The Murray - Darling Turtles: Gene Flow and Population Persistance in Dryland Rivers
Author Baggiano, Olivier
Published 2012
Thesis Type Thesis (PhD Doctorate)
School Griffith School of Environment
DOI https://doi.org/10.25904/1912/3361
Copyright Statement The author owns the copyright in this thesis, unless stated otherwise.
Downloaded from http://hdl.handle.net/10072/367471
Griffith Research Online https://research-repository.griffith.edu.au
The Murray – Darling Turtles:
Gene flow and Population Persistence
in Dryland Rivers
Olivier Baggiano, B. Sc. (Hons)
Griffith School of Environment Science, Environment, Engineering and Technology Griffith University
Submitted in fulfilment of the requirements of the degree of Doctor of Philosophy
February, 2012 Synopsis
Australia’s largest and most important waterway- the Murray-Darling Basin (MDB) - is under threat owing to predicted increases in temperature extremes and reduction in rainfall - runoff in the coming decades. Management strategies are required that incorporate an understanding of dispersal patterns of the MDB fauna and flora. Patterns of dispersal have typically been studied through direct organismal studies but genetic approaches, in which the movement of genes in the landscape is used as a correlate of species dispersal, can provide a more comprehensive view by investigating at a much larger temporal and spatial scale. Genetic connectivity (dispersal) is influenced by the biology of the species, and by flow regime and the dendritic pattern of the network in riverine landscapes. An understanding of the relative influence of each on connectivity is required to deliver informed management strategies. Decisions regarding whether management for conservation is necessary also require an understanding of a species susceptibility to a changing environment. Species already exhibiting deleterious trajectories under current flow regimes in the basin may require more drastic measures than those that have remained unaffected.
The dispersal ability of Chelodina expansa , Chelodina longicollis and Emydura macquarii macquarii , three species of turtles inhabiting the MDB, was inferred from patterns of gene flow in an unregulated dryland river that remains as a succession of permanent waterholes for most parts of the year (Chapter3). This provided a ‘benchmark’ estimate for each species under quasi natural conditions. Comparisons amongst multiple species enabled a better understanding of their respective ability and of the influence of their known ecology. It also provided a means to identify processes or features within the river network that represent shared or unique barriers to movement in these species. The susceptibility of each species to extended periods of no flow and associated habitat quality decline was also investigated in this system through identification of population(s) exhibiting genetic signature of extirpation (Chapter 3). The influence of flow regulation infrastructure on the population connectivity of each species was then examined through patterns of gene flow within a regulated river of the MDB (Chapter 4). Evidence for population extirpation was also searched for in this system with the expectation that flow regulation may have been beneficial by removing extended periods of drought and associated reduction in habitat quality (Chapter 4). The genetic structure of each species at the basin scale was then investigated with the expectation that genetic discontinuities were associated with structural features of the river network (e.g. dams, marshes, etc) and areas characterised by intermittent hydrological continuity (Chapter 5). These discontinuities were expected to identify regions of concern with regards to the long term persistence of the species under predicted rainfall-runoff scenarios in the basin. Finally, sex-biased dispersal was investigated in each species as there was no current knowledge about this dispersal pattern in the MDB turtles (Chapter 6). Based on our current knowledge of the MDB turtles, C. longicollis was expected to exhibit superior dispersal as it is able to undertake long (> 7 km) terrestrial migrations and aestivations, and dispersal between seasonally available habitats is an integral part of its ecology. The superior dispersal ability of the species was apparent with no genetic divergence in the unregulated river (global FST 0.000 at 521 km maximum distance). Low levels of divergence were also exhibited in the regulated river, showing that dams and weirs do not affect movements in this species (global FST 0.014 at 1876 km maximum distance). Despite the species ability to move terrestrially between habitats, evidence for population extirpation events were found in both systems (Chapter 3 and 4). These extirpation events were deemed to result from decline in habitat quality and resource availability in agreement with other studies. Despite its superior dispersal ability C. longicollis exhibited the highest FST (0.029) between populations inferred in GENELAND at the basin scale. This resulted from the presence of two distinct currently mixing lineages, concurring with suggestions made previously for this species (Chapter 5). No region of genetic discontinuity was found at the basin scale apart from this admixture. As expected from previous findings for this species, there was no consistent evidence for sex-biased dispersal, although a lack of analytical power stemming from low sample size in the latter analyses calls for caution (Chapter 6).
Although movements in C. expansa remain largely unstudied, the species was expected to exhibit excellent dispersal ability owing to its large size, and its ability to undertake terrestrial movement (>750 m) for nesting. The species showed more restricted connectivity than either of the other species
in the unregulated river (global FST 0.014 at 521 km maximum distance), with patterns of IBD emerging at an unexpectedly small spatial scale (< 500 km). Gene flow patterns were similar in the
regulated river (global FST 0.033 at 1758 km), with no evidence of influence from dams. Albeit described as ‘restricted’ relative to the other two species, dispersal ability in this species remains superior to many freshwater turtles. Believed to originate in more tropical latitudes, C. expansa was expected to show greater susceptibility to extended periods of no-flow than C. longicollis and E. m. macquarii . However, there was no evidence of population extirpation events in either river studied, suggesting a better ability to cope with habitat quality or resource decline in this species (Chapter 3 and 4). This was in agreement with findings from two recent studies. At the basin scale, C. expansa consisted of one large population with IBD, with no evidence of genetic discontinuity associated with spatially restricted structural features or hydrological discontinuity, although small sample size may have hampered results in some catchments (Chapter 5). Clear evidence for male-biased dispersal and female philopatry (fidelity to natal nesting site) was found in this species (Chapter 6).
Based on the limited information available on E. m. macquarii movements, expectations were for poor dispersal ability with movement restricted to the river network (aquatic environment). These
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expectations were only partially met as the species exhibited high connectivity in the unregulated
river with no IBD at the catchment scale (FST 0.005 at 435 km maximum distance). In the regulated
river, the global FST fell between that of the two other species (0.021 at 1789 km maximum distance) and there was no consistent evidence on the impact of dams on this species movement (Chapter 4). In contrast, and following our expectations, connectivity with backwater populations in the regulated
river was more restricted (FST 0.01 to 0.06) than connectivity between population within the river
proper ( FST < 0.005), suggesting a poor ability to move terrestrially (or along shallow aquatic corridors) (Chapter 4). Within these backwaters, female E. m.macquarii exhibited greater relatedness than males and greater relatedness than in the unregulated river (Chapter 6). The latter could reflect a plastic response of female Emydura to habitat stability, site-fidelity increasing with habitat permanence. Despite no consistent evidence on the effect of dams on its connectivity (Chapter 4), the identification of genetic break associated with structural features in the river network (waterfall, marshes, and headwater dams, Chapter 5) suggested that E. m. macquarii may nonetheless be restricted by barriers requiring out-of-network movements. These results were unique to this species. Aside from these populations and the Warrego catchment, the MDB appear to represent one large E. m. macquarii population with IBD (Chapter 5). Finally, E. m. macquarii exhibited some evidence of population size reduction within waterholes of the unregulated river (Chapter 3). No such evidence was found in the regulated river, although the pattern shown by backwater populations made identification of such events difficult with the method applied here (Chapter 4). The species is present in the driest catchments of Australia, where it is believed to have evolved traits enabling it to cope with the cycle of dry and wet typical of inland rivers of Australia. Extinction-recolonisation cycles may be more common in this species than first thought, its superior dispersal ability, early maturity and fast growth enabling it to recolonise failed refugia rapidly. This remains speculative.
These results highlighted the interaction between species biology, flow regime and features of the landscape in determining population connectivity and processes in riverine landscapes. They also highlighted the need to investigate across multiple systems to gain a better understanding of a species’ dispersal ability. Organisms can exhibit plasticity in their response to disturbance and it is this plasticity (resilience) that we should aim to understand, providing a better perception of a species ability to cope with predicted changes. On a more pragmatic note, the restoration of large seasonal floods in the lower MDB may be the most efficient management strategy to prevent further population size reduction in C. longicollis and to restore connectivity between E. m. macquarii populations. Current modifications of flow regulation infrasctructures in the basin should take movement of E. m. macquarii into consideration, averting the possible need for further modification in the future by enabling movement of fish as well as turtles through dams and weirs.
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Declaration
This work has not previously been submitted for a degree or diploma in any university. To the best of my knowledge and belief, the thesis contains no material previously published or written by another person except where the reference is made in the thesis itself.
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Olivier Baggiano
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Acknowledgments
First and foremost I thank my supervisor Jane Hughes for letting me run ahead with this project and despite my constant stress, fears, questions and worries, for keeping me on track and mindful of the reason why we put ourselves through this: we love asking questions and thinking about ways to answer them. I cannot think of anyone more patient, trusting and ready to listen to new ideas while finding a way to let you know that you should ‘get back on track’.
Dan Schmidt provided plenty of challenges through his constant betting schemes regarding laboratory work success and shared his extensive knowledge of population genetics theory. Dan was always ready to spare some time for my numerous questions, while laughingly brushing away any doubt I had (plenty!) about finishing this project. Don’t over rejoice Dan, you have not gotten rid of me just yet and you will still have to go through ‘turtle talks’ in the coming years.
Many thanks to Arthur Georges for taking me on board the herpetological wagon after only a very short conversation so many years ago. I do not think many people would have done it and this project would never have seen the light without your contributions. You opened the door to your endless knowledge about turtles, your lab and your Wildlife Tissue Collection to me and the time spent there while sub-sampling provided me the chance to meet other students passionate about turtles with which I have since shared knowledge and excitement about Australian freshwater turtles.
One of these students was Kate Hodges who showed me the way around UCAN, the lab, the freshwater turtle world and introduced me the many facets of the Murray-Darling turtles. Two apprentice ‘geneticists’ getting excited about each other’s findings. So thank you Kate for your patience, your time and your contagious excitement. Thanks to Kate, I also met Deborah Bower who happily shared all her findings with me when requested. Between the two of them, a large proportion of the samples included in the present thesis would not exist and therefore the thesis would not either.
My first encounter with a lowland muddy Australian river was exactly as expected: warm, slimy and painful (damn logs!) but the happy attitude of Stephen ‘Harry’ Balcombe and David Sternberg made it all worthwhile. Along the way many others have helped: Jimmy Fawcett, Jaye Lobergeiger, Nick Marsh and Jonathan Marshall amongst others. Two ‘volunteers’ deserve my utmost thanks: Kate Masci and Andrew Bentley who both came along with me to catch some turtles in warm and muddy waters. Kate you saved my fieldtrip and I owe you enormously for that. I think that our time with Gayle and Jay at Longswamp will definitively be the highlight of our trip and next time I will bring an even bigger drowned up generator for you to fix! Andrew ‘Bob’ Bentley: let’s hope that next time we won’t be caught in the largest flood recorded in the Moonie Border region although racing the flood to get out of St George was quite a thrill. I guess I should also thank you for your statistical knowledge, wizz bang IT skills, laughter, long conversations about things not related to genetics (and for those that were of course), running lengthy analyses on your home computer (K = 61 ?!) and so many other things which would make this list too long. Thank you and hope your own gigantic project will all work out for you.
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I am indebted to the owners of Caloola Bed and Breakfast, Bev and Richard Meyer for allowing us onto their property. I hope you will cherish your ‘gully’ as it is a wonder to me, and I think now to you too, having caught so many old C. expansa females there. I am also indebted to Gayle and Jay in Longswamp who opened the door to their property and their house after we had already spent quite a few days swimming in muddy waters: the best shower in years! Thanks also to Bruce Coward for letting us onto his Boomi property. Finally my immense gratitude to Rob Martman from Yarramildi Property who saved Bob and I from being stranded in the deep black soil of his property. Being pulled out by a tractor in heavy rain, knowing that the only way out was through the soon-to-be flooding Weir River was not exactly ‘part of the plan’.
The molecular Geeks provided information, conversations, much knowledge and time, but above all a great environment to work and study in. I especially thank Jemma, Ana, Joel, Ryan, Kathryn, Courtenay and Jodie and those already mentioned above. Thanks to John Spencer and Siti Amri from Griffith University for sharing GIS information and showing me some tricks of the trade. I am grateful to Erika Alacs and Mia Hillier for providing non- published microsatellite primers. Thanks to Jonathan Marshall from DERM for providing acoustic tags: it did not work as planned but it was worth a try and got me excited about different facets of the project.
Funding for the project and living expenses were provided by the Australian River Institute, a Griffith University Postgraduate Research Scholarship, an Endeavour International Postgraduate Research Scholarship and a grant from the Australian Capital Territory Herpetological Association.
During the three and a half years this thesis took to be completed I met and married my wonderful wife Beck. I am ever so grateful that you stayed around in the highs and the lows and did not laugh (too much) when asked what your husband was up to chasing turtles in rivers. Your hard working ethic and can-do attitude was a great motivation to see the end of this project. Here is to many full weekends together. To my far away family in Switzerland: Mum you believed in me like only a mother can. I promise I will try to move a bit closer next time.
Thank you
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Table of Contents Synopsis ...... i Acknowledgments...... v Table of Contents ...... vii List of Figures ...... x List of Tables ...... xii Chapter 1 General Introduction ...... 1 1.1 Australia’s Murray – Darling Basin ...... 1 1.2 Strategies for Species Persistence in Dryland Rivers ...... 2 1.3 Population Genetic Approaches to Studying Dispersal ...... 3 1.3.1 Gene flow ...... 3 1.3.2 Measure of Gene Flow ...... 4 1.3.3 Inferring Connectivity from Gene Flow ...... 5 1.4 Population Genetics in Dendritic Landscapes ...... 7 1.4.1 Models of Population Genetics Structure in Streams ...... 7 1.4.2 Dendritic Networks and Population Persistence ...... 8 1.5 Freshwater Turtles ...... 9 1.5.1 Ecological Role of Freshwater Turtles ...... 9 1.5.2 Freshwater Turtles of the Murray – Darling Basin ...... 10 1.6 Aims ...... 11 Chapter 2 General Methods ...... 13 2.1 Fieldwork ...... 14 2.1.1 Sampling ...... 14 2.1.2 Records per Individual ...... 17 2.1.3 Life Stage and Sex Determination ...... 17 2.1.4 Morphological Measurements ...... 18 2.1.5 Shell Notching and EPA Tags ...... 18 2.1.6 Skin Tissue Acquisition ...... 19 2.2 Laboratory Methods ...... 19 2.2.1 Total Genomic DNA Extraction ...... 19 2.2.2 Microsatellite Markers ...... 20 2.2.3 Microsatellite Primers ...... 21 2.2.4 Microsatellite Library ...... 22 2.2.5 Microsatellite Amplification and Assays ...... 24 2.3 Data Analysis ...... 29
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2.3.1 Intrapopulation ...... 29 2.3.2 Analysis of Population Genetic Structure ...... 32 Chapter 3 Genetic Connectivity in a Dryland River ...... 39 3.0 Introduction ...... 40 3.1 Methods...... 42 3.1.1 Catchments Description ...... 42 3.1.2 Laboratory and Statistical Methods ...... 44 3.2 Results ...... 46 3.2.1 Sample Sizes ...... 46 3.2.2 Intrapopulation Diversity Measure ...... 47 3.2.3 Genetic Structure ...... 56 3.3 Discussion ...... 69 3.3.1 Gene Flow and Genetic Structure ...... 69 3.3.2 Metapopulation Dynamics in a Dryland River ...... 72 3.4 Conclusion ...... 74 Chapter 4 Genetic Connectivity in a Regulated River ...... 75 4.0 Introduction ...... 76 4.1 Methods...... 77 4.1.1 Catchments Description ...... 77 4.1.2 Sample Sizes and Distribution ...... 78 4.1.3 Laboratory and Statistical Methods ...... 79 4.2 Results ...... 80 4.2.1 Intrapopulation Diversity Measure ...... 80 4.2.2 Genetic Structure in the Lower Murray-Darling Basin ...... 87 4.3 Discussion ...... 92 4.3.1 Intrapopulation Genetic Diversity...... 92 4.3.2 Population Structure...... 93 4.3.3 Effect of Flow Regulation Infrastructure ...... 95 4.4 Conclusion ...... 96 Chapter 5 Basin Scale Population Genetic Structure ...... 97 5.1 Introduction ...... 98 5.2 Methods...... 99 5.2.1 Laboratory Methods, Sample Sizes and Study Region ...... 99 5.2.2 Statistical Methods ...... 102 5.3 Results ...... 103 5.3.1 C. expansa ...... 103 viii
5.3.2 C. longicollis ...... 108 5.3.3 E. m. macquarii ...... 110 5.4 Discussion ...... 113 5.4.1 Sampling Scheme and Local Autocorrelation Influence on Clustering Methods . 113 5.4.2 C. expansa ...... 113 5.4.3 C. longicollis ...... 114 5.4.2 E. m. macquarii ...... 116 5.5 Conclusion ...... 118 Chapter 6 Sex-biased Dispersal ...... 119 6.1 Introduction ...... 120 6.2 Methods...... 122 6.2.1 Laboratory Methods and Sample Sizes ...... 122 6.2.2 Statistical Methods ...... 123 6.3 Results ...... 127 6.3.1 C. expansa ...... 127 6.3.2 C.longicollis ...... 134 6.3.3 E. m. macquarii ...... 137 6.4 Discussion ...... 141 6.4.1 C. expansa ...... 141 6.4.2 E. m. macquarii ...... 142 6.4.3 C. longicollis ...... 144 6.5 Conclusion ...... 145 Chapter 7 General Discussion ...... 146 7.1 Population Genetics of the MDB turtles ...... 146 7.2 How do the MDB turtles compare? ...... 148 7.3 Metapopulation in temporary-river ...... 149 7.4 Concluding Remark ...... 150 8 Appendices ...... 152 8.1 Freshwater Turtles of the Murray – Darling Basin ...... 152 8.2 Sampling Sites ...... 161
8.3 Pairwise and Global F ST Tables ...... 164 8.4 Decomposed Pairwise Regression Analyses ...... 171 8.5 Basin Scale Population Genetic Structure ...... 176 9 References ...... 177
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List of Figures
FIGURE 1. 1 ILLUSTRATION OF POSSIBLE RELATIONSHIPS BETWEEN GENETIC AND GEOGRAPHIC DISTANCES UNDER DIFFERENT GENE FLOW AND GENETIC DRIFT RELATIVE STRENGTH ...... 6 FIGURE 2. 1 DIAGRAM OF FYKE NET USED FOR TURTLES CAPTURE ...... 14 FIGURE 2. 2 FIELDWORK OF INTEREST ...... 16 FIGURE 2. 3 MORPHOLOGICAL MEASUREMENT AND NOTCHING PATTERN ...... 18 FIGURE 3. 1 MAP OF THE MOONIE RIVER , BORDER -BARWON RIVERS AND GWYDIR RIVER CATCHMENT WITH SAMPLING SITE LOCATION ...... 43 FIGURE 3. 2 STRAIGHT CARAPACE LENGTH (SSL) AGAINST TAIL LENGTH (TL) FOR C. EXPANSA INDIVIDUALS CAUGHT IN THE MOONIE AND BARWON RIVER ...... 46 FIGURE 3. 3 STRAIGHT CARAPACE LENGTH (SSL) AGAINST TAIL LENGTH (TL) FOR E. M. MACQUARII INDIVIDUALS CAUGHT IN THE MOONIE AND BARWON RIVER ...... 47 FIGURE 3. 4 DECOMPOSED PAIRWISE REGRESSION ANALYSIS (DPRA) FOR C. EXPANSA IN THE MOONIE AND BARWON RIVERS WITH CHANNEL DISTANCE ...... 56 FIGURE 3. 5 CORRELATION COEFFICIENT R AS A FUNCTION OF VARIABLE DISTANCE CLASS SIZE FOR C. EXPANSA IN THE MOONIE AND BARWON RIVERS ...... 58 FIGURE 3. 6 CORRELATION COEFFICIENT R PER AGE CLASS UNDER INCREASING DISTANCE CLASS SIZES FOR C. EXPANSA IN THE MOONIE AND BARWON RIVERS ...... 59 FIGURE 3. 7 DECOMPOSED PAIRWISE REGRESSION ANALYSIS (DPRA) FOR C. LONGICOLLIS IN THE MOONIE AND BARWON RIVERS WITH CHANNEL DISTANCE ...... 60 FIGURE 3. 8 CORRELATION COEFFICIENT R AS A FUNCTION OF VARIABLE DISTANCE CLASS SIZE FOR C. LONGICOLLIS IN THE MOONIE AND BARWON RIVERS ...... 61 FIGURE 3. 9 CORRELATION COEFFICIENT R PER AGE CLASS UNDER INCREASING DISTANCE CLASS SIZES (20 KM ) FOR C. LONGICOLLIS IN THE MOONIE AND BARWON RIVERS ...... 61 FIGURE 3. 10 DECOMPOSED PAIRWISE REGRESSION ANALYSIS (DPRA) FOR C. LONGICOLLIS IN THE MOONIE RIVER , BORDER RIVERS AND GWYDIR RIVER CATCHMENTS WITH EUCLIDEAN DISTANCE ...... 63 FIGURE 3. 11 GENELAND MAP OF POSTERIOR PROBABILITY TO BELONG TO POPULATION 2 FOR E. M .MACQUARII IN THE MOONIE AND BORDER RIVERS CATCHMENT ...... 64 FIGURE 3. 12 DECOMPOSED PAIRWISE REGRESSION ANALYSIS (DPRA) FOR E. M. MACQUARII IN THE MOONIE AND BARWON RIVERS WITH CHANNEL DISTANCE ...... 66 FIGURE 3. 13 CORRELATION COEFFICIENT R AS A FUNCTION OF VARIABLE DISTANCE CLASS SIZE FOR E. M. MACQUARII IN THE MOONIE AND BARWON RIVERS . COMBINED DATASET (N = 181). U AND L: UPPER AND LOWER 95% CI ABOUT THE NULL HYPOTHESIS OF A RANDOM DISTRIBUTION OF GENOTYPES . ERROR BARS : 95% CONFIDENCE INTERVAL ABOUT R FROM BOOTSTRAPPING ...... 66 FIGURE 3. 14 CORRELATION COEFFICIENT R PER AGE CLASS UNDER INCREASING DISTANCE CLASS SIZES (20 KM ) FOR E. M. MACQUARII IN THE MOONIE AND BARWON RIVERS ...... 67 FIGURE 3. 15 DECOMPOSED PAIRWISE REGRESSION ANALYSIS (DPRA) FOR E. M. MACQUARII IN THE BORDER RIVER AND GWYDIR RIVER CATCHMENTS WITH CHANNEL DISTANCE ...... 68 FIGURE 4. 1 MAP OF THE LACHLAN RIVER , MURRUMBIDGEE RIVER AND MURRAY RIVER CATCHMENTS WITH SAMPLING SITES AND MAJOR DAMS AND WEIRS ...... 79 FIGURE 4. 2 DECOMPOSED PAIRWISE REGRESSION ANALYSIS (DPRA) FOR C. EXPANSA IN THE LOWER MURRAY RIVER WITH CHANNEL DISTANCE...... 87 FIGURE 4. 3 DECOMPOSED PAIRWISE REGRESSION ANALYSIS (DPRA) FOR C. LONGICOLLIS IN THE LOWER MURRAY RIVER WITH CHANNEL DISTANCE ...... 89 FIGURE 4. 4 DECOMPOSED PAIRWISE REGRESSION ANALYSIS (DPRA) FOR E. M. MACQUARII IN THE LOWER MURRAY RIVER WITH CHANNEL DISTANCE ...... 91 FIGURE 4. 5 E. M. MACQUARII PLOT OF GENETIC DISTANCE (FST / (1-FST )) AGAINST CHANNEL DISTANCE ...... 91 FIGURE 5. 1 TOPOGRAPHIC MAP OF THE MURRAY -DARLING BASIN , WITH BASIN BOUNDARIES AND MAIN RIVERS ...... 101 x
FIGURE 5. 2 C. EXPANSA : MAP OF PROBABILITY OF BELONGING TO CLUSTER 1( UPPER MDB) UNDER THE UNCORRELATED MODEL OF ALLELE FREQUENCIES IN GENELAND...... 104 FIGURE 5. 3 C. EXPANSA : MAP OF POPULATION MEMBERSHIP UNDER THE CORRELATED MODEL OF ALLELE FREQUENCIES IN GENELAND...... 105 FIGURE 5. 4 ∆K (A MEASURE OF THE RATE OF CHANGE IN THE STRUCTURE LIKELIHOOD FUNCTION ) VALUES AS A FUNCTION OF K, THE NUMBER OF PUTATIVE POPULATIONS (A, B, C), AND MEAN OF ESTIMATE LN PROBABILITY OF DATA [L N(K)] (A A, BB, CC). A) C. EXPANSA ; B) C. LONGICOLLIS ; C) E. M. MACQUARII ...... 106 FIGURE 5. 5 GRAPHICAL OUTPUT OF STRUCTURE FOR EACH SPECIES AFTER ALIGNMENT OF REPLICATED RUNS IN CLUMPP. A) C. EXPANSA ; B) C. LONGICOLLIS ; C) E. M. MACQUARII ...... 107 FIGURE 5. 6 C. LONGICOLLIS : MAP OF PROBABILITY OF BELONGING TO CLUSTER 1( UPPER MDB) UNDER THE UNCORRELATED MODEL OF ALLELE FREQUENCIES IN GENELAND...... 108 FIGURE 5. 7 C. LONGICOLLIS : MAP OF POPULATION MEMBERSHIP UNDER THE CORRELATED MODEL OF ALLELE FREQUENCIES IN GENELAND...... 109 FIGURE 5. 8 ALLELIC RICHNESS (A R) (AFTER RAREFACTION FOR 6 GENES ), EXPECTED HETEROZYGOSITY (HE) AND PRIVATE ALLELE RICHNESS (AFTER RAREFACTION FOR 6 GENES ) FOR C. LONGICOLLIS POPULATIONS IN THE LOWER MDB...... 110 FIGURE 5. 9 E. M. MACQUARII : MAP OF PROBABILITY OF BELONGING TO CLUSTER 1( UPPER MDB) UNDER THE UNCORRELATED MODEL OF ALLELE FREQUENCIES IN GENELAND...... 111 FIGURE 5. 10 E. M. MACQUARII : MAP OF POPULATION MEMBERSHIP UNDER THE CORRELATED MODEL OF ALLELE FREQUENCIES IN GENELAND...... 111 FIGURE 6. 1 GENETIC CORRELATION COEFFICIENT R PER SEX -CLASS IN C. EXPANSA AS A FUNCTION OF INCREASING VARIABLE DISTANCES IN THE MOONIE RIVER (A) (F = 28; M = 14) AND THE LOWER MURRAY RIVER (B) (F = 34; M = 34)...... 127 FIGURE 6. 2 COANCESTRY OUTPUT FOR C. EXPANSA (UPPER MDB: A, B; LOWER MDB: C, D), C. LONGICOLLIS (UPPER MDB: E, F; LOWER MDB: G, H) AND E. M. MACQUARII (UPPER MDB: I, J; LOWER MDB: K, L) WITH QUELLER AND GOODNIGHT (1989) (A, C, E, G, I, K) AND TRIO ML (W ANG 2007) (B, D, F, H, L) ESTIMATOR ...... 129 CONTINUE FIGURE 6. 2 COANCESTRY OUTPUT FOR C. EXPANSA (UPPER MDB: A, B; LOWER MDB: C, D), C. LONGICOLLIS (UPPER MDB: E, F; LOWER MDB: G, H) AND E. M. MACQUARII (UPPER MDB: I, J; LOWER MDB: K, L) WITH QUELLER AND GOODNIGHT (1989) (A, C, E, G, I, K) AND TRIO ML (W ANG 2007) (B, D, F, H, L) ESTIMATOR ...... 130 FIGURE 6. 3 C. EXPANSA : PLOTS SHOWING RELATIONSHIP BETWEEN THE MEANS OF RANKED R (Q UELLER RELATEDNESS ) (A, C) VALUES AND RANKED A (R OUSSET ’S GENETIC DISTANCE ) VALUES (B, D) AND CORRESPONDING MEAN SPATIAL (CHANNEL ) DISTANCES IN MALES AND FEMALES IN THE UPPER (A, B) AND LOWER (C, D) MURRAY -DARLING BASIN POPULATIONS . . 133 FIGURE 6. 4 GENETIC CORRELATION COEFFICIENT R PER SEX -CLASS IN C. LONGICOLLIS AS A FUNCTION OF INCREASING VARIABLE DISTANCES IN THE LOWER MURRAY RIVER ...... 135 FIGURE 6. 5 C. LONGICOLLIS : PLOTS SHOWING RELATIONSHIP BETWEEN THE MEANS OF RANKED R (Q UELLER RELATEDNESS ) (A, C) VALUES AND RANKED A (R OUSSET ’S GENETIC DISTANCE ) VALUES (B, D) AND CORRESPONDING MEAN SPATIAL (CHANNEL ) DISTANCES IN MALE AND FEMALE IN THE UPPER (A, B) AND LOWER (C, D) MURRAY -DARLING BASIN POPULATIONS . ... 137 FIGURE 6. 6 GENETIC CORRELATION COEFFICIENT R PER SEX -CLASS IN E. M. MACQUARII AS A FUNCTION OF INCREASING VARIABLE DISTANCES IN THE MOONIE RIVER (A) (F = 46; M = 60) AND THE LOWER MURRAY RIVER (B) (F = 30; M = 13)...... 138 FIGURE 6. 7 E. M. MACQUARII : PLOTS SHOWING RELATIONSHIP BETWEEN THE MEANS OF RANKED R (Q UELLER RELATEDNESS ) (A, C) VALUES AND RANKED A (R OUSSET ’S GENETIC DISTANCE ) VALUES (B, D) AND CORRESPONDING MEAN SPATIAL (CHANNEL ) DISTANCES IN MALE AND FEMALE IN THE UPPER (A, B) AND LOWER (C, D) MURRAY -DARLING BASIN POPULATIONS . ... 140
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List of Tables
TABLE 2. 1 MICROSATELLITE PRIMERS FOR E. M. MACQUARII ...... 26 TABLE 2. 2 MICROSATELLITE PRIMERS FOR C. EXPANSA ...... 27 TABLE 2. 3 MICROSATELLITE PRIMERS FOR C. LONGICOLLIS ...... 28 TABLE 3. 1 C. EXPANSA DIVERSITY INDICES ...... 48 CONTINUED TABLE 3. 1 C. EXPANSA DIVERSITY INDICES ...... 49 TABLE 3. 2 C. LONGICOLLIS DIVERSITY INDICES ...... 50 CONTINUED TABLE 3. 2 C. LONGICOLLIS DIVERSITY INDICES ...... 51 TABLE 3. 3 E. M. MACQUARII DIVERSITY INDICES ...... 53 CONTINUED TABLE 3. 3 E. M. MACQUARII DIVERSITY INDICES ...... 54 CONTINUED TABLE 3. 3 E. M. MACQUARII DIVERSITY INDICES ...... 55 TABLE 3. 4 ANALYSIS OF MOLECULAR VARIANCE FOR C. EXPANSA , C. LONGICOLLIS AND E. M. MACQUARII IN THREE UPPER CATCHMENTS OF THE MURRAY -DARLING BASIN ...... 57 TABLE 3. 5 INTERCEPT AND SLOPE (WITH 95% CI) OF THE DECOMPOSED PAIRWISE REGRESSION ANALYSIS (DPRA) FOR C. EXPANSA IN THE MOONIE AND BARWON RIVERS ...... 57 TABLE 3. 6 INTERCEPT AND SLOPE (WITH 95% CI) OF THE DECOMPOSED PAIRWISE REGRESSION ANALYSIS (DPRA) FOR C. LONGICOLLIS IN THE MOONIE AND BARWON RIVERS ...... 60 TABLE 3. 7 INTERCEPT AND SLOPE (WITH 95% CI) OF THE DECOMPOSED PAIRWISE REGRESSION ANALYSIS (DPRA) FOR C. LONGICOLLIS IN THE MOONIE RIVER , BORDER RIVERS AND GWYDIR RIVER CATCHMENTS ...... 62 TABLE 3. 8 GENELAND OUTPUT FOR UNCORRELATED MODEL FREQUENCIES WITH SPATIAL INFORMATION FOR E. M. MACQUARII . K: NUMBER OF POPULATION ...... 64 TABLE 3. 9 INTERCEPT AND SLOPE (WITH 95% CI) OF THE DECOMPOSED PAIRWISE REGRESSION ANALYSIS (DPRA) FOR E. M. MACQUARII IN THE MOONIE AND BARWON RIVERS ...... 65 TABLE 3. 10 INTERCEPT AND SLOPE (WITH 95% CI) OF THE DECOMPOSED PAIRWISE REGRESSION ANALYSIS (DPRA) FOR E. M. MACQUARII IN THE BORDER RIVERS AND GWYDIR RIVER CATCHMENTS ...... 68 TABLE 4. 1 C. EXPANSA DIVERSITY INDICES IN THE LOWER MURRAY -DARLING BASIN ...... 81 CONTINUED TABLE 4.1 C. EXPANSA DIVERSITY INDICES IN THE LOWER MURRAY -DARLING BASIN . .. 82 TABLE 4. 2 C. LONGICOLLIS DIVERSITY INDICES IN THE LOWER MURRAY -DARLING BASIN ...... 83 CONTINUED TABLE 4. 2 C. LONGICOLLIS DIVERSITY INDICES IN THE LOWER MURRAY -DARLING BASIN ...... 84 TABLE 4. 3 E. M. MACQUARII DIVERSITY INDICES IN THE LOWER MURRAY -DARLING BASIN ...... 85 CONTINUED TABLE 4. 3 E. M. MACQUARII DIVERSITY INDICES IN THE LOWER MURRAY -DARLING BASIN ...... 86 TABLE 4. 4 OUTPUT OF WELTCH ’S T -TESTS (ASSUMING UNEQUAL VARIANCE ) FOR MEAN ALLELIC RICHNESS (AR) (N = 6 GENES ) WITH RAREFACTION , MEAN OBSERVED HETEROZYGOSITY (HO) AND MEAN EXPECTED HETEROZYGOSITY (HE) FOR C. EXPANSA , C. LONGICOLLIS AND E. M. MACQUARII BETWEEN THE MOONIE RIVER AND THE MURRAY RIVER ...... 86 TABLE 4. 5 GLOBAL FST FOR C. EXPANSA , C. LONGICOLLIS AND E. M. MACQUARII IN THE LOWER MDB...... 87 TABLE 4. 6 INTERCEPT AND SLOPE (WITH 95% CI) OF THE DECOMPOSED PAIRWISE REGRESSION ANALYSIS (DPRA) FOR C. EXPANSA IN THE LOWER MURRAY RIVER , SOUTH AUSTRALIA , WITH CHANNEL DISTANCE ...... 88 TABLE 4. 7 MANTEL AND PARTIAL MANTEL TEST OUTPUT FOR C. EXPANSA , C. LONGICOLLIS AND E. M. MACQUARII IN THE LOWER MDB...... 88 TABLE 4. 8 INTERCEPT AND SLOPE (WITH 95% CI) OF THE DECOMPOSED PAIRWISE REGRESSION ANALYSIS (DPRA) FOR C. LONGICOLLIS IN THE LOWER MURRAY RIVER , SOUTH AUSTRALIA , WITH CHANNEL DISTANCE...... 89
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TABLE 4. 9 INTERCEPT AND SLOPE (WITH 95% CI) OF THE DECOMPOSED PAIRWISE REGRESSION ANALYSIS (DPRA) FOR E. M. MACQUARII IN THE LOWER MURRAY RIVER , SOUTH AUSTRALIA , WITH CHANNEL DISTANCE...... 90 TABLE 5. 1 OUTPUTS OF CORRELATED AND UNCORRELATED MODELS OF ALLELE FREQUENCIES WITH SPATIAL INFORMATION IN GENELAND FOR C. EXPANSA , C. LONGICOLLIS AND E. M. MACQUARII ...... 103 TABLE 5. 2 F-STATISTICS FOR POPULATIONS INFERRED WITH THE UNCORRELATED MODEL OF ALLELES FREQUENCIES IN GENELAND...... 103 TABLE 5. 3 E. M. MACQUARII PAIRWISE FST COMPARISON FROM MICROSATELLITE DATA IN THE MURRAY -DARLING BASIN...... 112 TABLE 6. 1SAMPLE SIZE PER SEX -CLASS AND ANALYTICAL METHOD FOR C. EXPANSA , C. LONGICOLLIS AND E. M. MACQUARII ...... 122 TABLE 6. 2 COANCESTRY OUTPUT FOR SEX -BIASED DISPERSAL IN C. EXPANSA , C. LONGICOLLIS AND E. M. MACQUARII USING QUELLER AND GOODNIGHT (1989) AND TRIO ML (W ANG 2007) ESTIMATORS ...... 131 TABLE 6. 3 RESULTS OF MANN -WHITNEY U TESTS COMPARING REGRESSION SLOPES OF GENETIC ON SPATIAL DISTANCE FOR MALE AND FEMALE INDIVIDUALS OF THE MURRAY -DARLING BASIN TURTLES ...... 132 TABLE 6. 4 RESULTS OF MANTEL CORRELATION TESTS BETWEEN PAIRWISE GENETIC AND SPATIAL (CHANNEL ) DISTANCES AMONG INDIVIDUAL MURRAY -DARLING BASIN TURTLES ...... 134 TABLE 6. 5 SUMMARY OF TESTS FOR SEX -BIASED DISPERSAL IN C. EXPANSA , C. LONGICOLLIS AND E. M. MACQUARII ...... 141
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Chapter 1 General Introduction
The world’s freshwater ecosystems are under threat and are declining rapidly (Ricciardi and Rasmussen, 1999; Vance-Borland & Roux & Nel et al. , 2008; Vorosmarty & McIntyre & Gessner et al. , 2010; Kingsford, 2011). Primary threats are water diversion for irrigation (Doupé and Pettit, 2002), dams and weirs (Kingsford, 2000; Todd & Ryan & Nicol et al. , 2005), introduction of exotic species (Canonico & Arthington & McCrary et al. , 2005; Kennard & Arthington & Pusey et al. , 2005), decline in water and habitat quality (Goss, 2003; Brainwood & Burgin and Byrne, 2006) and habitat loss or fragmentation (Aarts & Van Den Brink and Nienhuis, 2004; Bennett & Keevil and Litzgus, 2009). All have led to severe biodiversity decline or loss (Ricciardi and Rasmussen, 1999; Bunn and Arthington, 2002; Dudgeon & Arthington & Gessner et al. , 2006; Arthington & Naiman & McClain et al. , 2010), especially in reptile and amphibian species (Gibbons & Scott & Ryan et al. , 2000; Stuart & Chanson & Cox et al. , 2004; Rowe, 2008; Buhlmann & Akre & Iverson et al. , 2009), and the predicted exacerbations of current impacts under most climate changes scenarios are alarming (Hughes, 2003; Araújo & Thuiller and Pearson, 2006; Vorosmarty et al. , 2010; Aldous & Fitzsimons & Richter et al. , 2011). Of special concern amongst freshwater ecosystems, dryland rivers are recognised as being of significance for the conservation of biodiversity (Bodie and Semlitsch, 2000; Bunn and Arthington, 2002; Larned & Datry & Arscott et al. , 2010), their highly variable flow regime providing an array of ecological and habitat conditions supporting diverse communities over time (Bunn & Thoms & Hamilton et al. , 2006; Leigh and Sheldon, 2008; Leigh & Sheldon & Kingsford et al. , 2010) that are unrepresented elsewhere. These fluctuations in ecological and habitat conditions are known as the boom and bust ecosystem processes (Bunn et al. , 2006). Floodplains and rivers are fragmented into patches of unconnected and more or less permanent habitats (referred to as refugia) during bust periods, while boom periods are characterised by the availability of highly productive seasonal habitats and the restoration of connectivity within the main channel (McMahon and Finlayson, 2003; Bunn et al. , 2006; Sheldon & Bunn & Hughes et al. , 2010). Regular seasonal floods in accordance with natural rhythms are therefore of prime importance in supporting and providing the ecological requirements for the communities inhabiting these systems (Walker & Sheldon and Puckridge, 1995; Puckridge & Sheldon & Walker et al. , 1998; Thoms and Sheldon, 2000a; Leigh et al. , 2010).
1.1 Australia’s Murray – Darling Basin
In Australia, predictions are for increased temperature and evaporation, and associated reduced rainfall-runoff to rivers and wetlands (Herron & Davis and Jones, 2002; Alexander & Hope & Collins et al. , 2007; Chiew & Vaze & Viney et al. , 2008; Pittock and Finlayson, 2011). These predictions are of particular concern for the Murray-Darling Basin (MDB), the sixth largest system in the world (1.073 million km 2) (Reid and Brooks, 2000) which is described as arid or semi-arid for the most part (Mackay and Eastburn, 1990). In its natural state, the basin exhibited highly variable flow periodically flushing and replenishing its extensive network of wetlands, oxbow lakes, anabranches and floodplains, and providing temporal and spatial connectivity (Walker and Thoms, 1993; Maheshwari & Walker and McMahon, 1995; Walker et al. , 1995; Kingsford, 2000). Now representing 70% of Australian irrigated agriculture (Davies & Harris & Hillman et al. , 2008), the hydrological regime of the basin has been subjected to large scale changes with an extensive suite of dams and weirs for regulation and irrigation (Reid and Brooks, 2000; Kingsford and Thomas, 2004; Walker, 2006). Seasonal floods no longer occur in the same frequency as they once did in the basin and this has led to severe species declines (Gehrke and Harris, 2001; Humphries & Serafini and King, 2002; Lind & Robson and Mitchell, 2006; King & Tonkin and Mahoney, 2009; Price & Gross and Whalley, 2010), impacted a number of nationally and internationally significant wetlands (Kingsford, 1999; Reid and Brooks, 2000; Roshier & Whetton & Allan et al. , 2001; Kingsford and Auld, 2005; Wilson & Spencer and Heagney, 2010), and reduce the ability of such wetlands to act as local refugia (Rayner & Jenkins and Kingsford, 2009). A decade of severe drought in the basin further highlighted the urgency; drought acting at such a large scale that it threatens not only the survival of individuals but of populations or even species (Lake, 2003; Bond & Lake and Arthington, 2008). A number of strategies have been put forward to mitigate the effect of predicted climate change and current alteration of flow regime in the basin (see Aldous et al. , 2011; Pittock and Finlayson, 2011). These include (a) the identification and establishment of freshwater protected areas (Saunders & Meeuwig and Vincent, 2002; Pittock and Finlayson, 2011) or refuge areas (Lake, 2003; Bond et al. , 2008; Aldous et al. , 2011), primarily wetlands on floodplains, and (b) modifying the operation of flow regulation infrastructure to sustain these refugia and maintain appropriate flow connectivity (Whittington & Bunn & Cullen et al. , 2002; Forester, 2008; Wilson et al. , 2010). The success of these strategies will however depend on the ability of the species to take advantage of these refuges and to recolonise them following periods of no flow (Magoulick and Kobza, 2003; Bond et al. , 2008).
1.2 Strategies for Species Persistence in Dryland Rivers
Species inhabiting drought prone systems have evolved a number of strategies that enable them to survive in these harsh conditions (Puckridge & Walker and Costelloe, 2000; Unmack, 2001; McMahon and Finlayson, 2003; Roe and Georges, 2009). Species can either ‘sit and wait’, essentially slowing down their metabolism (Kennett and Christian, 1994; Roe and Georges, 2009), or recolonise and recruit rapidly following the onset of wetter period (Lake, 2003; Bond et al. , 2008). A number of Australian freshwater turtles are known to ‘sit and wait’ on a seasonal basis (aestivation), awaiting the return of wetter conditions (Kennett and Christian, 1994; Cann, 1998; Roe and Georges, 2008b; Roe
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and Georges, 2008a). Should periods of drought or no flow extend past their ability to ‘sit and wait’ individuals will have to move to more permanent habitat (if available) or successful recolonisation of the failed refugia may be required for the long term persistence of the species in the system (Rees & Roe and Georges, 2009; Roe and Georges, 2009). Dispersal is therefore one of the key processes allowing survival of a species in fragmented landscapes (Hanski, 2001; Clobert & Ims and Rousset, 2004). An understanding of a species recolonisation ability, as well as its susceptibility to extended periods of no flow or drought and associated reduction in habitat quality (Chessman, 2011), is fundamental to assess management strategies proposed under the assumption ‘build it and they will come’ (see Hughes, 2007). Population genetic tools can provide a means to gain such understanding.
1.3 Population Genetic Approaches to Studying Dispersal
1.3.1 Gene flow
Following successful reproduction in the receiving patch, the movement of individuals leads to the exchange of genes between populations (Whitlock and McCauley, 1999). This movement of ‘genes’ will be interchangeably referred to as dispersal or gene flow (Slatkin, 1985; Bohonak, 1999) in this thesis. The spatial distribution of genes amongst patches can consequently be used as a correlate for connectivity, the greater the gene flow the higher the connectivity for a given population size (Slatkin, 1987; Bohonak, 1999; Lowe and Allendorf, 2010). The vast majority of species show some level of population substructuring, or genetic divergence, where the connectivity between patches of interbreeding individuals is unequal, external factors facilitating or impeding the movement of individuals (genes) (Wiens, 1997; 2001). By investigating the magnitude and spatial distribution of genetic substructures, the influence of structural features (e.g. barriers), processes in the landscape (e.g. flow) and species response to its surroundings (e.g. dispersal ability) can be inferred, and the evolutionary independence of each population assessed (Wiens, 1997; Avise, 2000; Scribner & Blanchong & Bruggeman et al. , 2005; Hughes, 2007; Hughes & Schmidt and Finn, 2009b). Comparison of multiple sympatric species provides an improved means to assess their respective dispersal abilities, by the consistent identification of factors intrinsic to the system (Bohonak, 1999).
In isolated patches, genetic divergence (the variation in allele frequencies amongst patches) results from the combination of genetic drift (the random loss of alleles in every generation through reproduction), mutation (the birth and accumulation of new alleles in a population) and selection (the differential selection of alleles/genes through generations) (Slatkin, 1987; Neuhauser, 2007). In connected patches, allele frequencies are further influenced by gene flow which reduces genetic divergence (Slatkin, 1985; 1987). High gene flow can result in the homogenisation of alleles amongst
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all interbreeding patches, while low levels of gene flow can result in distinct allele frequencies amongst demes depending on the speed of genetic drift within each deme (Slatkin, 1985; 1987). The speed of genetic drift is negatively related to the effective size of the population, the chance loss of alleles being faster in smaller effective populations ( Ne) (Lacy, 1987; Pages and Holmes, 1998). In large (infinite) randomly mating populations, Ne is approximately equivalent to the size of the breeding population, but in most true populations where size is finite and fluctuates through time, Ne represents only a portion of the breeding population (Frankham, 1996; Pages and Holmes, 1998). As Ne is linked to the rate of random loss of alleles, estimation of a population genetic diversity can be used as a correlate to its relative size (Frankham, 1996).
1.3.2 Measure of Gene Flow
A measure of population divergence, FST , was introduced by Wright (1943) enabling inference on how much gene flow must have occurred between populations for the level of (or lack of) differentiation revealed in the observed data (Slatkin, 1985; Neigel, 2002; Holsinger and Weir, 2009). The measure is most commonly interpreted as the variance in allele frequencies across populations Var (p) standardized by the mean allele frequency (p´):
(FST = Var (p)/p´(1-p´)
(Slatkin, 1985). Other definitions have been introduced, such as Nei’s (1972) common definition:
(GST = (H T - HS)/ H T)
which uses the expected heterozygosity HT at the total population level against the observed average
heterozygosity HS at the subpopulation level (measures reviewed in Balloux and Lugon-Moulin,
2002; Holsinger and Weir, 2009). Low FST values imply low divergence in allele frequency among populations, enough migrants per generation moving between patches to compensate for the effect of genetic drift, while high values imply fixation of different alleles within each population owing to isolation (Slatkin, 1985; Neuhauser, 2007; Lowe and Allendorf, 2010). Wright’s original method was devised for a single locus with two or more alleles. A model allowing unequal sample sizes, multiple alleles and loci was subsequently devised (Weir and Cockerham, 1984). Under this latter model, increasing the number of loci, populations and individuals from each population will increase the precision of the estimate (Weir and Cockerham, 1984; Cockerham and Weir, 1993; Holsinger and Weir, 2009).
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1.3.3 Inferring Connectivity from Gene Flow
Assuming a stepping-stone model of migration (Kimura and Weiss, 1964), where individuals (genes) cannot move over the entirety of an area during their lifetime (constrained by dispersal) and where local population sizes and dispersal remain stable for an extended period of time, a pattern of equilibrium between gene flow and genetic drift will appear (Hutchison and Templeton, 1999). In such cases a positive monotonic relationship will exist between the genetic and the geographic distances, where adjacent populations exhibit lower genetic divergence than more distant ones, a pattern referred to as Isolation by Distance (IBD) (Pattern 3 of Koizumi et al. 2006, Figure 1. 1 C) (Case I in Hutchison and Templeton, 1999) (Wright, 1943; Kimura and Weiss, 1964; Slatkin, 1993; Hutchison and Templeton, 1999). Under the same scenario, an absence of equilibrium between gene flow and genetic drift is inferred when no relationship exists between the two distances and divergence levels are low (Pattern 4 of Koizumi et al. 2006, Figure 1. 1D) (Case II in Hutchison and Templeton, 1999) (Hudson & Slatkin and Maddison, 1992; Hutchison and Templeton, 1999). This pattern may be indicative of a recent invasion from a homogeneous population (McCauley, 1993 cited in Hutchison and Templeton, 1999) as it exhibits consistently low genetic divergence amongst patches (similar allele frequencies at the start), and not enough time has passed for genetic drift and gene flow to differentially affect their frequencies (Slatkin, 1985; 1987). If, following the invasion, gene flow is strong enough at the region scale, the divergence level remains low and the region is referred to as panmictic (removal of the constraint on dispersal assumption of the model). The ensuing pattern would be identical to Pattern 4 (Figure 1. 1D) and differentiating between the two hypotheses can be difficult (Hutchison and Templeton, 1999; Koizumi & Yamamoto and Maekawa, 2006). Extending the study area to a scale where movement of individuals is unlikely between the most distant patches may be required. In contrast, if following the invasion genetic drift is stronger than gene flow, genetic divergence between populations will increase. If some level of gene flow is present and not enough time has passed or conditions suitable to dispersal have not been stable for long enough for equilibrium to be reached, a pattern of localised IBD will appear (Crow and Aoki, 1984 cited in Hutchison and Templeton, 1999). A relationship between the two distances will exist at the smallest scale, but a positive intercept will be exhibited as genetic drift remains the stronger process at the largest scale (Pattern 2 in Koizumi et al. 2006, Figure 1. 1B) (Case IV in Hutchison and Templeton, 1999). Finally, if genetic drift is the strongest process, a random pattern of divergence levels between patches will be observed (Pattern 1 of Koizumi et al. 2006, Figure 1. 1A) (Case III of Hutchison and Templeton, 1999). These patterns therefore provide insight into the recent and contemporary history of a species with regards to the stability and connectivity of its populations (Hutchison and Templeton, 1999; Koizumi et al. , 2006).
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A B
C D Figure 1. 1 Illustration of possible relationships between genetic and geographic distances under different gene flow and genetic drift relative strength. A: Pattern 1 (Genetic drift >> Gene flow); B: Pattern 2 (Genetic drift > Gene flow); C: Pattern 3 (Genetic drift = Gene flow); D: Pattern 4 (Genetic drift < Gene flow). See text for details. Modified from Koizumi et al . 2006 and Hutchison and Templeton 1999.
The rejection or acceptance of IBD, and subsequent classification into one of the four patterns described above, is over-simplistic because gene flow at large scales is unlikely to be equal and symmetric (Slatkin, 1993; Jenkins & Carey & Czerniewska et al. , 2010). With increased distance comes increased complexity and physical barriers (Neigel, 1997; Whitlock, 2001; Wiens, 2001). These barriers may lead to a number of populations demonstrating greater shift in allele frequencies than would be expected from distance alone owing to reduced gene flow and stronger genetic drift (Neigel, 1997; Koizumi et al. , 2006). Similar situations can arise when some populations have atypical characteristics such as extremely high gene flow (sink population) (Olivieri & Michalakis and Gouyon, 1995; Hanski, 2001) or high genetic drift relative to neighbouring populations for reasons other than the presence of barriers (e.g. small population size, bottleneck) (Kuo and Janzen, 2004; Groombridge & Dawson & Burke et al. , 2009; Huey & Schmidt & Balcombe et al. , 2011). These populations, if unidentified, would alter the overall relationship and result in erroneous inferences (Slatkin, 1993; Koizumi et al. , 2006). Just as importantly, atypical populations can help in the identification of factors influencing population structure of a species through consideration of their 6
shared genetic and physical attributes (Koizumi et al. , 2006; Koizumi, 2011). For instance, identification of population extirpation across multiple taxonomic groups could help formulate past history that is common to a system (e.g. Huey et al. , 2011). By integrating knowledge of a species ecology, drivers of population isolation and extirpation, as well as its dispersal ability (recolonisation potential), a better understanding of a species susceptibility to habitat alteration and fragmentation, and ultimately to local extinction, is attainable (Hess, 1996; Hanski, 1998; Lowe, 2002; Clobert et al. , 2004; Koizumi, 2011).
1.4 Population Genetics in Dendritic Landscapes
Understanding factors affecting a species population structure requires an understanding of the system that supports them. In riverine landscapes, connectivity is provided by streamflow (Ward & Tockner and Schiemer, 1999; Amoros and Bornette, 2002; Bunn and Arthington, 2002; Wiens, 2002; Thoms & Southwell and McGinness, 2005). In Australian dryland rivers, hydrological connectivity fluctuates seasonally with the dry (winter) and wet (summer) seasons (McMahon and Finlayson, 2003) and over longer period fluctuates with cyclic weather patterns such as the El Nino Southern Oscillation system (Puckridge et al. , 2000; Kershaw & Moss and Van Der Kaars, 2003; Bond et al. , 2008). Seasonal flows create hydrological connectivity between permanent and non-permanent waterbodies such as disconnected anabranches and floodplains, and between temporally isolated permanent waterbodies or waterholes along river corridors (Bunn & Davies and Winning, 2003; Balcombe & Bunn & Arthington et al. , 2007; Sheldon et al. , 2010). During extended periods of drought, populations isolated in refugia may diverge under the influence of genetic drift or witness a drastic size reduction, leading to changes in allele frequencies, and in reduced genetic diversity in short-lived organisms (Douglas & Brunner and Douglas, 2003; Huey et al. , 2011) but not necessarily in long-lived ones (Kuo and Janzen, 2004). Following the onset of wetter periods, species with good dispersal abilities are able to take advantage of the restored hydrological connectivity, gene flow rapidly erasing any signature of genetic divergence (Cook & Bunn and Hughes, 2002; Carini & Hughes and Bunn, 2006; Huey & Hughes and Baker, 2006; Hughes and Hillyer, 2006; Faulks & Gilligan and Beheregaray, 2010).
1.4.1 Models of Population Genetics Structure in Streams
Freshwater systems are notable for the restriction of connectivity to the stream network for obligate aquatic species. This characteristic has led to a number of predictive models for population structure and species persistence within dendritic networks (Meffe and Vrijenhoek, 1988; Fagan, 2002; Lowe, 2002; Grant & Lowe and Fagan, 2007; Hughes et al. , 2009b). The Stream Hierarchy Model (SHM) (Meffe and Vrijenhoek, 1988) predicts genetic divergence to be arranged hierarchically within the
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network with greater divergence occurring between populations found in distinct nodes of the network than between those found within the same node (Souza & Cunha & Oliveira et al. , 2002b; Escalona & Engstrom & Hernandez et al. , 2009). The partitioning of variance therefore increases as the stream distance between localities increases (Hughes et al. , 2009b). Most notably, patches that are nearby in terrestrial distance but distant along the river network will demonstrate high genetic divergence (Fagan, 2002; Hughes et al. , 2009b). A second model predicts an absence of relationship between genetic divergence and the network structure if populations are remnant populations of a previously hydrologically connected network (Meffe and Vrijenhoek, 1988; Hughes et al. , 2009b). The Death Valley Model predicts strong divergence between populations through genetic drift, mutation and selection (Meffe and Vrijenhoek, 1988) and represents the extreme in isolation. It appears appropriate to describe population connectivity between historically connected catchments, such as in some Australian dryland rivers systems (e.g. Cooper Creek and Diamantina River) but rarely applies for within catchment connectivity, even in dryland rivers (Cook et al. , 2002; Goodsell, 2002; Carini and Hughes, 2004; Huey & Baker and Hughes, 2008).
1.4.2 Dendritic Networks and Population Persistence
The two models above apply to obligate aquatic organisms, habitat specialists or where movements are restricted to stream corridors such as a number of aquatic insects with adult flying stage (Hughes, 2007; Hughes et al. , 2009b). For organisms with terrestrial (or overland) movement abilities, the SHM may not hold as two patches in distinct catchments can be connected through short overland (Euclidean) distance (Schmidt & Hughes and Bunn, 1995; Ponniah and Hughes, 2004; Schultheis and Hughes, 2005). First described in studies on river headwaters, this ‘Headwater’ model (Finn & Blouin and Lytle, 2007) predicts no pattern of IBD or genetic structure associated with the stream network but instead an IBD with direct overland distance if no barrier to movement exists. Owing to the branching pattern of freshwater systems, a similar pattern can be expected in low lying regions between close-by river corridors, either within catchments or between, if the organism is able to move overland (e.g. Mockford & McEachern & Herman et al. , 2005).
While all the above models make predictions with regards to the genetic structure of populations within dendritic networks, other models relate to the influence of the network structure on the persistence of a metapopulation (a population of populations/patches, Hanski, 1998). For network restricted species, persistence is primarily a function of connectivity (probability of next patch recolonisation) and of colonisation direction (upstream and downstream movement) (Fagan, 2002). Under high connectivity, one-way movement reduces the persistence time of a metapopulation in a dendritic network compared to a linear system, while two-way colonisation results in longer
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persistence time within a dendritic network. Under low connectivity (low probability of next patch colonisation), similar persistence times are expected in either configuration and with either mode of colonisation (Fagan, 2002). The restriction to within-network movement of obligate aquatic species also renders them more susceptible to fragmentation, with localised habitat perturbation removing the only connecting route between patches (Fagan, 2002; Grant et al. , 2007). Hence, barriers within the system network (e.g. dams) increase the risk of local extinction following disturbances by decreasing the chance of recolonisation. The ability to move overland between stream corridors greatly reduces the chance of local extinction following disturbance (e.g. drought, Scribner and Chesser, 2001), enabling population replenishment or recolonisation through multiple routes (Lowe, 2002; Fortuna & Gómez-Rodríguez and Bascompte, 2006; Grant et al. , 2007). This is especially relevant owing to the potential for large scale environmental damage in freshwater systems, resulting from the downstream movement of disturbance in the networks (Sandoz toxic spill in Rhine River, Switzerland, Reinhard, 2008).
