistent. This is most likely due to the fact that

C.tinnula has never been included in either of the Crinia species’ groups (“C.insignifera

species group” and “C.signifera species group”) described by Main (1957). The

relationships among species within these groups have been the main source of contention in

Crinia systematics.

The most recent taxonomic revision, based on a molecular phylogenetic assessment of two

mtDNA regions, was the first study to include and compare all Crinia species (Read et al.

2001). The molecular phylogeny supports the position of C.tinnula (and an unidentified

Crinia sp.) as a sister clade to C.parinsignifera and suggests a basal trichotomy for the

Genus Crinia (Figure 3).

Figure 3. Conservative summary (bootstrap support of 70% or more) of the phylogenetic

relationships among 11 of the 14 described Crinia species’ based on combined ND2 and 12S

sequence data. Reproduced from Read et al. (2001).

2.2.2 MORPHOLOGY

The physical appearance of the wallum froglet is characteristic of the Crinia genus, with

individuals highly polymorphic for back colour and pattern (classified as lyrate, ridged or

smooth), small size (20mm- 25mm) and granular belly pattern (Figure 4). Specific

distinguishing morphological attributes include a midline of white dots down the throat

(occurs on some C.signifera), pointed snout and a distinctive high pitched call, described by

Straughan and Main (1966) as ‘like the tinkling of a small bell’ from where the aboriginal

term ‘tinnula’ (tinkling) comes from.

halla

This figure is not available online. Please consult the hardcopy thesis available from the QUT Library


23

Figure 4. Wallum froglet, Crinia tinnula. A. Adult froglet next to a matchstick. Adults

range in size from 16mm to 24mm. B, C & D show the polymorphic back patterns and

colours characteristic of C.tinnula.

A.

B.

C. D.


24

C.tinnula is very similar in appearance to both C.parinsignifera and C.signifera. In optimal

wallum habitat, both C.parinsignifera and C.signifera are absent and so identification of

C.tinnula is non-problematic (C.tinnula is significantly morphologically different from other

acid frog species). In disturbed habitats, however, where C.parinsignifera and C.signifera

may be present, it can be very difficult to identify the three species based on external

phenotype, especially in the metamorph stages. Male calls are generally used to distinguish

the species, however, while all three species have somewhat distinctive calls, when males are

calling in a chorus it can be very difficult to identify which call belongs to which frog. In

some cases (e.g. at the Karawatha and Caboolture sites) it was only possible to distinguish

the species using genetic markers.

2.3 SAMPLING DESIGN AND SAMPLE COLLECTION

The design of the sampling regime was intended to document the pattern of broad scale

genetic structure for C.tinnula populations within southeast Queensland as well as to

describe the extent of local dispersal among populations. Approximately 27 sites were

chosen originally, including at least two sites on each of the major sand islands (sites were

restricted to those approved by the EPA under the sampling permit).

An inherent difficulty in studying a declining species is finding suitable sites (as populations

are often small and isolated) and obtaining adequate numbers of samples for analysis. At

least ten sites were visited along the mainland which had previously been known to, or

thought to support C.tinnula populations and frogs could not be heard calling or found after

intensive searching. Several of these sites were visited multiple times over the four year

sampling period. The difficulty in finding suitable sample sites produced geographical gaps

in the sampling regime.

As wallum habitat is coastal it forms a linear distribution pattern along the southeast

Queensland coastline. Sampling sites are also therefore, relatively linear in distribution.

The described distribution for C.tinnula within southeast Queensland stretches along the

coastline from Littabella National Park, Bundaberg down to Coolongatta on the Gold Coast.

In the current study, fourteen sites were sampled; Wathumba Creek (Fraser Island), Ungowa

(Fraser Island), Barga Lagoon (Fraser Island), Rainbow Beach, Cooloola (Great Sandy

National Park), Noosa, Peregian, Beerwah, Caboolture, White Patch (Bribie Island), Bellara

(Bribie Island), Karawatha Forest Park, Honeyeater Lake (Moreton Island) and Amity Point


25

(Stradbroke Island) (Figure 5).

A total of 262 individuals were collected from fourteen sites in southeast Queensland. These

samples were collected over four successive breeding seasons. For each individual

population, except Stradbroke Island and Ungowa, sampling occurred within a single

breeding season. The Stradbroke Island population was sampled across two successive

breeding seasons, (5 froglets sampled in one year and 30 froglets sampled the next year) and

the Ungowa population was sampled across a three year time period but only within two

breeding seasons (25 froglets sampled in 1998 and 2 sampled in 2000).

Sampling was usually undertaken at night when males were calling, however, sites were also

sampled during the day when visibility was greater and frogs could sometimes be found

under overhanging vegetation or were more likely to be seen as they swam through the

ponds. Individuals were caught by hand. Listening and searching for calling males and then

searching similar areas of vegetation where calling males had been located seemed to give

the most success for capture of the froglets. The most common position male froglets were

found was at the base of reeds or under overhanging vegetation close to the pond edge.

It is assumed that the majority of samples were taken from male froglets, however, females

approaching calling males and those found in amplexus were also sampled. It was

impossible to sex froglets in the field unless individuals were observed calling or were found

in amplexus, therefore sex of froglets was not recorded and sex ratios are not known.


26

Figure 5. Southeast Queensland sampling sites for C.tinnula. Sampling sites are shown as

green squares.


27

Tissue samples were collected from froglets by taking a very small toe-clip from the third toe

(Barker et al. 1995) on the back left foot. This was taken using sterile, sharp straight-edged

manicure scissors. The scissors were sterilised after each clip using an ethanol swab and a

new pair of scissors was used for each site. No evidence of mortality was observed on any

trip (generally, clipped males were calling again from the site of capture within 10 – 30

minutes of release).

Tissue samples were placed immediately in a 1.5ml eppendorf ring top tube containing an

80% dimethylsulphoxide (DMSO) saturated salt solution. At a small number of sites

(Cooloola and Barga Lagoon) where few adults were calling over successive nights or days,

up to ten tadpoles were also taken. Tadpoles were placed on an ice slurry to slow

metabolism and individuals were then placed in 70% ethanol.

Sample sizes per locality ranged from two to thirty-five (refer Table 1). Permit restrictions

limited the number of samples taken at each site (30 maximum at any one time and a total of

60 maximum per site - successive sampling had to occur at least six months apart, Permit

Numbers E6/000010/98/SAA, E6/000010/00/SAA, E6/000010/01/SAA).


28

Table 1. Collection sites and sample size for C.tinnula populations. Latitude and longitude

coordinates are shown in decimal degrees.

2.4 LABORATORY METHODS: MITOCHONDRIAL DNA TECHNIQUES

Two regions of the mtDNA genome, 12S ribosomal DNA and Cytochrome oxidase subunit

one (COI), were screened to document patterns of genetic diversity and to determine

population structure within and among sampled wallum froglet populations. The 12SrRNA

region was chosen to describe deep (historical) phylogenetic relationships among

populations because rRNA genes generally evolve slowly relative to other mtDNA genes,

and are therefore useful for applications such as inferring patterns of deeper evolutionary

divergence (Kumazawa and Nishida 1993). Conserved primers for this region were known

to amplify without problem across a diverse range of organisms (birds, reptiles and crayfish)

and had shown genetic variation in these organisms.

The COI region was used to document local population structure and also to describe relative

genetic diversity levels within populations. Substitutions at the 3rd codon position of the

amino acid are less constrained as they do not change amino acid sequences, therefore

Population Identification Code

Location Latitude Longitude

Sample Number

Wathumba Creek

Wc Fraser Island -24.98 153.23 30

Ungowa Un Fraser Island -25.45 153.00 27 Barga Lagoon Bg Fraser Island -25.50 153.05 27 Rainbow Beach Rb Rainbow Beach Rd -25.94 153.08 18 Cooloola Co Cooloola-Rainbow Rd -26.01 153.08 24 Noosa Ns North Shore Noosa -26.38 153.05 02 Peregian Pg Old Emu Road, Peregian -26.43 153.10 05 Beerwah Bw Scientific Area No 1,

Beerwah -26.84 153.01 04

White Patch WP Bribie Island -27.03 153.14 30 Bellara Bell Bribie Island -27.07 153.17 30 Caboolture Ct Porters Road, Caboolture -27.07 153.00 03 Honeyeater Lake

MI Moreton Island -27.09 153.44 11

Amity Point SI Stradbroke Island -27.40 153.45 16 Karawatha Kw Logan, Brisbane -27.64 153.11 35


29

synonymous substitutions evolve approximately 8-10 times faster than second position sites

and 2-4 times faster than first position sites in vertebrates (Kocher and Carleton 1997) and

can be used for estimating genetic diversity within and among populations (Bermingham et

al. 1997).

2.4.1 OUTGROUP SPECIES

C.parinsignifera and C.signifera were used as outgroup species for phylogenetic analyses in

this study because of their close genetic relationship to C.tinnula and also because parapatric

populations of these species are found in disturbed areas of wallum making access to tissue

samples relatively easy.

2.4.2 DNA EXTRACTION

A modified chelex extraction protocol was used to obtain genomic DNA (Walsh et al. 1991).

The chelex protocol provided a rapid method for extracting DNA for polymerase chain

reaction (PCR) amplification from the small amount of tissue available.

Tissue was washed in 1 ml of STE for 1 hour to rehydrate and then samples were placed in

500μl of 20 percent chelex solution (20g chelex resin in 80g distilled water) and 5μl of

20mg/ml proteinase K. Samples were then kept at 55oC on a heating block for 3 hours,

gently vortexed every hour, or placed on a rotating wheel in an oven kept at 55oC. After

digestion, samples were placed in boiling water for 8mins (or alternatively on a heat block at

100oC for 8mins) then allowed to cool after which 50µl of TE was added. Samples were

then spun down in a centrifuge at 13000rpm and the supernatant was removed to a new tube

(chelex beads discarded). Samples were stored at –20oC.

2.4.3 POLYMERASE CHAIN REACTION (PCR)

PCR was used to amplify target mtDNA regions. The 12SrRNA mtDNA region was

amplified using general vertebrate primers developed by Kocher et al (1989). The sequences

of the primers were as follows; light strand primer (12sf) 5’-AAA GCT TCA AAC TGG

GAT TAG ATA CCC CAC TAT-3’, heavy strand primer (12Sr) 5’ -TGA CTG CAG AGG

GTG ACG GGC GGT GTG T-3’. The COI mtDNA region was amplified using a


30

combination of general vertebrate (COIaH; Palumbi et al. 1991) and general amphibian

primers (Cox; Schneider et al. 1998). Sequences of the primers were as follows; Cox (light

strand primer) 5’-TGA TTC TTT GGG CAT CCT GAA G -3’; COIa-H (heavy strand

primer) 5’ – AGT ATA AGC GTC TGG GTA GTC – 3’.

The 12SrRNA and COI mtDNA fragments required different PCR protocols and as such

each of the specific PCR reaction conditions and PCR cycle protocols are included in

relevant chapters. To ensure correct fragments were targeted, amplified mtDNA was run out

on an agarose check gel next to a size marker. DNA sequencing further verified that the

correct fragment had been targeted successfully.

2.4.4 TEMPERATURE GRADIENT GEL ELECTROPHORESIS (TGGE)

There are a number of ways to identify mitochondrial DNA sequence differences

(haplotypes) among individuals. The most common of these include Random Fragment

Length Polymorphisms (RFLPs), Temperature- or Denaturation Gradient Gel

Electrophoresis (TGGE and DGGE, respectively) and direct sequencing. Population

analysis involves examining large data sets and therefore requires a quick, efficient and

informative method for identifying differences among individuals. TGGE combined with

heteroduplex analysis (HA) can provide a reliable and relatively quick method for explicit

identification of haplotypes. Further, as each individual can be assigned a particular

haplotype, a representative individual for each unique haplotype can be sequenced

eliminating the need to sequence every individual, so reducing labour and sequencing costs.

The method of TGGE is based on the separation of double-stranded PCR products in a

polyacrylamide gel with a superimposed linear temperature gradient (Po et al. 1987). DNA

migrates through the temperature gradient gel as a duplex until it reaches a destabilising

temperature (melting point), at which point the duplex partially denatures and will decrease

in mobility. Mutations alter the melting point and individuals with different mutations will

possess comparatively different migration patterns.

Heteroduplex analysis is used in conjunction with TGGE to enhance visual sensitivity. As

mtDNA is haploid, a heteroduplexing technique is used to combine comparative mtDNA

sequences from different individuals (or different species in the case of Outgroup

heteroduplex analysis – see Campbell et al. 1995) to identify mutation differences among


31

individuals.

Sequence fragments from two individuals (amplified by PCR) are subject to a brief

denaturation cycle (95oC) followed by a reannealing period (50oC). This reaction generates

two kinds of product; the homoduplex – fragment strands from the one individual combine

with each other with no mismatched bases as strands are homologous, and the heteroduplex -

fragment strands from the two different individuals combine and if the strands are not

completely homologous there will be mismatched base pairs and unmatched segments upon

recombination. These mismatched strands generally have lower melting points and different

electrophoretic mobility than do homoduplex bands and are therefore visualised at a different

position on the gel.

The sensitivity of TGGE and heteroduplex analysis (within short fragments <1000bp) is very

high and allows detection of mutations resulting from insertion/deletion (Lessa and

Applebaum 1993) and single (or multiple) base pair substitutions (Russ and Medjugorac

1992). In addition, the differences in the type and position of mutations, produces visually

distinct banding patterns so that the same base pair substitution at different locations along

the genome can be detected on an acrylamide gel using a silver staining technique (Wartell et

al. 1990; Lessa and Applebaum 1993). This allows relatively easy and reliable identification

of different haplotypes among individuals.

In the present study, the Diagen horizontal TGGE system was used to analyse C.tinnula 12S

and COI fragment samples. TGGE analysis was performed according to the specifications

outlined in the Diagen handbook (1993, DIAGEN Gmbh, QIAGEN Inc.). A summary of the

most important aspects of the procedure are outlined below.

Gel formation and Casting: A 5% polyacrylamide gel was used to maximize resolution

among different haplotypes. Gels were made up using 21.6g Urea, 7.5ml 30% Acrylamide,

0.9mls 50x buffer (1M MOPS, 50mM EDTA pH 8.0), 2.25ml 40% Glycerol, 14.8ml ddH20,

75µl TEMED (N,N,N’N’-tetramethylethylenediamine), and 136µl 10% Ammonium

persulphate. The gel was cast onto a polyethylene gel support film (Pagebond, FMC) and

left in a horizontal position undisturbed, for 60 minutes to set. The gel (still attached to the

support film) was then mounted on to the temperature plate over a 2ml layer of 0.1% Triton

(to ensure uniform heating). Buffer tanks were filled with 1 x ME buffer and bridges were

established to the gel by a layer of electrode wicks. DNA samples were then loaded and the

gel covered with a thin perspex sheet to prevent dehydration.


32

Optimisation of parallel TGGE: A perpendicular temperature gradient is used to establish

optimum running conditions for a particular fragment. This procedure determines the

electrophoresis running time and temperature range for the parallel TGGE. DNA from a

single individual is run perpendicular to a standard temperature gradient (20oC - 60oC) to

determine the temperature at which the homoduplex is no longer stable. This provides a

temperature range in which the less stable heteroduplex products should denature.

A polyacrylamide gel was cast using the method described above. In the absence of a

temperature gradient, 200-500ng of froglet mtDNA from a single individual, combined with

20µl 10x ME + dye (containing Bromophenol Blue and Xylene cyanol FF dyes; 0.5mg/ml

each) and ddH2O to a total volume of 200µl, was electrophoresed at 300 volts for 30

minutes. After allowing 25 minutes for stabilization of the temperature gradient (20oC –

60oC), the DNA was electrophoresed for a further hour.

DNA was visualized by silver staining using the following procedure: traces of triton were

carefully removed from the back of the gel support film and the gel covered with a buffer

containing 10% ethanol and 0.5% Acetic acid for 3 minutes. The buffer was discarded and

the step repeated, again the excess discarded and the gel overlaid with a 1% AgNO3 solution

for 10minutes. The gel was then washed twice with ddH20 and incubated for 15-20minutes

in a buffer containing 1.5% NaOH, 0.01% NaBH4, and 0.015% Formaldehyde (37%). After

discarding the excess, the gel was fixed for 10 minutes in 0.75% Na2CO3.

From the gel, it was possible to determine the temperature range of effective separation, the

migration rate of the native double stranded DNA, the subsequent migration rate of the DNA

duplex and the optimum electrophoretic running time. Optimisation procedures were carried

out independently for each of the 12S and COI fragments and specific running conditions for

each mtDNA marker are outlined in the relevant chapters.

Heteroduplexing: Heteroduplex analysis was performed immediately prior to parallel

TGGE. To each 500µl microcentrifuge tube was added: 3µl 8M Urea, 0.6µl 10xME +dye

buffer, 10-15ng reference PCR product, and 10-15ng sample PCR product to a total volume

of 7.5µl. Each DNA sample was heteroduplexed to a single reference individual. The

reference sample used in all routine analyses was selected by trialing a number of the most

divergent haplotypes (inferred by the degree of band separation on the gel) as reference

samples and choosing the sequence that allowed best discrimination of all haplotypes.

Temperature cycling was performed in an MJ Research PTC-100 programmable thermal

cycler. Solutions were denatured at 95oC for 5 minutes, reannealed at 50oC for 15 minutes


33

and then left for 10 minutes at room temperature.

One inherent difficulty of heteroduplex analysis is that the method requires a reference

individual which is genetically similar to the samples being run to allow effective

heteroduplexing (fragments from different individuals will anneal and show a relatively high

level of thermal stability to allow the heteroduplex to migrate through the gel to a point

where bands can be easily distinguished from the single stranded fragments and

homoduplexes), but it must be different enough to show mutational differences among

comparative sequences (this allows for the heteroduplex bands to be distinguished from the

homoduplex bands on the gel). For relatively conserved mtDNA regions, Campbell et al.

1995 suggest using the method of ‘outgroup’ heteroduplex analysis, which uses a closely

related species as the reference individual. This generally ensures a high level of homology

between the sequences which allows for effective heteroduplexing and also allows enough

mutational differences for heteroduplexes to separate clearly from homoduplex bands.

A separate set of problems were encountered with the 12S and COI Heteroduplex Analyses

when trying to identify suitable reference individuals. Problems are outlined in the relevant

chapters.

Parallel temperature gradient gel electrophoresis: Parallel TGGE (i.e with the temperature

gradient oriented parallel to the direction of sample migration) was performed on all DNA

samples (Figure 6). Heteroduplexed samples were loaded into individual wells on the gel

with a reference sample homoduplex in the first lane as a control. Gels were electrophoresed

at 300 volts for approximately two and a half to three hours for the 12SrRNA fragment and

approximately four hours for the COI fragment. DNA was visualized using the silver

staining procedure described above. Resulting heteroduplex DNA variants were each

assigned a distinguishing haplotype. All individuals assigned to the same haplotype were

run side by side on a parallel TGGE to confirm scoring. After individuals had been scored

for their haplotype, at least two individuals (where possible) representing each unique

haplotype were sequenced. To maximize the chance of identifying discrepancies in the

visual scoring of haploytpes, samples chosen for sequencing were selected from different

populations. This ensured that visual scoring had been carried out correctly.


34

Figure 6. Example of a parallel TGGE showing homoduplex and heteroduplex banding

patterns. The less stable heteroduplex bands have a lower melting point than the

homoduplex bands. The lane identified by ‘A’ is the reference homoduplex.

2.4.5 SEQUENCING

PCR products were cleaned using a QIAquick PCR Purification Kit (QIAGEN Inc). The

procedure for clean-up was carried out according to the PCR purification microcentrifuge

protocol in the QIAquick Spin Handbook. For each individual sequenced, between 80µl –

100µl of PCR product was obtained for initial clean up. The concentration for the cleaned

product was determined using a spectrophotometer; 60ng of 12SrRNA template and 95ng of

COI template was used in sequencing PCRs (as per the Australian Genome Research

Facility, AGRF, guidelines for gel separation sequencing using the ABI Prism BigDye

Terminator Version 2). The PCR reaction and running conditions are as follows;

PCR reaction: 95ng clean PCR product, 4µl BigDye terminator mix, 3.2 nm of sequencing

primer, ddH2O to make reaction up to 20µl.

PCR protocol: Step 1. 94oC 30 seconds; Step 2. 50oC 15 seconds; Step 3. 68oC 4

minutes;Step 4. ‘go to’ Step 1. for 25 cycles; Step 5. Hold at 4oC until ready to purify.

Products from this reaction were further cleaned using Sodium Acetate Ethanol precipitation.

Each sequencing reaction was mixed with 2.0µl 3M sodium acetate (pH 4.6) and 50µl of

95% ethanol in a 1.5ml microcentrifuge tube. Tubes were vortexed briefly and left at room

temperature for 15 minutes to precipitate the extension products. Subsequently, tubes were

Homoduplex

Heteroduplex

A


35

placed in a microcentrifuge and spun for 20 minutes at 13000rpm. The supernatant was

removed (contains unincorporated dye terminators) and the pellet rinsed with 250µl of 70%

ethanol. The tubes were vortexed and spun for 5 minutes at 13000rpm. The supernatant was

removed and the remaining pellet was dried by placing the tubes in heat block at 90oC for

one minute. Dried DNA templates were sent to AGRF for sequencing.

2.5 LABORATORY METHODS: NUCLEAR DNA TECHNIQUES

Microsatellite markers were used to compliment mtDNA analyses. One of the only

disadvantages with using microsatellite markers is that they must be developed anew for

each species, though primers developed for one species will often amplify in closely related

species (Vos et al. 2001; Primmer and Merila 2002). As no microsatellite loci had

previously been identified in any Crinia species, isolation of microsatellite loci and primer

design had to be carried out from first principles.

2.5.1 DEVELOPMENT OF MICROSATELLITE GENOMIC LIBRARY

DNA Extraction: Total genomic DNA was extracted from the muscle tissue of two Bribie

Island individuals using a phenol-chloroform procedure as follows: tissue was added to

700µl of 2x CTAB buffer (1M Tris HCL, 4M NaCl, 0.5M EDTA2, 0.5M CTAB3, 0.5M 2-

mercaptoethanol) and 13µl of Proteinase K (10mg/ml). Samples were placed on a rotating

wheel and incubated at 55oC overnight. Following digestion, DNA was extracted using

phenol-chloroform. DNA was precipitated with iso-propanol and cleaned with ethanol.

DNA was electrophoresed through a 1% TBE (1M Tris/0.83M Boric Acid/10mM EDTA)

agarose gel and visualised using ethidium bromide under a UV transilluminator (Pharmacia).

DNA concentration was calculated using a Quantagene spectrophotometer.

Isolation of microsatellites: C.tinnula genomic DNA was digested with Sau3A1. Digested

DNA ranging in size from 300-600bp was excised from a one percent (1%) agarose gel,

purified and ligated into a cut pUC18 vector (Pharmacia). Plasmids were transformed into

2 Ethylenedeamine Tetraacetic Acid

3 Hexadecyltrimethylammonium Bromide


36

Escherichia coli via electroporation (Biorad Gen Pulsar) and the cells grown on Hybond N+

membranes overnight at 37oC. Multiple membranes were made. Colonies were probed with

(dC-dA)n.(dG-dT)n labelled with α-32P dCTP, randomly primed using the Mega Prime Kit

(Amersham). After secondary screening, positive clones were purified using an

alkaline/PEG precipitation and sequenced using ABI (Applied Biosystems) automated DNA

sequencing.

A large number of positive clones were found to contain only very short microsatellite repeat

motifs, or microsatellite regions which were highly interrupted (the microsatellite motif was

interspersed with small regions of sequence which were not the repeat sequence).

Approximately 15 clones were found to have relatively good microsatellite sequences which

were not highly interrupted.

2.5.2 PRIMER DESIGN AND OPTIMISATION OF PCR

Primers were designed by eye according to; distance from the repeat region, GC content,

primer length (20-24 bp) and highly conserved 3’ end sequences. Optimisation of primer

sets was carried out on chelex-extracted individuals.

Optimisation of primer sets proved to be very difficult. Many primers would not amplify

and others produced numerous non-specific bands (resembling DNA fingerprints) on gels.

Primers were subject to rigorous optimisation procedures including; trialling a range of PCR

annealing and extension temperatures as well as different step-down and step-up cycle

protocols, trying different Taq enzymes to increase the chance of specific amplification

(Platinum Taq), and manipulating concentrations of PCR chemicals to try to increase

amplification success.

After very limited success, it was decided to develop a second genomic library to identify

other microsatellite regions and attempt to design primers for these new regions.

Development of the library was undertaken as above. Approximately 70 positive clones

were sequenced and again success in finding uninterrupted sequences of a good length (>20

repeats) was limited. Fifteen loci were chosen and primers were designed for these loci.

The second series of primers were designed using the program Oligo (Oligo: Primer

Analysis Software. Piotr & Wojciech Rychlik 2004, www.oligo.net). Default search

parameters were used (e.g. duplex free oligonucleotides, highly specific 3’ end stability, GC


37

clamp, eliminate false priming, hairpin free) and search stringency were set to high (if no

suitable primers could be found the search stringency was relaxed to moderate).

Primer design using Oligo revealed there were regions of DNA (approximately 20-30bp in

length) which were highly repetitive and because of this primer designed in one region were

sometimes found to bind at more than one site along a short region of sequence (within 300-

500bp). It proved very difficult to find primers which would only bind at one site and also

possessed other attributes which make good primers (annealing temp, GC content, length,

strict binding at the 3’ end).

Twenty-five primer sets were designed for fifteen microsatellite loci. These primer sets were

identified by Oligo and then checked manually by two laboratory technicians who had

previous success in designing microsatellite primers. Each of the primer sets were trialled

using varying PCR protocols and annealing temperatures. Most primer sets produced non-

specific banding which could not be resolved by altering PCR protocols, temperatures or

amplification cycles. Two primer sets produced what appeared to be a single monomorphic

band, however, success with repeatability was limited.

One primer set was optimized to a point where clear discernable bands were produced on the

autoradiograph films. This primer was F2.5 (forward primer: 5’ CAG aAC GGA TGa TGT

AAT ACC CTA 3’, reverse primer: 5’ GCG CTg Tag AAA GTA TAG TTC AAC 3’). F2.5

primers amplified a 152 bp product containing a (GT)n repeat region. The sequence, from

which the primers were designed, contained 20 repeats of the tandem nucleotide ‘GT’.

2.5.3 AMPLIFICATION OF F2.5

The initial optimisation of the F2.5 primer set was carried out on a subset of samples and

once all populations were screened with the primer set it was found that only a few

populations would amplify. The primers were subsequently modified using degenerate base

pairs to try increase the success of amplification across all populations, however, this had

limited success. Many attempts were made to amplify all samples with the F2.5 primer set.

Various PCR conditions were trialled which included using different Magnesium chloride

concentrations, different primer concentrations, different dNTP concentrations, different

DNA concentrations, changing PCR chemical systems (Biotech vs Roche) and using

different Taq systems. Variables were changed one at a time and then different

combinations of concentrations were trialled. A range of annealing temperatures was also


38

used to try to increase the amplification success of samples.

