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
KEYWORDS
Crinia tinnula; population genetic structure; phylogeography; Pliocene; mitochondrial DNA; 12S; COI; wallum; southeast Queensland.
ii
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
xii
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.
xiii Chapter One: General Introduction
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
1 Chapter One: General Introduction
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).
2 Chapter One: General Introduction
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
3 Chapter One: General Introduction
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
4 Chapter One: General Introduction 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).
5 Chapter One: General Introduction
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
6 Chapter One: General Introduction
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
7 Chapter One: General Introduction
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.
8 Chapter One: General Introduction 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.
9 Chapter One: General Introduction
(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.
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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;
11 Chapter One: General Introduction
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.
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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.
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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.
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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.
15 Chapter Two: General Methods
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.
16 Chapter Two: General Methods
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.
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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.
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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
19 Chapter Two: General Methods 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
20 Chapter Two: General Methods 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
21 Chapter Two: General Methods
‘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.
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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.
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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
24 Chapter Two: General Methods
(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.
25 Chapter Two: General Methods
Figure 5. Southeast Queensland sampling sites for C.tinnula. Sampling sites are shown as green squares.
26 Chapter Two: General Methods
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).
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Table 1. Collection sites and sample size for C.tinnula populations. Latitude and longitude coordinates are shown in decimal degrees.
Population Identification Location Latitude Sample Code Longitude Number Wathumba Wc Fraser Island -24.98 153.23 30 Creek 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, -26.84 153.01 04 Beerwah 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 MI Moreton Island -27.09 153.44 11 Lake Amity Point SI Stradbroke Island -27.40 153.45 16 Karawatha Kw Logan, Brisbane -27.64 153.11 35
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
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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
29 Chapter Two: General Methods 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
30 Chapter Two: General Methods
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.
31 Chapter Two: General Methods
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
32 Chapter Two: General Methods 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.
33 Chapter Two: General Methods
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.
Homoduplex
Heteroduplex
A
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
34 Chapter Two: General Methods
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
35 Chapter Two: General Methods
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
36 Chapter Two: General Methods
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
37 Chapter Two: General Methods
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
38 Chapter Two: General Methods
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.
39 Chapter Two: General Methods
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 2 th Hd = n/n-1(1-Σpi ) where n is the sample size and pi is the sample frequency of the i- haplotype. Nucleotide diversity is a measure of heterozygosity at the nucleotide level. Nucleotide diversity describes the average number of nucleotide differences per site between th a pair of sequences; π = Σxixjdij , xi is the population frequency of the i- allele, xj is the th population frequency of the j- 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
40 Chapter Two: General Methods
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
41 Chapter Two: General Methods
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
42 Chapter Two: General Methods
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).
43 Chapter Three: 12S mtDNA
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
44 Chapter Three: 12S mtDNA 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.
45 Chapter Three: 12S mtDNA
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.
46 Chapter Three: 12S mtDNA
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.
Number of Samples Species Population Location Analysed for 12S mtDNA variation C.tinnula 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 Amity Point Stradbroke Is. 35 C.parinsignifera CparC (Caboolture) Sunshine Coast 01 CparK (Karawatha) Brisbane 01 CparB (Barakula) ~350km NW Brisbane 01 C.signifera CsigG (Goomburra) ~120km SW Brisbane 01 CsigK (Karawatha) Brisbane 01
47 Chapter Three: 12S mtDNA
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.
48 Chapter Three: 12S mtDNA
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.
49 Chapter Three: 12S mtDNA
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
50 Chapter Three: 12S mtDNA 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.
51 Chapter Three: 12S mtDNA
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.
52 Chapter Three: 12S mtDNA
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).
53 Chapter Three: 12S mtDNA
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).
54 Chapter Three: 12S mtDNA
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?
55 Chapter Three: 12S mtDNA
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) .
56 Chapter Three: 12S mtDNA
Table 4. Distribution of 12S mtDNA haplotypes for southeast Queensland populations of C.tinnula.
Population Haplotypes Total
001 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028
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
57 Chapter Three: 12S mtDNA
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.
58 Chapter Three: 12S mtDNA
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
59 Chapter Three: 12S mtDNA
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
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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 VarФ Level of Variation Significance Among Regions 1 87.3 0.873 0.001 (ФCT) Among Populations within Regions 9 8.9 0.700 0.001 (ФSC)
Within Populations 249 3.8 0.962 0.001 (ФST)
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
61 Chapter Three: 12S mtDNA 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.
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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
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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.
Average genetic
Net average genetic distances among groups (DA) 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
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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 Cpar Cpar Csig Csig Barakula Caboolture Karawatha Goomburra 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.
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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).
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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
67 Chapter Three: 12S mtDNA structure and geographic location (Table 11). Of a total of 18 clades only 5 were significant.
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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
Southern Network
2-1
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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.
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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).