1.5 Freshwater Turtles
1.5.1 Ecological Role of Freshwater Turtles
Aside from recognition by professional and hobbyist herpetologists, the contribution of turtles to freshwater system health is seldom recognised despite the accumulating evidence supporting their importance (Klemens, 2000). Moll and Moll (2004 p. 65) underlined the irony of having a common snapping turtle on the front cover of a well recognised wetland ecology book which succeeded in briefly mentioning turtles only once in its entirety. This disregard for freshwater turtles is especially frustrating when applied to river health and biodiversity assessment. Audits more often than not do not mention turtles and if so, merely as presence or bycatch at a site of audit (Lintermans, 1997; Davies et al. , 2008; MDBC, 2008), despite growing evidence for the role these organisms may play in maintaining system health (reviewed in Klemens, 2000; Moll and Moll, 2004). Turtles occupy a broad trophic role by feeding on animals, microbes, algae and plants (Chessman, 1978; Kennett and Tory, 1996; Meathrel & Suter and Reid, 2004) and can represent large biomass and thus significantly impact energy flow and nutrient cycling in freshwater systems (Thompson, 1993; Souza and Abe, 2000; Moll and Moll, 2004). In Australia, the northern snapping turtle has been estimated to reach biomasses of 105-170 kg/ha, corresponding to 19 to 31 turtles per 100m of river channel (Woinarski, 1993), while estimates of up to 230,000 tonnes of turtles, believed to consume between 130 and 430 tonnes of carrion each day, or a conservative 180,000 tonnes of carrion per active season, have been made for the Murray River in Victoria (Thompson, 1993). Considering that these estimates only relate to the amount of carrion consumed, which represent between 20 and 80% of the diet depending on the
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species (Chessman, 1986; Georges & Norris and Wensing, 1986; Meathrel et al. , 2004), the overall effect of the turtles on the nutrient cycle and energy flow of the Murray River may be very significant. Other ecological services include seed dispersal of terrestrial riparian plants by Elseya dentate, Chelodina rugosa, Carettochelys insculpta , Emydura sublglobosa worrelli (Legler, 1976; Georges and Kennett, 1989; Kennett and Russell-Smith, 1993; Kennett and Tory, 1996) and other turtle species (Kimmons and Moll, 2010). The same applies to algae via carapace growth (epizoochonically) by C. longicollis (Burgin and Renshaw, 2008) and probably by most other species as well. Turtles therefore can provide vital links between permanent and temporary habitats (Burke, 2000; Meathrel & Suter and Radford, 2002; Roe and Georges, 2007), playing an important but often disregarded role in the dynamics of freshwater systems.
1.5.2 Freshwater Turtles of the Murray – Darling Basin
Three species of freshwater turtles occur in sympatry throughout the MDB. Chelodina expansa Gray 1857 (broad-shelled long neck), Chelodina longicollis (Shaw, 1794) (eastern long neck) and Emydura macquarii (Gray, 1830) (Macquarie turtle) are endemic to Australia and belong to the family Chelidae (suborder: Pleurodira) (Georges and Thompson, 2010). Emydura macquarii is split for convenience into four subspecies of which Emydura macquarii macquarii resides in the MDB. A fourth species, Myuchelys bellii (Gray, 1844) is confined to a restricted number of headwater streams in the basin (Georges and Thompson, 2010) and is not discussed further in this thesis. In this thesis I assume that the reader is somewhat familiar with the MDB turtles and only a brief description relating to known movements or dispersal abilities in each species is provided here. A more thorough description of each species life history and ecology can be found in Appendix 8.1 for those readers unfamiliar with the species.
A number of mark-recapture and radiotelemetry studies on C. longicollis have revealed a superior ability to use terrestrial habitats for extra-population movements relative to other turtles of the MDB and no evidence for sex-biased dispersal (Stott, 1987; Graham & Georges and McElhinney, 1996; Roe and Georges, 2008c). Despite the large number of studies of its movement, its dispersal in aquatic systems and in lotic systems in particular remains unassessed. Much less is known of movements in C. expansa , with only one recent study having investigated its local movement pattern and home range size through radiotelemetry in permanent backwaters (Bower & Hutchinson and Georges, 2011). The study revealed restricted seasonal movements within aquatic habitats, with males moving significantly further than females but returning to their site of origin as the active season ends. No knowledge of its propensity to terrestrial movement exists, with only nesting-related overland movements having been observed (Georges, 1984). Finally, there is no clear knowledge of E. m.
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macquarii dispersal within as well as outside the aquatic environment. A study on its population connectivity in a dryland river of the MDB suggested moderate dispersal abilities (Goodsell, 2002) but it remains unknown if this pattern was typical of the species or of the system studied. Other observations made in a backwater habitat revealed only localised movements, although some individuals revealed good homing abilities after experimental displacement (Goode and Russell, 1968). Its typical absence from sites away from to the river proper suggest poor terrestrial abilities (Chessman, 1988a). Hence, until Bower et al. (2011), C. expansa was assumed to have good dispersal abilities owing to its large size (Jenkins & Brescacin & Duxbury et al. , 2007), C. longicollis to have superior dispersal capability by being able to move overland and E. m. macquarii to have fairly poor abilities and to be mostly restricted to the water column.
1.6 Aims
Apart from Goodsell’s (2002) study on the population connectivity of Emydura in a dryland river, all the above information relating to the dispersal of MDB turtles was obtained through direct methods of study. Direct methods enable investigation of an individual’s range size, movement patterns and habitat preferences amongst other things, but are typically restricted in their temporal and spatial scale of investigation to movement that occurs within the lifetime of an individual (Bohonak, 1999; Bilton & Freeland and Okamura, 2001). As such these methods can miss rare dispersal events which can nonetheless influence demography (Lowe and Allendorf, 2010), lead to recolonisation of failed patches (Bilton et al. , 2001) and contribute to the maintenance of genetic diversity (Allendorf and Luikart, 2007). The first aim of the present thesis was therefore to further our understanding of dispersal abilities in the MDB turtles, to identify their population structure, and to clarify their susceptibility to local extinction under current and predicted flow regime scenarios in the basin. To this end, Chapter 2 describes the general methods used in this study. This includes fieldwork methods for sample acquisition, laboratory methods for microsatellite library creation and fragments amplification, and statistical methods of analysis used throughout this study. Analyses specific to each chapter are provided within each. Chapter 3 then focuses on assessing the dispersal ability of each turtle species using patterns of gene flow in an unregulated river. Evidence for population extirpation and intense genetic drift within temporally isolated waterholes of this system has been found previously across multiple taxa (e.g. golden perch Macquarie ambigua, eel-tailed catfish Tandanus tandanus , and a shrimp species Macrobrachium australiensi , Huey et al. , 2011). The presence of similar evidence will also be investigated for each species, enabling assessment of their respective susceptibility to large scale processes such as drought and reduced hydrological connectivity, processes believed responsible for population extirpation of the MDB turtles (Chessman, 2011).
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The second aim of this study was to assess the impact of barriers (e.g. dams) on the population connectivity of each species in a highly regulated system. As previously discussed, patterns of gene flow are determined by a species’ biology (Bohonak, 1999; Baggiano & Schmidt & Sheldon et al. , 2011b), by the presence or absence of structural barriers in the landscape (Bilton et al. , 2001; Wiens, 2001), and in riverine systems, by the flow regime (Meffe and Vrijenhoek, 1988; Hughes et al. , 2009b). Chapter 4 compares gene flow across the three species and searched for the presence of correlation with the number of dams in the system. Permanent flow in a regulated system may provide greater chances for dispersal but the presence of dams and other regulating infrastructure in the system may represent insurmountable barriers to species incapable of overland movements. In addition, flow regulation providing habitat stability, a lack of evidence for population extirpation is expected.
Chapter 5 looks at the population structure of each species at the basin scale to identify possible management units (Moritz, 1994). Identification of currently isolated regions and their association with known features of the landscape (i.e. human-made or natural barriers) will enable informed decisions to be made with regards to flow management, location of freshwater protected areas, and ultimately species persistence. Finally, Chapter 6 investigates for evidence of sex-biased dispersal in each species. Testudines commonly express female philopatry which can lead to increased risk of population extirpation following nesting site alteration (e.g. flooding of site, land reclamation) (Horne & Brauman & Moore et al. , 2003; Rioux Paquette & Louis and Lapointe, 2010; Sheridan & Spotila & Bien et al. , 2010) and sex-biased mortality following greater predation on females at nesting sites (Spencer and Thompson, 2003; Spencer and Thompson, 2005) and other associated mortality (e.g. road crossing en route to nest sites, Cann, 1998; Steen & Aresco & Beilke et al. , 2006). Species demonstrating female philopatry would necessitate the identification of locally important nesting sites, to ensure their long term successful recruitment at specific sites and in regions of current concern within the system.
The thesis finishes with a discussion integrating the key elements of the main chapters to reinforce and further develop the major contributions this thesis makes to our understanding of turtle gene flow, dispersal and persistence in the MDB.
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Chapter 2 General Methods
Hatchling Chelodina longicollis at Longswamps, NSW, 2010
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2.1 Fieldwork
2.1.1 Sampling
Samples were obtained via direct captures in the Moonie and Barwon River and via sub-sampling of the Wildlife Tissue Collection (Genbank UC
Stake Lead
Float
Direction of entry
Aperture to collect caught individual
Hoop Funnel (35cm Ø)
Stake
Mesh=10mm
Wing (8x1m)
Figure 2. 1 Diagram of fyke net used for turtles capture
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Baited traps were made of 45 mm mesh in the crab pot section enabling smaller organisms to escape when retrieved. A 22 cm long funnel (mesh of 10 mm) fitted with a float was added to the crab pot enabling trapped individuals to surface for air (Figure 2. 2). Bait consisted of pierced tinned cat food held in gutter guard mesh and attached to the centre of the crab pot section (Figure 2. 2). Nets and traps were set for approximately 19 hours, from 2pm to 9am the next day for the first site and 4pm to 11am the next day for the second site, covering the main daily activity periods for the three species (1988b; Bower, 2011). Fyke net distribution at each site was influenced by the abundance of snags and the waterhole profile. Traps were set approximately every 20 m along a 200 m stretch of the waterholes, targeting both the river edges and the mid-channel sections. Traps were never set in water more than 2.2 m deep, the length of the trap’s funnel. Nets and traps were set and retrieved simultaneously at each site.
Sampling in the Barwon River, northern New South Wales, was undertaken over a two-week period in late February 2010. Record flooding in the Barwon and Border Rivers resulted in the cancellation of two further weeks of fieldwork along these catchments.Capture methods were identical to the one described for the Moonie River but ephemeral waterbodies off the main channel where C. expansa and C. longicollis were likely to occur following summer rains were also sampled.
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Figure 2. 2 Fieldwork of interest: clockwise from top; modified crab pot design; skin acquisition; large C. expansa female at Caloola Bed & Breakfast; fyke net set at Boomi lagoon; large E. m. macquarii trapped and marked at Boomi lagoon.
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2.1.2 Records per Individual
Turtles were held in large open plastic tubs containing water from the site of capture while awaiting measurement. For individuals caught in the Barwon River, only the first ten of the following records were taken. For individuals captured in the Moonie River all the following were recorded:
1. Species
2. Life Stage (Adult – Juvenile);
3. Sex (mature male, mature female, juvenile); Maximum
4. Straight and Curved Carapace Length (SSL and CSL respectively); Midline
5. Plastron Length (PL);
6. Tail Length (from margin of carapace, TL);
7. Site name and coordinates (GPS:GDA94);
8. Date of capture;
9. Capture Method (Trap, Fyke net, Seine net, Hand)
10. Genetic sample ID; (detailed given below)
11. Notch ID; (detailed given below)
12. EPA Tag ID;
13. Acoustic tag ID (if applicable);
2.1.3 Life Stage and Sex Determination
Individuals were considered to be mature if they had a carapace longer than 175 mm for E. m. macquarii and 230 mm for C. expansa , juvenile if smaller. These sizes represent the minimum size at which sexually dimorphic features such as tail shape could confidently be used for sex discrimination (see results section Chapter 3). Sex was determined visually in the field from tail length and bulkiness, large bulky tails identifying males in both species (Chessman, 1978). I used Chessman’s (1978) size threshold (~175mm) for age class determination in C. longicollis as tail length measurement is difficult in this species, individuals retracting their short tail when handled. For this same reason and apart for those individuals showing clear external characters such as deeply convex or concave posterior lobe of plastron and visibly large tail (Kennett & Roe & Hodges et al. , 2009),
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sex determination was not attempted for this species. Differentiation between adults having or not attained sexual maturity was not undertaken for any species.
2.1.4 Morphological Measurements
Straight carapace length (SSL) was measured from the cervical scute and the right 12 th marginal scute and plastron length (PL) from the point of intersection of the gular scutes and the most anterior point of the anal notch using a set of 50 cm callipers (Calliper Haglof Mantax Precision) (Figure 2. 3). Tail length from carapace (TL) was measured with a metallic ruler placed at the junction of the 12 th marginal scutes. Curved carapace length (CSL) was measured from the anterior of the cervical scute to the posterior margin of the right 12 th marginal scute with a measuring tape. All above measures were taken to the closest millimetre.
Cervical Individual ‘AD’
X A W B Gular Gular V C
U D
T E
S F
R G
H Length(PL) Plastron Q 12 th marginal scute I
Straight Carapace Carapace (SSL) LengthStraight P Anal Notch O J N K M L
Tail-Carapace length (TL)
Figure 2. 3 Morphological measurement and notching pattern
2.1.5 Shell Notching and EPA Tags
Permanent marking was made by notching individuals with a hand held round file, the marginal scutes from but not including the cervical scute to the 12 th marginal designated A to X after Sajwaj et al. (1998). A notch in the 1 st and 4 th marginal scutes designated individual ‘AD’ (Figure 2. 3) while a further 3 rd notch in the 3 th marginal read ‘ACD’. The reading frame was always A to Z. Individuals
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were further marked using uniquely numbered tags provided by the Environmental Protection Agencies, Queensland. The self-piercing tags were attached to the webbing between the clawless and the third toe of the left back limb using pliers, leaving a gap in younger individuals between the tag and the webbing to allow future growth of the webbing without constriction.
2.1.6 Skin Tissue Acquisition
Skin samples were obtained from the trailing flaps of the clawless toe of the left and right rear foot with surgical scissors, a practice known to cause no long-term incapacities to the turtles (Arthur Georges, pers. comm.) (Figure 2. 2). Skin tissues were stored in 90% ethanol and instruments cleaned and disinfected between each individual using 96% ethanol and a 6% hydrogen peroxide solution. This protocol was submitted to the Griffith University Ethic Committee and approved on the 16 th October 2008 (GU Ref No: ENV/22/08/AEC).
Although some were not included in the present study, a total of 306 E. m. macquarii , 157 C. expansa and 243 C. longicollis were obtained through sub-sampling of the University of Canberra Wildlife Tissue Collection (Genbank UC
2.2 Laboratory Methods
2.2.1 Total Genomic DNA Extraction
Total genomic DNA was extracted using a modified CTAB/phenol-chloroform extraction protocol (Doyle and Doyle, 1987). A small skin or muscle section (~50 mg) of each individual was left in an open tube for 10 min allowing ethanol to evaporate before being soaked in 700 µL 2x CTAB extraction buffer (0.5 M Tris HCL pH 8.0, 2 M NaCl, 0.25 M EDTA, 0.05 M CTAB; Sigma, Sydney, Australia) and 5 µL Proteinase K (20 mg mL -1) and incubated in a shaker at 40ºC for forty eight hours. A further 5 µl Proteinase K (20 mg mL -1) was added after twenty four hours in samples showing low protein break down. Following incubation samples were homogenised with a plastic pestle ensuring complete break down of tissue material.
Following homogenisation, lipids and proteins were removed from each sample as follows: addition of 300 µL Phenol and 300 µl Chloroform: Isoamyl (24:1), 10 min carousel mixing, 5 min centrifugation at 13500 rpm and transfer of resulting supernatant into a newly labelled 1.5 ml tube. A
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second addition of 600 µl Chloroform: Isoamyl (24:1), 5 min carousel mixing and 10 min centrifugation at 13500 rpm. The resulting supernatant was transferred into a newly labelled tube, 600 µl cold isopropanol added, and left for incubation at -20ºC for 60 min to precipitate the DNA out of solution. Following incubation, tubes were centrifuged at 13500 rpm for 30 min to form a DNA pellet, before aspiration using a vacuum pipette. DNA pellets were washed with 1000 µl 70% ethanol, centrifuged one last time for 15 min and ethanol aspirated. Pellets were dried in a vacuum bell prior addition of 80 µl ddH 2O. Extractions were left to resuspend overnight prior amplification and subsequently stored at 4ºC awaiting further analysis.
2.2.2 Microsatellite Markers
A number of genetic markers are available for use in population genetic studies. The widespread use of mtDNA in population genetic studies stems from the easy access to the nucleotide sequence of a haploid genome, allowing for investigation of phylogeny and divergence time between populations (Harpending & Sherry & Rogers et al. , 1993; Lamb and Lydeard, 1994; Avise, 1995; Arbogast & Slowinski & B. et al. , 1998), and its typically high level of polymorphism (Vigilant & Stoneking & Harpending et al. , 1991; Lambert & Ritchie & Millar et al. , 2002) which makes it valuable in population genetic studies as well (Castro & Picornell and Ramon, 1998). Investigation of the rapidly evolving mitochondrial control region and ND4 (Vigilant et al. , 1991; Parsons & Muniec & Sullivan et al. , 1997) in Chelodina longicollis and Chelodina expansa revealed low haplotype diversity and divergence across the entire MDB (Kate Hodges, unpublished data). This echoed the unusually low genetic variability generally observed within turtles at mitochondrial DNA (Avise & Bowen & Lamb et al. , 1992). Variation at nuclear protein-coding genes such as allozymes, as well as in some non- coding introns, evolve relatively slowly compared to mtDNA (Spinks & Bradley Shaffer & Iverson et al. , 2004; Engstrom & Edwards & Osentoski et al. , 2007 and references therein; Thomson & Shedlock & Edwards et al. , 2008) and these markers were consequently not considered for this study. Despite their slower mutation rate in turtles compared to other vertebrate taxa (FitzSimmons & Moritz and Moore, 1995), the higher mutation rate of microsatellites owing to replication slippage during DNA synthesis process (Schlotterer, 2000; Ellegren, 2004; Buschiazzo and Gemmell, 2006) renders these markers appropriate for the spatial and temporal scale of this study, as they are more likely to detect differences between populations that have been isolated recently (Sunnucks, 2000; Selkoe and Toonen, 2006).
Microsatellites consist of short nucleotide sequences no longer than 6 nucleotides that are repeated a number of times, typically 5 to 40 times (Hancock, 1995; Goldstein and Pollock, 1997; Li & Korol & Fahima et al. , 2002). The different number of repeat units creates discrete allele sizes and generates
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the high genetic variation of interest in population genetic studies (Sunnucks, 2000; Selkoe and Toonen, 2006). Hence microsatellite polymorphism derives from variation in length rather than from changes in the nature of the nucleotide sequence per se as in mtDNA. These tandem repeats are found in a non-random manner within a species genome, the vast majority occurring within intergenic sequences and introns, the non-coding regions of the genome (Weissenbach & Gyapay & Dib et al. , 1992; Dietrich & Miller & Steen et al. , 1996; Tóth & Gáspári and Jurka, 2000). This is attributable to negative selection against mono, di and tetra-nucleotide mutations, each of which will cause shifts in the reading frame characteristically leading to non-functional proteins (Metzgar & Bytof and Wills, 2000; Tóth et al. , 2000). Hence microsatellites used in population genetic studies are considered to be by and large neutral-markers (Jarne and Lagoda, 1996; Schlotterer, 2000; Sunnucks, 2000; Selkoe and Toonen, 2006).
Praised for their high mutation rate and associated high allele diversity, this characteristic also elevates the rate of size homoplasy in microsatellites, where two amplified fragments are identical in size but not identical by descent (Estoup & Tailliez & Cornuet et al. , 1995; Buschiazzo and Gemmell, 2006). Homoplasy creates uncertainty in genetic studies (reviewed in Estoup & Jarne and Cornuet, 2002 for microsatellite), such as distortion of divergence levels between populations (Balloux & Brunner & Lugon-Moulin et al. , 2000), reduced estimates of allelic diversity and rapid loss of genealogical relationships (Goldstein and Pollock, 1997), although these uncertainties have been deemed negligible for routine population genetics analyses (Estoup et al. , 2002). Finally, the presence of microsatellites in coding regions, such as trinucleotide repeats which appear dominant in all vertebrate exons (Tóth et al. , 2000; Morgan & Garland & Irwin et al. , 2003) requires caution. Changes brought about by the insertion or deletion of these repeats do not necessarily detrimentally alter its functionality (in that the reading frame remains unchanged) and can even be positively selected for if the alteration is favourable (Morgan et al. , 2003). Further, although not selected for specifically, a microsatellite linkage to a genomic region under selection could lead to deviation from expectations under neutrality.
2.2.3 Microsatellite Primers
Emydura macquarii macquarii
Thirteen unpublished (but mentioned in Engstrom et al. , 2007) primer sets were developed for E. m. macquarii in 2000 by Jing Ma in the laboratory of Jane Hughes, Griffith University, under contract to Arthur Georges, University of Canberra (Table 2. 1). Following preliminary tests for polymorphism and amplification success, eight of these were kept for further analysis in this study, the remaining five being either monomorphic, not amplifying, or showing strong stuttering and unreliable scoring 21
(Table 2. 1). Of the thirteen primers designed for E. m. macquarii , two were found polymorphic in C. expansa and one in C. longicollis (Table 2. 2 and Table 2. 3). A primer set created in this study for C. expansa (see below) was found polymorphic in E. m. macquarii totalling nine loci for this species in this study.
Chelodina expansa and Chelodina longicollis
Microsatellite primers for Chelodina rugosa , a closely-related species to C. expansa and C. longicollis (Georges and Thompson, 2010), were created by Alacs et al. (2009). Following cross amplification of these primer sets on eight C. expansa and C. longicollis individuals, three primer sets showed moderate to high levels of polymorphism in the former species and four in the latter (Alacs et al. , 2009). As the loci were assessed on eight individuals only and from unknown regions, all seventeen primer sets were reassessed here on twenty four individuals of each species from the Moonie River catchment. Only two loci were found polymorphic in C. expansa and five in C. longicollis (Table 2. 2 and Table 2. 3). Both T31 and T11 loci in Alacs et al. (2009) were highly (>15 alleles) polymorphic in C. expansa but non-specific amplifications rendered them impossible to score and attempts to redesign these primers were unsuccessful. A further five primer sets, designed by Mia Hillyer (Griffith University) for Alacs et al. (2009) but not included in their primer note were tested on both Chelodina species. Only one was found polymorphic in C. expansa (Table 2. 2). Lower polymorphism in related species appears to be common in microsatellites studies (Ellegren & Primmer and Sheldon, 1995). In view of the low number of loci available for C. expansa and C. longicollis it was decided to create a new microsatellite library for each species.
2.2.4 Microsatellite Library
Genomic DNA was extracted from a skin section of three individuals of each species, obtained from distinct upper catchments of the MDB, using the modified cetyltrimethyl ammonium bromide (CTAB) / phenol-chloroform method described above (Doyle and Doyle, 1987). Microsatellite fragments were isolated using methods modified from Glen and Schable (2005) and Perrin and Roy (2000). The following protocol was identical for both species. Extracted total genomic DNA (10 µl with ~700 ng/µl DNA) was digested separately with 1µl restriction enzyme Dpn II (New England
Biolab), 1 µl Rnase (40U/µl), 1.5 µl Dpn II buffer (10x) and 1.5 µl ddH 2O at 37ºC overnight and an additional 1 µl of restriction enzyme added for three hours the following day. Digested samples were linker-ligated with a nonphosphorylated Sau Linker following assessment of digestion success on a 1% agarose gel (presence of smears). Linkers Sau-LA (5’-GCGGTACCCGGGAAGCTTGG-3’) and Sau-LB (5’-GATCCCAAGCTTCCCGGGTACCGC-3’) at a concentration of 200µM were combined into a single linker with 3.44 µl of Sau-LA and Sau-LB, 1.0 µl reaction buffer (10x) and 2.21 µl
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ddH 2O with a single cycle at 95ºC for 40 seconds, 65ºC for 15 min, 60ºC for 15 min, 50ºC for 1 min, 45ºC for 15 min, 40ºC for 15 min and then held at room temperature. Ligation to the digested DNA was made by incubation at room temperature for two hours followed by 16 ºC overnight with 2.0 µl linker, 2.0 µl digested DNA, 1.5 µl ligase buffer (10x), 1.5 µl T4 DNA ligase, 0.5 µl rATP and 7.5 µl ddH 2O.
Following ligation, samples were pooled and pre-enriched with a PCR containing 5 µl reaction buffer
(10x), 6.0 µl MgCl 2 (25 mM), 4.0 µl dNTP’s (10 mM), 3.0 µl Sau-LA (10 µM), 2 µl Taq (1 U/µl), 4
µl ligated DNA and ddH 2O to make a 50 µl reaction. Thermal conditions were 94ºC for 5 min, 35 cycles of 30 seconds at 58ºC, 40 seconds at 72ºC, 30 seconds at 95ºC and a final extension cycle at 72ºC for 5 min. Three simultaneous pre-enrichment reactions were made and subsequently pooled to obtain 150 µl of pre-enriched DNA product. The pre-enriched product (150 µl) was run on a 1% agarose gel and fragments ranging between 450 and 750 base pairs selected. Fragments were gel extracted with QIAquick (Qiagen) extraction kit. Hybridisation of the linker-ligated DNA with 6 tetramer (dAGAT, dAAGG, dAAAT, dACAT, dAAAG, dAAGT) (Sigma Genosys) and 6 dimer/trimer (dAAC 6, dAT 15 , dAC 13 , dACC 8, dAGC 6, dAAG 8) (Sigma Genosys) biotinylated oligo probes was made separately. Reactions contained 15 µl 2x Hyb solution (60 µl 20x SSC, 1.0 µl 10%
SDS and 39 µl ddH 2O), 6 µl prob mix, 6 µl linker-ligated PCR product and 3 µl ddH 2O, cycled at 95ºC for 5 min, at 70ºC for 5 seconds and reduced by 2ºC every 5 seconds down to 50ºC with a hold for 10 min, and reduced again by 2ºC every 5 seconds down to 40ºC and rapidly reduced down to 15ºC. The probes were then selectively maintained using Streptavidin MagneSphere Paramagnetic Particles (Promega) following manufacturer’s recommendations, to separate them from non-target fragment, with incubations made at 50ºC for either probe mixes. A post-enrichment PCR identical to the pre-enrichment described above and a second run on a 1% agarose gel to select fragment size was followed by another identical run of probes selection with Streptavidin Magneshpere Paramagnetic Particles (Promega). A final, identical, enrichment PCR followed by gel run and extraction completed the enrichment procedure.
Following the enrichment procedure, the library was ligated into pGEM-4Z cloning vector (Promega) and electroporated into One shot TOP10 electrocompetent Escherichia coli cells (Invitrogen). Cells were smeared onto agar LB ampicillin plates (AMP 100µg/ml) and incubated overnight at 37ºC for colony growth. Ninety six colonies of each probe type were hand-picked and transferred into a ninety
six well plate containing 20 µl of ddH 2O for direct PCR amplification. Another forty eight colonies per probe mix (Tetramer and Trimer/Dimer) were hand-picked and transferred into a ninety six well plate containing 50 µl of growth mix (40 ml LB, 10 ml autoclaved glycerol, 50 µl AMP) for future amplification if required. Direct amplification was carried out with the M13F (5 ′-
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GTAAAACGACGGCCAGT-3′) and M13R primers (5 ′-CAGGAAACAGCTATGAC-3′) with 1 µl buffer (10x), 0.6 µl MgCl 2 (25 mM), 0.4 µl each primers (10 µM), 0.2 µl dNTP’s (10 µM), 0.3 µl Taq
(1 U/µl), 2 µl template DNA and ddH 2O to make up 10 µl reactions. Thermal conditions were 94ºC for 5 min, 35 cycles of 30 seconds at 94ºC, 40 seconds at 53ºC, 60 seconds at 72ºC, and a final step of 4 min at 72ºC. PCR products were run on a 1% agarose gel and fragment between 250 and 550 bp were sequenced using the M13F primer.
Of 384 fragments, 96 were sequenced (24 colonies of each probe type for each species), and edited and aligned using Sequencher v4.9 (Gene Codes Corporation). Thirty six fragments were found to contain microsatellite repeats, including clones of identical fragments, and a final 21 fragments were selected for multiplex PCR primer design. Primers were designed using the web based software PrimerBlast ( http://www.nc.bi.nlm.nih.gov/tools/primer-blast/ ) and combination compatibilities (i.e. formation of hairpin and dimmers) were checked for using AutoDimer (Vallone and Butler, 2004). Seven primers, all from C. expansa fragments, were found polymorphic and provided acceptable PCR products for scoring in either C. expansa and/or C. longicollis (Table 2. 2 and Table 2. 3). A forward primer (TCE92.2) was redesigned for C. expansa to improve scoring, but kept unchanged for C. longicollis (Table 2. 3). These primers were also assayed on E. m. macquarii , one showing reliable and polymorphic results in this species (Table 2. 1). These new primers were published in Baggiano et al. (2011a).
2.2.5 Microsatellite Amplification and Assays
Genotyping costs were reduced by applying a multi-tailed approach to fluorescent labelling of primers combined with multiplex PCR as per Real et al. (2009). One of four unique 20-mer oligo tails (Table 2 in Real et al. 2009) was added to the 5 ′ end of each forward primer, enabling the incorporation of a corresponding fluorescently labelled tagging primer in the PCR (Schuelke, 2000). Use of four unique tails permits multiplex amplification with up to four different fluorescent labels in a single reaction (Missiaggia and Grattapaglia, 2006; Real et al. , 2009). The four tagging primers were labelled with 6- FAM, VIC, NED and PET from the G5 fluorescent dye set (Applied Biosystems). PCR amplifications were first conducted for each locus separately using 16 individuals to validate amplification and scoring of subsequent multiplex PCR trials.
All PCR reactions were carried out in 10 µl. Single primer PCR contained: 1× reaction buffer (Fisher
BioReagents, Fisher Scientific Inc., VIC, Australia), 2.25 mM MgCl 2, 0.1 µM tailed forward primer, 0.4 µM reverse primer, 0.4 µM corresponding fluorescent tag, 0.2 mM dNTP ′s, 0.044 U Thermus aquaticus (Taq) (Fisher BioReagents) polymerase and 20-60 ng/µl of template DNA. Thermocycling conditions for all PCR amplifications were 94 oC for 5 min, 35 cycles at 94 oC for 30 seconds, 55 oC
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for 30 seconds, 72 oC for 40 seconds, with a final extension at 72 oC for 30 min. Multiplex PCR’s contained 1x reaction buffer (10x) (Fisher BioReagents, Fisher Scientific Inc., VIC, Australia), 1.75 mM MgCl 2, 0.2 mM dNTP ′s, 1.5 µl Bovine Serum Albumin (10mg/ml) (Sigma Aldrich), an equal amount of fluorescently labelled tagging and reverse primers, 0.15 to 0.3 U of Taq (Fisher BioReagents) depending on the number of primer set included, and 20-60 ng/µl of template DNA. Primers concentration, reaction conditions and multiplex combinations are shown in Table 2. 2 for C. expansa , in Table 2. 3 for C. longicollis and in Table 2. 1for E. m. macquarii . Thermocycling conditions for multiplex PCR were as per single primer PCR. Product were analysed on an ABI 3130
(Applied Biosystem) and scored on G ENEMAPPER v.4.0 (Applied Biosystem).
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Table 2. 1 Microsatellite Primers for E. m. macquarii . Tail 5 ′: numbers refer to tail ID in Table 2 of Real et al. (2009). Multiplex Combination: 1- 2 multiplex PCR grouping; a - b multiplex genotyping (ABI 3130). TA: annealing temperature.
Primer concentration (µM) Multiplex Size Range GenBank Combination T (OC) (bp) Accession no. Source Single PCR Multiplex A Locus Primer Sequence (5'-3') Repeat Motif Tail (5')
TCE70 F:GCT TCC TCA ACC CCC TTG CAG GAT GGT(5) GGA(7) GGT(8) 3 0.1 0.02 1a 55 117-135 JF423193 Baggiano et al. 2011 R:TCG CTT TGG GAA GGC ACA GCC 0.4 0.08 TLE6.2 F:TCA AAT CTA ACG TAA TTG TGC C (GT)13 3 0.1 0.02 2b 55 116-152 Unpublished R:GTT TAC AGY YCA CCT CTT CAG 0.4 0.08 TLE7.2 F:ACA GCC ATC ACG TTT AGC CAC (CA)13 4(Pet) 0.1 0.04 1a 55 128-150 Unpublished R:TGA GTT TCA GGC ATC TCC TC 0.4 0.16 TLE10 F: TTC TGC TTC TGT GGT TCC ACC (AC)11 1 0.1 0.02 1a 55 138-158 JF423194 Baggiano et al. 2011 R:CTG TAT TTC AAG GAC TCT GCC 0.4 0.08 TLE13.1 F:TGG GTC TAA TTC AGT GAA GAG (TG)13 1 0.1 0.04 1a 55 218-246 Unpublished R:TGA GTT TCA GGC ATC TCC TC 0.4 0.16 TLE13.3 F:GTG TCA GCC CCT CCA GAA TGT C (TG)13 2 0.1 0.02 1a 58 124-178 Unpublished R:TCA ACG AGA AGC AAA TTG AAG 0.4 0.08 TLE19.1 F:CTA CCA CCT GCT TTA CCA ACC (CTT)6 4(Pet) 0.1 0.02 1a 55 209-221 Unpublished R:GTG AAA CCC GAT GCT CTT GAA CC 0.4 0.08 TLE28.21 F:GCT TTG CCT ATC ATC CTC TTG C (AC)10 2 0.1 0.02 2b 55 156-178 Unpublished R:CCT GGT CTC ATT CAG AAA GG 0.4 0.08
TLE31.1 F:TAA CGG AAG GTC TTC AAA GGT C (TC) 14 (AC)10 1 0.1 0.04 2b 55 319-363 Unpublished R:GTA GTG TGT CCC AGG CGA TTC GAC 0.4 0.16
TLE19.3 F:CAG CGT TTT GCC CAT GGT AAG (AC) 14 2 0.1 0.02 2b 55 274-320 Unpublished R:GTG CTA AAA CCA GTCTCA TTG TG 0.4 0.08
TLE2.1 F:ATG AAC TTT CCC GTG GTG CTC (GT)6T(TG)2TT(TC)2AA(TG)19 55 - Unpublished Multi-Peaks R: GTT CCG ATA CAG AGC TTC ACC
TLE23.41 F:CAC CCA AGA ATA CCC GTC ACC (GCT) 4CCT(GCT) 4 55 - Unpublished No Amplification R:GTA CAC CCA ATG ATC ACT CG
TLE16.31 F:GAC CCT AAT CCC CTC CTA ATC C (AC) 21 55 - Unpublished Stuttering R:CCA ACC CTT CTG ACT CTC ACT C TLE9.2 F:CAA ATG TTC AGC AGC ACC CTG 55 - Unpublished Monomorphic R:TGT GTT CGT GCG ATG CAA CTC (TG)13
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Table 2. 2 Microsatellite Primers for C. expansa . Tail 5 ′: numbers refer to tail ID in Table 2 of Real et al. (2009). Multiplex Combination: 0- Single PCR, 1- 2 multiplex PCR; a - b multiplex genotyping (ABI 3130). TA: annealing temperature.
Primer concentration (µM) Multiplex Size Range GenBank o Locus Primer Sequence (5'-3') Repeat Motif Tail (5') Single PCR Multiplex Combination TA ( C) (bp) Accession no. Source
TCE64 F:AGC CGT TCT CTG CCT TGC CCG (TGG) 10 2 0.1 0.02 1a 55 135-162 JF423188 Baggiano et al. 2011
R:GGT CCG GAG GCT CCA AAC GA 0.4 0.08
TCE70 F:GCT TCC TCA ACC CCC TTG CAG GAT (GGT) 5 (GGA) 7 (GGT) 8 3 0.1 0.02 1a 55 128-146 JF423193 Baggiano et al. 2011
R:TCG CTT TGG GAA GGC ACA GCC 0.4 0.08
TCE76.1 F:TGG TCA TGC CTC CTG AGT CAC AGT (CA) 11 3 0.1 0.02 1a 55 173-267 JF423189 Baggiano et al. 2011
R:GCC CTC ATG AAA CCA GAG GCC A 0.4 0.08
TCE74 F:CCA GGT GTA TGG AAT CTT AAA AGG (CCA) 7 1 0.1 0.02 1a 55 107-111 JF423187 Baggiano et al. 2011
R:GTT CAT CCC AAG GGA AGT TG 0.4 0.08
TCE86 F:TCC AGA ATT TGC CAG TCA TTG TAT CCC (CA) 19 1 0.1 0.04 1a 55 196-234 JF423191 Baggiano et al. 2011
R:TGA AGG TCG TCT TTG CTC ACG AAA 0.4 0.16
TCE89.1 F:CGG CCA TGC TAA CAC ACA TT (AC) 19 4(Pet) 0.1 0.02 1a 55 247-277 JF423190 Baggiano et al. 2011
R:TTG TTG CAG AGG AAG TAT TTC TGG 0.4 0.08
TCE92.2 F:ACA CAG TCT GAG CGG GTT TTT GT (CA) 21 2 0.1 NA 0a 55 282-452 JF423192 Baggiano et al. 2011
TCE92.1 R:AGT TCA GAT GCA GCC TAA CTC TCA CT 0.4
TLE10 F:TTC TGC TTC TGT GGT TCC ACC (AC) 11 1 0.1 NA 0a 55 134-142 JF423194 Baggiano et al . 2011
R:CTG TAT TTC AAG GAC TCT GCC 0.4
T48 F:TAC AGC AGC AAC AGA TGC AGC (GCA) 9 1 0.1 0.02 2b 55 173-194 JF423186 Baggiano et al. 2011
R:TTT GCT GGT GAA GA 0.4 0.08
T15 F:TGG TAA ATA AGG GCT GCA TGC (AC) 15 3 0.1 0.02 2b 55 182-194 EU522103 Alac et al. 2009
R:CAG TTT CCT TAC TTT GTC TGT C 0.4 0.08
T44 F:AAGGCAGTTGAGAACCAGGTG (AGC) 7 4(Fam) 0.1 0.02 2b 55 155-164 EU522098 Alac et al . 2009
R:GTAGATGCCACCCATGTTGTC 0.4 0.08
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Table 2. 3 Microsatellite Primers for C. longicollis . Tail 5 ′: numbers refer to tail ID in Table 2 of Real et al. (2009). Multiplex Combination: 1- 2 multiplex PCR; a-b multiplex genotyping (ABI 3130). TA: annealing temperature.
Primer concentration (µM) Multiplex Size Range GenBank Locus Primer Sequence (5'-3') Repeat Motif Tail (5') Combination T (oC) (bp) Accession no. Source Single PCR Multiplex A
TLE10 F:TTC TGC TTC TGT GGT TCC ACC (AC) 11 1 0.1 0.02 1a 55 138-164 JF423194 Baggiano et al. 2011
R:CTG TAT TTC AAG GAC TCT GCC 0.4 0.08
TCE70 F:GCT TCC TCA ACC CCC TTG CAG GAT (GGT) 5 (GGA) 7 (GGT) 8 3 0.1 0.02 1a 55 117-150 JF423193 Baggiano et al. 2011
R:TCG CTT TGG GAA GGC ACA GCC 0.4 0.08
TCE86 F:TCC AGA ATT TGC CAG TCA TTG TAT CCC (CA) 19 1 0.1 0.02 1a 55 179-221 JF423191 Baggiano et al. 2011
R:TGA AGG TCG TCT TTG CTC ACG AAA 0.4 0.08
TCE92.2 F:TCA GCC TTG TGC CTT CAA CCA TT (CA) 21 2 0.1 0.02 1a 55 331-403 JF423192 Baggiano et al. 2011
R:AGT TCA GAT GCA GCC TA 0.4 0.08
T11 F:CAG CCA AAA AAA TGT AGG TCC (CA) 24 4(Pet) 0.1 0.02 1a 55 155-199 EU522102 Alac et al. 2009
R:TGT GAC CAC CTG ATA ACA GGC 0.4 0.08
TCE76.1 F:TGG TCA TGC CTC CTG AGT CAC AGT (CA) 11 3 0.1 0.02 1a 55 170-172 JF423189 Baggiano et al. 2011
R:GCC CTC ATG AAA CCA GAG GCC A 0.4 0.08
T87 F:CAG CAC TGA TCT GCA ACT ACC (TGC) 9 2 0.1 0.02 1a 55 152-173 EU522100 Alac et al. 2009
R: GCT ACA CCA GTT TCA CTC TGC 0.4 0.08
T31 F:GGG ACC ACT CAT GGA ACT AAG (AC) 18 4(Pet) 0.1 0.02 2b 55 144-196 EU522104 Alac et al. 2009
R:GGG ATA GAA TTG GGA ATG TAT G 0.4 0.08
T12 F:GG ATC ACT CGG CCA CTC TGG (CAG) 9 GAG (CAG) 3 2 0.1 0.02 2b 55 174-186 EU522093 Alac et al. 2009
R:ACC CAA GAA TAC CCG TCA CCG 0.4 0.08
T17 F:AAC AGT ATT ATG GAT GCA GAC (TGC) 7 2 0.1 0.02 2b 55 135-144 EU522095 Alac et al. 2009
R:GAC ACA AAA GGT ACC ATT CCC 0.4 0.08
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2.3 Data Analysis
2.3.1 Intrapopulation
Prior assessment of processes that affect the genetic structure of population data must be investigated for the presence of additional factors that can create deviation from HWE within each subpopulation (or group of samples). Potential sources and means of testing for them are described below.
Null alleles and Large Allele Drop Out
Following scoring in GENEMAPPER v.4.0 (Applied Biosystems) and prior to the above HWE test, genotype scores were checked in MICRO-CHECKER 2.2.3 (Van Oosterhout & Hutchinson & Wills et al. , 2004). The latter was also used for evaluation of null alleles, potential misscoring owing to stuttering and large allele drop out at the subpopulation level. Large allele drop out where large alleles are faint or not observable owing to poor amplification (Wattier & Engel & Saumitou-Laprade et al. , 1998; Selkoe and Toonen, 2006) is primarily of concern in non-invasive studies owing to the low quantity and quality of extractions (Taberlet & Griffin & Goossens et al. , 1996; Bonin & Bellemain & Eidesen et al. , 2004). Large allele drop out is inferred when there is an excess of homozygotes compared to expectation under HWE, with homozygote distribution bias towards small allele size (Van Oosterhout et al. , 2004).
The presence of null alleles on the other hand results from mutation within the primer-binding region of one or more alleles, preventing binding of the primer and subsequent amplification (Callen & Thompson & Shen et al. , 1993; Jarne and Lagoda, 1996). As a general rule, the more genetically distant a population is from the population from which the primers were designed (the focal population), the greater the likelihood of null alleles in the non focal population (Selkoe and Toonen, 2006; Chapuis and Estoup, 2007). Null alleles reduce observed heterozygosity as the second allele is not amplified, resulting in deflated summary statistics of genetic diversity within populations and biased estimates of population differentiation (Chapuis and Estoup, 2007). Although a correction method for null alleles exists (Chapuis and Estoup, 2007), it provides only marginally better results for some genetic diversity statistics but not all and weaker power for population differentiation than non corrected datasets (Chapuis & Lecoq & Michalakis et al. , 2008). Moderate levels of null alleles (~ 0.20) also increases population discrimination under all levels of gene flow using clustering methods (Chapuis et al. , 2008; Guillot & Santos and Estoup, 2008). Loci demonstrating moderate levels of null alleles in this study were consequently kept and given no further investigative attention, while the influence of loci demonstrating higher frequencies of null alleles was assessed against results obtained following their removal from the dataset.
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Null alleles are inferred when an excess of homozygotes is found compared to expectations under HWE, with homozygote present across all allele size classes (Van Oosterhout et al. , 2004). A missing genotype (i.e. non-amplified) at a single locus in an individual helps to reject other explanations for the non-amplified data (e.g. poor extraction with non-amplified genotype at multiple loci) (Selkoe and Toonen, 2006). Where detected, null allele frequencies were calculated using the Expectation Maximization algorithm of Dempster, Laird and Rubin (1977) as implemented in FreeNA (Chapuis and Estoup, 2007).
Linkage Disequilibrium
Linkage disequilibrium (LD) between pairs of loci was investigated in GENEPOP 4.1 (Rousset, 2008). LD tests for a non-random association in allele frequencies between two or more loci, either on the same or different gametes (Slatkin, 1994; Weir, 1996). In population genetics, one is primarily interested in assessing if two loci are physically linked and hence share a similar genealogical history.
This, because the estimate of population differentiation θW is a weighted average over alleles over loci (Weir and Cockerham, 1984) and non-independent loci would over contribute to the calculation of the estimate, towards either lower or higher estimates depending on their genealogical history. One is therefore interested in knowing if any significant level of non-random association of alleles between loci is present and to include only one of the linked loci in the dataset (Selkoe and Toonen, 2006). Disequilibrium may however also result from a number of processes such as migration, selection and genetic drift (Hartl, 2000; Law & Buckleton & Triggs et al. , 2003). In a structured population, genetic drift alone will cause allele frequencies to differ within each subpopulation, resulting in different high frequency allele combinations at specific loci within each subpopulation (Cornuet & Piry & Luikart et al. , 1999; Holsinger and Weir, 2009). In other words, individual genotypes within a subpopulation will be more similar than with those of individuals in other subpopulations. If the pattern is present across multiple loci, non-random association of alleles between loci will be inferred when analysing the overall population as the allele combinations created by isolation are unlikely to occur following the randomisation procedure (Weir, 1996). This issue of unknown substructure in the data can be circumvented when testing for LD by using the test of composite linkage disequilibrium (Weir, 1996, p. 126-128), also known as the exact HW test, as it does not assume HWE. Tests were carried at the ‘site of capture’ level using the G-test for significance level computed via the Markov chain randomization procedure of Raymond and Rousset (1995) with 10,000 de-memorisation procedure, 500 batches and 5,000 iterations per batch.
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Hardy Weinberg Equilibrium
The genotypic proportions sampled from a population are expected to be at Hardy Weinberg Equilibrium (HWE) if a number of assumptions such as random mating, no migration, no mutation and no selection are met (Hartl, 2000; Wigginton & Cutler and Abecasis, 2005). HWE tests for deviation of observed genotype frequencies against expected genotype frequencies. Under the above assumptions, the expected frequencies are predicted from the observed allele frequencies and the laws of Mendelian inheritance (Hartl, 2000). Common explanations for deviation from HWE are the Wahlund effect where the dataset analysed is comprised of two or more genetically distinct populations (Sinnock, 1975), non-random mating and selection (Guo and Thompson, 1992;
Wigginton et al. , 2005). The presence of non-random mating in a population is estimated with the FIS statistic, the inbreeding coefficient, which measures the deviation in heterozygosity of individuals relative to their subpopulation (Pages and Holmes, 1998; Perrin and Goudet, 2001). If other factors
such as the Wahlund effect and the presence of null alleles have been invalidated, significant FIS value are associated with departure from HWE as individuals do not mate randomly (Perrin and Goudet, 2001). When closely related individuals breed, the chance of two identical alleles (homozygote) occurring in an offspring is increased relative to a randomly mating population, and produces FIS
values that are positive (Lande, 1988; Frankham, 1995; Keller and Waller, 2002). FIS values for each locus within each population and deviation from HWE for each locus within each population were computed in Genepop version 4.1 (Rousset, 2008). HWE was assessed with the exact HW test, via the complete enumeration procedure for loci with less than 5 alleles and the Markov chain algorithm of Guo and Thompson (1992) for loci with more than 5 alleles as implemented in GENEPOP 4.1 (Rousset, 2008), with 10,000 de-memorisation, 200 batches and 5,000 iterations per batch.
Measures of Genetic Diversity
The observed ( HO) heterozygosity provides information on genetic variance at a locus, with high heterozygosity seen as a good predictor of a population current fitness (Frankham, 1996; Reed and Frankham, 2003) and its potential for recovery in the short term following a bottleneck (Allendorf, 1986). Following a bottleneck, genetic drift is strong owing to the small population size but is less likely to result in high genetic diversity loss if all alleles are found at high and even frequencies than under low heterozygosity, where only a few alleles are found at high frequencies and a high proportion of allelic diversity is at risk of being loss through genetic drift (Allendorf, 1986; Lande,
1988). The observed ( HO) and expected ( HE) proportion of heterozygote at each locus were computed in Arlequin version 3.5.1.2 (Excoffier and Lischer, 2010).
The number of alleles present at a neutral marker (allelic diversity) has been suggested to be a better measure of genetic diversity than heterozygosity (Allendorf, 1986; El Mousadik and Petit, 1996; Petit & Mousadik and Pons, 1998; Kalinowski, 2004) and to correlate well with measures of diversity at 31
non-neutral markers (Bataillon & David and Schoen, 1996; Petit et al. , 1998). This is especially true if interest lies in the long term persistence of a species, allelic diversity being more important than allele frequencies in this instance by providing a ‘greater range’ of response to changing conditions (Allendorf, 1986; El Mousadik and Petit, 1996; Petit et al. , 1998). Therefore some advocate the use of allelic richness as a measure of population or species conservation prioritisation (Petit et al. , 1998).
This is measured by the total number of alleles ( AT) observed within a population. As AT is dependent on the number of samples, larger sample size revealing greater allelic diversity than smaller ones
(Petit et al. , 1998; Kalinowski, 2004), a corrected measure of Allelic richness ( AR) using rarefaction
methods for standardisation (Petit et al. , 1998) can be computed. The total ( AT) number of alleles and corrected mean allelic richness ( AR) at each locus within each population was calculated in HP-Rare v.2006 (Kalinowski, 2005).
2.3.2 Analysis of Population Genetic Structure
Estimates of Population Differentiation using F- statistics
Only the weighted average unbiased estimate θW (Weir and Cockerham, 1984), the estimate of FST ,
was computed in this study. The microsatellite specific unbiased estimate ρ for RST (Slatkin, 1995) has been repeatedly found to have high variance when samples sizes are small and unequal between
populations (Ruzzante, 1998; Gaggiotti & Lange & Rassmann et al. , 1999). RST takes into account the difference in repeat number between microsatellite alleles (a measure of allele relatedness) assuming the single stepwise mutation model where only one repeat unit is gained or lost per mutation event (Slatkin, 1995). As a result, this measure of population divergence is more appropriate for situations where the time since the ancestral population has split is long relative to its ancestral size (Slatkin, 1995; Balloux and Lugon-Moulin, 2002). This is due to mutations at microsatellite alleles being responsible for a larger component of the differentiation relative to genetic drift when population size is large and evolutionary time long (Balloux and Lugon-Moulin, 2002). In contrast, the weighted ~ unbiased estimate θW (Weir and Cockerham, 1984) of FST only considers the character of the alleles and does not take into account the size difference. This makes it a more appropriate measure of population differentiation under recent divergence time where genetic drift is a stronger evolutionary process relative to mutation (Slatkin, 1995; Ruzzante, 1998) and when gene flow between population
is expected to be moderate to high (Balloux and Lugon-Moulin, 2002). FST has also been found to be unaffected by unequal samples sizes and to provide low variance in estimates when using six or more loci (Ruzzante, 1998). Considering the sample size, number of loci, typically conservative turtle DNA (Avise et al. , 1992; FitzSimmons et al. , 1995; Amato & Brooks and Fu, 2008; Thomson et al. , 2008) and projected recent time since divergence between populations, this measure was deemed the most appropriate for investigation of population subdivision in freshwater turtles of the MDB.
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AMOVA
The Analysis of Molecular Variance (AMOVA) as implemented in Arlequin ver 3.5.1.2 (Excoffier and Lischer, 2010) was used to assess the population genetic structure of each species within and between catchments. AMOVA partitions the total sum of squared deviations to obtain covariance components following a hierarchical structure (Excoffier & Smouse and Quattro, 1992). The hypothesis of panmixia at the regional level is tested by carrying out an AMOVA with the following hierarchical levels:
Among catchments ( FCT )
Among populations within catchments ( FSC )
Within populations ( FST )
When the hypothesis was rejected and a subdivision was found at the among catchment level ( FCT )
but not within ( FSC ), AMOVA was rerun between two catchments at a time to determine among which region(s) the partitioning lies. Regardless of the results for FCT , pairwise comparisons among all subpopulations within each catchment were made if the fixation index for among populations
within catchment ( FSC ) was significant in order to identify the outliers. Significance of each variance
component was assessed by a non parametric permutation method where 1) for FST , individuals are randomly permuted among populations while keeping the sample size within each population fixed at the realised value; 2) for FSC individuals are permuted within region without regard to population. The third permutation scheme 3) assumes panmixia at the regional level and randomly permutes whole
populations among regions ( FCT ) (Excoffier et al. , 1992; Excoffier and Lischer, 2010). The variance components are then re-computed for each newly obtained matrix and their null distribution constructed.
Isolation by Distance
Isolation by distance (IBD) is typically tested for with the Mantel test (Mantel, 1967). Mantel test computes a Pearson product-moment correlation coefficient between genetic distance and geographical distance (or any two distance measures) (Legendre, 2000). The observed correlation is assessed against a null-distribution obtained from randomisation of the rows of a single distance matrix followed by re-calculation of the correlation coefficient from the permuted matrix (Legendre, 2000). IBD is inferred when the observed (positive) correlation between the two distance falls within the top five percentile (if choosing this significance level) of the null-distribution obtained through randomization. The procedure was implemented in Arlequin ver. 3.5.1.2 (Excoffier and Lischer, 2010) and was carried out with river distances and where appropriate with straight-line overland distances between sites. This allowed testing of both within-channel and overland movement between
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catchments. Channel and overland distances were measured in ArcView version 9.3 (ESRI). Mantel test were carried out with Slatkins linearised distance D = FST /(1-FST ) (Slatkin, 1995) a modification of FST that is appropriate for analyses of IBD in one-dimensional habitat (Rousset, 1997; 2000) such as streams and rivers.
Decomposed Pairwise Regression Analysis
Isolation by distance (IBD) assumes equilibrium between gene flow and genetic drift in all populations, an assumption seldom met in real studies (McCauley, 1991; 1993; Hutchison and Templeton, 1999; Whitlock, 2001). The classical Mantel test (1967) for IBD does not accommodate for the usual increase in complexity and physical barriers with increasing distance (Neigel, 1997; Whitlock, 2001; Wiens, 2001). These barriers may lead to a number of populations demonstrating greater shift in allele frequencies than would be expected from distance alone owing to reduced gene flow and resulting stronger genetic drift effect (Neigel, 1997). Inclusion of such populations in the Mantel test would provide a correlation between genetic and geographic distances either stronger than it truly is if these populations are located distantly to most other populations, or weaker if located at an intermediate distance to all other populations (Slatkin, 1993; Koizumi et al. , 2006). Hence removal of such populations would provide greater confidence in how truly representative of the species overall population structure the correlation between geographical and genetic distances is (Koizumi et al. , 2006). These atypical populations also provide the opportunity to investigate external factors influencing a species population genetic structure through identification of shared characters in the identified populations.
Detection of populations with atypical allelic frequencies was made following the Decomposed Pairwise Regression Analysis (DPRA) of Koizumi et al. (2006). In the DPRA, pairwise geographic
(channel distances unless stated otherwise) and linearised genetic distances [ FST / (1-FST )] (Slatkin, 1995; Rousset, 1997) were regressed and the mean Residual Sums of Squares ( RSS ) calculated for each population. The population with the highest statistically significant ( α = 0.05) mean RSS was removed (i.e. all pairwise comparisons involving this population were removed from analysis) and a new regression line was computed. Mean RSSs for each population were calculated again and the ‘removal’ process reiterated until no population showed mean RSS significantly different from the overall line of best fit. All populations identified as outlier at this stage were considered ‘putative outliers’ (Koizumi et al. , 2006). Secondly, the sequential models obtained (i.e. All populations, All populations minus one population, All populations minus two populations etc) were assessed using the Akaikes’ information criteria (AIC) (Burnham and Anderson, 2002 cited in Koizumi et al. 2006) following Koizumi et al. ’s (2006) equation AIC=2 K+nLn ( RSS /n), where K is the number of parameters, n the number of populations and RSS the residual sums of squares. The model with the highest AIC value was then selected as the best model. Where two or more models were equally
34
likely ( ∆AIC ≤ 2) (Burnham and Anderson, 2002 cited in Koizumi et al. 2006) the R2 value from the regression of each model, which provides an estimate of how much of the variance is explained by the model, was used as the selection criterion to identify the best models amongst them (Koizumi et al. , 2006). Populations still identified as outliers following AIC selection were then considered ‘true outliers’(Koizumi et al. , 2006). Finally, ‘true’ non outlier populations were regressed against each other while each ‘true’ outlier population was regressed against all ‘true’ non outlier populations. The line of best fit for each of the populations were then assessed using the least-square regression method and categorised following Koizumi et al. (2006) (see Figure 1. 1) into either:
1) ‘strong genetic drift’ (high significant intercept, no slope)
2) ‘genetic drift with limited gene flow’ (high significant intercept, positive slope)
3) ‘Isolation by distance’ (zero intercept, positive significant slope)
4) ‘high gene flow’ (zero intercept and no/low slope)
Spatial Autocorrelation Analysis
Spatial autocorrelation analysis (SAA) also tests for the presence of structure resulting from IBD, albeit usually at a smaller scale (Sokal and Wartenberg, 1983). SAA uses spatial variables such as Euclidean distance or connection in a network between two sampling locations or individuals to describe their spatial relationship (Sokal and Wartenberg, 1983; Epperson and Li, 1997; Smouse and Peakall, 1999), and assesses it against the level of (genetic) autocorrelation between individuals with the expectation that the more distant the individuals are the weaker the autocorrelation between them. A matrix of pairwise genetic relationships between individuals, computed for multi-allelic codominant markers following Peakall et al. (1995) and Smouse and Peakal (1999) methods was used to calculate the autocorrelation of individuals within the different classes following Cliff and Ord (1981). At each locus a squared linear distance was calculated between genotypes, and the squared distance obtained for each locus was summed up across all loci to obtain an overall pairwise genetic distance between individuals (Peakall et al. , 1995; Peakall & Ruibal and Lindenmayer, 2003). As for all other methods described so far, evolutionary and hence statistical independence between loci is assumed. The spatial autocorrelation coefficient r obtained is then plotted against the different distance classes in what is called a correlogram (Sokal and Wartenberg, 1983). Locations are grouped into distance classes using the individuals pairwise distance matrix and the average number of joins (distance between alleles based on identity) between genotypes within a specific distance class is compared to the number of joins under a random distribution of genotypes through permutations (see Epperson and Li, 1997 p. 673). High coefficients of autocorrelation are linked to genotype homogeneity, owing to individuals mating with more closely related individuals than under panmixia or complete random mating (Sokal and Wartenberg, 1983). 35
An interesting feature of SAA is its ability to provide an estimate of the distance at which population structure can no longer be detected, which has been equated to the ‘dispersal distance’ or patch size of the species (Sokal, 1979; Sokal and Wartenberg, 1983; Smouse and Peakall, 1999; Peakall et al. , 2003). This distance has been equated to the average distance of gene flow per generation, being positively related to the parents’ dispersal abilities (Epperson, 1993; Epperson and Li, 1997). Hence the genetic patch size should vary with respect to the species vagility (Bohonak, 1999) and the influence of physical barriers in the population, the larger the patch size the better the dispersal of the species (Bowman & Forbes and Dilworth, 2000; Peakall et al. , 2003). This ‘ patch size’ is inferred from the x-intercept, which corresponds to the average distance at which any two sites are as genetically similar to the study-wide similarity level under random distribution of genotypes (Schweiger & Frenzel and Durka, 2004).
The geographical distances used in the SAA were identical to those used in the DPRA. Consequently all pairwise comparisons between individuals collected within a single sampling location have a geographic distance of zero. Investigation were made with all loci combined as the interest was in the patterns resulting from gene flow and historical association between populations, not in investigating potential selection or mutation affecting loci differently (see Slatkin and Arter, 1991).
Clustering Methods of Population Structure Inference
Apart from the SAA, all above methods are frequentist or summary statistics methods, where inferences about the structure (or lack of) of a population are based on allele frequencies obtained from the presence or absence of alleles in individuals belonging to predefined groups (Hey and Machado, 2003; Mank and Avise, 2004; Kelly & Oliver & Sivasundar et al. , 2010). Groups are typically defined by sampling location and henceforth considered a population unit for analysis, with potential migrants unknowingly considered residents (Manel & Gaggiotti and Waples, 2005). These methods make a number of unrealistic assumptions such as identical levels of gene flow between populations and most importantly attainment of equilibrium between genetic drift and gene flow for all populations (Bossart and Pashley-Prowell, 1998; Whitlock and McCauley, 1999; Whitlock, 2001; Koizumi et al. , 2006). The DPRA introduced above can be used post pairwise comparison analysis to identify if and where some of these assumptions were not met, but the method still relies on arbitrarily grouped samples. Analysis made at the individual level would avoid such limitations (Pritchard & Stephens and Donnelly, 2000; Mank and Avise, 2004; Manel et al. , 2005; Kelly et al. , 2010).