There was success with amplification for some populations, however, in those populations

which did work not all individuals could be amplified.

2.5.4 AMPLIFIED FRAGMENT LENGTH POLYMORPHISM (AFLP)

As a last resort, it was decided that amplified fragment length polymorphism primers should

be trialled. The AFLP technique is a DNA fingerprinting technique which is based on the

detection of genomic restriction fragments by PCR amplification (Vos et al. 1995).

Although AFLPs are dominant markers and therefore do not provide the same level of

information as microsatellites, AFLPs have been shown to exhibit high levels of variability

and have been used in many studies to address levels of genetic diversity within and among

populations (Drummond et al.2000; Keiper and McConchie 2000; Tero et al. 2003; Haig et

al. 2004) . AFLPs are also very cost effective as primers work across a range of species and

primers were readily available within the lab.

AFLP procedures were performed according to the protocols of Adjome-Mardsen et al.

(1997). The AFLP technique required five steps; a restriction digestion, ligation of specific

adaptors, pre-selective amplification, selective amplification and gel electrophoresis.

Approximately 400ng of DNA was incubated with the Taq1 restriction enzyme at 65oC for

one hour. Taq1 (recognition sequence TCGA) was used as the dominant cutter and EcoR1

as the rare cutter. The EcoR1 restriction enzyme was added to the Taq1 digest and incubated

at 37oC for one hour. After fragments were generated, specific adaptors were ligated to the

sticky ends of the restriction site with DNA ligase. The template DNA created by the

digestion ligation process was then diluted 10x in TE buffer for use in pre-selective

amplification.

Amplification procedures were undertaken on an MJ Research Incorporated, PTC-100 PCR

machine. Pre-selective PCR was performed with two primers, the EcoR1 primer (5’-

GACTGCGTACCAATTCA-3’) and the Taq1 primer (5’-GATGAGTCCTGACCGAA-3’).

The following temperature cycle profile was used; 30 cycles of denaturing at 94oC for 30

seconds, annealing and extension for one minute at 72oC and 56oC respectively and a final

step of 10 minutes at 72oC. The amplification reaction was diluted 10x with TE buffer.

Three EcoR1 and Taq1 primer combinations (E33-T49, E33-T50 and E42-T49) which were


39

known to be highly variable in animal populations were chosen for selective PCR.

Fragments were labeled with 33P-dATP. Selective amplification began with a denaturing

step of 94oC for 30 seconds, annealing and extension at 61.5oC for 30 seconds and 72oC for

one minute, respectively. The 61.5oC step was reduced by 0.7oC in temperature for every

succeeding cycle until 56oC was reached. 30 cycles of the above cycle profile were

performed with 56oC as the first annealing temperature. This was then followed by a final

step of 5 minutes at 72oC. PCR products were then mixed with 7µl of formamide loading

dye and denatured for five minutes at 95oC before screening with polyacrylamide gel

electrophoresis.

PCR products were electrophoresed at 100 watts through a pre-heated (50oC) 5% denaturing

polyacrylamide sequencing gel (8M Urea, 5% acrylamide:bis-acrylamide 38:2, in 10x TBE

buffer (890 mmol/L Tris, 890 mmol/L boric acid, 25 mmol/L EDTA pH 8.30)) and run for

two hours and 45 minutes. Gels were dried and then exposed to AGFA x-ray films

overnight.

The AFLP procedure was performed twice on six initial samples using the same primer sets

to test if the procedure was reproducible for these species. Samples were not reproducible

across the two trials and bands were found to be difficult to score.

Very few studies have used chelex extraction with AFLPs because the procedure tends to

require relatively high quality DNA and accurate quantities. While some studies have had

success with chelex and AFLPs (pers communication), the combination of time since

extraction and the original quality of the DNA in this study may have caused problems with

amplification and ligations.

Due to the limited results obtained for the microsatellite loci and AFLPs it was decided to

present the results as an Appendix (Appendix 1). The results obtained for individuals that

amplified consistently and produced clear, scorable bands for the microsatellite locus F2.5

are presented in the Appendix.


40

2.5.5 DATA ANALYSES

Sequence data were cleaned manually with the aid of the GAP program in WebANGIS

(www1.angis.org.au) and Chromas 2.13 (Technelysium Pty Ltd,

www.technelysium.com.au). Clean sequences were aligned using EclustalW (WebANGIS)

and ClustalX (Thompson et al. 1997) and all final alignments were checked manually.

Fu and Li’s (1993) D- and F-tests, which test the conformity of DNA sequence evolution to

neutrality, were performed using DnaSP (version 4; Rozas and Rozas 1999). These tests are

based on the neutral model prediction that estimates of n/a1(n-1)ns/n and of k, are unbiased

estimates of θ, where, n is the total number of mutations, a1 = Σ(n/i) from i=1 to n-1, n is the

number of nucleotide sequences, ns is the total number of singletons (mutations appearing

only once among the sequences), k is the average number of nucleotide differences between

pairs of sequences and θ = 2Nu (for haploid-autosome; N and u are the effective population

size, and the mutation rate per DNA sequence per generation, respectively).

Haplotype diversity (Hd) and nucleotide diversity (π) were calculated in DnaSP. Haplotype

diversity is an index of relative frequency of the haplotypes (Nei 1987) and is estimated as

Hd = n/n-1(1-Σpi2) where n is the sample size and pi is the sample frequency of the i-th

haplotype. Nucleotide diversity is a measure of heterozygosity at the nucleotide level.

Nucleotide diversity describes the average number of nucleotide differences per site between

a pair of sequences; π = Σxixjdij , xi is the population frequency of the i-th allele, xj is the

population frequency of the j-th allele and dij is the number of nucleotide differences or

substitutions per site between the i-th and j-th sequences.

Pairwise genetic distances and between-group net sequence divergences, DA, (Nei 1987)

were calculated in MEGA (version 2.1; Kumar et al. 2001). Genetic distance estimates were

calculated using Jukes-Cantor method, which is based on the assumption that nucleotide

substitution occurs at any nucleotide site with equal frequency and that at each site a

nucleotide changes to one of the three remaining nucleotides with a probability of alpha per

unit time. Jukes Cantor distances were used for the analyses instead of more complicated

distance corrections following the recommendations of Nei and Kumar (2000). This was

because the Jukes-Cantor distances were low (d ≤ 0.05 for 12S; d ≤ 0.2 for COI), and there

was no strong transition/transversion bias for either 12S or COI. Nei and Kumar (2000)

suggest that under these conditions the simplest distance correction method is preferred

because more complex methods tend to give similar estimates but with greater variance.

Standard errors of sequence divergence estimates were obtained using 10,000 bootstrap


41

replications.

The spatial distribution of genetic variation was examined using the Analysis of Molecular

Variance (AMOVA; Excoffier et al. 1992). AMOVA examines the partitioning of genetic

variation at various hierarchical levels to estimate levels of population differentiation.

Populations were partitioned a priori, to examine population differentiation at different

levels of geographic scale. Population subdivision analyses were assessed using Ф-statistics

generated by AMOVA under the permutational procedures in the Arlequin program (version

2.0; Schneider et al. 2000). Ф-statistics were used to estimate population subdivision among

designated geographic regions (ФCT), among populations within regions (ФSC) and within

populations (ФST). Tests of significance (at α = 0.05) were conducted using 100 000

permutations of the data. Variance component estimates were calculated from the following

equations; Var ФST (fraction of the total variation among populations and regions) = (VAP +

VAR)/(VWP + VAP + VAR); Var ФSC (fraction of within-region variation among populations) =

VAP/(VWP +VAP) and Var ФCT (fraction of the total variation among regions) = VAR/(VWP +

VAP + VAR), following Excoffier et al. (1992).

Nested Clade Analysis (NCA) was used to infer historical patterns of gene flow among

populations. Nested Clade Analysis (NCA) (Templeton et al. 1995) tests the null hypothesis

of no association between clades and geographical location. A haplotype tree is used to

define a nested series of clades that are used in the analysis of the spatial distribution of

genetic variation (Templeton 1998). By virtue of its coalescent approach (i.e. modeling how

the most derived haplotypes coalesce with ancestral types, back through time) the NCA can

potentially distinguish between recurrent gene flow and historical evolutionary processes

(e.g. population fragmentation and expansion) by mapping the geographical distribution of

haplotypes within successive temporal clade groupings in the genealogy network.

TCS 1.13 (Clement et al. 2000) was used to estimate a haplotype network under statistical

parsimony as described by Templeton et al. (1992). Using the rules outlined in Templeton et

al. (1987), Templeton and Sing (1993) and Crandall (1996) an evolutionary clade hierarchy

was superimposed on the TCS network. Degenerate clades (i.e those containing fewer than

two known haplotypes or those with no geographical variation) were not included in the

analysis. For each clade in a nested group, information on the relative network position

(interior or tip), frequency at each site and geographical location (based on latitude/longitude

coordinates) is input into GeoDis (version 2.2; Posada et al. 2000).

GeoDis first performs a simple categorical test for geographical association. This test treats


42

each sample location as a categorical variable and permutation tests are implemented in a

nested fashion (i.e. clade types within a nested category vs. geographical location;

Templeton and Sing 1993).

The program then incorporates geographical (straight-line) distance among sample sites and

calculates two parameters: clade distance and nested clade distance (which are then tested for

significance at α = 0.05 using a permutation procedure). As described in Templeton et al.

(1995), clade distance (Dc) is the average distance of individuals bearing a haplotype from

clade A from the calculated geographical center of that clade, whereas the nested clade

distance (Dn) is the average distance of individuals in clade A from the calculated

geographical centre of nesting clade B (the next highest nesting level). A P-value is output

in GeoDis for Dc and Dn for each clade within each nesting. If a significant value is recorded

(P < 0.05), then the null hypothesis is rejected and that clade is considered to be significantly

associated with geographical location. An inference key is then used for each significant

clade to make predictions about the distribution and magnitude of Dc and Dn values, using a

coalescent approach. The rules of the key should enable discrimination between

contemporary and historical processes which have influenced the genetic structure of the

species (Templeton et al. 1995; Gottelli et al. 2004) (NOTE: the July 2004 Inference Key

was used in the current study).

A distance-based procedure (mismatch distribution in Arlequin 2.0, Schneider et al. 2000)

was used to determine if evidence existed for population expansion events. The mismatch

distribution is the distribution of the observed number of differences between pairs of

haplotypes. This distribution is usually multimodal in samples drawn from populations at

demographic equilibrium, but it is usually unimodal in populations having passed through a

recent demographic expansion (Rogers and Harpending 1992; Slatkin and Hudson 1991).

Phylogenetic analysis of mitochondrial DNA sequence data was used to look at patterns of

divergence and evolutionary relationships among haplotypes. Neighbour-joining

phylogenies were produced using MEGA (version 2.1; Kumar et al. 2001). All trees were

subject to 10 000 bootstrap replications.

Maximum Parsimony trees were generated using PAUP* (Swofford 2003). Trees were

subject to 100 bootstrap replications (1000 for the COI tree). Best trees were found using a

heuristic search, with a step-wise addition algorithm to generate the provisional tree and

nearest-neighbour interchange to generate the most parsimonious trees.

To test for adherence to a clock-like evolution of the mtDNA sequences, a log-likelihood


43

ratio test was conducted in TREE-PUZZLE (version 5.3; Schmidt et al. 2002) with

C.parinsignifera used as the outgroup species. TREE-PUZZLE compared trees generated

under the assumption of a molecular clock, to trees unconstrained by a molecular clock

(Felsenstein 1988). C.parinsignifera was used for the outgroup species. The timing of

divergence among clades identified in the phylogenies was then inferred by way of

molecular clock approximation. Although the accuracy of dates of divergence based on a

molecular clock is debatable (Knowlton et al. 1993; Knowlton and Weigt 1998; Arbogast

and Slowinski 1998) they do none the less provide a relative time frame for investigating

phylogeographical relationships (Arbogast et al. 2002).

Chapter Three: 12S mtDNA

44

CHAPTER THREE.

3 HISTORICAL POPULATION STRUCTURE INFERRED FROM MITOCHONDRIAL 12S

RRNA.

3.1 INTRODUCTION

Aligning patterns of population genetic structure with spatial distribution of habitat is not

always as simple as overlaying patterns of genetic variation onto geographical distributions.

Population structure can often appear inconsistent with present day geographic landscapes

and land formations. For example, populations that are geographically isolated can display

genetic homogeneity (Boulton et al. 1998) and conversely populations which have a

seemingly continuous distribution may show significant levels of differentiation (Selander

1970; Avise 1992).

One reason for this is that the genetic structure observed in a species at one point in time is

likely to reflect extrinsic and intrinsic dynamics of populations that existed at a point in time

in the past. Changes to the physical characteristics of landscapes occur continually over both

ecological and geological time-scales (Crowley and North 1991). These changes can have

significant effects on population dynamics, in particular the levels and patterns of dispersal

among populations both within an organism’s individual lifetime and across a species’

evolutionary history (Avise 1992). Discrepancies exist between population genetic structure

and observed geographic distribution of habitat because the impact of these landscape

changes on genetic population structure occurs at an evolutionary time scale (Bernatchez and

Dodson 1991; Hughes et al. 1999).

Describing population genetic structure in relation to historical processes allows us to

understand how populations have responded to past changes in the environment and the

evolutionary consequences of historical barriers to dispersal and gene flow among

populations (Hewitt 2001).

Studies have shown that environmental changes during the Pliocene and Pleistocene epochs

had a significant effect on population distribution and population structure of many plant and

animal species, both terrestrial and aquatic (Saunders et al. 1986; Bowen and Avise 1990;

Walker and Avise 1998; McGuigan et al. 1998; Waits et al. 1998; Milot et al. 2000;

Schultheis et al. 2002). The late Tertiary and Quaternary periods in particular, were

characterised by extreme fluctuations in global climates (Shackleton 1988; Crowley and

North 1991) that brought about changes in habitat structure and distribution and often


45

resulted in populations being isolated in glacial habitat refugia (Hewitt 2001). The long term

isolation of populations in different habitat refugia often led to the differentiation of distinct

phylogeographic lineages within species (Avise 1992).

Studies of a wide range of Australian fauna and flora have revealed significant

phylogeographic structure among conspecific populations due to bioclimatic and landscape

changes during the Tertiary and Quaternary periods (Schneider et al. 1998; McGuigan et

al.1998; Hughes et al. 1999; Wong et al.2004). Evidence from geological and

palaeontological studies suggest eastern Australia experienced glacial-interglacial climatic

fluctuations and changes in sea level and the land formations and coastline underwent

environmental and geographic changes associated with these events. There is evidence for

changes to river drainage patterns (Jones 1992), range contraction and expansion of habitat

(Kershaw 1994) and probably the most dynamic change during the Pleistocene era was the

formation of large sand islands which now lie along the southeast Queensland coast (Clifford

and Specht 1979).

There are some regions where species inhabiting Queensland show congruent

phylogeographic breaks, for example the Black Mountain Corridor in the Wet Tropics

rainforests of northeast Queensland (Joseph et al. 1995; Schneider et al. 1998, 1999) and the

Burdekin Gap in coastal central Queensland (Cracraft 1986; Joseph and Moritz 1993; James

and Moritz 2000). The common factor in many of these vicariant breaks appears to be range

contraction of habitat and the formation of habitat isolates during glacial-interglacial climate

cycles.

The wallum, or its structural equivalent, is believed to be at least as old as the Pliocene

(Coaldrake 1961, 1962). Assuming that ancestral Crinia tinnula populations have been

associated with wallum habitat since this time, bioclimatic changes associated with the

glacial-interglacial cycles could have had a significant influence on the pattern of dispersal

and hence the development of population structure of C.tinnula across southeast Queensland.

In the paper in which Straughan and Main (1966) first described C.tinnula, the authors

suggested that sea level fluctuations could have influenced the evolution of this species.

Straughan and Main (1966) proposed that C.tinnula evolved from an ancestral Crinia

parental stock which became isolated in wallum habitat (or its structural equivalent) during

the Pliocene. The authors suggested that speciation within this group (Crinia restricted to

the wallum) may have occurred during periods of rising sea levels due to isolation of

favourable habitat.


46

An alternative hypothesis for C.tinnula evolution was proposed by Ingram and Corben

(1975). They argued that C.tinnula (acid frogs generally) were not necessarily Tertiary in

origin, rather the relatively large number of glacio-pluvial periods during the Pleistocene

provided many opportunities for the parent stocks of acid frogs to invade the wallum. Thus

instead of a single evolutionary event giving rise to the C.tinnula species (and subsequent

isolation from sea level fluctuations), Ingram and Corben (1975) propose that there may

have been multiple speciation events and therefore each wallum “island” could possibly

represent a separate C.tinnula speciation event.

As wallum is a coastal habitat, it is likely that sea level changes would have influenced the

distribution of wallum habitat and therefore would have influenced the distribution of

wallum froglet populations and the potential for dispersal among populations. Wallum

habitat may have been restricted to small habitat isolates when sea levels rose and climatic

conditions changed during glacial periods. Consequently, froglet populations may have

undergone significant range contractions and subsequent expansions in accordance with

changes in habitat spatial dynamics.

The objective of this chapter was to document the broad scale historical population structure

of C.tinnula across southeast Queensland using mitochondrial DNA and to look at the

historical processes which may have influenced gene flow among populations. Information

from the genetic data may potentially also distinguish between the two hypotheses put

forward to explain the evolutionary history of C.tinnula. 12S rRNA was used to describe the

genetic population structure and phylogeography of southeast Queensland populations of

C.tinnula.


47

3.2 MATERIALS AND METHODS

3.2.1 SAMPLING LOCALITIES AND SAMPLE NUMBERS

A total of 262 C.tinnula individuals from 14 populations were analysed for variation at the

mitochondrial 12S rRNA region (Table 1). C.parinsignifera and C.signifera individuals

were used as outgroups in analyses.

Table 1. C.tinnula populations and sample sizes for 12S mtDNA analyses.

C.parinsignifera and C.signifera individuals used as outgroups in analyses are listed

at the bottom of the table.

Species

Population

Location

Number of Samples Analysed for 12S mtDNA variation

Wathumba Creek Fraser Is. 30 Ungowa Fraser Is. 27 Barga Lagoon Fraser Is. 27 Rainbow Beach Cooloola Coast 18 Cooloola Cooloola Coast 24 North Shore Sunshine Coast 02 Peregian Sunshine Coast 05 Beerwah Sunshine Coast 04 White Patch Bribie Is. 30 Bellara Bribie Is. 30 Caboolture Sunshine Coast 03 Honeyeater Lake Moreton Is. 11 Karawatha Brisbane 16

C.tinnula

Amity Point Stradbroke Is. 35 CparC (Caboolture) Sunshine Coast 01 CparK (Karawatha) Brisbane 01

C.parinsignifera

CparB (Barakula) ~350km NW Brisbane 01 CsigG (Goomburra) ~120km SW Brisbane 01 C.signifera CsigK (Karawatha) Brisbane 01


48

3.2.2 DNA EXTRACTION AND AMPLIFICATION OF 12S RRNA MITOCHONDRIAL DNA

FRAGMENT

For all samples included in 12S mtDNA analyses, DNA extraction followed the Chelex

protocol outlined in Chapter 2.

A 380bp fragment of the 12S mtDNA region was amplified using general vertebrate primers

developed by Kocher et al. (1989). The sequences of the primers were as follows; light

strand primer (12Sf) 5’-AAA GCT TCA AAC TGG GAT TAG ATA CCC CAC TAT-3’,

heavy strand primer (12Sr) 5’ -TGA CTG CAG AGG GTG ACG GGC GGT GTG T-3’.

The 3’ end of the 12Sf primer corresponds to position 2509 in Xenopus laevis 12S rRNA

(Roe et al. 1985).

The 12s mtDNA fragment was amplified in a 25μl reaction containing; 3μl of Biotech 10X

Buffer solution, 2μl of 10mM dNTPs, 2μl of 2mM MgCl2, 0.5μl of 3.2nM forward primer,

0.5μl of 3.2nM reverse primer, 0.08μl Taq (Tth plus polymerase Taq – Biotech), 1μl genomic

DNA (concentrations varied from ~2ng/μl to 60ng/μl) and 15.92μl ddH2O. Due to the nature

of the chelex extraction procedure, genomic DNA varied considerably in quantity and purity.

This made it difficult to standardise the genomic DNA used in PCR reactions. To ensure the

correct fragment was amplified the mtDNA was run on an agarose check gel next to a size

marker. DNA sequencing further verified that the correct fragment had been targeted.

Reaction mixtures were subject to 30 cycles of the following profile in a hot lid thermal

cycler (PTC-100, MJ Research, Inc); Step 1. 03 minutes at 94oC; Step 2. 30 seconds at 94oC;

Step 3. 30 seconds at 55oC; Step 4. 30 seconds at 72oC; Step 5. Go to ‘Step 2’ for 30 cycles;

Step 6. 8 minute extension at 72oC.

For all PCR reactions a negative control (master mix excluding DNA template) was included

to test for potential contamination. Presence of the amplified product was determined by

running 3μl of PCR sample + 3μl bromophenol blue 6X loading dye, through a 1% 0.5X

TBE agarose gel. A DNA size marker (φ174 Hae III) was loaded alongside the PCR

products to identify the size and intensity (relative quantity) of the product.


49

3.2.3 TEMPERATURE GRADIENT GEL ELECTROPHORESIS (TGGE), HETERODUPLEX

ANALYSIS (HA) AND SEQUENCING

TGGE combined with HA was used to screen for 12S haplotype diversity among the

sampled populations. TGGE and HA procedures were carried out according to the methods

outlined in Chapter 2. A Bribie Island individual (B20) was run on a perpendicular TGGE to

identify the melting point for the 12S fragment and to establish the temperature gradient for

further parallel gels. The optimum temperature gradient used for all parallel gels was 23.7oC

to 58.6oC and the electrophoretic running time was 2 hours 30 minutes (migration rate of

3.2cm/hr).

A preliminary TGGE was run to determine an appropriate reference individual for

subsequent parallel gels. Three reference individuals were trialed; a C.parinsignifera

individual (outgroup heteroduplex analysis, Campbell et al. 1995) and two C.tinnula

individuals; a White Patch individual and an Ungowa individual. The resulting TGGE

showed that no single individual could be used as a reference for all samples. Individuals

representative of populations in the Fraser-Cooloola region were as different

electrophoretically to the White Patch reference as they were to the C.parinsignifera

reference. The same was true for individuals from the Sunshine coast and Brisbane region

(including the three sand islands); these samples were as different to the Ungowa reference

as they were to the C.parinsignifera reference.

Sequencing of these three reference individuals (the White Patch, Ungowa and

C.parinsignifera individuals) confirmed a large number of base pair differences between all

three samples (Table 2). For subsequent parallel TGGE runs, given the large amount of

reference DNA available for the C.tinnula samples, the Ungowa individual (U20) was used

as the reference for all samples from the Peregian, Beerwah, Caboolture, White Patch,

Bellara, Moreton Island and Stradbroke Island populations and the Bribie Island (B20)

individual was used as the reference for samples from Wathumba Creek, Ungowa, Barga

Lagoon, Rainbow Beach, Cooloola and Noosa populations.


50

Table 2. Pairwise nucleotide differences among C.tinnula and C.parinsignifera TGGE

reference samples. B20 = C.tinnula Bribie Is. individual; U20 = C.tinnula Fraser Is.

individual; CparK=C.parinsignifera individual. bp = base-pairs.

U20 B20

B20 15bp

Cpar Karawatha 18bp 17bp

Heteroduplexing was carried out according to methods outlined in Chapter 2. Parallel runs

were carried out according to the DIAGEN TGGE handbook and gels were stained using the

silver staining procedure outlined in Chapter 2.

Haplotypes were scored based on visual interpretation of the gels and each individual was

assigned a distinguishing haplotype number. Where possible, two representative individuals

of each haplotype were sequenced (in both directions to ensure strand homology) to confirm

phenotypic designations.

PCR products for sequencing were cleaned and sequenced according to procedures outlined

in Chapter 2.

3.2.4 DATA ANALYSIS

Gaps in the 12S sequence were treated as fifth state nucleotides and given equal weighting as

all other nucleotide changes. The single multi-residue gap (more that one gap at an indel

site) was inferred as being derived via a step-wise mutation model (the multi-residue gap

was indicative of a mono-nucleotide microsatellite repeat, therefore step-wise mutation was

inferred). Individuals which shared a common number of gap sites at an indel region were

assumed to be identical by descent (Lloyd and Calder 1991).

AMOVA was used to describe the partitioning of genetic variation at three different levels of

scale; among regions, among populations within regions, and within populations, to

determine levels of population differentiation. Nested Clade Analysis was used to infer


51

historical and/or contemporary processes which may have influenced evolutionary

relationships among haplotypes.

Isolation by distance tests were carried out using IBD (version 1.5; Bohanak 2002).

Population pairwise distance estimates were calculated in Arlequin (Schneider et al. 2000)

and then these estimates were correlated with geographical distance using matrix correlation

methods based on the Mantel tests in the IBD v1.5 program. Both straight line distance and

habitat distance (measured via habitat corridors) was used for geographical distance and

there was no difference observed for the IBD results (the distribution of wallum is close to

linear along the coast so there was minimal difference in the results for the different distance

measure treatments). Only results for straight line distance are presented. The genetic and

geographic distance estimates were both log transformed. The strength of the isolation by

distance relationship was determined with reduced major axis (RMA) regression, which is

more appropriate than standard ordinary least squares regression when the independent axis

(geographical distance) is measured with error (Sokal and Rolf 1995), and calculated with

IBD v1.5 (Bohanak 2002).

Population expansion hypotheses were tested using mismatch analysis. Phylogenetic trees

were constructed to infer patterns of divergence and evolutionary relationships among

C.tinnula populations. Phylogenetic trees were generated using neighbour-joining (MEGA;

Kumar et al. 2001) and maximum parsimony (PAUP; Swofford 2003) methods.


52

3.3 RESULTS

3.3.1 MITOCHONDRIAL DNA SEQUENCES

Universal or conserved mitochondrial DNA primers which have been developed to work

across a range of species have the potential to amplify nuclear copies of the same gene

(numts; Zhang and Hewitt 1996; Mirol et al. 2000). Macey et al. (1998a) suggest that a

strong strand bias against guanine on the light strand of sequence data is characteristic of the

mitochondrial genome but not the nuclear genome. Macey et al. (1998a, 1999a, 1999b,

2001) have shown that this strand bias is present in a number of lizard and anuran species.

C.tinnula 12S sequences were assessed for the presence of a light strand bias. Average

nucleotide composition for the C.tinnula light strand was; A = 29.8%, G = 21.1%, C =

29.8% and T = 19.3%.

The percentage of ‘G’ nucleotides for C.tinnula 12S sequences was significantly higher than

that found for Bufo bufo in Macey et al.(1998a) therefore sequences of C.tinnula were

aligned with myobatrachid frogs to determine if a similar pattern of nucleotide composition

was observed in other myobatrachid species (sequences obtained from Genbank, Accession

Numbers are given in Appendix 2)4. Sequence alignment and nucleotide composition for the

myobatrachid frogs suggested that the C.tinnula 12S sequence was mitochondrial and not a

nuclear pseudogene. The nucleotide composition was similar for all myobatrachid frogs

analysed and showed a bias against ‘G’ and ‘T’ nucleotides in comparison to ‘C’ and ‘A’

nucleotides. Appendix 2 shows the alignment of myobatrachid 12S sequences and Table 3

gives the average nucleotide composition for the myobatrachid frogs analysed.