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Figure 5. 12S mtDNA mismatch distribution for southern mainland (Peregian, Beerwah, Caboolture and Karawatha) and Bribie Island populations.
2000
1600
1200 Observed 800 Expected Frequency 400
0 0123456
Number of Pairwise Differences
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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
73 Chapter Three: 12S mtDNA 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
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Figure 6. mtDNA haplotype frequencies of Oxleyan Pygmy Perch populations from southeast Queensland (reproduced from Hughes et al. 1999).
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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.
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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.
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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
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(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
79 Chapter Three: 12S mtDNA 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
80 Chapter Three: 12S mtDNA
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.
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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
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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
83 Chapter Four: COI mtDNA among mainland and island populations and to determine processes which may be affecting dispersal at a local level.
84 Chapter Four: COI mtDNA
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 C.tinnula 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 Mungo Myall Lakes, NSW 01 C.parinsignifera CparB (Barakula) ~350km NW Brisbane 01 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.
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4.2.2 DNA EXTRACTION AND AMPLIFICATION OF COI MITOCHONDRIAL DNA FRAGMENT
For all samples included in mitochondrial analyses, DNA extraction followed the Chelex protocol outlined in Chapter 2.
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 th 10mM dNTPs, 2μl 2mM MgCl2, 0.5μl 3.2nm COX, 0.5μl 3.2nm M31, 0.08μl Taq (T 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.
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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.
87 Chapter Four: COI mtDNA
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.
Moreton Stradbroke North South Island 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.
88 Chapter Four: COI mtDNA
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.
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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 27.6 28.6 22.9 20.8 aquilonius 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 30.6 23.7 28.8 16.9 melanoscaphus 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
90 Chapter Four: COI mtDNA
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).
91 Chapter Four: COI mtDNA
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
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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......
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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. ...??????? ?????????? ?
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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.
Average genetic distance among
Net average genetic distances among groups (DA) 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
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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 Ф Level of Variation Significance Among Regions 1 84.0 0.841 0.001
(ФCT) Among Populations within Regions 9 15.0 0.940 0.001
(ФSC) Within
Populations(ФST) 249 1.0 0.990 0.001
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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
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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
98 Chapter Four: COI mtDNA 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).
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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
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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 S0.009 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
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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
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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).
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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 Ф Level of Variation Significance
Among Regions 1 98.6 0.986 0.001
(ФCT) Among Populations 9 0.8 0.577 0.001 within Regions
(ФSC) Within Populations 249 0.6 0.994 0.001
(ФST)
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
104 Chapter Four: COI mtDNA 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.
3000
2500
2000 Observed 1500 Expected
Frequency 1000
500
0 01234 Number of pairwise differences
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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. + northern NSW + 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.
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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;
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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).
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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
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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* 0.00360 0.00218 Strad.-NSW** 0.00126 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.
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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.
116 Chapter Four: COI mtDNA
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.
117 Chapter Five: General Discussion
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
118 Chapter Five: General Discussion 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
119 Chapter Five: General Discussion 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
120 Chapter Five: General Discussion 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.
121 Chapter Five: General Discussion
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
122 Chapter Five: General Discussion
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
123 Chapter Five: General Discussion 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,
124 Chapter Five: General Discussion 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
125 Chapter Five: General Discussion 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
126 Chapter Five: General Discussion
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
127 Chapter Five: General Discussion 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.
128 Appendices
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
129 Appendices
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.
130 Appendices
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.
Number of Samples Analysed for Species Population Microsatellite Variation (F2.5 locus) C.tinnula 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 Mungo 1 C.parinsignifera Barakula 1 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 protocol outlined in Chapter 2.
The PCR conditions and PCR protocol which were the most successful for amplifying the
131 Appendices
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 th 2mM MgCl2, 1μl of 25μM forward primer, 1.0 of 25μM reverse primer, 0.08μl of Taq (T 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.
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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
Number of Samples Analysed for Number of Samples Species Population Microsatellite that Amplified Variation (F2.5 locus) (F2.5 locus) C.tinnula 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 Mungo 1 0 C.parinsignifera Barakula 1 0 Karawatha 1 0 C.signifera Karawatha 1 0
133 Appendices
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. No. No. A D E F G individuals heterozygotes homozygotes WP 16 5 11 0.25 0.03 0.03 0.03 - Bellara 18 6 12 0.25 - 0.03 - 0.11 South* 42 12 30 0.23 0.01 0.02 0.06 0.05 North** 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 - - - - -
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 - - - -
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
134 Appendices
(Table 4).
Table A1.4. Observed and Expected Heterozygosity for Locus F2.5
No. Observed Expected individuals Heterozgosity Heterozygosity (HO) (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
135 Appendices 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.
136 Appendices
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
137 Appendices
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 ..
138 Appendices
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
139 Appendices
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
140 Appendices
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
141 Appendices
Appendix 3.1. Continued.
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
142 Appendices
Appendix 3.1. Continued.
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
143 Appendices
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
144 References
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