The individual based Bayesian clustering method of Pritchard et al. (2000) (STRUCTURE ) uses HWE and linkage disequilibrium arising from substructure to infer populations or ‘clusters’, assuming linkage equilibrium within each subpopulation. The number of populations is therefore obtained by finding the sample arrangement that optimally reduces linkage disequilibrium between loci and attain HWE for each locus (Pritchard et al. , 2000). This stems from the assumptions that individuals 36
(genotype) from the same population are more closely related to each other than to individuals (genotype) from other populations (Cornuet et al. , 1999; Pritchard et al. , 2000). In STRUCTURE all clusters are equally likely, no information on the spatial distribution of the individual being included in the standard model (Pritchard et al. , 2000). The number of clusters ( K) is a fixed parameter and hence five replicated runs at different ( K) were computed to obtain a mean posterior probability across runs and assess consistency of output (posterior probability) for each fixed ( K) following Coulon et al. ’(2006)recommendations. The basic admixture model (without LD), where individual genotypes are assumed to have mixed ancestry, was used for investigation of structure without sample location information (LOCPRIOR). Although this add-on (LOCPRIOR) to the original models of Falush (2003) and Pritchard (2000) was found to help clustering when the signal in the data was weak, while not interfering with the inference when the signal is strong, here the interest lay in identification of strong structure while reducing time for analyses. The best K estimate for all runs was obtained from STRUCTURE outputs via the online software STRUCTURE Harvester (Earl, 2011). STRUCTURE Harvester applies the ad hoc method of Evanno (2005) where ∆K is inferred from the rate of change of the log posterior probability of the data with respect to K, averaged over all runs for each K. The optimal alignment of replicated runs for the best K inferred above, and the mean membership coefficient of the realigned matrices across runs (i.e. replicate) was then obtained via the software CLUMPP v1.2.2 (Jakobsson and Rosenberg, 2007) and results visualised in Excel.
The R package GENELAND (Guillot & Estoup & Mortier et al. , 2005a; Guillot & Mortier and Estoup, 2005b) was also used to infer cluster number as the use of multiple clustering methods for population structure inference has been encouraged following small but generally consistent discrepancies amongst their outputs (Rowe and Beebee, 2007; Latch & Scognamillo & Fike et al. , 2008). GENELAND is a Bayesian clustering method similar to STRUCTURE but allows inclusion of the geographic location of individuals in the analysis. As such it does not assume equal probability for any two samples to belong to one population regardless of the geographical distance between them, but rather assumes that the joint probability that two samples belong to the same population decreases with increasing distance between them (Guillot et al. , 2005a; Guillot et al. , 2005b). An uncertainty factor around the spatial location of the sample, which can be associated with the assumed average dispersal distance in the species of focus, can also be included (Guillot et al. , 2005b). By including spatial information GENELAND is able to map population boundaries from genetic discontinuities in the sample locations, in addition to other features found in STRUCTURE .
In either clustering method, two allele frequency models are available for ( K) inference. The uncorrelated model assumes clusters to be independent from each other and hence allele frequencies in one cluster are independent from frequencies in other clusters (Pritchard et al. , 2000; Falush et al. , 2003). The second model, known as the correlated frequencies model assumes that all clusters ( K) have diverged from a (relatively recent) common ancestral population (the LD model of Falush et al. , 37
2003) (Guillot and Santos, 2009). Hence variance in allele frequencies amongst them is assumed to results from (uniform) genetic drift since divergence (Falush et al. , 2003; Guillot and Santos, 2009) and allele frequencies within a population are assumed to provide information about frequencies in the other populations. The correlation in the model can therefore be seen as a summary of the shared recent micro-evolutionary history of the populations (Guillot, 2008; Guillot and Santos, 2009). The correlated model is able to identify more subtle differentiation than the uncorrelated model (Falush et al. , 2003) which can become ‘blind’ below a differentiation threshold, but the former may be more sensitive to the presence of IBD pattern and upward bias of ( K) estimate (Falush et al. , 2003; Guillot and Santos, 2009). In Geneland, investigations were started with the uncorrelated model, identifying old divergences if any were present, before investigating for more subtle and more recent differentiation with the correlated model (Coulon et al. , 2006; Guillot, 2008). In STRUCTURE only the correlated admixture model was run owing to extensive computation time and resources (computing) limitations.
A number of studies have shown that GENELAND can infer ‘Ghost’ populations (Excoffier and Heckel, 2006; Rowe and Beebee, 2007; Coulon & Fitzpatrick & Bowman et al. , 2008; Guillot, 2008), where no samples were actually obtained under the correlated frequencies model. This issue has been improved upon since GENELAND version 2.0, although such populations can still be inferred when the model assumptions are not met (e.g. presence of an IBD pattern) (Guillot, 2008). To assess output consistency and reduce the likelihood of ‘Ghost’ population inference a minimum of 5 runs with large iterations (~ 2x10 6) were computed under each model/parameter (Guillot, 2008). Under both frequency models the maximum rate of Poisson process, which controls the number of polygons in the geographical area studied, was set to a value equal to the number of individual in the analysis as recommended by Guillot et al. (2005a). The maximum number of nuclei was also set following GENELAND’s manual recommendation (3 x maximum rate Poisson process). Specific run parameters for both STRUCTURE and GENELAND (iterations number, thinning, burnin, etc) are provided in the method section of chapters where the methods are applied. Further methods of analysis unique to specific chapters are described within each chapter methods section.
38
Chapter 3 Genetic Connectivity in a Dryland River
Sampling in the Moonie River at Verena (VE), 2008
39
3.0 Introduction
Predicted rapid climatic changes (Aldous et al. , 2011), growing extraction of already scarce water resources, and current habitat modifications raise concerns for the freshwater fauna of Australia’s inland waters (Kingsford, 2000; Ciofi & Milinkovitch & Gibbs et al. , 2002; Bond et al. , 2008; Aldous et al. , 2011). These concerns further highlight the significance of permanent waterholes as refugia for freshwater fauna in dryland rivers (Knighton and Nanson, 1994; Morton & Stafford Smith & Friedel et al. , 1995; Landcaster and Belyea, 1997; Magoulick and Kobza, 2003; Balcombe & Bunn & Davies et al. , 2005). Refugia enable organisms to withstand periods of no flow by providing a non-optimal habitat patch in which organism metabolic and ecological activity is typically slowed down (Robinson & Tockner and Ward, 2002; White, 2002; Humphries and Baldwin, 2003; Rayner et al. , 2009) until the system reenters a period of boom (Bunn et al. , 2006). The presence of refugia in a system is however not the only factor driving species persistence. Refugium size, persistence time and spatial distribution are also key factors (Landcaster and Belyea, 1997; Stanley & Fisher and Grimm, 1997; Wiens, 1997; Magoulick and Kobza, 2003), while the dispersal ability, movement behavior and perceptual range of a species are biologically important drivers of successful recolonisation dynamics within these systems (Wiens, 1997; 2001; Lowe, 2002; Schooley and Wiens, 2003). Knowledge of species dispersal ability is therefore key to our understanding of their potential for successful recolonisation following periods of no flow.
Populations typically follow a variety of connectivity patterns which reflect the dispersal potential of the species as well as the morphology and flow regime of the system; these patterns are reflected in their population genetics (see Meffe and Vrijenhoek, 1988; Hughes et al. , 2009b; Faulks et al. , 2010). In dryland rivers, these range from panmixia where individuals are able to move over the entire system or drainage over their lifetime (Hutchison and Templeton, 1999; Waples and Gaggiotti, 2006; Faulks et al. , 2010), isolation by distance (IBD) where individuals only move within a restricted portion of the system over a single or multiple flow events (Slatkin, 1993); highly isolated populations that follow their own evolutionary trajectory (Meffe and Vrijenhoek, 1988; Hutchison and Templeton, 1999) as the organisms are unable to take advantage of flow events because refugia are too distant for successful dispersal or flow events are too rare to allow sufficient gene flow to counterbalance the effect of genetic drift within isolated populations. Although the genetic structuring of a species is clearly affected by the presence of barriers and ecological gradients (Cushman & McKelvey & Hayden et al. , 2006; Hughes, 2007; Huey et al. , 2008; McLean & Schmidt and Hughes, 2008), under identical conditions it is the intrinsic biological traits and abilities of each species that ultimately dictate their spatial genetic structure (Bohonak, 1999; Baggiano et al. , 2011b).
40
Populations of freshwater turtles have exhibited all patterns of connectivity, from isolation over short distances (< 2 km) among connected mountainous streams (Hydromedusa maximiliani ; Souza and Abe, 1997) to high connectivity over large areas of the Amazon - Orinoco basins ( Podocnemis unifilis ; Escalona et al . 2009; Podocnemis expansa ; Pearse et al . 2006) and among wetlands of a desert-spring ecosystem ( Terrapene Coahuila , Howeth & McGaugh and Hendrickson, 2008). In Australia, despite the importance of population connectivity for persistence of species (Hanski, 2001; Fagan, 2002; Lowe, 2002; Clobert et al. , 2004) assessment of population connectivity in inland turtles is lacking. This is startling considering indications that these may be highly dependent on refugia for long term persistence (Goodsell, 2002; White, 2002), enabling recolonisation of the system in periods of high flow. A few direct-method studies have investigated movement patterns of the Chelodina species of focus in the present study, providing knowledge on movement cues, movement patterns, habitat range, and proportion of migrants in the populations (Graham et al. , 1996; Roe and Georges, 2007; Roe and Georges, 2008c; Bower, 2011). Other studies have extended our understanding of basic demographics for MDB turtles (Georges, 1984; 1988; Kennett and Georges, 1990; Spencer and Thompson, 2005; Georges et al. , 2006; Spencer & Janzen and Thompson, 2006), but a knowledge gap remains regarding their population connectivity at larger spatial and temporal scale. Such information, combined with the demographics and movement information already gathered would make informed assessment possible on which species may be most detrimentally affected by current and predicted changes in the MDB.
In this chapter, I assessed the dispersal abilities, inferred from levels of gene flow, of the MDB freshwater turtles in a dryland river that remains as a succession of permanent to semi-permanent waterholes for most parts of the year. This provides a ‘benchmark’ of gene flow levels for each species under natural conditions before investigation on the potential impact of human made regulation on the structure of their populations in Chapter 4. Comparisons across the three species enabled a better understanding of the intrinsic abilities of each species relative to each other. Based on existing studies and anecdotal evidence, expectations were that C. longicollis would exhibit high levels of gene flow, C. expansa more moderate levels, and E. m. macquarii the lowest levels of connectivity of all three species. Owing to the variable nature of dryland rivers, it was further expected that the populations would follow a metapopulation structure where some populations experienced relatively recent population extirpation events following extended periods of no flow and would therefore demonstrate unusual patterns of genetic structure (see Huey et al. , 2011). As documentation of pathways along which dispersal takes place is critical for our understanding of recolonisation potential (Fagan, 2002; Lowe & Likens and Power, 2006), overland genetic connectivity in C. longicollis was also assessed, terrestrial movements between permanent and non permanent habitats representing a central part of its ecology (Stott, 1987; Graham et al. , 1996; Roe, 2007; Roe and Georges, 2008c).
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3.1 Methods
3.1.1 Catchments Description
The Moonie River
The Moonie River is a 525 kilometer long dryland system draining primarily in south-west Queensland (CSIRO, 2008). The Moonie River joins the Barwon River south of the NSW border before the latter becomes the Darling River, a main tributary of the MDB, one of the world’s longest river systems (Walker, 2006) (Figure 3. 1). Surface flow in the Moonie River depends on unreliable seasonal rainfall, with no groundwater contribution to the system (Sternberg & Balcombe & Marshall et al. , 2008). The region is semi-arid with a mean annual rainfall of 528mm/yr, varying from 700mm in the east to 450mm in the west of the catchment and a mean annual pan evaporation of 1800-2200 mm/ yr (CSIRO, 2008). Both annual temperature and rainfall are highly seasonal, most of the rain falling in summer. The system is thus characterised by long period of no flow punctuated by well defined flow events, the River existing as a serie of unconnected waterholes acting as refugia during no flow spells (Department of Environment and Resources Management, 2010). The longest recorded period of now flow was recorded over a 13 month period in 1979-1980 (Biggs & Power & Silburn et al. , 2005) and current models have estimated waterhole persistence time of up to 820 days in the absolute absence of flow and rainfall (Department of Environment and Resources Management, 2010). The average annual streamflow discharge is 27m3/s at Nindigully (NI) gauge and 37m3/s further south at Fenton (FE) near the NSW border (Sternberg et al. , 2008). The Moonie catchment has no major public storage along its course (Department of Natural Resources, 1999).
Border Rivers
The Border Rivers catchment is composed of three main tributaries, the Macintyre River, the Dumaresq River and the Macintyre Brook, all rising in the western slopes of the Great Dividing Range along the NSW and Queensland border (Figure 3. 1). The main branch, the Macintyre River which joins the Severn River at Kwiamble National Park (KN), is joined by both the Dumaresq and the Macintyre Brook near Goondiwindi before ultimately becoming the Barwon River. The catchment covers 4% of the MDB area and is bound in the North by the Moonie River catchment, the Gwydir River catchment in the south, the Great Dividing Range in the east and the Barwon-Darling catchment in the west (CSIRO, 2007a). The catchment has experienced significant alteration with the construction of major water storage structures in the 1960’s onwards in the upper catchment and numerous public weirs along the main channels (Kingsford, 1999; Thoms et al. , 2005; CSIRO, 2007a) (Figure 3. 1). Water resource development is also greater than in the Moonie region, with large numbers of farm dams and ring tanks present in the Goondiwindi (GO) - Boomi (BOM) area.
42
KI KU Moonie River VE KO
AL CAR AP Border Rivers MI NI GO Macintyre Brook BOM SK NU Macintyre BOPU WA River Dumaresq River BA FE BW BD KN
BC PA Severn LO WC PI River
Gwydir River MY CAL Barwon River Macintyre River BB
N KG O E 0 100 km S
Figure 3. 1 Map of the Moonie River, Border-Barwon Rivers and Gwydir River catchment with sampling site location. Triangle: Sampling location; Half circles: Major Dam; Shaded area: major wetlands and lagoons. See Appendix 8.2.1 for site names. Note that not all sites have samples from all three species
An allogenic catchment, the eastern section of the Border Rivers is well watered from precipitation in the higher elevations but consists of a dry low lying floodplain in the west (Thoms et al. , 2005). Mean annual rainfall ranges from 1100mm in the east to 480mm in the west and has remained constant over the last 112 years of records (CSIRO, 2007a). Rainfall patterns are similar to the Moonie region. Flows in the Macintyre River ranged from 61,000 to 4,488,000 Ml/d (mega litres per day) in the last hundred years of records (Thoms et al. , 2005). During these high summer rainfall events, the low lying section downstream of Goondiwindi (GO) can experience extensive flooding (Kingsford, 1999). Annual evaporation in the dry western section of the catchment is analogous to the Moonie River (1900mm), but is much lower in the mountainous area (200mm) (Thoms et al. , 2005). Owing to the high surface water diversion and regulation in the last thirty years, mean annual volume has decreased by up to 39% at Goondiwindi and flow duration downstream of the major impoundments has been
43
significantly altered by decreasing the magnitude and frequency of major flow and increasing the frequency of smaller ones (Kingsford, 1999; Thoms et al. , 2005). This has significantly altered the nationally important wetland of Morella Watecourse / Boobera (BO) lagoon / Pungbougal (PU) lagoon south of Goondiwindi (GO) (Kingsford, 1999).
Barwon River
The hydrology and geomorphology of the Barwon River section sampled is similar to that of the lower end of the Border Rivers. The Barwon River is considered unregulated with no major water storage along its course but public weirs are present. Rainfall is low (328mm mean) throughout the year but is higher in summer. Flows in the Barwon River are largely a reflection of upstream tributary flow and regulation. As opposed to the other catchments described here, rainfall patterns are erratic with high inter-annual variation. The region experienced significantly wetter climate in the last fifty years than in the fifty years prior (Thoms and Sheldon, 2000b). No significant wetlands are present along the section sampled, but a large number of oxbows and anabranches exist and extensive wetland areas are formed in the region by off-takes from the Moonie, Culgoa and Barwon River.
Gwydir River
Approximately 300 kilometer long, the Gwydir River catchment is located south of the Border Rivers region (CSIRO, 2007b) (Figure 3. 1). This semi-arid lowland river system culminates in an extensive wetland complex of national and international significance for colonial waterbirds (RAMSAR) (Wilson et al. , 2010). Significantly water-resource developed, the catchment is regulated by Copeton Dam and flow patterns are substantially affected by the important surface water diversion to ring tanks and farm dams (CSIRO, 2007b), leading to a 90% decrease in the core wetland area (Crabb, 1997 cited in Wilson et al. , 2010). Wet areas of tablelands in the east (mean annual rainfall 850mm) give way to central slope in the west (mean annual rainfall 500mm), characteristic of drainages in the upper eastern MDB. Mean annual rainfall (688mm) has remained constant since the early 1900’s, and follows a seasonal pattern identical to the Border River and Moonie River regions (CSIRO, 2007b). The catchment contributes around 3.4% of the total runoff in the MDB, less than the 5% of the Border Rivers but four times that of the Moonie River (0.8%).
3.1.2 Laboratory and Statistical Methods
A description of genotyping methods can be found in Chapter 2 ‘General Methods’. These methods are identical across all four data chapters. In depth description of statistical methods can also be found in Chapter 2. Specifics of statistical methods for the current chapter only are as follows. AMOVA, pairwise FST comparisons and Mantel tests for IBD were carried out using sampling locations with three samples or more (see Appendix 8.2.1 for capture per sites), reflecting the low number of samples obtained at some sites in the Moonie River. AMOVA analyses’were carried with catchments 44
set as the highest hierarchical level (i.e. Moonie vs Border-Barwon vs Gwydir). Within the Moonie River, Mantel tests for IBD using channel distance were carried out with and without populations identified as outliers through the DPRA for all three species. DPRA and Mantel tests for IBD with channel distance were also performed on the combined Border Rivers and Gwydir River populations for E. m. macquarii to test for possible influence of flow connectivity on genetic structure. This was not possible for C. expansa owing to the low number of sites in this catchment. DPRA was performed with channel distance within the Moonie River for C. longicollis, as well as across the three catchments using Euclidean distances. Mantel tests for IBD were run with and without populations identified as outliers in the Moonie, Border and Gwydir catchments in C. longicollis using Euclidean and channel distances. The increased Type I error associated with multiple tests (Rice, 1989; Benjamini and Yekutieli, 2001; Narum, 2006) was taken into account by applying the correction method of Benjamini and Yukieteli (2001). This correction method (B-Y) is deemed suitable for dependence amongst tests, such as the relative dependence amongst loci from common inheritance, while also providing good control of Type II error (Narum, 2006).
Spatial autocorrelation analyses (SAA) were performed with all individuals genotyped within the Moonie and Barwon Rivers for each species. SAA were performed separately for adults and juveniles for each species. The minimum distance class size of 20 km for SAA enabled inclusion of all samples from a single waterhole into the first distance class, as distances between individual captures within each waterhole were not recorded (maximum distance between captures within waterholes < 400m). No spatial autocorrelation analyses were performed in the Border and Gwydir Rivers, sample distribution in these catchments being too inconsistent (see Epperson and Li, 1997; Peakall et al. , 2003).
Subsequent to the pairwise FST analysis, five simultaneous runs with 500,000 iterations, a thinning of 500, a burn-in of 200 and an uncertainty factor of 0.2 (corresponding to a square of 20 km sides), with the uncorrelated frequency model were performed in GENELAND to identify a possibly divergent E. m. macquarii populations in the region. The number of iterations was set following ‘dummy’ runs assessing the number of iterations required for MCMC acceptable convergence. The uncertainty factor was chosen based on radiotelemetric studies of C. longicollis (Roe and Georges, 2008c) and C. expansa (Bower, 2011) in which dispersal of up 7 km and 17 km were observed respectively. No such study has been carried out on E. m. macquarii . Three extra runs of 2’000’000 iterations (2000 thinning and 200 burning) were performed for each species to further assess consistency of posterior density of the model and consistency of output with shorter runs (Guillot & Santos and Estoup, 2011).
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3.2 Results
3.2.1 Sample Sizes
A total of 276 individuals were captured in the Moonie River main channel over the course of this study, in addition to 37 individuals caught in the Barwon River. Within the Moonie River E. m. macquarii represented 63% of the total catch, C. expansa 21% and C. longicollis the remainder (Appendix 8.2.1). Including samples obtained from the University of Canberra Wildlife Tissue Collection a total of 99 C. expansa , 90 C. longicollis and 281 E. m. macquarii individuals were genotyped from the Moonie River, Border Rivers and Gwydir River catchments, apart from C. expansa for which no samples were obtained from the Gwydir catchment (Appendix 8.2.1).
The capture composition of both rivers, relevant for the SAA analysis (Juvenile and Adults), is discussed conjointly as samples were combined for most analyses. A total of 42 adults (14 male, 28 female) and 34 juvenile C. expansa were captured in the Moonie and Barwon Rivers. C. expansa individuals were classified as mature Adults if their straight carapace length (SSL) was larger than 230mm (Figure 3. 2). Adults were classified as male if their tail length (TL) was greater than 40mm and female if smaller (Figure 3. 2). This classification was compared against visual classification in the field using tail morphology to confirm identification. Individuals around 180-190mm are not reliably identifiable as either sex owing to the rapid increase in tail length around this size (Figure 3. 2) and individuals with SSL smaller than 230mm were therefore classify as Juvenile even though the onset of tail elongation was observable in some individuals (Figure 3. 2).
80
70
60
50
40
30 Tail Length TailLength (mm) 20
10
0 0 50 100 150 200 250 300 350 400 450 Straight Carapace Length (mm)
Figure 3. 2 Straight Carapace Length (SSL) against Tail Length (TL) for C. expansa individuals caught in the Moonie and Barwon River. Threshold Juvenile – Adult = 230 (mm); Female: lower right hand; Male: upper right hand; Juveniles: lower left hand.
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Tail length dimorphism was observed around a SSL of 175 (mm) in E. m. macquarii individuals (Figure 3. 3). E. m. macquarii individuals with SSL equal to or larger than 175mm were hence classified as Adults. Adults with TL larger than 40 mm were further classified as male (Figure 3. 3). Individuals collected during the first field trip were only classified visually using tail morphology and a minimum Plastron size of 147 (mm) the mean size at which tail elongation was apparent in male individuals (Spencer, 2002b). These individuals are therefore not included in Figure 3. 3. Although maximum carapace length appears highly variable across catchments in this species (Judge, 2001), these measures fall just short of Spencer’s (2002b) and White’s (2002) findings for E. m. macquarii . Following this classification, a total of 106 adults (60 male and 46 female) and 75 juvenile were captured within the Moonie and Barwon Rivers.
120.00
100.00
80.00
60.00
40.00 Tail Length Length Tail (mm)
20.00
0.00 0.00 50.00 100.00 150.00 200.00 250.00 300.00 350.00
Straight Carapace Length (mm)
Figure 3. 3 Straight Carapace Length (SSL) against Tail Length (TL) for E. m. macquarii individuals caught in the Moonie and Barwon River. Threshold Juvenile – Adult = 175 (mm); Female: lower right hand; Male: upper right hand; Juveniles: lower left hand.
A SSL size of 175 (mm), based on Chessman (1978) estimates, was applied for age class determination in C. longicollis . Forty one adults and 14 juvenile were captured in the Moonie and Barwon Rivers for this species.
3.2.2 Intrapopulation Diversity Measure
Chelodina expansa
No linkage disequilibrium amongst locus pairs ( P > 0.05) was detected and no sign of null alleles was detected at any locus (Table 3. 1) in this species. Three populations, each at different loci, showed deviations from HWE expectations before correction ( P < 0.05) but none after correction for multiple comparisons (Benjamini and Yukieteli Correction, α = 0.009). These populations are consequently considered to follow HWE expectations. Indices of genetic diversity revealed a moderate level of polymorphism in C. expansa with mean allelic richness ( AR) ranging between 2.55
47
(BW) to 3.16 (NI) and heterozygosity levels ranging from 0.25 (BW) to 0.67 (NI) with N = 3, the smallest sample size included in the rarefaction analysis (Table 3. 1).
Table 3. 1 C. expansa diversity indices. N, samples size; AT, total number of alleles (per locus and per site); AR, corrected mean allelic richness (n = 6 genes); HO, observed heterozygosity; HE, expected heterozygosity; P-
value, significant level for HWE expectation; FIS , inbreeding coefficient; NA , null allele frequency (*: not enough samples for NA estimation; - : no null alleles). See Appendix 8.2.1for site names and Figure 3. 1for site locations.
Locus Population A CAL LO BW BOM GO AP CAR FE NI KI KO KU VE T TCE64 7 N 15 4 3 6 11 5 3 13 5 6 11 8 5
AT 6 3 2 5 5 3 4 6 6 4 5 3 3
AR 3.69 2.96 2.00 3.79 3.45 2.60 4.00 3.58 4.26 2.50 3.64 2.86 2.60
HO 0.67 0.25 0.67 1.00 0.73 0.20 1.00 0.69 0.80 0.50 0.82 0.63 0.40
HE 0.79 0.75 0.53 0.82 0.77 0.64 0.87 0.78 0.84 0.46 0.80 0.71 0.64 P-value 0.341 0.143 1.000 0.711 0.792 0.048 1.000 0.257 0.799 1.000 0.191 0.227 0.365
FIS 0.16 0.70 -0.33 -0.25 0.06 0.71 -0.20 0.11 0.06 -0.11 -0.02 0.13 0.41 NA - - * - - - * ------TCE86 14 N 15 4 3 6 11 5 3 13 5 6 11 8 5
AT 8 6 4 6 6 6 4 11 6 4 6 5 5
AR 3.46 4.93 4.00 4.15 3.83 4.26 4.00 4.52 4.50 3.31 3.95 3.77 4.00
HO 0.87 0.75 1.00 1.00 0.82 0.80 1.00 0.92 0.80 0.83 0.91 0.88 0.80
HE 0.70 0.93 0.87 0.85 0.81 0.84 0.80 0.87 0.89 0.76 0.83 0.82 0.84 P-value 0.964 0.325 1.000 1.000 0.391 0.802 1.000 0.712 0.618 1.000 0.731 0.949 0.848
FIS -0.25 0.22 -0.20 -0.20 -0.01 0.06 -0.33 -0.06 0.11 -0.11 -0.10 -0.08 0.06 NA - - * - - - * ------TCE70 4 N 15 4 3 6 11 5 3 13 5 6 11 8 5
AT 3 3 2 2 3 3 3 3 3 3 3 2 2
AR 2.56 2.75 2.00 1.99 2.72 2.73 3.00 2.19 2.86 2.49 2.41 1.99 2.00
HO 0.60 0.50 0.33 0.50 0.46 0.40 1.00 0.31 0.80 1.00 0.82 0.50 0.60
HE 0.62 0.68 0.33 0.53 0.65 0.62 0.73 0.54 0.71 0.62 0.57 0.53 0.56 P-value 1.000 1.000 - 1.000 0.274 0.238 1.000 0.120 0.035 0.091 0.224 1.000 1.000
FIS 0.03 0.29 0.00 0.06 0.32 0.39 -0.50 0.44 -0.14 -0.71 -0.48 0.07 -0.09 NA - - * - - - * ------TCE74 2 N 15 4 3 6 11 5 3 13 5 6 11 8 5
AT 2 1 2 2 2 2 2 2 2 2 2 2 2
AR 1.98 1.00 2.00 1.99 1.98 2.00 2.00 1.76 2.00 1.99 1.93 1.88 2.00
HO 0.80 0.00 0.33 0.83 0.36 0.60 0.67 0.23 0.80 0.50 0.27 0.50 0.40
HE 0.52 0.00 0.33 0.53 0.52 0.56 0.53 0.32 0.53 0.53 0.46 0.40 0.53 P-value 0.045 - - 0.394 0.543 1.000 1.000 0.374 0.429 1.000 0.232 1.000 1.000
FIS -0.59 NA 0.00 -0.67 0.31 -0.09 -0.33 0.29 -0.67 0.06 0.41 -0.27 0.27 NA - - * - - - * ------TLE10 3 N 15 4 3 6 11 5 3 13 5 6 11 8 5
AT 2 1 1 1 2 2 2 3 2 1 2 2 1
AR 1.50 1.00 1.00 1.00 1.48 1.60 2.00 1.79 1.60 1.00 1.27 1.38 1.00
HO 0.07 0.00 0.00 0.00 0.18 0.20 0.67 0.31 0.20 0.00 0.91 0.13 0.00
HE 0.19 0.00 0.00 0.00 0.17 0.20 0.53 0.28 0.20 0.00 0.09 0.13 0.00 P-value 0.103 - - - 1.000 - 1.000 1.000 - - - - -
FIS 0.65 NA NA NA -0.05 0.00 -0.33 -0.10 0.00 NA 0.00 0.00 NA NA - - * - - - * ------TCE89.1 7 N 15 4 3 6 11 5 3 13 5 6 11 8 5
AT 4 2 4 4 6 4 3 7 5 5 5 5 5
AR 2.64 2.00 4.00 2.91 3.25 3.43 3.00 3.98 4.00 3.81 3.45 3.71 3.76
HO 0.67 0.75 0.67 0.67 0.64 1.00 0.67 0.77 1.00 0.83 0.82 0.88 0.80
HE 0.56 0.54 0.87 0.64 0.68 0.78 0.80 0.83 0.84 0.80 0.77 0.80 0.80 P-value 1.000 1.000 0.467 0.212 0.379 1.000 0.467 0.964 0.849 1.000 0.673 0.789 0.462
FIS -0.19 -0.50 0.27 -0.05 0.07 -0.33 0.20 0.07 -0.21 -0.04 -0.07 -0.10 0.00 NA - - * - - - * ------TCE76.1 20 N 15 4 3 6 11 5 3 13 5 6 11 8 5
AT 8 5 3 7 8 3 2 12 5 4 9 6 4
AR 3.31 4.21 3.00 4.38 4.09 2.20 2.00 4.22 3.67 3.05 3.65 3.53 2.80
HO 0.67 1.00 0.33 1.00 0.82 0.40 0.33 0.69 0.60 0.67 0.72 0.75 0.60
HE 0.67 0.86 0.73 0.86 0.84 0.38 0.33 0.82 0.76 0.49 0.74 0.73 0.53 P-value 0.572 1.000 0.200 1.000 0.583 1.000 - 0.081 0.245 1.000 0.543 0.642 1.000
FIS 0.00 -0.20 0.60 -0.18 0.02 -0.07 0.00 0.16 0.23 -0.03 0.01 -0.02 -0.14 NA - - * - - - * ------
48
Continued Table 3. 1 C. expansa diversity indices. N, samples size; AT, total number of alleles (per locus and per site); AR, corrected mean allelic richness (n = 6 genes); HO, observed heterozygosity; HE, expected heterozygosity; P-value, significant level for HWE expectation; FIS , inbreeding coefficient; NA , null allele frequency (*: not enough samples for NA estimation; - : no null alleles). See Appendix 8.2.1for site names and Figure 3. 1 for site locations.
Locus Population A CAL LO BW BOM GO AP CAR FE NI KI KO KU VE T T15 2 N 15 4 3 6 11 5 3 13 5 6 11 8 5
AT 3 1 2 2 2 2 2 2 2 2 2 2 2
AR 1.50 1.00 2.00 1.77 1.64 1.97 2.00 1.95 1.97 1.97 1.98 1.79 2.00
HO 0.07 0.00 0.33 0.33 0.27 0.20 0.33 0.23 0.20 0.67 0.27 0.13 0.40
HE 0.19 0.00 0.33 0.30 0.25 0.47 0.33 0.47 0.47 0.49 0.51 0.33 0.53 P-value 0.103 - - 1.000 1.000 0.333 - 0.092 0.330 1.000 0.215 0.200 1.000
FIS 0.65 NA 0.00 -0.11 -0.11 0.60 0.00 0.52 0.60 -0.43 0.47 0.63 0.27 NA - - * - - - * ------T48 6 N 15 4 3 6 11 5 3 13 5 5 10 8 5
AT 5 3 2 3 2 4 2 3 2 5 3 3 3
AR 2.37 2.71 2.00 2.49 1.96 3.07 2.00 2.11 2.00 3.71 2.27 2.42 2.57
HO 0.47 0.50 0.33 0.67 0.55 0.80 0.33 0.62 0.60 0.83 0.50 1.00 0.40
HE 0.48 0.61 0.33 0.62 0.49 0.64 0.33 0.47 0.56 0.80 0.56 0.60 0.60 P-value 1.000 0.429 - 1.000 1.000 1.000 - 0.631 1.000 0.777 1.000 0.049 0.619
FIS 0.04 0.20 0.00 -0.08 -0.13 -0.28 0.00 -0.34 -0.09 -0.04 0.12 -0.75 0.36 NA - - * - - - * ------T44 3 N 15 4 3 6 11 5 3 13 5 6 11 8 5
AT 3 3 2 3 3 3 2 3 3 3 3 3 3
AR 2.64 2.75 2.00 2.00 2.68 2.60 2.00 2.06 2.60 2.41 2.47 2.57 2.73
HO 0.60 1.00 0.67 0.17 0.64 0.60 0.33 0.23 0.80 0.67 0.46 0.75 0.40
HE 0.63 0.68 0.53 0.32 0.65 0.64 0.33 0.43 0.64 0.53 0.55 0.61 0.62 P-value 0.485 0.314 1.000 0.091 1.000 1.000 - 0.076 0.619 1.000 0.108 1.000 0.238
FIS 0.04 -0.60 -0.33 0.50 0.01 0.08 0.00 0.47 -0.28 -0.29 0.18 -0.25 0.39 NA - - * - - - * ------TCE92.1 47 N 15 4 3 6 11 5 3 13 5 6 11 8 5
AT 19 7 4 10 17 8 5 17 8 10 16 14 9
AR 5.35 5.46 4.00 5.55 5.41 5.17 5.00 5.44 5.33 5.55 5.51 5.75 5.67
HO 1.00 1.00 1.00 1.00 0.91 0.80 1.00 0.85 1.00 1.00 1.00 1.00 0.80
HE 0.95 0.96 0.87 0.97 0.95 0.93 0.93 0.96 0.96 0.97 0.97 0.98 0.98 P-value 1.000 1.000 1.000 1.000 0.444 0.345 1.000 0.145 1.000 1.000 1.000 1.000 0.125
FIS -0.05 -0.04 -0.20 -0.04 0.05 0.16 -0.09 0.12 -0.05 -0.04 -0.04 -0.02 0.20 NA - - * - - - * ------
All Loci AR 2.82 2.80 2.55 2.91 2.95 2.87 2.82 3.06 3.16 2.89 2.96 2.88 2.83
HO 0.59 0.72 0.57 0.72 0.58 0.55 0.66 0.53 0.69 0.75 0.61 0.65 0.56
HE 0.57 0.75 0.57 0.64 0.62 0.61 0.59 0.61 0.67 0.66 0.62 0.60 0.66
Chelodina longicollis
No linkage disequilibrium amongst locus pairs ( P > 0.05) and no sign of null alleles were detected at any locus (Table 3. 2) in C. longicollis . Two populations showed deviations from HWE expectations at locus T92.2 before correction for multiple tests and one after correction (Table 3. 2). As only one population showed deviation from HWE at a single locus, both selection and non-random mating were rejected as explanations of deviations. Overall heterozygosity levels ( HE) were moderate 0.48
(AP) to high 0.71 (KU) and allelic richness ( AR) was slightly higher than those observed in C. expansa , ranging from 2.63 (AP) to 3.4 (KU) after rarefaction ( N = 3) (Table 3. 2).
49
Table 3. 2 C. longicollis diversity indices. N, samples size; AT, total number of alleles (per locus and per site);
AR, corrected mean allelic richness (n = 6 genes); HO, observed heterozygosity; HE, expected heterozygosity; P-value, significant level for HWE expectation (#: significant after B-Y correction for multiple comparisons);
FIS , inbreeding coefficient; NA , null allele frequency (*: not enough samples for NA estimation; - : no null alleles). See Appendix 8.2.1for site names and Figure 3. 1 for site locations
Locus Population
AT LO CAL BC KN BA BB KG AP NU AL KI KO KU VE
TLE10 7 N 6 6 6 4 6 4 5 4 7 6 6 4 3 12
AT 4 3 4 2 4 1 4 1 4 2 2 3 3 4
AR 2.77 2.00 3.05 1.75 2.50 1.00 3.19 1.00 2.55 1.97 1.50 2.71 3.00 2.75
HO 0.67 0.17 0.67 0.25 0.50 0.00 0.80 0.00 0.43 0.33 0.17 0.75 0.67 0.67
HE 0.32 0.65 0.25 0.46 0.00 0.73 0.00 0.50 0.49 0.17 0.61 0.73 0.58 P-value 1.000 0.091 0.515 - 1.000 - 1.000 - 0.441 1.000 - 1.000 1.000 0.621
FIS -0.21 0.50 -0.03 0.00 -0.11 NA -0.10 NA 0.14 0.33 0.00 -0.29 0.11 -0.15 NA ------* - TCE70 8 N 6 6 6 4 6 4 5 4 7 6 6 4 3 12
AT 2 5 3 2 6 3 3 2 4 4 4 3 3 5
AR 1.77 3.00 2.00 2.00 4.15 2.50 2.47 1.96 2.29 2.91 2.50 2.50 3.00 2.39
HO 0.33 0.67 0.33 0.25 0.83 0.50 0.60 0.50 0.43 0.33 0.33 0.50 1.00 0.50
HE 0.30 0.58 0.32 0.54 0.85 0.46 0.51 0.43 0.40 0.64 0.46 0.46 0.73 0.44 P-value 1.000 1 1.000 0.429 0.437 1.000 1.000 1.000 1.000 0.091 0.273 1.000 1.000 1.000
FIS -0.11 -0.18 -0.05 0.57 0.02 -0.09 -0.20 -0.20 -0.09 0.50 0.28 -0.09 -0.50 -0.15 NA ------* - TCE92.2 24 N 6 6 6 4 6 4 5 4 7 6 6 4 3 12
AT 9 8 7 7 6 7 5 5 7 8 11 5 6 12
AR 4.97 5.09 4.73 5.46 3.94 5.46 4.00 4.21 4.22 4.74 5.77 4.21 6.00 4.86
HO 1.00 1.00 0.67 1.00 0.67 1.00 0.80 1.00 0.71 0.67 1.00 1.00 1.00 0.92
HE 0.91 0.94 0.91 0.96 0.82 0.96 0.84 0.86 0.85 0.89 0.99 0.86 1.00 0.91 P-value 1 0.399 0.025 1 0.609 1 0.006# 1 0.55 0.099 1 0.659 1 0.604
FIS -0.11 -0.07 0.29 -0.04 0.20 -0.04 0.06 -0.20 0.17 0.27 -0.02 -0.20 0.00 -0.01 NA ------* - TCE86 14 N 6 6 6 4 6 4 5 4 7 6 6 4 3 12
AT 3 4 3 5 3 6 5 6 5 5 4 6 3 5
AR 2.74 2.98 2.68 4.21 2.27 4.93 3.93 4.93 3.83 3.67 2.97 4.93 3.00 2.88
HO 0.83 0.67 0.67 1.00 0.33 1.00 0.80 1.00 0.86 1.00 0.67 1.00 0.33 0.67
HE 0.67 0.70 0.62 0.86 0.44 0.93 0.82 0.93 0.82 0.79 0.68 0.93 0.73 0.66 P-value 1.00 1.00 1.00 1.00 0.273 1.00 0.34 0.31 0.82 0.79 1.00 1.00 0.2 0.33
FIS -0.28 0.05 -0.08 -0.20 0.26 -0.09 0.03 -0.09 -0.04 -0.30 0.02 -0.09 0.60 -0.02 NA ------* - T11 18 N 6 6 6 4 6 4 5 4 7 6 6 4 3 12
AT 7 8 8 5 7 5 6 6 7 9 6 7 4 13
AR 4.73 4.74 4.95 4.39 4.38 4.21 4.50 4.75 4.45 5.32 4.29 5.46 4.00 4.93
HO 1.00 1.00 1.00 1.00 1.00 0.75 1.00 1.00 0.86 1.00 0.83 0.75 1.00 0.92
HE 0.91 0.89 0.92 0.89 0.86 0.86 0.89 0.89 0.88 0.96 0.86 0.96 0.87 0.92 P-value 0.601 1.000 1.000 1.000 0.857 0.653 0.614 1.000 0.852 1.000 0.833 0.142 1.000 0.902
FIS -0.11 -0.13 -0.09 -0.14 -0.18 0.14 -0.14 -0.14 0.03 -0.05 0.04 0.25 -0.20 0.00 NA ------* - T31 14 N 6 6 6 4 6 4 5 4 7 6 6 4 3 12
AT 5 7 7 5 3 5 5 3 6 6 6 5 4 9
AR 3.67 4.38 4.52 4.21 2.74 4.21 4.00 2.75 3.56 3.90 4.04 4.00 4.00 3.58
HO 0.83 1.00 1.00 0.75 1.00 0.75 0.80 0.25 0.71 0.83 0.83 0.75 0.67 0.67
HE 0.79 0.86 0.88 0.86 0.67 0.86 0.84 0.68 0.74 0.80 0.82 0.79 0.80 0.71 P-value 0.794 1.000 0.755 0.658 0.204 0.657 0.845 0.086 0.406 1.000 0.955 0.768 0.600 0.453
FIS -0.06 -0.18 -0.15 0.14 -0.58 0.14 0.06 0.67 0.03 -0.04 -0.02 0.05 0.20 0.06 NA ------* - TCE76.1 2 N 6 6 6 4 6 4 5 4 7 6 6 4 3 12
AT 2 1 1 1 2 2 2 1 2 2 2 2 2 2
AR 1.50 1.00 1.00 1.00 1.50 1.75 1.87 1.00 1.43 1.77 1.50 1.75 2.00 1.71
HO 0.17 0.00 0.00 0.00 0.17 0.25 0.40 0.00 0.14 0.33 0.17 0.25 0.33 0.33
HE 0.17 0.00 0.00 0.00 0.17 0.25 0.36 0.00 0.14 0.30 0.17 0.25 0.33 0.29 P-value ------1.000 - - 1.000 - - - 1.000
FIS 0.000 - - - 0.00 0.00 -0.14 - 0.00 -0.11 0.00 0.00 0.00 -0.16 NA ------* -
50
Continued Table 3. 2 C. longicollis diversity indices. N, samples size; AT, total number of alleles (per locus and per site); AR, corrected mean allelic richness (n = 6 genes); HO, observed heterozygosity; HE, expected heterozygosity; P-value, significant level for HWE expectation (#: significant after B-Y correction for multiple comparisons); FIS , inbreeding coefficient; NA , null allele frequency (*: not enough samples for NA estimation; - : no null alleles). See Appendix 8.2.1for site names and Figure 3. 1for site locations
Locus Population
AT LO CAL BC KN BA BB KG AP NU AL KI KO KU VE
T12 4 N 6 6 6 4 6 4 5 4 7 6 6 4 3 12
AT 3 4 2 3 3 2 2 1 3 3 3 1 2 4
AR 2.27 2.97 1.91 2.71 2.49 1.96 1.87 1.00 2.40 2.00 2.68 1.00 2.00 2.56
HO 0.50 0.67 0.50 0.50 0.50 0.50 0.40 0.00 0.43 0.33 0.67 0.00 0.33 0.75
HE 0.44 0.68 0.41 0.61 0.62 0.43 0.36 0.00 0.58 0.32 0.62 0.00 0.33 0.56 P-value 1.000 1.000 1.000 0.429 0.654 1.000 1.000 - 0.627 1.000 1.000 - - 0.580
FIS -0.15 0.02 -0.25 0.20 0.21 -0.20 -0.20 - 0.28 -0.05 -0.08 NA 0.00 -0.36 NA ------* - T17 4 N 6 6 6 4 6 4 5 4 7 6 6 4 3 12
AT 3 4 4 3 4 4 3 2 4 4 4 3 4 4
AR 2.76 3.56 3.48 2.96 2.97 3.68 2.93 1.96 3.20 3.45 3.23 2.96 4.00 2.86
HO 1.00 0.83 1.00 0.75 0.50 1.00 0.80 0.50 0.57 0.83 0.83 0.75 0.67 0.42
HE 0.68 0.80 0.79 0.75 0.68 0.82 0.73 0.43 0.74 0.77 0.74 0.75 0.87 0.66 P-value 0.654 0.148 0.317 0.657 0.515 1.000 1.000 1.000 0.265 0.088 1.000 0.657 0.467 0.213
FIS -0.54 -0.04 -0.30 0.00 0.29 -0.26 -0.10 -0.20 0.24 -0.09 -0.14 0.00 0.27 0.38 NA ------* - T87 5 N 6 6 6 4 6 4 5 4 7 6 6 4 3 12
AT 3 4 3 3 3 3 3 3 4 4 3 4 3 4
AR 2.41 2.98 2.76 2.96 2.87 2.75 2.83 2.71 3.20 3.23 2.74 3.50 3.00 2.98
HO 0.67 0.83 0.83 1.00 0.67 0.75 0.60 0.75 0.71 1.00 0.67 1.00 0.67 0.83
HE 0.53 0.70 0.68 0.75 0.71 0.68 0.69 0.61 0.74 0.74 0.67 0.79 0.73 0.69 P-value 1.000 0.654 0.654 1.000 1.000 1.000 0.619 1.000 0.393 0.354 0.307 0.057 1.000 0.063
FIS -0.29 -0.22 -0.25 -0.41 0.07 -0.13 0.14 -0.29 0.03 -0.40 0.00 -0.33 0.11 -0.22 NA ------* -
All Loci AR 2.96 3.27 3.11 3.17 2.98 3.25 3.16 2.63 3.11 3.3 3.12 3.3 3.4 3.15
HO 0.70 0.76 0.74 0.72 0.62 0.72 0.70 0.71 0.59 0.67 0.62 0.75 0.70 0.67
HE 0.60 0.71 0.69 0.72 0.63 0.69 0.68 0.69 0.64 0.67 0.62 0.71 0.71 0.64
E. m. macquarii
No evidence of linkage disequilibrium was found in E. m. macquarii . Locus TLE 13.3 deviated from HWE in 10 populations with evidence of null alleles at frequencies ranging between 0.135 and 0.389 (Table 3. 3). Most populations showing evidence of null alleles were found in the Moonie River catchment (8). Locus TCE7.2 deviated from HWE in three populations, with a unique allele in a single individual in two populations (BW, KU). These individuals were re-PCRed and genotypes checked for consistency. The third population (GO) showed an excess of homozygotes with nine out
of ten individuals homozygous for one of the two alleles present at this locus and high positive FIS values at a number of loci (Table 3. 3). After correction for multiple tests, only locus TLE 13.3 showed significant deviation from HWE.
Pairwise comparisons were run with and without TLE13.3 to assess the influence of null alleles on levels of divergence. As expected, removal of TLE13.3 affected FST values (see Appendix 8.3.1 and Appendix 8.3.2) but these shifted only slightly and remained high, apart from comparisons involving the (BO) and (PU) subpopulations in the Border Rivers. Pairwise comparisons involving (BO) decreased sharply, three of them involving subpopulations with high levels of null alleles (NI, KI and
51
KU). Five individuals out of eight in (BO) were homozygous for the same alleles at the TLE13.3 locus with no sign of null alleles detected in this population (Table 3. 3). This pattern was also apparent in the (PU) population, another lagoon off the Macintyre River (Figure 3. 1). Four individuals out of five were heterozygous for the same alleles at this locus with no sign of null alleles (Table 3. 3). Following removal of TLE13.3, (PU) was no longer significantly divergent from (KN) and (PI) although all other locations sampled between them were. The above suggested that the shifts observed in the level of divergence following removal of TLE13.3 are owing to allele frequencies at this locus rather than to the presence of null alleles. This was also suggested by the absence of variation in divergence levels for most comparisons involving (NI), (KU) and (KI) populations (and any other population with null alleles). As the overall pattern of divergence between populations sampled remained unchanged, locus TLE13.3 was kept for all subsequent analyses.
Diversity indices showed Pindari Dam (PI) to have the lowest overall mean allelic richness ( AR =
3.049) and heterozygosity ( HE = 0.588). Two other populations (KN and WC) in the upper Border Rivers also showed lower allelic richness at loci with highest allelic diversity (TLE 13.1; 13.3; 19.3;
31.1) (Table 3. 3). Overall mean allelic diversity ( AR) was low owing to the smallest samples size included in this analysis ( N = 3) as a result of missing data for one individual (loci TLE13.3 in (PA)). Overall allele numbers at each locus fell within the range expected in Testudine studies (FitzSimmons and Hart, 2007).
52
Table 3. 3 E. m. macquarii diversity indices. N, samples size; AT, total number of alleles (per locus and per site); AR, corrected mean allelic richness (n = 6 genes); HO, observed heterozigosity; HE, expected heterozygosity; P-value, significant level for HWE expectation (#: significant after B-Y correction for multiple comparisons); FIS , inbreeding coefficient; NA , null allele frequency (*: not enough samples for NA estimation; - : no null alleles). See Appendix 8.2.1for site names and Figure 3. 1 for site locations
Locus Population
AT LO BW BO GO PU KN PI WC BB PA AP CAR FE NI NU KI KO KU VE
TLE10 5 N 6 17 8 10 5 16 14 6 14 4 37 19 11 15 9 17 12 46 9
AT 3 4 2 4 1 2 3 2 2 2 4 3 3 3 3 4 3 4 4
AR 2.00 1.96 1.79 2.34 1.00 1.89 2.06 1.91 1.53 1.75 2.27 1.76 1.75 2.24 2.06 2.54 2.05 2.66 2.90
HO 0.33 0.35 0.38 0.50 0.00 0.31 0.43 0.50 0.21 0.25 0.41 0.32 0.27 0.67 0.44 0.59 0.50 0.59 0.67
HE 0.32 0.32 0.33 0.44 0.00 0.42 0.37 0.41 0.20 0.25 0.47 0.28 0.26 0.50 0.39 0.54 0.41 0.57 0.65 P-value 1.000 1.000 1.000 1.000 NA 0.530 1.000 1.000 1.000 NA 0.250 1.000 1.000 0.292 1.000 0.719 1.000 0.497 0.368
FIS -0.05 -0.11 -0.17 -0.15 NA 0.26 -0.16 -0.25 -0.08 NA 0.13 -0.13 -0.07 -0.36 -0.16 -0.08 -0.23 -0.03 -0.03 NA - - - - * - - - - * ------TLE 6.2 13 N 6 17 8 10 5 16 14 6 14 4 37 19 11 15 9 17 12 46 9
AT 5 10 5 7 7 8 7 5 7 6 9 10 8 10 8 9 7 10 7
AR 3.67 4.26 3.71 4.37 5.00 4.22 3.58 3.46 4.22 4.93 4.02 4.42 4.42 4.52 4.58 4.11 4.05 4.01 3.70
HO 0.83 0.82 1.00 0.80 1.00 1.00 0.71 0.67 0.86 1.00 0.78 0.90 0.64 0.87 0.78 0.88 0.92 0.76 0.78
HE 0.79 0.85 0.80 0.87 0.93 0.86 0.77 0.76 0.86 0.93 0.83 0.87 0.88 0.88 0.89 0.33 0.84 0.83 0.75 P-value 0.791 0.264 0.891 0.203 1.000 0.307 0.829 0.379 0.861 1 0.46 0.398 0.085 0.201 0.211 0.861 1 0.04 0.936
FIS -0.06 0.04 -0.27 0.08 -0.08 -0.18 0.07 0.13 0.00 -0.09 0.05 -0.13 0.28 0.02 0.13 -0.05 -0.10 0.08 -0.05 NA - - - - * - - - - * ------TLE 7.2 4 N 6 17 8 10 5 16 14 6 14 4 37 19 11 15 9 17 12 46 9
AT 2 3 2 2 2 2 2 2 2 2 2 2 2 2 3 2 2 3 2
AR 1.99 2.29 1.94 1.99 2.00 1.48 1.86 1.97 1.97 1.96 1.97 1.98 1.98 1.93 2.31 1.98 1.86 2.09 1.99
HO 0.83 0.59 0.38 0.10 0.40 0.19 0.50 0.33 0.36 0.50 0.35 0.53 0.73 0.40 0.33 0.88 0.50 0.44 0.78
HE 0.53 0.56 0.46 0.52 0.53 0.18 0.39 0.49 0.50 0.43 0.51 0.51 0.52 0.46 0.57 0.81 0.39 0.52 0.53 P-value 0.394 0.029 1.000 0.015 1.000 1.000 0.513 1.000 0.570 1.000 0.097 1.000 0.257 1.000 0.171 0.162 0.529 0.012 0.226
FIS -0.67 -0.05 0.19 0.82 0.27 -0.07 -0.30 0.33 0.29 -0.20 0.31 -0.03 -0.43 0.13 0.43 -0.40 -0.29 0.16 -0.51 NA - - - - * - - - - * ------TLE13.1 12 N 6 17 8 10 5 16 14 6 14 4 37 19 11 15 9 17 12 46 9
AT 7 6 8 6 5 6 4 6 8 6 9 8 7 9 6 7 8 11 6
AR 4.73 3.58 4.33 3.65 3.43 3.49 2.69 4.36 3.99 4.93 4.15 3.86 3.97 4.23 3.63 3.81 4.27 4.21 4.05
HO 1.00 0.77 0.88 0.90 0.40 0.81 0.43 0.67 0.79 1.00 0.84 0.68 0.64 0.80 0.89 0.71 0.92 0.80 0.89
HE 0.91 0.78 0.86 0.78 0.78 0.77 0.63 0.88 0.83 0.93 0.85 0.80 0.82 0.85 0.77 0.51 0.86 0.85 0.84 P-value 1.000 0.469 0.973 1.000 0.187 0.618 0.227 0.055 0.302 1.000 0.479 0.072 0.190 0.178 0.838 0.824 0.987 0.304 0.923
FIS -0.11 0.02 -0.02 -0.17 0.52 -0.06 0.33 0.26 0.05 -0.91 0.01 0.15 0.24 0.06 -0.17 -0.09 -0.07 0.05 -0.06 NA - - - - * - - - - -
53
Continued Table 3. 3 E. m. macquarii diversity indices. N, samples size; AT, total number of alleles (per locus and per site); AR, corrected mean allelic richness (n = 6 genes);
HO, observed heterozigosity; HE, expected heterozygosity; P-value, significant level for HWE expectation (#: significant after B-Y correction for multiple comparisons); FIS , inbreeding coefficient; NA , null allele frequency (*: not enough samples for NA estimation; - : no null alleles). See Appendix 8.2.1for site names and Figure 3. 1 for site locations
Locus Population
AT LO BW BO GO PU KN PI WC BB PA AP CAR FE NI NU KI KO KU VE
TLE19.1 3 N 6 17 8 10 5 16 14 6 14 4 37 19 11 15 9 17 12 46 9
AT 3 3 2 2 2 3 3 3 3 2 3 3 3 3 3 3 3 3 3
AR 2.47 2.15 1.97 1.87 1.97 2.36 2.19 2.49 2.46 2.00 2.00 2.21 2.41 2.54 2.32 2.35 2.31 1.98 2.31
HO 0.83 0.47 0.50 0.30 0.60 0.56 0.50 0.67 0.71 0.50 0.51 0.58 0.73 0.53 0.33 0.53 0.67 0.46 0.67
HE 0.59 0.54 0.50 0.40 0.47 0.54 0.55 0.62 0.58 0.57 0.43 0.51 0.57 0.61 0.58 0.54 0.51 0.39 0.57 P-value 1.000 0.802 1.000 0.479 1.000 1.000 1.000 1.000 0.450 1.000 0.439 0.652 0.554 0.078 0.171 0.559 0.535 0.310 1.000
FIS -0.47 0.13 0.00 0.25 -0.33 -0.05 0.09 -0.08 -0.24 0.14 -0.21 -0.13 -0.30 0.13 0.44 0.02 -0.33 -0.19 -0.19 NA - - - - * - - - - * ------TCE70 5 N 6 17 8 10 5 16 14 6 14 4 37 19 11 15 9 17 12 46 9
AT 4 4 4 4 4 4 3 3 5 5 5 4 3 5 3 4 4 5 4
AR 3.18 2.59 2.96 2.69 3.43 2.71 2.07 2.47 2.85 4.21 2.85 2.41 2.25 2.88 2.54 2.55 2.66 2.79 2.74
HO 0.50 0.59 0.63 0.40 1.00 0.63 0.43 0.50 0.50 1.00 0.51 0.63 0.73 0.67 0.67 0.65 0.42 0.59 0.67
HE 0.71 0.62 0.69 0.60 0.78 0.62 0.44 0.59 0.62 0.86 0.64 0.58 0.56 0.61 0.62 0.59 0.63 0.64 0.60 P-value 0.100 0.427 0.590 0.068 1.000 1.000 1.000 1.000 0.256 1.000 0.196 0.896 0.371 0.901 1.000 0.822 0.150 0.253 0.276
FIS 0.32 0.05 0.10 0.34 -0.33 -0.02 0.03 0.17 0.20 -0.20 0.20 -0.09 -0.32 -0.10 -0.08 -0.10 0.35 0.08 -0.17 NA - - - - * - - - - * ------TLE 31.1 21 N 6 17 8 10 5 16 14 5 14 4 37 19 11 15 9 17 12 46 9
AT 6 11 8 10 7 7 6 5 7 7 11 8 8 8 6 10 8 12 6
AR 4.04 4.55 3.62 4.87 5.00 3.93 3.06 3.40 3.24 5.46 3.12 3.85 3.32 4.05 3.46 3.55 3.29 3.83 3.99
HO 0.67 0.88 0.75 0.80 1.00 0.81 0.64 0.60 0.71 1.00 0.60 0.84 0.73 0.73 0.56 0.71 0.67 0.83 0.78
HE 0.82 0.88 0.70 0.92 0.93 0.82 0.66 0.67 0.66 0.96 0.63 0.79 0.65 0.83 0.73 0.72 0.66 0.78 0.83 P-value 0.185 0.199 0.915 0.120 1.000 0.221 0.651 0.617 0.886 1.000 0.537 0.724 0.909 0.099 0.105 0.291 0.666 0.669 0.170
FIS 0.20 0.00 -0.08 0.13 -0.08 0.01 0.03 0.11 -0.09 -0.04 0.05 -0.07 -0.12 0.12 0.25 -0.09 -0.02 -0.06 0.07 NA - - - - * - - - - * ------TLE 19.3 16 N 6 17 8 10 5 16 14 4 14 4 37 19 11 15 9 17 12 46 9
AT 7 10 6 9 8 8 8 3 11 3 13 10 10 10 8 10 9 10 5
AR 4.86 4.18 3.88 4.59 5.33 4.19 4.19 2.75 4.80 2.71 4.48 4.46 4.73 4.29 4.38 4.52 4.16 4.19 3.17
HO 1.00 0.94 0.88 0.70 1.00 0.88 0.93 0.50 1.00 0.50 0.92 0.79 0.73 0.87 0.78 0.82 0.83 0.74 0.44
HE 0.92 0.88 0.82 0.89 0.96 0.86 0.85 0.68 0.91 0.61 0.88 0.88 0.91 0.86 0.87 0.88 0.83 0.85 0.71 P-value 1.000 0.575 0.549 0.278 1.000 0.091 0.643 1.000 0.797 0.429 0.892 0.256 0.061 0.135 0.320 0.163 0.410 0.293 0.091
FIS -0.09 -0.13 -0.08 0.22 -0.05 -0.02 -0.10 0.29 -0.10 0.20 -0.05 0.10 0.20 -0.01 0.11 0.07 0.00 0.13 0.39 NA - - - - * - - - - * ------
54
Continued Table 3. 3 E. m. macquarii diversity indices. N, samples size; AT, total number of alleles (per locus and per site); AR, corrected mean allelic richness (n = 6 genes);
HO, observed heterozigosity; HE, expected heterozygosity; P-value, significant level for HWE expectation (#: significant after B-Y correction for multiple comparisons); FIS , inbreeding coefficient; NA , null allele frequency (*: not enough samples for NA estimation; - : no null alleles). See Appendix 8.2.1for site names and Figure 3. 1 for site locations
Locus Population
AT LO BW BO GO PU KN PI WC BB PA AP CAR FE NI NU KI KO KU VE
TLE13.3 22 N 6 17 7 10 4 16 14 6 14 3 35 19 11 13 9 17 11 44 9
AT 6 14 4 5 3 5 4 5 7 4 12 6 7 7 7 10 7 11 5
AR 4.36 4.62 2.29 3.27 2.75 3.24 2.85 3.46 4.01 4.00 4.05 3.36 4.12 3.97 4.00 4.12 3.47 3.82 2.84
HO 0.33 0.59 0.29 0.50 1.00 0.50 0.50 0.67 0.64 1.00 0.51 0.42 0.55 0.31 0.33 0.53 0.36 0.43 0.56
HE 0.88 0.59 0.40 0.73 0.68 0.74 0.63 0.76 0.83 0.87 0.82 0.74 0.84 0.81 0.82 0.83 0.72 0.80 0.60 P-value 0.003# 0.000# 0.231 0.222 0.314 0.047 0.111 1.000 0.196 1.000 0.000# 0.002# 0.019 0.001# 0.001# 0.001# 0.020 0.000# 0.075
FIS 0.64 0.34 0.29 0.33 -0.60 0.33 0.21 0.13 0.23 -0.20 0.38 0.44 0.36 0.63 0.61 0.37 0.51 0.46 0.08 NA 0.26 0.14 - * - - - - * 0.22 0.17 0.14 0.39 0.25 0.16 0.30 0.25 -
All Loci AR 3.48 3.35 2.94 3.29 3.32 3.06 2.73 2.92 3.23 3.55 3.21 3.14 3.22 3.41 3.25 3.28 3.13 3.29 3.08
HO 0.7 0.67 0.63 0.56 0.80 0.63 0.56 0.57 0.64 0.75 0.6 0.63 0.64 0.65 0.57 0.70 0.64 0.63 0.69
HE 0.72 0.70 0.62 0.68 0.76 0.64 0.59 0.65 0.66 0.71 0.67 0.66 0.67 0.71 0.69 0.70 0.65 0.69 0.67
55
3.2.3 Genetic Structure
Results describing the genetic structure of the three species in the Moonie, Border and Gwydir River catchments are presented for each species separately. For the Mantel test, DPRA, and SAA, sites located in the Barwon River (LO and CAL) were either included in the Moonie River or in the Border Rivers, depending on the catchment in focus, as the former represent an extension of either rivers (see Figure 3. 1). For AMOVAs where analyses were conducted including all catchments, Barwon River sites were grouped within the Border Rivers catchment as the former is the natural continuation of the the primary river in the Border Rivers catchment.