The region analysed by Macey et al. (1998a) extended from the ND1 (subunit one of NADH

dehydrogenase) through to the ND2 gene. The mitochondrial strand bias against guanine

indicated by Macey et al. (1998a, 1999a, 1999b, 2001) may not be as strong in the 12S

region of the myobatrachid mitochondrial genome.

4 It is assumed that myobatrachid sequences obtained from Genbank are mitochondrial in origin.


53

Table 3. Average nucleotide frequencies for myobatrachid 12S mtDNA sequences.

3.3.2 SEQUENCE VARIATION

Analysis of 12S sequence data for 262 C.tinnula individuals revealed a total of 28 unique

haplotypes. Thirty-one (8.5%) nucleotide sites were variable (Figure 1) and 20 of these sites

were parsimony informative (total sequence length of 362bp). The observed

transition/transversion ratio was 1.86:1.

When sequences of the two outgoup species (C.parinsignifera, C.signifera) were included,

forty-five sites were variable (Figure 1) and 34 of the nucleotide changes were parsimony

informative (total sequences length of 362bp). Transition/transversion ratio was 1.18:1.

Alignment of the haplotypes produced a gap (indel) of varying length (1-4bp) beginning at

position 13 (Figure 1). The gap occurred in a repeat region of a ‘C’ nucleotide and

resembled a small mononucleotide microsatellite repeat. The largest indel observed in a

single individual was four base pairs in length. The gap created in the alignment appeared to

be due to a number of individuals having gained additional nucleotides (rather than deletion)

as evidenced by alignment with other Crinia species (Note: Studies have shown that

different methods for treating gaps can influence phylogenetic relationships among

operational taxonomic units e.g. Eernisse and Kluge 1993 and Bogler and DeSalle 1994. In

the current study, analysis of the 12S dataset was also conducted excluding the indel and

results showed that the overall phylogenetic signal was not altered).

halla

This table is not available online. Please consult the hardcopy thesis available from the QUT Library


54

3.3.3 NEUTRALITY TESTS

No significant deviation from neutrality was evident (D = -0.47551, p>0.10 NS;F = -

0.06860, p>0.10 NS).

3.3.4 TEST FOR CLOCK-LIKE EVOLUTION

A log-likelihood ratio test could not reject the hypothesis that lineages were evolving

according to a clock-like model of evolution (-ln L = 931.87 with molecular clock enforced

vs.-ln L 912.09 without molecular clock enforced, χ2 = 39.56, d.f. = 33, P > 0.10).


55

Figure 1. Alignment of variable sites from the 362bp of mitochondrial 12S sequenced for

C.tinnula, compared with outgroup species C.parinsignifera (Cpar) and C.signifera (Csig).

Position of the base subsitutions are included above each nucleotide, identical sites = ‘.’;

missing data = ‘?’; indel = ‘-‘.

1111111 1112222222 2222222222 23333 1111111112 3670111368 9990334666 6778888999 90034 0123456794 9847578952 2694681345 6456789578 90869 001 CCC--AACAA CGTGTTTCCT ATCGACCCTT GTCACTACTA GTCAT 002 ...--..... ..C....... .......... .....C.... ..... 003 ...C-..... ........T. .......... .......... ..... 004 ...--..... .......... .......... A......... ..... 005 ...CC..... ........T. .......... .......... ..... 006 ..T--..... .A......T. .......... .......... ..... 007 ...C-..... ........T. .......... .......... .C... 008 ...--..... ........T. .......... .......... ..... 009 ...--....G ........T. .......... .......... ..... 010 ...--....G ........T. ....G..... .......... ..... 011 ..T--..... ........T. .......... .......... A.... 012 T..T-CC.T. A.......T. GC.....GC. A.....TTCC ..... 013 ...T-CC.T. A.......T. .C.....GC. A.T...TTCC ..... 014 T..T-CC.T. A.......T. .C.....GC. A.T...TTCC ..... 015 TT.T-CC.T. A.......T. .C.....GC. A.T...TTCC ..... 016 T..T-CC.T. A.......T. .C.....GCC A.T...TTCC ..... 017 T..---C.T. A.......T. .C.....GC. A.T...TTCC ..... 018 T..----.T. A.......T. .C.....GC. A.T...TTCC ..... 019 T.-T-CC.T. A.......T. .C.....GCC A.T...TTCC ..... 020 T..T-CC.T. A.......T. .C.....GCC A.T...T.CC ..... 021 T..C-CC.T. A.......T. .C.....GC. A.T...TTCC ..... 022 T..T-CC.T. A.......T. .C.....GC. A.T...T.CC ..... 023 T..--CC.T. A.......T. .C.....GC. A.....TTCC ..... 024 T..---C.T. A.......T. .C.....GC. A.....TTCC ..... 025 T..--CC.T. A.......T. .C.....GC. A.....TTCC ..A.. 026 A..--CC.T. A.......T. .C.....GC. A.....TTCC ..... 027 A..--CC.T. A....C..T. .C.....GC. A.....TTCC ..... 028 A..--CC.T. A...CC..T. .C.....GC. A.....TTCC ..... CparB T..-----C. A....C.ATC ...A.TA... A.T.....CC ..... CparC T..-----C. A..A.C.ATC ...A.TA... A.T.....CC ..... CparK T..-----C. A.C..C.ATC ...A.TA... A.T.....CC ..... CsigG ..A----.TC ......A.T... AT.T.TA.AC .TT..TCC.. .GC CsigK ..A----.TC ..C...A.T... AT.T.TA.AC .TT..TCT.. .G?


56

3.3.5 POPULATION GENETIC DIVERSITY AND STRUCTURE

Southeast Queensland

The distribution of haplotypes among the sampled C.tinnula populations is shown in Table 4.

Each of the 14 populations sampled contained a single dominant haplotype, and one to six

haplotypes at lower frequencies. Haplotype diversity varied considerably among populations

(due in part to differences in sample size). In general, most populations had high haplotype

diversity due to the presence of multiple rare alleles (Table 5). Nucleotide diversity was

relatively low in all populations (Table 5).

Populations sampled along the Sunshine Coast (Peregian, Beerwah and Caboolture), the

Bribie Island populations and the Karawatha population all shared a common haplotype

(haplotype 014). Haplotype 014 was found in the highest frequency in all these populations,

including populations which contained only a small number of individuals (Peregian,

Beerwah and Caboolture). The Stradbroke and Moreton Island populations shared haplotype

026. All other haplotypes identified in the Stradbroke and Moreton Island populations were

unique to individual populations (for a reference guide to population locations, refer to the

foldout map at the back of the thesis).

In populations sampled north of Peregian, the geographic distribution of haplotype variation

was highly structured and individual populations differed in haplotype composition or

frequency. A few haplotypes were shared among populations across the Cooloola and Fraser

Island region, however, no single haplotype was common to all populations. Haplotype 003

was found in the Ungowa, Barga Lagoon, Rainbow Beach and Cooloola populations and

haplotype 008 was found in the three Cooloola mainland populations. The common

haplotype in the Wathumba Creek population was found in a single Barga Lagoon

individual.

Across the entire sampled distribution, populations divided into distinct geographic clusters

based on sharing of haplotypes; a ‘northern’ group which consisted of populations from

Wathumba Creek to Noosa, a ‘southern’ group consisting of populations from Peregian to

Karawatha and including Bribie Island populations and a Moreton Island-Stradbroke Island

group (Pariwise ΦST = 0.9212, p<0.05) .


57

Table 4. Distribution of 12S mtDNA haplotypes for southeast Queensland populations of C.tinnula.

Population Haplotypes Total

001

002

003

004

005

006

007

008

009

010

011

012

013

014

015

016

017

018

019

020

021

022

023

024

025

026

027

028

Wathumba Ck 28 2 30 Ungowa 24 2 1 27 Barga Lagoon 1 21 1 3 1 27 Rainbow Beach 1 17 18 Cooloola 1 20 1 1 1 24 Noosa 1 1 2 Peregian 1 2 1 1 5 Beerwah 1 3 4 Caboolture 2 1 3 White Patch 16 2 7 1 2 1 1 30 Bellara 21 3 5 1 30 Karawatha 14 2 16 Moreton Is. 7 2 1 1 11 Stradbroke Is. 1 33 1 35

Total 29 2 47 2 2 3 1 38 2 1 1 1 1 58 1 5 13 2 2 1 1 3 7 2 1 2 33 1 262


58

Table 5. 12S mtDNA haplotype diversity (Hd) and nucleotide diversity (πd) within southeast

Queensland populations of C.tinnula. n = number of individuals.

Population

n

Hd

πd

Wathumba Ck 30 0.13 ± 0.08 0.0007 Ungowa 27 0.21 ± 0.10 0.0014 Barga Lagoon 27 0.39 ± 0.14 0.0025 Rainbow Bch 18 0.11 ± 0.09 0.0003 Cooloola 24 0.31 ± 0.12 0.0014 Noosa 02 1.00 ± 0.50 0.0028 Peregian 05 0.90 ± 0.16 0.0055 Beerwah 04 0.50 ± 0.26 0.0014 Caboolture 03 0.67 ± 0.31 0.0018 White Patch 30 0.67 ± 0.08 0.0040 Bellara 30 0.49 ± 0.10 0.0025 Karawatha 16 0.23 ± 0.13 0.0006 Moreton Is. 11 0.60 ± 0.15 0.0019 Stradbroke Is. 35 0.11 ± 0.09 0.0003

A pairwise genetic distance matrix (Jukes-Cantor distance) was generated to examine the

genetic relationships among haplotypes. Genetic distance estimates indicated that all

haplotypes found within northern populations were highly divergent from all haplotypes

found within southern populations (Table 6). The distance matrix showed that haplotypes

from the Moreton Island and Stradbroke Island populations were genetically related to other

southern haplotypes.

Genetic distances ranged from 3.7 to 5.7 percent divergence between respective northern and

southern haplotype sets. These estimates were significantly higher than the estimates

calculated for pairwise haplotype differences within regions; values ranged from 0.3 to 1.4

percent within the northern region and from 0.3 to 1.7 percent within the southern region.

An AMOVA was performed and the northern and southern groupings were designated a

priori as a regional partition. The results of the AMOVA supported the differentiation of

northern and southern populations, indicating that the majority of mtDNA variation was

explained by regional differences (87.3%) with only a small percent of variation found

among populations within regions (8.9%) and within populations (3.8%) (Table 7).

In a broad scale context, genetic data analyses clearly support the presence of two

significantly differentiated groups of populations among southeast Queensland C.tinnula.


59

Table 6. Pairwise genetic distances for C.tinnula 12S mtDNA haplotypes. Outgroup species, C.parinsignifera (Cpar) and C.signifera (Csig), are included at the bottom of the table. Jukes-Cantor pairwise distances are shown below the diagonal. Absolute base-pair differences are shown above the diagonal. The ‘N’ or ‘S’ shown after the haplotype number in the first column indicates whether the haplotype was found in a ‘northern’ (N) or ‘southern’ (S) population.

001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 001 N 2 2 1 3 3 3 1 2 3 3 16 15 16 17 17 15 002 N 0.006 4 3 5 5 5 3 4 5 5 18 17 18 19 19 17 003 N 0.006 0.011 3 1 3 1 1 2 3 3 15 14 15 16 16 15 004 N 0.003 0.008 0.008 4 4 4 2 3 4 4 15 14 15 16 16 14 005 N 0.008 0.014 0.003 0.011 4 2 2 3 4 4 16 15 16 17 17 16 006 N 0.008 0.014 0.008 0.011 0.011 4 2 3 4 2 17 16 17 18 18 16 007 N 0.008 0.014 0.003 0.011 0.006 0.011 2 3 4 4 16 15 16 17 17 16 008 N 0.003 0.008 0.003 0.006 0.006 0.006 0.006 1 2 2 15 14 15 16 16 14 009 N 0.006 0.011 0.006 0.008 0.008 0.008 0.008 0.003 1 3 16 15 16 17 17 15 010 N 0.008 0.014 0.008 0.011 0.011 0.011 0.011 0.006 0.003 4 17 16 17 18 18 16 011 N 0.008 0.014 0.008 0.011 0.011 0.006 0.011 0.006 0.008 0.011 17 16 17 18 18 16 012 S 0.046 0.051 0.043 0.043 0.046 0.048 0.046 0.043 0.046 0.048 0.048 3 2 3 3 4 013 S 0.043 0.048 0.04 0.04 0.043 0.046 0.043 0.04 0.043 0.046 0.046 0.008 1 2 2 3 014 S 0.046 0.051 0.043 0.043 0.046 0.048 0.046 0.043 0.046 0.048 0.048 0.006 0.003 1 1 2 015 S 0.048 0.054 0.046 0.046 0.048 0.051 0.048 0.046 0.048 0.051 0.051 0.008 0.006 0.003 2 3 016 S 0.048 0.054 0.046 0.046 0.048 0.051 0.048 0.046 0.048 0.051 0.051 0.008 0.006 0.003 0.006 3 017 S 0.043 0.048 0.043 0.04 0.046 0.046 0.046 0.04 0.043 0.046 0.046 0.011 0.008 0.006 0.008 0.008 018 S 0.043 0.048 0.043 0.04 0.046 0.046 0.046 0.04 0.043 0.046 0.046 0.014 0.011 0.008 0.011 0.011 0.003 019 S 0.051 0.057 0.048 0.048 0.051 0.051 0.051 0.048 0.051 0.054 0.051 0.011 0.008 0.006 0.008 0.003 0.011 020 S 0.046 0.051 0.043 0.043 0.046 0.048 0.046 0.043 0.046 0.048 0.048 0.011 0.008 0.006 0.008 0.003 0.011 021 S 0.046 0.051 0.04 0.043 0.043 0.048 0.043 0.043 0.046 0.048 0.048 0.008 0.006 0.003 0.006 0.006 0.006 022 S 0.043 0.048 0.04 0.04 0.043 0.046 0.043 0.04 0.043 0.046 0.046 0.008 0.006 0.003 0.006 0.006 0.008 023 S 0.04 0.046 0.04 0.037 0.043 0.043 0.043 0.037 0.04 0.043 0.043 0.006 0.008 0.006 0.008 0.008 0.006 024 S 0.04 0.046 0.04 0.037 0.043 0.043 0.043 0.037 0.04 0.043 0.043 0.008 0.011 0.008 0.011 0.011 0.003 025 S 0.043 0.048 0.043 0.04 0.046 0.046 0.046 0.04 0.043 0.046 0.046 0.008 0.011 0.008 0.011 0.011 0.008 026 S 0.04 0.046 0.04 0.037 0.043 0.043 0.043 0.037 0.04 0.043 0.043 0.008 0.008 0.008 0.011 0.011 0.008 027 S 0.043 0.048 0.043 0.04 0.046 0.046 0.046 0.04 0.043 0.046 0.046 0.011 0.011 0.011 0.014 0.014 0.011 028 S 0.046 0.051 0.046 0.043 0.048 0.048 0.048 0.043 0.046 0.048 0.048 0.014 0.014 0.014 0.017 0.017 0.014 Cpar 0.048 0.054 0.048 0.046 0.051 0.051 0.051 0.046 0.048 0.051 0.051 0.051 0.048 0.046 0.048 0.048 0.04 Csig 0.06 0.066 0.06 0.057 0.063 0.06 0.063 0.057 0.057 0.06 0.06 0.06 0.057 0.06 0.063 0.063 0.054


60

Table 6. Continued.

018 019 020 021 022 023 024 025 026 027 028 Cpar Csig 001 N 15 18 16 16 15 14 14 15 14 15 16 17 21 002 N 17 20 18 18 17 16 16 17 16 17 18 19 23 003 N 15 17 15 14 14 14 14 15 14 15 16 17 21 004 N 14 17 15 15 14 13 13 14 13 14 15 16 20 005 N 16 18 16 15 15 15 15 16 15 16 17 18 22 006 N 16 18 17 17 16 15 15 16 15 16 17 18 21 007 N 16 18 16 15 15 15 15 16 15 16 17 18 22 008 N 14 17 15 15 14 13 13 14 13 14 15 16 20 009 N 15 18 16 16 15 14 14 15 14 15 16 17 20 010 N 16 19 17 17 16 15 15 16 15 16 17 18 21 011 N 16 18 17 17 16 15 15 16 15 16 17 18 21 012 S 5 4 4 3 3 2 3 3 3 4 5 18 21 013 S 4 3 3 2 2 3 4 4 3 4 5 17 20 014 S 3 2 2 1 1 2 3 3 3 4 5 16 21 015 S 4 3 3 2 2 3 4 4 4 5 6 17 22 016 S 4 1 1 2 2 3 4 4 4 5 6 17 22 017 S 1 4 4 2 3 2 1 3 3 4 5 14 19 018 S 5 5 3 4 3 2 4 4 5 6 13 18 019 S 0.014 2 3 3 4 5 5 5 6 7 18 22 020 S 0.014 0.006 3 1 4 5 5 5 6 7 16 23 021 S 0.008 0.008 0.008 2 2 3 3 3 4 5 16 21 022 S 0.011 0.008 0.003 0.006 3 4 4 4 5 6 15 22 023 S 0.008 0.011 0.011 0.006 0.008 1 1 1 2 3 16 19 024 S 0.006 0.014 0.014 0.008 0.011 0.003 2 2 3 4 15 18 025 S 0.011 0.014 0.014 0.008 0.011 0.003 0.006 2 3 4 17 20 026 S 0.011 0.014 0.014 0.008 0.011 0.003 0.006 0.006 1 2 17 19 027 S 0.014 0.017 0.017 0.011 0.014 0.006 0.008 0.008 0.003 1 16 20 028 S 0.017 0.02 0.02 0.014 0.017 0.008 0.011 0.011 0.006 0.003 17 21 Cpar 0.037 0.051 0.046 0.046 0.043 0.046 0.043 0.048 0.048 0.046 0.048 22 Csig 0.051 0.063 0.066 0.06 0.063 0.054 0.051 0.057 0.054 0.057 0.06 0.063


61

Table 7. AMOVA showing partitioning of 12S mtDNA variation within and among regions

of southeast Queensland populations of C.tinnula.

Source of Variation d.f Percentage of Variation

VarФ Level of Significance

Among Regions (ФCT)

1 87.3 0.873 0.001

Among Populations within Regions (ФSC)

9

8.9

0.700

0.001

Within Populations (ФST)

249

3.8

0.962

0.001

3.3.6 POPULATION STRUCTURE ACROSS THE NATURAL DISTRIBUTION OF C.TINNULA

To determine if differentiation was also evident among sets of populations of C.tinnula

outside southeast Queensland, samples were obtained from across the species’ natural

distribution. Due to time constraints and the difficulty in obtaining permits to collect in New

South Wales, samples were sourced from the preserved tissue collection at the South

Australia museum (samples kindly provided to the museum by Dr. Michael Mahony). The

number and position of additional sites accessible to sample outside Queensland was

therefore, limited to only those available from the SA museum collection.

Samples from four New South Wales sites (Tyagarah, Newrybar Swamp, Richmond Ranges,

Mungo Brush-Myall Lakes) and one Queensland site (Gympie) were used. Two of the New

South Wales sites (Tyagarah and Newrybar) are geographically close to each other

(approximately 13 km apart – direct linear distance) and are the two closest sampled

populations to the most southern Queensland population sampled (Tyagarah is

approximately 100kms from the Karawatha population). The Richmond Ranges are

approximately 75km south of Newrybar and Mungo Brush-Myall Lakes is at the southern

end of the known distribution of C.tinnula (550kms from Newrybar) (Figure 2).

A single tissue sample was obtained as a representative from each of the five sites. DNA

extraction and PCR protocols were as per methods used for the southeast Queensland

C.tinnula samples. The PCR products were run out on a TGGE gel and sequences were

obtained for each unique haplotype. The same 362bp sequence of 12S mtDNA fragment


62

used for analysis of the southeast Queensland C.tinnula samples was recovered and used in

analysis for each of the five museum samples.

Figure 2. Locations of C.tinnula samples obtained from the South Australian museum

(sample locations represented by green squares). Note: The Richmond Ranges sample and the

Gympie sample were subsequently found to be C.parinsignifera and C.signifera, respectively.


63

TGGE and subsequent sequence analysis revealed that the Richmond Range and Gympie

samples had been incorrectly identified as C.tinnula during sampling and were

C.parinsignifera and C.signifera samples, respectively. Sequence analysis revealed the

Richmond sample had an identical sequence to the C.parinsignifera Barakula sample and the

Gympie sample had an identical sequence to the C.signifera Goomburra sample. The three

other New South Wales samples; Tyagarah, Newrybar and Mungo Brush, were identified as

C.tinnula (based on sequence similarity to other C.tinnula samples) and were included in

subsequent analyses.

The New South Wales C.tinnula samples (Tyagarah, Newrybar, Mungo Brush) showed

unique 12S haplotypes. When included in an alignment with other southeast C.tinnula

samples (Appendix 3), the NSW haplotypes produced 34 variable sites (9.4%), 20 of which

were parsimony informative (total length of sequence 362bp).

Average pairwise distances between the three New South Wales samples and Queensland

C.tinnula haplotypes showed the Tyagarah and Newrybar samples to be very similar

genetically to the southern Queensland haplotypes and significantly differentiated from all

northern C.tinnula haplotypes (Table 8; Pairwise distance values for individual haplotypes

are given in Appendix 3.1). The Mungo haplotype did not show a close affiliation to either

the northern or southern samples. Divergence estimates between the Mungo haplotype and

all other haplotypes ranged from 2.5 to 3.7 percent with an average of 3.1 percent

differentiation from all northern samples and 3.3 percent differentiation from all southern

haplotypes. Thus, the Mungo individual appears to be representative of a third C.tinnula

‘clade’.

Table 8. Range of pairwise genetic distances for New South Wales C.tinnula 12S

mtDNA haplotypes.

Northern Southern Tyagarah Newrybar Tyagarah 0.037-0.043 0.008-0.020 - - Newrybar 0.040-0.051 0.008-0.017 0.011 - Mungo 0.025-0.037 0.031-0.040 0.037 0.031


64

3.3.7 GENETIC COMPARISONS WITHIN CRINIA GENUS

To put the genetic distance estimates between northern and southern C.tinnula populations in

perspective, average pairwise distance values were calculated between C.tinnula haplotypes

and the outgroup species (Table 9). Pairwise comparisons of C.tinnula haplotypes with

C.parinsignifera ranged from 4.0 to 5.7 percent. This level of divergence was similar to the

average divergence observed between northern and southern C.tinnula haplotypes (3.7% to

5.7%). The pairwise comparisons with C.signifera haplotypes were slightly higher, ranging

from 5.4 to 6.6 percent.

To determine if the level of genetic divergence observed among C.tinnula haplotypes was

characteristic of species in the Crinia genus or specific to C.tinnula, genetic distances among

haplotypes within each outgroup species were estimated. Although samples of the two

outgroup species were few in number, they were collected from populations that were

separated by larger geographic distances than those between sampled C.tinnula populations.

Results indicated that C.signifera and C.parinsignifera showed no appreciable genetic

divergence among haplotypes at either small or large geographic scales (Table 10).

Table 9. Average genetic distances (DA) between C.tinnula population groups

(northern vs southern region) and outgroup species C.parinsignifera and C.signifera.

Jukes-Cantor net average genetic distances shown below diagonal. Standard error,

based on 10000 bootstrap replications, shown above the diagonal. Comparative

genetic distances among C.tinnula haplotypes within a region are shown on the right

hand columns of the table. Standard error (SE) is based on 10000 bootstrap

replications. n/c – not calculated.

Net average genetic distances among groups (DA)

Average genetic distance among haplotypes within a region

North South Mungo Cpar Csig D SE North - 0.010 0.009 0.012 0.012 0.008 0.002 South 0.036 - 0.008 0.010 0.012 0.010 0.003 Mungo 0.027 0.029 - 0.012 0.012 n/c n/c Cpar 0.046 0.042 0.049 - 0.014 0.004 0.003 Csig 0.055 0.054 0.052 0.063 - 0.006 0.004


65

Table 10. Pairwise genetic distances for C.parinsignifera (Cpar) and C.signifera (Csig) 12S

mtDNA haplotypes. Pairwise distances given below the diagonal, approximate geographic

distances in kilometres (km) are shown above the diagonal (direct linear distance).

Cpar Barakula

Cpar Caboolture

Cpar Karawatha

Csig Goomburra

Csig Karawatha

CparB - 295km 330km CparC 0.003 - 70km CparK 0.003 0.006 - CsigG - 120km CsigK 0.006

NOTE: C.parinsignifera Richmond Ranges shared the same 12S haplotype as to

C.parinsignifera Barakula; these populations are approximately 430km apart. C.signifera

Gympie shared the same haplotype as C.signifera Goomburra and these populations are

approximately 215km apart.

3.3.8 PHYLOGENETIC ANALYSES

A neighbour-joining tree revealed three monophyletic clades among the sampled C.tinnula

haplotypes (Figure 3). There was a strong correlation between the phylogenetic structuring

of the clades and broad geographic distributions. Haplotypes from populations found in the

northern region formed one clade, haplotypes from the southern region formed a second

clade and the single Mungo haplotype formed a third clade. Relative to the C.signifera

outgroup, C.parinsignifera also formed a fourth monophyletic clade with the three C.tinnula

clades. There was strong support for all clades with bootstrap values close to 100.

No significant structuring was observed within either the northern or southern clades and

there was no differentiation of island and mainland haplotypes (75% bootstrap limits were

imposed on all branches). Haplotypes from southern populations (Peregian to Newrybar,

including the sand islands), formed a polyphyletic assemblage of haplotypes, as did those

haplotypes from the northern (Wathumba to Noosa) populations. The two haplotypes from

the northern New South Wales populations, Tyagarah and Newrybar, clustered with the

Queensland southern haplotypes.


66

Figure 3. Neighbour-joining (NJ) tree showing inferred phylogenetic relationships among

C.tinnula 12S mtDNA haplotypes. C.parinsignifera and C.signifera used as outgroups. “S”

denotes the ‘southern’ C.tinnula clade; “N” denotes the ‘northern’ C.tinnula clade.

Bootstrap values greater than 75% are shown above branches, Parsimony bootstraps are

shown in italics (e.g. NJ / Pars).


67

3.3.9 GENETIC STRUCTURE WITHIN REGIONS

The frequencies and distribution of haplotypes across the region and the high level of genetic

divergence observed between northern and southern populations show patterns of population

genetic structure that support a significant period of isolation in the past. This would suggest

that populations in the two regions were likely to have evolved independently. Coalescent-

based analysis, i.e. Nested Clade Analysis (NCA) was used to determine processes which

may have influenced population structure within the northern and southern regions.

The Mungo haplotype was not included in the analysis due to the large number of mutational

differences between this haplotype and all other C.tinnula haplotypes and because only a

single sample of this clade was available for analysis.