C. expansa
AMOVA found significant differentiation between the Moonie River and the Border Rivers catchments (there were no samples from the Gwydir River catchment) and among all populations in C. expansa, albeit with a large proportion of variation present within populations (97%) (Table 3. 4). DPRA in the Moonie River found no outliers and a pattern of IBD (Pattern 3) (Appendix 8.4.1 and Figure 3. 4). Subpopulations in the ‘upper’ Moonie River catchment displayed slightly steeper slopes (Pattern 4) than ‘lower’ subpopulations (Table 3. 5 and Figure 3. 4). Further examination of pairwise comparisons showed ‘upper’ Moonie populations (KI) and (KO) to have higher divergence levels
relative to all other pairwise comparisons within the Moonie River (Appendix 8.3.4). Global FST for
C. expansa in the Moonie and in the Moonie-Border catchments ( FST = 0.014 and 0.018 respectively) were greater than for both other species (Appendix 8.3.6). This held when standardizing E. m. macquarii sample size that obtained in both other species (nmax = 6) and removing outliers identified in C. longicollis and E. m. macquarii (see DPRA for C. longicollis and E. m.macquarii ).
0.1
0.09
0.08 KI
0.07
0.06 ) ST F 0.05 /(1-
ST KO F 0.04
0.03 VE 0.02
0.01
0 0 100 200 300 400 500 600 Channel Distance (Km)
Figure 3. 4 Decomposed Pairwise Regression Analysis (DPRA) for C. expansa in the Moonie and Barwon Rivers with channel distance. Full lines: Non-outlier line of best fit; dashed line: mean line of best fit. See Appendix 8.2.1for site names and Figure 3. 1 for site locations
56
Table 3. 4 Analysis of Molecular Variance for C. expansa , C. longicollis and E. m. macquarii in three upper catchments of the Murray-Darling Basin. Fixation indices that deviate from zero are indicated with an asterix. See Figure 3.1 for river location.
Among populations Among Catchments Within populations within catchments
% % % Species Source F F F Variation CT Variation SC Variation ST C. expansa Moonie River vs 1.52 0.015** 0.99 0.010 97.49 0.025** Border Rivers C. longicollis Moonie River vs Border Rivers vs 0.2 0.003 0.7 0.007 98.9 0.010* Gwydir River
E. m. macquarii Moonie River vs Border Rivers vs 1.35 0.014* 1.64 0.017** 97.01 0.029** Gwydir River
Moonie River vs Border Rivers vs 2.54 0.025** 0.86 0.008* 96.6 0.034** Gwydir River vs KN,PI,WC Moonie River vs Border Rivers (without 0.52 0.005* 0.86 0.009* 98.62 0.014** KN, PI, WC) vs Gwydir River
Moonie River vs Border Rivers vs 0.79 0.008* 0.32 0.003 98.89 0.011 Gwydir River (without outliers) Moonie River vs Border Rivers (without 0.97 0.010* 0.32 0.003 98.71 0.013
outliers) Border Rivers vs Gwydir River (without 0.09 0.004 0.00 0.000 99.91 0.001
outliers) *α= 0.05 ** α= 0.005
Table 3. 5 Intercept and slope (with 95% CI) of the Decomposed Pairwise Regression Analysis (DPRA) for C. expansa in the Moonie and Barwon Rivers. See Figure 1. 1for ‘Pattern’ descriptions. See Appendix 8.2.1for site names and Figure 3. 1for site locations. # P < 0.05.
C. expansa Intercept * 10 -2 95% CI Slope * 10 -7 95% CI r2 n Pattern Non outlier CAL 1.52 (-1.47_4.52) 0.06 (-0.05_0.17) 0.347 10 4 LO 0.95 (-3.09_4.99) 0.1 (-0.06_0.25) 0.229 10 4 AP 0.41 (-1.76_2.59) 0.04 (-0.09_0.17) 0.071 10 4 CAR -0.21 (-2.30_1.89) 0.1 (-0.02_0.23) 0.347 10 4 FE 0.32 (-2.32_2.95) 0.04 (-0.09_0.16) 0.067 10 4 NI -0.79 ( 2.21_0.64) 0.12 # ( 0.03_0.20) 0.592 10 3 KI -1.45 (-4.13_1.22) 0.17 # ( 0.08_0.26) 0.755 10 3 KO -0.33 (-2.36_1.42) 0.14 # ( 0.04_0.24) 0.600 10 3 KU -0.67 (-1.16_0.51) 0.03 (-0.01_0.06) 0.347 10 4 VE -0.56 (-1.96_0.61) 0.07 # ( 0.01_0.13) 0.529 10 3
57
Supporting the outcome of the DPRA, the Mantel test for an IBD pattern using channel distance was highly significant ( P = 0.001) for the Moonie River. Plotting of linearised Slatkin’s genetic distance
(FST /(1-FST )) against channel distance showed genetic divergence starting around 70 to 100 km (Figure 3. 4). The population genetic structure of C. expansa in the Moonie River catchment was also apparent in the SAA. Under variable distance classes, significant genetic autocorrelation was observed for the class 0-20 and up to the 20-80 km class (Figure 3. 5), reflecting autocorrelation levels within (0-20) and between neighboring waterholes (20-80). The estimated x-intercept was around 150 and 200 km under variable distance classes (Figure 3. 5), which is taken as an estimate of the genetic patch size (Sokal, 1979; Sokal and Wartenberg, 1983; Peakall et al. , 2003).
0.060 r 0.040 U L 0.020
r 0.000 -0.020 -0.040 -0.060 20 80 120 150 210 300 550 Distance Class Size (Km)
Figure 3. 5 Correlation coefficient r as a function of variable distance class size for C. expansa in the Moonie and Barwon Rivers. Combined dataset ( N =76). U and L: upper and lower 95% CI about the null hypothesis of a random distribution of genotypes. Error bars: 95% confidence interval about r from bootstrapping.
Under increasing distance class sizes the pattern of non-random distribution of genotypes disappeared for Adults but became more apparent for Juveniles (Figure 3. 6). The coefficient of correlation r doubled ( r = 0.60) for the first distance class (0-20 km) and remained different from zero up to the 0- 80 (km) distance class size. The error margins in the smaller distance classes were relatively large owing to small sample sizes.
No DPRA were performed on the Border Rivers catchment owing to the low number of sites and low sample sizes, but the Mantel test carried out on channel distances within the Moonie and Border Rivers combined was highly significant ( P value = 0.004).
58
0.140 r 0.120 U 0.100 L 0.080 0.060
r 0.040 0.020 0.000 -0.020 -0.040 -0.060 20J 40J 60J 80J 20A 40A 60A 80A 100J 120J 140J 160J 180J 200J 220J 240J 100A 120A 140A 160A 180A 200A 220A 240A
Distance Class Size (Km)
Figure 3. 6 Correlation coefficient r per age class under increasing distance class sizes for C. expansa in the Moonie and Barwon Rivers. X-axis: A (blue) - Adult (Male and Female) ( N = 42); J (Green) - Juvenile ( N = 34). U and L: upper and lower 95% CI about the null hypothesis of a random distribution of genotypes. Error bars: 95% confidence interval about r from bootstrapping. Note: 0 to 240 km distance class size showed only.
C. longicollis
AMOVA carried out over the three catchments for C. longicollis found no significant differentiation at the catchment level ( FCT : 0.003; P = 0.255) or at the subpopulation within catchment level ( FSC :
0.007; P = 0.062) (Table 3. 4). This was reflected in the near zero global FST at the Moonie and the Moonie-Border scale (Appendix 8.3.6). The DPRA carried out for the Moonie River catchment identified (AP) and (KO) as outlier subpopulations (Appendix 8.4.1). (AP) showed a significantly greater than zero intercept and no slope categorising it as Pattern 1 (High genetic drift) (Table 3. 6 and Figure 3. 7). (AP) population had the lowest heterozygosity level and allelic richness, and the highest
FIS value (at locus T31) of all populations – locus combination (Table 3. 2). As (KO) slope and intercept were non-significant but it was identified as a ‘true’ outlier it remained uncategorised. All other subpopulations were categorised as Pattern 4 (High gene flow) (Table 3. 6). Mantel tests for
IBD with and without outliers in the Moonie River catchment using Slatkin’s linearised FST were non- significant ( P = 0.481 and P = 0.137 respectively) further supporting the ‘High gene flow’ pattern inferred from the DPRA. FST table for C. longicollis populations in the Moonie, Border and Gwydir River is found in Appendix 8.3.5.
59
Table 3. 6 Intercept and slope (with 95% CI) of the Decomposed Pairwise Regression Analysis (DPRA) for C. longicollis in the Moonie and Barwon Rivers. See Figure 1. 1 for ‘Pattern’ descriptions. See Appendix 8.2.1for site names and Figure 3. 1 for site locations
C. longicollis Intercept * 10 -2 95% CI Slope * 10 -7 95% CI r2 n Pattern Non outlier LO 0.47 (-2.77_3.70) -1.03 (-111.98_109.92) 0.000 7 4 CAL 0.07 (-1.93_2.06) 15.87 (-38.53_70.28) 0.141 7 4 NU 0.23 (-0.23_0.70) -9.20 (-31.60_13.20) 0.245 7 4 AL -0.57 (-2.36_1.23) 74.51 (-13.55_162.58) 0.580 7 4 KI 0.03 (-0.06_0.11) -0.53 (-3.21_2.14) 0.071 7 4 KU -0.24 (-0.96_0.49) 19.44 (-7.85_46.73) 0.494 7 4 VE -0.24 (-0.88_0.39) 22.49 (-6.07_51.05) 0.544 7 4 Outlier AP 8.25 # (1.40_15.10) -208.88 (-543.82_126.06) 0.339 8 1 KO 1.05 (-2.02_4.12) 62.65 (-96.25_221.54) 0.170 8 - # P < 0.05
0.09
0.08
0.07 AP 0.06 ) ST
F 0.05
/( 1- /( 0.04 ST
F KO 0.03
AL 0.02
0.01
0 0 100 200 300 400 500 600 Channel Distance (Km)
Figure 3. 7 Decomposed Pairwise Regression Analysis (DPRA) for C. longicollis in the Moonie and Barwon Rivers with channel distance. Full line: Non-outlier line of best fit; Dashed line: outlier line of best fit; See Appendix 8.2.1for site names and Figure 3.1 for site locations
The global correlogram for C. longicollis within the Moonie River was significant suggesting the presence of structure within this catchment. The correlation coefficient r was however never significantly different from that under a random distribution of genotypes in the catchment except for the 120 km distance class size (Figure 3. 8). (AP) and (KO), the two outliers identified in the DPRA make up the majority of the individuals included in the SAA at this distance class size. The coefficient of autocorrelation r was half that of C. expansa (r = 0.17 vs 0.32 respectively) at the 20 km distance
60
size class and oscillated between high and low across all other size classes, fitting a random genotype distribution pattern (Smouse and Peakall, 1999; Smouse & Peakall and Gonzales, 2008) (Figure 3. 8). As r was found not significantly different to zero at all distance class sizes investigated but for the 120 km under a combined dataset, the first x-intercept should not be interpreted as an estimate of genetic patch size (Sokal, 1979).
0.080 r 0.060 U 0.040 L 0.020 r 0.000 -0.020 -0.040 -0.060 20 80 120 150 210 300 550
Distance class size (Km)
Figure 3. 8 Correlation coefficient r as a function of variable distance class size for C. longicollis in the Moonie and Barwon Rivers. Combined dataset ( N=56). U and L: upper and lower 95% CI about the null hypothesis of a random distribution of genotypes. Error bars: 95% confidence interval about r from bootstrapping.
SAA showed juveniles to be non-randomly distributed at the 0-20 km size class (Figure 3. 9), reflecting the sampling of young individuals from the same clutch or from related parents within each waterhole. No new samples were added between the 0-20 km and the 0-40 km class size. The pattern of non-random distribution of genotypes for juveniles therefore may disappears well before the 60 (km) distance class. Adults showed no correlation at all class sizes.
0.300 r 0.250 U 0.200 L 0.150 0.100 r 0.050 0.000 -0.050 -0.100 -0.150 20J 40J 60J 80J 20A 40A 80A 60A 100J 140J 180J 120J 160J 200J 220J 240J 100A 120A 140A 160A 180A 200A 220A 240A Distance class size (Km) Figure 3. 9 Correlation coefficient r per age class under increasing distance class sizes (20 km) for C. longicollis in the Moonie and Barwon Rivers. A (blue): Adult ( N =42); J (Green): Juvenile ( N = 14). U and L: upper and lower 95% CI about the null hypothesis of a random distribution of genotypes. Error bars: 95% confidence interval about r from bootstrapping. Note: 0 to 240 km distance class size showed only.
61
Moonie, Border and Gwydir River
To assess overland dispersal of C. longicollis , DPRA and Mantel tests were carried out over the three catchments using Euclidean distance between subpopulations. DPRA identified two more outlier subpopulations (BA and KN) in the Border Rivers catchment in addition to (AP) and (KO) identified previously in the Moonie River catchment (Appendix 8.4.1). (BA) displayed Pattern 1 (High genetic drift), while (KN) remained an unclassified outlier (Table 3. 7 and Figure 3. 10). (AP) and (KO) subpopulations remained classified as previously. Removal of the outliers showed a pattern of high gene flow which deviates only slightly from the pattern observed when including all populations (Appendix 8.4.3). Pairwise comparisons are shown in Appendix 8.3.5.
Table 3. 7 Intercept and slope (with 95% CI) of the Decomposed Pairwise Regression Analysis (DPRA) for C. longicollis in the Moonie River, Border Rivers and Gwydir River catchments. See Figure 1. 1 for ‘Pattern’ descriptions. See Appendix 8.2.1for site names and Figure 3. 1 for site locations
C. longicollis Intercept * 10 -2 95% CI Slope * 10 -7 95% CI r2 n Pattern
Non outlier LO 0.46 (-1.60_2.52) -8.70 (-111.26_93.86) 0.006 10 4 CAL 0.16 (-1.35_1.68) 10.84 (-53.90_75.58) 0.022 10 4 BC 0.00 ( 0.00_0.00) 0.00 (0.00_0.00) 1.000 10 4 BB 0.92 (-0.16_2.00) -36.16 (-88.69_16.36) 0.275 10 4 KG 0.7 (-1.09_2.49) -12.03 (-76.64_52.58) 0.027 10 4 NU 0.13 (-0.08_0.33) -5.64 (-16.86_5.59) 0.168 10 4 AL 0.37 (-1.20_1.94) 9.13 (-72.90_91.16) 0.010 10 4 KI -0.05 (-0.27_0.17) 5.38 (-4.85_15.62) 0.181 10 4 KU -0.1 (-0.71_0.51) 16.8 (-14.06_47.67) 0.191 10 4 VE -0.37 (-1.15_0.40) 41.35 # (0.87_81.83) 0.455 10 3 Outlier KN 0.19 (-3.90_4.28) 102.19 (-118.31_322.68) 0.125 11 - BA 3.69 # ( 0.01_7.39) -20.16 (-174.40_134.09) 0.011 11 1 AP 4.32 # ( 0.60_8.03) -8.49 (-212.60_195.63) 0.001 11 1 KO 1.68 (-0.66_4.03) 21.94 (-107.95_151.83) 0.019 11 - # P < 0.05
62
0.06
0.05
0.04 AP ) ST
F 0.03 BA KN /(1-
ST KO F 0.02
0.01
0 0 50 100 150 200 250 300 350 400
Euclidean Distance (Km)
Figure 3. 10 Decomposed Pairwise Regression Analysis (DPRA) for C. longicollis in the Moonie River, Border Rivers and Gwydir River catchments with Euclidean distance. Full line: Non-outlier line of best fit; Dashed line: outlier line of best fit; See Appendix 8.2.1for site names and Figure 3. 1 for site locations
Mantel tests for IBD carried out over the three catchments returned no significant results with either Euclidean or Channel distance when including all subpopulations (Euclidean P = 0.238; Channel Distance P = 0.691) or after removing outliers (Euclidean P = 0.274; Channel Distance P = 0.189).
Global FST values were close to zero for both the Moonie and the Moonie-Border catchments, the lowest value of all three species (Appendix 8.3.6).
E. m. macquarii
Significant genetic structure was found at the catchment, among subpopulations within catchment and among all subpopulations in E. m. macquarii with the Moonie, Border-Barwon and Gwydir Rivers catchment at the highest hierarchical level (Table 3. 4). A large amount of variation was nonetheless present within populations (97%). Pairwise comparisons showed that (KN), (PI) and (VE) had higher divergence values than the majority of other populations, even with adjacent populations (Appendix 8.3.1 and Appendix 8.3.2). This pattern remained when running pairwise comparisons with standardised samples size (nmax = 6) (Appendix 8.3.3). GENELAND, used to further elucidate the uncharacteristic pattern of (KN), (PI) and (VE), consistently clustered the Moonie, Border-Barwon and Gwydir Rivers’ sampling locations into two populations (Figure 3. 11 and Table 3. 8); (KN), (PI) and (WC) formed a divergent population ( FST = 0.053; FIS = 0.054) from the rest of the region, the
latter lumped into one large population including (VE) (FIS = 0.075).
63
Figure 3. 11 GENELAND map of posterior probability to belong to Population 2 for E. m .macquarii in the Moonie and Border Rivers catchment. High probability: light colors; Low probability: darker colors. See Figure 3.1 for river names and site codes. Numbers in contours are posterior probability values.
AMOVA with this ‘new’ population (KN, PI and WC) set as a fourth highest level group returned a higher among catchments and lower among populations within catchment partitioning of variance (Table 3. 4). Removing this fourth group from the AMOVA still returned a significant partitioning of variance at the among catchment (Moonie vs Border-Barwon vs Gwydir) and among subpopulations within catchment level albeit with a much lower percentage of variance found at the highest hierarchical level (0.52% after removal vs 2.54% before) (Table 3. 4). Significant but very low partition of variance was present between the Moonie River and the Border Rivers, but not between the Gwydir River and the latter (Table 3. 4). This result held when removing locus TLE13.3 (null alleles) (not shown). This very low partition of variance among catchments was reflected in the near zero global FST at theMoonie and Moonie-Border scale after removal of outliers identified in the DPRA (see below for DPRA) (Appendix 8.3.6).
Table 3. 8 GENELAND output for uncorrelated model frequencies with spatial information for E. m. macquarii . K: number of population.
Run (iterations) Log Posterior Density Model % Inferred K Inferred 5x10 5 1 -12825.316 79.40 2 2 -12833.420 83.80 2 3 -12840.441 82.00 2 4 -12844.377 86.00 2 5 -12847.019 83.60 2 2x10 6 1 -12832.779 80.00 2 2 -12840.558 83.13 2 3 -12840.735 83.75 2
64
As expected DPRA identified (VE) as an outlier (Appendix 8.4.1); (VE) showed a pattern of ‘Genetic drift > Gene flow’ (Pattern 2), while all other population were categorised as Pattern 4 (High gene flow) (Table 3. 9). (VE) showed low mean allelic richness relative to other subpopulations. A pattern of high gene flow is apparent when plotting all non-outliers separately (Figure 3. 12) or combined (Appendix8.4.2). Not identified as a ‘true’ outlier in the DPRA, (KO) showed high level of divergence from a large number of subpopulations within the Moonie River catchment (Table 3. 9and Figure 3. 12). Mantel tests on channel distance within the Moonie River were non-significant with and without (VE) ( P = 0.572 and P = 0.350 respectively).
Table 3. 9 Intercept and slope (with 95% CI) of the Decomposed Pairwise Regression Analysis (DPRA) for E. m. macquarii in the Moonie and Barwon Rivers. See Figure 1. 1 for ‘Pattern’ descriptions. See Appendix 8.2.1for site names and Figure 3. 1 for site locations
E. m. macquarii Intercept * 10 -2 95% CI Slope * 10 -7 95% CI r2 n Pattern Non outlier LO 0.43 (-1.52_2.39) 0.02 (-0.06_0.09) 0.050 9 4 AP 0.53 (-0.15_1.20) -0.01 (-0.06_0.04) 0.032 9 4 CAR 0.57 # ( 0.09_1.05) -0.02 (-0.06_0.01) 0.292 9 - FE 0.22 (-0.75_1.18) 0.01 (-0.04_0.06) 0.054 9 4 NI 0.88 (-0.29_2.06) 0.01 (-0.07_0.09) 0.013 9 4 NU 0.19 (-0.87_1.26) 0.02 (-0.05_0.09) 0.089 9 4 KI 0.41 (-0.37_1.19) -0.01 (-0.04_0.02) 0.116 9 4 KO 0.46 (-1.27_2.19) 0.07 (-0.04_0.18) 0.287 9 4 KU 0.61 (-1.01_2.23) 0.02 (-0.05_0.09) 0.079 9 4 Outlier VE 5.56 # ( 4.31_6.81) -0.08 # (-0.16_0.00) 0.474 10 2 # P < 0.05
65
0.07
0.06
0.05
) 0.04 ST F
/(1- 0.03 VE ST F KO 0.02
0.01
0 0 50 100 150 200 250 300 350 400 450 500 Channel Distance (Km) Figure 3. 12 Decomposed Pairwise Regression Analysis (DPRA) for E. m. macquarii in the Moonie and Barwon Rivers with channel distance. Full line: Non-outlier line of best fit; Dashed line: outlier line of best fit; See Appendix 8.2.1for site names and Figure 3. 1 for site locations
In the SAA, the correlation coefficient r in E. m. macquarii under variable distance class sizes was not significantly different to zero at all distance classes when analysing the combined dataset (Figure 3. 13). When not combined, Adults in the Moonie River showed a non-zero r coefficient between 60 and 160 (km) but non-significant values at the smaller distance class (Figure 3. 14) while Juveniles showed a negative relatedness up to the 80 km distance class but remained not significantly different from zero.
r 0.015 U
0.010 L
0.005
0.000 r -0.005
-0.010
-0.015
-0.020 20 80 120 150 210 300 550 Distance Class Size (Km)
Figure 3. 13 Correlation coefficient r as a function of variable distance class size for E. m. macquarii in the Moonie and Barwon Rivers. Combined dataset ( N = 181). U and L: upper and lower 95% CI about the null hypothesis of a random distribution of genotypes. Error bars: 95% confidence interval about r from bootstrapping.
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0.025 r 0.020 U 0.015 L 0.010 0.005
r 0.000 -0.005 -0.010 -0.015 -0.020 -0.025 20J 40J 60J 80J 40A 20A 60A 80A 240J 100J 120J 140J 160J 180J 200J 220J 260J 280J 120A 180A 220A 240A 260A 280A 140A 160A 200A 100A
Distance Class Size (Km)
Figure 3. 14 Correlation coefficient r per age class under increasing distance class sizes (20 km) for E. m. macquarii in the Moonie and Barwon Rivers. X-axis: A (Blue) - Adult ( N = 106); J (Green) - Juvenile ( N = 75). U and L: upper and lower 95% CI about the null hypothesis of a random distribution of genotypes. Error bars: 95% confidence interval about r from bootstrapping. Note: 0 to 280 km distance class size showed only.
Border Rivers and Gwydir River
DPRA in the Border Rivers and Gwydir River catchments identified three populations as outliers (Appendix 8.4.1). (KN) and (PI), which were clustered with (WC) into a separate population in GENELAND, had a non significant but high intercepts and slopes (Table 3. 10) and were therefore not categorised into one of the four patterns. The non-significance of their respective intercepts was owing to the large confidence intervals around them, resulting from their low divergence level with (WC) and (PA) (Appendixes 8.3.1 and 8.3.2). The third subpopulation identified as a true outlier by the DPRA, (BO) showed similar pattern to (KN) and (PI) (Figure 3. 15 and Table 3. 10). Although not statistically ‘categorised’ in the DPRA all three outliers displayed a pattern where genetic drift is stronger than gene flow (Pattern 2). The Mantel test for IBD in the Border Rivers and Gwydir River catchments combined using channel distance was not significant when including all identified outliers (P = 0.474), when removing them ( P = 0.120), or when removing all outliers identified in DPRA and as well as (WC) from the analysis ( P = 0.082). The maximum distance between two subpopulations included in the Mantel test analysis in the latter catchments was twice as large (~ 900 km) as in the Moonie River (~ 550 km).
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Table 3. 10 Intercept and slope (with 95% CI) of the Decomposed Pairwise Regression Analysis (DPRA) for E. m. macquarii in the Border Rivers and Gwydir River catchments. See Figure 1. 1 for ‘Pattern’ descriptions. See Appendix 8.2.1for site names and Figure 3. 1 for site locations
E. m. macquarii Intercept * 10 -2 95% CI Slope * 10 -7 95% CI r2 n Pattern Non outlier LO 0.17 (-1.19_1.53) 0.00 (-0.03_0.03) 0.001 7 4 BW 0.44 (-0.62_1.50) 0.00 (-0.02_0.02) 0.005 7 4 GO 0.38 (-0.67_1.43) 0.01 (-0.02_0.04) 0.218 7 4 PA 0.17 (-0.55_0.90) 0.00 (-0.01_0.01) 0.017 7 4 PU 0.31 (-0.90_1.51) 0.00 (-0.03_0.03) 0.027 7 4 WC 0.76 (-2.31_3.83) 0.01 (-0.05_0.06) 0.026 7 4 BB -0.36 (-3.09_2.37) 0.02 (-0.02_0.06) 0.308 7 4 Outlier BO 2.95 (-1.89_7.80) 0.03 (-0.10_0.16) 0.072 8 - KN 2.64 (-1.19_6.47) 0.03 (-0.05_0.11) 0.121 8 - PI 2.55 (-0.85_5.96) 0.03 (-0.04_0.09) 0.179 8 -
0.09
0.08
0.07
0.06
) BO
ST 0.05 KN PI F /(1-
ST 0.04 F
0.03
0.02
0.01
0 0 100 200 300 400 500 600 700 800 900 1000
Channel Distance (Km)
Figure 3. 15 Decomposed Pairwise Regression Analysis (DPRA) for E. m. macquarii in the Border River and Gwydir River catchments with channel distance. Full line: Non-outlier line of best fit; Dashed line: outlier line of best fit; See Appendix 8.2.1for site names and Figure 3. 1 for site locations
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3.3 Discussion
3.3.1 Gene Flow and Genetic Structure
C. expansa
C. expansa revealed lower levels of gene flow than expected with structure found among ( FCT ) but not
within catchments ( FSC ). This pattern of non-restricted gene flow within but somewhat restricted between catchments fits the Stream Hierarchy Model (SHM, Meffe and Vrijenhoek, 1988), which predicts greater movement of genes within river branches than between branches, and still lower movement between catchments. Although the SHM model appears to accommodate the population genetic structure of C. expansa well, a similar partitioning of variance may emerge under an IBD pattern where adjacent populations show lower levels of divergence than more distant ones as individuals move over a limited portion of the population range (Wright, 1943; Slatkin, 1985; Slatkin, 1993). Under IBD, hierarchical grouping of populations per catchment may create ‘artificially’ divergent groups (i.e. each catchment) as populations distant from confluence between catchments generate high fixation indices at the catchment level ( FCT ) when grouped, despite populations located near the confluence showing no or low level of divergence in pairwise comparisons. A pattern of IBD for C. expansa populations in the upper MDB was supported by both the DPRA and the Mantel tests, rejecting the SHM of population structure in this species. Despite larger than for C. longicollis, sample sizes for C. expansa were small at a number of populations. Marked variances in sample sizes between populations and small overall sample sizes result in larger confidence interval (Ruzzante, 1998). Hence larger sample size would return more precise and statistically significant levels of divergence in C. expansa, potentially producing a stronger IBD pattern at a smaller geographical scale.
Under a stepping stone model of population structure, which characterises the Moonie River waterholes, migration-drift equilibrium must be attained for an IBD pattern to emerge (Kimura and Weiss, 1964; Slatkin, 1993; Hutchison and Templeton, 1999). Equilibrium implies that the relative influence of gene flow and genetic drift within each subpopulation are constant throughout the region (Slatkin, 1993; Hutchison and Templeton, 1999). Assuming no barriers and a resulting equal potential for gene flow over the entire system, subpopulations need to have been reasonably stable and finite in size for an extended period of time for equilibrium to be reached, population size fluctuations resulting in genetic divergence not correlated with geographical distances (McCauley, 1993; Hastings and Harrison, 1994; Hutchison and Templeton, 1999). In such cases, the relative influence of genetic drift to gene flow would have been altered as genetic drift strength is negatively correlated with effective population size (Lacy, 1987; Frankham, 1996; Willi & Van Buskirk & Schmid et al. , 2007) .
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C. expansa populations have therefore been stable within the study region for an extended period of time.
Little is known of juvenile movement and habitat requirement in the MDB turtles. Expectations were that juveniles would show high levels of relatedness at short distance classes as they are thought to remain within the natal habitat (waterhole) for predator avoidance and/or until attainment of a suitable carapace size for protection from predators (Bodie and Semlitsch, 2000; Burke, 2000; Gibbs and Amato, 2000). The positive level of correlation of juvenile C. expansa at small distance classes could reflect the species slow rate of growth up to four years old (Spencer, 2002b) and the late maturing of both sexes in comparison to the two other species (Chessman, 1978; Spencer, 2002b). Juvenile C. expansa may remain within the natal waterhole for a number of years (~ 7 years if until attainment of maturity) before moving in search of potential mating partners (Bower, 2011). In addition, as females appear to have a reduced propensity for movement (see Bower, 2011 and Chapter 6), juveniles from multiple years within a single waterhole are more likely to share their mother than juveniles in species more prone to movement.
C. longicollis
Low sample sizes for C. longicollis reduce the reliability of the results obtained for this species. Low sample size typically lead to high variance in divergence estimate, although the number of loci and alleles may have helped reduce such variance (Ruzzante, 1998; Gaggiotti et al. , 1999). Despite the low sample size which may have lead to large variance in divergence estimate, and considering the geographical scale of the present study, C. longicolllis demonstrated higher levels of contemporary gene flow among and within catchments than was expected, as subpopulations are only expected to show equal allele frequencies under panmixia (Waples and Gaggiotti, 2006). The absence of IBD with either distance supports extensive terrestrial movements in this species. Long lived C. longicollis individuals may therefore move terrestrially over somewhat restricted distances (< 7 km, considered extreme for Australian turtles) within a limited time frame, as observed in a number of studies using direct methods (Georges et al. , 1986; Kennett and Georges, 1990; Graham et al. , 1996; Roe and Georges, 2007; Roe and Georges, 2008c), but may be able to move over a significant portion of the population range during their lifetime which can extend well over 50 yrs (Parmenter, 1985).
An alternative explanation to the panmixia observed in C. longicollis relates to the suggestion made by some of a recent range expansion of C. longicollis into the western edges of its current distribution in the MDB; individuals taking advantage of the extensive network of irrigation canals and reservoirs constructed since European arrival, acting as stepping stones in an otherwise hostile region for the species (Parmenter, 1985; Beck, 1991; Cann, 1998). If originating from a homogeneous source population, these newly colonised areas should be characterised by populations with low divergence and low variance in divergence estimates (case II in Hutchison and Templeton, 1999), as not enough
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time has passed for genetic drift to differentially alter allele frequencies within each newly colonised region. C. longicollis is however believed to have occupied the Moonie, Border and Gwydir catchments long before the arrival of European settlements (Arthur Georges, pers.comm), and these catchments do not fall within the ‘western edges’ of the species distribution in the MDB. It is therefore unlikely that the almost complete absence of genetic divergence observed in these catchments reflects the hypothesised expansion and recent shared history of these populations. Should a recent expansion into these catchment have occurred, the FST values obtained would be an overestimate of the current gene flow rate (Lowe and Allendorf, 2010) as summary statistics methods assume attainment of migration –drift equilibrium (Whitlock and McCauley, 1999).
The small sample size, the timing and limited number of waterholes in which C. longicollis juveniles were caught suggested that the high level of autocorrelation observed resulted from the capture of a few highly related individuals, possibly from a few clutches only. The disappearance of autocorrelation in C. longicollis at small distance classes in this study should therefore not be interpreted as support for early dispersal in this species, but rather as the disappearance of the ‘clutch’ effect. Immature C. longicollis individuals have nevertheless shown high rates of movement between permanent lakes and temporary habitats, and no sign of age-bias in movement pattern were found in the species previously with direct methods (Roe, 2007; Roe and Georges, 2008c).
E. m. macquarii
The overall results obtained for E. m. macquarii in the Moonie, Border and Gwydir Rivers region were contrary to expectations, as, based on a previous study (Goodsell, 2002), moderate levels of gene flow were expected in this species. A weakly divergent population located above the Macintyre waterfall (~20 m high) in Kwiambal National Park (KN), NSW, and extending beyond Pindari Dam (PI) along the Severn River was identified in this species. Connectivity between the Severn River and the Macintyre River is probably restricted to one-way gene flow (FST = 0.053), with individuals ‘flushed’ downstream during flash flood events but unable to overcome the waterfall when moving upstream. Following removal of this population, partition of variance at the ‘among catchments’ level decreased fivefold but remained significant even after further removal of outlier populations identified in the DPRA (VE and BO). This partitioning of variance appeared somewhat contrary to the ‘high gene flow’ pattern (Pattern 4 in Koizumi et al., 2006 or Case II in Hutchison and Templeton 1999) revealed by all non outlier populations in the three catchments, but the high variance present within populations (98.6 %) suggested extensive gene flow among catchments in E. m. macquarii ,
concurring with the low global FST and the absence of spatial autocorrelation in this species.
Juvenile E. m. macquarii within a waterhole were found no more related than those in different waterholes. A combination of hypotheses for this pattern can be put forward. Firstly, the random
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distribution of genotypes could be the result of large population size and high movement by both sexes as adults. As the species shows no genetic structure within and arguably among catchments, sampled juveniles are likely to be the product of numerous clutches laid by many non-local females (en mass nesting and large population, see Spencer et al. , 2006) following opportunistic mating with transitory males. Secondly, this could also reflect widespread juvenile movement as E. m. macquarii juveniles have a fast growth rate prior to maturity and attain maturity at an earlier age than both sympatric Chelodina species, and could therefore be ‘on the move’ earlier on (see Chessman, 1978; Kennett and Georges, 1990; Spencer, 2002b). Thirdly, the size threshold used here to identify juveniles may have lead to the inclusion of near mature sub-adults with a propensity to move, as size and age to maturity varies with latitude in this species (Georges, 1982; Spencer, 2002b). Finally, although mostly documented in Cryptodiran ( see Valenzuela, 2000 for a Pleurodiran example), multiple paternity clutches and sperm retention by females is a possibility (Pearse and Avise, 2001; Pearse & Janzen and Avise, 2001). Without further information, it is impossible to support one hypothesis over another.
3.3.2 Metapopulation Dynamics in a Dryland River
Populations having undergone a recent extinction-colonisation cycle, a bottleneck owing to small population size, or being isolated by some barrier to gene flow are expected to demonstrate divergence levels consistently differing from the ‘background’ genetic pattern of the metapopulation (Koizumi et al. , 2006). Allendorf and Phelps (1981) showed that despite the presence of high gene flow at the population level, sampling offspring from a small subset of a larger population could also lead to significant FST values owing to a small number of parents in the sampled subset and consequent high genetic drift. This is similar to the patchy-recruitment hypothesis of Bunn and Hughes (1997) where within-stream structures result from sampling the offspring of a few individuals having oviposited over a short stream distance. Both effects can lead to genetic signature comparable to a metapopulation signature where extinction-colonisation of populations accelerates the rate of random loss of alleles (Whitlock and McCauley, 1990; Hastings and Harrison, 1994). In both cases however, the structure observed is expected to change rapidly through time as drift alters allele frequencies differentially each generation. This enables distinction with a founder effect signature which lasts longer through time (Boileau & Hebert and Schwartz, 1992). Both the Allendorf-Phelps and Patchy Recruitment effects cannot explain the outlier populations identified in the Moonie River as sampled individuals were adults of differing size. Similarly, as all outliers in the Moonie River were located in the mid-reach, the possibility of a barrier isolating these populations while allowing gene flow between the upper and lower part of the River was rejected. C. longicollis and E. m. macquarii , but not C. expansa , therefore seemed to follow our predictions with evidence of extirpation-recolonisation dynamic within this dryland river. This finding concurs with recent findings
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by Chessman (2011), where both E. m. macquarii and C. longicollis, but not C. expansa, revealed a marked reduction in catch per unit effort and a fall in recruitment in the Murray River.
Owing to the distinct dispersal abilities and behavior of C. longicollis and E. m. macquarii , two processes can explain the presence of outlier populations in the two species, namely drought related habitat quality crash and/or high (nest) predation. Verena (VE) is a relatively small waterhole with an estimated persistence time of 550 days under extreme drought conditions (Department of Environment and Resources Management, 2010). This is longer than the maximum no flow spell recorded in the Moonie (approximately 400 days) (Biggs et al. , 2005), but water quality and waterhole productivity crash well before a complete drying out of waterhole occurs (Jolly & Williamson & Gilfedder et al. , 2001; Boulton, 2003; Hall & Baldwin & Rees et al. , 2006; van Vliet and Zwolsman, 2008). Unable to move overland to more favorable habitats, E. m. macquarii in (VE) and possibly (KO) may have experienced a recent size reduction owing to water quality and resource crash. C. longicollis may also have undergone a population size reduction in both (AP) and (KO) (and possibly (AL)), the former having a similar estimated persistence time to (VE) (Department of Environment and Resources Management, 2010). The ability of this species to aestivate terrestrially (escape in time) or to move overland (escape in space) (Roe and Georges, 2008a; c) may not have protected it from being affected by waterhole quality crash, as these strategies only work if the drought is shorter than the maximum aestivation time and if more permanent waterbodies are available in close vicinity (Roe and Georges, 2008c; Roe and Georges, 2009).
The second process relates to the high contribution by a few individuals to the genetic material of subsequent generations (Chesser, 1991; Scribner & Congdon & Chesser et al. , 1993; Arnaud-Haond & Vonau & Rouxel et al. , 2008) following high nest predation (up to 96%) by the introduced fox (Cann, 1998; Spencer and Thompson, 2003; Chessman, 2011). Although the latter could have partially contributed to the pattern observed, foxes occurring throughout the basin, population crashes owing to stochastic environmental factors are well supported in the Moonie River, with (AL) and (VE) previously identified as outliers in Golden Perch ( Macquaria ambigua ) and (KO) in the Australian shrimp Macrobrachium australiense, all under strong genetic drift (Pattern 1) (Huey et al. , 2011). All outlier populations are positioned consecutively along the upper Moonie River (see Figure 3. 1) and a single event is likely responsible for these population crashes, such as the extended period of no flow of 1979-1980 (Biggs et al. , 2005), the variance in population sizes between waterholes affecting their relative speed of recovery. Similar population reductions have been suggested for E. m. macquarii in the Cooper Creek catchment owing to the early 1980s’ drought (White, 2002), and in the Murray River for both C. longicollis and E. m. macquarii owing to habitat quality and resource crash associated with the Millenium drought (1997-2009) (Chessman, 2011). In the latter study, C. expansa was deemed much better at accessing scarce resources than both above species, as suggestion also
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made by Georges et al. (1986). The latter species was also able to withstand higher salinity level associated with long period of no-flow than both other species (Bower 2011).
Outliers identified in the Border Rivers for C. longicollis and E. m. macquarii resulted from isolation from the main channel (eg. (BO) lagoon in E. m. macquarii ) or from structural features in the landscape such as waterfall and high elevations (eg. (BA) and (KN) in C. longicollis ). These are consequently not relevant to the current discussion. The absence of ‘river-proper’ outliers in this moderately regulated, albeit admittedly more mesic, catchment nonetheless suggests that flow regulation may have removed the flow stochasticity inherent to dryland rivers.
3.4 Conclusion
Comparison of population genetic structure across multiple species enabled a better understanding of their respective dispersal abilities. Although greater than expected, the dispersal abilities inferred for C. longicollis mostly followed our expectations of widespread dispersal, and provided further support for the previously suggested extensive terrestrial movements in this species. In contrast, C. expansa revealed lower dispersal ability and population connectivity than expected although the partitioning of variance observed at the ‘within populations’ level in the AMOVA across catchments alluded to elevated levels of gene flow nonetheless. E. m. macquarii demonstrated greater population connectivity, at both the within and among catchment scales, than expected based on a previous study in the Warrego River.
The absence of any sign of population extirpation in the DPRA and hence apparent ability of C. expansa populations to better respond to temporal flow stochasticity was unexpected and deserves further investigation. This result nevertheless concurred with recent findings by Chessman (2011). E. m. macquarii on the other hand appeared to follow the genetic structure of a metapopulation associated with an obligate aquatic disperser in Dryland Rivers where subpopulation extinction and recolonisation is not uncommon and where waterhole persistence plays a key role in the species population dynamic. This was also true of C. longicollis . The increased sedimentation rate of the Moonie River waterholes in the past decades should therefore be of concern, and protection of waterholes with large persistence times should be a priority for the long term persistence of turtle species (see Fachín-Terán & Vogt and Thorbjarnarson, 2006).
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Chapter 4 Genetic Connectivity in a Regulated River
Dam of the Murray – Darling Basin, 2010
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4.0 Introduction
Water retention structures typically lead to flow modification and subsequent loss and fragmentation of habitats in freshwater systems (Walker and Thoms, 1993). Detrimental impacts of river regulation on turtle species worldwide (reviewed in Bodie, 2001; Moll and Moll, 2004, p. 254-256) include community structure changes and reduced species diversity, reduced growth rates (Bennett et al. , 2009; Ashton & Bettaso and Hartwell, 2011), loss of biological cues (Alho, 2011) and loss of suitable habitat. In Australia, impacts include, but are not limited to, loss of essential habitat (Tucker & Limpus & Priest et al. , 2001; Thompson & Hamman & Latta et al. , 2006; Clark & Gordos and Franklin, 2009) and food sources (Tucker, 1999), as well as direct injuries and mortalities (Donnelly, 2004; Limpus & Hodge and Limpus, 2006). Although dams and weirs have been associated with reduced population persistence of freshwater fauna (Vaughn and Taylor, 1999; Morita and Yamamoto, 2002; Yamamoto & Morita & Koizumi et al. , 2004; Alho, 2011) very few studies have attempted to assess their potential to act as barriers to freshwater turtle movements and population connectivity. Population connectivity of freshwater turtle populations in the extensively regulated lower MDB, Australia, for instance, remains to be assessed.
Isolated populations in freshwater systems, either from natural (e.g. waterfalls) or anthropogenic (e.g. dams) obstacles, are more likely to experience size reduction or extirpation than those in non- freshwater systems owing to restricted dispersal routes typical of river networks (Martin-Smith and Laird, 1998; Morita and Yamamoto, 2002; Grant et al. , 2007; Koizumi, 2011). This is especially problematic for obligate aquatic species that are unable to get around within-network barriers such as dams and locks or to undertake out-of-network movements (sensu Grant et al. , 2007), but this may not necessarily be the case for species capable of terrestrial movements (Lowe, 2002; 2003). Many freshwater turtles can move terrestrially and evidence shows that some are able to move directly through such man-made barriers (e.g. Bennett & Keevil and Litzgus, 2010). In such cases dams and weirs represent a buffer rather than a barrier to population connectivity. The differentiation in allele frequencies between upstream and downstream populations following fragmentation therefore depends on dam ‘permeability’, the effective population size of each fragment and the number of generations since construction (Frankham, 1996; Yamamoto et al. , 2004). Only weak levels of connectivity are sufficient to erase the effect of genetic drift (Lacy, 1987). Even small levels of differentiation would therefore demonstrate demographic independence of the populations, with not enough individuals moving between populations to significantly influence their respective demographics (Lowe and Allendorf, 2010). Demographic independence of populations would have management implication for these species in the Murray River where evidence of fall in recruitment exists (Chessman, 2011).
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Although flow regulation structures have typically been seen as negative to fauna persistence within river networks (Richter & Braun & Mendelson et al. , 1997), the freshwater turtles of arid and semi- arid Australian systems may have benefited from them. Indeed, the more stable, albeit lower surface flow resulting from flow regulation in the Murray River (Maheshwari et al. , 1995) may have enhanced the overall connectivity of turtle populations in this system by removing the ‘bust’ periods (where connectivity was absent for extended period of time) for obligate riverine species that can thrive in permanent lentic waters (Puckridge et al. , 1998; Puckridge et al. , 2000; Bunn et al. , 2006). Flow stability has also led to larger and more permanent, albeit homogeneous, habitats within the lower Murray River main channel (Walker & Boulton & Thomas et al. , 1994), which may be able to sustain larger populations of freshwater turtles than under historical natural conditions. Hence, while the high flow variability of the Moonie River (Chapter 3) led to the expectations of a genetic signature corresponding to a metapopulation where some populations revealed patterns of population extirpation events following extended periods of no flow (see Huey et al. , 2011) such pattern is not expected in the lower Murray River.
This chapter looks at the potential impact of flow regulation on the population connectivity of C. longicollis , C. expansa and E. m. macquarii . Flow regulation infrastructure in the Murray River was expected to represent a buffer rather than a barrier to dispersal in C. longicollis and C. expansa , owing to their potential for overland movement, but to represent substantial barriers to E. m. macquarii owing to its obligate aquatic nature. Despite the more stable and likely larger populations present in the Murray River, each species was expected to show a more pronounced pattern of IBD as connectivity between sampled populations was buffered by dams, locks and weirs along the system. The three species were also expected to show comparable or higher genetic diversity at the population level in the Murray River than in the Moonie River catchment owing to the temporal fragmentation and reduction in habitat in the latter, metapopulation theory predicting within-population genetic diversity reduction following recurrent extinction-colonization events (Pannell and Charlesworth, 1999).
4.1 Methods
4.1.1 Catchments Description
The lower part of the MDB (lower MDB) is primarily made up of the Lachlan, the Murrumbidgee and the Murray River catchments, covering almost 29% of the entire basin area (Davies et al. , 2008). The Murray River drains approximately 2540 km before discharging into the Coorong/Murray mouth near Goolwa in South Australia (Figure 4. 1). To increase transport efficiency, ten weirs (referred to as ‘locks’) were built in the lower part of the Murray River (Lower Murray) from 1922 to 1935, in addition to a number of levees and small regulating structures forming a cascade of potential barriers
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to organism dispersal within the system (Walker, 2006). The river flow is also regulated by large storage infrastructures near its source in the Snowy Mountains, and only 36% of the natural mean annual discharge now reaches the Murray mouth (Walker, 1992; 2006). Water levels have been kept artificially high in a number of lakes and floodplains along the Lower Murray (Kingsford & Walker & Lester et al. , 2011) and the system is now typified by seasonal low flow, with larger flood events capable of replenishing floodplains and wetlands rarely part of the natural cycle (Walker, 2006). Prior to modifications, the river demonstrated seasonal and erratic flow patterns with spring snow melt and rainfalls (Walker, 2006; Kingsford et al. , 2011). This fragmentation and deterioration of habitats are great causes for concern for declining native fishes, waterbirds and macroinvertebrate populations in the system (Walker, 1992; Walker et al. , 1994; Walker, 2006; Kingsford et al. , 2011).
Located north of the Murray River catchment, the Murrumbidgee River rises in the Snowy Mountains and flows eastward before joining the main Murray River (Figure 4. 1). The system is considered to be in a very poor (lowland) to moderate condition (upland) (Davies et al. , 2008), with the construction of large dam infrastructure in the early 1900’s and land reclamation for human use leading to extensive degradation (up to 75%) of the floodplains in the lower parts of the catchment (Kingsford and Thomas, 2004; Kingsford & Lemly and Thompson, 2006). Characterised by seasonal flow prior to regulation (Kingsford and Thomas, 2004), the region now boasts extensive irrigation schemes which have altered surface flow regimes both seasonally and in magnitude (Davies et al. , 2008). The third and last river of interest, the Lachlan River rises in the Southern Tablelands of NSW, west of the Great Dividing Range, and flows for 1500 km eastward before discharging into the Great Cumbung Swamp (Davies et al. , 2008) (Figure 4. 1). The catchment is considered to be in poor condition overall and connection with the Murrumbidgee River occurs only 15 to 20% of years through the Great Cumbung Swamps (O'Brien and Burne, 1994). Owing to the number of structures for flow regulation along both the Murrumbidgee and The Murray Rivers, and especially the Lower Murray, these systems have witnessed an increased stability but reduced magnitude in their flow level for the last 50 to 70 years (Walker, 2006).
4.1.2 Sample Sizes and Distribution
A total of 99 C. expansa , 74 C. longicollis and 110 E. m. macquarii samples were obtained from the Wildlife Genetics Tissue Collection, University of Canberra ( http://iae.canberra.edu.au/locations.cgi , last access 14-Feb-2011, Genbank UC
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LFO Lower Murray River Darling River Lachlan River
MOC MUR WEN WAI GBS
LBO BL10 LGU
BOW MUB Murrumbidgee River GOL
GUC ALB
Murray River N O E 0 100 km S
Figure 4. 1 Map of the Lachlan River, Murrumbidgee River and Murray River catchments with sampling sites and major dams and weirs. Yellow (larger) Circles: sampling sites; Black (smaller) circles: major dams and weirs. See Appendix 8.2.2 for site names and coordinates.
4.1.3 Laboratory and Statistical Methods
I redirect the reader to Chapter 2 ‘General Methods’ for information relating to genotyping methods. Background information on the statistical methods of analysis performed here can also be found in Chapter 2. Specifics for these analyses are provided here. Decomposed pairwise regression analysis (DPRA) and Mantel tests for IBD were carried out on channel distances for C. longicollis C. expansa and E. m. macquarii . Based on mtDNA, C. longicollis in the MDB is composed of two lineages (Kate Hodges, unpublished data), with evidence of overlapping and expanding distribution along the Murrumbidgee and Lachlan Rivers (see Chapter 5). Inclusion of these populations in the present analyses provided confusing population genetic patterns owing to the presence of two lineages in some populations but not all. As a consequence, no tests on Euclidean distance were carried out in C. longicollis as only site sampled along the Murray River were included for analyses, resulting in the channel and Euclidean distance being approximately equal. Partial Mantel tests with Channel distance and the number of major dams and weirs present between sites were carried out in Arlequin v.3.5 (Excoffier and Lischer, 2010) for all three species. Dam and weir locations were obtained from the freely available Dams and Water Storages 1990 (3 rd Edition 2004) (http://www.ga.gov.au/meta/ANZCW0703005382.html - last accessed 11 October 2010) GIS layer
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and visualised in ArcMap (v.9.3). Following the DPRA outcome for this species, partial Mantel tests were also performed on ‘off-channel’ distance for E. m. macquarii populations sampled in lakes and lagoons (‘non-river’ sites) while controlling for channel distance (see results). For this analysis, populations located within the main channel (referred to as ‘within-river’ sites) had a distance of zero; ‘non-river’ populations had a distance equivalent to the distance from the main channel following side channels or direct overland distance if no side channels were present. Off-channel distances for each pair of sites were added when performing pairwise comparisons between ‘non-river’ sites. Off- channel distances were measured in Google Earth (2010) as it provided sufficient accuracy for the small distances measured.
Welch’s t-test for comparison of mean allelic richness ( AR) (carried out with equal number of genes
for rarefaction in both systems), mean observed heterozigosity ( HO) and mean expected heterozygosity ( HE) for C. expansa, C. longicollis and E. m. macquarii between the Moonie River and the Murray River populations were carried out in the freely available trial version of Analyse-it (http://www.analyse-it.com/support/download.aspx - accessed 02 November 2011).
4.2 Results
4.2.1 Intrapopulation Diversity Measure
Eleven loci were amplified in C. expansa, ten in C. longicollis , and nine in E. m. macquarii (Table 4. 1, Table 4. 2, Table 4. 3 respectively). No linkage disequilibrium amongst locus pairs ( P > 0.05) was detected. (LBO) in E. m. macquarii showed HWE deviations at multiple loci (four) before correction for multiple tests, with high FIS values each time. Three (LBO) individuals carried a unique allele at loci TLE 19.1, TLE10 and TLE6.2. These individuals were rerun for confirmation. After correction, only (BOW) was found to deviate from HWE expectation at one locus in this species and only (MUB) in C. expansa , the latter showing lower heterozygosity than under HWE expectations at locus T48. Mean allelic richness was similar across populations in all three species except for (ALB) in C. expansa and C. longicollis (Table 4. 1, Table 4. 2), (LBO) in C. longicollis, and (LFO) in E. m. macquarii which showed a lower mean allelic richness and mean expected heterozygosity level
relative to other populations (Table 4. 3). Mean allelic richness ( AR), mean observed heterozygosity
(HO) and mean expected heterozygosity (HE) were all significantly higher in the Moonie River for C. longicollis (Table 4. 4) compared to the Murray River populations. Mean allelic richness ( AR) and
mean expected heterozygosity (HE) were both higher in the Moonie River for C. expansa but no measures of genetic diversity were found significantly different between the two systems in E. m. macquarii .
80
Table 4. 1 C. expansa diversity indices in the lower Murray-Darling Basin. N, samples size; AT, total number of alleles (per locus per sites); AR, corrected mean allelic richness ( N = 6 genes); HO, observed heterozygosity; HE, expected heterozygosiy; P-value, significant level for HWE expectation (#: significant after B-Y correction for multiple comparisons); FIS , inbreeding coefficient, NA , null allele frequency (-: no null alleles detected). See Appendix 8.2.2 for site names and Figure 4. 1 for site location.
Locus Population
AT MUB MOC LGU MUR WAI BL10 WEN GBS ALB TCE64 7 N 16 5 8 15 12 11 6 13 13 AT 4 4 5 4 6 4 5 4 5 AR 3.08 3.43 3.45 2.81 3.77 2.99 3.46 2.93 2.97 HO 0.75 0.80 0.75 0.60 0.75 0.55 0.83 0.92 0.85 HE 0.72 0.78 0.77 0.66 0.80 0.70 0.76 0.69 0.67 P-value 0.619 0.695 0.605 0.875 0.258 0.455 1.000 0.020 0.553 FIS -0.04 -0.03 0.02 0.09 0.06 0.23 -0.11 -0.35 -0.28 NA ------TCE86 9 N 16 5 8 15 12 11 6 13 13 AT 5 4 4 7 6 6 6 5 7 AR 3.29 3.43 3.04 3.86 3.61 3.81 4.15 3.01 3.40 HO 0.75 0.80 0.75 0.67 0.67 0.91 0.83 0.62 0.69 HE 0.73 0.78 0.69 0.80 0.79 0.81 0.85 0.68 0.74 P-value 0.589 1.000 0.612 0.178 0.532 0.441 0.433 0.258 0.637 FIS -0.03 -0.03 -0.09 0.18 0.16 -0.13 0.02 0.09 0.07 NA ------TCE70 3 N 16 5 8 15 12 11 6 13 13 AT 3 2 2 3 3 2 2 2 2 AR 2.04 1.97 1.94 2.56 2.31 1.98 1.91 1.88 1.42 HO 0.44 0.60 0.63 0.87 0.50 0.45 0.85 0.38 0.15 HE 0.43 0.47 0.46 0.62 0.51 0.51 0.41 0.41 0.15 P-value 1.000 1.000 0.488 0.154 1.000 1.000 0.273 1.000 1.000 FIS -0.01 -0.33 -0.40 -0.42 0.01 0.11 0.62 0.06 -0.04 NA ------TCE74 2 N 16 5 8 15 12 11 6 13 13 AT 2 2 2 2 2 2 2 2 2 AR 1.96 1.97 1.79 1.97 1.94 1.98 1.99 1.92 1.96 HO 0.63 0.20 0.38 0.67 0.33 0.64 0.50 0.62 0.62 HE 0.48 0.47 0.33 0.50 0.46 0.51 0.53 0.44 0.49 P-value 0.319 0.333 1.000 0.289 0.518 0.555 1.000 0.243 0.566 FIS -0.30 0.60 -0.17 -0.36 0.29 -0.27 0.06 -0.41 -0.26 NA ------TLE10 2 N 16 5 8 15 12 11 6 13 13 AT 2 1 2 2 2 2 2 2 2 AR 1.67 1.00 1.88 1.70 1.45 1.48 1.50 1.23 1.56 HO 0.19 - 0.25 0.33 0.17 0.18 0.17 0.08 0.23 HE 0.27 - 0.40 0.29 0.16 0.17 0.17 0.08 0.21 P-value 0.306 - 0.385 1.000 1.000 1.000 - - 1.000 FIS 0.32 - 0.39 -0.17 -0.05 -0.05 - - -0.09 NA ------TCE89.1 6 N 16 5 8 15 12 11 6 13 13 AT 4 4 3 6 4 4 3 3 5 AR 2.76 3.60 2.60 3.14 2.89 2.94 2.47 2.70 3.26 HO 0.69 0.80 0.50 0.93 0.67 0.64 0.33 0.85 0.62 HE 0.60 0.80 0.63 0.69 0.68 0.69 0.59 0.66 0.72 P-value 1.000 1.000 0.432 0.115 0.644 0.806 0.151 0.437 0.276 FIS -0.15 0.00 0.22 -0.37 0.02 0.08 0.46 -0.31 0.15 NA ------TCE76.1 17 N 16 5 8 15 12 11 6 13 13 AT 6 4 4 8 8 5 3 9 6 AR 2.38 2.80 2.79 3.11 3.63 3.02 2.00 4.33 2.69 HO 0.44 0.40 0.63 0.67 0.75 0.45 0.33 0.92 0.62 HE 0.44 0.53 0.59 0.63 0.74 0.63 0.32 0.86 0.55 P-value 0.362 0.330 1.000 0.708 0.567 0.309 1.000 0.881 0.280 FIS 0.00 0.27 -0.06 -0.06 -0.02 0.29 -0.05 -0.08 -0.12 NA ------
81
Continued Table 4.1 C. expansa diversity indices in the lower Murray-Darling Basin. N, samples size; AT, total number of alleles (per locus per sites); AR, corrected mean allelic richness ( N = 6 genes); HO, observed heterozygosity; HE, expected heterozygosiy; P-value, significant level for HWE expectation (#: significant after
B-Y correction for multiple comparisons); FIS , inbreeding coefficient, NA , null allele frequency (-: no null alleles detected). See Appendix 8.2.2 for site names and Figure 4. 1 for site location.