The nesting design generated by TCS produced two networks; one network contained all

northern haplotypes and the other network contained all southern haplotypes (Figure 4).

Individual networks were not joined because divergence between the networks exceeded the

95% confidence limits of parsimonious connections derived from the estimation procedure

(Templeton et al. 1992). A minimum of 14 mutational steps separated the northern and

southern networks.

There were three cases where haplotypes were symmetrically stranded (Templeton and Sing

1993); haplotype 008, haplotype 024 and haplotype 026. Each of these haplotypes was

subsequently nested with the nearest clade with the lowest number of individuals (Templeton

and Sing 1993). The network containing the southern haplotypes showed evidence of

ambiguous relationships among two groups of haplotypes (broken lines in Figure 4). These

ambiguities resulted in closed loops among haplotypes. These loops were resolved using

criteria from Crandall and Templeton (1993). Owing to their close geographic proximity

and small sample number, the two northern NSW haplotypes were pooled. The geographical

centre between the pair of populations was used for the geographical coordinates.

Northern and southern networks were analysed separately in GeoDis (Posada et al. 2000).

The large number of mutational steps between the networks, in conjunction with data from

genetic divergence estimates and the strict geographic partitioning among sampled northern

and southern haplotypes would strongly suggest historical allopatric fragmentation for these

population groups.

NCA revealed that very few clades showed significant non-random associations of genetic


68

structure and geographic location (Table 11). Of a total of 18 clades only 5 were significant.


69

Figure 4. Nested cladogram of southeast Queensland and northern New South Wales

C.tinnula 12S mtDNA haplotypes. Underlined haplotypes (008, 014) are considered to be

ancestral haplotypes for the network. Small open circles represent missing steps in the

cladogram. Broken lines represent resolved ambiguous loops. The number of mutational

steps between the cladograms is 14. Haplotype 029 = Tyagarah, haplotype 030 = Newrybar.

Northern Network

2-1

Southern Network


70

Table 11. Permutational chi-squared probabilities for geographical structure of the clades

identified in Figure 4 from 1000 resamples. ‘*’ significant at P<0.05. Abbreviations in the

Inference Key: CRE = Contiguous Range Expansion; SDI = Sampling Design Inadequate;

LDC = Long Distance Colonisation; PF = Past Fragmentation; IBD = Isolation by Distance.

Clade

X2 Statistic

P value

Inference Key Steps

Northern Network 1-1 0.00 0.000* 1, 2, 11, 12NO: CRE 1-3 20.76 0.068 1-4 1.29 0.762 1-5 4.00 0.231 2-1 0.21 1.000 2-2 89.17 0.000* 1, 2, 3, 5, 6, 7, 8NO: SDI (IBD or LDC) Total Cladogram 113.29 0.000* 1, 2, Inconclusive Outcome (No

Tips/Interiors in this clade) Southern Network 1-2 1.20 1.000 1-3 48.27 0.017* 1, 2, 11 12NO: CRE 1-5 0.22 1.000 1-8 3.00 1.000 2-1 1.60 0.475 2-2 15.49 0.150 2-3 25.00 0.000* 1, 19NO Allopatric Fragmentation 2-4 37.28 0.024* 1, 2, 3, 4NO Restr. Gene flow with IBD 3-1 6.50 0.249 3-2 59.20 0.000* No significant Dc or Dn values Total Cladogram 0.00 0.000* 1, 2, Inconclusive Outcome (No

Tips/Interiors in this clade)

Within the northern network two clades, Clade 1-1 and Clade 2-2, were found to show

geographical association of haplotypes. Clade 1-1 contained haplotypes sampled from the

Wathumba Creek population and a single haplotype (004) from the Ungowa population.

NCA analysis suggests distance values are concordant with contiguous range expansion for

the Ungowa haplotype.

Clade 2-2 contained haplotypes from all northern populations except the Wathumba

population. NCA inferred that the sampling design was inadequate to discriminate between

Isolation by Distance and Long Distance Colonisation for haplotypes found in the island

populations (Ungowa and Barga Lagoon populations). The difficulty in accessing

populations across the regional distribution resulted in a very patchy sampling distribution

which would have contributed to the lack of resolution for NCA.


71

Haplotype frequencies and distributions across the northern region appear to exhibit a pattern

where geographically close populations are more genetically similar. Although not

evidenced by NCA, restricted dispersal with isolation-by-distance could produce the pattern

of haplotype distribution observed in the northern region. Isolation-by-distance was tested

using the program IBD (Bohonak, 2002). Significant isolation-by-distance was observed

(Mantel test; Z = -19.0371, r = 0.6335, p<= 0.0012), however, the strength of the relationship

was very low (R2 = 0.40, 95% CI 0.135 and 0.821).

Within the southern network, three clades showed significant geographic associations among

haplotypes. At the lowest nesting level (haplotype level) Dc and Dn distances within clade 1-

3 (mainland and Bribie Island haplotypes) suggested contiguous range expansion for

haplotype 022. This was the only haplotype within clade 1-3 to show significant Dc and Dn

values. The pattern observed in the TCS network, where mainland and Bribie Island

haplotypes formed a starlike pattern around haplotype 014 (the haplotype considered to be

ancestral by TCS due to 014 being an interior haplotype with a widespread geographic

distribution; Neigel and Avise 1993; Templeton et al. 1995) is indicative of a demographic

expansion (Slatkin and Hudson 1991). Although a population range expansion is not

necessarily evidence of a demographic population expansion or vice versa, a higher survival

rate of metamorphs and an increase in population size may have been a precursor for a range

expansion. Mismatch analysis was carried out to determine if southern mainland and Bribie

Island populations showed evidence of population expansion. Results of the mismatch

analysis suggested that there was evidence of a population expansion; i.e. no differences

between observed values and those expected under a pattern of sudden population expansion

were observed (SSD = 0.0546; p=0.191; Figure 5).

NCA suggested allopatric fragmentation for clades within Clade 2-3, which contained

haplotypes from Bribie Island and the mainland and Moreton Island haplotypes. Allopatric

fragmentation was inferred because these haplotypes are currently found in separate

geographic areas with no overlap and because the species is not present in areas between the

separate clades. A pattern of restricted gene flow with isolation-by-distance was inferred for

the clade containing two Stradbroke Island haplotypes. This clade nested with two other

clades containing the haplotypes from the NSW populations and the single haplotype shared

among the Moreton and Stradbroke Island populations (haplotype 026).


72

0

400

800

1200

1600

2000

0 1 2 3 4 5 6Number of Pairwise Differences

Freq

uenc

y

Observed

Expected

Figure 5. 12S mtDNA mismatch distribution for southern mainland (Peregian, Beerwah,

Caboolture and Karawatha) and Bribie Island populations.


73

3.4 DISCUSSION

3.4.1 BROAD SCALE POPULATION STRUCTURE

12S mitochondrial DNA analyses clearly indicate that two distinct evolutionary lineages of

C.tinnula are present within southeast Queensland. Haplotypes from populations in the

northern region of the sampled distribution (Wathumba Ck to Noosa) show a high level of

genetic divergence (an estimated average of 3.6%), from all haplotypes sampled in southern

populations (Peregian to Stradbroke Is). This level of divergence in 12S rRNA is

comparable to that observed among previously described Crinia species for the same gene

(approximately 5.0% - Read et al. 2000).

There are no (obvious) geological or geographical barriers to dispersal that exist across the

sampled distribution currently which could explain such a high level of divergence. The

Noosa River is the only significant biogeographical structure present between the Noosa and

Peregian populations which could potentially impede dispersal, however, two other major

river systems are present in the Sunshine Coast and Brisbane regions and there is no

evidence for phylogeographic breaks in sampled populations either side of these rivers.

The geographical distribution of common haplotypes within both the northern and southern

regions indicates that dispersal across distances greater than the distance between the Noosa

and Peregian sites has been possible. The distribution of wallum habitat that currently exists

between these two populations is no more or less patchy or isolated than any other areas of

wallum habitat found within southeast Queensland, so there appears to be no indication in

the current geographical or ecological characteristics of the region that offer an explanation

for the dichotomy between the southern and northern C.tinnula clades observed here.

In a study by Read et al. (2001) that investigated the phylogenetic relationships among

myobatrachid frogs, two samples identified morphologically as C.tinnula also showed a high

level of sequence divergence (approximately 3.7% for 12S mtDNA), suggesting that the

pattern observed in this study is consistent across the species distribution. Interestingly, the

two samples included in the Read et al. (2001) study were from Mungo brush-Myall Lakes

and the Coffs Harbour region, respectively. Read et al. (2001) identified the Mungo

individual as C.tinnula and the Coffs Harbour individual as a possible undescribed species.

The Coffs Harbour individual could potentially ‘fall’ within the southern clade identified in

the present study, or may indeed represent an additional differentiated group of populations.

Unfortunately, sequence data were not available for comparison and so this issue must


74

remain, at present, unresolved.

Studies which have looked at population structuring of Oxleyan pygmy perch (Nannoperca

oxleyana; Hughes et al. 1999) and atyid shrimp populations (Caridina spp.; Woolschot et al.

1999) across the same southeast Queensland geographical region as that sampled for

C.tinnula populations, have also reported relatively high levels of genetic differentiation

among populations which were geographically close. Both studies found a high degree of

genetic similarity among populations distributed along the Sunshine and Cooloola Coasts

and a relatively high degree of differentiation among these populations and populations

associated with the Mary River catchment and Fraser Island streams (streams sampled within

the Cooloola region are closer geographically to the Mary River sites than to the Noosa and

other mainland sites; refer Figure 6). Both studies proposed that historical changes to

drainage patterns in the Mary and Brisbane Basins could account for comparably large levels

of differentiation among populations which were otherwise geographically close.

As recently as 8000 years ago the coastline in the region would have extended out past the

major sand islands and there is some evidence to suggest that the Brisbane River, which

currently flows out through Moreton Bay may have flowed a long way further north before

entering the sea. Hughes et al. (1999) proposed that mainland streams south of Fraser

Island, including those in the Cooloola area that currently flow north into Tin Can Bay, may

have once flowed south and formed part of the Brisbane River drainage system. This

hypothesis remains purely speculative, however, as there is a lack of geological information

relating to this region of the coastline. If, mainland streams in the Cooloola region did

historically flow south, then dispersal of freshwater obligate and freshwater dependent

species would be more likely among streams along the Cooloola and Sunshine Coasts and

less likely among the Cooloola and Mary River streams as observed in the Woolschot et al.

(1999) and Hughes et al. (1999) studies.

Applying this hypothesis to the southeast Queensland C.tinnula populations does not,

however, explain the pattern of divergence observed here. If C.tinnula was influenced by

changes in historical drainage patterns in the same way as the atyid shrimp and Pygmy Perch

it is likely that the Rainbow Beach, Cooloola and Noosa populations would be more similar

genetically to the Sunshine Coast populations (Peregian, Beerwah, Caboolture and Bribie

Island) than to the Fraser


75

Figure 6. mtDNA haplotype frequencies of Oxleyan Pygmy Perch populations from

southeast Queensland (reproduced from Hughes et al. 1999).

halla



76

Island populations. For this hypothesis to explain the observed pattern of differentiation,

northern populations would have had to have been restricted to an area north of Cooloola

during the time the Cooloola rivers and lakes flowed south and have expanded subsequently

and colonised wallum habitat in the Cooloola-Noosa region. In addition, southern

populations would have had to have been restricted to an area south of the Noosa River and

dispersal could not have occurred, historically or recently, north of the sampled Noosa

population.

Because C.tinnula are not restricted to or dependent on permanent waters of rivers and

creeks for survival like the atyid shrimp and the Pygmy Perch, they are less likely to be

affected by changes in drainage channels. Froglets are able to take advantage of ephemeral

water bodies for breeding and larval development and then continue development in a

terrestrial environment. It may also be possible that the mtDNA genetic structure observed

in C.tinnula populations pre-dates the proposed changes in the configuration of the Brisbane

and Mary catchment systems.

The average genetic divergence estimate among northern and southern populations (3.6%)

suggests differentiation among northern and southern populations occurred approximately 3

– 5.2 million years ago, during the Pliocene (using a minimum rate of mutation of 0.69%

estimated for the average mtDNA rate of mutation in frogs, Martin and Palumbi 1993; and a

maximum rate of mutation of 1.2 % per million years for 12SrRNA derived from Rana spp.;

Sumida et al. 20005). Straughan and Main (1966) proposed that differentiation of C.tinnula

may have occurred during periods of rising sea levels that and isolation of favourable habitat.

Sea level fluctuations during the Pliocene could have created opportunities for patches of

wallum habitat and consequently, C.tinnula populations, to have become isolated by marine

or brackish water intrusion of low lying areas.

The period of isolation, however, would have needed to have been quite extensive to

produce such a relatively high level of divergence. The interglacial periods, during which

time sea levels would have risen and created isolated patches of wallum, are believed to have

been of a relatively short duration, approximately 10ka. In comparison, the glacial cycles

were believed to have a periodicity of ~100 ka in the last 550ka, and prior to that glacial

5 The pairwise sequence divergence estimate of 1.2% is supported by geological land formation data

(Macey et al. 1998a; 1998b). Sumida et al. 2000 also found that divergence estimates based on the

rate of 1.2% were compatable with divergence times estimated from allozyme data.


77

periods varied between 41 ka and 100ka back to approximately 2.4 million years ago

(Longmore 1997).The high level of differentiation between regions would also mean that

when sea levels fell dispersal did not occur between wallum patches which contained

‘northern’ and ‘southern’ populations. During the last glacial period, sea levels dropped

between 80m and 190m, therefore it seems unlikely that pockets of wallum would have

remained isolated by higher sea levels for the length of time required to produce and

maintain such a high level of divergence (a fall in sea level of 28m would move the present

south eastern Queensland coastline 40km eastwards and terrestrial habitat would have

extended to the eastern sides of Moreton and Stradbroke Islands). However, climates in

glacial periods were generally cooler, dryer and windier than in interglacial periods (Bowler

1976; Longmore and Heijnis 1999) thus wallum habitat may have experienced not only

isolation due to rises in sea level but also range contractions due to climate change.

Dispersal among patches of wallum habitat may not have been possible due to unfavourable

environmental conditions for ancestral C.tinnula.

The climatic changes which occurred during the Pliocene and the coastal location of wallum

would make it highly likely that populations of wallum froglets would have been affected by

sea level changes and may have been isolated in, and restricted to, habitat refugia. The

geographical partitioning of variation indicates that populations were isolated in a north –

south pattern. There is little geological or palaeontological evidence, however, that exists on

the distribution of wallum habitat during the Tertiary or the Quaternary periods therefore it is

very difficult to propose where potential habitat isolates may have been present.

3.4.2 POPULATION STRUCTURE WITHIN REGIONS

Results of Nested Clade Analysis (NCA) revealed very few significant associations between

spatial patterns of genetic diversity and geography. While this may be indicative of a lack of

phylogeographic structure, it may also be due to inadequacies in the sampling regime

(Templeton 2004). Several sites were sampled for C.tinnula where no males could be heard

calling, or, where males were heard calling but either the site was inaccessible or the cryptic

behaviour of the frogs made it difficult to sample large numbers. Additionally, there was

also temporal variation in activity at sites, e.g. during one sampling season at the Ungowa

site, large choruses of male frogs were calling and 25 froglets were sampled over a period of

5 days. However, during the next sampling season only a few male individuals were calling

and only 2 froglets were caught over a similar period.


78

The need for comprehensive sampling across the species’ distribution (to identify signatures

of historical processes) and adequate sample numbers may limit the application of NCA for

studies of organisms which are difficult to sample spatially due to poor access to habitat

patches and/or show temporal variability in activity patterns. It was assumed that

undisturbed areas of wallum habitat which were visited and where no frogs were heard

calling did in fact support C.tinnula populations.

As very little is known about the distribution of wallum habitat during the Pliocene and

Pleistocene epochs or about the relative dispersal abilities of wallum froglets it is difficult to

make any sound biological interpretation for the few inferences resulting from NCA without

the support of patterns observed at either higher or lower nesting levels.

The aforementioned limitations not withstanding, some interesting hypotheses can be

advanced from the results obtained. In the northern region, nested clade analysis suggested

contiguous range expansion (CRE) for haplotype 004 from its inferred ancestral haplotype

(haplotype 001) which was present in both the Wathumba Creek and Barga Lagoon

populations. The assumption of CRE was based on a large Dn value (and an insignificant Dc

value). It is possible that haplotype 001 may have been more widespread (as evidenced by

the presence of 001 in the Barga Lagoon population) and that haplotype 004 may have arisen

in situ in the Ungowa or Barga Lagoon populations.

The visual overlay of northern haplotype distribution and frequencies on geography

suggested an isolation-by-distance pattern of dispersal among populations, however,

isolation-by-distance was not inferred from NCA and results from IBD provided only weak

statistical support for this pattern of population structure. While this may be an artefact of

the extent of population sampling across the region, it is possible that dispersal occurs at a

very local scale and gene flow is rare among more geographically isolated populations.

Most frogs are generally, relatively poor dispersers and are often highly philopatric to natal

ponds (Blaustein et al. 1994; Beebee 1996). As dispersal is often restricted to the

metamorph stage (Beebee 1996) and because breeding success can be highly variable

(Pechmann et al. 1991; Semlitsch et al. 1996), gene flow among local populations may be

limited.

The Fraser Island and the Cooloola sand masses were connected during glacial cycles of the

Pleistocene and continuous during most of the Holocene (Longmore and Heijnis 1999). It is

also believed that the Cooloola wallum plains, which now drain into Hervey Bay, once

formed a fairly continuous habitat with areas of wallum on the western side of Fraser Island


79

(Ward 1977). Under coalescent theory, the three dominant haplotypes in the northern region

(001, 003 and 008), which in the TCS network are internal haplotypes, are likely to be

ancestral (Donnelly and Tavare 1986; Golding 1987). The retention of these ancestral

haplotypes in different populations and the lack of sharing of other haplotypes across the

region may be indicative of fragmentation of a once continuously distributed population or a

range expansion/colonisation event from a large ‘source’ population and subsequent

isolation among local populations. The presence of ancestral haplotypes in low frequency

among populations within the northern region, e.g. the presence of haplotype 003 in the

Cooloola mainland populations and haplotype 001 in the Barga Lagoon population, would

suggest that these haplotypes were once more widely distributed and/or in high frequency in

the ancestral population and stochastic lineage sorting resulting from genetic drift and/or

population bottlenecks may have affected the frequency of specific alleles in different areas.

The restricted distribution of tip haplotypes (more recently derived haplotypes) to

populations which are geographically close (< 10km) suggests that contemporary gene flow

may occur at a very local scale. More intensive sampling across sites within the northern

distribution may increase the resolution of NCA analysis.

The range expansion suggested for haplotype 022 in Clade 1-3 which contained southern

mainland and Bribie Island haplotypes is consistent with post glacial expansion observed in

many other species which have been isolated in glacial refugia during eustatic oscillations of

the Pliocene and Pleistocene (Hewitt 2001; Schneider et al. 1998). Given that only a single

haplotype showed evidence for a range expansion, these results should be interpreted with

caution. It is possible that southern populations may have been restricted to a single habitat

isolate during the dry climates of the Pliocene and Pleistocene epochs and once

environmental conditions became more favourable for dispersal (the early Holocene is

thought to have seen an increase in precipitation rates over Asia: Crowley and North 1991),

froglets may have been able to move into other adjacent wallum habitat. A demographic

expansion may have coincided with a range expansion if conditions were more favourable

for tadpole and metamorph survival.

The high level of genetic similarity among southern mainland and Moreton and Stradbroke

Island haplotypes, the evolutionary relationships among haplotypes and the proposed age of

mainland wallum habitat would suggest that sand islands were colonised from the mainland

and have since experienced isolation resulting in a small degree of genetic differentiation and

changes in haplotype frequencies. While both the southern mainland and Moreton Island

haplotypes are interior on the Nested Clade network, it is assumed, given that the age of


80

mainland heath is estimated to be late Tertiary, that C.tinnula populations existed on the

mainland before the sand islands were formed. During times of lower sea levels when

Moreton Bay was dry, froglets may have been able to move out and colonise patches of

wallum habitat on Moreton and Stradbroke Island (TCS analysis also indicated that

haplotype 014, a mainland haplotype, is most likely the ancestral haplotype from which

Moreton and Stradbroke Island haplotypes are derived). Reciprocal gene flow among the

islands and the mainland would have also been possible during this time.

The pattern of allopatric fragmentation inferred by NCA for a mainland clade which

appeared to be descended from Moreton Island haplotypes lends support to this hypothesis.

The inference of allopatric fragmentation should be treated with caution, however, as this is

based on current land formation patterns, and we know that for long periods in the past, these

populations were connected by land. Isolation by distance could have just as likely given

rise to the divergence observed, (this pattern would also support the hypothesis of reciprocal

gene flow during lower sea levels).

The absence of common haplotypes among island and mainland populations is likely to be

due to a combination of founder events and drift due to subsequent isolation as sea levels

rose and filled Moreton Bay, restricting gene flow between mainland populations and island

populations.

3.4.3 EVOLUTION OF C.TINNULA

Of the two hypotheses proposed by Straughan and Main (1966) and Ingram and Corben

(1975) to explain the evolution of C.tinnula populations, Straughan and Mains’ (1966)

hypothesis appears to be more consistent with the data reported here. Straughan and Main

(1966) proposed that Crinia are a Tertiary species and preliminary results from the present

study and results of Read et al. (2001) would lend support to this hypothesis, at least for

C.signifera, C.parinsignifera and C.tinnula. Studies of immunological comparisons among

several myobatrachid genera, including Crinia also suggest species level divergences pre-

Pleistocene (Maxson 1985, 1988).

The level of divergence observed between the C.tinnula clades and the proposed time of

speciation for Crinia species’ is consistent with a Pliocene divergence. If Ingram and

Corbens’ theory of multiple speciation events during the Pleistocene is valid, more than two

differentiated sets of populations might have been expected and frogs’ speciating on spatially


81

and temporally isolated wallum islands or wallum patches would be expected to show

different evolutionary lineages with lower divergence estimates.

The Mungo sample is quite distinct from either the northern or southern haplotypes. The

genetic distances between the Mungo sample and the northern and southern samples are

difficult to explain. This sample could represent another historically isolated group of

populations, or, given that wallum habitat is thought to have had a very patchy distribution

between Coffs Harbour and Newcastle in the past (Coaldrake, 1961), this sample may

represent a groups of frogs with a unique evolutionary lineage to that of the southeast

Queensland and northern New South Wales C.tinnula. The genetic divergence observed

between this sample and other C.tinnula samples was similar to the divergence observed

between northern and southern samples, which may support the hypothesis of a single

parental stock becoming isolated in patches of wallum and subsequent differentiation during

glacial-interglacial periods.

Chapter Summary: 12SmtDNA data analysis suggests that two distinct evolutionary

lineages are present among southeast Queensland populations of C.tinnula. It is

hypothesised that a combination of fluctuating sea levels and unfavourable environmental

conditions during the Pliocene glacial period caused fragmentation of remnant wallum

refugia resulting in subsequent divergence of C.tinnula populations located in separate

isolated regions.

Chapter Four: COI mtDNA

82

CHAPTER FOUR.

4 LOCAL SCALE POPULATION STRUCTURE AND GENE FLOW INFERRED FROM

MITOCHONDRIAL CYTOCHROME OXIDASE SUBUNIT I (COI) SEQUENCE DATA.

4.1 INTRODUCTION

A large number of studies have used multiple mtDNA markers to describe population

structure and diversity among populations. This is primarily because patterns detected using

sequences from different mtDNA genes should be concordant due to a shared evolutionary

history (Hay et al. 1995; Read et al. 2001) but also because different regions of the

mitochondrial molecule exhibit variation in their rate of mutation (Upholt and Dawid 1977).

Different mtDNA markers can therefore be effective for describing variation at different

levels of scale (de Bruyn et al. 2004a, 2004b). For example, slower evolving mtDNA

regions such as 16S are often used to address questions at the genus or family level (Hay et

al. 1995; de Bruyn et al. 2004a), and faster evolving regions such as the d-loop region or

cytochrome oxidase regions are used to infer relationships at or below the species level

(Nagata et al. 1998; de Bruyn et al. 2004b).

Results from the 12S mtDNA region in the present study suggest that a broad-scale pattern

of regional divergence has occurred among southeast Queensland populations of C.tinnula.

Phylogenetic analysis revealed complete monophyly among populations which are currently

geographically separated by the Noosa River. Very little structuring was observed, however,

among populations within regions. The 12S rRNA gene is known to be one of the slower

evolving mtDNA genes (Randi 2000). The functional products of ribosomal RNA genes are

single-stranded RNA molecules that exhibit secondary structure and bind with ribosomal

proteins to form the ribosomal subunits involved in the assembly of proteins. The molecular

interactions result in a complex hierarchy of functional constraints on the stem and loop

regions of the protein structure, governing the process of nucleotide substitution in rRNA

genes. Studies have shown a high level of conservation of stem sequences associated with

rRNA secondary structure. Constraints on substitution have been shown to inhibit base pair

mutations, thus 12S rRNA may be too conservative to detect local scale structuring of

populations within regions, where it exists.

The Cytochrome oxidase subunit I gene often possesses a greater range of phylogenetic

signal than does the 12S rRNA mitochondrial gene (Hebert et al. 2003). Third-position

nucleotides show a high incidence of synonymous mutations, leading to a rate of molecular

evolution that has been estimated to be approximately two to three times greater than that of


83

12S rRNA (although rates of evolution are extremely variable even within similar species;

Knowlton et al 1993; Knowlton and Weigt 1998; McMillen-Jackson and Bert 2003). The

higher rate of evolution of COI is rapid enough to allow discrimination of fine scale

phylogeographic structuring within a single species (McGuigan et al. 1999; Cox and Hebert

2001; Wares and Cunningham 2001). Thus while 12S was able to discriminate patterns of

historical broad-scale divergence in C.tinnula populations, variation at COI may provide

greater insight into more recent population structure within regions. A higher rate of

mutation may also provide evidence for patterns of colonisation of southeast Queensland

sand islands.

The sand islands off the coast of southeast Queensland (Fraser Island, Bribie Island, Moreton

Island and North Stradbroke Island) are believed to have been formed episodically during

periods of fluctuating sea levels in the late-Quaternary (Ward 1977, Clifford and Specht

1979). (For more detail refer to Chapter 2, Section 2.1.2). Geological evidence suggests that

Fraser Island was linked to the mainland for the majority of the last one million years, except

for relatively brief interglacial periods (Longmore 1997) and both Moreton Island and

Stradbroke Island would have also been linked by dry land to the mainland during glacial

periods (Clifford and Specht 1979). Isolation of the sand islands from the mainland is

estimated to have occurred relatively recently, approximately 6000 years ago (Jones 1992).

If ancestral C.tinnula populations evolved in association with mainland wallum habitat, then

island populations are likely to have been established via dispersal from the mainland during

periods of lower sea levels. Once island populations were established, the potential for

dispersal among mainland and island populations would have continued to have been

influenced by sea level changes. 12S results indicated that island populations were

characterised by unique haplotypes and showed variation in haplotype frequency compared

with mainland populations. Given a faster mutation rate, patterns of COI variation are likely

therefore to exhibit genetic differentiation of island populations and may help to resolve

patterns of colonisation for these populations.

Additionally, if geologic or environmental conditions causing the divergence observed

between northern and southern populations of C.tinnula for 12S were maintained, then

variation at the COI gene should show a greater level of differentiation among northern and

southern regions and the Mungo haplotype.

The objective of this chapter was to document local scale population structure of C.tinnula

across southeast Queensland using COI mtDNA markers to investigate patterns of dispersal


84

among mainland and island populations and to determine processes which may be affecting

dispersal at a local level.


85

4.2 MATERIALS AND METHODS

4.2.1 SAMPLE LOCALITIES AND SAMPLE NUMBERS

A total of 244 C.tinnula individuals6 from 17 populations were analysed for variation at

cytochrome oxidase subunit one (COI) mtDNA region (Table 1). C.parinsignifera was used

as the outgroup in all analyses.

Table 1. C.tinnula populations and sample sizes for COI mtDNA analyses.

C.parinsignifera outgroup sample is listed at the bottom of the table.

Species

Population

Location

Number of Samples Analysed for COI mtDNA variation

Wathumba Creek Fraser Is. 29 Ungowa Fraser Is. 27 Barga Lagoon Fraser Is. 27 Rainbow Beach Cooloola Coast 16 Cooloola Cooloola Coast 20 Noosa (Nth Shore) Sunshine Coast 02 Peregian Sunshine Coast 05 Beerwah Sunshine Coast 03 White Patch Bribie Is. 27 Bellara Bribie Is. 26 Caboolture Sunshine Coast 03 Honeyeater Lake Moreton Is. 11 Karawatha Brisbane 15 Amity Point Stradbroke Is. 30 Tyagarah Northern NSW 01 Newrybar Northern NSW 01

C.tinnula

Mungo Myall Lakes, NSW 01 CparB (Barakula) ~350km NW Brisbane 01 C.parinsignifera CparK (Karawatha) Brisbane 01

6 244 individuals were used (compared to 262 for 12S analyses) because some individuals would not

amplify at the COI fragment.


86

4.2.2 DNA EXTRACTION AND AMPLIFICATION OF COI MITOCHONDRIAL DNA FRAGMENT

For all samples included in mitochondrial analyses, DNA extraction followed the Chelex


The extent of COI mtDNA diversity present among C.tinnula populations caused a

significant problem for finding a primer set that would anneal and amplify successfully in

individuals from all sample localities. Several primers were trialed including the general

vertebrate primers COIf-L and COIa-H (Palumbi et al. 1991) and the general amphibian

primers Cox and Coy (Schneider et al. 1998).

Using the primer combination of Cox-COIaH, with a specific PCR protocol developed for

C.tinnula, a 639bp fragment was generated. Sequences of the primers were as follows; COX

(light strand primer) 5’-TGA TTC TTT GGG CAT CCT GAA G -3’; COIa-H (heavy strand

primer) 5’ – AGT ATA AGC GTC TGG GTA GTC – 3’. The light strand primer

corresponded to position 8104 in the Xenopus laevis COI region (Roe et al. 1985).

Samples were amplified in 25μl reactions containing; 3μl Biotech 10x Buffer solution, 2μl

10mM dNTPs, 2μl 2mM MgCl2, 0.5μl 3.2nm COX, 0.5μl 3.2nm M31, 0.08μl Taq (Tth plus

polymerase Taq – Biotech), 2μl genomic DNA and made up to 25μl with ddH2O.

To amplify all samples, two different PCR protocols were used. Samples from the southern

populations (Peregian, Beerwah, Caboolture, Bribie Island, Karawatha, Moreton Island and

North Stradbroke Island), the NSW samples and the C.parinsignifera were amplified using

the step-down PCR protocol outlined below (‘PCR 1’). Samples from northern populations

(Wathumba, Ungowa, Barga Lagoon, Cooloola and Noosa) were amplified using a simple

PCR protocol with a single annealing temperature, listed below as ‘PCR 2’.

PCR 1: Step1. 94oC 3minutes; Step2. 94oC 1minute; Step3. 42oC 1minute; Step4. 68oC

1minute; Step5. Go to Step 2 for two cycles; Step6. 94oC 1minute; Step7. 40oC 1minute;

Step8. 68oC 1minute; Step9. Go to Step 6 for 25 cycles; Step10. 68oC 8 minutes; END.

PCR 2: Step1. 94oC 3minutes; Step2. 94oC 1minute; Step3. 42oC 1minute; Step4. 68oC

1minute; Step5. Go to Step 2 for 25 cycles; Step6. 70oC 8minutes; END.


87

4.2.3 TEMPERATURE GRADIENT GEL ELECTROPHORESIS (TGGE), HETERODUPLEX

ANALYSIS (HA) AND SEQUENCING

TGGE combined with HA was used to screen for COI haplotype diversity among the

sampled populations. TGGE and HA procedures were carried out according to the methods

outlined in Chapter 2. An individual from the Beerwah population (Bw605) was run on a

perpendicular TGGE to identify the melting profile for the COI fragment and to determine

the optimum temperature gradient for further parallel gels. The running conditions for all

parallel gels were consistent across gels; the temperature gradient was set at 10oC to 45oC

and the optimum electrophoretic running time was 3 hours 40 minutes (migration rate of

1.7cm/hr).

For the COI fragment, references were chosen which showed clear mutational differences

among samples. Table 2 shows the extreme variability in base pair differences among the

COI sequences from within regions (or populations) compared to that between regions.

Crinia parinsignifera, the closest known outgroup, is also given as a reference at the bottom

of the table.

Two different reference individuals were used in parallel TGGE gels due to the large number

of substitutions that were present along the sequence. A North Stradbroke individual was

used as the reference for all samples from the following populations; Peregian, Beerwah,

Caboolture, White Patch, Bellara and Moreton Island. A Moreton Island individual was

used as the reference for samples from Wathumba Creek, Ungowa, Barga Lagoon, Rainbow

Beach, Cooloola, Noosa and North Stradbroke. Heteroduplexing was carried out according

to methods outlined in Chapter 2.

Gels were stained using the silver staining procedure outlined in Chapter 2. Haplotypes were

scored based on visual interpretation of the gels and each individual was assigned a

distinguishing haplotype number. Where possible, two representative individuals for each

unique haplotype were sequenced, ensuring that scoring was accurate and reliable. Clean

sequenced products yielded a 543bp fragment which was used for sequence analysis.


88

Table 2. Average pairwise differences within and between C.tinnula population groups and

the outgroup species C.parinsignifera. Average pairwise differences among haplotypes

within regions (and populations) are shown in bold on the diagonal. Average pairwise

differences between regions (and populations) are given below the diagonal. C.par =

C.parinsignifera, n/c = not calculated.

North

South

Moreton Island

Stradbroke Island

Mungo

C.par

North 3.33 South 58.28 2.00 Moreton Is 56.13 4.71 1.00 Stradbroke Is 57.23 39.05 37.33 5.33 Mungo 63.00 68.14 65.50 71.40 n/c C.par 86.83 82.14 80.00 81.33 82.00 n/c

Visual scoring of the TGGE thermal phenotypes indicated the presence of 22 unique

haplotypes7 among the C.tinnula samples. After sequencing this number increased to 25.

Sequence analysis revealed one to two base pair differences between haplotypes which had

been tentatively scored as identical. This may cast some uncertainty on those samples scored

as ‘001’ and ‘013’ haplotype, simply because of the relatively few samples sequenced and

the large number of individuals in these groups. For this reason, individuals that were

sequenced for each of these haplotypes (ten individuals were sequenced for each haplotype)

were chosen from different populations to potentially maximise the chance of finding unique

haplotypes which appeared to show identical phenotypes on the TGGE gel. None of the

samples sequenced for either the 001 or 013 haplotypes were found to show any mutational

differences.

7 Twenty-three haplotypes were identified during initial visual scoring of the gels. A single

Karawatha individual (ctk19) showed a unique TGGE thermal profile however, despite numerous

attempts, I was unable to obtain clean COI sequence data for this individual. This individual was

excluded from all COI analyses.


89

4.2.4 DATA ANALYSIS

AMOVA was used to describe the partitioning of genetic variation at three different levels of

scale; among regions, among populations within regions, and within populations, to

determine levels of population differentiation. Nested Clade Analysis was used to infer

historical and/or contemporary processes which may have influenced evolutionary

relationships among haplotypes. Population expansion hypotheses were tested using

mismatch analysis. Phylogenetic trees (Neighbour-Joining and Maximum Parsimony) were

constructed to infer patterns of divergence and evolutionary relationships among C.tinnula

populations.

4.3 RESULTS

4.3.1 MITOCHONDRIAL DNA SEQUENCES

Several observations suggest that the DNA sequences analysed here are from the

mitochondrial genome and do not represent nuclear mitochondrial psuedogenes; the COI

sequences obtained from C.tinnula samples showed a strand bias against guanine on the light

strand (A = 24.5%, G = 19.6%, C = 27.0%, T = 28.9%) which is characteristic of the

mitochondrial genome but not the nuclear genome (Macey et al. 1998, 1999a, 1999b, 2001);

no insertions or deletions were present in either the nucleotide or protein sequence and no

premature stop codons were evident in the protein sequence.

The absence of stop codons and indels, however, has been observed in nuclear copies of

mitochondrial sequences (e.g. Mirol et al. 2000; Collura et al. 1996). To ensure that the COI

sequence in the current study did not represent a pseudogene, C.tinnula COI sequences were

aligned with myobatrachid COI sequences and the nucleotide strand bias among sequences

was compared as well as the amino acid sequences (sequences were obtained from Genbank;

it is assumed that these sequences are mitochondrial in origin). Table 3 shows that the

C.tinnula COI strand bias is very similar to that of other myobatrachid COI sequences and

the alignment of the amino acid sequence (not presented) showed only three parsimonious

amino acid substitutions among the Crinia and Neobatrachus sequences.


90

Table 3. Average nucleotide frequencies for myobatrachid COI mtDNA sequences.

Neobatrachus aquilonius (Accession Number: NAU66853); N.albipes (Acc. No:

NAU66855); N.centralis (Acc. No: NCU66852); N.fulvus (Acc. No: NFU66859); N.pictus

(Acc. No: NPU66857); N.pelobatoides (Acc. No: NPU66858); Notaden melanoscaphus

(Acc. No: NMU66861).

T (%) C (%) A (%) G (%) Neobatrachus aquilonius

27.6 28.6 22.9 20.8

N.albipes 26.7 29.2 23.9 20.2 N.centralis 28.4 27.8 25.1 18.6 N.fulvus 26.6 28.7 23.6 21.1 N.pictus 27.8 28.2 23.9 20 N.pelobatoides 26.5 29.5 24 20 Notaden melanoscaphus

30.6 23.7 28.8 16.9

C.tinnula013c 28 26.5 25.5 20 C.tinnula001c 28.6 26.3 24.5 20.6 C.parinsignifera 32.2 23.5 26.1 18.2

Average. 28.1 27.4 24.8 19.6

These results, combined with the observed lack of indels or stop codons and the fact that

there were no sequence ambiguities encountered on alignment of forward and reverse strands

and individuals which were amplified multiple times showed no variability in TGGE

analyses or sequencing, strongly suggests that the COI gene analysed in this studywas

mitochondrial in origin and did not orignate from a pseudogene.

4.3.2 SEQUENCE VARIATION

Analysis of COI sequence data for 244 C.tinnula individuals revealed a total of 25 unique

haplotypes. One hundred and twelve nucleotide sites were variable across the 25 haplotypes

and 83 of these sites were parsimony informative (total sequence length 543bp). The

transition/transversion ratio was 3.2:1.

Figure 1 shows the alignment of the sequenced haplotypes. Of the 25 haplotypes identified,

ten unique haplotypes were present only in northern populations. Among these ten

haplotypes 12 sites were variable, four of which were parsimony informative (one

transversion). Fourteen unique haplotypes were present only in southern populations

(including Tyagarah and Newrybar) and 51 sites were variable, 44 of which were parsimony


91

informative with a transition/transversion ratio of 6.2:1. The Mungo Brush sample showed a

unique haplotype. When the C.parinsignifera sequence was included in the alignment, 139

sites were variable, 93 of which were parsimony informative and the transition/transversion

ratio was 3.2:1. No evidence was found for saturation in transitions or transversions in the

COI data set (graphs not shown).

Conversion of the nucleotide sequence to an amino acid sequence showed that the amino

acid sequence was conserved completely across all C.tinnula samples and the

C.parinisignifera sample (Figure 2). All codon base pair changes were found to occur at 1st

(2.9%) or 3rd (97.1%) position sites. The C.tinnula and Xenopus laevis amino acid sequences

were aligned to determine a comparative level of variability of amino acid sequences among

highly divergent frog species. The alignment produced 13 amino acid changes across a total

of 164 comparative amino acid codons (Figure 2).


92

Figure 1. Alignment of variable sites from the 543bp of mitochondrial COI sequenced for C.tinnula, compared with outgroup species C.parinsignifera (Cpar).

Position of the base substitution included above each nucleotide, identical sites = ‘.’; missing data = ‘?’; CparB = C.parinsignifera Barakula; CparK =

C.parinsignifera Karawatha. An “N” or “S” after the haplotype name signifies a ‘northern’ or ‘southern’ haplotype, respectively.

1111111 1111111111 1111122222 2222222222 2222222222 2223333333 3333333333 3333333333 3444444444 11122344 5566667778 8990001222 3334456677 8889900122 2333445555 6667778889 9990011122 2333344445 5666788899 9011122233 3925814928 1403692584 7092587369 0581432514 0692514625 8147092568 1470692581 4780925814 7036902581 4069214703 6514703625 001cN CCAGCTCGCC CAAGAAAACG CGTCAAGAAC CCCCTTGCAC CCCAAATTGC GATCGAAGCG GCTCCTGTTT GGCACTGCCC CTTCATATCC CCCCCTCTGG TAGGCTCCCC 002cN .......... ...A...... .......... ......A... .......... .......... .......... .......... .......... ......A... .......... 003cN .......... ...A...... .......... ......A... .........T .......... .......... .......... .......... ......A... .......... 004cN .......... ...A...... .......... ......A... .......... .......... .......... .......... .......... ......A... .......... 005cN .......... .......... .......... .......... .......... .......... .......... .......... .C........ .......... .......... 006cN .......... ...A...... .......... .......... .......... .......... .......... .......... .......... .......... .......... 007cN .......... .......... .......... .......... .......C.. .......... .......... .......... .......... .......... ...C...... 008cN .......... .......... .......... ......A... .......... .G........ .......... .......... .......... .......... .......... 009cN .......... ...A...... .......... .......... .......... .......... .......... .......... .......... .......... .......... 010cN .......... ...A...... .......... .......... .......... .......... .......... .......... .......... .......C.. .......... 011cS .....C..T. ......G.TC .......T.. ......A.GT ..GGG..... A.GTA.C..A AA..T.CCCA CAT..GA... G.CTC..C.A ...T.CT.A. ..A...A..T 012cS .....C..T. ......G.TC .......T.. ......A.GT ..GGG..... A.GTA.C..A AA..T.CCCA C.T..GA... G.CTC..C.A ...T.CT... ..A...A..T 013cS .....C..T. ......G.TC .......T.. ......A.GT ..GGG..... A.GTA.C..A AA..T.CCCA CAT..GA... G.CTC..C.A ...T.CT... ..A...A..T 014cS .....C..T. ......G.TC .......T.. ......A.GT ..GGG..... A.GTA.C..A AA..T.CCCA CAT..GA... G.CTC.GC.A ...T.CT... ..A...A..T 015cS .....C..T. ......G.TC .......T.. ......A.GT ..GGG..... A.GTA.C... AA..T.CCCA CAT..GA... G.CTC..C.A ...T.CT... ..A...A..T 016cS .....C..T. ......G.TC .......T.. ......A.GT ..TGG..... A.GTA.C..A AA..T.CCCA CAT..GA... G.CTC..C.A ...T.CT... ..A...A..T 017cS .....C..T. ......G.TC .......T.. ......A.GT ..GGG..... A.ATA.C..A AA..T.CCCA CAT..GA... G.CTC..C.A ...T.CT..A ..A...A..T 018cS .....C..T. ........TC .......T.. ......A..T ..GGG..... A.GTA.C..A AA..T.CCCA CAT..GA... G.CTC..C.A ...T.CT..A ..A...A... 019cS .....C..T. ........TC .......T.. ......A..T ..GGG..... A.GTA.C..A AA..T.CCCA CAT..GA... G.CTC..C.A .....CT..A ..A...A... 020cS ....TC..T. .GGA..GG.T .A..G..T.. ......A..T .A....C... A.ATA.T... A.....CCCA CAT..GA..T G.C.T..C.A ....T.T.AA .C..T.A... 021cS .....C..T. .GGA..GG.T .A..G..T.. ......A..T .A...GC... A.ATA.T... A.....CCCA CAT..GA..T G.C.T..C.A ....T.T.AA .C..T.A... 022cS .....CT.T. .GGA..GG.T TA..G..T.. ......A..T .A...GC... A.ATA.T... A.....CCCA CAT..GA..T G.C.T..C.A ....T.T.AA .C..T.A... 023cS ....TC..T. .GGA..GG.T .A..G..T.. ......A..T .A...GC..T A.ATA.T... A.....CCCA CA...GA..T G.C.T..C.A ....T.T.AA .C..T.A... TyagS ....TC..T. .GGA..GG.T .A..G..T.. ......A..T .A...GC... A.ATA.T... A.....CCCA CA...GA..T G.C.T..C.A ....T.T.AA .C..T.A... Mungo .......A.. .G..G..GT. .ACT..AT.T .TAT..A..T TTAG....A. AG.TAGT... AT.T.....G .A..T.A.TT G..TCC.CTA TTTT...C.A .....CA.TA CparB TTTATC..TT TGGCGG.GTA TA..GCACG. TTTTCCAA.. .TAT....AT CG.TA.CATA A.C.TCA..A CA.G..AT.. A.CTCC..TA .T...CT.AA CTAA..ATTT CparK ???ATC..TT TGGCGG.GTA TA..GCACG. TTTTCCAA.. .TAT....AT CG.TA.CATA A.C.TCA..A CA.G..AT.. A.CTCC..TG .T...CT.AA CTAA..ATTT


93

Figure 2. Alignment of amino acid sequence for 543bp mitochondrial COI sequenced for C.tinnula, compared with outgroup species C.parinsignifera (Cpar).

Xenopus laevis (X.lae) is included at the bottom of the alignment. Sequence is completely conserved. Missing data = '?'. CparB = C.parinsignifera Barakula;

CparK = C.parinsignifera Karawatha. An “N” or “S” after the haplotype name signifies a ‘northern’ or ‘southern’ haplotype, respectively.

001cN ISHVVSYYSS KKEPFGYMGM VWAMMSIGFL GFIVWAHHMF TTDLNVDTRA YFTSATMIIA IPTGVKVFSW LATMHGGVIK WDAAMLWALG FIFLFTVGGL TGIVLANSSL 002cN .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... 003cN .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... 004cN .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... 005cN .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... 006cN .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... 007cN .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... 008cN .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... 009cN .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... 010cN .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... 011cS .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... 012cS .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... 013cS .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... 014cS .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... 015cS .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... 016cS .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... 017cS .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... 018cS .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... 019cS .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... 020cS .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... TyagS .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... 021cS .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... 022cS .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... 023cS .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... Mungo .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... CparB .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... CparK ?????..... .......... .......... .......... .......... .......... .......... .......... .......... .......... .......... X.lae ...I.T...G .......... ........L. .......... .V........ .......... .......... .......T.. ...P...... .......... ..........


94

Figure 2. Continued.

001cN DIVLHDTYYV VAHFHYVLSM GAVFAIMAGF VHWFPLFTGY TLHKTWTKAH FGVMFTGVNL TFFPQHFLGL A 002cN .......... .......... .......... .......... .......... .......... .......... . 003cN .......... .......... .......... .......... .......... .......... .......... . 004cN .......... .......... .......... .......... .......... .......... .......... . 005cN .......... .......... .......... .......... .......... .......... .......... . 006cN .......... .......... .......... .......... .......... .......... .......... . 007cN .......... .......... .......... .......... .......... .......... .......... . 008cN .......... .......... .......... .......... .......... .......... .......... . 009cN .......... .......... .......... .......... .......... .......... .......... . 010cN .......... .......... .......... .......... .......... .......... .......... . 011cS .......... .......... .......... .......... .......... .......... .......... . 012cS .......... .......... .......... .......... .......... .......... .......... . 013cS .......... .......... .......... .......... .......... .......... .......... . 014cS .......... .......... .......... .......... .......... .......... .......... . 015cS .......... .......... .......... .......... .......... .......... .......... . 016cS .......... .......... .......... .......... .......... .......... .......... . 017cS .......... .......... .......... .......... .......... .......... .......... . 018cS .......... .......... .......... .......... .......... .......... .......... . 019cS .......... .......... .......... .......... .......... .......... .......... . 020cS .......... .......... .......... .......... .......... .......... .......... . TyagS .......... .......... .......... .......... .......... .......... .......... . 021cS .......... .......... .......... .......... .......... .......... .......... . 022cS .......... .......... .......... .......... .......... .......... .......... . 023cS .......... .......... .......... .......... .......... .......... .......... . Mungo .......... .......... .......... .......... .......... .......... .......... . CparB .......... .......... .......... .......... .......... .......... .......... . CparK .......... .......... .......... .......... .......... .......... .......... . X.lae ..M....... .......... .......G.. I......... ...E..A.I. ...??????? ?????????? ?


95

4.3.3 NEUTRALITY TESTS

No significant deviation from neutrality was evident (D = -2.16220, p>0.10 NS; F = -

0.16835, p>0.10 NS).

4.3.4 TEST FOR CLOCK-LIKE EVOLUTION

A log-likelihood ratio test could not reject the hypothesis that lineages were evolving

according to a clock-like model of evolution (-ln L = 1830.54 with molecular clock enforced

vs.-ln L 1816.36 without molecular clock enforced, χ2 = 28.35, d.f. = 24, P > 0.10).

4.3.5 BROAD-SCALE POPULATION STRUCTURE

On average, northern haplotypes were approximately ten percent divergent from all southern

haplotypes. The Mungo individual was approximately 12.7 percent divergent from all

northern sequences and 12.1 percent divergent from all southern populations. The outgroup,

C.parinsignifera, was approximately 17 percent divergent from C.tinnula haplotypes (Table

4).

Table 4. Average genetic distances between C.tinnula population groups (northern

vs southern region) and outgroup species C.parinsignifera (Cpar). Jukes-Cantor net

average genetic distances shown below diagonal. Standard error, based on 10,000

bootstrap replications, shown above the diagonal. Average genetic distances within

regions are shown as a comparison.

Net average genetic distances among groups (DA)

Average genetic distance among haplotypes within a region

North South Mungo Cpar D SE North - 0.013 0.016 0.021 0.006 0.002 South 0.099 - 0.015 0.018 0.045 0.006 Mungo 0.127 0.121 - 0.020 n/c n/c Cpar 0.183 0.150 0.168 - 0.004 0.003


96

An AMOVA that examined variation among the northern and southern regions indicated that

the majority of COI variation was evident among regions (84%), 15 percent was present

among populations within regions and one percent of the variation was maintained within

populations (Table 5).

The distribution of the 25 unique haplotypes found among the southeast Queensland and

New South Wales C.tinnula populations are presented in Table 6. Populations within the

northern region (Wathumba Creek to Noosa) shared no haplotypes in common with any

populations from the southern region (Peregian to Tyagarah).

Table 5. AMOVA showing partitioning of variation within and among regions of

southeast Queensland populations of C.tinnula.

Source of Variation d.f Percentage of

Variation Ф Level of

Significance Among Regions (ФCT)

1 84.0 0.841 0.001


9

15.0

0.940

0.001

Within Populations(ФST)

249

1.0

0.990

0.001


97

Table 6. Distribution of COI mtDNA haplotypes for C.tinnula populations.

Population Haplotypes Total

001

002

003

004

005

006

007

008

009

010

011

012

013

014

015

016

017

018

019

020

021

022

023

024

025

Wathumba Ck 29 29 Ungowa 25 1 1 27 Barga Lagoon 25 1 1 27 Rainbow Beach 13 1 1 1 16 Cooloola 15 1 3 1 20 Noosa 02 2 Peregian 5 5 Beerwah 1 1 1 3 Caboolture 3 3 White Patch 27 27 Bellara 22 1 1 1 1 26 Karawatha 15 15 Moreton Is. 9 1 1 11 Stradbroke Is. 28 1 1 30 Tyagarah 1 1 Newrybar 1 1 Mungo 1 1 Total 109 2 1 1 1 1 1 1 3 1 1 1 74 1 1 1 1 9 1 1 28 1 1 1 1 244


98

4.3.6 LOCAL-SCALE DIVERSITY AND POPULATION STRUCTURE

Northern Region

Levels of diversity within populations were generally low (Table 7). The Rainbow Beach

and Cooloola populations showed the highest levels of haplotype and nucleotide diversity,

however, nucleotide diversity was particularly low in all populations. The Wathumba and

Noosa populations were fixed for a single haplotype.

Table 7. COI mtDNA haplotype diversity (Hd) and nucleotide diversity (πd) within southeast

Queensland populations of C.tinnula. n=number of individuals.

Population n Hd πd Wathumba Ck 29 0.00 0.0000 Ungowa 27 0.14 ± 0.09 0.0009 Barga Lagoon 27 0.14 ± 0.09 0.0009 Rainbow Beach 16 0.35 ± 0.15 0.0012 Cooloola 20 0.43 ± 0.13 0.0017 Noosa 2 0.00 0.0000 North Total 121 0.19 ± 0.05 0.0009 Peregian 5 0.00 0.0000 Beerwah 3 1.00 ± 0.23 0.0025 Caboolture 3 0.00 0.0000 White Patch 27 0.00 0.0000 Bellara 26 0.29 ± 0.12 0.0007 Karawatha 15 0.00 0.0000 Moreton Island 11 0.34 ± 0.17 0.0133 Stradbroke Island 30 0.13 ± 0.08 0.0009 South Total* 122 0.59 ± 0.04 0.0313

*includes Tyagarah and Newrybar sequences.

Based on COI haplotype frequencies and distribution there was little evidence of population

structure within the northern region. A single haplotype (haplotype 001c) was common

across the region and this haplotype was the dominant haplotype in all northern populations.

Apart from this single shared haplotype there was very little sharing of additional haplotypes

among populations. Ungowa and Barga Lagoon were the only populations to share a rare


99

haplotype (haplotype 002c). Most of the rare haplotypes (6 of the 8 found across the

northern region) were found in only a single individual.

Genetic pairwise distance estimates suggested that the highest level of observed sequence

divergence among northern haplotypes was 1.1 percent which equated to a six base pair

difference across a total of 543 base pairs (Table 8). The average sequence divergence

among haplotypes was 0.6 percent (3 base pair difference).