Locus Population
AT MUB MOC LGU MUR WAI BL10 WEN GBS ALB T15 2 N 16 5 8 15 12 11 6 13 13 AT 2 2 2 2 2 2 2 2 2 AR 1.85 1.60 1.88 1.83 1.60 1.89 2.00 1.88 1.98 HO 0.38 0.20 0.50 0.33 0.08 0.55 0.33 0.54 0.62 HE 0.39 0.20 0.40 0.37 0.23 0.42 0.55 0.41 0.52 P-value 1.000 - 1.000 1.000 0.130 0.505 0.481 0.499 0.601 FIS 0.03 - -0.27 0.10 0.65 -0.33 0.41 -0.33 -0.20 NA ------T48 5 N 16 5 8 15 12 11 6 13 13 AT 3 3 3 3 3 3 2 4 2 AR 2.67 2.57 2.41 2.18 2.66 2.45 1.99 2.74 1.68 HO 0.44 0.60 0.50 0.67 0.67 0.73 0.50 0.69 0.31 HE 0.65 0.60 0.51 0.55 0.65 0.60 0.53 0.65 0.27 P-value 0.002 # 0.620 0.591 0.429 0.857 0.771 1.000 0.151 1.000 FIS 0.33 0.00 0.02 -0.22 -0.03 -0.22 0.06 -0.07 -0.14 NA ------T44 4 N 16 5 8 15 12 11 6 13 13 AT 3 3 3 3 3 4 3 3 2 AR 2.72 2.93 2.67 2.78 2.62 2.95 2.76 2.19 1.88 HO 0.69 0.60 0.88 0.73 0.58 0.64 0.83 0.38 0.54 HE 0.66 0.73 0.63 0.68 0.62 0.65 0.68 0.54 0.41 P-value 0.361 0.541 0.328 0.906 0.296 0.075 0.654 0.258 0.499 FIS -0.04 0.20 -0.44 -0.08 0.06 0.03 -0.25 0.29 -0.33 NA ------TCE92.1 41 N 16 5 7 15 12 11 6 13 13 AT 19 7 10 13 15 16 11 13 13 AR 5.39 5.00 5.05 5.08 5.19 5.47 5.77 5.08 5.07 HO 1.00 0.80 0.86 0.93 0.92 0.82 1.00 0.85 0.92 HE 0.96 0.93 0.92 0.93 0.94 0.96 0.98 0.93 0.93 P-value 0.728 0.064 0.550 0.246 0.281 0.059 1.000 0.160 0.501 FIS -0.05 0.16 0.08 0.00 0.02 0.15 -0.02 0.10 0.01 NA ------All Loci AR 2.71 2.75 2.68 2.82 2.88 2.81 2.73 2.72 2.53 HO 0.58 0.58 0.60 0.67 0.55 0.60 0.53 0.62 0.56 HE 0.58 0.63 0.57 0.61 0.60 0.60 0.58 0.58 0.51
82
Table 4. 2 C. longicollis diversity indices in the lower Murray-Darling Basin. N, samples size; AT, total number of alleles (per locus per sites); AR, corrected mean allelic richness ( N = 6 genes); HO, observed heterozygosity;
HE, expected heterozygosiy; P-value, significant level for HWE expectation (None significant after B-Y correction for multiple comparisons); FIS , inbreeding coefficient, NA , null allele frequency (-: no null alleles detected). See Appendix 8.2.2 for site names and Figure 4. 1 for site location.
Locus Population
AT GOL MUB MOC LBO LGU MUR WAI BL10 ALB TLE10 8 N 11 10 8 5 4 13 8 6 9 AT 5 4 3 2 3 3 4 3 5 AR 2.66 2.50 2.16 1.60 2.96 2.09 2.38 2.47 2.57 HO 0.64 0.40 0.38 0.20 1.00 0.46 0.50 0.83 0.56 HE 0.53 0.50 0.43 0.20 0.75 0.39 0.44 0.59 0.48 P-value 1.000 0.444 0.384 - 1.000 1.000 1.000 0.636 1.000 FIS -0.22 0.21 0.13 - -0.41 -0.18 -0.14 -0.47 -0.16 NA ------TCE70 5 N 11 10 8 5 4 13 8 6 9 AT 4 3 4 3 3 4 3 2 3 AR 2.39 2.17 2.94 2.57 2.71 2.48 2.57 1.77 2.24 HO 0.45 0.30 0.38 0.60 0.50 0.46 0.38 0.33 0.56 HE 0.46 0.47 0.68 0.60 0.61 0.53 0.61 0.30 0.50 P-value 0.605 0.133 0.018 0.617 0.427 0.380 0.164 1.000 0.349 FIS 0.02 0.37 0.46 0.00 0.20 0.14 0.40 -0.11 -0.11 NA ------TCE92.2 22 N 11 10 8 5 4 13 8 6 9 AT 11 8 10 6 5 10 10 6 10 AR 4.82 4.51 5.25 4.50 4.21 4.80 5.16 4.50 4.78 HO 0.91 0.80 1.00 1.00 0.50 0.85 1.00 0.83 0.78 HE 0.91 0.88 0.95 0.89 0.86 0.91 0.94 0.89 0.90 P-value 0.916 0.341 1.000 1.000 0.086 0.729 1.000 0.667 0.381 FIS 0.00 0.10 -0.06 -0.14 0.45 0.07 -0.07 0.07 0.15 NA ------TCE86 17 N 11 10 8 5 4 13 8 6 9 AT 4 6 4 2 3 7 5 3 5 AR 2.50 3.54 3.04 1.87 2.96 3.65 3.64 2.41 3.36 HO 0.64 0.80 0.63 0.40 1.00 0.77 0.75 0.33 0.67 HE 0.52 0.76 0.69 0.36 0.75 0.78 0.79 0.53 0.72 P-value 1.000 0.331 0.494 1.000 1.000 0.874 0.076 0.515 0.345 FIS -0.25 -0.05 0.10 -0.14 -0.41 0.02 0.06 0.39 0.08 NA ------T11 15 N 11 10 8 5 4 13 8 6 9 AT 7 6 8 5 4 8 6 8 5 AR 4.14 3.81 4.03 3.40 3.25 3.71 3.69 5.09 3.14 HO 0.91 0.90 0.75 0.80 0.75 0.77 0.50 1.00 0.78 HE 0.85 0.81 0.80 0.67 0.64 0.76 0.78 0.94 0.69 P-value 0.313 0.519 0.504 1.000 1.000 0.645 0.046 1.000 0.922 FIS -0.08 -0.12 0.07 -0.23 -0.20 -0.01 0.37 -0.07 -0.13 NA ------T31 11 N 11 10 8 5 4 13 8 6 9 AT 5 5 3 3 3 4 5 4 4 AR 3.02 3.18 1.75 2.47 2.50 2.66 2.91 3.18 2.24 HO 0.73 0.70 0.25 0.60 0.50 0.54 0.75 0.50 0.44 HE 0.63 0.67 0.24 0.51 0.46 0.58 0.60 0.71 0.40 P-value 0.916 0.272 1.000 1.000 1.000 0.896 1.000 0.341 1.000 FIS -0.16 -0.04 -0.04 -0.20 -0.09 0.07 -0.27 0.32 -0.12 NA ------
83
Continued Table 4. 2 C. longicollis diversity indices in the lower Murray-Darling Basin. N, samples size; AT, total number of alleles (per locus per sites); AR, corrected mean allelic richness ( N = 6 genes); HO, observed heterozygosity; HE, expected heterozygosiy; P-value, significant level for HWE expectation (None significant after B-Y correction for multiple comparisons); FIS , inbreeding coefficient, NA , null allele frequency (-: no null alleles detected). See Appendix 8.2.2 for site names and Figure 4. 1 for site location.
Locus Population
AT GOL MUB MOC LBO LGU MUR WAI BL10 ALB TCE76.1 3 N 11 10 8 5 4 13 8 6 9 AT 2 2 1 2 2 2 2 2 2 AR 1.48 1.52 1.00 1.97 1.75 1.68 1.97 1.97 1.84 HO 0.18 0.20 - 0.20 0.25 0.15 0.75 0.00 0.44 HE 0.17 0.19 - 0.47 0.25 0.27 0.50 0.48 0.37 P-value 1.000 1.000 - 0.333 - 0.236 0.441 0.031 1.000 FIS -0.05 -0.06 - 0.60 - 0.44 -0.56 1.00 -0.23 NA ------T12 3 N 11 10 8 5 4 13 8 6 9 AT 2 2 2 2 2 2 2 2 3 AR 1.98 1.99 1.97 2.00 2.00 1.95 1.94 2.00 2.17 HO 0.55 0.50 0.75 0.60 0.75 0.54 0.38 0.67 0.33 HE 0.52 0.52 0.50 0.56 0.54 0.47 0.46 0.55 0.45 P-value 1.000 1.000 0.441 1.000 1.000 1.000 1.000 1.000 0.531 FIS -0.05 0.04 -0.56 -0.09 -0.50 -0.15 0.19 -0.25 0.27 NA ------T17 4 N 11 10 8 5 4 13 8 6 9 AT 2 2 2 2 2 2 2 2 2 AR 1.48 1.87 1.88 1.97 1.75 1.88 1.94 2.00 1.95 HO 0.18 0.50 0.50 0.20 0.25 0.54 0.63 0.67 0.44 HE 0.17 0.39 0.40 0.47 0.25 0.41 0.46 0.55 0.47 P-value 1.000 1.000 1.000 0.333 - 0.499 0.487 1.000 1.000 FIS -0.05 -0.29 -0.27 0.60 -0.33 -0.40 -0.25 0.06 NA ------T87 6 N 11 10 8 5 4 13 8 6 9 AT 4 4 4 4 4 6 4 4 3 AR 3.04 3.13 3.19 3.67 3.50 3.51 3.33 3.35 2.52 HO 0.64 0.70 0.88 0.80 0.50 0.77 0.63 0.83 0.44 HE 0.70 0.72 0.71 0.82 0.79 0.76 0.76 0.77 0.60 P-value 0.589 0.864 0.733 0.543 0.313 1.000 0.402 0.197 0.382 FIS 0.09 0.03 -0.26 0.03 0.40 -0.02 0.19 -0.09 0.27 NA ------All Loci AR 2.75 2.82 2.72 2.6 2.76 2.84 2.95 2.87 2.68 HO 0.58 0.58 0.61 0.54 0.60 0.58 0.63 0.60 0.54 HE 0.55 0.59 0.59 0.55 0.59 0.59 0.63 0.63 0.56
84
Table 4. 3 E. m. macquarii diversity indices in the lower Murray-Darling Basin. N, samples size; AT, total number of alleles (per locus per sites); AR, corrected mean allelic richness ( N = 6 genes); HO, observed
heterozygosity; HE, expected heterozygosiy; P-value, significant level for HWE expectation (#: significant after
B-Y correction for multiple comparisons); FIS , inbreeding coefficient, NA , null allele frequency (-: no null alleles detected). See Appendix 8.2.2 for site names and Figure 4. 1 for site location.
Locus Population A T MUB MOC LBO LGU MUR WAI WEN GUC ALB GBS BOW LFO TLE10 7 N 7 10 8 8 10 7 9 6 10 10 14 11
AT 2 2 3 2 2 5 3 5 4 3 4 4 AR 1.43 1.68 2.51 1.38 1.79 2.71 2.06 3.27 2.39 1.82 2.31 2.44 HO 0.14 0.30 0.25 0.13 0.40 0.57 0.44 0.67 0.40 0.10 0.43 0.45 HE 0.14 0.27 0.57 0.13 0.34 0.51 0.39 0.67 0.49 0.28 0.43 0.54 P-value - 1.000 0.021 - 1.000 1.000 1.010 0.758 0.653 0.053 0.629 0.619
FIS 0.00 -0.13 0.58 0.00 -0.20 -0.14 -0.16 0.00 0.19 0.65 0.01 0.17 NA ------TLE 6.2 14 N 7 10 8 8 10 7 9 6 10 10 14 11
AT 7 8 7 6 7 9 8 4 10 9 9 5 AR 4.52 4.20 3.90 4.24 4.18 5.07 4.26 3.35 4.37 4.70 4.52 3.22 HO 0.86 0.90 0.88 0.75 0.90 1.00 0.89 0.50 0.80 1.00 0.93 0.55 HE 0.89 0.85 0.79 0.87 0.85 0.93 0.85 0.77 0.85 0.90 0.89 0.70 P-value 0.801 0.390 0.926 0.675 0.644 1.000 0.421 0.529 0.458 0.559 0.239 0.032
FIS 0.40 -0.07 -0.11 0.14 -0.07 -0.08 -0.05 0.38 0.06 -0.12 -0.05 0.23 NA ------TLE 7.2 2 N 7 10 8 8 10 7 9 6 10 10 14 11
AT 2 2 2 2 2 2 2 2 2 2 2 2 AR 1.93 1.98 1.99 1.97 1.96 1.99 1.95 1.99 1.98 1.99 1.93 1.93 HO 0.29 0.40 0.88 0.75 0.50 0.29 0.44 0.50 0.40 0.40 0.64 0.64 HE 0.44 0.51 0.53 0.50 0.48 0.53 0.47 0.53 0.51 0.53 0.45 0.45 P-value 0.440 0.573 0.139 0.441 1.000 0.441 1.000 1.000 0.573 0.563 0.221 0.480
FIS 0.37 0.22 -0.75 -0.56 -0.05 0.48 0.06 0.06 0.22 0.25 -0.44 -0.43 NA ------TLE13.1 12 N 7 10 8 8 10 7 9 6 10 10 14 11
AT 6 8 6 6 7 6 7 5 9 8 9 8 AR 3.99 4.36 3.69 4.05 4.15 3.92 4.57 3.41 4.44 4.62 4.31 4.13 HO 0.86 0.80 0.75 1.00 0.90 1.00 0.89 0.67 0.90 0.90 0.93 0.82 HE 0.84 0.87 0.78 0.84 0.85 0.80 0.90 0.73 0.87 0.89 0.85 0.83 P-value 1.000 0.845 0.449 0.987 0.693 1.000 0.874 0.515 0.186 0.608 0.998 0.567
FIS -0.03 0.08 0.03 -0.20 -0.07 -0.27 0.01 0.09 -0.03 -0.01 -0.10 -0.03 NA ------TLE19.1 5 N 7 10 8 8 10 7 9 6 10 10 14 11
AT 2 2 3 2 3 2 2 2 2 3 2 2 AR 1.97 1.96 2.77 1.94 2.27 2.00 1.99 1.91 1.98 2.80 1.97 1.93 HO 0.43 0.50 0.25 0.63 0.40 1.00 0.67 0.50 0.80 0.70 0.36 0.45 HE 0.49 0.48 0.67 0.46 0.56 0.54 0.52 0.41 0.51 0.69 0.49 0.45 P-value 1.000 1.000 0.035 0.487 0.309 0.037 0.539 1.000 0.173 0.872 0.570 1.000
FIS 0.14 -0.05 0.64 -0.40 0.30 -1.00 -0.30 -0.25 -0.64 -0.02 0.29 0.00 NA - - 0.241 ------TCE70 6 N 7 10 8 8 10 7 9 6 10 10 14 11
AT 4 4 4 5 4 2 6 3 5 4 5 3 AR 3.31 2.90 3.11 3.32 2.85 2.00 3.99 2.47 3.21 2.92 2.88 2.46 HO 0.57 0.50 0.50 0.75 0.70 0.71 0.89 0.50 0.70 0.90 0.64 0.73 HE 0.75 0.67 0.73 0.72 0.64 0.54 0.83 0.59 0.70 0.68 0.64 0.61 P-value 0.509 0.199 0.393 0.685 0.772 0.511 0.172 1.000 0.638 0.544 0.479 0.310
FIS 0.25 0.26 0.33 -0.05 -0.10 -0.36 -0.08 0.17 0.00 -0.34 0.00 -0.21 NA ------TLE 31.1 18 N 7 10 8 8 10 7 9 6 10 10 14 11
AT 5 7 7 9 7 5 9 8 6 13 6 5 AR 3.83 3.93 4.46 4.24 4.07 3.47 4.72 4.95 3.69 5.07 3.66 3.27 HO 0.86 0.80 0.63 0.75 0.80 0.71 1.00 1.00 0.50 0.90 0.79 0.55 HE 0.82 0.82 0.88 0.82 0.83 0.76 0.90 0.92 0.77 0.93 0.79 0.74 P-value 0.414 0.903 0.035 0.439 0.186 0.769 0.448 0.501 0.014 0.240 0.337 0.219
FIS -0.04 0.02 0.31 0.09 0.04 0.06 -0.12 -0.09 0.36 0.03 0.01 0.28 NA ------
85
Continued Table 4. 3 E. m. macquarii diversity indices in the lower Murray-Darling Basin. N, samples size; AT, total number of alleles (per locus per sites); AR, corrected mean allelic richness ( N = 6 genes); HO, observed heterozygosity; HE, expected heterozygosiy; P-value, significant level for HWE expectation (#: significant after
B-Y correction for multiple comparisons); FIS , inbreeding coefficient, NA , null allele frequency (-: no null alleles detected). See Appendix 8.2.2 for site names and Figure 4. 1 for site location.
Locus Population A T MUB MOC LBO LGU MUR WAI WEN GUC ALB GBS BOW LFO 1 TLE 19.3 2 N 7 10 8 8 10 7 9 6 10 10 14 11
AT 6 8 7 10 6 7 6 6 9 7 8 4 AR 4.06 4.36 4.26 4.82 3.93 4.63 3.56 4.29 4.59 3.66 4.27 2.94 HO 1.00 0.70 0.75 0.88 0.80 0.86 0.78 0.83 0.80 0.80 0.86 0.73 HE 0.84 0.87 0.86 0.90 0.82 0.90 0.76 0.86 0.89 0.76 0.86 0.69 P-value 0.298 0.181 0.078 0.763 0.588 0.722 0.839 0.324 0.274 0.908 0.004 # 0.807
FIS -0.07 0.20 0.13 0.03 0.03 0.05 -0.03 0.04 0.11 -0.05 0.01 -0.06 NA ------1 TLE13.3 5 N 7 10 8 8 10 7 9 6 10 10 14 11
AT 8 7 4 8 8 7 6 4 9 4 8 5 AR 4.79 3.92 2.63 4.48 4.45 4.09 3.78 2.91 4.67 2.96 3.59 3.12 HO 0.71 0.89 0.63 0.63 0.70 0.86 0.63 0.67 0.70 0.75 0.71 0.82 HE 0.91 0.82 0.53 0.88 0.87 0.81 0.78 0.64 0.90 0.69 0.73 0.69 P-value 0.218 0.769 1.000 0.044 0.045 0.980 0.378 0.212 0.234 0.031 0.481 1.000
FIS 0.23 -0.09 -0.21 0.30 0.21 -0.06 0.21 -0.05 0.23 -0.09 0.02 -0.19 NA ------
All Loci AR 3.31 3.25 3.26 3.38 3.29 3.32 3.43 3.17 3.48 3.39 3.27 2.83 HO 0.63 0.64 0.61 0.69 0.68 0.78 0.74 0.65 0.67 0.72 0.70 0.64 HE 0.68 0.68 0.70 0.68 0.69 0.70 0.71 0.68 0.72 0.71 0.68 0.63
Table 4. 4 Output of Weltch’s t-tests (assuming unequal variance) for mean Allelic richness ( AR) ( N = 6 genes)
with rarefaction, mean observed heterozygosity ( HO) and mean expected heterozygosity ( HE) for C. expansa , C . longicollis and E. m. macquarii between the Moonie River and the Murray River. Two-tail test.
Species Mean
AR HO HE C. expansa Moonie 2.91 0.63 0.63 Lower Murray 2.74 0.59 0.58 P-value 0.003 0.138 0.025
C. longicollis Moonie 3.14 0.69 0.67 Lower Murray 2.78 0.58 0.59 P-value 0.001 0.000 0.000
E. m. macquarii Moonie 3.25 0.65 0.68 Lower Murray 3.27 0.67 0.69 P-value 0.743 0.611 0.207
86
4.2.2 Genetic Structure in the Lower Murray-Darling Basin
C. expansa
No AMOVA was carried out for any species in the lower MDB as the sampling distribution was unbalanced across catchments. Global FST for C. expansa was higher than for either other species at a similar maximum distance (Table 4. 5) (see Appendix 8.3.6 for Moonie River comparison).
Table 4. 5 Global FST for C. expansa , C. longicollis and E. m. macquarii in the lower MDB.
Maximum Channel Species FST P-value Distance (km) C. expansa 0.033 0.000 1758 C. longicollis 0.014 0.022 1876 E. m. macquarii 0.021 0.000 1789
The DPRA revealed no outlier populations in the lower MDB in C. expansa (Appendix 8.4.4) and Table 4. 6) and plotting of linearised genetic distance against channel distance showed a strong IBD pattern in this species (Figure 4. 2). Divergence levels within the Murray River were lower than those observed in the Moonie River ( FST ~ 0.025 vs 0.045 respectively at 500 km). Correlation between genetic distance and channel distance was highly significant, the latter explaining a slightly larger
proportion of the variation when controlling for the presence of dams and weirs (Table 4. 7). FST table for C. expansa in the lower MDB can be found in Appendix 8.3.7.
0.1200
0.1000
0.0800 ) ST F 0.0600 / (1- / ST F
0.0400
0.0200
0.0000 0 200 400 600 800 1000 1200 1400 1600 1800 2000 Channel Distance (km)
Figure 4. 2 Decomposed Pairwise Regression Analysis (DPRA) for C. expansa in the lower Murray River with channel distance. Full line: non-outlier populations line of best fit (no outlier populations were identified).
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Table 4. 6 Intercept and slope (with 95% CI) of the Decomposed Pairwise Regression Analysis (DPRA) for C. expansa in the lower Murray River, South Australia, with channel distance. See Figure 1. 1 for ‘Pattern’ descriptions. See Appendix 8.2.2 for site names and Figure 4. 1 for site location.
C. expansa Site code Intercept * 10 -2 95% CI Slope * 10 -7 95% CI r2 n Pattern Non outlier MUB 1.14 (-4.88_0.67) 0.12 # (0.33_0.94) 0.815 9 3 MOC -1.10 (-3.86_1.66) 0.56 # (0.20_0.92) 0.709 9 3 LGU 0.08 (-1.47_1.64) 0.66 # (0.42_0.91) 0.881 9 3 MUR -0.92 (-1.73_-0.10) 0.52 # (0.39_0.66) 0.937 9 3 WAI -0.01 (-1.90_1.88) 0.43 # (0.09_0.77) 0.621 9 3 BL10 -0.51 (-4.02_3.00) 0.67 # (0.04_1.30) 0.530 9 3 WEN 0.92 (-1.53_3.37) 0.27 (-0.17_0.71) 0.273 9 4 GBS 1.33 (-5.18_7.84) 0.43 (-0.19_1.04) 0.321 9 4 ALB -1.61 (-10.31_7.09) 0.70 # (0.07_1.34) 0.549 9 3
# P < 0.05
Table 4. 7 Mantel and Partial Mantel test output for C. expansa, C. longicollis and E. m. macquarii in the lower MDB.
Species Parameter r P-value
C. expansa Channel distance 0.705 0.001 Weirs & Dams 0.279 0.034 Channel distance: controlling for Weirs and Dams 0.852 0.008 Weirs & Dams: controlling for Channel Distance -0.124 0.825
C. longicollis Euclidean distance 0.013 0.587 Channel distance 0.012 0.584 Weirs and Dams 0.003 0.542 Channel distance: controlling for Weirs and Dams 0.026 0.638 Weirs and Dams: controlling for Channel Distance -0.008 0.330
E. m. macquarii Channel distance 0.059 0.128 Weirs and Dams 0.167 0.023 Channel distance: controlling for Weirs and Dams -0.075 0.807 Weirs and Dams: controlling for Channel Distance 0.283 0.036 Off-channel distance 0.240 0.036 Off-channel distance: controlling for Channel distance 0.238 0.037 Off-channel distance: controlling for Weirs and Dams 0.201 0.061 Channel distance: controlling for Off-channel distance 0.044 0.132 Weirs and Dams: controlling for Off-channel distance 0.123 0.056
C. longicollis
C. longicollis had the lowest global FST despite having the largest maximum distance between populations of all three species (Table 4. 5). Two true outlier populations were identified in the DPRA (Appendix 8.4.4 and Table 4. 8), both showing a pattern of high genetic drift (Pattern 1; Figure 4. 3). The genetic background pattern of C. longicollis in the Murray River was one of high gene flow
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(Pattern 4). This overall pattern of high gene flow was supported by the absence of correlation between channel (and Euclidean) distance and linearised genetic distance (Table 4. 7). Dams and weirs had no apparent influence on the level of genetic divergence between populations in the Murray River in C. longicollis (Table 4. 7). After removal of true outlier populations, divergence levels in the Murray River were similar to those observed in the Moonie River at comparable distances (Appendix
8.4.2 and 8.4.5). FST table for C. longicollis in the lower MDB can be found in Appendix 8.3.8.
Table 4. 8 Intercept and slope (with 95% CI) of the Decomposed Pairwise Regression Analysis (DPRA) for C. longicollis in the lower Murray River, South Australia, with channel distance. See Figure 1. 1 for ‘Pattern’ descriptions. See Appendix 8.2.2 for site names and Figure 4. 1for site location.
C.longicollis Site Code Intercept * 10 -2 95% CI Slope * 10 -8 95% CI r2 n Pattern Non outlier GOL 0.83 (-0.60_2.27) 0.87 (-0.73_2.47) 0.364 8 4 MUB 0.21 (-0.42_0.83) 0.18 (-0.59_0.95) 0.099 8 4 MOC 1.11 (-0.05_2.27) -0.79 (-2.45_0.87) 0.305 8 4 LGU 0.25 (-1.02_1.53) 3.52 # (1.51_5.54) 0.855 8 3 MUR -0.10 (-0.70_0.51) 1.37 # (0.40_2.34) 0.795 8 3 WAI 0.48 (-1.68_2.63) 0.06 (-3.26_3.38) 0.001 8 4 ALB 1.78 (-12.27_15.84) -0.12 (-9.37_9.14) 0.000 8 4 Outlier LBO 5.07 # (1.55_9.58) -1.79 (-7.83_4.25) 0.081 9 1 BL10 3.73 # (1.29_6.18) 0.79 (-3.33_4.92) 0.036 9 1
# P < 0.05
0.0700
0.0600
LGU 0.0500 LBO
) 0.0400 ST F BL10 / (1- / ST
F 0.0300
0.0200
0.0100
0.0000 0 200 400 600 800 1000 1200 1400 1600 1800 2000 Channel Distance (km)
Figure 4. 3 Decomposed Pairwise Regression Analysis (DPRA) for C. longicollis in the lower Murray River with channel distance. Full line: non-outlier line of best fit; Dashed line: true outlier line of best fit. See Appendix 8.2.2 for site names and Figure 4. 1 for site location.
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E. m. macquarii
Owing to the wide scatter of pairwise comparison values, no populations were singled out as outliers in the lower MDB in E. m. macquarii (Appendix 8.4.4). Strikingly, all ‘non-river’ populations showed higher intercepts and flatter slopes than populations sampled within (‘within-river’) the main channel (Figure 4. 4 and Table 4. 9). All but one ‘non-river’ population followed a pattern of high genetic drift (Pattern 1) while all ‘within-river’ populations showed a pattern of high gene flow (Pattern 4) (Table 4. 9). ‘Within-river’ populations showed low divergence with other ‘within-river’ populations even at relatively large channel distances, while ‘non-river’ populations showed moderate to large divergence levels with other ‘non-river’ populations even at small distances (Figure 4. 5 a, b). As expected from the above results, the Mantel test with channel distance did not support a pattern of IBD in the lower MDB for this species when including all populations (Table 4. 7). A Partial Mantel test showed that a good proportion of the variation between populations was explained by ‘weirs and dams’ when controlling channel distance and by ‘off-channel’ distance (the latter with and without controlling for channel distances) (Table 4. 7). FST table for E. m. macquarii in the lower MDB can be found in Appendix 8.3.9.
Table 4. 9 Intercept and slope (with 95% CI) of the Decomposed Pairwise Regression Analysis (DPRA) for E. m. macquarii in the lower Murray River, South Australia, with channel distance. See Figure 1. 1 for ‘Pattern’ descriptions. See Appendix 8.2.2 for site names and Figure 4. 1for site location.
E. m. macquarii Site Code Intercept * 10 -2 95% CI Slope * 10 -7 95% CI r2 n Pattern Non outlier Non river sites LFO 4.51 # (0.56_8.46) 0.00 (-0.30_0.31) 0.000 11 1 MUR 2.01 # (0.36_3.65) 0.12 (-0.08_0.33) 0.167 11 1 LBO 2.69 # (0.50_4.87) 0.07 (-0.18_0.31) 0.039 11 1
BOW 4.75 # (1.57_7.94) -0.14 (-0.40_0.12) 0.149 11 1
GBS 3.75 # (1.27_6.23) -0.15 (-0.40_0.10) 0.169 11 1 LGU 1.85 (-0.27_3.96) 0.01 (-0.24_0.27) 0.002 11 4 River sites MUB 0.43 (-1.98_2.84) 0.13 (-0.09_0.34) 0.167 11 4 MOC 0.44 (-1.03_1.90) 0.07 (-0.08_0.21) 0.098 11 4 WAI -0.13 (-1.45_1.18) 0.14 (-0.05_0.32) 0.228 11 4 WEN 0.58 (-1.55_2.70) 0.13 (-0.18_0.44) 0.090 11 4 GUC 0.02 (-4.11_4.15) 0.27 (-0.20_0.73) 0.159 11 4 ALB -0.164 (-3.57_3.24) 0.09 (-0.16_0.35) 0.074 11 4
# P < 0.05
90
0.06
0.05
LFO BOW
0.04
MUR LBO GBS ) ST
F 0.03 / (1- / ST F
0.02 LGU
0.01
0 0 200 400 600 800 1000 1200 1400 1600 1800 2000 Channel Distance (km)
Figure 4. 4 Decomposed Pairwise Regression Analysis (DPRA) for E. m. macquarii in the lower Murray River with channel distance. Full line: non-outlier (‘within-river’ population) line of best fit; Dashed line: singular (‘non-river’ population) non-outlier line of best fit. See Appendix 8.2.2 for site names and Figure 4. 1 for site location.
A 0.04 B 0.08
0.035 0.07
0.03 0.06 ) ) 0.025 0.05 ST ST F F 0.02 0.04 / (1- / (1- / ST ST F
0.015 F 0.03
0.01 0.02
0.005 0.01
0 0 0 500 1000 1500 2000 0 500 1000 1500 2000 Channel Distance (km) Channel Distance (km)
C 0.06
0.05 Figure 4. 5 E. m. macquarii plot of genetic distance 0.04 ) (FST / (1-FST )) against channel distance. A) between ST F 0.03
/ (1- / ‘within-river’ sites only; B) between ‘non-river’ sites ST F 0.02 only; C) between ‘within-river’ and ‘non-river’ sites
0.01 only (‘within-river’ to ‘within-river’ and ‘non-river’ to ‘non-river’ comparisons excluded). See Figure 1. 1 for 0 0 500 1000 1500 2000 Channel Distance (km) pattern description.
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4.3 Discussion
4.3.1 Intrapopulation Genetic Diversity
Anthropogenic disturbance such as fragmentation is known to reduce genetic variation (see DiBattista, 2008) with rare alleles first to disappear (Lande, 1988), a consequence of increased genetic drift within each smaller fragment (Frankham, 1996; Frankham & Ballou and Briscoe, 2002). The low divergence level observed across all three species in the Murray River showed dams did not create significant fragmentation in the Murray River, at least not to the extent where their effect could be observable at the allelic richness level. The genetic diversity of C. longicollis , C. expansa and E. m. macquarii in the lower MDB were comparable to diversity levels observed in other genetic studies on turtles (Hauswaldt and Glenn, 2005; Tessier & Rioux Paquette and Lapointe, 2005; FitzSimmons and Hart, 2007; Bennett et al. , 2010; Rioux Paquette et al. , 2010), and by and large showed no evidence of genetic depletion.
In C. expansa , genetic diversity between the Murray and the Moonie Rivers was statistically significant but variations between individual populations within each catchment were almost as high as between catchments. Nevertheless, some populations in the Moonie River showed a higher mean allelic richness than those in the Murray River, the probable result of infrequent genetic input from neighboring catchments; a restricted number of individuals sampled in the Condamine and Moonie Rivers carried alleles commonly found in the Fitzroy, Burnett and Brisbane catchments north-east of the MDB (data not shown, samples from the latter catchments obtained to test primers usefulness across catchments), but only rarely found in the northern part of the upper MDB and never found (in this study) in the lower MDB.
Contrary to expectations, the populations of C. longicollis in the Lower Murray River showed lower genetic diversities than the Moonie River populations. These small, but significant, differences were explained by the presence of two mixing lineages in the upper MDB but only one in the Lower Murray River. One lineage is believed to have originated east of the Great Dividing Range along the East Coast, and in more recent times to have (re) colonised the Castlereagh-Namoi-Gwydir-Border- Moonie Rivers region in the upper MDB (Kate Hodges, pers. comm.). The second lineage currently occupies the entire MDB, the Southern Coast and the Fitzroy and Burnett catchments in the north (Kate Hodges, pers. comm.). The genetic diversity observed in the Moonie River therefore reflects the current mixing of these two lineages, the East Coast Lineage contributing extra genetic diversity in the form of new or old but retained allele sizes. The Murray River is genetically poorer in comparison, but genetically healthy nonetheless, being currently solely, or primarily, occupied by the Southern Lineage with no extra input of new or old but retained alleles (see Chapter 5 for further discussion and
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analyses on this admixture). These differences are therefore not to results of the distinct hydrological regime and extinction-recolonisation dynamic in the Moonie River, as hypothesised in the introduction.
The only turtle taxon truly endemic to the MDB, E. m. macquarii showed no difference in genetic diversity between the two catchments, and the population extirpation events inferred in the Moonie River appear to have had no negative effect on the overall genetic diversity in this system.
4.3.2 Population Structure
The larger scale of investigation in the lower MDB provided further insight into the population genetic structure of all three species. In concordance with the more moderate level of dispersal inferred for C. expansa in the Moonie River (Chapter 3), this species showed an IBD pattern in the Murray River, and once again no sign of outlier populations. The barely differing DPRA regression slope obtained for this species revealed comparable levels of gene flow and genetic drift within each population and long term stability of the populations in the lower Murray River. The lower divergence levels in the Lower Murray River compared to those of the Moonie River could suggest that the cost associated with movement to new patches (see Switzer, 1993; Wiens, 2001) was reduced and/or the ease of such movement enhanced by low but reliable flows compared to unpredictable ones in the Moonie. This remains speculative as this result could also stem from smaller population sizes and increased genetic drift within waterholes of the Moonie River, the hydrological connectivity only playing a secondary role in the pattern observed. The absence of effect of dams but high correlation between genetic and channel distance suggests that the level of dispersal in C. expansa results from limitations intrinsic to the species (e.g. metabolic or behavioral). Ectothermic organisms have been found to have a greater chance to demonstrate a pattern of IBD than endotherms, which suggest that metabolic restriction may be central to these organisms dispersal rate (gene flow) (Jenkins et al. , 2010). It should be noted that male C. expansa in the Lower Murray River were previously found to be restricted in their movements by dams using radiotraking (Bower et al. 2011).
C. longicollis populations showed moderate to high levels of gene flow even at the relatively large maximum distance investigated (1800 km). This mirrored the pattern of ‘lack of regional equilibrium’ where gene flow is much stronger than drift (case II in Hutchison and Templeton1999 or Pattern 1 in Koizumi et al. 2006) observed in the upper MDB. Yet, the larger scale of investigation in the Murray River provided for a clearer pattern with divergence levels between populations increasing with distance albeit at a low rate (Appendix 8.4.5). Despite this superior dispersal ability, all off-channel habitats sampled (LBO, LGU, MUR) showed patterns of IBD or high genetic drift. This suggested a small population size in these off-channel habitats, with genetic drift strong enough to balance the high gene flow observed in other populations. This could stem from the suggested reduction in
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population sizes in this species along the Murray River (Cann 1998, Chessman 2011), with one of these off-channel populations exhibiting evidence of recent extirpation. Although no causale evidences are available, the above pattern was deemed to reflect crashes in habitat and resource availability in lakes and other off-channel habitats following the Millennium drought (1999 – 2012), further exacerbated by the removal of overbank replenishing floods in the system through flow regulation. (LBO), which demonstrated the highest level of divergence, and other wetlands of the region have been detrimentally affected by anthropological activites and the removal of seasonal floods, leading to a substantial pH shift and sustained salinity rises (Gell & Tibby & Little et al. , 2007; Kingsford et al. , 2011). These processes were also deemed responsible for the reduction in catch per unit effort in populations of C. longicollis along the mid-Murray River (Chessman, 2011), and the high C. longicollis mortality rate observed within large wetlands along the Murray River in 2007 – 2010 (Leah Beesley, pers. comm.). The only outlier population found within the river proper, where habitat quality crash was not expected, (LB10) exhibited presence of admixed individuals (see Chapter 5) and is discussed further in the next chapter where admixture in the MDB is analysed.
E. m. macquarii population genetic structure in the lower MDB to some extent mirrored that observed for this species in the upper MDB (Chapter 3). High gene flow characterised populations sampled within the Lower Murray River main channel while restricted gene flow and higher genetic drift typified populations in backwater habitats (see (BO) in Chapter 3). E. m. macquarii therefore appears capable of extensive movement within the aquatic environment but less able to overcome barriers requiring some terrestrial or shallow water movement, which could be due to its high evaporative water loss (Chessman, 1984a) and its preference for deeper water (Chessman, 1988a) and more secretive or complex habitats (Meathrel et al. , 2004). This is noteworthy as these ‘non-river’ populations would have been connected to the main channel via infrequent large flood events in the past, floods now quasi permanently removed from the Murray River natural cycle through regulation (Walker et al. , 1994; Maheshwari et al. , 1995; Kingsford, 2000). The long term alteration of connectivity in these lakes and lagoons has significant implication for their populations’ viability. The depauperate level of allelic richness in (LFO) in the Lachlan catchment is the first sign of long term isolation and small population size resulting in the removal of rare alleles through genetic drift (Lande, 1988). Inbreeding and loss of genetic diversity has been linked to shell malformation in turtles (Velo-Anton & Becker and Cordero-Rivera, 2010), and more generally to lower resistance to disease (Spielman & Brook & Briscoe et al. , 2004) and higher propensity to infections (Acevedo- Whitehouse & Gulland & Greig et al. , 2003).
A similar conclusion to that of MacCulloch and Secoy (1983) on Chrysemys picta bellii is reached here for E. m. macquarii , where movements within the river main channel may be more common and over much larger distances than those occurring in lakes. Movements of E. m. macquarii individual were not observed between Murtho Reserve (MUR) and the river proper by Bower (2011) over her
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four year study in the Lower Murray River, while the recapture of a female E. m. macquarii individual more than 400 kilometers away from the site of first capture (Colin Limpus, pers. comm.) may not be as unusual as it first seemed. The present results add to those obtained previously in the Cooper Creek catchment, where (restricted) movement between waterholes in E. m. macquarii were believed to occur during flood events but where terrestrial movements were deemed nonexistent (Goodsell, 2002).
4.3.3 Effect of Flow Regulation Infrastructure
The weak correlation present between genetic distance and the number of dams in E. m. macquarii disappeared when the distant population (LFO), separated by a large number of dams, was removed from the analysis (r = 0.094; P = 0.290). The following discussion consequently applies to all three species, no noticeable effect of flow regulation infrastructure on their population genetic structure having been found. As the impact of increased genetic drift from population fragmentation is known to increase with the number of generations since fragmentation (Steinberg and Jordan, 1997), the possibility remains that not enough time/generations has elapsed since first fragmentation for a large enough level of divergence to be detected, genetic drift proceeding slowly in long-lived organisms (see Scribner and Chesser, 2001; Tessier et al. , 2005). The sampling of only a few juveniles further reduced the chance of detecting an effect; allele frequencies reflected population structure present a few generations ago, further reducing the time since fragmentation at which the investigation took place. As rate of genetic drift is inversely proportional to the effective size of the population (Frankham, 1995), the estimated large populations of E. m. macquarii in the Murray River (Thompson, 1993; Bower, 2011) would result in a slow rate of genetic drift within each hypothetical fragment (pool between locks). This is also true for C. expansa and C. longicollis albeit at a higher rate considering current, smaller, size estimates (see Thompson, 1993; Bower, 2011).
Downstream movement past dams and weirs may still occur as observed in catchments outside the MDB (Tucker, 1999; Limpus et al. , 2006), individuals further slowing down the effect of genetic drift if contributing to the gene pool of downstream populations. The presence of locks may also be of assistance in reducing the impact of this infrastructure, individuals moving directly through them as observed in the northern map turtle ( Graptemys geographica ) (see Bennett et al. , 2010). In the latter, the northern map turtle showed smaller home range in regulated compared to unregulated habitats and infrastructure was inferred to represent a barrier to the species movement despite no impact being detected on the genetic structure of the populations (Bennett et al. , 2010). Male C. expansa were also hypothesised as being limited in their range by the Lower Murray River locks, individuals covering up to 86% of the weir pool length at times (Bower et al. , 2011). It remains unclear therefore if the presence of dams and weirs act as a barrier or buffer to movements in the MDB turtles, an issue of particular concern for the obligate aquatic E. m. macquarii .
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4.4 Conclusion
The absence of apparent population extirpation events within the regulated Lower Murray River main channel suggests that regulation may have been of benefit, at least over the short term, to the MDB turtles. The evident stability of C. expansa populations in the Murray River is a positive sign given the current ‘Vulnerable’ status of the species in South Australia (but see Bower, 2011 for recent density estimate in the region), while the absence of genetic divergence between backwater and river populations in C. expansa alludes to regular movements between the two habitats, a suggestion also made by Bower (2011). The status of C. expansa may therefore be of less concern than anticipated, the species able to access and take advantage of most available habitats, albeit moving over shorter distances than both other species over their lifetime.
In contrast, the inability of E. m. macquarii to move freely in and out of backwater habitats highlighted the detrimental effect that flow regulation can have on this species in the long term, removing potentially important habitats (nurseries, nesting sites, feeding and mating ground, etc) from its reach. Flow regulation in the Lower Murray River may have benefited this species in the short term (Thompson, 1993; Bower, 2011) thanks to the establishment of littoral vegetation and habitat permanence within the main channel (Walker et al. , 1994), but the removal of connecting floods could prove deleterious to the long term viability of its backwater populations by increasing the likelihood of inbreeding depression and stochastic extinction. Controlled release-flow may be sufficient to prevent habitat quality crashes in these ecologically important habitats (Walker and Thoms, 1993; Thoms et al. , 2005) but not to provide genetic connectivity. As genetic connectivity occurs before demographic connectivity (Lowe and Allendorf, 2010), these backwater populations should be considered demographically independent and unlikely to be replenished naturally under current hydrological conditions if required.
Finally, the superior dispersal ability of C. longicollis was once again confirmed with both river- proper and backwater populations exhibiting excellent connectivity. Despite this, signs of high genetic drift were exhibited in some populations. Habitat collapses following extended drought, as well as the disappearance of seasonally productive habitats owing to the removal of replenishing floods were suggested as primary causes here and elsewhere. This is particularly true of (LBO) which has received much unwanted attention with regards to its lack of replenishing flow in the last decade. Evidence is now starting to accumulate with regards to shrinking populations and lack of successful recruitment in this species in parts of the MDB as a result of wetland reclamation, and flow regulation and associated disappearance of seasonally productive and suitable habitats (Cann, 1998; Bower, 2011; Chessman, 2011; this study) .
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Chapter 5 Basin Scale Population Genetic Structure
Darling River below Wilcannia April 2005 (Modified from http://www.d-r-a-g.org.au/report , 2012)
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5.1 Introduction
Management units have been defined as populations ‘connected by such low levels of gene flow that they are functionally independent’ (Moritz, 1994) and can be identified by assessing the level of population genetic divergence, preferentially with clustering methods (Palsboll & Bérubé and Allendorf, 2007). An understanding of landscape features responsible for a species substructure into potential management units is of primary importance for ecosystem management (Moritz, 1994; Caballero & Rodríguez-Ramilo & Ávila et al. , 2010). Substructure is influenced by individual movements, or connectivity, which is itself a function of changes in the landscape, of a species’ perception of its surrounding landscape and of its dispersal abilities (Wiens, 1997). In riverine landscapes, population substructure is influenced by the hierarchical or dendritic nature of the network (Fagan, 2002; Grant et al. , 2007; Hughes et al. , 2009b) and opportunities for dispersal are dictated by the hydrological regime of the system (Cook et al. , 2002; Huey et al. , 2006; Faulks et al. , 2010). In dryland rivers, such opportunities are temporally restricted (Larned et al. , 2010). The high hydrological variability of dryland rivers creates temporal heterogeneity in the landscape which, in combination with the structural features of the fluvial landscape, affects the movement of genes in a manner that leaves a signal on the spatial genetic structure of the species (Meffe and Vrijenhoek, 1988; Hughes et al. , 2009b). Resolution of a species population genetic structure at large geographical scales helps identify and formulate hypotheses about physical and environmental processes restricting or promoting movements of individuals (Manel & Schwartz & Luikart et al. , 2003), processes deemed of primary importance for species persistence (Moritz, 1994; 2002).
Understanding how environmental and physical processes such as those discussed above influence the population connectivity of the MDB turtles is timely considering the predictions for reduced precipitation and increased mean annual temperature in the southern part of the basin (with uncertainty of direction in rainfall changes in the northern parts) in the coming decades (Chiew et al. , 2008; Preston and Jones, 2008; Chiew & Cai and Smith, 2009). Predictions made on the influence of highly variable hydrological regime on the population genetic structure of fish species in the basin have not always been supported, low levels of differentiation having been observed in some species at the basin scale (e.g. Faulks et al. , 2010). This further highlighted the ability of many dryland river organisms to disperse rapidly following wetter periods (Balcombe & Arthington & Foster et al. , 2006; Balcombe et al. , 2007; Arthington and Balcombe, 2011). Genetic differentiation between catchments was found in other species but was more related to the structural features of the river network, such as terminal wetlands in some catchments of the basin (Rourke & McPartlan & Ingram et al. , 2011) or human-made impoundments (Haynes & Gilligan & Grewe et al. , 2009), than to the hydrological regime. How predicted climatic changes and current structural features of the basin will combine to influence a species population structure and persistence therefore remains uncertain. Resolving the
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current population structure of a species would help in formulating hypotheses about future potential impacts and in identifying those regions or populations of concern.
Current knowledge of dispersal ability of the MDB turtles suggests an aptitude to disperse out of refugia following erratic periods of high flow (Goodsell, 2002; Chapter 3). E. m. macquarii exhibited moderate levels of divergence between highly persistent waterholes separated by large stretches of intermittently wetted channel in the Warrego River (Goodsell, 2002), but the species appears unable to undertake non-nesting overland movement. At the basin level, the species is therefore expected to reveal substructure more related to the structural features of the basin, such as wetlands and human- made impoundments similar to those discussed above, than to the hydrological regime. C. longicollis on the other hand migrates overland at the first opportunity following rain, in search of ephemeral but productive habitat (Graham et al. , 1996; Roe and Georges, 2007). Hence the species is expected to demonstrate no or low substructure in relation to structural features of the basin and possibly some relationship, albeit weak, with the hydrological regime of some catchments. From the results obtained so far in this study and by Bower et al. (2011), C. expansa may also be able to take advantage of sporadic hydrological flows but to a lesser extent than either of the other species, having exhibited more restricted dispersal abilities in the Moonie and the Lower Murray Rivers. The species is expected to be restricted by extremes in periods of low or no flow, characteristic of the western parts of the basin, but to demonstrate low substructuring in the wetter parts owing to its ability to move overland, possibly across catchments.
In this chapter, I address the question of whether the MDB represents one large population for each of the three species, with little or no substructuring, considering our current knowledge of dispersal abilities of the MDB turtles and genetic structure at the catchment level (Moonie and Lower Murray) (Chapter 3 and 4) ? If not, does the population genetic structure of each species relate to known landscape or hydrological regime features of the basin?
5.2 Methods
5.2.1 Laboratory Methods, Sample Sizes and Study Region
Information relating to sampling and genotyping methods can be found in chapter 2 General Methods, as these methods are identical across chapters. Microsatellite primers used in this chapter were identical to those described in chapter 2 and used in previous chapters. Samples were obtained from all major catchments in the MDB (Figure 5. 1) with the exception of C. expansa samples from the Castlereagh and Gwydir catchments and C. longicollis samples from the Condamine catchment. Across the entire MDB, eleven loci and 241 C. expansa individuals were included in GENELAND
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and STRUCTURE analyses. For E. m. macquarii 486 individuals and 9 loci were used and 300 individuals and 10 loci for C. longicollis .
Descriptions of a number of catchments from the MDB were already provided in chapters 3 and 4 and only a brief description of the basin is provided here. The MDB is Australia’s largest river system, covering 1.07 x 10 6 km 2 or approximately 14% of the continent surface area (Murray-Darling River Basin Commission, http://www.mdbc.gov.au/ ). Flow sources for most catchments are found outside dryland regions along the inland slopes of the Great Dividing Range in the eastern part of the basin (see Figure 5. 1). The only system sampled here with headwaters in the arid region of the basin, the Warrego River, an aboriginal word meaning ‘River of Sand’, exhibits extremely variable and intermittent flows (Young and Kingsford, 2006) and contributes to increase the duration of high flow in the lower Darling during intense rainfall (Thoms and Sheldon, 2000b). At the basin scale, high interannual precipitation variability owing to the El Nino-Southern oscillation, high evapotranspiration in lowland areas and significant rainfall-runoff processes result in one of the most variable streamflows in the world (Thoms and Sheldon, 2000a; Young and Kingsford, 2006). The upper part of the basin, partially described in chapter 3, and the lower part described in chapter 4, are linked by the Barwon – Darling River which is a highly regular, low complexity channel with high flow variability (Thoms and Sheldon, 2000b). The basin is also home to numerous significant wetlands (Kingsford, 1999; Kingsford and Thomas, 2004; Kingsford et al. , 2011) such as the RAMSAR listed Macquarie Marshes (Kingsford and Auld, 2005; Ren & Kingsford and Thomas, 2011) (Figure 5. 1).
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3 2 4 1 5 7 8 6 9 13 10 11
12 LFO 17 MOC MUR BL10 LBO WAI LGU 14 TOD N MUB NAD PIR 15 O E GOL 16 S ALB
0 100 km
Figure 5. 1 Topographic map of the Murray-Darling Basin, with basin boundaries and main rivers. Cold to warm colours represent steep to low slopes areas. Dark full circles are geo-referenced sampling locations. Sample locations not positioned on a river outline were sampled in second or third order channels not shown on the map. Rivers: 1) Warrego; 2) Maranoa; 3) Balonne-Condamine; 4) Moonie; 5) Border; 6) Border; 7) Gwydir; 8) Barwon; 9) Namoi; 10) Castlereagh; 11) Macquarie; 12) Lachlan; 13) Darling; 14) Murrumbidgee; 15) Yass- Molonglo; 16) Murray; 17) Lower Murray. Dashed line: Great Dividing and Border Ranges. Black Star: Macquarie Marshes. Black Arrow: route for MDB re-invasion (see text). Site code in the lower MDB provided for C. longicollis results only: refer to Appendix 8.2.2 for coordinates and name. Note: each species may not have samples from all locations shown.
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5.2.2 Statistical Methods
The individual based clustering methods of Guillot et al. (2005a; 2005b) (G ENELAND ) and Pritchard et al. (2000) and Falush et al. (2003) (STRUCTURE ) were used to infer hidden structure and admixture levels at the basin level. Description of both methods can be found in chapter 2. For C. longicollis in the lower MDB, allelic richness and private alleles averaged across all loci were calculated in HP- Rare (Kalinowski, 2005) (see results).
Geneland
For all three species, five runs of 2 x 10 6 iterations (thinning of 2000 and a burn-in of 200) were carried out with the uncorrelated (Dirichlet) frequencies model, with spatial input, which test for the presence of substantial differentiation (Guillot et al. , 2005a). For C. expansa , the maximum number of nuclei was set at 750, maximum rate at 241 and K maximum at 10. Delta coordinate (uncertainty of coordinate in the spatial model) was set at 0.2 which equated to a square of 20 km sides, corresponding to observations made on male movement in this species (Bower et al. , 2011). No correction for null alleles was required. For each species, a similar run with the correlated frequencies model (F-model in Guillot et al. , 2005a) (K = 15) and 4 x 10 6 iterations was performed, lower iteration numbers providing inconsistent outputs. For E. m. macquarii , the maximum number of nuclei was set at 1200, maximum rate at 486 and K maximum at 15. Delta coordinate was also set at 0.2, no previous information of the species movement being available although it is expected that individuals may be able to move farther over their lifetime. Null alleles present at locus TLE 13.3 in some populations (see chapter 3) were not corrected for since low levels of null alleles (~ 0.2) was found advantageous to clustering inference (Chapuis et al. , 2008; Guillot et al. , 2008). The maximum number of nuclei was set at 900 for C. longicollis , maximum rate at 300 and K maximum at 15. Delta coordinate was also set at 0.2 which is larger than the largest recorded overland movement for this species (~ 7 km) (Roe and Georges, 2008c) but may compare with the lower limit of individual movements within aquatic habitat over their lifetime considering previous results obtained in this study. No correction for null alleles was required. No ‘non-spatial’ models were carried out in GENELAND.
STRUCTURE
In STRUCTURE, the default admixture model with correlated allele frequencies (F-model of Falush et al. , 2003) was used, without population information, as populations were expected to be closely related (i.e. low level of genetic differentiation). Five runs for each K, with K maximum set at 25, a burnin of 5 x 10 5 and 2 x 10 6 iterations per run, and alpha set as 1.0 (individuals were likely to be admixed) were performed for all three species. Multiple runs were aligned in the CLUster Matching and Permutation Program (CLUMPP) of Jakobsson and Rosenberg (2007).
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5.3 Results
5.3.1 C. expansa
The uncorrelated model of allele frequencies inferred K = 3 for the MDB (Table 5. 1) in GENELAND. The third population was composed of a single individual with unique alleles in Storm King Dam (SK) in the Border Rivers (see Figure 3.1 for location), likely a migrant from outside the MDB. Hence, the MDB was composed of two C. expansa populations covering the northern and southern parts of the basin respectively (Figure 5. 2). The Macquarie River, located in the northern section, was lumped with the southern section in the map of probability of population membership.
The level of differentiation between the two clusters was low (FST = 0.023) and each population showed low inbreeding levels and clear evidence of random mating (FIS ) (Table 5. 2).
Table 5. 1 Outputs of correlated and uncorrelated models of allele frequencies with spatial information in GENELAND for C. expansa , C. longicollis and E. m. macquarii. K: number of population
Model C. expansa C. longicollis E. m. macquarii
Iterations Log Posterior % K Log Posterior % K Log Posterior % K Run Density Model Inferred Inferred Density Model Inferred Inferred Density Model Inferred Inferred Uncorrelated 2x10 6
1 -11289.660 37.40 3 -14430.581 41.00 2 -23376.621 64.25 2 2 -11313.979 34.80 3 -14446.993 46.00 2 -23389.145 67.88 2 3 -11327.465 34.60 3 -14474.263 50.75 2 -23390.359 68.25 2 4 -11346.039 36.20 3 -14497.651 55.88 2 -23393.892 69.13 2 5 -11360.544 37.50 3 -14559.322 68.38 2 -23402.847 70.88 2
Correlated 4x10 6
1 6715.154 33.63 10 -7829.726 26.14 8 -8758.183 29.63 13 2 6280.985 31.63 10 -7987.390 32.29 8 -11449.918 30.13 11 3 6158.493 34.75 10 -8257.639 30.43 8 -11600.406 35.63 11 4 6080.245 39.63 10 -8448.057 31.29 7 -11947.78 36.00 10 5 2989.571 40.25 8 -9043.028 39.57 6 -12105.557 28.63 11
Table 5. 2 F-statistics for populations inferred with the uncorrelated model of alleles frequencies in GENELAND. No significance level or standard error provided.
Species Population FST FIS
C. expansa upper MDB 0.073 0.023 lower MDB 0.030 C. longicollis upper MDB 0.046 0.029 lower MDB 0.032 E. m. macquarii upper MDB 0.080 0.010 lower MDB 0.066
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Figure 5. 2 C. expansa : map of probability of belonging to cluster 1(upper MDB) under the uncorrelated model of allele frequencies in GENELAND. Black circles are geo-referenced samp le locations. Samples not on rivers outline were sampled in second or third order channels. Sample location may consist of one or more individuals. Lighter colours imply high probability to belong to cluster 1 (values are posterior probabilities). Note: St orm King (SK) dam migrant in upper right corner creates sudden drop in probability to belong to cluster 1 over short distances.
The correlated model of allele frequencies inferred 10 populations of C. expansa in the MDB (Table 5. 1). One population was a ghost population with no samples. The remainder consisted of 1) the Lower Murray, 2) the Murrumbidgee River, 3) the Murray River , 4) the Macquarie River, 5) the Warrego River, 6) The Culgoa- Barwon -Namoi, 7) the Moonie-Border River , 8) the upper Condamine, 9) the Dumaresq River (see numbering in Figure 5. 3). Four runs provided similar log posterior density of the model suggesting good convergence.
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8 5
7 9
6
4
1 GBS 2
3
Figure 5. 3 C. expansa : map of population membership under the correlated model of allele frequencies in GENELAND. Black circles are geo-referenced sample locations. Samples not on rivers outline were sampled in second order channels. Sample location may consist of one or more individuals. Y axis: latitude; X axis: longitude. Red Circle: Maquarie Marshes. Numbers: see text.
Calculation of ∆K in STRUCTURE Harvester produced a modal value at K = 2 using Evanno et al. ’s (2005) method. All other ∆K were virtually equal to zero apart for K = 3 (Figure 5. 4A). The substructure signal was clear as made evident by the height of the modal value (Evanno et al. 2005). The graphical output showed no clear-cut distinction between the two populations inferred however, with a significant number of individuals within each cluster assigned with a probability less than 70%, indicative of weak structure (Figure 5. 5 A). Only the proportion of individuals assigned to each cluster varied within each cluster inferred. The structure inferred followed an IBD pattern from north to south (left to right in Figure 5. 5 A), locations sampled in the north exhibiting a larger number of individuals with posterior probability greater than 0.70 of belonging to cluster 1 (upper MDB), probability decreasing the further south the sampled location.