100

Table 8. Pairwise genetic distances for C.tinnula COI mtDNA haplotypes. Outgroup species, C.parinsignifera is included at the bottom of the table. Jukes-Cantor

pairwise distances are shown below the diagonal, absolute base-pair differences are shown above the diagonal. ‘N’ = ‘northern’ haplotype, ‘S’ = ‘southern’

haplotype.

001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 001 N 3 4 4 2 1 2 2 2 2 59 61 61 60 61 59 60 58 57 002 N 0.006 1 1 5 2 5 3 3 3 59 61 61 60 61 59 60 58 57 003 N 0.007 0.002 2 6 3 6 4 4 4 60 62 62 61 62 60 61 59 58 004 N 0.007 0.002 0.004 6 3 6 4 4 4 59 61 61 60 61 59 60 58 57 005 N 0.004 0.009 0.011 0.011 3 4 4 4 4 59 61 61 60 61 59 60 58 57 006 N 0.002 0.004 0.006 0.006 0.006 3 3 1 1 60 62 62 61 62 60 61 59 58 007 N 0.004 0.009 0.011 0.011 0.007 0.006 4 4 4 61 63 63 62 63 61 62 60 59 008 N 0.004 0.006 0.007 0.007 0.007 0.006 0.007 4 4 59 61 61 60 61 59 60 58 57 009 N 0.004 0.006 0.007 0.007 0.007 0.002 0.007 0.007 2 61 63 63 62 63 61 62 60 59 010 N 0.004 0.006 0.007 0.007 0.007 0.002 0.007 0.007 0.004 61 63 63 62 63 61 62 60 59 011 S 0.117 0.117 0.12 0.117 0.117 0.12 0.122 0.117 0.122 0.122 2 3 1 2 2 2 6 7 012 S 0.122 0.122 0.124 0.122 0.122 0.124 0.126 0.122 0.126 0.126 0.004 3 1 2 2 2 6 7 013 S 0.122 0.122 0.124 0.122 0.122 0.124 0.126 0.122 0.126 0.126 0.006 0.006 2 3 3 3 5 6 014 S 0.12 0.12 0.122 0.12 0.12 0.122 0.124 0.12 0.124 0.124 0.002 0.002 0.004 1 1 1 5 6 015 S 0.122 0.122 0.124 0.122 0.122 0.124 0.126 0.122 0.126 0.126 0.004 0.004 0.006 0.002 2 2 6 7 016 S 0.117 0.117 0.12 0.117 0.117 0.12 0.122 0.117 0.122 0.122 0.004 0.004 0.006 0.002 0.004 2 6 7 017 S 0.12 0.12 0.122 0.12 0.12 0.122 0.124 0.12 0.124 0.124 0.004 0.004 0.006 0.002 0.004 0.004 6 7 018 S 0.115 0.115 0.117 0.115 0.115 0.117 0.12 0.115 0.12 0.12 0.011 0.011 0.009 0.009 0.011 0.011 0.011 1 019 S 0.113 0.113 0.115 0.113 0.113 0.115 0.117 0.113 0.117 0.117 0.013 0.013 0.011 0.011 0.013 0.013 0.013 0.002 020 S 0.122 0.117 0.12 0.115 0.126 0.12 0.126 0.122 0.122 0.122 0.082 0.078 0.075 0.08 0.082 0.078 0.08 0.075 0.073 021 S 0.12 0.115 0.117 0.117 0.124 0.117 0.124 0.12 0.12 0.12 0.086 0.082 0.08 0.084 0.086 0.082 0.084 0.08 0.078 022 S 0.12 0.115 0.117 0.113 0.124 0.117 0.124 0.12 0.12 0.12 0.08 0.075 0.073 0.078 0.08 0.075 0.078 0.073 0.071 023 S 0.126 0.122 0.124 0.12 0.13 0.124 0.13 0.126 0.126 0.126 0.086 0.082 0.08 0.084 0.086 0.082 0.084 0.08 0.078 Tyagarah S 0.126 0.122 0.12 0.12 0.13 0.124 0.13 0.126 0.122 0.126 0.092 0.088 0.086 0.09 0.092 0.088 0.09 0.086 0.084 Mungo 0.124 0.126 0.128 0.128 0.128 0.126 0.128 0.12 0.128 0.124 0.139 0.139 0.135 0.137 0.139 0.135 0.137 0.13 0.133 CparB 0.19 0.187 0.185 0.19 0.194 0.19 0.192 0.185 0.192 0.192 0.178 0.173 0.173 0.176 0.178 0.178 0.176 0.171 0.169 CparK 0.188 0.186 0.183 0.188 0.193 0.188 0.191 0.183 0.191 0.191 0.178 0.174 0.174 0.176 0.178 0.178 0.176 0.171 0.169


101

Table 8. Continued.

020 021 022 023 Tyagarah Mungo CparB CparK 001 N 61 60 60 63 63 62 91 88 002 N 59 58 58 61 61 63 90 87 003 N 60 59 59 62 60 64 89 86 004 N 58 59 57 60 60 64 91 88 005 N 63 62 62 65 65 64 93 90 006 N 60 59 59 62 62 63 91 88 007 N 63 62 62 65 65 64 92 89 008 N 61 60 60 63 63 60 89 86 009 N 61 60 60 63 61 64 92 89 010 N 61 60 60 63 63 62 92 89 011 S 42 44 41 44 47 69 86 84 012 S 40 42 39 42 45 69 84 82 013 S 39 41 38 41 44 67 84 82 014 S 41 43 40 43 46 68 85 83 015 S 42 44 41 44 47 69 86 84 016 S 40 42 39 42 45 67 86 84 017 S 41 43 40 43 46 68 85 83 018 S 39 41 38 41 44 65 83 81 019 S 38 40 37 40 43 66 82 80 020 S 3 3 4 5 71 85 83 021 S 0.006 4 5 4 70 84 82 022 S 0.006 0.007 3 6 70 86 84 023 S 0.007 0.009 0.006 7 73 87 85 Tyagarah S 0.009 0.007 0.011 0.013 73 85 83 Mungo 0.144 0.141 0.141 0.148 0.148 83 81 CparB 0.176 0.173 0.178 0.180 0.176 0.171 2 CparK 0.176 0.174 0.178 0.181 0.176 0.171 0.004


102

The haplotype network, generated using TCS, revealed a reasonably well resolved network

among haplotypes (Figure 3). All mainland haplotypes clustered around the regionally

common 001c haplotype. TCS analysis suggested haplotype 006c (Rainbow Beach

haplotype) was the most likely ancestral haplotype and all island haplotypes appeared to be

descended from haplotype 006c.

NCA analysis was conducted on the northern network to determine if there were any

significant associations between genetic structure at COI and geography. Only a single clade

at the highest nesting level was significant (Clade 2-1), however, the clade was keyed out to

“Inconclusive Outcome” (Permutation chi-squared probabilities are given in Appendix 4).

Figure 3. Nested Cladogram for northern C.tinnula COI mtDNA haplotypes.

Haplotype 006c was considered to be the ancestral haplotype for the network.

Small open circles without haplotype numbers are missing steps in the

network.

Northern Network


103

Southern Region

Haplotype and nucleotide diversity were low in most southern populations; Moreton Island

was the only population to show a relatively high level of nucleotide diversity (Table 6;

Newrybar and Tyagarah were excluded from diversity analyses). Four populations in the

region were fixed for a single common haplotype (Peregian, Caboolture, White Patch and

Karawatha). All other populations were characterised by the presence of a single common

haplotype with a few additional rare haplotypes.

Haplotype 013c was common among sites across the southern mainland region (Peregian to

Karawatha) and the Bribie Island populations. Stradbroke Island, Moreton Island and the

northern NSW samples shared no haplotypes in common with the southern mainland or

Bribie Island populations. The Newrybar and Moreton Island populations were the only

populations across the southern region which shared a rare haplotype and this was found

only in a single individual in the Moreton Island population.

Haplotypes were classified into three groups based on genetic similarity (Table 8).

Haplotypes found in Sunshine Coast populations (Peregian to Bribie Island) and the

Karawatha population, were very similar; separated by a maximum of 0.6 percent

(approximately 3 base pairs different). Two haplotypes which were unique to the Moreton

Island population formed a second group. These two Moreton haplotypes (018c and 019c)

were separated by a single base pair difference (0.2% divergence). The Moreton haplotypes

differed from the Sunshine Coast and Karawatha populations by an average of 1.2 percent

and, notably, from Stradbroke Island, northern NSW and the third Moreton Island haplotype

(020c) by an average of 7.4 percent. The third group of genetically similar haplotypes

consisted of the three North Stradbroke Island haplotypes the Tyagarah and Newrybar

haplotypes and a single Moreton Island haplotype. Within this group, haplotypes differed by

an average of 0.8 percent (4.5bp).

Considering the observed division of the southern group into Sunshine Coast-Moreton

Island, and North Stradbroke Island-northern NSW groups, an AMOVA was carried out to

partition genetic variation across the southern distribution. The results of the AMOVA

indicated that the vast majority of COI variation (98.6%) was present among groups; less

than one percent of variation was present among populations within groups and also less

than one percent of variation was maintained within populations (Table 9).


104

Table 9. AMOVA showing the partitioning of variation within and among southern

population groups of C.tinnula. The two population groups analysed were i). Peregian,

Beerwah, Caboolture, White Patch, Bellara, Karawatha, Moreton Is. and ii). Stradbroke

Island, Newrybar, Tyagarah.

Source of Variation d.f Percentage of

Variation Ф Level of

Significance Among Regions (ФCT)

1 98.6 0.986 0.001


9 0.8 0.577 0.001

Within Populations (ФST)

249 0.6 0.994 0.001

To examine the evolutionary relationship among haplotypes, a parsimony network was

created using TCS (Figure 4). Two separate networks were generated; the first southern

network included haplotypes from the Peregian, Beerwah, Caboolture and Karawatha

populations, as well as all Bribie Island haplotypes and two Moreton Island haplotypes. The

second southern network contained a single Moreton Island haplotype, the North Stradbroke

Island haplotypes and the Newrybar and Tyagarah haplotypes. Individual networks are not

joined because divergence between the networks exceeded 95% confidence limits for

parsimonious connections derived from the estimation procedure (Templeton et al. 1992).

Within networks, the maximum number of mutational steps was seven and between

networks the minimum number of mutational steps was 37.

NCA analysis was carried out to examine historical and contemporary processes that may

have influenced population structure in the southern region. Very few clades in the southern

network showed significant geographic associations (refer Figure 4 for nested cladogram and

Table 10 for permutational chi-squared probabilities). Clade 1-1, which contained the

majority of the mainland and Bribie Island haplotypes, had a significant chi-squared value

but no significant Dc or Dn values. Clade 2-3, which contained Stradbroke Island, northern

New South Wales and a single Moreton Island haplotype was suggested to show a pattern of

restricted gene flow with isolation by distance. Clade 3-1 keyed out to allopatric


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fragmentation for the Moreton Island haplotypes.

Figure 4. Nested Cladogram for southern C.tinnula COI mtDNA haplotypes.

Haplotypes 013c and 021c were considered to be ancestral haplotypes. Small

open circles without haplotype numbers are missing steps in the network.


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Table 10. Permuational chi-squared probabilities for geographical structure of southern

clades identified in Figure 4. ‘*’ significant at P<0.05. Abbreviations used in the Inference

Key; Dc = clade distance; Dn = nested clade distance; IBD = Isolation by Distance.

Clade

X2 Statistic

P value

Inference Key Steps

Southern Network 1-1 57.83 0.031* No significant Dc or Dn values 2-1 2.06 0.664 2-3 31.00 0.001* 1, 2, 3, 4NO Restr. gene flow with

IBD 2-4 2.00 1.000 3-1 89.00 0.000* 1, 19NO Allopatric Fragmentation 3-2 7.24 0.184

Interestingly, a starlike relationship of mainland and Bribie Island haplotypes was observed

for the COI network as was shown for the 12S network. NCA did not detect a geographic

range expansion (although Clade 1-1 did show a significant geographic association),

however, mismatch analysis suggested there was evidence of a demographic population

expansion (SSD = 0.0001, p = 0.548; Figure 5).

Figure 5. COI mtDNA mismatch distribution for southern mainland (Peregian,

Beerwah, Caboolture and Karawatha) and Bribie Island populations.

0

500

1000

1500

2000

2500

3000

0 1 2 3 4

Number of pairwise differences

Freq

uenc

y

Observed

Expected


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4.3.7 PHYLOGENETIC ANALYSIS

The tree of the southeast Queensland and New South Wales C.tinnula sequences showed a

regional pattern of evolution, with populations from the northern and southern regions

forming two separate, well supported monophyletic clades (Figure 6). The Mungo sequence

formed a third clade, separate from either the northern or southern C.tinnula clades.

Branches were restricted to a 75% consensus which removed much of the un-resolved

structuring within the major clades. Within the southern clade; the North Stradbroke Island,

northern New South Wales sequences and a single Moreton Island sequence (020) formed a

well supported sub-clade separate to all other southern sequences. Within the northern clade

no definitive structuring of either mainland or island sequences was evident. The two

C.parinsignifera sequences were basal in the C.tinnula phylogeny.


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Figure 6. Neighbour-joining (NJ) tree showing inferred phylogenetic relationships among

C.tinnula COI mtDNA haplotypes. C.parinsignifera used as an outgroup. “N” = ‘northern’

clade; “S” = ‘southern’ clade. Bootstrap values greater than 75% are shown above branches,

Parsimony bootstrap values are shown in italics (e.g. NJ / Pars).

‘southern’mainland+ Bribie Is.

Moreton Is.

Stradbroke Is.+ northernNSW + Moreton Is.Haplotype (020)


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4.4 DISCUSSION

4.4.1 BROAD SCALE POPULATION STRUCTURE: CONCORDANCE OF MARKERS

Analysis of COI sequence data for southeast Queensland populations and northern New

South Wales samples of C.tinnula showed broad scale consensus with 12S sequence

analysis. The genetic partitioning of populations into northern and southern regions within

southeast Queensland is concordant with 12S mtDNA results; C.tinnula populations from

Wathumba Creek to Noosa form a northern population group and populations from Peregian

to Newrybar form a southern population group. The estimated net average sequence

divergence between the two lineages was 9.9 percent, compared to 3.6 percent for 12S.

Using a range of COI mutation rate estimates published for amphibians (1.3%, Macey et al.

1998b; 2.0%, McGuigan et al. 1998; James and Moritz 2000), the average corrected pairwise

difference observed among northern and southern clades for COI suggests that divergence

occurred between 5 and 7.6 million years ago during the late Miocene-early Pliocene. While

this is slightly earlier than the time estimated for 12S, the range of estimates from both

markers suggests that divergence is most likely to have occurred during the Pliocene.

4.4.2 POPULATION STRUCTURE WITHIN REGIONS

Northern Region

There appeared to be very little genetic structure among populations in the northern region.

A single dominant haplotype was found in all populations across the region. This may

indicate high levels of gene flow among populations or the retention of an ancestral

haplotype among northern populations. The frequency and distribution of rare haplotypes

would suggest, however, that the observed pattern is not simply the result of ongoing high

levels of gene flow within the region.

The common haplotype (001c) although not considered to be the ancestral haplotype by TCS

analysis, was shown to be an interior haplotype in the northern network. Under coalescent

theory, interior haplotypes (and those of high frequency in the population) are likely to be

older haplotypes. Haplotypes of recent evolutionary origin occur preferentially at the tips of

the network (Donnelly and Tavare 1986; Golding 1987). In the northern network, this would

suggest that haplotype 001c is an older, more ancestral haplotype than others in the network.


110

The distribution of this haplotype among populations across the region suggests that either i).

populations may have once been connected by high levels of gene flow or formed a

continuous panmictic unit. The presence of unique tip haplotypes endemic to certain

populations would infer that there has since been fragmentation and isolation, and gene flow

among populations is restricted. While this hypothesis is concordant with that proposed for

the 12S northern region dataset, if populations do represent fragments of an historical

panmictic unit, large effective population sizes should decrease the effects of genetic drift

such that high haplotype diversity would be expected, similar to that observed for 12S.

ii.) Alternatively, the fixation of a single haplotype across the region may be suggestive of an

historical range expansion (Ferris et al. 1995; Barber 1999). Although NCA did not detect

patterns indicative of a range expansion, if migration rates between populations were low,

then gene genealogies could resemble those of stationary populations because coalescent

events would tend to occur before migration events to other populations (Ray et al. 2003).

This may explain the presence of a few unique haplotypes across the northern region and the

widespread distribution of an ancestral haplotype. A population bottleneck caused before the

expansion event may account for the presence of single dominant haplotype across the

region.

It is difficult to suggest historical or contemporary patterns of gene flow based on

concordance of 12S and COI data sets. A number of possible scenarios could explain the

pattern of genetic structure observed for 12S and COI, respectively. One common feature of

both data sets is the geographic widespread distribution of interior (ancestral) haplotypes and

the restricted distribution of the tip (more recently derived) haplotypes. This would suggest

that either a large ancestral population has experienced fragmentation (which would seem

unlikely given the retention of a single common haplotype) or there has been a contiguous

range expansion across the northern region with low levels of dispersal among populations.

The lack of genetic differentiation among island and mainland populations suggests that

gene flow, although potentially restricted, was occurring until relatively recently. Fraser

Island has been connected to the mainland for much of the last million years and unlike the

relatively large geographic distance separating Moreton and Stradbroke Islands from the

mainland, the distance between Fraser Island and the Cooloola coast is very small (< 2km).

This may mean that dispersal between Fraser Is. and Cooloola has been possible for longer

periods and until more recently, than between the mainland and other sand islands in the

region.


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Southern Region

Nested Clade Analysis revealed similar geographic associations for COI haplotypes as was

observed for 12S haplotypes. Mismatch analysis suggested that populations on the mainland

showed evidence of a demographic population expansion, however, the unimodal pattern

observed for the pairwise haplotype differences may also be due to a single ancestral

haplotype being dominant among mainland populations. The common haplotype (haplotype

013c) was observed in 73 out of 79 mainland and Bribie Island individuals. The remaining

six haplotypes were tip haplotypes found only in a single individual, respectively, with a

maximum of 3 base-pairs among the seven haplotypes. Also, all rare haplotypes were found

in a single geographic location, there was no sharing of haplotypes, suggesting that these

haplotypes evolved in situ in relatively isolated populations.

The two other clades which showed geographic associations were consistent with the

findings for 12S. The genetic structure of Moreton Island and mainland haplotypes was

indicative of allopatric fragmentation and the Stradbroke Island haplotypes showed a pattern

of isolation by distance from the northern NSW and the Moreton Island haplotype.

Inferences for the Stradbroke Island/NSW clade should be interpreted with caution, however,

as no other mainland populations were sampled between these sites. The Moreton Island

haplotype which consistently clustered with this clade creates a geographic overlap among

the clades and may be altering the Dc and Dn values, due to its distant geographic location

from the other haplotypes.

In general, the COI data showed greater differentiation among southern populations than that

revealed in the 12S study. The Stradbroke Island, Newrybar and Tyagarah samples (and a

single Moreton Island haplotype) formed a sub-clade within the southern clade and the other

two Moreton Island haplotypes clustered with all other southern populations forming another

sub-clade. The two southern sub-clades were well supported in the neighbour joining tree

(bootstrap value of 92%) and pairwise distance estimates showed a net average

differentiation of 7.4 pecent among haplotypes found in the two groups.

Historical geological patterns for the Brisbane River drainage system may explain the

clustering of Moreton Island haplotypes with the southern mainland, Bribie Island and

Karawatha haplotypes. Moreton Bay which lies between Moreton and North Stradbroke

Islands and the mainland coastline, was often exposed during past periods of low sea levels

and geological studies suggest that the Brisbane river once flowed out across the exposed

continental shelf of Moreton Bay and then northward alongside Moreton Island (Figure 7;


112

Jones 1992). The area surrounding the river may have provided a sandy low nutrient soil

which could have supported extensive areas of wallum heathland. This could have provided

a route for C.tinnula dispersal between the mainland and Moreton Island. Alternatively, or

perhaps in conjunction, there may have been dispersal between the area which is now Bribie

Island and Moreton Island. The distance between the northern part of Moreton Island, which

supports the largest continuous extent of wallum habitat on the island, and the southern area

of Bribie Island is the shortest distance from the mainland to Moreton Island. This may have

provided a dispersal route between the two islands when sea levels were low and the bay

floor was exposed.

Sea levels are thought to have fluctuated in the range of -200m up to +43m during the

Pleistocene (Jongsma 1970) and reached their present level approximately 6000 years ago.

Brackish and sea water is generally a deterrent, if not a total barrier to dispersal for most frog

species (Duellman and Trueb 1986), therefore once Moreton Bay began to fill, dispersal

among Moreton Island and mainland populations would not have been possible.

Differentiation of Moreton Island haplotypes and mainland haplotypes would have

subsequently occurred due to periodic isolation and low levels of dispersal. Levels of gene

flow may have been insufficient to counter the effects of genetic drift. Figure 8 shows

fluctuations in sea level over the last 200 000 years and indicates the periods when Moreton

Bay would have been completely dry.

The single haplotype from the Moreton Island population (haplotype 020) which clustered

with the North Stradbroke group could be explained by episodic dispersal events spanning

sea level fluctuations and may be a relict haplotype from an earlier dispersal event. Moreton

Bay emptied during many of the ice ages, providing multiple potential opportunities for

frogs to disperse to suitable habitat and breeding sites on Moreton Island. This haplotype

may have persisted in the Amity Point population due to stochastic lineage sorting. This

seems unlikely, however, over such a long period of time (Avise 1994). The relationships

among haplotypes indicated by TCS would suggest, that the haplotype 020 is more closely

related to the northern NSW and Stradbroke Island haplotypes than to other Moreton Island

haplotypes and therefore is more likely to represent a dispersal event among North

Stradbroke Island or northern NSW populations.


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Figure 7. Channels of Brisbane and Pine Rivers across the Moreton Bay plain when sea

levels were low during the last Ice Age. Reproduced from Jones (1992).

Figure 8. Sea level fluctuations over the last 200 000 years. Reproduced from Jones

(1992) (compiled from Chappell 1983).

halla


halla



114

The high level of differentiation observed between the southern mainland, Bribie and

Moreton Island populations and the northern New South Wales and North Stradbroke Island

populations may be indicative of an extreme case of restricted dispersal among population

groups or of a second isolation event. James and Moritz (2000) showed that Litoria fallax

exhibited strong phylogeograhic structuring among populations distributed in the vicinity of

the McPerson Ranges (south of Brisbane). Average sequence divergence estimates between

the two lineages were 11.5 – 12.1 percent (for the same COI sequence fragment analysed in

this study for C.tinnula). It is possible that biogeographical processes associated with this

region created a temporary barrier to dispersal in the past for C.tinnula populations in the

northern New South Wales area.

The high level of divergence among Stradbroke Island populations and other southern

southeast Queensland populations and the genetic similarity with northern NSW samples

would suggest that North Stradbroke Island populations may have been colonised from

northern NSW. Dispersal may have occurred across the bay and islands adjacent to the Gold

Coast, rather than directly across Moreton Bay. This would imply that isolation of the

northern NSW populations occurred before the formation of the islands. The high level of

divergence observed among other southern populations and the northern NSW populations

(7.4%) supports this hypothesis.

4.4.3 GENETIC VARIATION WITHIN POPULATIONS

Among the southern mainland populations and the Bribie Island populations, only a single

common haplotype was observed in all populations. Multiple haplotypes were found in only

two of the six populations. The 12S data for the southern populations likewise showed a

high degree of sharing of haplotypes across the southern region, however, in comparison to

the 12S data, COI haplotype diversity within populations was reduced. Similarly, within the

northern region, COI analysis revealed that a single haplotype (001c) was present in all

populations and that this haplotype was most common. This pattern of haplotype

distribution and frequency differs markedly from the pattern seen in the 12S data.

Interpreting the COI data in isolation, the lack of haplotypes and low genetic diversity within

regions could be explained by repeated bottlenecks and/or ongoing small effective

population sizes. Range contraction during the glacial periods and subsequent expansion in

the interglacials could explain the single dominant ancestral haplotype and the low haplotype

diversity observed in both the northern and southern regions. However, as both COI and 12S


115

are found on the mitochondrial genome they share the same evolutionary history, therefore,

low levels of diversity observed for COI should also be evident in the 12S data set. In the

current study, haplotype diversity was consistently higher for 12SmtDNA than for COI and

comparisons of nucleotide diversity were very similar across both datasets (refer Table 11).

Thus, it would appear that past bottlenecks alone cannot explain the lack of diversity

observed at COI.

Table 11. Haplotype diversity (Hd) and nucleotide diversity (πd) for 12S and COI mtDNA

haplotypes.

12S COI Hd πd Hd πd North 0.730±0.0190 0.00329 0.189±0.0480 0.00085 South 0.750±0.0082 0.00642 0.588±0.0420 0.03132 Mainland-MI* Strad.-NSW**

0.00360 0.00126

0.00218 0.00151

Total 0.871±0.009 0.02333 0.696±0.020 0.06373

*Mainland-MI = Peregian, Beerwah, Caboolture, White Patch, Bellara, Karawatha,

Moreton Is; **Strad-NSW = Stradbroke Is., Newrybar, Tyagarah.

In the absence of a population bottleneck, selective sweeps can also produce a significant

decrease in nucleotide diversity (Bohonak 1999; Nurminsky et al. 2001). A recent selective

sweep should create a characteristic decrease in the level of polymorphism in a region as

well as an expected excess of singleton sites (Nurminsky et al. 2001). Although the pattern

of a selective sweep appears to fit the COI data (low levels of polymorphism and excess

singleton sites), it is unlikely that this could explain the lack of diversity across the sampling

distribution. For a selective sweep to account for the lack of diversity in both the northern

and southern clades, a sweep would have had to occur prior to the divergence of the northern

and southern clades (levels of polymorphism should be restored relatively rapidly after a

selective sweep; Nurminsky et al. 2001) or it would have had to occur simultaneously in

each of the two clades. Neither of these two scenarios seems likely.

Another explanation for the observed trend of comparatively fewer COI haplotypes than 12S

haplotypes is that there have been functional constraints on the evolution of the COI gene.


116

Lunt et al. (1996) used the insect mitochondrial COI genes to examine within-gene

heterogeneity of evolutionary rate and found that patterns of sequence variability were

associated with functional constraints on different regions of the protein (also observed by

Howland and Hewitt 1995).

The COI DNA sequence for the C.tinnula samples showed low levels of variation within

geographical regions which is consistent with functional constraints affecting the COI

protein sequence. Unlike the insect sequences which showed some degree of variability

outside constrained regions (Lunt et al. 1996), the C.tinnula sequences showed very little

variability across the entire 565 base pair sequence. Of a potential 181 silent substitution

sites (at third position sites), there were only 12 variable sites in northern haplotypes, 12

variable sites in southern mainland, Bribie Island and Moreton Island haplotypes and 10

variable sites in the North Stradbroke Island, Tyagarah and Newrybar haplotypes.

An alignment (not shown) of the C.tinnula COI sequence with the insect sequence showed

that the C.tinnula sequence falls in an area of low, moderate and high variability sites,

respectively (regions I3, M7, E4, M8, I4 M9, E5, M10, I5 and M11 in Lunt et al. 1996), so

lack of nucleotide diversity cannot solely be explained by restrictive functional constraints.

In comparison to studies which have examined COI phylogenetics of other anuran species,

C.tinnula shows very low levels of COI haplotype diversity. James and Moritz (2000)

sequenced 87 Litoria fallax individuals and found 84 distinct COI mtDNA haplotypes and

McGuigan et al. (1998) sequenced 49 Litoria pearsoniana individuals and found 31 distinct

COI mtDNA haplotypes. While the James and Moritz (2000) study encompassed a larger

geographic area than that here, the sampling distribution of McGuigan et al. (1998) was

comparable to the current study. COI haplotype diversity in the present study, was also

generally lower than other studies which have used mtDNA regions with comparative

mutation rates (e.g. cytochrome b; Barber 1999; Bos and Sites 2001; Babik et al. 2004).

The number of base pair changes observed between northern and southern sequences, and

also between the two clades present within the southern region, is consistent with a faster

mutation rate for the COI gene region (compared with the number of base pair changes

observed for the 12S fragment), however, the lack of variation within regions contradicts the

underlying pattern of relative diversity levels. Both the McGuigan et al. (1998) and James

and Moritz (2000) show comparatively similar levels of divergence among differentiated

lineages as observed for C.tinnula. Both these studies, however, show higher levels of

within region variability.


117

Comparisons with other studies regarding the discrepancy observed between the 12S and

COI datasets are difficult to make, as studies which have used multiple mitochondrial

markers are generally phylogenetic studies with little information on population diversity

and also because the 12S mitochondrial region is not generally used for population analyses.

As both markers share a common history (by virtue of being effectively ‘linked’ on the

mitochondrial genome), then it would be expected that if population bottlenecks were

responsible for the low levels of COI diversity found within regions for C.tinnula then this

general pattern should have also been evident in the 12S data set. Selective constraints on

COI as described by Lunt et al. (1996) seem unlikely, given that the COI fragment used in

the present study encompasses high variability sites and also because it is the same region as

that used by James and Moritz (2000). Selective sweeps would also appear an unlikely

scenario.

There appears to be no simple explanation as to why the C.tinnula dataset possessed levels

of diversity lower than would be expected following genetic diversity studies of 12S in the

same individuals. While this issue must remain unresolved, the phylogenetic patterns that

result from analysis of the two data sets appears consistent and robust. Both 12S and COI

datasets support monophyly of the northern and southern C.tinnula clades.

Chapter Summary: The broad scale structure observed for COI was similar to that observed

for the 12S mitochondrial region. Two distinct evolutionary lineages exist within the

southeast Queensland populations of C.tinnula. In the southern region, the COI results

suggested differentiation of the island populations from the mainland populations and

significant differentiation of the North Stradbroke Island, Newrybar and Tyagarah samples

from all other southern populations.

Chapter Five: General Discussion

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CHAPTER FIVE

5 GENERAL DISCUSSION

5.1 MODEL FOR THE PAST EVOLUTIONARY HISTORY OF C.TINNULA POPULATIONS IN

SOUTHEAST QUEENSLAND

Historical climatic conditions and biogeographic processes related to the changing coastline

of eastern Australia appear to have had a significant impact on the evolution of C.tinnula

populations. Populations within southeast Queensland exhibit a distinct north-south

dichotomy, with a high level of divergence observed between northern and southern

populations and a significant level of differentiation also evident among southern population

groups. This would suggest that historically, wallum froglets have been exposed to more

than a single period of long term isolation in the past.

The deep phylogeographical split within C.tinnula is likely to represent a late Miocene –

Pliocene divergence. The magnitude of the split is comparable to deep divergences observed

in some Wet Tropics rainforest herpetofauna and an open forest species of Litoria (Schneider

et al. 1998; James and Moritz 2000). The congruence of sequence divergence among

species suggests that climatic conditions during the Tertiary may have affected the

population structure of herpetofauna across a wide geographic distribution within eastern

Australia. Range contraction of suitable habitat due to unfavourable bioclimatic conditions

is thought to be the reason for diversification of many of the reptile and amphibian species

studied in northern Queensland and this seems to be the most appropriate explanation for the

population divergence observed for C.tinnula.

Assuming that the ancestral parental stock of C.tinnula evolved in wallum habitat, and given

that wallum froglets are restricted to breeding in creeks and ponds of the wallum, it is

possible that dry climatic conditions during the Pliocene restricted this species to two (main)

areas of wallum habitat which were suitable for breeding and larval development and this

isolation resulted in the divergence of northern and southern populations. This hypothesis is

not necessarily reliant on wallum habitat having experienced a range contraction, only that

wallum froglet populations were restricted. Climatic conditions and sea level fluctuations

may, however, have caused range contractions of wallum habitat such as those observed for

rainforest habitat in Queensland (Kershaw et al. 1994; McGuigan et al. 1998; Schneider et

al. 1998). A trend of increasing aridity since the early Pliocene (Crowley and North 1991)

may have limited opportunities for froglets to breed in temporary ponds and C.tinnula may

have been restricted to areas of wallum habitat where permanent water bodies were


119

available. Bos and Sites (2001) suggested a similar scenario for divergent populations of

Rana luteiventris. The authors proposed that hydrologic isolation during cold and dry glacial

periods during the Tertiary restricted dispersal of R.luteiventris and resulted in

phylogeographical divergence. In northern Queensland, the “dry” corridor of the Burdekin

Gap has been shown to influence the differentiation of anuran populations which span this

ecological barrier (James and Moritz 2000).

Periodic, and often abrupt, transitions in atmospheric and climatic conditions during the mid-

late Pliocene (Crowley and North 1991) may have created opportunities for wallum froglets

to disperse to other areas when permanent and/or temporary new breeding sites became

available and also to undergo subsequent range contractions during drier periods and sea

level fluctuations. A pattern of range expansions and contractions could account for the

second isolation event which resulted in differentiation of northern New South Wales and

southern Queensland populations. Southern populations may have expanded their range

during conditions favourable to dispersal and when additional permanent water sources

become available. Populations may have then experienced range contractions or extinctions

due to rises in sea levels and changes in climatic conditions, resulting in the isolation and the

differentiation of the two southern lineages.

The above hypothesis proposes that there were at least two periods during the last 3-7

million years when wallum froglet populations were restricted in distribution or experienced

restrictions in dispersal, and isolated for enough time to result in the level of differences

observed between the northern and southern clades, and among southern clade populations.

The lack of detailed palaeoecological and bioclimatic information, however, for this region

during the Pliocene period makes it difficult to suggest the likely mechanisms which may

have led to such long term isolation. The lack of any current significant geographic or

geological landscape feature(s) that could impede froglet dispersal, and the distinct non-

overlapping delineation of geographic distribution of the northern and southern clades,

suggests that distribution of suitable habitat, in particular the distribution of suitable breeding

habitat, may have been the primary biogeographic factor that has influenced the evolution of

wallum froglet populations.

Climatic conditions in general during the last ice-ages, are thought to have been very windy,

dry and cold with intervals as warm as today occupying only 10% of the late Quaternary

(Crowley and North 1991). General unfavourable dispersal conditions in conjunction with

low dispersal capacity may have limited the potential for range expansions in C.tinnula and

may explain why there is no evidence of ancestral southern haplotypes in northern


120

populations and vice versa, and why there was no mixing of ancestral lineages observed

within the southern clades.

Within the southern populations, the widespread distribution of ancestral haplotypes, star-

like phylogeny and low nucleotide diversity observed for southern mainland and Bribie

Island populations are all indicative of a range expansion subsequent to a period of range

contraction. The fairly continuous distribution of wallum habitat along the southeast

Queensland coast, the increase in precipitation rates and the increase in climatic stability in

the Holocene could have provided the opportunity for wallum froglets to move out of habitat

refugia and to establish other, more permanent, breeding populations. Population sample

sizes from within the southern region, however, are generally low so it is difficult to infer

patterns of gene flow among populations.

The colonisation of Moreton Island and Stradbroke Island is most likely to have occurred

after the second long term isolation event, as (most) Moreton Island haplotypes are more

closely related to southern mainland and Bribie Island individuals, and Stradbroke Island

haplotypes are more closely related to northern New South Wales haplotypes. The

relationship among Stradbroke Island and northern New South Wales populations also

suggests that there was a northward range expansion of individuals from the northern New

South Wales clade.

The single Moreton Island individual that clustered with the northern NSW and Stradbroke

Island populations may represent a further northward dispersal event and introgression of the

two divergent southern clades or indicate insufficient time for coalescence processes to

achieve reciprocal monophyly of the two regions. The low levels of variation within the

clades and the high levels of variation between them suggest there has been enough time for

complete coalescence of haplotypes. The small number of samples collected from Moreton

Island and the limited sampling of populations on Moreton Island, Stradbroke Island and

northern New South Wales makes it difficult to provide definitive support for the argument

of a northward expansion. More intensive sampling in this area may help to clarify historical

dispersal patterns among northern NSW and southern mainland populations.

In the northern area, it appears that historical processes have also dominated the population

structure of Fraser Island and Cooloola C.tinnula populations. The oldest dune systems in

the northern region indicate that Fraser Island formed over the last million years, therefore

populations on Fraser Island must have been colonised from mainland populations. The

Great Sandy Strait which separates Fraser Island from the Cooloola-Maryborough mainland


121

region is a very narrow and shallow estuary which would have been dry land during low sea

levels. Populations on Fraser Island could have been established by dispersers from a large

mainland source population or multiple mainland populations adjacent to the island.

Different ancestral haplotypes found in Fraser Island populations for 12S analysis would

support either of these scenarios; COI data, however, would support colonisation from a

single source. The lack of sharing of rare haplotypes across the region suggests very little

dispersal among populations subsequent to colonisation.

The hypotheses proposed above for the historical processes which have shaped C.tinnula

population structure in Queensland and northern New South Wales suggest that there are two

potential expansion fronts, one occurring between Noosa and Peregian and the other

occurring in the region between Karawatha and northern New South Wales. More detailed

sampling within these regions will be necessary to understand the fine scale relationships and

current gene flow patterns among populations that have experienced such a long history of

isolation. Further analysis may reveal expansion boundaries and/or may show evidence of

introgression of divergent haplotypes in areas where different lineages have recently

contacted (Barber 1999a; Bos and Sites 2001).

The current study has demonstrated clearly that historical processes have played a significant

role in shaping the population structure of C.tinnula populations in southeast Queensland and

northern New South Wales. It will be critical, however, for future conservation management

of this species that levels of contemporary gene flow be examined. While this study

attempted to address this issue using microsatellite markers, the attempts were largely

unsuccessful. Recommendations for conservation management based on contemporary

patterns of diversity and structure are therefore beyond the scope of this study. This

information will be necessary, however, to ensure that any future management practices fully

represent modern potential for population interaction.

This study, in conjunction with other studies on the herpetofauna of eastern Australia, also

highlights the significant role that past climatic conditions have had in shaping population

structure (McGuigan et al. 1998; Schneider et al. 1998; James and Moritz 2000). Restriction

to habitat refugia during glacial periods appears to have been an important process in

creating intraspecific diversification of many of the species studied within Queensland.


122

5.2 COMPARATIVE STUDIES

Comparative phylogeographic studies have shown that different species that inhabit the same

geographic area often exhibit a shared evolutionary history separate from conspecific

populations due to exposure to common historical bioclimatic and biogeographic influences

(Moritz 1994a; Moritz and Faith 1998).

Phylogeographic structuring of two other acid frog species (Litoria cooloolensis and

L.olongburensis; James 1997) a freshwater crayfish species (Cherax robustus; Garvie 1998)

which are endemic to wallum habitat, have shown similar broad scale pattern of genetic

divergence to those observed in this study for C.tinnula (levels of divergence were, however,

lower than those observed here; among population divergence 2.9% for L.cooloolensis and

2.6% for C.robustus). Unfortunately, the difference in sample number and sampling effort

of these studies does not permit a comprehensive comparison between the levels and patterns

of divergence observed for C.tinnula and those observed for the other wallum species.

It is possible that many species endemic to wallum habitat have experienced a shared

evolutionary history. Support for this hypothesis comes from a recent phylogenetic study of

ornate rainbow fish (Rhadinocentrus ornatus), a species that inhabits wallum streams, that

showed a very similar tree topology to that observed for C.tinnula (Page et al. 2004).

Page et al. (2004) found four divergent clades across a sampling distribution from Central

Queensland to northern New South Wales. Three clades showed distinct geographic

partitioning; a Central Queensland clade (CEQ) which incorporated populations from Fraser

Island and the Cooloola area (another clade representing a single population at Seary’s

Creek, fell within this CEQ clade), a southeast Queensland clade (SEQ), which incorporated

populations from Noosa, Bribie Island, Moreton Island, Stradbroke Island, Logan (Qld) and

Cudgera Creek in northern New South Wales; and a third clade represented populations from

south of the Richmond Ranges (NSW). Central Queensland and southeast Queensland

populations of the rainbow fish cluster in a nearly identical fashion to northern and southern

C.tinnula population groups. The major difference between the two studies was the

placement of the Noosa populations.

Pairwise distance estimates among haplotypes of the two major Queensland rainbow fish

clades (CEQ and SEQ) put the time of divergence at around the late Miocene - early

Pliocene. These times are consistent with divergence times estimated for C.tinnula. While

Page et al. (2004) did not offer any further alternative explanations to those proposed in the

present study, as to the cause of the observed vicariant break, results from both studies


123

support the hypothesis that local biogeographic processes significantly influenced the

distribution and dispersal patterns of species inhabiting wallum heathland.

Genetic analyses of other wallum species and/or more comprehensive sampling of other acid

frogs may reveal patterns consistent with a general north-south dichotomy among wallum

population groups. The fragmentation of wallum habitat has the potential to influence

population dynamics of all wallum-restricted species and studies should be conducted to

determine if divergent lineages are also present in other wallum species. Future conservation

plans for particular areas of wallum habitat could, therefore encompass conservation of a

number of species rather than adopt an individual species management approach.

Two other comprehensive studies on anurans have been carried out in the southeast

Queensland and northern New South Wales area. McGuigan et al. (1998) looked at the

phylogeography of Litoria pearsoniana, a wet-forest restricted species and James and Moritz

(2000) described the phylogeography of Litoria fallax, a open-forest restricted species.

Although neither of these species are wallum habitat species, these studies found significant

phylogeographic structure similar to that observed for the southern populations of C.tinnula.

The degree of divergence among populations of the two species examined differed; 11

percent (James and Moritz 2000) compared with 4 percent (McGuigan et al. 1998), however

both studies attributed the divergence to suppression of gene flow due to habitat range

contraction and persisting biogeographical barriers to dispersal. Geographically, the pattern

of population differentiation is similar to that observed among southern populations of

C.tinnula. It is likely that biogeographical processes associated with this region created

barriers to dispersal and/or range contraction of habitat that have affected a number of

species.

As studies on other taxa in this area become available, it will be interesting to see whether

these patterns will reflect a broad ecological response to historical changes in the distribution

of habitat (Moritz and Faith 1998; James and Moritz 2000).

5.3 CONSERVATION IMPLICATIONS

Current legislation views C.tinnula as a single conservation ‘issue’. If the aim of

conservation strategies is to protect and preserve biodiversity, results from the current study

and those of Read et al. (2001) would suggest that the current legislation does not satisfy this

objective for C.tinnula populations. The lineages identified in this study, represent distinct


124

evolutionarily significant units due to reciprocal monophyly of mtDNA clades (sensu Moritz

1994a) and the significant level of divergence among each lineage (results from nuclear

markers will be required to confirm ESU status as defined by Moritz 1994a). Significant

structuring is also evident within the southern clade and populations groups in this area may

warrant additional conservation status.

A review of the current systematics of C.tinnula is also required to ensure that discrete

population groups are recognized as distinct conservation units and will therefore be

protected accordingly. An apparent lack of morphological differentiation among individuals

collected from across the species distribution may require that future identification of

wallum froglet lineages be confined solely to genetic analyses or may require new attempts

to identify morphological, behavioural or perhaps acoustic markers unique to discrete

lineages.

Ideally, conservation strategies for C.tinnula would involve conserving all existing

populations to preserve as much genetic variation as possible. In this way, even if dispersal

is reduced among isolated populations, different populations will retain different alleles

through genetic drift. Ensuring the survival of the maximum number of populations

therefore, ensures more genetic variation is maintained the long term for the species as a

whole than could be held in a smaller number of large populations (Simberloff 1988).

Conservation of a species is often limited by a number of constraints (political, social,

economic), however, and it may not be possible to conserve all known populations.

Assuming that not all populations are able to be conserved I propose the following

suggestions for the conservation management of C.tinnula based on the mitochondrial

evidence and available information on habitat distribution. Given that habitat decline in both

quality and quantity represents a major determining factor in the survival of C.tinnula

populations in southeast Queensland, conservation strategies must take into account the

genetic information available and information on longer term availability of habitat

(Blaustein et al. 1994).

Populations of C.tinnula found on Fraser Island and Moreton Island appear to be the most

viable populations over the long term with respect to both habitat security and the relatively

high levels of genetic diversity observed at mtDNA markers. Both islands are protected

National Parks and Fraser Island is a declared World Heritage site. Fraser Island in

particular has large expanses of wallum heath and freshwater lakes associated with wallum

habitat that are for the most part untouched by human disturbance. It is unlikely therefore,


125

that C.tinnula populations on Fraser Island or Moreton Island will decline due to future

habitat disturbance or modification through human-mediated development. While both these

populations are likely to have been colonized by dispersers from mainland populations, it is

unlikely, given the loss and degradation of wallum habitat on adjacent mainland areas, that

these source populations (if they are still in existence) would be more secure than protected

island populations. Protection of populations on these islands will preserve, at the least,

representative diversity of each of the two evolutionary lineages observed for southeast

Queensland populations of C.tinnula.

The two other major sand islands in southeast Queensland, Stradbroke Island and Bribie

Island, support relatively large human populations and both islands are also popular tourist

destinations. Wallum habitat on Stradbroke Island appears relatively undisturbed and in the

absence of future large developments on the island and the continued protection of wallum

habitat, C.tinnula populations should remain relatively unaffected by human disturbance.

Bribie Island, however, has undergone major development over the last 15 years and large

areas of heathland and freshwater swamps are under threat from local urban development. It

is likely that many sites on Bribie Island once favourable to C.tinnula will not persist due to

loss of habitat, fragmentation of habitat and a gradual decrease in habitat quality. Sites such

as the ‘White Patch’ population which are found in the National Park are likely to provide

the best chance of long term survival (particularly from a habitat quality perspective),

however, modification to the surrounding area may increase the potential for non-wallum

species, such as the introduced cane toad Bufo marinus to move into wallum habitats and

potentially ‘disturb’ local habitat specialists.

Without data from nuclear markers on contemporary levels of genetic variation, it is difficult

to suggest that any one population will have a greater chance of survival due to higher levels

of diversity, however, the Bribie Island populations exhibited relatively high levels of

mtDNA diversity and appear to be good representative populations for the diversity observed

among southern mainland populations. The Bellara population, in particular, showed

relatively high haplotype diversity for both the 12S and COI mtDNA regions. During

sample collection the Bribie Island populations also appeared to have higher numbers of

calling males than other southern mainland populations. Ensuring that the Bribie Island

populations are protected, in conjunction with populations on Stradbroke Island, will

additionally preserve unique diversity representative of the lineages observed within the

southern Queensland and northern New South Wales clade.

Mainland C.tinnula populations that have protection in National Parks, such as the Cooloola


126

and Noosa populations (that are part of the Great Sandy National Park and populations in the

Noosa National Park and Peregian Environmental Park) may have the highest chance of

persistence on the mainland over the long term. The high degree of fragmentation of wallum

along the mainland coastal area is likely to mean that any extant populations will be

increasingly isolated and if local populations suffer extinctions it is unlikely that they be

recolonised from adjacent populations. Where possible, any large areas of wallum habitat

that remain should be protected and corridors of freshwater swamps and heathlands

connecting them should be conserved. Mainland populations, particularly ‘southern’

populations contain unique genetic diversity and where possible, large populations of

C.tinnula which are likely to be unaffected by human disturbance should therefore be given

high conservation priority.

5.3.1 FUTURE CLIMATE CHANGE

In light of the recent focus on the potential impacts of climate change on biodiversity

(Pounds et al. 1999; Hughes 2003; Thomas et al. 2004), information on climate change

relevant to C.tinnula populations and wallum heath is briefly discussed. From other studies

on amphibians, there appear to be a range of consequences attributed to recent changes in

climate. Migration and spawning of amphibians is occurring earlier (Beebee 1995),

population declines correlated with low precipitation have been observed in Puerto Rican

rainforest frogs (Stewart 1995) and extinction of the golden toad (Bufo periglenes) has been

attributed to unusually warm and dry conditions in Costa Rica (Pounds and Crump 1994;

Pounds et al. 1999). The overall emerging picture from the effects of climate change on

species distribution is that conservation biologists need to monitor the effects of climate

change on populations and consider this issue when planning for conservation (Blaustein et

al. 1994; McCarty 2001).

Sea level changes are also likely to impact C.tinnula populations (and wallum species in

general). The low relief of wallum heath (1-10m above sea level) could mean that even

small rises in sea level may result in relatively large areas being affected by salt-water

intrusion, with the expansion of estuarine and mangrove systems encroaching on freshwater

systems (Hughes 2003).

In the Northern Territory, expansion of tidal creek systems has been observed in the Lower

Murray River system. Two creeks have extended more than 4km inland, invading

freshwater wetlands and at present 17 000ha of wetlands in NT have been adversely affected


127

by salt-water intrusion (Mulrennan and Woodroffe 1998). In addition to the impacts of sea

level rise, changes in fire regimes are also likely to occur and climate models project that this

will increase the fire danger over much of Australia (Keith et al. 2002). As heathlands are

particularly susceptible to fires this will mean an increased risk to wallum habitat. Keith et

al. (2002) suggest that heathland areas around urbanized areas will be particularly vulnerable

because of the increased probability of ignition by accidental fires and arson.

Many of the plant species that are found in wallum heathlands also show limited dispersal

capabilities, this means that vegetation dynamics in heathlands is governed largely by

disturbance and population processes at local spatial scales, much more so than in other plant

communities (Keith et al. 2002). Once eliminated from local areas of heathland, species are

thus likely to remain absent over very long time-scales before dispersal and re-establishment.

The naturally patchy distribution of heathlands and increase fragmentation due to land use

changes would exacerbate this effect.

5.4 CONCLUSION

The southeast Queensland and New South Wales coastline is one of the most densely human

populated regions in Australia. Development in this area is progressing rapidly and it is

therefore essential that information regarding endemic fauna and flora in this area be

incorporated into future land management decisions. While C.tinnula has been listed as

Vulnerable under the Queensland and New South Wales Threatened Species Acts,

populations are still under significant threat from anthropogenic modification of coastal areas

in and around existing wallum habitat (Ehmann 1997; Hero et al. 2000; personal

observations).

Evidence from the current study clearly indicates a high level of divergence among northern

and southern population groups and appreciable genetic structuring of populations within

regions. It is proposed that historic habitat distribution (influenced by climatic oscillations

during the Tertiary) has played a key role in shaping the population structure of C.tinnula.

To preserve the unique genetic diversity, and until data from nuclear markers becomes

available, these lineages should be recognized as having distinct evolutionary potential and

be protected accordingly.

Intensive sampling of populations in areas where divergent lineages are found in close

geographic proximity may elucidate patterns of range expansion. Information from nuclear


128

markers will assist in describing levels and patterns of genetic diversity, contemporary gene

flow patterns. This data may provide a better understanding of how local populations are

connected and whether metapopulation dynamics exist for C.tinnula populations.

Conservation of habitat and maintenance of habitat quality in mainland coastal areas is likely

to be the major factor that determines wallum froglet population persistence in the mainland

and Bribie Island populations. Populations in these areas should be monitored to ensure that

any future population declines are not the result of human disturbance. Populations on the

sand islands (Fraser Island, Moreton Island and Stradbroke Island) afford protection from

development under current National Park listings, however these populations should also be

monitored to ensure populations do not decline.

This study has provided data that are highly relevant to conservation strategies for C.tinnula

in the southeast Queensland region, and has identified areas of future research that should be

conducted to increase our understanding of the genetic and ecological relationships among

extant wallum froglet populations.

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129

APPENDIX 1

MICROSATELLITE CHAPTER: FINE SCALE POPULATION STRUCTURE AND CONTEMPORARY

GENE FLOW AMONG WALLUM FROGLET POPULATIONS.

Introduction

Documenting levels of genetic diversity and understanding fine scale population structure

within regions is important for the development of effective conservation strategies.

Estimates of genetic variation within and among populations can provide important

information on the level of interaction among local populations.

The subdivision of a species into partly isolated subpopulations can either increase or

decrease total genetic variation because while each subpopulation loses variation due to drift,

differentiation among population increases the diversity among populations as a whole.

Complex outcomes may arise from interactions between local population dynamics, limited

dispersal and habitat connectivity (Hassell et al. 1991).

Metapopulation theory has become a popular basis for conserving species in patchy or

fragmented environments (Harrison 1996). In particular, metapopulations are increasingly

used to describe anuran population structure (Berven and Grudzien 1990; Sjogren 1991a,

1991b; Hecnar and M’Closkey 1996; Driscoll 1998). This is because breeding ponds form

discrete habitat patches than can be easily identified and characterized and studies have

shown that local anuran populations are subject to local extinction events and recolonisation

via dispersal of juveniles from nearby populations (Rowe et al. 2000; Newman and Squire

2001).

Understanding the dynamics of population structure enables conservation managers to

conserve processes which are likely to sustain population persistence and levels of diversity

by maintaining patterns of dispersal and gene flow among local populations. Results from

12S and COI mtDNA suggest that at the regional scale, much of the genetic diversity

observed among populations may be due to historical associations rather than to

contemporary gene flow. Using nuclear markers it is possible to address the question of

whether populations exist as a metapopulation system or whether the observed patterns of

diversity are due primarily to historical connections.

Microsatellites are hypervariable nuclear markers that offer good prospects for quantifying

genetic population structure over short spatial and temporal scales (e.g. Bruford and Wayne

Appendices

130

1993, Jarne and Lagoda 1996). Mutations at microsatellite loci occur at relatively high

frequency (typically around 10-4 per locus per generation), are abundant throughout most

animal genomes. Microsatellites are expected to provide a good estimate of overall genomic

heterozygosity due to their random and abundant distribution across the genome and because

they are selectively neutral (Schlotterer and Tautz 1992; Bachtrog et al. 1999).

The relatively high levels of diversity commonly exhibited by microsatellites also makes

them ideal markers to address the issue of relative importance of individual populations for

conservation based on levels of diversity. Within a region, certain populations may warrant

increased conservation value because they possess greater allelic diversity, are larger in size

and are potentially more stable, i.e. populations which represent ‘source’ rather than ‘sink’

populations.

The purpose of this section was to determine the levels and patterns of contemporary gene

flow among local C.tinnula populations and to describe levels of microsatellite diversity

within populations. With an understanding of current population dynamics it will be

possible to assign relative conservation status to populations and to determine possible

effects that fragmentation could have on population persistence.