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A Aa
B Bb
C Cc
Figure 5. 4 ∆K (a measure of the rate of change in the structure likelihood function) values as a function of K, the number of putative populations (A, B, C) , and Mean of estimate Ln probability of data [Ln(k)] (Aa, Bb, Cc). A) C. expansa ; B) C. longicollis ; C) E. m. macquarii . Outputs obtained from the freely available online version of STRUCTURE Harvester. Only a portion of the putative K tested are shown ( K max = 25).
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Upper MDB Lower MDB
1 Warrego
5 Moonie 8 Lower Murray 6 Namoi 9 Murray 2 Condamine 3 Barwon 4 Border 7 Macquarie 10 Murrumbidgee
A
Upper MDB Lower MDB
5 Barwon 6 Border 7 Gwydir 11 Lachlan 1 Warrego 8 Namoi 12 Murrumbidgee 2 Maranoa 9 Castlereagh 13 Murray 3 Balonne 4 Moonie 10Macquarie 14 Lower Murray
B
Upper MDB Lower MDB
7 Barwon 8 Gwydir 12 Lachlan 1 Warrego 9 Namoi 13 Murrumbidgee 2 Maranoa 10 Castlereagh 14 Murray 3 Condamine 4 Moonie 5 Borders 6 Severn 11 Macquarie 15 Lower Murray
C Figure 5. 5 Graphical output of STRUCTURE for each species after alignment of replicated runs in CLUMPP. A) C. expansa ; B) C. longicollis ; C) E. m. macquarii . Vertical bars represent a single individual made up of K colours, proportional to its posterior probability of belonging to that cluster. Outputs arranged from north to south with regards to catchment location in the MDB. X axis values refer to catchment names provided. Vertical black lines added to help visualisation of distinct catchment groups. Dark rectangles on X axis in figure ‘B’ added to help visualisation (see text). Non-discernible label ‘2’ on X axis of figure ‘C’ owing to low sample size for that catchment (Maranoa: 2 individuals). See Figure 5. 1 for catchments location in the MDB.
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5.3.2 C. longicollis
Two populations of C. longicollis were inferred in the MDB in GENELAND with the uncorrelated model of allele frequencies ( Table 5. 1). The populations consisted of the lower and upper sections of the basin, connected by the Darl ing River (Figure 5. 6). The differentiation level between the two populations was low (FST = 0.0291) , but higher than the global levels observed within each sections of the basin in chapter 3 and 4, and marginally higher than for the two other species (Table 5.2). Two southern populations were included in the northern group , namely Tony Dam (TOD) on the Yass River and Lake Forbes (LFO) on the Lachlan River (Figure 5. 6).
Figure 5. 6 C. longicollis : map of probability of belonging to cluster 1(upper MDB) under the uncorrelated model of allele frequencies in GENELAND. Black circles are geo-referenced sample locations. Samples not on rivers outli ne were sampled in second or third order channels. Arrows indicate lower MDB populations lumped with upper MDB. Sample location may consist of one or more individuals. Lighter colours imply high probability to belong to cluster 1 (values are posterior prob abilities).
Outputs of the correlated model of allele frequencies were inconsistent across the five runs (Table 5. 1). Based on the model with best log posterior density, Figure 5. 7 showed only six clusters despite inference for eight, with two ‘ghost populations’ inferred by the best three runs. Again, (TOD) and (LFO) were not included in southern clusters (Figure 5. 6). Across al l runs, the six clusters were consistently composed of: 1) the Murray River (lower and upper); 2) The Murrumbidgee River; 3) the Yass and Molonglo River; 4) Lake Forbes on the Lachlan River and the Macquarie and Castlereagh River; 5) the Warrego River and 6) all other catchments in the upper MDB from the Namoi River northward (see numbers in Figure 5. 7).
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6 5
4
2 1 3
Figure 5. 7 C. longicollis : map of population membership under the correlated model of allele frequencies in GENELAND. Black circles are geo-referenced sample locations. Samples not on rivers outline were sampled in second order channels. Sample location may consist of one or more individuals. Y axis: latitude; X axis: longitude. Red bar: Burrinjuck Dam.
In STRUCTURE , a clear mode was produced at K = 3 (Figure 5. 4 B). Only two distinct clusters at K = 3 can however be seen in the graphical output (Figure 5. 5 B), with presence of admixture throughout most the MDB. Most individuals in the lower MDB were assigned with a posterior probability greater than 0.85, indicative of a strong signal. Similarly to GENELAND output, the two lower MDB populations (LFO) and (TOD) exhibited a large proportion of individuals assigned to the upper MDB cluster. Three other locations along the Murrumbidgee River, up to the locality of Narrandera (NAD in Figure 5. 1), showed a similar pattern. These three locations, as well as (TOD), are highlighted in Figure 5. 5 B by a black rectangle on the X axis. (LFO) is found in Catchment 11 (Lachlan) in the same figure. The presence of multiple admixed individuals was also apparent for (BL10) in the Lower Murray, highlighted by a square on the X axis in Figure 5. 5 B.
To further investigate STRUCTURE ’s outcome in the lower MDB for C. longicollis , allele richness
(A R), private alleles (alleles unique to a single population, Kalinowski, 2004), calculated with
rarefaction, and expected heterozygosity ( HE) were plotted for locations with more than four samples. This showed a weak pattern of increase in allelic richness and expected heterozygosity from south to north-east (left to right) (Figure 5. 8) and a sudden increase in private alleles in Narrandera (NAD) and in populations further east along the Murrumbidgee River (TOD, PIR) and in the Lachlan River (LFO) (Figure 5. 8). No sign of increase in private allele numbers was present in Albury (ALB) along the Murray River. Indices of genetic diversity for these populations can be found in Appendix 8.5.1. and Table 4.2.
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0 GOL MUB MOC LBO LGU MUR WAI BL10 ALB NAD TOD PIR LFO
Figure 5. 8 Allelic richness (A R) (after rarefaction for 6 genes), Expected heterozygosity ( HE) and Private Allele richness (after rarefaction for 6 genes) for C. longicollis populations in the lower MDB. Plot ordered from south
to north-east (left to right). Full horizontal bar: HE; Empty circle: Log 10 AR; Full triangle: Private alleles. X axis: sampling site codes. See figure 5.1 for site locations.
5.3.3 E. m. macquarii
Two clusters were inferred for E. m. macquarii in the MDB with the uncorrelated model of allele frequencies in GENELAND (Table 5. 1). The Macquarie, Warrego and Maranoa catchments were clustered with the lower section in the map of probability of belonging to cluster 1 (the upper MDB) despite their spatial location in the northern section of the basin (Figure 5. 9). This partitioning of populations was relatively similar to C. expansa output despite the more extensive sampling distribution and size. The level of differentiation between the two clusters ( FST = 0.009) and the coefficient of inbreeding ( FIS ) within each cluster were low (Table 5. 2).
The best run with the correlated model of allele frequencies in GENELAND inferred thirteen clusters for E. m. macquarii but only ten clusters contained samples in the map of population membership (Figure 5. 10). The following groups were consistent across all five runs: 1) the entire Murray and the Murrumbidgee Rivers, 2) locations sampled upstream of the Burrinjuck Dam in the upper Murrumbidgee (Yass and Molonglo River), 3) the upper Warrego River which was clustered with the upper Maranoa River, 4) the Condamine River, 5) the Balonne, Moonie, Border, Gwydir, Namoi and Castlereagh Rivers, 6) the Macquarie groups (circled), 7) the lower Warrego and 8) the Severn River, which was already discussed in chapter 3 (see numbers in Figure 5. 10). The Lachlan River was not consistently clustered with a specific group across runs, forming a group on its own in some runs.
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Burrendong Dam
Figure 5. 9 E. m. macquarii : map of probability of belonging to cluster 1(upper MDB) under the uncorrelated model of allele frequencies in GENELAND. Black circles are geo-referenced sample locations. Samples not on rivers outline were sampled in second or third order channels. Sample location may consist of one or more individuals. Lighter colours entail high probability to belong to cluster 1 (values are posterior probabilities).
3 4
BE 5 TH 7 KW 8
MM
BG 6
GBS 1 BK
2
Figure 5. 10 E. m. macquarii : map of population membership under the correlated model of allele frequencies in GENELAND. Small black circles are geo-referenced sample locations. Samples not on rivers outline were sampled in second order channels. Sample location may consist of one or more individuals. Y axis: latitude; X axis: longitude. Red bar: dam (BE: Beardmore, BK: Burrinjuk, BG: Burrendong); Blue bar: Kwiamble waterfall (KW); Red circle: Macquarie Marshes (MM); Large circle: the Macquarie River group; TH: Thurrulgoona population in the Warrego Catchment (see text); GBS: Griffith Barren Box Swamps (see text). Note: the Warrego-Maranoa and Condamine are not clustered together despite the close colour shades (dashed line).
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A clear mode at K = 3 was produced for E. m. macquarii (Figure 5. 4 C) when plotting ∆K over K from STRUCTURE output. The graphical output did not show any strong subdivision (Figure 5. 5 C) but followed the correlated model output in GENELAND (Figure 5.10), with weak subdivisions in admixture levels between the Severn River, the upper MDB catchments and the lower MDB catchments. Similarly to the uncorrelated model in GENELAND, admixture levels in the Warrego were more similar to those of the lower MDB than to those of adjacent catchments in the upper MDB (i.e. Moonie, Borders, Condamine, etc).
Owing to the unexpected clustering of the Warrego with the Lower MDB in GENELAND (and STRUCTURE ), pairwise comparisons between the Warrego as a separate unit, the lower MDB, the upper MDB, and the Severn River populations were performed. These groups were chosen following the F-model’s output in GENELAND, which showed the lower MDB, upper MDB and Warrego as forming three large groups (Figure 5. 10), while the Severn River population was already identified previously in chapter 3 (as well as in the F-model). Two analyses were performed, one following the uncorrelated model’s output in GENELAND (i.e. the Macquarie River with the lower MDB) and one following STRUCTURE ’s output (i.e. the Macquarie River with the upper MDB). These showed low differentiation between the upper and lower MDB populations in both cases, and equally high differentiation of these populations from both the Warrego and the Severn River (Table 5. 3). Results
were identical when taking microsatellite size into account ( RST ) (results not shown).
Table 5. 3 E. m. macquarii pairwise FST comparison from microsatellite data in the Murray-Darling Basin. Above diagonal: Macquarie River lumped with the lower MDB (GENELAND), below diagonal: Macquarie River lumped with upper MDB ( STRUCTURE ). All comparisons are significant at α = 0.05
FST Upper MDB Severn River Lower MDB Warrego River Upper MDB 0.051 0.009 0.039 Severn River 0.050 0.046 0.058 Lower MDB 0.009 0.046 0.035 Warrego River 0.038 0.058 0.037
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5.4 Discussion
5.4.1 Sampling Scheme and Local Autocorrelation Influence on Clustering Methods
The influence of sampling scheme on outputs of clustering methods (specifically STRUCTURE ) have been discussed by Schwartz and McKelvey (2008). Discerning between true barriers to dispersal and the effect of proximal mating may be difficult under some sampling schemes, as neighbour matings will lead to large differentiation at larger scales. Should sampling be made at smaller or equal scale to the pattern of autocorrelation created by neighbour mating, clustering methods may wrongly infer each sampling ‘region’ to represent an isolated population (see Schwartz and McKelvey, 2008). The detrimental effect of IBD on clustering methods was also underlined by their authors as potentially leading to overestimation of cluster numbers following departure of the data from the model assumptions (Guillot et al. , 2005a; Pritchard & Wen and Falush, 2007). To reduce the likelihood of misinterpretation, a number of studies recommended considering small scale autocorrelation patterns before undertaking larger scale investigation with clustering methods (Schwartz and McKelvey, 2008; Frantz & Cellina & Krier et al. , 2009; Safner & Miller & McRae et al. , 2011). These investigations were carried out in Chapters 3 and 4 and revealed the presence of IBD in all three species albeit at different spatial scales. In addition, irregular sampling schemes can also lead to inference of substructure in the larger population by creating a sudden increase in the level of differentiation (Schwartz and McKelvey, 2008), a sampling issue present in this study with no samples available from the Darling River between the upper and lower MDB.
5.4.2 C. expansa
Analyses for C. expansa showed signs of sampling at a scale consistent with local relatedness. The correlated model of allele frequencies in GENELAND clustered samples over short distances without following any known structural or hydrological features of the basin, but rather following the spatial distribution or grouping of samples. This is especially apparent in the lower MDB, where gene flow between the inferred clusters was known to occur (see chapter 4). The lack of samples from the Darling River and the high sample density in the upper MDB led to the inference of two large populations in the MDB by GENELAND despite STRUCTURE showing a clear pattern of IBD in the basin. This was deemed an artefact of the high sampling density in the Moonie-Border Rivers region and more sparse distribution of samples in the remainder of the Basin. This follows Schwartz and McKelvey’s (2008) and Coulon et al. (2006) finding that areas of high and low relatedness, associated with a ‘mixed-sampling’ strategy (sensu Schwartz and McKelvey, 2008, where many samples are obtained through research type sampling, and fewer, more scattered one are obtained through more or less opportunistic gathering by third part) can provide erroneous output. The lengthy Darling River,
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which represents the only connection between the two regions, and the more restricted movement of C. expansa (but no spatially restricted barrier) hence prevent a greater homogenisation of genes at the basin scale. The reduced susceptibility of C. expansa to barriers compared to E. m. macquarii (see below) was further highlighted by the clustering of (GBS) with the Murrumbidgee River populations, showing no restriction to movements by the irrigation canals connecting the former and the Murrumbidgee River proper (Kingsford, 2000; Kingsford and Thomas, 2004; Wassens & Hall & Osborne et al. , 2010). A possible buffer to C. expansa movements identified in the basin consisted of the Macquarie Marshes area, but the low sample size and an influence of IBD on the F-model in GENELAND did not allow for confident inference. Hence, C. expansa exhibited no apparent population connectivity restriction owing to extrinsic factors (i.e. barriers), but rather its comparatively lower intrinsic dispersal abilities were further highlighted.
5.4.3 C. longicollis
GENELAND and STRUCTURE revealed the presence of two mixing C. longicollis groups in the MDB. These groups showed no sign of effects from having been sampled at a scale commensurate with areas of local relatedness or of issues associated with sampling scheme, samples having an evenly spread density in this species. The presence of two groups or lineages in the basin was previously suggested by Kate Hodges (unpublished data) using mtDNA, while Beck (1991), as well as Cann (1998), suggested the presence of a southern and eastern group or race based on morphological evidence. The increase in allelic richness and heterozygosity between populations in the Lower Murray River and those sampled further east along the Lachlan (LFO) and Murrumbidgee River (NAD, TOD and PIR) suggested the mixing of these two lineages in the latter rivers, each lineage contributing to a higher genetic diversity through their non-shared alleles (Pemberton & Coltman & Coulson et al. , 1999; Lohmueller & Bustamante and Clark, 2010; Sakaguchi & Takeuchi & Yamasaki et al. , 2011) (see also Weltch test on mean allelic richness and heterozygosity between the Moonie and the Lower Murray River in chapter 4). Although the allelic richness and heterozygosity follow a pattern typically associated with a species recent expansion, where a reduction in genetic diversity is progressive towards the expansion front with each new founder event representing only a small proportion of the parent population (Ibrahim & Nichols and Hewitt, 1996; Hewitt, 2001; Rowe and Beebee, 2007), the presence of a greater number of rare alleles in the east (the back of the expansion, if following the above logic) than in the west (the front) does not concur with the idea of a recent expansion of C. longicollis in the region.
I argue that this increase in the number of private alleles (sensu Kalinowski, 2004) in populations along the Murrumbidgee and Lachlan River reflects the presence of migrants from the upper MDB and/or East Coast carrying low frequency alleles, not enough time having passed for these alleles to
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spread and accumulate in adjacent populations (Lohmueller et al. , 2010). The upper MDB, which exhibited higher polymorphism than populations in the Lower Murray River in Chapter 4, is believed to be the region where the ‘East Coast’ lineage identified by Hodges and suggested by Beck (1991) re-entered the MDB and mixed with the ‘Southern’ or ‘inland’ lineage (Kate Hodges, pers. comm.). From the restricted geographical distribution of these private alleles it can be suggested that the eastern lineage is moving southward from the east coast and/or upper MDB along the Murrumbidgee and Lachlan River, but has not yet reached the (ALB) population in the Murray River (see genetic diversity indices in Figure 5. 8). Some admixture could however already be occurring in the upper sections of the Lower Murray River, where both (WAI) and (BL10) showed a greater proportion of admixed individuals in STRUCTURE and slightly higher allelic richness and expected heterozygosity (albeit with no sign of increase in the number of private alleles) than populations further downstream. These admixed migrants could originate from the upper MDB, having progressed along the Darling River which converges with the Murray River not far upstream of (BL10). Although the allelic richness observed in the (MUB) population further downstream could cast doubt onto this hypothesis, catch and release of C. longicollis individuals by humans across the basin is well known (Georges, 1993; Greer, 2006). A number of admixed individuals may have been displaced by people into (MUB) and other populations along the Lower Murray River. Alteration of C. longicollis genetic pool (and hence structure pattern) has been suggested following displacement of individuals by humans (Greer, 2006), at times leading to the introduction of individuals in areas where it did not occur previously in Victoria and South Australia (Beck, 1991).
The geographical distribution of populations with a large proportion of individuals carrying ‘Southern’ lineage genotype (high proportion of red in STRUCTURE output) at the edges of the species distribution in the upper MDB may reflect the current boundary of the ongoing admixture event in the north and west of the upper basin. Populations in the upper parts of the Warrego (West) and Maranoa River (North), populations most distant from the East Coast, showed a greater proportion of genes of ‘Southern’ rather than ‘Eastern’ lineage origin. From all above results, a short and speculative history of C. longicollis can be made as follows. Spreading over the MDB and the East Coast, populations of C. longicollis retracted to more northern latitudes during the last glaciations (Georges and Thomson, 2006). These once widespread and connected populations would have been separated along the Great Dividing Range and the Border Ranges (see Figure 5. 1) during the last glacial between 30 to 10 kya (Augustinus and Macphail, 1997), extreme cold climate in these high altitudes an effective barrier to gene flow. During this time, genetic drift and mutation resulted in the retention of different alleles and the rise and accumulation of new alleles within each population (Kate Hodges, unpublished data). Following the retraction of cold climate the ‘Southern’ lineage would have recolonised the basin before the re-invasion of the ‘East Coast’ lineage in more recent time, as suggested by the comparatively less or non-admixed populations in the southern, northern
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and western edges of the MDB. This ‘re-expansion’ of the East Coast lineage into the MDB appears to be still underway, with pockets of primarily ‘Southern’ lineage still remaining within the upper MDB (see STRUCTURE output). Similar history of recent admixing of intraspecific lineages separated during past glaciations were observed in a number of species (Walter and Epperson, 2001; Sakaguchi et al. , 2011), leading to increase in genetic diversity relative to non-admixed populations. Such increase in genetic diversity, and especially rare alleles, is also found in newly colonised populations with multiple source populations (Kelly & Muirhead & Heath et al. , 2006). As the aim of this chapter was only to assess the population structure of each species within the basin, no further analyses such as estimation of time since admixture events, were carried out (but see Beaumont, 2007 for possible methods). This work is currently undertaken by Kate Hodges at UCAN using mtDNA.
Returning to the primary aim of this chapter, the identification of isolating processes in the MDB, no clear genetic break associated with structural features of the landscape or hydrological connectivity was apparent in C. longicollis, except for Burrinjuck Dam in the upper Murrumbidgee River (see Figure 5. 7). As stated previously, Burrinjuck dam represents the upper limit of E. m. macquarii natural range with regard to cold climate and it is possible that the high elevation and cold climate, rather than the dam itself, represent a barrier for C. longicollis as well, despite its known ability for continued activity at temperatures lower than E. m. macquarii and C. expansa (Chessman, 1988b; Beck, 1991; Cann, 1998). Hence the clustering of populations in C. longicollis reflected areas of genetic relatedness and current admixture distribution rather than the presence of barriers and hydrological discontinuity in the basin.
5.4.2 E. m. macquarii
The number and composition of populations inferred in E. m. macquarii with the uncorrelated model of allele frequencies in GENELAND is believed to have been biased by the presence of IBD, the gap in the sample distribution along the Darling River, and the sampling scheme. In particular, the clustering of the Warrego and the Maranoa River with the lower part of the MDB was dubious, especially with regards to the spatial location of the Maranoa River in the upper MDB (see Figure 5.1). Pairwise comparisons carried out subsequently to further elucidate the main clusters showed the upper and lower MDB to represent one large, quasi panmictic, population, while the Warrego and the Severn River represented two somewhat isolated populations, both exhibiting comparatively higher divergence levels with both MDB populations. These latter divergence values were still low but fall within those observed between populations of the giant Amazon turtle Podocnemis expansa located in different sub-basins and separated by more than 3000 km along the main-stem Amazon (Pearse & Arndt & Valenzuela et al. , 2006). These divergences are consequently significant with regards to the smaller scale at which they occured in this study, but also relative to that obtained between the much larger upper and lower MDB populations.
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There was evidence of hydrological influence on E. m. macquarii population connectivity in the Warrego, Murrumbidgee and Macquarie River. The clustering, on its own, of (TH) in the lower Warrego (see Figure 5. 10) supported Goodsell (2002)’s finding that the large, but sporadic, flows in the Warrego do not lead to apparent panmixia in E. m. macquarii , but to moderate, primarily downstream, connectivity between the spatially and temporally isolated waterholes of this catchment. This is in contrast to a number of organisms with good dispersal abilities which demonstrated panmixia in this system (Cook et al. , 2002; Carini et al. , 2006; Huey et al. , 2006). In the Murrumbidgee catchment, the segregation of Griffith Barren Swamp (GBS) (Figure 5. 10) from the rest of the populations sampled in this catchment further highlighted the inability of E. m. macquarii to take advantage of shallow aquatic corridors, man-made irrigation canals the sole connection between (GBS) and the river proper. Finally, GENELAND output suggested that the Macquarie Marshes act as a buffer to E. m. macquarii individual movement. The water reaching the Marshes are now half that of historical levels (Kingsford and Auld, 2005; Ren et al. , 2011) and sufficient connectivity would only occurr during high rainfall-runoff events, which have followed a15 to 20 years cycle since first records in 1886 (see Brock, 1998). The Macquarie Marshes area has previously been identified as a barrier to movement in the Murray cod (Rourke et al. , 2011), the common carp (Haynes et al. , 2009) and the silver perch (Faulks et al. , 2010).
A number of structural barriers were also identified in the river network. The Burrendong Dam in the Macquarie River built in 1967 and the Burrinjuck Dam in the Murrumbidgee River built in 1928 were responsible for breaks in E. m. macquarii genetic continuity. Burrinjuck Dam is however believed to represent the breeding and range limit of E. m. macquarii in cold regions of eastern Australia (Georges and Thomson, 2006). It is therefore plausible that individuals collected in 2005 upstream of the dam and analysed here were introduced or descendants of introduced specimens (see Lintermans, 1997 in Greer 2006). The identification of Burrendong Dam (see Figure 5. 10) in the Macquarie River on the other hand implied low to no downstream gene flow and small population size, or that the dam location represented a historical barrier in the river network as change in allele frequencies in long lived species is expected to be slow (Scribner and Chesser, 2001).
Overall, E. m. macquarii substructuring in the MDB followed very closely that of the common carp (Cyprinu carpio ) (Haynes et al. , 2009) and the Murray cod ( M. peeli ) (Rourke et al. , 2011), where low or no genetic divergence was exhibited over large distances in hydrologically connected areas, and substructure followed the spatial distribution of structural features (e.g. large dams, waterfall and overland isolation) and low flow areas (e.g. Warrego, Macquarie Marshes) in the basin.
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5.5 Conclusion
The MDB did not represent one large population for all three species, although this was primarily owing to IBD. E. m. macquarii appeared to be highly connected in the wetter (eastern) parts of the basin, and the identification of weak discontinuity associated with structural features of the basin, as well as with hydrological disconnectivity, further supports our previous inference regarding the species susceptibility to ‘within-network’ barriers. Yet, despite a lack of panmixia in the Warrego, the larger presence of E. m. macquarii in this catchment compared to both other species suggest greater ability by this species to colonise sporadically connected catchments. The population structure of C. longicollis in contrast reflected the hypothesised recent re-invasion of the MDB by a coastal lineage and admixture with an ‘inland’ lineage. Still under way, this re-invasion will likely lead to one large population of C. longicollis in the basin through time, the species not appearing to be affected by barriers and hydrological discontinuity. C. expansa population structure once again did not reveal any influence of barriers in the landscape, but rather showed high level of local relatedness associated with restricted dispersal distances. Although the presence of IBD may have hindered the identification of breaks in genetic connectivity in this species at the basin scale, no evidence suggests such breaks should be expected.
Taken together these results suggested that E. m. macquarii is capable of taking advantage of large infrequent flows in highly dynamic systems, in essence adjusting to the unpredictability of the system, but to be susceptible to barriers in the river network. In contrast, C. expansa is likely to be more affected by large stretches of temporarily dry river beds than by barriers within the rivers network, the species not as able to take advantage of large, sporadic, flows for large distance dispersal (intrinsic limitations). Although no strong inference can be made on C. longicollis , the species appeared not affected by barriers in the river network and would be best able to recolonise failed refugia, provided stepping stone habitats exist. As C. longicollis is known to primarily stroll the river-bed rather than swim within the water column, recolonisation would occur at a probably slower pace than in E. m. macquarii if no barrier is present within the system and surface flow is sufficient for the latter species.
Finally, the presence of multiple admixture components in both E. m. macquarii and C. longicollis which were not clearly associated with current population distribution alluded to a complex past population history, such as retraction and re-expansion from a small number of remnant populations in the basin after isolation during the last glacial (e.g. The Severn River population in Emydura ) (and see Georges and Thomson, 2006). In contrast, C. expansa showed a less complex population structure and a probably simpler past history of basin invasion.
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Chapter 6 Sex-biased Dispersal
Large Chelodina expansa females at Dead Man’s gully, Caloola B & B, NSW
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6.1 Introduction
Inbreeding depression is deemed a prerequisite for the evolution of sex-biased dispersal (Gandon, 1999) but factors other than inbreeding avoidance are required to specify a direction in the bias (Motro, 1991; Perrin and Mazalov, 1999). A large body of literature has shown that polygynous mammals, where males mate with multiple females, primarily exhibit male-biased dispersal (Greenwood, 1980; Dobson, 1982; Zenger & Eldridge and Cooper, 2003; Devillard & Allainé & Gaillard et al. , 2004; Hazlitt & Eldridge and Goldizen, 2004; Lawson Handley and Perrin, 2007) while monogamous birds have typically demonstrated female-biased dispersal (Greenwood, 1980; Pusey, 1987; Clarke & Saether and Roskaft, 1997). In polygynous species, male-biased dispersal is associated with greater mating opportunity and a lower dispersal cost, as males have a reduced reproductive energy requirement compared to females (Greenwood, 1980; Dobson, 1982; Lawson Handley and Perrin, 2007). Female-biased dispersal on the other hand primarily occurs when males hold territories, such as in birds, thereby reducing the cost of reproductive energy requirement in females owing to their familiarity with available surrounding resources (Clarke et al. , 1997). The sedentary sex, in this case males, ‘forces’ females to be the dispersing sex for inbreeding avoidance (Greenwood, 1980). Unsurprisingly, many exceptions exist showing mating system is not the only driver of direction in dispersal bias (see review by Lawson Handley and Perrin, 2007 for mammals and Clarke et al. 1997 for birds). Additional factors to inbreeding avoidance may be especially required to explain the direction of sex-biased dispersal in Testudines species where promiscuity, multiple paternity clutches and female sperm storage capacity in some species could represent effective inbreeding avoidance methods (Brooker & Rowley & Adams et al. , 1990; Pearse and Avise, 2001).
Until recently, few genetic studies have assessed sex-biased dispersal in reptiles (see Tucker & McCallum & Limpus et al. , 1998; Dubey & Brown & Madsen et al. , 2008; Lane and Shine, 2011). In chelonians, the large majority of studies have focused on marine turtles (reviewed in Bowen and Karl, 2007) which showed male-biased dispersal in all species (Roberts & Schwartz and Karl, 2004). Adult males move large distances for breeding and feeding, mating with multiple partners during their migration (polygyny), while females move to distant feeding grounds but return to their natal ground for nesting (called ‘natal homing’ or ‘nest-site philopatry’) (Bowen and Karl, 2007). Hence, gene flow appears male-driven in marine turtles (FitzSimmons & Limpus & Norman et al. , 1997; Bowen & Bass & Soares et al. , 2005a; Bowen and Karl, 2007). A number of genetic studies specifically assessing sex-biased dispersal in freshwater turtles have also revealed male-biased dispersal in this group (Freedberg & Ewert & Ridenhour et al. , 2005; Ciofi & Wilson & Beheregaray et al. , 2006; Rioux Paquette et al. , 2010; Sheridan et al. , 2010). Although in chelonians neither sex invests in juvenile upbringing (Klemens, 2000; Pearse and Avise, 2001), females bear a higher energetic cost from reproduction than males and high predation risk while nesting. Therefore, females have more to gain
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from local habitat familiarity for nest site quality and resource requirements for reproduction (Reinhold, 1998; Freedberg and Wade, 2001).The latter advantage applies to non-migrating turtle species only, where the species do not undertake long distance movements between feeding and nesting ground. The advantages afforded by nest site fidelity are higher in species demonstrating environmental sex determination (ESD), where the parents are able to manipulate offspring sex-ratio with habitat quality (Reinhold, 1998; Freedberg and Wade, 2001; Hulin and Guillon, 2007), than in genetically sex determined (GSD) species. Advantages remain nonetheless significant in the latter as well, as clutch success is affected by environmental factors (Kolbe and Janzen, 2002; Hughes & Greaves and Litzgus, 2009a). Further, nest site fidelity provides greater certainty in reproductive success if clutches have successfully hatched in previous years (Freedberg and Wade, 2001). The cost associated with patch quality uncertainty following dispersal will also be greater in females, male cost being associated with the number of mating partners available in the new patch (es) in contrast to failed reproduction from poor resources and nesting site quality in females. Hence nest site fidelity, or female philopatry, is expected to be common in freshwater turtles (Morreale & Gibbons and Congdon, 1984; Freedberg et al. , 2005; Rioux Paquette et al. , 2010; Sheridan et al. , 2010).
Strong female philopatry can however increase the risk of local demographic extirpation following poor reproductive success owing to alteration of nesting habitat through changed water level (e.g. flooding of nesting areas), and changed vegetation cover and soil type (reviewed in Klemens, 2000; Bodie, 2001; Moll and Moll, 2004). This is of particular concern where large areas of the system are affected, such as through flow regulation and wetland reclamation, two processes omnipresent in the MDB (Walker and Thoms, 1993; Brinson and Malvárez, 2002; Jensen, 2002; Hall et al. , 2006; Gell et al. , 2007). Further concern exists for subsequent recolonisation, theoretical models predicting severe male-biased dispersal to be associated with reduced ability to adapt to poor marginal habitats (sinks) (Kawecki, 2003).
In this chapter, sex-biased dispersal was assessed in the MDB turtles. Following previous information (see Bower, 2011), severe sex-biased dispersal was expected in C. expansa with females exhibiting strong natal nesting site fidelity. Few direct observations on E. m. macquarii movements have been reported, with only a few females having been observed returning to the same nesting site over multiple years (Goode and Russell, 1968). It is however unknown if these were an expression of fidelity to the natal nesting site. Following the patterns common in many chelonian species, E. m. macquarii was expected to demonstrate male-biased dispersal, albeit not at a severe level. A female has been recaptured more than 400 km from its first site of capture (Colin Limpus, pers. comm.), alluding to the potential for some long distance movements in this sex also. Although this could be an exception, if female movements do also occur, albeit at a smaller scale than male, only a weak sex- biased pattern should be expected. Higher levels of local genetic relatedness in females compared to males, and a stronger correlation between genetic and spatial distances in females would support these
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predictions. Finally, no sex-biased dispersal was expected in C. longicollis (i.e. identical correlation level between genetic and spatial distance in both sexes), all studies using direct methods having found no such pattern albeit investigating at much smaller scales (Stott, 1987; Graham et al. , 1996; Roe and Georges, 2008c).
6.2 Methods
6.2.1 Laboratory Methods and Sample Sizes
Genotyping protocols can be found in chapter 2 ‘General Methods’. Samples used in the present chapter represent subsamples of those already included in Chapter 3, 4 and 5, depending on the region of focus (upper vs lower MDB) and the method of analysis. Sample sizes per sex-class, species, region and method are provided in Table 6. 1. Microsatellite primers used for DNA amplification in each species were described in Chapter 2. Following the removal of biallelic or effectively biallelic loci (sensu Wang 2011, where two of the alleles are common with frequencies summing up to a value close to one), a total of 8 loci were used for all IBD based methods and COANCESTRY analyses in C. expansa (see methods description below). In C. longicollis, 7 loci were included in all analyses. In E. m. macquarii 8 loci were used in the upper MDB population for all IBD based methods and COANCESTRY analyses following removal of TLE13.3 owing to the presence of null alleles. All loci (9) were used in the lower MDB as no null alleles were found in these populations previously (see Chapter 4). Measures of genetic diversity within each population were provided previously in chapter 3 (upper MDB) and chapter 4 (lower MDB).
Table 6. 1 Sample size per sex-class and analytical method for C. expansa , C. longicollis and E. m. macquarii . Note that smaller sample size may have greater number of pairwise comparisons than larger one in COANCESTRY depending on sample size within each location (see methods).
IBD methods Coancestry SAA F M F M F M
C. expansa Upper MDB 38 20 30 25 28 14 Lower MDB 41 36 46 43 34 34
C. longicollis Upper MDB 49 31 48 32 NA NA Lower MDB 32 28 42 32 29 25
E. m. macquarii Upper MDB 65 75 80 87 46 60 Lower MDB 34 19 45 26 30 13
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6.2.2 Statistical Methods
As stated previously sex determination in C. longicollis can be difficult. Despite this difficulty, all following analyses but for SAA in the Moonie River were carried out on all three species as sex- classification for C. longicollis samples included here were carried out by experienced researchers (Arthur Georges, Brad Schaffer, Deborah Bower, Kate Hodges, Scott Snyder, and Colin Limpus). Only a small number of C. longicollis sexed with confidence were included from the Moonie River for IBD-based methods and COANCESTRY. As mating removes any signal of sex-biased dispersal in nuclear DNA in the following generation (Goudet & Perrin and Waser, 2002; Prugnolle and de Meeus, 2002) only ‘mature’ adults, which could exhibit sex-biased natal dispersal, were included in analyses. For all three species, analyses were carried out within each population identified at the largest scale by clustering methods in chapter 5, as some of the methods relied on IBD pattern (see below) and hence required the individuals to be able to move between sampled locations (see Knight & Oppen & Smith et al. , 1999; Rioux Paquette et al. , 2010). A suitable sampling scale must be large enough to pick up an IBD pattern while not being so large that individuals cannot disperse freely over the study area (see Campbell and Strobeck, 2006). In the upper MDB population, IBD based analyses were conducted at the Moonie and Border Rivers scale for C. expansa and E. m. macquarii as IBD was present in both species at this scale, while C. longicollis was analysed across multiple catchments to increase the sample size and the chance of picking up an IBD pattern. In the lower MDB population, all three species were analysed at the Murray River scale for all IBD based analyses. Descriptions of other methods of analysis are provided below along with the corresponding sampling scale. For all analyses, spatial distances correspond to channel distances.
A number of methods that test for sex-biased dispersal using bi-parentally inherited genetic markers are available (reviewed in Prugnolle and de Meeus, 2002). Methods proposed by Goudet et al . (2002) were not suitable for the present study as the sampling obtained in this study was not considered exhaustive and bias level was not expected to be severe, two of the methods’ main requirements. This was also true of the assignment method of Favre et al. (1997). Instead the following approaches were used:
1. Isolation by Distance and comparison of regression slopes by sex-class 2. Isolation by Distance and comparison of regression slopes on means ranked genetic and spatial distance 3. Mean level of relatedness per sex-class with Queller relatedness estimate in COANCESTRY 4. Mean level of relatedness per sex-class with TrioML relatedness estimate in COANCESTRY 5. Spatial Autocorrelation Analysis (SAA)
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6.2.2.1 Isolation by Distance Based Analyses
Regression Slope Two Isolation by Distance (IBD) based methods appear more sensitive and are applicable to more realistic datasets for detecting sex-biased dispersal than the method of Goudet et al . (2002). These two methods are individual based and hence suffer from pseudo-replication as the high number of pairwise comparisons artificially increases the number of degrees of freedom resulting in an artificially increased power of the test (Prugnolle and de Meeus, 2002). To circumvent this issue, Knight et al. (1999) proposed two approaches recently successfully applied in a study on sex-biased dispersal in Astrochelys radiate (radiated tortoise) (see Rioux Paquette et al. , 2010). These two approaches use pairwise genetic and corresponding spatial distances between individuals of the same sex. In the first approach, a regression slope was calculated for each individual using the distances (genetic and spatial) obtained from its comparison against all other same-sex individuals. The large number of pairwise comparisons obtained for that individual were thus reduced to a single regression slope value. Sex-biased dispersal was then assessed by comparing all ‘individual’ slopes obtained for one sex-class against those of the other sex-class using a Mann-Whitney U test computed in Analyse-it freely downloadable version ( http://analyse-it.com/download , last accessed on the 29 th of November 2011).
Ranked Means
In the second approach, the analysis was based on comparison of the means of ranked genetic and corresponding spatial distance across all individuals of each sex-class (Knight et al. , 1999). Within each sex-class the following was carried out. Firstly, all pairwise genetic distances obtained for each individual against all other same sex individuals were ranked from distantly related to closely related. That is [n-1] comparisons per individual with ‘n’ equal to the sample size per sex-class as an individual cannot be compared to itself. Following the ranking of all genetic distances (which still have their corresponding spatial distance) for each individual, a mean genetic distance across all individuals was then calculated with the largest ranked relatedness value of each individual. The corresponding spatial distances across all individuals were also used to calculate a mean spatial distance. This was repeated with the second largest genetic distance of all individuals, the third largest and so on. The large number of pairwise comparisons was hence reduced to (n-1) with ‘n’ the number of samples within each sex-class (Knight et al. , 1999). The means were then used to calculate a regression slope of genetic distance on geographic distance for each sex-class. The regressions were then compared between sex-classes with an analysis of covariance in ‘R’ (R Foundation for Statistical Computing, 2010), testing for an interaction between sex and distance, a significant interaction providing evidence for difference in regression slopes between sex-classes.
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Measures of Genetic Distance for Isolation by Distance Methods
Both individual based methods discussed above were computed with both Rousset’s (2000) genetic distance estimate ( a) and Queller and Goodnight’s (1989) (R) relatedness estimate. Rousset’s (2000)
(a) is analogous to Slatkin’s (1995) linearised FST / (1- FST ) but calculated between pairs of individuals, while Queller and Goodnight’s (1989) estimator estimates the probability that any shared alleles between two individuals are shared by descent or by chance based on the frequency of that allele in the population (Queller and Goodnight, 1989; Van De Casteele & Galbusera and Matthysen, 2001). The latter estimator therefore varies between -1 and 1, with 0 corresponding to the overall population relatedness level (Queller and Goodnight, 1989). Groups showing a strong positive relationship (slope) between channel distance and Rousset genetic distance ( a) would be considered more sedentary as genetic distance increases with channel distance, while a flatter (absence of) relationship would indicate a greater propensity to dispersal. The opposite (strong negative slope for the sedentary group) is expected with the coefficient of relatedness ( R) as closely related individuals (large positive R values) are found at smaller distances. Rousset’s (2000) (a) and Queller and Goodnight’s (1989) (R) measures were computed in SPAGEDi v1.3 (Hardy and Vekemans, 2002) and means and regression slopes calculated in Excel. Pairwise channel distances between individuals were obtained in ArcView 9.3 and individual distance matrices created in Excel.
Using SPAGeDi’s (Hardy and Vekemans, 2002) outputs, the correlation between genetic and geographic distance matrices in both sexes was also assessed with the expectation that females should show higher correlation coefficients than males owing to the expected restricted dispersal of the former. Following Rioux Paquette et al. (2010), Queller and Goodnight’s pairwise ( R) estimates were transformed into distance measures by [ D = 1- R] as Mantel tests should be carried out between two distance matrices. Mantel tests were carried out in PopTools v3.2.5 (Hood, 2010) and correlation coefficient ( rm) significance assessed with 10,000 permutations. As the Mantel test is a permutation based method, this analysis does not suffer from the pseudo-replication issue discussed above.
6.2.2.2 Mean Level of Relatedness Method in COANCESTRY
A fourth test based on the mean level of relatedness between females and between males within each sampled location was carried out in COANCESTRY (Wang, 2011). Here, no relationship between genetic relatedness and spatial distance was investigated, instead focusing on the level of relatedness between same-sex individuals at each sampled location. The following was carried out within each of the large populations identified with clustering methods in the MDB (chapter 5) and included all samples but those from the Warrego River. First, relatedness between all individuals was estimated. Then all pairwise relatedness values between same-sex individuals within each sampled location were pooled and the difference in relatedness between the two groups (female vs male) tested with a permutation procedure implemented in COANCESTRY v1.0.0.1 (Wang, 2011). A significantly
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greater level of relatedness in a sex-class would result from a lower propensity to disperse and associated close relationship between individuals within each sampled location while a lower or diluted level of relatedness would result from greater dispersal in that sex-class.
Both Queller and Goodnight’s (1989) (R) and the Triadic likelihood relatedness estimator of Wang (2007) (TrioML) were chosen as the performance of relatedness estimators is highly marker dependent (see Van De Casteele et al. , 2001; Milligan, 2003; Wang, 2007). The former estimator appears to perform better with moderate locus and allele numbers (Lynch and Ritland, 1999; Wang, 2002) but also performs well with multiple highly polymorphic loci under some circumstances (Van De Casteele et al. , 2001) such as with positively skewed allele distributions (where a few alleles are very common); a distribution believed common for microsatellites (Rubinsztein & Amos & Leggo et al. , 1995) and observed at multiple loci in the present study. The TrioML estimator of Wang (2007) is a maximum likelihood method that uses a third individual as a reference to estimate the coefficient of relatedness of the focal dyad and which can account for the coefficient of identity by descent to obtain a more accurate estimate of the relatedness coefficient r (see Wang, 2007; 2011). The TrioML is deemed to yield results close to the true relatedness under a number of relationships and alleles per locus (Wang, 2007). Bootstrapping of 10,000 for calculation of 95% CI around the coefficient of relatedness was used and 10,000 permutations were performed to test the difference in mean level of relatedness between the two sex-classes.
Relatedness estimators are detrimentally affected by biallelic markers (Van De Casteele et al. , 2001; Wang, 2007), and biallelic or ‘effectively biallelic’ loci, where two alleles are common with a combined frequency close to one (Wang, 2011). These were therefore removed from all analyses discussed so far. Prior analysis showed no detrimental effect of locus removal on levels of relatedness, instead narrowing the variance in relatedness amongst individuals (observable when plotted against spatial distance, not shown). As they are known to produce erroneous results when estimating relatedness (Dakin and Avise, 2004), a single locus showing the presence of null alleles was removed (TLE13.3) from the upper MDB E. m. macquarii population. A number of correcting methods exist that take account of null alleles (e.g. Wagner & Creel and Kalinowski, 2006) but none were applied here for ease of computation.
6.2.2.3 Spatial Autocorrelation Analysis
Finally, a Spatial Autocorrelation Analysis (SAA) was performed as this analysis has been suggested to detect genetic structuring at smaller scales than are detected by most of the above methods (Rioux Paquette et al. , 2010) and to be able to provide support for sex-biased dispersal if performed separately by sex-class (Peakall et al. , 2003). Significantly higher correlation in one sex would support sex-biased dispersal (Peakall et al. , 2003). Only populations sampled along the Lower Murray River were included in the SAA as the method requires a continuous sampling distribution and large
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un-sampled sections separated the former populations to other populations in the lower MDB. SAA along the Moonie River in the upper MDB were already introduced in chapter 3.
6.3 Results
6.3.1 C. expansa
Spatial Autocorrelation Analysis
Neither sex showed levels of correlation signficiantly different from zero in the Moonie River (Figure 6. 1 A) despite showing opposite patterns. The small sample sizes resulted in large confidence intervals in both sexes, reducing the statistical power to reject the null hypothesis of random distribution of genotypes in this species. In the Lower Murray River C. expansa females were significantly more related than under a random distribution of genotypes at the smallest distance class, a pattern not present in males (Figure 6. 1 B). The level of autocorrelation for females was higher in this system than in the Moonie River ( r = 0.047 vs r = 0.028 respectively). Although not significant, males had a negative correlation at small distances which turned positive at the same distance class as in the Moonie River populations. A
0.150 r 0.100 U L 0.050
r 0.000
-0.050
-0.100
-0.150 20F 60F 80F 20M 60M 80M 120F 180F 300F 420F 120M 180M 300M 420M
Distance (km) B
0.100 r 0.080 0.060 U 0.040 L
r 0.020 0.000 -0.020 -0.040 -0.060 20F 60F 80F 80M 20M 60M 120F 180F 300F 500F 120M 280M 300M 500M Distance (km)
Figure 6. 1 Genetic correlation coefficient r per sex-class in C. expansa as a function of increasing variable distances in the Moonie River (A) (F = 28; M = 14) and the Lower Murray River (B) (F = 34; M = 34). X-axis: F (blue) - Female; M (Green) - Male; U and L: upper and lower 95% CI about the null hypothesis of random distribution of genotypes. Error bars: 95% CI about r from bootstrapping.
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COANCESTRY Analyses
A total of 101 pairwise comparisons in C. expansa (58 female and 43 male pairwise comparisons obtained from 30 females and 25 males) were used for comparison of level of relatedness within sampling locations between sex-classes in the upper MDB population. These were not significantly different using either Queller and Goodnight (1989) or TrioML (Wang, 2007) estimators (Continue Figure 6. 3 A, B and Table 6. 2). In the lower MDB, the larger number of pairwise comparisons (116 female and 102 male pairwise comparisons, obtained from 46 females and 43 males) showed the females to be more closely related within each sampled location than males using Queller’s estimators but not TrioML, although there was a similar pattern of higher relatedness in females (Continue Figure 6. 3 C, d and Table 6. 2). The smaller number of pairwise comparisons included in the upper MDB analysis results from fewer samples obtained within each sampled location compared to the lower MDB where more samples were obtained from fewer locations.
Isolation by Distance Based Analyses
Slopes
IBD based methods showed outputs fairly consistent with those of the SAA and COANCESTRY. The individual regression slopes of genetic on spatial distance for male and female C. expansa in the upper MDB population were not significantly different. These were significantly higher in males in the lower MDB using Queller estimator and albeit not significant at the 0.05 level were also higher in females using Rousset’s distance (Table 6. 3). The latter results correspond to expectations under a male-biased model of dispersal where ‘ a’ slopes should be high in females (high mean rank value) as individual similarity decreases with distance, but low (negative) with R (low mean rank value) (Rioux Paquette et al. , 2010) for the same reasons.
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A B
C D
E F
Figure 6. 2 COANCESTRY output for C. expansa (upper MDB: A, B; Lower MDB: C, D), C. longicollis (upper MDB: E, F; Lower MDB: G, H) and E. m. macquarii (upper MDB: I, J; Lower MDB: K, L) with Queller and Goodnight (1989) (A, C, E, G, I, K) and TrioML (Wang 2007) (B, D, F, H, L) estimator. Y-axis: Frequency of mean difference in relatedness between ‘within-female’ and ‘within-male’ groups following the permutation procedure; X-axis: mean difference in relatedness (r) between groups. Full light lines: 1 and 99% quantile; Dashed lines: 2.5 and 97.5 %; Dotted line: 5 and 95 %; Full dark line: observed difference level between groups. Group 1: Female; Group 2: Male.
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G H
I J
K L
Continue Figure 6. 3 COANCESTRY output for C. expansa (upper MDB: A, B; Lower MDB: C, D), C. longicollis (upper MDB: E, F; Lower MDB: G, H) and E. m. macquarii (upper MDB: I, J; Lower MDB: K, L) with Queller and Goodnight (1989) (A, C, E, G, I, K) and TrioML (Wang 2007) (B, D, F, H, L) estimator. Y- axis: Frequency of mean difference in relatedness between ‘within-female’ and ‘within-male’ groups following the permutation procedure; X-axis: mean difference in relatedness (r) between groups. Full light lines: 1 and 99% quantile; Dashed lines: 2.5 and 97.5 %; Dotted line: 5 and 95 %; Full dark line: observed difference level between groups. Group 1: Female; Group 2: Male.
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Table 6. 2 COANCESTRY output for sex-biased dispersal in C. expansa , C. longicollis and E. m. macquarii using Queller and Goodnight (1989) and TrioML (Wang 2007) estimators. Bold values: significant at α = 0.05. Underlined largest ‘Mean’ values identifies group with highest mean relatedness.
Population Species Estimator Group n Mean 2.5 % Quantile 97.5 % Quantile
Upper MDB C. expansa Queller F 58 0.0240 M 43 0.0399 F - M -0.0159 -0.0843 0.0818 TrioML F 58 0.1067 M 43 0.1113 F - M -0.0046 -0.0526 0.0516 C. longicollis Queller F 64 0.0060 M 30 0.0520 F - M -0.0460 -0.0724 0.0516 TrioML F 64 0.1182 M 30 0.1176 F - M 0.0006 -0.0544 0.0484 E. m. macquarii Queller F 224 0.0420 M 288 -0.0010 F - M 0.0430 -0.0392 0.0392 TrioML F 224 0.1322 M 288 0.1046 F - M 0.0277 -0.0253 0.0258
Lower MDB C. expansa Queller F 116 0.0870 M 102 -0.0089 F - M 0.0969 -0.0619 0.0614 TrioML F 116 0.1625 M 102 0.1346 F - M 0.0279 -0.0448 0.0463 C. longicollis Queller F 90 0.0218 M 48 0.0298 F - M -0.0080 -0.0760 0.0725 TrioML F 90 0.1242 M 48 0.0951 F - M 0.0291 -0.0479 0.0465 E. m. macquarii Queller F 114 0.0074 M 41 -0.0165 F - M 0.0238 -0.0783 0.0773 TrioML F 114 0.1149 M 41 0.1205 F - M -0.0056 -0.0530 0.0512
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Table 6. 3 Results of Mann-Whitney U tests comparing regression slopes of genetic on spatial distance for male and female individuals of the Murray-Darling Basin turtles. a :Rousset genetic distance; R: Queller relatedness coefficient; n: number of samples;
Genetic Sum of Population Species Sex Mean Rank U z P value n Distance Ranks
Upper MDB C. expansa a Males 30.35 607 363 -0.28 0.7809 20
Females 29.05 1104 397 38
R Males 27.45 549 421 0.67 0.5024 20
Females 30.58 1162 339 38
C. longicollis a Males 45.97 1425 590 -1.67 0.0941 31
Females 37.04 1815 929 49
R Males 35.32 1095 920 1.59 0.1130 31
Females 43.78 2145 599 49
E. m. macquarii a Males 58.93 4420 3305 3.62 0.0003 75
Females 83.85 5450 1570 65
R Males 73.71 5528 2197 -1.00 0.3150 75
Females 66.8 4342 2678 65 Lower MDB C. expansa a Males 34.44 1240 902 1.67 0.0941 36
Females 43.00 1763 574 41
R Males 45.36 1633 509 -2.34 0.0194 36
Females 33.41 1370 967 41
C. longicollis a Males 36.36 1018 284 -2.43 0.0151 28
Females 25.38 812 612 32
R Males 32.71 916 386 -0.92 0.3583 28
Females 28.56 914 510 32
E. m. macquarii a Males 35.89 682 154 -3.13 0.0017 19
Females 22.03 749 492 34
R Males 22.26 423 413 1.67 0.0951 19
Females 29.65 1008 233 34
Ranked Means
The second method, which compared the regression slope of ranked mean genetic and spatial distances showed somewhat similar results with non-significantly different slopes between sex-classes in the upper MDB (Figure 6. 4 A, B), and a significant difference in the lower MDB using Queller’s ‘R’ but not with Rousset’s ‘ a’ (Figure 6. 4 C, D). Female correlation coefficients of regression slopes were consistently higher with both estimators and in both populations. Female R slopes showed a clearly negative relationship while males showed a less negative (upper MDB) or positive one (lower MDB) (Figure 6. 4 A, C). The difference in correlation coefficients was not as apparent with Rousset’s distance. The weaker correlation coefficient of male slopes related to the concentration of their mean genetic distance, in both the upper and lower MDB, in the lower range of the mean spatial distances. This implied that genetic distance or relatedness was not related to spatial distance,
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providing intermediate mean spatial distances for all ‘individuals’ when ranked, as large spatial distances are mixed with shorter ones within each relatedness-based rank. If a correlation existed, the distribution of genetic distances should show more widespread distribution along the x axis such as is observable in females, as larger spatial distances are consistently found with lower ( a) or higher ( R) ranked genetic distances. Caution is nonetheless required as a few more females than males were sampled from distant locations in the lower MDB (four females) and also in the upper MDB (two females), which would have contributed to larger mean spatial distance and hence a more widespread distribution of data points.
A) ( F = 0.275 P = 0.398) B) (F = 0.040 P = 0.841)
0.5 0.25
0.4 R = -0.0024d + 0.812 0.2 r = - 0.4397 a= 0.0013d - 0.4332 0.3 0.15 r = 0.5387 0.2 0.1
0.1 0.05 a R 0 0 a = 0.0011d - 0.3309 R = -0.0014d + 0.3959
mean r = 0.4575 r = - 0.3724 mean -0.1 -0.05
-0.2 -0.1
-0.3 -0.15
-0.4 -0.2
-0.5 -0.25 150 200 250 300 350 400 450 140 190 240 290 340 390 440 490 mean distance (km) mean distance (km)
C) ( F = 11.960 P = 0.001) D) ( F = 0.016 P = 0.901)
0.4 0.6
R = -0.0012d + 0.7474 0.3 0.4 r = - 0.5768
0.2
0.2 a = 0.0007d - 0.2608 0.1 r = 0.3501 a R a= 0.0008d - 0.3871 0 r = 0.7517 Mean 0 mean
-0.2 -0.1 R = 0.001d - 0.4027 r = 0.2673 -0.4 -0.2
-0.6 -0.3 200 300 400 500 600 700 800 200 300 400 500 600 700 800 900 mean distance (km) mean distance (km)
Figure 6. 4 C. expansa : Plots showing relationship between the means of ranked R (Queller relatedness) (A, C) values and ranked a (Rousset’s genetic distance) values (B, D) and corresponding mean spatial (channel) distances in males and females in the upper (A, B) and lower (C, D) Murray-Darling Basin populations. Diamond show female data point; Crosses show male data point. Full line represents female regression; Dashed line represents male regression. Regression equation and corresponding correlation value shown next to regression lines. Values along the x and y axis do not represent true spatial and genetic distances for each individual. See method section for ranking protocol, mean calculation and analysis explanation.
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Finally, the correlation between genetic and spatial distances tested with Mantel tests was higher in females than males in the lower MDB with ( a) but not with (1-R) (Table 6. 4). In the upper MDB, males showed higher coefficients of correlation than females and both sex-classes showed significant correlations depending on the genetic distance used (Table 6. 4).
Table 6. 4 Results of Mantel correlation tests between pairwise genetic and spatial (channel) distances among individual Murray-Darling Basin turtles. a: Rousset’s genetic distance; R: Queller relatedness coefficient; rm: Mantel test correlation coefficient. Bold value show highest correlation coefficient.
Population Species Genetic Distance Sex rm P value
Upper MDB C. expansa a Males 0.1612 0.074 Females 0.1223 0.034 1-R Males 0.1604 0.033 Females 0.0764 0.079 C. longicollis a Males 0.1117 0.122 Females 0.0892 0.034 1-R Males 0.1012 0.098 Females 0.0777 0.017 E. m. macquarii a Males 0.0055 0.461 Females 0.0969 0.041 1-R Males 0.0309 0.173 Females 0.0599 0.031
Lower MDB C. expansa a Males 0.0076 0.453 Females 0.1421 0.032 1-R Males -0.1018 0.974 Females 0.0846 0.067 C. longicollis a Males 0.0896 0.176 Females -0.0515 0.671 1-R Males 0.0007 0.498 Females 0.0385 0.261 E. m. macquarii a Males 0.0605 0.219 Females -0.0836 0.746 1-R Males -0.0027 0.471 Females -0.0739 0.810
6.3.2 C.longicollis
Spatial Autocorrelation Analyses
At the smaller scale, both C. longicollis sexes did not covary away from the genetic mean (Figure 6. 5) in the SAA on the Lower Murray River populations. No SAA by sex-classes were carried out in the Moonie River population owing to insufficient samples having been identified by sex-class in this river.
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0.080 r
0.060 U 0.040 L 0.020 r 0.000 -0.020 -0.040 -0.060 20F 60F 80F 20M 60M 80M 120F 180F 300F 500F 120M 180M 300M 500M Distance (km)
Figure 6. 5 Genetic correlation coefficient r per sex-class in C. longicollis as a function of increasing variable distances in the lower Murray River. X-axis: F (blue) - Female ( N = 29); M (Green) - Male ( N =25); U and L: upper and lower 95% CI about the null hypothesis of random distribution of genotypes. Error bars: 95% CI about r from bootstrapping.
COANCESTRY Analyses
A total of 94 pairwise comparisons (64 female and 30 male pairwise comparisons obtained from 48 females and 32 males) were used for comparisons of mean level of relatedness in COANCESTRY between sex-classes in the upper MDB population. Both sex-classes had similar levels of relatedness within all sampled locations using either estimator, although males were shown to have a non- significantly higher level of relatedness than females with Queller’s estimator (Table 6. 2, Continue Figure 6. 3 E, F). There was no evidence for sex-biased dispersal in the lower MDB either with COANCESTRY which had a similar sample size but a larger number of pairwise comparisons (90 female and 48 male pairwise comparisons obtained from 42 females and 32 males). As opposed to the upper MDB, females were shown to be more related than males with TrioML but not significantly so (Table 6. 2, Continue Figure 6. 3 G, H). Again, the larger number of pairwise comparisons resulted from a larger number of individuals sampled within fewer locations in the lower MDB.
Isolation by Distance Based Analyses
Slopes
Following the indication from COANCESTRY that males were more related (albeit non-significant) than females using Queller’s estimator, the mean rank of individual regression slopes of genetic on spatial distances was non-significantly higher in males than females with both estimators in the upper MDB (Mann-Whitney U test) (Table 6. 3). Queller’s estimator was used in both COANCESTRY and the latter method and hence both analyses were expected to show a similar pattern with this estimator. Male mean ranks of individual regression slopes were also non-significantly higher with Rousset’s genetic distance (Mann-Whitney U test) (Table 6. 3). In the lower MDB, the results were contradictory. Males had significantly higher regression slopes than females using Rousset’s distance, which concurred with the results obtained so far, but both sex-classes had similar regression slopes using Queller’s relatedness estimator (Table 6. 3).
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Ranked Means
Ranked mean of R and a regression slopes had higher correlation coefficients in males in the upper MDB, but the slopes of both sex-classes were not significantly different using either estimator (Figure 6. 6 A, B). Unbalanced numbers of samples, with regards to sex-class, were obtained from each location in the upper MDB resulting in larger mean spatial distances for females (Figure 6. 6 A, B). In the lower MDB, slopes of ranked means of relatedness were not found significantly different between sexes (Figure 6. 6 C, D). Similar numbers of samples from each sex-class were obtained from each sampled location in the lower MDB providing a balanced data point distribution of ranked means (Figure 6. 6 C, D). Not shown here, all slopes were also tested with a t-test for slope (Zar, 1999 p. 360) in R which showed identical results.