NOTE: Only amplification results for a single microsatellite locus (F2.5) are presented in

the following sections. See General Methods, Section 2.6 for detailed methods regarding

development of microsatellite genomic library and optimisation of microsatellite primers for

C.tinnula.

Appendices

131

Methods and Materials

Sample Localities and Sample Numbers

A total of 223 C.tinnula individuals from 17 populations were analysed for variation at the

F2.5 microsatellite locus (Table 1). C.parinsignifera and C.signifera were used as

outgroups.

Table A1.4 C.tinnula sites sampled and sample sizes for microsatellite analyses.

C.parinsignifera and C.signifera samples used as outgroups are listed at the bottom

of the table.

Species

Population

Number of Samples Analysed for Microsatellite

Variation (F2.5 locus) Wathumba Creek 20 Ungowa 14 Barga Lagoon 14 Rainbow Beach 18 Cooloola 24 Noosa 2 Peregian 5 Beerwah 4 White Patch 30 Bellara 30 Caboolture 3 Moreton Island 11 Karawatha 15 Stradbroke Island 30 Tyagarah 1 Newrybar 1

C.tinnula

Mungo 1 Barakula 1 C.parinsignifera Karawatha 1

C.signifera Karawatha 1

DNA extraction and amplification of the F2.5 locus

For all samples included in microsatellite analyses, DNA extraction followed the Chelex


The PCR conditions and PCR protocol which were the most successful for amplifying the

Appendices

132

F2.5 locus were as follows; the PCR master mix contained 3μl of 10xBuffer (Biotech), 2.4μl

of 2mM dNTPs (containing 30% each of dATP, dTTP and dGTP and 10% dCTP), 1.6l of

2mM MgCl2, 1μl of 25μM forward primer, 1.0 of 25μM reverse primer, 0.08μl of Taq (Tth

Plus polymerase Taq – Biotech), 0.08μl of 32P-dCTP, 1-2μl of DNA and up to total volume

of 20μl with dH2O.

PCR Protocol: (Microsatellite loci were amplified in a mini-thermocycler with a hot bonnet;

Bresatec); Step 1. 94oC 3 minutes; Step 2. 94oC 1 minute; Step 3. 48-52oC 1 minute; Step 4.

72oC 1 minute; Step 5. Got to Step 2 for 25 cycles; Step 6. 72oC 8 minutes; END.

Following amplification loading dye (95% formamide and 50mM EDTA) was added to each

PCR product.

Gel Running Conditions

PCR products mixed with loading dye were denatured at 94oC for 2-5 minutes and placed on

ice immediately before loading the gel. PCR products were electrophoresed at 100 watts

through a pre-heated (50oC) 5% denaturing polyacrylamide sequencing gel to separate

alleles. Gels were run for one and a half hours at 50oC and then dried for 1-2 hours. Once

dry, gels were exposed to autoradiograph film overnight. Reference individuals were run on

each gel and used as a baseline for genotyping other individuals.

Data Analysis

The program GenAlEx (Peakall and Smouse 2001) was used to test for deviations from

Hardy-Weinberg (HW) equilibrium and calculate of genetic diversity, measured as observed

(HO) and expected (HE) heterozygosity.

Appendices

133

Microsatellite Results

A total of forty-two individuals from four ‘southern’ populations amplified at locus F2.5

(Table 2). These individuals were from the Bribie Island populations (Bellara and White

Patch), the Karawatha and the Peregian populations. Only seven individuals from two

‘northern’ populations (Cooloola and Noosa) amplified. A total of 24 alleles were observed.

Twelve individuals were heterozygotes and 35 were homozygotes (Table 3).

Table A1.2. Samples which amplified at microsatellite Locus F2.5

Species

Population

Number of Samples Analysed for Microsatellite

Variation (F2.5 locus)

Number of Samples

that Amplified (F2.5 locus)

Wathumba Creek 20 0 Ungowa 14 0 Barga Lagoon 14 0 Rainbow Beach 18 0 Cooloola 24 5 Noosa 2 2 Peregian 5 3 Beerwah 4 0 White Patch 30 16 Bellara 30 18 Caboolture 3 0 Moreton Island 11 0 Karawatha 15 5 Stradbroke Island 30 0 Tyagarah 1 0 Newrybar 1 0

C.tinnula

Mungo 1 0 Barakula 1 0 C.parinsignifera Karawatha 1 0

C.signifera Karawatha 1 0

Appendices

134

Table A1.3. Allele frequencies (A-A2) for the F2.5 locus. WP=White Patch.

South*=Peregian, Karawatha and Bribie Island individuals; North**=Cooloola and Noosa

individuals.

Population No. individuals

No. heterozygotes

No. homozygotes

A D E F G

WP 16 5 11 0.25 0.03 0.03 0.03 - Bellara 18 6 12 0.25 - 0.03 - 0.11South* 42 12 30 0.23 0.01 0.02 0.06 0.05North** 07 0 07 - - - 0.14 -

Popn H I J K L M N O P Q R WP 0.09 0.03 0.06 - 0.09 - - - - - 0.06 Bellara - 0.03 - 0.06 0.08 - 0.03 0.06 - 0.06 0.03 South 0.04 0.02 0.02 0.08 0.07 - 0.01 0.05 0.01 0.02 0.04 North - - - - 0.29 0.29 - - - - -

Rowe et al. (2000) suggest that different systems of population structure will produce

different genetic expectations with respect to HW results. For the case of single patchy

panmictic populations, data from the pooled populations conform to HW equilibrium with no

evidence of differentiation. For sets of completely isolated demes, each subpopulation will

conform to HW equilibrium, but the pooled data will not and differentiation among

subpopulations will be high. Partially interconnected metapopulations will exhibit results

which lie in-between these extremes. The two Bribie Island populations were independently

tested for conformation to HW equilibrium and then southern samples were pooled and

tested for HW equilibrium. Tests indicated that neither the White Patch or Bellara

populations nor pooled populations conformed to HW equilibrium (pooled data set; X2 = ∞,

d.f = 6, p= 0.00001).

Analysis of expected and observed heterozygosity suggested an excess of homozygotes

Popn S U V X Y Z A1 A2 WP 0.03 0.06 0.06 0.06 0.03 0.06 0.06 - Bellara - 0.08 0.08 0.03 0.03 - - South 0.01 0.04 0.08 0.04 0.02 0.02 0.02 0.02 North - - - 0.29 - - - -

Appendices

135

(Table 4).

Table A1.4. Observed and Expected Heterozygosity for Locus F2.5

No. individuals

Observed Heterozgosity(HO)

Expected Heterozygosity (HE)

South 42 0.29 0.91 North 07 0.00 0.73 Total 49 0.24 0.92

Discussion

Due to the low number of samples which amplified at the C.tinnula microsatellite loci and

the lack of reproducible results for the AFLP markers (refer General Methods, Section 2.6) it

is difficult to make any confident inferences about genetic diversity at nuclear loci or fine

scale population structure within and among C.tinnula populations.

The large homozygote excess could have resulted from a number of factors including; high

levels of inbreeding, bottleneck effects or the presence of null alleles. The microsatellite

genomic library was developed from a tissue sample from a Bribie Island individual. While

microsatellites are known for cross-species amplification, studies have shown mixed success

with using primers designed for one species across related species (Rowe et al. 1997; Call et

al. 1998). Null alleles can result where primers are unable to bind to the priming site due to

mutational changes. This may be particularly relevant to C.tinnula, given the high degree of

mtDNA divergence observed among different population groups. While inbreeding could

also potentially result in an excess of homozygotes, given the difficulties in amplifying

individuals it is most likely that the observed excess of homozygotes was due to presence of

null alleles.

Levels of allelic diversity were relatively high in the Bribie Island samples and although

results are only for a single locus, the number of alleles observed at the F2.5 locus was equal

to, or greater than found in other anuran studies (Rowe et al. 2000; Newman and Squire

2001; Burns et al. 2004). Relatively high levels of allelic diversity may persist because

population fragmentation of this species has occurred relatively recently and populations

Appendices

136

were once large and/or experienced high levels of gene flow. Levels of diversity may not

persist, however, due to increasing isolation, as increased drift will result in a loss of

diversity within populations.

Results showed that southern and northern populations share common microsatellite alleles,

however, because the number of individuals which amplified successfully was low it is not

possible to determine if there has been contemporary gene flow among divergent lineages or

whether sharing of alleles is a result of homoplasy. If a larger number of individuals had

amplified then frequencies of alleles could have been compared and this may have indicated

whether populations share alleles as a result of gene flow. Hewitt (2001) suggests that

shared alleles between geographically allopatric species of low vagility are unlikely to result

from hybridization.

Summary: The use of microsatellite markers has proved invaluable in many anuran studies

that have examined contemporary gene flow (Newman and Squire 1991; Rowe et al. 2000;

Burns et al. 2004). Information on genetic diversity and local population structure is

necessary for effective design of future conservation management strategies and while this

study was unable to design and optimize microsatellite markers for C.tinnula this will be

something that needs to be addressed in the future.

Appendices

137

APPENDIX 2.

ALIGNMENT OF MYOBATRACHID FROGS TO CHECK THE MITOCHONDRIAL 12S SEQUENCED

FOR C.TINNULA IS NOT A NUCLEAR INSERT.

Limnodynastes dorsalis (Accession Number: AF261250), Limnodynastes salmini (Acc. No:

AY326071), Myobatrachus gouldii (Acc. No: AY364361), Limnodynastes peronii (Acc. No:

LPE440770), Neobatrachus pelobatoides (Acc. No: MTNP12S1), Pseudophryne guentheri

(Acc. No: PGU39989).

L.dorsalis CGCCAGGGAA CTACAAGCCC ACCCTTAAAA CCCAAAGGAC L.salmini .......... ....G..... .......... .......... M.gouldii ....C...C. ........T. .GA....... .......... L.peronii .......... ....G..... .......... .......... N.pelobatoides ....C..... ....G..... .G........ .......... P.guentheri ....C...T. ........TA .AG....... .......... C.tinnula014 ........C. ....G....A .AA....... .......... C.tinnula005 ........C. ....G..... .AA....... .......... L.dorsalis TTGACGGTGC CCCACATCCC CCTAGAGGAG CCTGTCCTAT L.salmini .......... ....T..... .......... .......... M.gouldii .......... ....T....A .......... .......... L.peronii .......... ....T..... .......... .......... N.pelobatoides .......... .......... .......... .......... P.guentheri .......... .........A .......... .......... C.tinnula014 .......... ....T....A .......... .......... C.tinnula005 .......... ....T....A .......... .......... L.dorsalis AATCGATGAT CCACGTTAAA CCTCACCTCT TCTTGCCCTC L.salmini .......... .......... .......A.. .......TA. M.gouldii .......... .......T.. .......A.. ...A....C. L.peronii .......... .......T.. .......A.. ......AAA. N.pelobatoides .......... .......TT. .......... .......AA. P.guentheri .......... .......T.. .......A.. ...A...TC. C.tinnula014 .......... ..C....TT. .......A.. ...A...TA. C.tinnula005 .......... ..C....TT. .......A.. ...A...TA. L.dorsalis CCGCCTGTAT ACCTCCGTCG TCAGCTCACC GCATGAGCGA L.salmini .......... .......... .......... .......... M.gouldii .A........ .......... .......... .........C L.peronii .A........ .......... .......... .......... N.pelobatoides .......... .......... .......... ......T..G P.guentheri .A........ .......... C......... .........C C.tinnula014 .A........ .......... .......... .........C C.tinnula005 .A........ .......... .......... .........C

Appendices

138

Appendix 2. Continued. L.dorsalis TTAATAGTGA GCATAATGGC CC---CACCA AAACGTCAGG L.salmini ..T....... ...A..A... .ATT-.G... .......... M.gouldii CA........ A..A...... ..CT-.G... .......... L.peronii .......... ...A.....T TACC-..... .......... N.pelobatoides .C........ ...A...... .AAG-.G... .......... P.guentheri C......... ...AG..... ..TTT.G... .......... C.tinnula014 ACT....... ...C..C... A.CC--G... .......... C.tinnula005 ACT....... ...C...... A.CC--G... .......... L.dorsalis TCAAGGTGCA GCTTATGAAG TGGGAAGAGA TGGGCTACAT L.salmini .......... ..AC...... .......... .......... M.gouldii .......... ..AA.C.... C...C..... .......... L.peronii .......... ..A....T.. .......... .......... N.pelobatoides .......... ..AC.....T A...T..... .......... P.guentheri .......... ...A...... C...C..... .......... C.tinnula014 .......... ..AA...... C..CC..... .......... C.tinnula005 .......... ..AA...... C..CC..... .......... L.dorsalis TTTCTAA-TC TAGAAAA--A ACGAAAGACC T-AGTGAAAC L.salmini .......C.. .......--. .......... .-........ M.gouldii ......C-CA .......C-T .........T G-T....... L.peronii .......-.T .......--T .........T C-........ N.pelobatoides .......-AT .......TT. ...G.....T C-.A...... P.guentheri ......C-.. .......C-C .........T G-C....... C.tinnula014 .....GC-.A C......T-T ...G.....T ACTT.....T C.tinnula005 .....CT-.G C......T-C ...G.....T ACTA...... L.dorsalis CCTGTCAGAA GGCGGATTTA GCAGTAAAG- AGAGATCAGA L.salmini .......... .......... .........- .A...C..A. M.gouldii ..CAG..... .......... .T.......C G.......AC L.peronii .......... .......... .........- .A.A.C..A. N.pelobatoides ..A....... .......... ........A- G..A.C.... P.guentheri ..C....... ..A....... .T.......C ........AT C.tinnula014 ..C....... .......... .........C ...A.C.... C.tinnula005 .TA....... .......... .........C ...A.C.... L.dorsalis ACACTCTCTT TAACACGGCC CA L.salmini .TG....... .......... .. M.gouldii .A....CA.. .....A...A .. L.peronii .T...T.... .......... .. N.pelobatoides .AGTC..T.. .......... .. P.guentheri GAG....... .....A...A .. C.tinnula014 .AGT...... ...T.A...A .. C.tinnula005 .AGT...... ...T.A...A ..

Appendices

139

APPENDIX 3.

ALIGNMENT OF VARIABLE SITES FROM 12S MITOCHONDRIAL SEQUENCE DATA FOR

SOUTHEAST QUEENSLAND AND NEW SOUTH WALES C.TINNULA SAMPLES.

Cpar = C.parinsignifera; Csig = C.signifera.

111111 1111222222 2222222222 223333 111111111 2367111368 8999033466 6677888899 990034 7012345679 4984578952 9269468134 5645678957 890869 001_{northern} ACCC--AACA ACGTTTTCCT AATCGACCCT TGTCACTACT AGTCAT 002_{northern} ....--.... ...C...... .......... ......C... ...... 003_{northern} ....C-.... ........T. .......... .......... ...... 004_{northern} ....--.... .......... .......... .A........ ...... 005_{northern} ....CC.... ........T. .......... .......... ...... 006_{northern} ...T--.... ..A.....T. .......... .......... ...... 007_{northern} ....C-.... ........T. .......... .......... ..C... 008_{northern} ....--.... ........T. .......... .......... ...... 009_{northern} ....--.... G.......T. .......... .......... ...... 010_{northern} ....--.... G.......T. .....G.... .......... ...... 011_{northern} ...T--.... ........T. .......... .......... .A.... 012_{southern} .T..T-CC.T .A......T. .GC.....GC .A.....TTC C..... 013_{southern} ....T-CC.T .A......T. ..C.....GC .A.T...TTC C..... 014_{southern} .T..T-CC.T .A......T. ..C.....GC .A.T...TTC C..... 015_{southern} .TT.T-CC.T .A......T. ..C.....GC .A.T...TTC C..... 016_{southern} .T..T-CC.T .A......T. ..C.....GC CA.T...TTC C..... 017_{southern} .T...--C.T .A......T. ..C.....GC .A.T...TTC C..... 018_{southern} .T...---.T .A......T. ..C.....GC .A.T...TTC C..... 019_{southern} .T.-T-CC.T .A......T. ..C.....GC CA.T...TTC C..... 020_{southern} .T..T-CC.T .A......T. ..C.....GC CA.T...T.C C..... 021_{southern} .T..C-CC.T .A......T. ..C.....GC .A.T...TTC C..... 022_{southern} .T..T-CC.T .A......T. ..C.....GC .A.T...T.C C..... 023_{southern} .T...-CC.T .A......T. ..C.....GC .A.....TTC C..... 024_{southern} .T...--C.T .A......T. ..C.....GC .A.....TTC C..... 025_{southern} .T...-CC.T .A......T. ..C.....GC .A.....TTC C..A.. 026_{southern} .A...-CC.T .A......T. ..C.....GC .A.....TTC C..... 027_{southern} .A...-CC.T .A...C..T. ..C.....GC .A.....TTC C..... 028_{southern} .A...-CC.T .A..CC..T. ..C.....GC .A.....TTC C..... Tyagarah {NSW} .A...--C.C .A......T. ..C.....GC .A.....TTC C..... Newrybar {NSW} TA..C-CC.T .A......T. ..C.....GC .A.....TTC C..... Mungo {NSW} ....C-CC.T ........T. G......... CAC......C C..... Cpar .T...----C .A...C.ATC ....A.TA.. .A.T.....C C..... Csig ...A.---.T C.....A.T. ...AT.T.TA .AC.TT..TC C...GC

Appendices

140

APPENDIX 3.1. PAIRWISE GENETIC DISTANCES FOR 12S MTDNA SOUTHEAST QUEENSLAND AND NEW SOUTH WALES HAPLOTYPES

Haplotype 001 002 003 004 005 006 007 008 001 2 2 1 3 3 3 1 002 0.006 4 3 5 5 5 3 003 0.006 0.011 3 1 3 1 1 004 0.003 0.008 0.008 4 4 4 2 005 0.008 0.014 0.003 0.011 4 2 2 006 0.008 0.014 0.008 0.011 0.011 4 2 007 0.008 0.014 0.003 0.011 0.006 0.011 2 008 0.003 0.008 0.003 0.006 0.006 0.006 0.006 009 0.006 0.011 0.006 0.008 0.008 0.008 0.008 0.003 010 0.008 0.014 0.008 0.011 0.011 0.011 0.011 0.006 011 0.008 0.014 0.008 0.011 0.011 0.006 0.011 0.006 012 0.046 0.051 0.043 0.043 0.046 0.048 0.046 0.043 013 0.043 0.048 0.04 0.04 0.043 0.046 0.043 0.04 014 0.046 0.051 0.043 0.043 0.046 0.048 0.046 0.043 015 0.048 0.054 0.046 0.046 0.048 0.051 0.048 0.046 016 0.048 0.054 0.046 0.046 0.048 0.051 0.048 0.046 017 0.043 0.048 0.043 0.04 0.046 0.046 0.046 0.04 018 0.043 0.048 0.043 0.04 0.046 0.046 0.046 0.04 019 0.051 0.057 0.048 0.048 0.051 0.051 0.051 0.048 020 0.046 0.051 0.043 0.043 0.046 0.048 0.046 0.043 021 0.046 0.051 0.04 0.043 0.043 0.048 0.043 0.043 022 0.043 0.048 0.04 0.04 0.043 0.046 0.043 0.04 023 0.04 0.046 0.04 0.037 0.043 0.043 0.043 0.037 024 0.04 0.046 0.04 0.037 0.043 0.043 0.043 0.037 025 0.043 0.048 0.043 0.04 0.046 0.046 0.046 0.04 026 0.04 0.046 0.04 0.037 0.043 0.043 0.043 0.037 027 0.043 0.048 0.043 0.04 0.046 0.046 0.046 0.04 028 0.046 0.051 0.046 0.043 0.048 0.048 0.048 0.043 Tyagarah 0.04 0.046 0.04 0.037 0.043 0.043 0.043 0.037 Newrybar 0.046 0.051 0.04 0.043 0.043 0.048 0.043 0.043 Mungo 0.031 0.037 0.025 0.028 0.028 0.034 0.028 0.028 Cpar 0.048 0.054 0.048 0.046 0.051 0.051 0.051 0.046 Csig 0.06 0.066 0.06 0.057 0.063 0.06 0.063 0.057

Appendices

141

Appendix 3.1. Continued.

Haplotype 009 010 011 012 013 014 015 016 017 001 2 3 3 16 15 16 17 17 15 002 4 5 5 18 17 18 19 19 17 003 2 3 3 15 14 15 16 16 15 004 3 4 4 15 14 15 16 16 14 005 3 4 4 16 15 16 17 17 16 006 3 4 2 17 16 17 18 18 16 007 3 4 4 16 15 16 17 17 16 008 1 2 2 15 14 15 16 16 14 009 1 3 16 15 16 17 17 15 010 0.003 4 17 16 17 18 18 16 011 0.008 0.011 17 16 17 18 18 16 012 0.046 0.048 0.048 3 2 3 3 4 013 0.043 0.046 0.046 0.008 1 2 2 3 014 0.046 0.048 0.048 0.006 0.003 1 1 2 015 0.048 0.051 0.051 0.008 0.006 0.003 2 3 016 0.048 0.051 0.051 0.008 0.006 0.003 0.006 3 017 0.043 0.046 0.046 0.011 0.008 0.006 0.008 0.008 018 0.043 0.046 0.046 0.014 0.011 0.008 0.011 0.011 0.003 019 0.051 0.054 0.051 0.011 0.008 0.006 0.008 0.003 0.011 020 0.046 0.048 0.048 0.011 0.008 0.006 0.008 0.003 0.011 021 0.046 0.048 0.048 0.008 0.006 0.003 0.006 0.006 0.006 022 0.043 0.046 0.046 0.008 0.006 0.003 0.006 0.006 0.008 023 0.04 0.043 0.043 0.006 0.008 0.006 0.008 0.008 0.006 024 0.04 0.043 0.043 0.008 0.011 0.008 0.011 0.011 0.003 025 0.043 0.046 0.046 0.008 0.011 0.008 0.011 0.011 0.008 026 0.04 0.043 0.043 0.008 0.008 0.008 0.011 0.011 0.008 027 0.043 0.046 0.046 0.011 0.011 0.011 0.014 0.014 0.011 028 0.046 0.048 0.048 0.014 0.014 0.014 0.017 0.017 0.014 Tyagarah 0.04 0.043 0.043 0.014 0.014 0.014 0.017 0.017 0.008 Newrybar 0.046 0.048 0.048 0.011 0.011 0.011 0.014 0.014 0.014 Mungo 0.031 0.034 0.034 0.034 0.031 0.034 0.037 0.031 0.037 Cpar 0.048 0.051 0.051 0.051 0.048 0.046 0.048 0.048 0.04 Csig 0.057 0.06 0.06 0.06 0.057 0.06 0.063 0.063 0.054

Appendices

142


Haplotype 018 019 020 021 022 023 024 025 026 001 15 18 16 16 15 14 14 15 14 002 17 20 18 18 17 16 16 17 16 003 15 17 15 14 14 14 14 15 14 004 14 17 15 15 14 13 13 14 13 005 16 18 16 15 15 15 15 16 15 006 16 18 17 17 16 15 15 16 15 007 16 18 16 15 15 15 15 16 15 008 14 17 15 15 14 13 13 14 13 009 15 18 16 16 15 14 14 15 14 010 16 19 17 17 16 15 15 16 15 011 16 18 17 17 16 15 15 16 15 012 5 4 4 3 3 2 3 3 3 013 4 3 3 2 2 3 4 4 3 014 3 2 2 1 1 2 3 3 3 015 4 3 3 2 2 3 4 4 4 016 4 1 1 2 2 3 4 4 4 017 1 4 4 2 3 2 1 3 3 018 5 5 3 4 3 2 4 4 019 0.014 2 3 3 4 5 5 5 020 0.014 0.006 3 1 4 5 5 5 021 0.008 0.008 0.008 2 2 3 3 3 022 0.011 0.008 0.003 0.006 3 4 4 4 023 0.008 0.011 0.011 0.006 0.008 1 1 1 024 0.006 0.014 0.014 0.008 0.011 0.003 2 2 025 0.011 0.014 0.014 0.008 0.011 0.003 0.006 2 026 0.011 0.014 0.014 0.008 0.011 0.003 0.006 0.006 027 0.014 0.017 0.017 0.011 0.014 0.006 0.008 0.008 0.003 028 0.017 0.02 0.02 0.014 0.017 0.008 0.011 0.011 0.006 Tyagarah 0.011 0.02 0.02 0.014 0.017 0.008 0.006 0.011 0.006 Newrybar 0.017 0.017 0.017 0.008 0.014 0.008 0.011 0.011 0.006 Mungo 0.04 0.034 0.028 0.031 0.031 0.031 0.034 0.034 0.031 Cpar 0.037 0.051 0.046 0.046 0.043 0.046 0.043 0.048 0.048 Csig 0.051 0.063 0.066 0.06 0.063 0.054 0.051 0.057 0.054

Appendices

143


Haplotype 027 028 Tyagarah Newrybar Mungo Cpar Csig 001 15 16 14 16 11 17 21 002 17 18 16 18 13 19 23 003 15 16 14 14 9 17 21 004 14 15 13 15 10 16 20 005 16 17 15 15 10 18 22 006 16 17 15 17 12 18 21 007 16 17 15 15 10 18 22 008 14 15 13 15 10 16 20 009 15 16 14 16 11 17 20 010 16 17 15 17 12 18 21 011 16 17 15 17 12 18 21 012 4 5 5 4 12 18 21 013 4 5 5 4 11 17 20 014 4 5 5 4 12 16 21 015 5 6 6 5 13 17 22 016 5 6 6 5 11 17 22 017 4 5 3 5 13 14 19 018 5 6 4 6 14 13 18 019 6 7 7 6 12 18 22 020 6 7 7 6 10 16 23 021 4 5 5 3 11 16 21 022 5 6 6 5 11 15 22 023 2 3 3 3 11 16 19 024 3 4 2 4 12 15 18 025 3 4 4 4 12 17 20 026 1 2 2 2 11 17 19 027 1 3 3 12 16 20 028 0.003 4 4 13 17 21 Tyagarah 0.008 0.011 4 13 15 19 Newrybar 0.008 0.011 0.011 11 19 21 Mungo 0.034 0.037 0.037 0.031 17 18 Cpar 0.046 0.048 0.043 0.054 0.048 22 Csig 0.057 0.06 0.054 0.06 0.051 0.063

Appendices

144

APPENDIX 4.

PERMUATIONAL CHI-SQUARED PROBABILITIES FOR GEOGRAPHICAL STRUCTURE OF THE

CLADES IDENTIFIED IN FIGURE 3, CHAPTER 4. Clades with a probability value less than 0.05

suggest significant geographical structure. Clades with no genetic or geographical variation

are excluded. ‘*’ significant at P<0.05. Abbreviations used in the Inference Key; Dc = clade

distance; Dn = nested clade distance; IBD = Isolation by Distance

Clade

X2 Statistic

P value

Inference Key Steps

Northern Network 1-1 2.00 1.000 1-2 5.00 0.386 2-1 8.00 0.014* 1, 2, 11, 17 Inconclusive Outcome 2-2 12.56 0.089 Total Cladogram 7.10 0.219

References

145

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