Finally, the Mantel test for IBD revealed a slightly higher correlation coefficient in male C. longicollis with both distances ( a and R-1) in the upper MDB than in females, although only female correlation coefficients were significant (Table 6. 4). In the lower MDB, only Rousset’s distance showed a higher correlation coefficient in males, the coefficient of correlation being higher in females with Queller’s estimator. The coefficients were significant with neither estimator (Table 6. 4).
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A) ( F = 0.524 P = 0.472) B) ( F = 0.265 P = 0.608)
0.5 0.15 a = 0.0007d - 0.627 0.4 0.1 r = 0.4902
0.3 a = 0.0008d - 0.6001 0.05 r = 0.6808 0.2 0 0.1 R
a -0.05 0
mean -0.1 -0.1 mean
-0.2 -0.15
-0.3 -0.2 R= -0.0015d + 0.8742 r = - 0.5727 R= -0.0019d + 1.433 -0.4 r = - 0.5477 -0.25 -0.5 -0.3 460 510 560 610 660 710 760 810 860 910 480 530 580 630 680 730 780 830 880 mean distance (km) mean distance (km)
C) ( F = 0.353 P = 0.555) D) ( F = 2.796 P = 0.100)
0.6 0.3
0.5
0.4 0.2 a = 0.0005d - 0.2887 0.3 r = 0.4733 0.1 0.2 R = -0.0004d + 0.1978
R r = - 0.1336 0.1 a 0 0 mean mean a = -0.00004d - 0.0169 R = 0.00005d - 0.0339 -0.1 r = 0.0135 r = - 0.0868 -0.1 -0.2
-0.3 -0.2 -0.4
-0.5 -0.3 270 320 370 420 470 520 570 620 670 220 320 420 520 620 720 mean distance (km) mean distance (km)
Figure 6. 6 C. longicollis : Plots showing relationship between the means of ranked R (Queller relatedness) (A, C) values and ranked a (Rousset’s genetic distance) values (B, D) and corresponding mean spatial (channel) distances in male and female in the upper (A, B) and lower (C, D) Murray-Darling Basin populations. Diamond show female data point; Crosses show male data point. Full line represents female regression; Dashed line represents male regression. Regression equation and corresponding correlation value shown next to regression lines. Values along the x and y axis do not represent true spatial and genetic distances for each individual. See method section for ranking protocol, mean calculation and analysis explanation.
6.3.3 E. m. macquarii
Spatial Autocorrelation Analyses
Males and females revealed an almost identical (random) level of correlation at all distance classes in the Moonie River (Figure 6. 7 A). In the Lower Murray River, females E. m. macquarii were positively related at the smallest distance classes (up to 80 km), while males showed a random, albeit negative, pattern of genotype distribution in the SAA (Figure 6. 7 B). The correlation level for females at the smallest scale in the Lower Murray River was larger than that obtained in the Moonie River population ( r = 0.044 and r = 0.010 respectively).
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A
0.050
0.040 r U 0.030 L 0.020
r 0.010
0.000
-0.010
-0.020
-0.030 20F 60F 80F 20M 60M 80M 120F 180F 300F 420F 120M 180M 300M 420M Distance (km) B
0.150 r 0.100 U L 0.050
r 0.000
-0.050
-0.100
-0.150 20F 60F 80F 20M 60M 80M 120F 180F 300F 500F 650F 300M 500M 120M 180M 650M Distance (km)
Figure 6. 7 Genetic correlation coefficient r per sex-class in E. m. macquarii as a function of increasing variable distances in the Moonie River (A) (F = 46; M = 60) and the lower Murray River (B) (F = 30; M = 13). X-axis: F (blue) - Female; M (Green) - Male; U and L: upper and lower 95% CI about the null hypothesis of random distribution of genotypes. Error bars: 95% CI about r from bootstrapping.
COANCESTRY Analyses
A total of 512 pairwise comparisons (224 female and 288 male pairwise comparisons obtained from 80 females and 87 males) were included for comparison of mean level of relatedness between sex- classes in the upper MDB population with COANCESTRY. Females were found to be more closely related than males within sampled locations with both TrioML and Queller estimators suggesting male-biased dispersal in this species (Table 6. 2, Continue Figure 6. 3 I, J). Fewer pairwise comparisons were available in the lower MDB (114 female and 41 male obtained from 45 females and 26 males). No difference in relatedness between the two sex-classes was found with this method (Table 6. 2, Continue Figure 6. 3 K, L).
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Isolation by Distance Based Analyses
Slopes
Comparison of individual regression slopes of genetic on spatial distance in the upper MDB provided further evidence for male biased dispersal, with females showing a significantly higher mean rank with Rousset’s distance ( a). Males also showed a high mean rank using relatedness ( R), but the latter was clearly not significantly higher than that of females. The exact opposite was true in the lower MDB where males had a significantly higher mean rank with ‘ a’ distance and females a non- significantly higher mean rank with ‘ R’ (Table 6. 3), which would support a female biased model of dispersal.
Ranked Means
This contradiction was also apparent with the second method which compared the means of ranked genetic distances in the upper MDB population, where the female sex-class had a higher correlation coefficient than males and somewhat supported male biased dispersal, even though the slopes were not significantly different (Figure 6. 8 A, B). The distribution of E. m. macquarii male pairwise comparisons in the upper MDB population was similar to that of male C. expansa , being somewhat restricted within the lower range along the mean spatial distance axis, while females showed a more widespread distribution consistent with an IBD pattern (Figure 6. 8 A, B). In the lower MDB, slopes were opposite to expectations in females (Figure 6. 8 C, D) and were significantly different between sex-class with ‘ R’ relatedness and almost so with ‘ a’ distance. This is contradictory to expectations but likely reflected the isolation of several populations in the lower MDB inferred in chapter 4 (see discussion).
All above results were further supported by the mantel tests which showed female correlation coefficients to be much higher than male in the upper MDB, and the correlation coefficient of both sex-classes to follow no particular pattern in the lower MDB, although being higher for male with both distances ( a and R-1) (Table 6. 4) as expected from the slopes in Figure 6.7 C and D.
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A) ( F = 0.335 P = 0.564) B) ( F = 1.492 P = 0.224)
0.6 0.4
0.4 0.3
R = -0.0026d + 0.8741 a = 0.0015d - 0.4908 r = - 0.4278 0.2 r = 0.5138 0.2
0.1 R a 0 R = -0.002d + 0.5727 r = - 0.2619 a = 0.0008d - 0.2307
mean r = 0.2126 mean 0
-0.2 -0.1
-0.4 -0.2
-0.6 -0.3 200 250 300 350 400 200 250 300 350 400 450 mean distance (km) mean distance (km)
C) ( F = 5.330 P = 0.025) D) ( F = 3.151 P = 0.082)
0.5 0.5 R = 0.001d - 0.432 0.4 r = 0.3560 0.4 0.3
0.3 0.2 a = -0.0004d + 0.2328 r = - 0.2219 0.1 0.2 a R 0 mean mean 0.1 -0.1 a = 0.0002d - 0.1154 r = 0.3325 -0.2 0 R = -0.0002d + 0.0999 -0.3 r = - 0.2017 -0.1 -0.4
-0.5 -0.2 250 350 450 550 650 750 850 950 1050 1150 200 400 600 800 1000 1200 mean distance (km) mean distance (km)
Figure 6. 8 E. m. macquarii : Plots showing relationship between the means of ranked R (Queller relatedness) (A, C) values and ranked a (Rousset’s genetic distance) values (B, D) and corresponding mean spatial (channel) distances in male and female in the upper (A, B) and lower (C, D) Murray-Darling Basin populations. Diamond show female data point; Crosses show male data point. Full line represents female regression; Dashed line represents male regression. Regression equation and corresponding correlation value shown next to regression lines. Values along the x and y axis do not represent true spatial and genetic distances for each individual. See method section for ranking protocol, mean calculation and analysis explanation.
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To clarify the evidence gathered here in support or rejection of sex-biased dispersal in each species, and facilitate interpretation, results are summarised in Table 6. 5.
Table 6. 5 Summary of tests for sex-biased dispersal in C. expansa , C. longicollis and E. m. macquarii . -: no support for sex-biased dispersal; ~: non-significant but provides support; √: statistically significant support, ♂: male biased dispersal; ♀: female biased dispersal; NA: not analysed; *: see results and discussion for interpretation of contradictory results. R: Queller’s relatedness estimate; a: Rousset’s distance. SAA: Spatial Autocorrelation Analysis.
IBD Individual Method Slopes IBD Ranked Mantel Test COANCESTRY SAA
Species Population R a R a (1-R) a Queller TrioML
C. expansa Upper MDB ------~♂
Lower MDB √♂ ~♂ √♂ - - √♂ √♂ ~♂ √♂
C. longicollis Upper MDB ~♀ ~♀ - - ~♀ ~♀ ~♀ - NA
Lower MDB - √♀ - - ~♂ ~♀ - ~♂ -
E. m. macquarii Upper MDB - √♂ - - √♂ √♂ √♂ √♂ -
Lower MDB ~♀* √♀* √♀* ~♀* ~♀* ~♀* - - √♂
6.4 Discussion
Testing for sex-biased dispersal with small sample sizes in large populations such as those investigated here can be problematic (see Goudet et al. , 2002; Rioux Paquette et al. , 2010). Pairwise relatedness estimators can provide variable outcomes depending on a range of factors such as levels of polymorphism, allele frequency, number of loci and population composition (proportion of each relationship, full sibs, half sibs, unrelated, etc) even under appropriate sampling schemes (Lynch and Ritland, 1999; Van De Casteele et al. , 2001). Nevertheless, the combination of methods used here provided some evidence for sex-biased dispersal in two of the three MDB turtle species analysed.
6.4.1 C. expansa
C. expansa exhibited a pattern associated with male biased dispersal in both the upper and lower MDB populations in the SAA, although the correlation level was not statistically different from that under a random distribution of genotypes in the former. The strong signal of male-biased dispersal obtained by SAA in the lower MDB concurred with COANCESTRY output, which showed females to be more closely related than males within each sampled location. The higher mean rank of regression slopes in females (Method 1 in Knight et al. , 1999) also supported male-biased dispersal, but these results were neither supported nor rejected by the second method of ranked means. Both above IBD based methods as well as the Mantel tests for IBD were likely affected by the presence of four more female samples than males from the distant (ALB) populations in the lower MDB, as each
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extra sample contributed multiple pairwise comparisons. This did not explain the absence of an IBD type distribution of data point in males. Further, no such issues existed in the SAA and COANCESTRY analyses which both showed male-biased dispersal in C. expansa . All above results supported a male-biased dispersal in C. expansa in the lower MDB and provided comprehensive support for the shorter dispersal distances and mean linear range size in females compared to males measured by Bower (2011) in this species using radiotelemetry.
Apart from the pattern observable in the SAA, no other support for sex-biased dispersal was found in the upper MDB. Although the lower sample size and shorter spatial distances of investigation in the upper MDB population may explain the disparity between the two outcomes, this cannot be confirmed with the current data. A biological explanation for the disparity could relate to the difference in predictability of the two systems, as the propensity to disperse is known to be influenced by inbreeding avoidance and competition for mates, but also by habitat quality and risk of stochastic extinction of the population (Olivieri et al. , 1995; Perrin and Mazalov, 1999; Gros & Hovestadt and Poethke, 2008). The greater flow variability or unpredictability in the upper MDB catchments could therefore lead to greater movement by females in response to the increased stochastic extinction risks and variation in habitat quality compared to the more predictable Murray River in the lower MDB. Although high site fidelity is expected in unpredictable habitats, this is only the case when the mean habitat qualities are equal (Switzer, 1993). Higher habitat quality heterogeneity leads to greater chance of gain when moving, the new habitat potentially providing a better reproductive outcome than the previous one, while the potential for gain will be reduced or non-existent in a predictable or more homogeneous system (Switzer, 1993). For instance, Bowne et al. (2006) found female Chrysemys picta to be more influenced by the habitat quality of the recipient patch than other age-sex categories, and hypothesised this to be owing to energy requirements of reproduction. Further, difference in food availability was hypothesised to account for dissimilar direction in sex-biased dispersal in populations of two sea snake species, Laticauda saintgironsi and L. laticaudata (Lane and Shine, 2011), sex- biased level and direction appearing not necessarily fixed within a phylogenetic group or species.
6.4.2 E. m. macquarii
Multiple lines of evidence for male-biased dispersal in E. m. macquarii were found in the upper MDB. Both Queller’s and Wang’s estimators showed females to be more closely related than males within each sampled location, while the regression slopes (method 1 in Knight et al. , 1999) and the Mantel tests for IBD had stronger correlation between genetic and spatial distances in females than males with both estimators. This was not apparent in the SAA which showed genotypes of either sex- class to follow a random distribution within the Moonie River. The latter result could be owing to the spatial scale at which the SAA was performed in the Moonie, the scale possibly not large enough for the manifestation and loss of non-random genotype distribution to take place in this species (Sokal
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and Oden, 1991; Peakall et al. , 2003). Despite not providing statistical support for male-biased dispersal, male ranked means of genetic relatedness (Method 2 in Knight et al. , 1999) in the upper MDB were primarily found in the lower to mid range of the mean spatial distance while female ranked means extended over the entire mean spatial distance distribution. Further, female slopes had consistently higher correlation coefficients than males. This was not due to a significant imbalance in the spatial distribution of samples, only mid-distance populations having a few less males than females. Pairwise genetic distance for females therefore increased with increasing mean spatial distance, whereas male pairwise genetic distances did not appear to follow any relationship with mean spatial distance, and returned intermediate ranked mean spatial distance as a result. Female E. m. macquariii therefore seemed to disperse over shorter distances than males, but based on the lack of SAA at the scale of the Moonie River both sexes show a propensity to disperse over large distances nonetheless, and at the very least, over larger distances than C. expansa individuals.
In the lower MDB, results for E. m. macquarii were more ambiguous. Firstly, at the smaller multiple distance classes (up to 80 km) females but not males were more related than under a random distribution of genotypes (SAA). This was not the case when testing relatedness within each sampled location with Queller’s and Wang’s estimators (COANCESTRY), each sex-class having identical mean relatedness levels within sampled locations. To confuse matters further, in both the regression slopes and ranked means IBD based methods, males had a weak correlation between genetic and spatial distance while females showed no such pattern. Based on the distribution of the ranked means relatedness for each sex-class (Method 2 in Knight et al. , 1999), the above contradictions were likely due to the relative isolation of ‘off-channel’ populations in the lower Murray River as inferred in Chapter 4. Female ranked means data points were restricted to the extreme lower end of the spatial mean distance distribution and no apparent relationship existed between the genetic and spatial distances in this sex-class. Larger numbers of females than males were obtained in the lower MDB and comparable spatial distances were covered in both sex-classes. The data point distribution could therefore not be explained by fewer numbers of ‘large’ spatial distances contributing to the ranked means of spatial distance in females.
Instead, the pattern observed in the lower MDB could be explained by a higher ‘susceptibility’ of females than males to isolation within ‘off-channel’ populations, removing any relationship between genetic and spatial distance in this sex-class. Isolation would result in two females sampled at short spatial distances (e.g. within neighbouring sampled populations) to potentially exhibit large genetic distance, while males being less susceptible to isolation would show some correlation between the two distances. The high coefficient of correlation at small distances in the SAA highlighted this isolation, with females up to four times more related in the lower Murray than in the Moonie River up to the 80 kilometres multiple distance class. This distance class equated to the limit where only within-location pairwise comparisons were included in the SAA, a pattern of random distribution of
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genotypes appearing at larger distances where ‘between-locations’ comparisons were included. The relative isolation of E. m. macquarii populations in the lower Murray River also meant that all IBD based methods (Mantel test for IBD, both IBD methods by Knight et al. , 1999) were unsuitable to assess sex-biased dispersal in this species in the lower MDB population.
The above ‘isolation of off-channel populations’ reasoning did not explain COANCESTRY’s output where both Queller’s and Wang’s estimators showed sex-classes to have identical mean levels of relatedness within each sampled location in the lower MDB. A credible reason for this discrepancy was the few male pairwise comparisons available for comparison in the latter method. The small sample size relative to the large population provided weak power to estimate the level of relatedness between individuals and provided few pairwise relatedness comparisons to test for differences between groups. This issue was prevalent in most COANCESTRY analyses, only two analyses with suitable sample sizes providing consistent output between both estimators ( E. m. macquarii in the upper MDB and C. expansa in the lower MDB). Small sample size is an even greater problem where only weak differences in mean relatedness level is expected between sex-classes, such as in species with a low to moderate sex-biased dispersal pattern (Goudet et al. , 2002; Wang, 2007; Rioux Paquette et al. , 2010).
6.4.3 C. longicollis
The absence of consistent evidence for sex-biased dispersal in C. longicollis followed our expectations based on direct studies (Stott, 1987; Graham et al. , 1996; Roe and Georges, 2008c), although the inconsistent outputs may reflect a lack of power to detect a bias in our study. As discussed above, the sample size obtained for analysis in COANCESTRY provided weak power to test for differences in mean levels of relatedness within the sampled locations in most cases. Male C. longicollis appeared more closely related than females within each location in the upper MDB with Queller’s estimator, but both sexes showed similar relatedness levels with Wang’s TrioML estimator. Female biased dispersal was also suggested by the higher mean rank for regression slope with Rousset’s estimator in both the lower and the upper MDB as well as the marginally higher correlation levels for ranked means in males with both Rousset’s and Queller’s estimators although none of the slopes were significantly different. The distinct distribution of ranked means data points between males and females in the upper MDB population was the result of female samples obtained from more distant populations. In contrast to all above results, TrioML in the lower MDB showed females to have a non-significantly higher level of relatedness than males within each location, while Queller showed both sex-classes to have similar level of relatedness. This lack of consistent definitive evidence for sex-biased dispersal, either male or female biased, may have resulted from a lack of power to detect it with the sample size and loci at hands. Should some level of sex-biased dispersal
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exist in C.longicollis or not, both sexes exhibited widespread movements as suggested by others previously (Graham et al. , 1996; Roe and Georges, 2008c; Kennett et al. , 2009).
The conflicting results obtained in COANCESTRY, and in other analyses, between the upper MDB and the lower MDB for C. longicollis may have to some extent reflected the admixture present in the former population but having not yet spread throughout the latter. Likelihood estimators such as TrioML are deemed better when many highly polymorphic loci are available but are less accurate than moment estimators such as Lynch and Ritland (1999) when these are not available (Wang, 2007). In contrast, the regression based estimator used here (Queller and Goodnight, 1989) is deemed to work better with closely related dyads (Van De Casteele et al. , 2001), relationships doubtful here considering the sample size and scale of sampling in C. longicollis . To decide which estimator best described the available dataset, data mimicking the data available in sample and loci number should be simulated and the different estimators assessed (Van De Casteele et al. , 2001; Wang, 2007). This was not performed here and Queller and Goodnight (1989) estimator was chosen owing to the low number of alleles and the positively skewed allele distribution at most loci, two factors believed unfavourable to the Lynch and Ritland (1999) estimator but not to the former or at least not to the same extent (Van De Casteele et al. , 2001). It is nonetheless surprising that a pattern of IBD was noticeable in the upper MDB population, where an expansion of the ‘East Coast’ lineage created admixture (Chapter 5 and Kate Hodges, unpublished data), whereas no such pattern could be seen in the lower MDB where this admixture was not present. The higher level of polymorphism at some loci in the upper MDB may have increased the power of the estimator by reducing the sampling variance per locus (Lynch and Ritland, 1999; Van De Casteele et al. , 2001) but the results remain difficult to explain. Moderate levels of female-biased dispersal in C. longicollis remains a possibility and will require further analysis, ideally with larger sample and number of loci, and a rigorous selection of the ‘best’ estimator (see Van De Casteele et al. , 2001).
6.5 Conclusion
In this chapter, some evidence for sex-biased dispersal in E. m. macquarii and C. expansa was found. In contrast no definitive evidence for sex-biased dispersal was found in C. longicollis , with evidence of male and female-biased dispersal depending on the method used. The ‘en mass’ nesting and possible philopatry of E. m. macquarii call for the identification of critical nesting ground in the highly regulated and altered lower MDB, where evidence of shrinking populations is already apparent (Chessman, 2011). Less affected by high nest predation rate owing to its more ‘isolated’ nesting behaviour (Bowen & Spencer and Janzen, 2005b; Spencer and Thompson, 2005; Chessman, 2011), C. expansa populations may nonetheless be affected by the current reclamation of wetland and backwaters in the basin, as females may inherit their nesting site through generations. The highly nomadic C. longicollis does not appear to suffer of the same restriction with regards to nesting site.
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Chapter 7 General Discussion
7.1 Population Genetics of the MDB turtles
The primary objective of this thesis was to assess the relative dispersal ability of each species of MDB turtle as inferred from levels of gene flow. A ‘benchmark’ estimate of dispersal ability for each species was obtained in an unregulated Dryland River (Chapter 3), and used as a correlate for the recolonisation potential of each species in unaltered hydrological conditions. The susceptibility of each species to extended periods of no flow and associated drop in habitat quality was assessed concurrently through identification of populations showing evidence of past population extirpation. Analysis of gene flow within the regulated Lower Murray River (Chapter 4) considered the impact of flow regulation infrastructure on the movement of turtles, and further assessed the influence of flow regime on the population genetics of each species. At a much larger scale, the identification of genetic discontinuity within each species enabled the identification of landscape features and processes of possible concern with regard to extinction - recolonisation dynamics at the basin scale (Chapter 5). Finally, sex-biased dispersal, a pattern common in testudines and with conservation implications, was assessed in each species (Chapter 6).
C. expansa
C. expansa exhibited limited dispersal distance relative to the other two species studied here but nonetheless excellent dispersal ability having shown no evidence of genetic structuring associated with physical barriers or variable flow regime. Limitations to re-colonisation in C. expansa would appear to be related to its intrinsic abilities (shorter dispersal distances) rather than to extrinsic features of the system. The absence of any signs of recent population extirpation in the systems studied, suggesting a better ability to cope with a decline in habitat quality than either C. longicollis or E. m. macquarii , is a surprising but positive outcome for the persistence of this species in the basin. Reclamation or permanent flooding of historically important nesting grounds through flow regulation could however be of concern for this species considering the natal habitat fidelity exhibited by female C. expansa . Removal of successful nesting grounds could lead to local population extirpation, forcing females to grounds of lower reproductive success while increasing their predation risk when nesting in unfamiliar territory (Sheridan et al. , 2010).
C. longicollis
The superior dispersal ability of C. longicollis was further highlighted by this study, with some evidence for movements in both sexes albeit uncertainty remained with regards to power in the latter analyses. In contrast, the expectation that this species is best adapted to withstand extended periods of
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drought (Roe, 2007; Roe and Georges, 2009) was challenged by evidence for multiple population extirpation events (Chapter 3 and 4), with refugia failing this species before failing C. expansa . These failures were deemed to result from a fall in habitat quality and resources availability for C. longicollis, but not for C. expansa which is better able to access scarce resource (Chessman, 2011) and withstand increased salinity (Bower, 2011). Nest predation by the introduced fox (Spencer and Thompson, 2005) was rejected as a primary driver of these extirpation events, at least in the Moonie River. A number of fish and invertebrate species have shown signs of population extirpation in these same refugia (Huey et al. , 2011), organisms unlikely to have been affected by fox predation. Chessman (2011) reached a similar conclusion regarding the driver of C. longicollis population extirpation in the Murray River.
The hypothesis by Kate Hodges (unpub. data) for two distinct lineages occupying the MDB was supported, and an admixture event appeared to still be under way in parts of the basin (Chapter 5). The impact of this admixture through introduction of genes adapted to different conditions on the health and local adaptation of the species may be worth investigating as episodic admixture events may be positive to a species, but this cannot be assumed (Verhoeven & Macel & Wolfe et al. , 2011).
E. m. macquarii
Previously assumed a poor disperser, E. m. macquarii exhibited an excellent ability to move within the river network, provided no significant structural barrier exists. The lack of genetic divergence correlated with distance within the Moonie River alluded to an ability to take advantage of the sporadic flows typical of this system for large distance movement (Chapter 3). The identification of populations isolated by headwater dams and waterfalls (Chapter 5) suggested that an impact of locks in the Lower Murray River on the population genetics of E. m. macquarii may not yet have been detectable (Chapter 4) owing to the larger size of populations in this system compared to headwaters, and to insufficient time having passed since their construction. The presence of locks may also have enabled sufficient gene flow between ‘weir pool’ populations to maintain allele frequencies, as observed in other species (see Bennett et al. , 2010); but the demographic connectivity (sensu Lowe and Allendorf, 2010) of these ‘populations’ remains to be assessed. A poor ability for out-of-network movements between the river proper and backwater habitats was also exhibited by E. m. macquarii (Chapter 4). Females may choose to remain within these permanent backwater habitats while males move in and out (Chapter 6), but the divergence levels and direct observations (Deborah Bower, pers. comm.) suggest that connectivity with these habitats may also be restricted in the latter sex.
Not specifically tested for, some results suggested that female E. m. macquarii may exhibit a plastic response to flow variability with regard to movement. No sex-biased dispersal was inferred in the hydrologically erratic Warrego River (Goodsell, 2002) while some evidence for male-biased dispersal in the more mesic and ‘predictable’ upper MDB was found (Chapter 6). The ‘Slopes’, ‘Ranked
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Means’ and SAA outputs in the lower MDB (Chapter 6) were interpreted as evidence for male-biased dispersal in this over-regulated region of the MDB, possibly at a more severe level than in the upper MDB considering the level of relatedness in females. Female E. m. macquarii may therefore demonstrate greater site fidelity in hydrologically more stable parts of the basin. Based on the results obtained for this species, this plasticity may also exist in C. expansa (Chapter 6).
7.2 How do the MDB turtles compare?
In their review of 57 population genetics studies on turtles (aquatic) and tortoises (terrestrial), FitzSimmons and Hart (2007) found no trend of increasing genetic distance with increasing spatial distance. This lack of trend across studies was interpreted as reason for strong caution when extrapolating results from one species or habitat to another. A trend was however present when categorising studies per habitat type (semi aquatic, terrestrial, lake and riverine) and it is reasonable to draw some tentative comparisons with other species occupying lotic systems.
The levels of divergence obtained for each of the MDB turtles fell within the lower end of those obtained for freshwater turtles in riverine systems, which was somewhat unexpected considering the intermittent flow dynamic of the system studied. At a small scale of up to 25 km, Terrapene coahuila exhibited pairwise FST between 0.006 and 0.049 among sub-populations of a drying wetland complex (Howeth & McGaugh and Hendrickson, 2008), while the northern map turtle ( Graptemys geographica ) showed similar levels of differentiation between populations separated by up to 50 km of regulated river channel (FST ; 0.010 to 0.044) (Bennett et al. , 2010). The extreme in small dispersal distance was exhibited by Hydromedusa maximiliani in Brazilian mountainous rainforest streams, with a maximum mean daily movement of 2 m (Souza and Abe, 1997), supported by a global differentiation level of 0.293 (Random Amplified Polymorphic DNA) over an area covering less than 9 km 2 (Souza & Cunha & Oliveira et al., 2002a). The differentiation levels observed at the smaller scale within the Moonie River clearly suggested greater connectivity in the MDB turtles. At the other end of the connectivity spectrum, and closer to that observed in the MDB turtles, the diamondback terrapin ( Malaclemys terrapin ) revealed low to moderate differentiation levels (FST ; 0.000 to 0.060) at distances of up to 1300 km along the southeastern USA coast (Hauswaldt and Glenn, 2005). This was mirrored by the giant Amazon river turtle ( Podocnemis expansa ) which revealed no genetic structure at distances up to 1000 km within drainages of the Amazon, but more limited gene flow among drainages (> 1000 km) (Pearse et al. , 2006). The divergence values obtained for C. longicollis , C. expansa and E. m. macquarii alluded to similar connectivity.
From the above it could be hypothesised that species inhabiting large rivers should show high connectivity, but the connectivity of Podocnemis unifilis within and between the Amazon-Orinoco
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does not support this hypothesis (Escalona et al. , 2009). Others have attempted to correlate dispersal distance with organism body size and found a somewhat loose correlation between the two in active dispersers (Jenkins et al. , 2007). The dispersal ability of the MDB turtles could therefore owe to their moderately large body size when compared to many, but not all, aquatic turtles. This could partly explain the pattern observed in E. m. macquarii and C. expansa , but that of C. longicollis is more difficult to explain the species not considered a large turtle. A more plausible and shared explanation relate to the drying out of the Australian continent. The drying out of the Australian continent has a long history (Quilty, 1994) and species occupying habitats with periodic flood-drought cycles have evolved traits that enable them to withstand such cycles (see Kingsford & Georges and Unmack, 2006; Roe and Georges, 2009). C. longicollis is well known for its drought coping abilities, with high water retention and terrestrial aestivation for up to 16 months (Roe, 2007; Roe and Georges, 2008a). Similarly, an Emydura lineage inhabiting one of the driest Australian catchment has been suggested to have evolved drought coping traits and population dynamics, adults able to forego growth and reproduction during periods of no flow (White, 2002). Believed to have evolved in the more tropical latitudes of the continent, C. expansa nonetheless revealed a higher tolerance to increased salinity resulting from extended periods of no flow than both other species (Bower, 2011). The superior dispersal ability of the MDB turtles may therefore also have evolved in response to the increasingly variable flow regime of Australian inland rivers that came with the drying out of the continent, only those individuals capable of moving out of failing refugia during sporadic flows surviving.
7.3 Metapopulation in temporary-river
The MDB turtles, possibly with the exception of C. longicollis , appeared to follow the ‘networker’ type of dispersal mode, where individuals are restricted to waterholes in times of no flow but are able to disperse rapidly among waterholes within the river network when hydrological connectivity is reinstated (Sheldon et al. , 2010). As opposed to waterbirds and other organisms able to perceive the landscape at a broader scale (see Sheldon et al. , 2010), but similarly to fish (Magoulick and Kobza, 2003), MDB turtles are therefore restricted to local habitat patches and dependent upon regular connection between these patches for their long term persistence and population stability at the regional scale. The increasing fragmentation and alteration of surface flow in the basin renders these species more reliant than ever on this rate of dispersal and recolonisation among waterholes for their local persistence.
It has been suggested that organisms in temporary rivers more commonly follow an expansion- contraction type of dynamic rather than a cycle of colonisation-extinction typical of metapopulations (Larned et al. , 2010). These contraction-expansions follow the occurrence of large floods linking the productively poor river channel to the highly productive floodplains, before contracting back into permanent waterholes of the channel following the drying out of floodplains (Bunn et al. , 2006;
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Balcombe et al. , 2007; Arthington and Balcombe, 2011). Here, population ‘explosion’ might be a more accurate description. White (2002) suggested such increase in flow or resource-related recruitment to also occur in Emydura inhabiting the Cooper Creek catchment, albeit at an obviously much lower ‘turtle’ rate, the species delaying recruitment to wetter, more productive, years. Owing to their limited ability to ‘expand’, Emydura in the basin may therefore be better described as following a ‘colonisation-extinction’ population dynamic at the reach scale, although probably sitting somewhere between the two: populations experience extirpation during extended periods of no flow followed by recolonisation of failed refugia and increased recruitment during wetter periods. The ability of some Emydura to delay recruitment (White, 2002), combined with a plastic growth rate and earlier maturity (Spencer, 2002b) and a superior dispersal ability, could prove a successful strategy to contend with extirpation events such as those observed in the Moonie River and possibly more common in the drier western catchments.
Despite having suggested a metapopulation dynamic in C. longicollis , it remains to be seen if a cycle of extinction-recolonisation is typical of the species or if the pattern observed here is more representative of a decline in populations. The ability of the species to remain within ‘permanent’ waterholes during dry periods, awaiting large rainfall before returning to productive wetlands and backwaters habitats is well documented (Chessman, 1978; Kennett and Georges, 1990; Roe, 2007; Roe and Georges, 2008c). This strategy would theoretically confer the species with an advantage for local recolonisation of failed waterholes, and the amount of evidence for extirpation events in both systems studied may be considered as strong support for an extinction-recolonisation dynamic in this species. However, the accumulating evidence, both through research and observation, all points toward a decline in this species populations across the MDB in the last decades (Thompson, 1983; Cann, 1998; Bower, 2011; Chessman, 2011). This evidence of extirpation may be a reflection of this trend rather than that of a metapopulation. Increasing rate of ‘extirpation’ events through time in this species would provide more certainty to this conclusion.
7.4 Concluding Remark
Management strategies for the MDB should include the modification of barriers in the river network and the restoration of replenishing seasonal flow. Indeed, E. m. macquarii , which with C. longicollis is considered to be increasingly of concern by experts in the field, may be able to move through locks and small weirs and dams, but not through larger infrastructure. The species may therefore be able to move over large distances during the large flow typical of unregulated rivers, but be restricted by large regulation infrastructure typical of upper reaches. Although not inferred in this study using genetic methods, the smaller types of infrastructure may also restrict movement in C. expansa (Bower et al. , 2011). More importantly and better supported in this study, the restoration of large seasonal flows enabling connectivity and recolonisation of backwater habitats appears essential to the health of
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turtle populations in the lower MDB (see Roe and Georges, 2009). Indeed, the findings of this study when combined to those of Bower (2011), Kennett and Georges (1990), and Chessman (2011) make a strong case against isolation of backwaters, as it can lead to salinity induced stress, smaller hatchlings, lower reproductive outcome, inbreeding and possibly to population extirpation following habitat quality crash. These seasonal flows would also greatly contribute to slowing down the decline in population size apparent in C. longicollis (Cann, 1998; Chessman, 2011) by providing the complex suite of seasonally productive habitats it requires.
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8 Appendices
8.1 Freshwater Turtles of the Murray – Darling Basin
Phylogeny
The freshwater turtles Chelodina expansa Gray, 1857, Chelodina longicollis (Shaw, 1794) and Emydura macquarii macquarii (Gray, 1830) are endemic to Australia (Goode, 1967; Georges and Thompson, 2010) and belong to the family Chelidae (suborder: Pleurodira) (Seddon & Georges & Baverstock et al. , 1997; Georges & Birrell & Saint et al. , 1998). The family has a clear Gondwanan origin with no fossil forms found outside their current distribution in the Australasian region and South America (Gaffney, 1991; Legler and Georges, 1993; Georges and Thompson, 2010). Chelids are side-necked turtles (Pleurodira), the head retracted sideways under the carapace. Molecular evidence shows that the Australian long-necked chelids are more closely related to the Australian short-necked chelids than to any of the South American species (Seddon et al. , 1997; Georges et al. , 1998), the long neck a perfect example of parallel evolution (Pritchard, 1984). Within the Chelidae, C. longicollis belongs to the subgenus Chelodina comprising Chelodina canni, Chelodina mccordi , Chelodina novaeguineae , Chelodina pritchardi , Chelodina reinammi and Chelodina steindachneri , a group typified by a shorter and thinner neck, narrow head and broader plastron. C. expansa is ascribed to the subgenus Macrochelodina along with Chelodina burrungandji , Chelodina parkeri and Chelodina rugosa (Georges et al. , 1998; Georges & Adams and McCord, 2002; Georges and Thompson, 2010) which possess a longer, stronger neck, broader head and thinner plastron (Georges and Thomson, 2006). A number of Australian turtle species are still in the process of being thoroughly assessed for classification, with some populations seen as potentially distinct to the recognised groups, but the status of both C. longicollis and C. expansa is uncontroversial (Georges and Thompson, 2010). E. m. macquarii is the holotype species for the genus Emydura . The status of the species is also uncontroversial (Georges and Adams, 1996; Georges and Thompson, 2010). Found in broad sympatry throughout the MDB from South Australia to Queensland, the three species have distinct life histories and distributions outside the MDB.
Chelodina expansa
Considering its large distribution, it comes as a surprise how much remains to be learned of the life history and ecology of C. expansa . The lack of information on this species was already underlined by Cogger (1993) when he described it as ‘Rare or Insufficiently Known’ in his ‘‘Action Plan for Australian Reptiles’’. Two decades later and Spencer and Thompson’s (2005) as well as Bower’s (2011) studies remind us of the slow advances made on the knowledge of this species. Some believe the paucity of information resulted from the secretive habits of the species, but it may in part also
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have resulted from the hesitation of researchers to study elusive taxa. Still patchy, the data collected in the last decade or so provides nonetheless a clearer image the life history and ecology of this species.
The largest Australian long-necked turtle (Goode, 1967; Cann, 1998) (largest recorded individual SCL = 485 mm (Legler and Georges, 1993), weighing in excess of 5.5 kg (Goode, 1967)), the species is surprisingly elusive (Chessman, 1988a). Field studies in the MDB have revealed a low capture rate for this species relative to E. m. macquarii and C. longicolllis , whichever the method used and the habitat type targeted (Chessman, 1988a; Meathrel et al. , 2002; Bower, 2011). It is therefore unknown if its classification as ‘Threatened’ in Victoria (Flora and Fauna Guarantee Act 1988, last updated 2010, Department of Natural Resources and Environment, 1999) and ‘Vulnerable’ in South Australia under the National Park and Wildlife Act (1972) (Australian Natural Resources Atlas, 2007; Department of Environment and Heritage, 2009) reflects the true status of the species or a lack of data (Spencer, 2002b; Spencer and Thompson, 2005).
C. expansa has a large distribution, extending from the Brisbane River in south east Queensland up to Rockhampton along the eastern coast, including freshwater lakes of Fraser (Cann, 1998), Moreton and Stradbrock Islands outside of the MDB. Within the MDB, the species may have extended its range into the lower parts of the Murray River in South Australia recently, some suggesting the species did not occur far into South Australia (Cogger, 1992), while other have clearly shown their occurrence in lower sections of the Murray River (Thompson, 1993; Cann, 1998; Bower, 2011). The system of dams and weirs and associated permanent waters along the South Australian section of the Murray River are believed to be the likely driver of this expansion (Thompson, 1993). The species is present in the Condamine River and its tributaries at the northern limits of the MDB, and occur as far West as the Warrego River, although at a lower density than in the rest of the Basin.
C. expansa is primarily found in the river main channel, although individuals are also common in permanent lakes, swamps, lagoons and oxbows in proximity of the main channel (Chessman, 1988a; Meathrel et al. , 2004). This association with the main channel could relate to the moderately high rate of evaporative water loss in this species (Chessman, 1984a) and the absence of recorded terrestrial movements, preventing the species from easily reaching neighbouring less permanent water bodies. Within the water column individuals are most abundant in the deeper sections (Chessman, 1988a), although individuals have also been observed to occupy the upper layer hiding among root-matts and debris to ambush potential prey (Legler, 1978) and individuals have been recorded in both deep and shallow waters using radiotelemetry (Ercolano, 2008; Bower, 2011). Aside from a (weak) association with depth, C. expansa abundance was found to be negatively correlated with distance from the main channel (Chessman, 1988a). Based on this single study, and contrary to expectations, no correlation between C. expansa abundance and water permanence seems to exist.
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Until recently not much was known of the aquatic and terrestrial habitat use of C. expansa . Rarely observed on land, it is believed that the few sightings made were of individuals moving to or from nesting sites (Legler, 1978; Cann, 1998), all tracks followed by Ercolano (2008) were also believed to be linked to nesting. Radiotelemetry showed that individuals can move up to 500 m in a twenty four hour period in aquatic habitats and were primarily found near submerged logs, dead trees and stumps (Ercolano, 2008; Bower, 2011). The study was carried out in mid autumn (April) and individuals moved further in the warmer months (Bower, 2011). Radiotelemetry showed the mean linear home range of females to cover 1.5 km and 11.2 km for males (Bower, 2011). Some males, but not females, moved up to 20 km over the course of the study. Such long distance movements are believed to be associated with searches for mating partners in the breeding season.
As alluded to above, the species is predominantly active during the warmer months, typically October to April, a pattern somewhat similar to E. m. macquarii (Chessman, 1988b; Bower, 2011). Rarely seen basking, activity in C. expansa appears correlated with water temperature. The species possess the highest minimum water temperature threshold for activity of the three MDB turtles (~18ºC compared to 16ºC and 12ºC in E. m. macquarii and C. longicollis respectively) (Chessman, 1988b; Greer, 2006). Active from spring to autumn, peak in activity are observed in October - November and then again in March (Chessman, 1988a). Individuals are mostly active during the day, with peaks at dusk and dawn, although juveniles demonstrate weaker cyclic patterns (Chessman, 1988b).
The nesting pattern of this species is best described as tropical, primarily nesting in Autumn (March to June) and less commonly in Spring (Georges, 1984; Legler, 1985; Booth, 1998b; a) in contrast to the exclusively Spring breeding season common to C. longicollis and E. m. macquarii and other temperate species. Winter nesting may only occur in Queensland due to the more clement climate at this time of the year in the region (Greer, 2006). This unusual nesting pattern in addition to its high minimum temperature threshold for activity (~18ºC) (Chessman, 1988b) has lead to the suggestion that the species originates in the Australian tropics and invaded the lower range of its present distribution in relatively recent times while retaining its breeding pattern (Legler, 1985; Legler and Georges, 1993), although some question this hypothesis (Greer, 2006). No data currently exisst that supports this hypothesis. Nests are typically dug during the daylight hours and on rainy days (Georges, 1984; Bowen et al. , 2005b) within 7 to 750m of the river channel and within 75m of the nearest neighbouring nest (Georges, 1984; Spencer and Thompson, 2003). Clutch size ranges from 5 to 28 (Goode and Russell, 1968; Georges, 1984; Booth, 1998a) with egg incubation averaging between 324 and 350 days (Goode and Russell, 1968; Georges, 1984). Moderate to high nest predation rate (50 to 70%) from foxes ( Vulpes vulpes ) and ravens (Corvus spp.) have been observed in this species, nest predation falling down to 18-38% after fox removal (Spencer and Thompson, 2003). Once hatched, juveniles are known to be preyed upon by spotted barramundi (Phillott and Parmenter,
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2000)(Phillot and Parmenter 2000) but other species may also prey upon them. As for all both sympatric species, sex is probably genetically determined in this species (Georges, 1988; Ezaz & Valenzuela & Grutzner et al. , 2006; Martinez & Ezaz & Valenzuela et al. , 2008).
The species is a specialised predator primarily feeding on macrocrustacean and fish (Legler, 1977; 1978; Chessman, 1983). Two individuals caught at a single site revealed distinct stomach content, with one individual containing only fish and the other containing only shrimps (most likely Macrobrachium sp.) (Cann, 1998). Hence the diet appears to be choice driven rather than ‘availability-dependent’, individuals perhaps varying their diet in response to their metabolic requirement at the time. The bulk of C. expansa diet appears to consist of fish (Meathrel et al. , 2004) and shrimp, but other type of live foods have been recorded such as frogs (Chessman, 1983) and crayfishes (Legler, 1977; 1978; Chessman, 1983). The diet seems to be similar between adult and juvenile, although larger individuals may target fewer but larger preys than juveniles (Chessman 1983) reflecting the feeding strategy of the species. Although foraging also occurs as evidenced by the presence of small benthic and terrestrial insects in the diet (Chessman, 1983), C. expansa and other long necked turtles are known for their sit-and-wait strategy, striking at passing prey items; a method known as ‘gape and suck’ (Legler, 1978). Individuals will sometimes bury their body in silt or hide in thick roots matts, the neck folded in a semisigmoid curve, remaining motionless while firmly anchoring themselves so as to be able to deliver an explosive and accurate extension of the neck, preventing backward motion of the body during the strike (Legler, 1978). Prior to the strike, the head and neck are slowly oriented towards the prey, while during the strike the mouth rapidly opens as it nears the prey, the hyoid apparatus depressed, creating a vacuum for the prey and surrounding water (Legler and Georges, 1993). This motion can be repeated every 1 to 6 seconds, depending on the quantity of detritus ingested during the strike (Legler, 1978). C. expansa has the longest neck of all Australian Chelids and is considered to have perfected this technique (Legler, 1978). An elegant description of C. expansa strikes, complete with step by step illustration of the gape and suck motion, can be found in Legler and Georges (1993).
Chelodina longicollis
The ecology and life history of the first described chelid turtle C. longicollis is well understood thanks to a number of studies. The species is considered of least concern (Cogger et al. , 1993; Kennett et al. , 2009) although some points to a worrying trend with few juveniles sighted within ageing and shrinking populations throughout its range (Cann, 1998; Chessman, 2011). Threats to the species include high nest predation (Thompson, 1983; 2005), high road-induced mortality (Cann, 1998; Rees et al. , 2009), waterways and wetlands degradation (Chessman, 1988a; Cann, 1998; Burgin and Ryan, 2008), and habitat disappearance either through repossession in urban areas (Burgin and Ryan, 2008;
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De Lathouder & Jones and Balcombe, 2009) or through prolonged drought and flow regulation (Kennett et al. , 2009). The species range extends from South Australia up to Charters Towers along the eastern coast, including the entire MDB, the Paroo drainage and the headwaters of the Cooper Creek drainage in western Queensland (Cann, 1998; Kennett et al. , 2009). Morphological variations exist on each side of the Great Dividing Range with the eastern form referred to as Chelodina longicollis sulcifera and the western form as Chelodina longicollis longicollis (Cann, 1998). Molecular studies show the eastern lineage to be an ancient lineage retained via incomplete lineage sorting which lies outside the recognised C. longicollis lineage which includes Chelodina pritchardii and Chelodina canni (Kate Hodges, unpublished data).
C. longicollis has evolved a number of traits to cope with highly variable habitat conditions. It is able to store and absorb water through its cloaca, adjusts its urine composition to retain salt and reduce water loss (Rogers, 1966; Roe and Georges, 2008a; Kennett et al. , 2009) and has developed a superior resistance to desiccation (Chessman, 1984a). These traits and its propensity to terrestrial movement are key factors in the widespread distribution of this species, although some populations may have been introduced by humans where it did not occur previously such as in some waterways of Victoria and South Australia (Beck, 1991). Concerns are raised that such introductions of new populations across its range, widespread pet trade of the species as well as people catching them in the wild and releasing them somewhere else may have lead to the alteration of the genetic pool of wild populations (Greer, 2006). The emergence of large scale irrigations and smaller farm dams since European colonisation in inland Australia further contributed to the extensive inland distribution of this species (Parmenter, 1985; Beck, 1991). The species low minimum temperature for activity (<12ºC) (Parmenter, 1976; Chessman, 1988b; Beck, 1991) enables it to enter higher latitudes and elevations than other Australian Chelids (Chessman, 1988b; Cann, 1998; Cogger, 2000); individuals have been trapped at altitudes of 1100m the highest altitude recorded for any Australian Chelid (Greer, 2006). This lower temperature threshold enables C. longicollis to be active throughout most of the year, June and July the only months the species was not trapped in the Murray River (Chessman, 1988b). In the more northern areas of its range where climatic conditions are more clement, the species may be active throughout the year (Cann, 1998; Kennett et al. , 2009).
C. longicollis occupies a wide range of habitat types, from permanent lakes and rivers, to temporary swamps, wetlands and other slow flowing water bodies (Chessman, 1988a; Burgin and Ryan, 2008; Kennett et al. , 2009). Its habitat preferences appear most distinct to those of E. m. macquarii and to a lesser extent to those of C. expansa , being always found at highest abundance relative to the other two species in water bodies with shallow, ephemeral waters, located at some distance from the main river channel (Chessman, 1988a). Although more abundant in distant and ephemeral water bodies, the species is nonetheless commonly found in all habitat type (Chessman, 1988a; Meathrel et al. , 2004).
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Its ability to move long distance overland may render the more ‘distant’ water bodies readily accessible and highly attractive owing to the reduced competition for food resources, both from fishes and from other turtle species (Georges et al. , 1986; Chessman, 1988a). Stomach volume content of C. longicollis caught in waters bodies without fishes were nearly eight times greater than those from waters where fish were present (Chessman, 1984b), while the absence of E. m. macquarii and C. expansa may be of advantage to C. Longicollis , individuals growing faster and having a higher reproductive output in the absence of such competition (Georges et al. , 1986; Kennett and Georges, 1990). This hypothesis of passive competition for resources has little direct experimental support and remains speculative (Georges & Limpus and Parmenter, 1993). Within the water column, C. longicollis occupies the lower layer (Legler, 1978; Chessman, 1988a) and is rarely seen swimming at the surface.
The wide range of habitat used by C. longicollis is associated with its ability to move between productive and unproductive habitats. It is known to spend the dry and unproductive periods in permanent water bodies (Kennett and Georges, 1990; Meathrel et al. , 2004; Roe and Georges, 2007; Roe and Georges, 2008c) and disperse to more productive, temporarily available, habitats during the warm and wet Australian summer period where recruitment and exchange of genes are believed to take place rendering terrestrial dispersal an integral part of this species’ ecology (Georges et al. , 1986; Kennett and Georges, 1990; Roe and Georges, 2007; Roe and Georges, 2008c). The species has been observed to move en mass overland (Cann, 1998; Roe and Georges, 2007) and is regularly seen crossing roads and other potential barriers to such movements; several tens of individuals have been observed unsuccessfully trying to go through the Rabbit Proof fence in the vicinity of Stanthorpe, Qld (Susan Fuller, pers. comm.). Exact cues for the onset of such movements are still unknown, although these have commonly been recorded following rain events (Stott, 1987; Roe and Georges, 2008c). This is especially true for movement from permanent to ephemeral water bodies (but not the opposite) and for juveniles in particular (Roe and Georges, 2008c). Movement from permanent to temporary wetlands following rains ensure a less desiccating ‘en route’ environment and increases the chance of reaching a newly flooded and highly productive water body (Roe and Georges, 2007; Roe and Georges, 2008c). A large proportion of overland movements appear associated with purposeful movement to and fro water body rather than with nesting, although incidences where individuals do both over the course of a single overland journey appear common (Stott, 1987). Contrary to the expectations of the ‘reproductive strategy hypothesis’ (Morreale et al. , 1984) that males should move further in search of a mating partner while females should move more frequently but over short distances in search of the most suitable nesting sites, the number and range of local movements in this species appear not to be sex, age or size related (Roe and Georges, 2008c).Terrestrial movement occurs in reasonably straight lines punctuated by marked alteration in direction toward the end destination, alluding to an acute navigational capacity (Stott, 1987; Graham et al. , 1996). Individuals
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probably use a combination of navigational cues, such as sun direction, landscape features and olfactory information (Graham et al. , 1996). C. longicollis uses terrestrial habitats for additional reasons to nesting and movements to and fro water bodies. Ninety percent of males and 75% of females used terrestrial habitats for aestivation at least once in Booderee National Park, Australia, staying on average 64 days without returning to waters with an extreme aestivation lasting for up to 480 days (Roe and Georges, 2007). The use of terrestrial habitat is often associated with wetland drying, individuals finding refuge buried deep in the leaf litter (Roe and Georges, 2008c; Roe and Georges, 2008b; Kennett et al. , 2009), the length of terrestrial stay increasing with decreasing wetland hydroperiod (Roe and Georges, 2008b).
C. longicollis is a carnivore feeding on a wide range of food sources such as plankton, nekton, benthic macro-invertebrates, carrion and terrestrial organisms, and hence broadly overlaps in diet with C. expansa (Chessman, 1984b; Georges et al. , 1986; Meathrel et al. , 2004). Individuals are not selective in the animals they eat, all studies having found the abundance of food items in the stomach to be positively related to their abundance in the surrounding environment (Chessman, 1984b; Georges et al. , 1986; Meathrel et al. , 2002). Adults and juveniles have similar diet range, although adults feed to a greater extent on carrion and juveniles on benthic-littoral invertebrates reflecting the differing habitat usage between age classes (Chessman, 1984b). The species also apply the ‘suck and gape’ feeding method described for C. expansa . The activity of individuals, related to feeding or not, peak in the early daylight hours and late afternoon (Chessman, 1988b).
As for C. expansa and E. m. macquarii , C. longicollis show size dimorphism with males maturing at a smaller size and attaining a smaller maximum size than females (Parmenter, 1985). Breeding occurs in early September to October and nesting from October to December (Parmenter, 1976; 1985; Kennett and Georges, 1990), a typical temperate zone sexual cycle. Nests are dug in close proximity to the water body (Stott, 1987; Roe and Georges, 2008c) and the number and sizes of clutches varies geographically, although a single clutch per year appears to be the norm (Parmenter, 1985; Kennett and Georges, 1990; Greer, 2006). Incubation ranges from 105 to 123 days, hatchlings emergence coinciding with rainfall (Parmenter, 1985; Greer, 2006). Typical of K species which experience high egg and juvenile mortality and high adult survival, nest predation is high (96%) (Thompson, 1983) and only 3% of eggs survive to sub–adulthood (Thompson, 1983; Parmenter, 1985). Sex is genetically determined (Georges, 1988; Ezaz et al. , 2006).
Emydura macquarii macquarii
E. m. macquarii is considered the most abundant species in the MDB (Thompson, 1993; Cann, 1998; Georges and Thomson, 2006). Considered subpopulations by some based on shared allozymes
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haplotypes (Georges and Adams, 1996; Georges and Thompson, 2010), but subspecies by others based on morphological examination (Cann, 1998), a number of E. m. macquarii populations occupy the coastal catchments of Eastern Australia from the Nepean-Hawkesbury catchment, NSW, in the south up to the Brisbane and Pine Rivers drainage, Qld, in the north, as well as in the Paroo and Bulloo catchment in western Queensland (Cann, 1998; Georges and Thompson, 2010). More recent, unpublished, mitochondrial sequence analyses (Schaffer and Georges, unpublished data in Georges and Thompson, 2010) shows highly divergent haplotypes within the broad southern complex of this species (MDB and Coastal catchments), while pilot study showed microsatellite alleles at multiple loci not shared amongst coastal catchments and the MDB (this study, data not shown). These populations are nonetheless considered subpopulations and not subspecies (Georges and Thomson, 2006; Georges and Thompson, 2010).
Classified as ‘Vulnerable’ in South Australia (Department of Environment and Heritage, 2009) there is no imminent concern for E. m. macquarii , numerous populations remaining across its range although individual numbers may be on the decreasing trend (Cann, 1998; Chessman, 2011). Reduced surface water in the basin may pose a threat to the long term persistence of this species; approximately ten thousand E. m. macquarii died when Lake Numalla, Queensland, dried up in 2007 (Roe and Georges, 2009) and similar drought and low flow related deaths have been reported for this species (Cann, 1998; Chessman, 2011).
Caught in larger numbers relative to the other two species in the deeper section of river and connected oxbows and anabranches, the species share greater habitat preference with C. expansa than with C. longicollis . Its occurrence and abundance negatively correlates with distance from the main channel and positively associates with water permanence, transparency, depth and flow rate (Chessman, 1988a). The species absence from non-permanent water bodies could relate to its greater evaporative water loss compared to the others two species (Chessman, 1984a), which suggests poor terrestrial dispersal ability. Quantitative data on non-nesting movement remains sparse: in ephemeral systems of the Cooper creek catchment E. m. macquarii only disperse between habitats during large floods (Goodsell, 2002), while good homing abilities were demonstrated by individuals returning to site of capture within three months although displacements were of relatively short distances (< 3 km) (Goode and Russell, 1968). These studies suggest restricted dispersal abilities in this species, although a marked individual was recaptured at distances of more than 400 km from site of first capture (Colin Limpus, pers. comm.) but such movement is believed to be the exception.
E. m. macquarii can be active at moderately low temperature, its minimum temperature threshold (16ºC) being approximately midway between that of C. longicollis and C. expansa (Chessman, 1988b). Individuals have been observed slowly swimming in 9.0ºC waters and in water as cold as 5-
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7ºC (Chessman, 1988b) but movement at such low temperatures are deemed not to be the norm. A clear seasonal activity trend exists in this species from low but increasing activity in early spring, to high activity in the warmer part of Summer rapidly decreasing in the early Autumn (March –April), the level of activity relating to ambient temperature (Chessman, 1986; Chessman, 1988a; Chessman, 1988b). Aquatic and terrestrial basking is common in this species, its frequency associated with external temperature and with difference in water surface to external temperature (Chessman, 1988b). In the wild, E. m. macquarii is mostly active around dusk and dawn with some additional nocturnal activities during the warmer months of the year (Chessman, 1988b). These movements are associated with feeding activity on filamentous algae and carrion primarily and invertebrates to a lesser extent (Chessman, 1986; Spencer & Thompson and D. Hume, 1998) the propensity towards each items changing with age but not sex (Chessman, 1986). Activity is mostly restricted to the middle layer of the water column, made up of logs and other debris (Legler, 1978). A short-necked turtle, E. m. macquarii is unable to capture rapidly moving prey available to both Chelodina species and its omnivorous diet reflects a more opportunistic, scavenging, behaviour, partly overlapping with that of the other two species (Chessman, 1986; Spencer et al. , 1998; Meathrel et al. , 2002).
Nesting occurs between October and mid-December, comparable to C. longicollis (Goode and Russell, 1968; Spencer, 2002b; Spencer and Thompson, 2003). Females nest close to the river channel (2 to 40m), distance a trade-off between predation on the clutch (higher closer to stream) and predation on the laying female (increase with increasing distance) (Spencer, 2002a). Nesting time is primarily at night and driven by rain event, probably owing to the high cutaneous water loss in this species and the soil softening qualities of water for nest digging (Chessman, 1984a; Greer, 2006) , although predator avoidance may play a role as well (Spencer, 2002a; Spencer and Thompson, 2003). Incubation time ranges from 66 to 85 days and clutch size from 6 to 30 eggs (Goode and Russell, 1968). Sex in E. m. macquarii is genetically determined, sex-ratio in wild population typically biased towards females (Thompson, 1983). The species is sexually dimorphic, females growing larger than males (Murphy and Lamoreaux, 1978; Thompson, 1983; Spencer, 2002b).
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8.2 Sampling Sites
8.2.1 Moonie, Barwon, Border and Gwydir Rivers (Chapters 3, 5 and 6)
Sites names, codes, coordinates and sample sizes from the Moonie, Barwon, Border and Gwydir Rivers. A: Included in summary statistic analyses (Chapter 3); R: site included in relatedness analysis for sex-biased dispersal (Chapter 6) (note: juveniles at each marked sites were not included). All samples were included in clustering analyses at the basin scale (Chapter 5).
Site Coordinates Sample size Catchment Site Name Code Latitude Longitude C. expansa C. longicollis E. m. macquarii Moonie River Kilawara KI -27.8948 150.2929 6 A 6 A 18 AR Kurmala KU -27.7875 149.9572 8 AR 3 A 46 AR Verena VE -27.8948 149.5599 5 A 12 AR 9 AR Kooroon KO -27.9567 149.3826 11 AR 4 A 12 AR Altonvale AL -27.9718 149.2757 0 6 AR 0 Carbeen CAR -28.1758 148.9358 3 A 0 19 AR Appletree AP -28.3233 148.8465 5 A 4 A 37 AR Nindigully NI -28.4276 148.8165 5 A 0 15 AR Nullera NU -28.6457 148.8581 1 7 AR 9 AR Fenton FE -28.9331 148.7351 13 AR 2 11 AR Barwon River Caloola CAL -29.7418 148.6937 15 AR 6 AR 0 Longswamp LO -29.4327 148.6937 4 A 6 A 6 AR Border Rivers Warramildi WA -28.8295 149.1065 0 0 1 Boomi BOM -28.6763 149.4029 6 AR 1 1 Pungbougal PU -28.6651 150.2681 0 0 5 AR Boobera BO -28.6151 150.2681 0 0 8 A Goondiwindi GO -28.5484 150.3014 11 AR 2 10 AR Macintyre Inglewood MI -28.4653 150.9587 1 0 2 Bonshaw Weir BW -28.9889 151.2777 3 A 2 17 AR Beardy Creek BC -29.2107 151.3789 0 6 AR 2 Beehive Dam BD -28.9889 151.9447 0 2 0 Kwiamble NP KN -29.1381 150.9843 0 4 AR 16 AR Ballandine Creek BA -28.8667 151.7834 0 6 AR 0 Pindari Dam PI -29.3936 151.2563 0 0 14 AR Well Crossing WC -29.3619 151.1445 0 0 6 AR Storm King Dam SK -28.7333 151.9833 2 0 0 Gwydir River Bingarra Bridge BB -29.8595 150.5812 0 4 AR 13 AR Kingstown KG -30.4956 151.1329 0 5 AR 0 Pallamallawa PA -29.4941 150.1648 0 1 4 AR Myall Creek MY -29.7547 151.0483 0 1 0 Total 99 90 281
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8.2.2 Sites from remaining catchments in the MDB (Chapters 4, 5 and 6)
Sites names, codes, coordinates and sample sizes from all catchments not included in Appendix 8.2.1. A: Included in summary statistic analyses (Chapter 4); R: site included in relatedness analysis for sex-biased dispersal (Chapter 6) (note: juveniles at each marked sites were not included). All samples were included in clustering analyses at the basin scale (Chapter 5).
Coordinates Sample size E. m. Catchment Site Name Site Code Latitude Longitude C. expansa C. longicollis macquarii Ballone River Kapunda Dam KUP -27.987 148.657 1 0 1 Ballone River St George STG -28.072 148.653 0 1 0 Castlereagh River Coonabarabran COO -31.267 149.281 0 11 R 5 R Castlereagh River Joppa jappa JOP -31.400 149.343 0 11 R 0 Condamine_middle Chinchilla CIC -26.801 150.679 6 R 1 13 R Condamine_middle Leslie Dam LES -28.227 151.919 0 0 4 R Condamine_upper Bowenville Reserve BOR -27.330 151.470 0 0 1 Condamine_upper Canal Creek CAC -28.009 151.580 1 0 0 Condamine_upper Gowrie Creek GOW -27.470 151.745 5 0 0 Condamine_upper Middletons Bri Bri MBB -27.680 151.890 7 R 0 0 Condamine_upper Oakey Creek OAK -27.330 151.470 1 0 0 Condamine_upper Warwick WAR -28.207 152.037 1 0 0 Condamine_upper Westbrook Creek WEB -27.502 151.745 0 0 1 Culgoa River Kookaburra KOO -29.164 147.279 2 0 0 Culgoa River Talawanta TAL -29.516 146.945 1 0 0 Culgoa River Westmunda WES -29.709 146.617 1 0 0 Lachlan River Lake Forbes LFO -33.384 147.990 0 6 R 11 AR Lower Murray River Below Lock 10 BL10 -34.113 141.891 11 AR 6 AR 0 Lower Murray River Goolwa GOL -35.499 138.791 0 11 A 0 Lower Murray River Lake Bonney LBO -34.218 140.454 2 5 AR 8 AR Lower Murray River Gura Gura Lake LGU -34.314 140.662 8 AR 4 AR 8 AR Lower Murray River Morgan Cadel MOC -34.046 139.747 5 AR 8 AR 10 AR Lower Murray River Murray Bridge MUB -35.087 139.307 16 AR 10 AR 7 AR Lower Murray River Murtho Reserve MUR -34.085 140.782 15 AR 13 AR 10 AR Lower Murray River Walpolla Island WAI -34.143 141.629 12 AR 8 AR 7 AR Lower Murray River Wentworth WEN -34.112 141.917 6 AR 0 9 AR Macquarie River Cudgegong CUG -32.588 149.589 0 0 15 R Macquarie River Gin Gin GIN -31.888 148.092 1 5 R 5 Macquarie River Lake Burrendong LBU -32.665 149.168 0 11 0 Macquarie River Narromine NAR -32.225 148.247 0 2 R 1 Maranoa River Bollon BOL -28.020 147.381 0 1 0 Maranoa River Mitchell Dam MIT -26.489 147.981 0 1 2 Maranoa River North Wallam NOW -28.020 147.491 0 5 0 Maranoa River Russell Dam RUD -25.803 148.240 0 9 0 Maranoa River Thruston THR -27.717 147.700 0 5 R 0
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8.2.2 Continued: Sites from remaining catchments in the MDB (Chapters 4, 5 and 6)
Sites names, codes, coordinates and sample sizes from all catchments not included in Appendix 8.2.1. A: Included in summary statistic analyses (Chapter 4); R: site included in relatedness analysis for sex-biased dispersal (Chapter 6) (note: juveniles at each marked sites were not included). All samples were included in clustering analyses at the basin scale (Chapter 5).
Coordinates Sample size Site E. m. Catchment Site Name Code Latitude Longitude C. expansa C. longicollis macquarii Molonglo River Pine Ridge PIR -35.231 149.004 0 10 1 Molonglo River Pine Ridge Substation PIS -35.217 148.998 0 3 0 Murray_Riverina Albury ALB -36.092 146.948 13 AR 9 AR 10 AR Murray_Riverina Gunbower Creek GUC -35.937 144.397 0 0 6 AR Murrumbidgee River Bowman lagoon BOW -35.112 147.342 0 0 14 AR Murrumbidgee River Childowlah CHI -34.900 148.550 0 2 0 Murrumbidgee River Currawunanna CUR -35.032 147.101 1 1 2 Murrumbidgee River Griffith Barren Swamp GBS -34.173 145.802 13 AR 1 10 A Murrumbidgee River Narranderra NAD -34.755 146.545 1 7 1 Murrumbidgee River Wagga wagga WAG -35.123 147.352 3 1 0 Namoi River Gunnedah GUN -30.974 150.258 2 5 R 0 Namoi River Maules Creek MAL -30.500 150.117 0 1 0 Namoi River Manilla MAN -30.756 150.717 3 0 0 Namoi River Norrie Property NOP -30.243 149.684 1 4 R 2 Narran River Black Lake BLA -29.717 147.450 0 4 0 Warrego River Anganalla Creek ANG -26.412 146.887 0 0 2 Warrego River Charleville CHA -26.478 146.102 0 0 1 Warrego River Clear Lagoon CLL -27.075 145.958 0 0 3 Warrego River Cunnamulla CUN -28.068 145.679 0 9 0 Warrego River Kay KAY -28.205 145.714 0 3 0 Warrego River Lake Darmouth LDA -26.011 145.317 0 0 7 Warrego River Quilberry QUI -27.080 145.923 0 1 5 Warrego River Sandford Park SAP -26.923 146.037 3 7 10 Warrego River Thurulgoona THU -28.770 145.977 0 0 9 Yass River Tony Dam TOD -34.864 149.009 0 8 R 2 Total 142 210 203
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8.3 Pairwise and Global FST Tables
8.3.1 E. m. macquarii pairwise FST (θ) from microsatellite data (9 loci) Moonie River, Border – Barwon Rivers and Gwydir River. Below diagonal: FST (θ); Bold value: significant FST (θ); Above diagonal: P-value; Underlined value: significant at α = 0.05. See Appendix 8.2.1for site names and Figure 3.1 for site locations
LO BW BO KO PU KN PI WC BB PA AP CA FE NI NU NI KO KU VE LO 0.863 0.121 0.453 0.566 0.042 0.105 0.780 0.707 0.945 0.266 0.454 0.731 0.285 0.810 0.573 0.060 0.205 0.036 BW -0.013 0.026 0.449 0.674 0.000 0.000 0.294 0.342 0.383 0.006 0.433 0.381 0.170 0.694 0.186 0.002 0.010 0.012 BO 0.023 0.030 0.142 0.184 0.000 0.000 0.006 0.091 0.017 0.178 0.188 0.093 0.016 0.471 0.022 0.482 0.018 0.001 GO 0.009 0.004 0.026 0.959 0.010 0.010 0.412 0.280 0.507 0.302 0.803 0.610 0.468 0.545 0.329 0.051 0.276 0.019 PU -0.007 -0.008 0.017 -0.028 0.046 0.024 0.377 0.631 0.393 0.217 0.818 0.489 0.180 0.826 0.221 0.067 0.276 0.008 KN 0.036 0.049 0.132 0.042 0.038 0.024 0.635 0.000 0.213 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 PI 0.025 0.044 0.135 0.044 0.050 0.028 0.880 0.000 0.108 0.000 0.000 0.000 0.000 0.006 0.000 0.000 0.000 0.000 WC -0.012 0.011 0.077 0.013 0.011 -0.007 -0.020 0.074 0.757 0.007 0.112 0.038 0.033 0.344 0.089 0.000 0.038 0.007 BB -0.007 0.004 0.020 0.011 -0.007 0.062 0.053 0.029 0.403 0.276 0.497 0.868 0.041 0.669 0.390 0.011 0.028 0.001 PA -0.033 0.005 0.067 0.006 -0.006 0.016 0.033 -0.019 0.003 0.209 0.498 0.310 0.109 0.533 0.601 0.025 0.499 0.020 AP 0.012 0.022 0.013 0.008 0.016 0.076 0.076 0.049 0.005 0.021 0.312 0.752 0.164 0.396 0.879 0.523 0.201 0.001 CAR 0.002 0.002 0.012 -0.007 -0.014 0.064 0.051 0.023 0.000 -0.001 0.004 0.666 0.209 0.831 0.747 0.253 0.327 0.004 FE -0.008 0.004 0.020 0.000 0.000 0.066 0.070 0.040 -0.010 0.010 -0.004 -0.003 0.229 0.643 0.663 0.137 0.165 0.008 NI 0.011 0.010 0.033 0.005 0.019 0.076 0.075 0.037 0.020 0.030 0.009 0.008 0.009 0.795 0.453 0.036 0.016 0.004 NU -0.008 -0.003 0.005 0.007 -0.012 0.068 0.048 0.019 -0.003 0.005 0.005 -0.007 0.000 -0.005 0.525 0.167 0.158 0.070 KI -0.004 0.007 0.029 0.006 0.011 0.059 0.049 0.025 0.002 -0.009 -0.006 -0.005 -0.004 0.001 0.001 0.163 0.955 0.004 KO 0.026 0.042 -0.002 0.032 0.027 0.129 0.119 0.075 0.028 0.048 0.001 0.007 0.013 0.021 0.018 0.010 0.022 0.001 KU 0.014 0.017 0.030 0.008 0.012 0.059 0.062 0.032 0.016 0.003 0.004 0.003 0.010 0.018 0.013 -0.007 0.020 0.004 VE 0.034 0.030 0.077 0.054 0.068 0.105 0.108 0.065 0.058 0.058 0.049 0.046 0.039 0.041 0.031 0.040 0.060 0.038
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8.3.2 E. m. macquarii pairwise FST (θ) from microsatellite data after removal of locus TLE13.3 owing to null alleles presence. Moonie
River, Border – Barwon Rivers and Gwydir River. Below diagonal: FST (θ); Bold value: significant FST (θ); Above diagonal: P-value; Underlined value: significant at α = 0.05. Sites codes see Appendix 8.2.1 and Figure 3.1 for site location.
LO BW BO GO PU KN PI WC BB PA AP CAR FE NI NU KI KO KU VE LO 0.706 0.234 0.392 0.655 0.015 0.107 0.562 0.562 0.810 0.168 0.383 0.597 0.204 0.587 0.430 0.038 0.127 0.047 BW -0.011 0.246 0.503 0.886 0.000 0.001 0.208 0.355 0.230 0.004 0.600 0.279 0.084 0.554 0.102 0.002 0.005 0.017 BO 0.009 0.007 0.298 0.317 0.000 0.000 0.012 0.208 0.058 0.596 0.329 0.445 0.380 0.637 0.266 0.543 0.167 0.005 GO 0.008 0.001 0.013 0.995 0.035 0.014 0.285 0.176 0.480 0.236 0.761 0.491 0.426 0.408 0.290 0.044 0.211 0.014 PU -0.013 -0.021 0.007 -0.040 0.171 0.143 0.480 0.641 0.807 0.293 0.864 0.599 0.478 0.870 0.377 0.061 0.360 0.021 KN 0.041 0.049 0.105 0.030 0.016 0.037 0.782 0.000 0.141 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 PI 0.021 0.044 0.110 0.046 0.025 0.022 0.880 0.002 0.130 0.000 0.001 0.000 0.000 0.008 0.000 0.000 0.000 0.000 WC -0.008 0.013 0.068 0.022 0.004 -0.016 -0.022 0.047 0.708 0.003 0.058 0.021 0.027 0.200 0.044 0.000 0.015 0.005 BB -0.006 0.003 0.010 0.015 -0.009 0.055 0.048 0.038 0.289 0.158 0.394 0.831 0.024 0.486 0.322 0.006 0.014 0.001 PA -0.025 0.014 0.041 0.008 -0.027 0.023 0.030 -0.015 0.010 0.130 0.478 0.195 0.090 0.433 0.432 0.023 0.399 0.016 AP 0.016 0.024 -0.003 0.009 0.011 0.075 0.079 0.059 0.009 0.027 0.325 0.617 0.118 0.222 0.868 0.584 0.166 0.001 CAR 0.001 -0.003 0.003 -0.007 -0.019 0.051 0.045 0.029 0.001 -0.004 0.003 0.755 0.276 0.758 0.789 0.139 0.260 0.003 FE -0.007 0.005 0.000 0.003 -0.006 0.066 0.076 0.050 -0.010 0.016 -0.002 -0.007 0.116 0.433 0.601 0.166 0.115 0.004 NI 0.011 0.014 0.002 0.004 0.000 0.074 0.078 0.040 0.022 0.031 0.010 0.004 0.014 0.658 0.268 0.083 0.008 0.003 NU -0.004 -0.002 -0.005 0.010 -0.018 0.065 0.047 0.027 0.001 0.009 0.010 -0.008 0.004 -0.004 0.453 0.096 0.098 0.078 KI -0.003 0.010 0.004 0.006 0.001 0.057 0.050 0.031 0.002 -0.003 -0.007 -0.007 -0.005 0.004 0.000 0.225 0.907 0.001 KO 0.028 0.042 -0.006 0.033 0.026 0.123 0.113 0.082 0.034 0.047 -0.002 0.010 0.009 0.014 0.021 0.005 0.023 0.001 KU 0.017 0.020 0.011 0.009 0.006 0.055 0.063 0.038 0.019 0.006 0.004 0.003 0.011 0.020 0.015 -0.007 0.019 0.002 VE 0.030 0.028 0.045 0.059 0.045 0.102 0.121 0.075 0.057 0.058 0.049 0.044 0.039 0.042 0.028 0.043 0.050 0.038
165
8.3.3 E. m. macquarii pairwise FST (θ) from microsatellite data with nmax = 6. Moonie River, Border – Barwon Rivers and Gwydir
River. Below diagonal: FST (θ); Bold value: significant FST (θ); Above diagonal: P-value; Underlined value: significant at α = 0.05. Sites codes see Appendix 8.2.1 and Figure 3.1 for site location.
LO BW BO GO PU KN PI WC BB PA AP CAR FE NI NU KI KO KU VE LO 0.692 0.189 0.150 0.592 0.077 0.464 0.789 0.518 0.954 0.599 0.417 0.652 0.376 0.169 0.911 0.175 0.300 0.125 BW -0.015 0.038 0.653 0.439 0.064 0.267 0.133 0.597 0.183 0.420 0.677 0.282 0.300 0.558 0.725 0.054 0.721 0.045 BO 0.022 0.038 0.106 0.266 0.002 0.002 0.011 0.094 0.029 0.089 0.080 0.179 0.115 0.628 0.090 0.753 0.034 0.020 GO 0.037 -0.009 0.044 0.860 0.172 0.138 0.178 0.596 0.219 0.471 0.511 0.148 0.332 0.459 0.214 0.063 0.688 0.042 PU -0.007 -0.008 0.013 -0.025 0.035 0.166 0.382 0.745 0.401 0.114 0.422 0.328 0.433 0.755 0.189 0.114 0.313 0.025 KN 0.042 0.036 0.145 0.033 0.055 0.124 0.180 0.011 0.112 0.025 0.021 0.001 0.008 0.000 0.008 0.000 0.142 0.009 PI 0.001 0.010 0.125 0.045 0.022 0.045 0.786 0.063 0.233 0.102 0.124 0.005 0.010 0.065 0.131 0.004 0.104 0.007 WC -0.012 0.025 0.082 0.037 0.011 0.026 -0.021 0.216 0.748 0.210 0.196 0.009 0.092 0.060 0.176 0.004 0.234 0.034 BB 0.005 -0.006 0.044 0.005 -0.016 0.079 0.050 0.026 0.318 0.232 0.627 0.593 0.274 0.489 0.740 0.097 0.321 0.381 PA -0.033 0.010 0.062 0.029 -0.006 0.039 0.017 -0.019 0.014 0.673 0.460 0.106 0.110 0.102 0.468 0.054 0.475 0.060 AP -0.002 0.001 0.039 0.010 0.030 0.051 0.039 0.026 0.024 -0.011 0.442 0.443 0.261 0.538 0.702 0.575 0.746 0.027 CAR 0.003 -0.017 0.038 0.005 0.006 0.063 0.036 0.024 -0.006 -0.005 0.002 0.825 0.623 0.769 0.747 0.247 0.596 0.282 FE -0.010 0.001 0.014 0.029 0.001 0.086 0.059 0.063 -0.004 0.020 0.004 -0.025 0.358 0.892 0.668 0.562 0.097 0.021 NI 0.013 0.014 0.036 0.023 0.002 0.089 0.069 0.046 0.020 0.043 0.022 -0.003 0.013 0.664 0.437 0.051 0.128 0.028 NU 0.029 0.002 0.002 0.017 -0.008 0.101 0.056 0.060 0.012 0.047 0.006 -0.012 -0.020 0.006 0.244 0.897 0.272 0.057 KI -0.022 -0.019 0.032 0.026 0.015 0.076 0.030 0.030 -0.012 -0.004 -0.010 -0.016 -0.014 0.007 0.025 0.307 0.739 0.451 KO 0.021 0.029 -0.014 0.055 0.019 0.144 0.086 0.085 0.034 0.041 -0.003 0.021 -0.005 0.044 -0.018 0.010 0.086 0.058 KU 0.015 -0.016 0.047 -0.006 0.014 0.028 0.048 0.031 0.018 0.001 -0.007 -0.005 0.028 0.043 0.029 -0.011 0.039 0.105 VE 0.024 0.036 0.063 0.083 0.066 0.131 0.113 0.061 0.011 0.052 0.065 0.021 0.046 0.066 0.051 0.003 0.045 0.037
166
8.3.4 C. expansa pairwise FST (θ) from microsatellite data (11 loci) in the Moonie River, Border – Barwon Rivers and Gwydir
River. Below diagonal: FST (θ); Bold value: significant FST (θ); Above diagonal: P-value; Underlined value: significant at 0.05. See Appendix 8.2.1 for site names and Figure 3.1 for site location.
CAL LO BW BOM GO AP CAR FE NU KI KO KU VE CAL 0.143 0.008 0.077 0.158 0.278 0.216 0.009 0.073 0.001 0.001 0.065 0.157 LO 0.022 0.109 0.075 0.233 0.226 0.119 0.437 0.276 0.024 0.086 0.630 0.270 BW 0.089 0.070 0.010 0.041 0.240 0.102 0.302 0.128 0.039 0.093 0.042 0.304 BOM 0.021 0.038 0.089 0.555 0.115 0.128 0.281 0.100 0.295 0.189 0.430 0.627 GO 0.011 0.023 0.064 -0.005 0.210 0.379 0.017 0.479 0.064 0.028 0.365 0.438 AP 0.010 0.041 0.053 0.033 0.023 0.565 0.489 0.899 0.287 0.198 0.550 0.953 CAR 0.017 0.035 0.099 0.007 0.011 -0.004 0.776 0.361 0.074 0.182 0.481 0.651 FE 0.035 0.011 0.028 0.012 0.037 0.008 -0.012 0.671 0.064 0.530 0.584 0.784 NI 0.027 0.019 0.034 0.019 0.001 -0.029 -0.002 -0.005 0.058 0.770 0.559 0.660 KI 0.063 0.079 0.075 -0.005 0.035 0.008 0.036 0.034 0.031 0.089 0.226 0.871 KO 0.053 0.033 0.038 0.012 0.032 0.020 0.022 0.001 -0.016 0.020 0.790 0.926 KU 0.019 -0.013 0.061 -0.005 0.003 -0.005 -0.013 -0.001 -0.010 0.004 -0.013 0.955 VE 0.021 0.031 0.040 -0.007 0.007 -0.029 -0.009 -0.007 -0.005 -0.031 -0.022 -0.037
167
8.3.5 C. longicollis pairwise FST (θ) from microsatellite data (10 loci) in the Moonie River, Border – Barwon Rivers and Gwydir River.
Below diagonal: FST (θ); Bold value: significant FST (θ); Above diagonal: P-value; Underlined value: significant at 0.05. Sites codes see Appendix 8.2.1 and Figure 3.1 for site location.
LO CAL BC KN BA BB KG AP NU AL KI KO KU VE LO 0.531 0.646 0.020 0.027 0.330 0.414 0.018 0.674 0.024 0.881 0.019 0.443 0.607 CAL -0.014 0.526 0.547 0.580 0.875 0.391 0.328 0.832 0.173 0.874 0.056 0.294 0.173 BC -0.017 -0.009 0.223 0.073 0.638 0.391 0.010 0.527 0.505 0.742 0.042 0.857 0.902 KN 0.050 -0.010 0.012 0.142 0.766 0.305 0.088 0.136 0.151 0.418 0.058 0.477 0.023 BA 0.040 -0.006 0.032 0.032 0.185 0.066 0.021 0.031 0.042 0.158 0.005 0.303 0.017 BB -0.007 -0.027 -0.016 -0.025 0.023 0.293 0.310 0.863 0.465 0.917 0.172 0.455 0.675 KG -0.011 -0.002 -0.003 0.015 0.048 0.012 0.113 0.520 0.418 0.382 0.354 0.968 0.093 AP 0.041 0.005 0.059 0.081 0.069 0.019 0.040 0.303 0.093 0.087 0.198 0.141 0.004 NU -0.012 -0.016 -0.003 0.039 0.046 -0.022 -0.004 0.020 0.493 0.808 0.559 0.623 0.835 AL 0.026 0.015 -0.006 0.030 0.043 0.001 0.001 0.037 0.003 0.496 0.815 0.990 0.536 KI -0.027 -0.021 -0.015 0.007 0.023 -0.033 0.004 0.040 -0.013 0.001 0.049 0.912 0.857 KO 0.030 0.030 0.035 0.069 0.103 0.018 0.005 0.033 -0.003 -0.019 0.041 0.742 0.033 KU -0.010 0.011 -0.036 0.001 0.019 0.006 -0.052 0.063 0.000 -0.052 -0.030 -0.028 0.734 VE -0.010 0.010 -0.020 0.036 0.037 -0.012 0.017 0.074 -0.012 -0.004 -0.015 0.035 -0.017
168
8.3.6 Global FST Table for C. e xpansa , C. longicollis and E. m. macquarii in the Moonie and Border Rivers catchments. E. m. macquarii values in brackets obtained with (n = 6) following standardisation of sample size.
Maximum Channel Distance Species Catchment FST P-value (km) C. expansa Moonie 0.014 0.026 521 Moonie - Border 0.018 0.002 929 C. longicollis Moonie 0.004 0.186 521 Moonie (No outlier) 0.000 0.764 521 Moonie - Border 0.009 0.040 1078 Moonie - Border (No outlier) 0.000 0.861 965 E. m. macquarii Moonie 0.009 (0.013) 0.011 (0.132) 435 Moonie (No outlier) 0.005 (0.006) 0.155 (0.394) 435 Moonie - Border 0.024 (0.026) 0.000 (0.000) 996 Moonie - Border (No outlier) 0.007 (0.006) 0.064 (0.292) 929
8.3.7 C. expansa pairwise FST (θ) from microsatellite data (11 loci) in the lower Murray-
Darling Basin. Below diagonal: FST (θ); Bold value: significant FST (θ); Above diagonal: P-value; Underlined value: significant at α = 0.05. Site codes see Appendix 8.2.2 and Figure 4.1 for site location.
MUB MOC LGU MUR WAI BL10 WEN GBS ALB MUB 0.373 0.041 0.22 0.043 0.727 0.686 0.000 0.000 MOC 0.006 0.293 0.196 0.836 0.469 0.257 0.09 0.000 LGU 0.024 0.011 0.403 0.718 0.037 0.086 0.000 0.000 MUR 0.004 0.011 -0.003 0.482 0.414 0.429 0.000 0.000 WAI 0.021 -0.013 -0.008 -0.001 0.238 0.202 0.004 0.000 BL10 -0.007 0.003 0.032 -0.001 0.010 0.717 0.001 0.000 WEN -0.008 0.027 0.034 -0.002 0.020 -0.010 0.018 0.019 GBS 0.067 0.025 0.068 0.042 0.038 0.053 0.043 0.000 ALB 0.090 0.092 0.078 0.054 0.054 0.083 0.039 0.088
169
8.3.8 C. longicollis pairwise FST (θ) from microsatellite data (10 loci) in the lower
Murray-Darling Basin. Below diagonal: FST (θ); Bold value: significant FST (θ); Above diagonal: P- value; Underlined value: significant at α = 0.05. Site codes see Appendix 8.2.2 and Figure 4.1 for site location.
GOL MUB MOC LBO LGU MUR WA BL10 ALB GOL 0.544 0.132 0.016 0.098 0.158 0.091 0.049 0.053 MUB -0.003 0.312 0.174 0.408 0.851 0.736 0.078 0.340 MOC 0.012 0.007 0.050 0.192 0.346 0.560 0.020 0.759 LBO 0.041 0.025 0.039 0.024 0.027 0.564 0.065 0.144 LGU 0.023 0.006 0.019 0.087 0.408 0.353 0.068 0.048 MUR 0.009 -0.013 0.004 0.045 0.003 0.623 0.041 0.126 WAI 0.019 -0.011 -0.004 -0.004 0.011 -0.006 0.218 0.980 BL10 0.031 0.030 0.045 0.059 0.050 0.036 0.019 0.026 ALB 0.021 0.006 -0.011 0.031 0.049 0.017 -0.026 0.048
8.3.9 E. m. macquarii pairwise FST (θ) from microsatellite data (9 loci) in the lower
Murray-Darling Basin. Below diagonal: FST (θ); Bold value: significant FST (θ); Above diagonal: P- value; Underlined value: significant at α = 0.05. Site codes see Appendix 8.2.2 and Figure 4.1 for site location.
MUB MOC LBO LGU MUR WAI WEN GBS BOW LFO GUC ALB MUB 0.765 0.150 0.568 0.179 0.874 0.881 0.299 0.051 0.008 0.108 0.667 MOC -0.011 0.141 0.756 0.196 0.764 0.637 0.212 0.083 0.009 0.71 0.976 LBO 0.032 0.024 0.013 0.031 0.565 0.301 0.059 0.012 0.002 0.150 0.277 LGU -0.003 -0.011 0.052 0.056 0.210 0.044 0.049 0.017 0.011 0.288 0.760 MUR 0.019 0.014 0.040 0.024 0.237 0.102 0.029 0.062 0.000 0.015 0.085 WAI -0.021 -0.014 -0.002 0.006 0.009 0.497 0.124 0.169 0.018 0.469 0.799 WEN -0.017 -0.006 0.010 0.022 0.018 -0.007 0.324 0.041 0.003 0.085 0.487 GBS 0.008 0.012 0.030 0.025 0.032 0.013 0.003 0.004 0.014 0.041 0.119 BOW 0.027 0.018 0.037 0.029 0.019 0.010 0.020 0.036 0.000 0.020 0.020 LFO 0.052 0.035 0.061 0.038 0.054 0.035 0.046 0.038 0.063 0.110 0.037 GUC 0.035 -0.009 0.031 0.010 0.046 -0.003 0.025 0.035 0.038 0.024 0.528 ALB -0.004 -0.019 0.015 -0.010 0.023 -0.013 0.000 0.018 0.028 0.029 0.001
170
8.4 Decomposed Pairwise Regression Analyses
8.4.1 Fit of alternative models DPRA in Moonie, Barwon, Border and Gwydir Rivers (Chapter 3)
Fit of alternative models from Decomposed Pairwise Regression Analysis (DPRA) for C. expansa , C. longicollis and E. m. macquarii in the Moonie, Border and Gwydir Rivers. Model: population excluded; n, number of populations included; K, number of parameters (distance); AIC c, AIC value with correction for model complexity. See Appendix 8.2.1for site names and Figure 3.1 for site location.
2 Species Catchments Model n K r AIC c ∆AIC c Included C. expansa Moonie None 10 1 0.370 -65.28 - KU 9 1 0.526 -61.32 3.96 KU, LO 8 1 0.528 -57.30 7.98 KU, LO, VE 7 1 0.621 -51.68 13.59 C. longicollis Moonie AP, KO 7 1 0.091 -60.24 - AP 8 1 0.009 -56.96 3.27 None 9 1 0.007 -54.63 5.60 AP, KO, KI 6 1 0.237 -51.66 8.57 AP, KO, KI, NU 5 1 0.216 -41.88 18.36 E. m. macquarii Moonie VE 9 1 0.007 -73.18 - VE, KO 8 1 0.011 -70.81 2.37 VE, KO, KI 7 1 0.297 -64.53 8.64 None 10 1 0.010 -64.29 8.89 VE, KO, KI, NI 6 1 0.550 -59.71 13.47 VE, KO, KI, NI, FE 5 1 0.445 -48.98 24.20 C. longicollis Moonie AP, BA, KO, KN 10 1 0.012 -86.37 - Border AP, BA, KO 11 1 0.004 -78.29 8.08 Gwydir AP, BA 12 1 0.000 -77.02 9.35 AP, BA, KO, KN, BC 9 1 0.021 -76.97 9.40 None 14 1 0.004 -75.45 10.92 AP 13 1 0.009 -75.12 11.25 AP, BA, KO, KN, BC, KI 8 1 0.023 -67.55 18.82 AP, BA, KO, KN, BC, KI, NU 7 1 0.014 -58.25 28.12 E. m. macquarii Border BO, KN, PI 7 1 0.070 -59.10 - Gwydir BO 9 1 0.022 -55.31 3.79 BO, KN 8 1 0.042 -52.25 6.85 BO, KN, PI, PA 6 1 0.126 -50.09 9.02 None 10 1 0.001 -49.47 9.64 BO, KN, PI, PA, LO 5 1 0.192 -41.58 17.53 BO, KN, PI, PA, LO, WC 4 1 0.186 -39.33 19.78 BO, KN, PI, PA, LO, WC, PU 3 1 0.081 -28.14 30.97
171
8.4.2 Plot of linearised distance DPRA in the Moonie – Barwon (Chapter 3)
0.1
0.09
0.08
0.07
0.06 ) ST F 0.05 /(1- ST F 0.04
0.03
0.02
0.01
0 0 100 200 300 400 500 600 Channel Distance (Km)
Plot of linearised genetic distance against geographic distance with line of best fit for C. expansa in the Moonie and Barwon Rivers following the DPRA. Filled diamond: non outlier only; Full line: line of best fit non outlier only. Note: no outliers were identified in the DPRA.
0.09
0.08
0.07
0.06
) 0.05 ST F
/(1- 0.04 ST F 0.03
0.02
0.01
0 0 100 200 300 400 500 600
Channel Distance (Km)
Plot of linearised genetic distance against geographic distance with line of best fit for C. longicollis in the Moonie and Barwon Rivers following the DPRA. Filled diamond: non outlier only; Empty Diamond: All pairwise comparisons; Full line: line of fit best non outlier only; Dashed line: line of best fit all pairwise comparisons.
172
Continued 8.4. 2 Plot of linearised distance DPRA in the Moonie – Barwon (Chapter 3)
0.07
0.06
0.05
) 0.04 ST F /(1-
ST 0.03 F
0.02
0.01
0 0 50 100 150 200 250 300 350 400 450 500
Channel distance (Km)
Plot of linearised genetic distance against geographic distance with line of best fit for E. m. macquarii in the Moonie and Barwon Rivers following the DPRA. Filled diamond: non outlier only; Empty Diamond: All pairwise comparisons; Full line: line of fit best non outlier only; Dashed line: line of best fit all pairwise comparisons.
173
8.4.3 Plot of linearised distance DPRA in the Moonie, Border, and Gwydir Rivers (Chapter 3)
0.12
0.1
0.08 ) ST F 0.06 /(1- ST F
0.04
0.02
0 0 50 100 150 200 250 300 350 400
Euclidean Distance (Km)
Plot of linearised genetic distance against Euclidean geographic distance with line of best fit for C. longicollis in the Moonie River, Border Rivers and Gwydir River catchments following DPRA. Filled diamond: non outlier only; Empty Diamond: All pairwise comparisons; Full line: line of best fit non outlier only; Dashed line: line of best fit all pairwise comparisons.
0.18
0.16
0.14
0.12
) 0.1 ST F /(1-
ST 0.08 F
0.06
0.04
0.02
0 0 100 200 300 400 500 600 700 800 900 1000 Channel Distance (Km) Plot of linearised genetic distance against geographic distance with line of best fit for E. m. macquarii in the Border Rivers and Gwydir River catchment following the DPRA. Filled diamond: non outlier only; Empty Diamond: All pairwise comparisons; Full line: line of fit best non outlier only; Dashed line: line of best fit all pairwise comparisons.
174
8.4.4 Fit of alternative models DPRA in the Lower Murray River (Chapter 4)
Fit of alternative models from Decomposed Pairwise Regression Analysis (DPRA) for C. expansa , C. longicollis and E. m. macquarii in the lower Murray River. Model: population excluded from model; n, number
of population included; K, number of parameters (distance); AIC c, AIC value with correction for model complexity. See Appendix 8.2.2 for site names and Figure 4.1 for site location.
2 Model n K r AIC C ∆ AIC C C. expansa None 9 1 0.727 -58.201 0.000 LGU 8 1 0.731 -52.799 5.402 C. longicollis None 9 1 0.006 -53.497 0.000 LBO 8 1 0.016 -52.591 0.907 LBO, BL10 7 1 0.109 -52.026 1.431 LBO, BL10, MUB 6 1 0.120 -44.146 9.352 E. m. macquarii None 12 1 0.053 -73.904 0.000 LFO 11 1 0.002 -72.214 1.689 LFO, WAI 10 1 0.005 -66.497 7.407 LFO, WAI, MOC 9 1 0.022 -61.139 12.764 LFO, WAI, MOC, WEN 8 1 0.074 -55.624 18.280 LFO, WAI, MOC, WEN, ALB 7 1 0.024 -50.687 23.217
8.4.5 Plot of linearised distance DPRA in the Lower Murray River (Chapter 4)
0.1000
0.0900
0.0800
0.0700
0.0600 ) ST F 0.0500 /(1- ST F 0.0400
0.0300
0.0200
0.0100
0.0000 0 200 400 600 800 1000 1200 1400 1600 1800 2000 Channel Distance (km) Plot of linearised genetic distance against geographic distance with line of best fit for C. longicollis in the Murray River following the DPRA. Filled diamond: non outlier only; Empty diamond: all pairwise comparison. Full line: line of best fit non-outlier only; Dashed line: line of best fit all populations.
175
8.5 Basin Scale Population Genetic Structure
8.5.1 Diversity indices C. longicollis admixed populations in the Lower Murray-Darling Basin (Chapter 5)
C. longicollis diversity indices for population showing evidence of admixture in the lower Murray-Darling
Basin. N, samples size; AT, total number of alleles (per locus per sites); AR, corrected mean allelic richness (n = 6 genes); HO, observed heterozygosity; HE, expected heterozygosiy; P-value, significant level for HWE expectation (None significant after B-Y correction for multiple comparisons); FIS , inbreeding coefficient, NA , null allele frequency (-: no null alleles detected). All loci: includes locus present in both columns. See Appendix 8.2.2 for site names and Figure 5.1 for site location.
Locus Population Locus Population NAD TOD PIR LFO NAD TOD PIR LFO TLE10 N 7 8 13 6 T31 N 7 8 13 6 AT 4 4 6 4 AT 5 5 7 4 AR 3.08 3.37 3.13 3.05 AR 3.72 3.49 3.35 3.56 HO 0.57 0.63 0.69 0.83 HO 0.57 1.00 0.62 0.67 HE 0.71 0.77 0.69 0.65 HE 0.80 0.78 0.71 0.80 P-value 0.357 0.360 0.969 1.000 P-value 0.058 0.696 0.504 0.418 FIS 0.21 0.20 -0.01 -0.32 FIS 0.30 -0.32 0.14 0.18 NA - - - - NA - - - - TCE70 N 7 8 13 6 TCE76.1 N 7 8 13 6 AT 3 2 5 4 AT 2 1 2 1 AR 2.42 1.88 2.71 2.91 AR 1.43 1.00 1.23 1.00 HO 0.43 0.25 0.69 0.50 HO 0.14 - 0.08 - HE 0.60 0.40 0.58 0.64 HE 0.14 - 0.08 - P-value 0.441 0.385 0.275 0.759 P-value - - - - FIS 0.31 0.39 -0.20 0.23 FIS - - - - NA - - - - NA - - - - TCE92.2 N 7 8 13 6 T12 N 7 8 13 6 AT 7 12 10 5 AT 3 2 2 3 AR 4.63 5.50 4.76 4.00 AR 2.66 1.88 1.96 2.41 HO 0.86 0.88 0.92 0.67 HO 1.00 0.50 0.62 0.50 HE 0.90 0.97 0.91 0.85 HE 0.65 0.40 0.49 0.53 P-value 0.722 0.296 0.771 0.566 P-value 0.090 1.000 0.566 0.515 FIS 0.05 0.10 -0.02 0.23 FIS -0.62 -0.27 -0.26 0.06 NA - - - - NA - - - - TCE86 N 7 8 13 6 T17 N 7 8 13 6 AT 6 6 11 8 AT 2 3 2 4 AR 3.81 3.86 4.63 4.95 AR 1.93 2.82 1.99 2.91 HO 0.86 0.63 0.92 1.00 HO 0.00 0.38 0.38 0.83 HE 0.79 0.81 0.89 0.92 HE 0.44 0.69 0.52 0.64 P-value 1.000 0.285 0.991 1.000 P-value 0.021 0.031 0.578 1.000 FIS -0.09 0.24 -0.04 -0.09 FIS 1.00 0.48 0.27 -0.35 NA - - - - NA 0.302 - - - T11 N 7 8 13 6 T87 N 7 8 13 6 AT 5 7 10 7 AT 4 4 3 3 AR 3.50 4.46 3.88 4.27 AR 3.33 3.50 2.75 2.00 HO 0.86 0.88 0.69 0.67 HO 0.71 0.63 0.77 0.33 HE 0.74 0.88 0.77 0.83 HE 0.76 0.79 0.67 0.32 P-value 1.000 0.524 0.402 0.154 P-value 0.822 0.549 0.805 1.000 FIS -0.18 0.01 0.11 0.22 FIS 0.06 0.22 -0.16 -0.05 NA - - - - NA - - - - All Loci AR 3.05 3.18 3.04 3.11 HO 0.60 0.64 0.64 0.67 HE 0.65 0.72 0.63 0.68
176
9 References
Aarts, B.G.W., Van Den Brink, F.W.B. & Nienhuis, P.H. (2004) Habitat loss as the main cause of the slow recovery of fish faunas of regulated large rivers in Europe: the transversal floodplain gradient. River Research and Applications, 20 , 3-23. Acevedo-Whitehouse, K., Gulland, F., Greig, D. & Amos, W. (2003) Disease susceptibility in California sea lions. Nature, 422 , 35-35. Alacs, E.A., Hillyer, M.J., Georges, A., Fitzsimmons, N.N. & Hughes, J.M. (2009) Development of microsatellites markers in the Australian snake-necked turtle Chelodina rugosa , and cross- species amplification. Molecular Ecology Resources, 9, 350-353. Aldous, A., Fitzsimons, J., Richter, B. & Bach, L. (2011) Droughts, floods and freshwater ecosystems: evaluating climate change impacts and developing adaptation strategies. Marine and Freshwater Research, 62 , 223-231. Alexander, L., Hope, P., Collins, D., Trewin, B., Lynch, A. & Nicholls, N. (2007) Trends in Australia's climate means and extremes: a global context. Australian Meteorological Magazine, 56 , 1-18. Alho, C.J.R. (2011) Environmental effects of hydropower reservoirs on wild mammals and freshwater turtles in Amazonia: a review. Oecologia Australis, 15 , 593-604. Allendorf, F.W. (1986) Genetic drift and the loss of alleles versus heterozygosity. Zoo Biology, 5, 181-190. Allendorf, F.W. & Luikart, G. (2007) Conservation and the Genetics of Populations, Blackwell Publishing, Singapore. Allendorf, F.W. & Phelps, S.R. (1981) Use of Allelic Frequencies to Describe Population Structure. Canadian Journal of Fisheries and Aquatic Sciences, 38 , 1507-1514. Amato, M.L., Brooks, R.J. & Fu, J. (2008) A Phylogeographic analysis of populations of the wood turtle ( Glyptemys insculpta ) throughout its range. Molecular Ecology, 17 , 570-581. Amoros, C. & Bornette, G. (2002) Connectivity and biocomplexity in waterbodies of riverine floodplains. Freshwater Biology, 47 , 761-776. Araújo, M.B., Thuiller, W. & Pearson, R.G. (2006) Climate warming and the decline of amphibians and reptiles in Europe. Journal of Biogeography, 33 , 1712-1728. Arbogast, B.S., Slowinski, B., J., Klicka, J., Zink & M., R. (1998) Pleistocene Speciation and the Mitochondrial DNA Clock. Science, 282 , 1955a-. Arnaud-Haond, S., Vonau, V., Rouxel, C., Bonhomme, F., Prou, J., Goyard, E. & Boudry, P. (2008) Genetic structure at different spatial scales in the pearl oyster ( Pinctada margaritifera cumingii ) in French Polynesian lagoons: beware of sampling strategy and genetic patchiness. Marine Biology, 155 , 147-157. Arthington, A.H. & Balcombe, S.R. (2011) Extreme flow variability and the ‘boom and bust’ ecology of fish in arid-zone floodplain rivers: a case history with implications for environmental flows, conservation and management. Ecohydrology, 4, 708-720. Arthington, A.H., Naiman, R.J., Mcclain, M.E. & Nilsson, C. (2010) Preserving the biodiversity and ecological services of rivers: new challenges and research opportunities. Freshwater Biology, 55 , 1-16. Ashton, D.T., Bettaso, K.B. & Hartwell, H.W.J. (2011) Comparative Ecology of Western Pond Turtle (Actinemys marmorata ) populations on the Free-flowing South Fork and Regulated Main Fork Trinity River: Demography, Size and Body Conditions Comparisons, Thermal Ecology and Spatial Dynamics. Final Report to the Trinity River Restoration Program. Usda Forest Service). Augustinus, P.C. & Macphail, M.K. (1997) Early Pleistocene stratigraphy and timing of the Bulgobac Glaciation, western Tasmania, Australia. Palaeogeography, Palaeoclimatology, Palaeoecology, 128 , 153-167.
177
Australian Natural Resources Atlas (2007) Biodiversity Assessment-Nandewar. Department of the Environment Water Heritage and the Arts, Canberra. Avise, J.C. (1995) Mitochondrial DNA Polymorphism and a Connection Between Genetics and Demography of Relevance to Conservation. Conservation Biology, 9, 686-690. Avise, J.C. (2000) Phylogeography: the history and formation of species, Harvard University Press, London, England. Avise, J.C., Bowen, B.W., Lamb, T., Meylan, A.B. & Bermingham, E. (1992) Mitochondrial DNA evolution at a turtle's pace: evidence for low genetic variability and reduced microevolutionary rate in the Testudines. Molecular Biology and Evolution, 9, 457-473. Baggiano, O., Schmidt, D.J. & Hughes, J.M. (2011a) Nine microsatellite markers for the Australian side-necked turtle Chelodina expansa (Chelidae) and cross species amplifications. Marine Genomics, 4, 297-300. Baggiano, O., Schmidt, D.J., Sheldon, F. & Hughes, J.M. (2011b) The role of altitude and associated habitat stability in determining patterns of population genetic structure in two species of Atalophlebia (Ephemeroptera: Leptophlebiidae). Freshwater Biology, 56 , 230-249. Balcombe, S.R., Arthington, A.H., Foster, N.D., Thoms, M.C., Wilson, G.A. & Bunn, S.E. (2006) Fish assemblages of an Australian dryland river: abundance, assemblage structure and recruitment patterns in the Warrego River, Murray-Darling Basin. Marine and Freshwater Research, 57 , 619-633. Balcombe, S.R., Bunn, S.E., Arthington, A.H., Fawcett, J.H., Mckenzie-Smith, F.J. & Wright, A. (2007) Fish larvae, growth and biomass relationships in an Australian arid zone river: links between floodplains and waterholes. Freshwater Biology, 52 , 2385-2398. Balcombe, S.R., Bunn, S.E., Davies, P.M. & Mckenzie-Smith, F.J. (2005) Variability of fish diets between dry and flood periods in an arid zone floodplain river. Journal of Fish Biology, 67 , 1552-1567. Balloux, F., Brunner, H., Lugon-Moulin, N., Hausser, J. & Goudet, J. (2000) Microsatellites can be misleading: an empirical and simulation study. Evolution, 54 , 1414-1422. Balloux, F. & Lugon-Moulin, N. (2002) The estimation of population differentiation with microsatellite markers. Molecular Ecology, 11 , 155-165. Bataillon, T.M., David, J.L. & Schoen, D.J. (1996) Neutral genetic markers and conservation genetics: simulated germplasm collections. Genetics, 144 , 409-417. Beaumont, M.A. (2007) Conservation Genetics. In: Handbook of Statistical Genetics, Third Edition. (Ed(s) D.J. Badling & M. Bishop & C. Cannings). John Wiley & Sons, Ltd. Beck, R.G. (1991) The common Longnecked Tortoise Chelodina longicollis (Shaw 1802) (Testudinata: Chelidae): A comparative study of the morphology and field behaviour of disjunct populations. South Australian Naturalist, 66 , 4-22. Benjamini, Y. & Yekutieli, D. (2001) The control of false discovery rate under dependency. Annals of Statistics, 4, 1165-1188. Bennett, A.M., Keevil, M. & Litzgus, J.D. (2009) Demographic differences among populations of Northern Map Turtles ( Graptemys geographica ) in intact and fragmented sites. Canadian Journal of Zoology, 87 , 1147-1157. Bennett, A.M., Keevil, M. & Litzgus, J.D. (2010) Spatial Ecology and Population Genetics of Northern Map Turtles ( Graptemys geographica ) in Fragmented and Continuous Habitats in Canada. Chelonian Conservation and Biology, 9, 185-195. Biggs, A.J., Power, R.E., Silburn, D.M., Owens, J.S., Burton, D.W.G. & Hebbard, C.L. (2005) Salinity audit-Border Rivers and Moonie catchments, Queensland Murray-Darling Basin. Vol. QNRM05462. Department of Natural Resources and Mines, Queensland. Bilton, D.T., Freeland, J.R. & Okamura, B. (2001) Dispersal in Freshwater Invertebrates. Annual Review of Ecology and Systematics, 32 , 159-181. Bodie, J.R. (2001) Stream and riparian management for freshwater turtles. Journal of Environmental Management, 62 , 443-455. Bodie, J.R. & Semlitsch, R.D. (2000) Spatial and temporal use of floodplain habitats by lentic and lotic species of aquatic turtles. Oecologia, 122 , 138-146. 178
Bohonak, A.J. (1999) Dispersal, gene flow, and population structure. Quarterly Review of Biology, 74 , 21-45. Boileau, M.G., Hebert, P.D.N. & Schwartz, S.S. (1992) Non-equilibrium gene frequency divergence: persistent founder effects in natural populations. Journal of Evolutionary Biology, 5, 25-39. Bond, N., Lake, P. & Arthington, A. (2008) The impacts of drought on freshwater ecosystems: an Australian perspective. Hydrobiologia, 600 , 3-16. Bonin, A., Bellemain, E., Eidesen, P.B., Pompanon, F., Brochmann, C. & Taberlet, P. (2004) How to track and assess genotyping errors in population genetics studies. Molecular Ecology, 13 , 3261-3273. Booth, D. (1998a) Egg Size, Clutch Size, and Reproductive Effort of the Australian Broad-shelled River Turtle, Chelodina expansa . Journal of Herpetology, 32 , 592-596. Booth, D. (1998b) Nest temperature and respiratory gases during natural incubation in the broad- shelled river turtle, Chelodina expansa (Testudinata: Chelidae). Australian Journal of Zoology, 46 , 183-191. Bossart, J.L. & Pashley-Prowell, D. (1998) Genetic estimates of population structure and gene flow: limitations, lessons and new directions. Trends in Ecology & Evolution, 13 , 202-206. Boulton, A.J. (2003) Parallels and contrasts in the effects of drought on stream macroinvertebrate assemblages. Freshwater Biology, 48 , 1173-1185. Bowen, B.W., Bass, A.L., Soares, L. & Toonen, R.J. (2005a) Conservation implications of complex population structure: lessons from the loggerhead turtle ( Caretta caretta ). Molecular Ecology, 14 , 2389-2402. Bowen, B.W. & Karl, S.A. (2007) Population genetics and phylogeography of sea turtles. Molecular Ecology, 16 , 4886-4907. Bowen, K.D., Spencer, R.J. & Janzen, F.J. (2005b) A comparative study of environmental factors that affect nesting in Australian and North American freshwater turtles. Journal of Zoology, London, 267 , 397-404. Bower, D.S. (2011) The conservation biology of freshwater turtles in the lower Murray River. Ph.D. Thesis, University of Canberra, Canberra. Bower, D.S., Hutchinson, M. & Georges, A. (2011) Movement and habitat use of Australia's largest snake-necked turtle: implications for water management. Journal of Zoology, Early View. Bowman, J., Forbes, G. & Dilworth, T. (2000) The spatial scale of variability in small-mammal populations. Ecography, 23 , 328-334. Bowne, D.R., Bowers, M.A. & Hines, J.E. (2006) Connectivity in an Agricultural Landscape as Reflected by Interpond Movements of a Freshwater Turtle. Conservation Biology, 20 , 780- 791. Brainwood, M., Burgin, S. & Byrne, M. (2006) Is the decline of freshwater mussel populations in a regulated coastal river in south-eastern Australia linked with human modification of habitat? Aquatic Conservation: Marine and Freshwater Ecosystems, 16 , 501-516. Brinson, M.M. & Malvárez, A.I. (2002) Temperate freshwater wetlands: types, status, and threats. Environmental Conservation, 29 , 115-133. Brock, P.M. (1998) The significance of the physical environment of the Macquarie Marshes. Australian Geographer, 29 , 71-90. Brooker, M.G., Rowley, M.A., Adams, M. & Baverstock, P.R. (1990) Promiscuity: an inbreeding avoidance mechanim in a socially monogmous species? Behavioral Ecology and Sociobiology, 26 , 1991-1199. Buhlmann, K.A., Akre, T.S.B., Iverson, J.B., Karapatakis, D., Mittermeier, R.A., Georges, A., Rhodin, A.G.J., Van Dijk, P.P. & Gibbons, J.W. (2009) A global analysis of tortoise and freshwater turtle distributions with identification of priority conservation areas. Chelonian Conservation and Biology, 8, 116-149. Bunn, S.E. & Arthington, A.H. (2002) Basic Principles and Ecological Consequences of Altered Flow Regimes for Aquatic Biodiversity. Environmental Management, 30 , 492-507. Bunn, S.E., Davies, P.M. & Winning, M. (2003) Sources of organic carbon supporting the food web of an arid zone floodplain river. Freshwater Biology, 48 , 619-635. 179
Bunn, S.E. & Hughes, J.M. (1997) Dispersal and recruitment in streams: evidence from genetic studies. Journal of the North American Benthological Society, 16 , 338-346. Bunn, S.E., Thoms, M.C., Hamilton, S.K. & Capon, S.J. (2006) Flow variability in dryland rivers: boom, bust and the bits in between. River Research and Applications, 22 , 179-186. Burgin, S. & Renshaw, A. (2008) Epizoochory, Algae and the Australian Eastern Long-Necked Turtle Chelodina Longicollis (Shaw). The American Midland Naturalist, 160 , 61-68. Burgin, S. & Ryan, M. (2008) Comparison of sympatric freshwater turtle populations from an urbanized Sydney catchment. Aquatic Conservation: Marine and Freshwater Ecosystems, 18 , 1277-1284. Burke, V.J. (2000) Conservation of freshwater turtles. In: Turtle Conservation. (Ed(s) M.W. Klemens), pp. 156-179. Smithsonian Institution Press, Washington, D.C. Burnham, K.P. & Anderson, D.R. (2002) Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach, Springer, New York. Buschiazzo, E. & Gemmell, N.J. (2006) The rise, fall and renaissance of microsatellites in eukaryotic genomes. BioEssays, 28 , 1040-1050. Caballero, A., Rodríguez-Ramilo, S., Ávila, V. & Fernández, J. (2010) Management of genetic diversity of subdivided populations in conservation programmes. Conservation Genetics, 11 , 409-419. Callen, D.F., Thompson, A.D., Shen, Y., Phillips, H.A., Richards, R.I. & Mulley, J.C. (1993) Incidence and origin of 'null' alleles in the (AC) n microsatellite markers. The American Journal of Human Genetics, 19 , 233-257. Campbell, V. & Strobeck, C. (2006) Fine-scale genetic structure and dispersal in Canada lynx ( Lynx canadensis ) within Alberta, Canada. Canadian Journal of Zoology, 84 , 1112-1120. Cann, J. (1998) Australian Freshwater Turtles, Beaumont Publishing Pty Ltd, Singapore. Canonico, G.C., Arthington, A., Mccrary, J.K. & Thieme, M.L. (2005) The effects of introduced tilapias on native biodiversity. Aquatic Conservation: Marine and Freshwater Ecosystems, 15 , 463-483. Carini, G. & Hughes, J.M. (2004) Population structure of Macrobrachium australiense (Decapoda: Palaemonidae) in Western Queensland, Australia: the role of contemporary and historical processes. Heredity, 93 , 350-363. Carini, G., Hughes, J.M. & Bunn, S.E. (2006) The role of waterholes as ‘refugia’ in sustaining genetic diversity and variation of two freshwater species in dryland river systems (Western Queensland, Australia). Freshwater Biology, 51 , 1434-1446. Castro, J., Picornell, A. & Ramon, M. (1998) Mitochondrial DNA: a tool for populational genetics studies. International Microbiology, 1, 327-332. Chapuis, M.-P. & Estoup, A. (2007) Microsatellite Null Alleles and Estimation of Population Differentiation. Molecular Biology and Evolution, 24, 621-631. Chapuis, M.-P., Lecoq, M., Michalakis, Y., Loiseau, A., Sword, G.A., Piry, S. & Estoup, A. (2008) Do outbreaks affect genetic population structure? A worldwide survey in Locusta migratoria , a pest plagued by microsatellite null alleles. Molecular Ecology, 17 , 3640-3653. Chesser, R.K. (1991) Gene Diversity and Female Philopatry. Genetics, 127 , 437-447. Chessman, B.C. (1978) Ecological studies of freshwater turtles in south-eastern Australia. Ph.D. Thesis, Monash University, Melbourne. Chessman, B.C. (1983) Observations on the diet of the broad-shelled turtle, Chelodina expansa Gray (Testudines : Chelidae). Australian Wildlife Research, 10 , 169-172. Chessman, B.C. (1984a) Evaporative water loss from three south-eastern Australian species of freshwater turtle. Australian Journal of Zoology, 32 , 649-655. Chessman, B.C. (1984b) Food of the snake-necked turtle, Chelodina longicollis (Shaw) (Testudines : Chelidae) in the Murray Valley, Victoria and New South Wales. Australian Wildlife Research, 11 , 573-578. Chessman, B.C. (1986) Diet of the Murray turtle, Emydura macquarii (Gray) (Testudines : Chelidae). Australian Wildlife Research, 13 , 65-69.
180
Chessman, B.C. (1988a) Habitat preferences of freshwater turtles in the Murray Valley, Victoria and New South Wales. Australian Wildlife Research, 15 , 485-491. Chessman, B.C. (1988b) Seasonal and Diel Activity of Freshwater Turtles in the Murray Valley, Victoria and New South-Wales. Wildlife Research, 15 , 267-276. Chessman, B.C. (2011) Declines of freshwater turtles associated with climatic drying in Australia's Murray-Darling Basin. Wildlife Research, 38 , 664-671. Chiew, F.H.S., Cai, W. & Smith, I.N. (2009) Advice on defining climate scenarios for use in Murray- Darling Basin Plan modelling. Report for the Murray-Darling Basin Authority. CSIRO, Australia. Chiew, F.H.S., Vaze, J., Viney, N., Jordan, P., Perraud, J.-M., Zang, L., Teng, J., Pena Arancibia, J., Morden, R., Freebairn, A., Austin, J., Hill, P., Wiesemfeld, C. & Murphy, R. (2008) Rainfall- runoff modelling across the Murray-Darling Basin. A report to the Australian Government from the CSIRO Murray-Darling Basin Sustainable Yields Project. CSIRO, Australia. Ciofi, C., Milinkovitch, M.C., Gibbs, J.P., Caccone, A. & Powell, J.R. (2002) Microsatellite analysis of genetic divergence among populations of giant Galapagos tortoises. Molecular Ecology, 11 , 2265-2283. Ciofi, C., Wilson, G.A., Beheregaray, L.B., Marquez, C., Gibbs, J.P., Tapia, W., Snell, H.L., Caccone, A. & Powell, J.R. (2006) Phylogeographic History and Gene Flow Among Giant Galapagos Tortoises on Southern Isabela Island. Genetics, 172 , 1727-1744. Clark, N.J., Gordos, M.A. & Franklin, C.E. (2009) Implications of river damming: the influence of aquatic hypoxia on the diving physiology and behaviour of the endangered Mary River turtle. Animal Conservation, 12 , 147-154. Clarke, A.L., Saether, B.-E. & Roskaft, E. (1997) Sex Biases in Avian Dispersal: A Reappraisal. OIKOS, 79 , 429-438. Cliff, A.D. & Ord, J.K. (1981) Spatial Processes: Models and Applications. Pion, London. Clobert, J., Ims, R.A. & Rousset, F. (2004) Causes, Mechanisms and Consequences of Dispersal. In: Ecology, Genetics and Evolution of Metapopulations. (Ed(s) I. Hanski & O.E. Gaggiotti), pp. 307-335. Elsevier Academic Press, San Diego, California. Cockerham, C.C. & Weir, B.S. (1993) Estimation of gene flow from F-statistics. Evolution, 47 , 855- 863. Cogger, H.G. (1992) Reptiles and amphibians of Australia, Reed, Chatswood, NSW. Cogger, H.G. (2000) Reptiles and Amphibians of Australia, Reed New Holland, Sydney. Cogger, H.G., Cameron, E.E., Sadlier, R.A. & Eggler, P. (1993) Action plan for Australian reptiles. Project No. 124. Australian Nature Conservation Agency, Canberra. Cook, B.D., Bunn, S.E. & Hughes, J.M. (2002) Genetic structure and dispersal of Macrobrachium australliense (Decapoda: Palaemonidae) in western Queensland, Australia. Freshwater Biology, 47 , 2098-2112. Cornuet, J.-M., Piry, S., Luikart, G., Estoup, A. & Solignac, M. (1999) New Methods Employing Multilocus Genotypes to Select or Exclude Populations as Origins of Individuals. Genetics, 153 , 1989-2000. Coulon, A., Fitzpatrick, J.W., Bowman, R., Stith, B.M., Makarewich, C.A., Stenzler, L.M. & Lovette, I.J. (2008) Congruent population structure inferred from dispersal behaviour and intensive genetic surveys of the threatened Florida scrub-jay ( Aphelocoma cœrulescens ). Molecular Ecology, 17 , 1685-1701. Coulon, A., Guillot, G., Cosson, J.F., Angibault, J.M.A., Aulagnier, S., Cargnelutti, B., Galan, M. & Hewison, A.J.M. (2006) Genetic structure is influenced by landscape features: empirical evidence from a roe deer population. Molecular Ecology, 15 , 1669-1679. Crabb, P. (1997) Murray-Darling Basin Resources. Murray-Darling Basin Commission, Canberra. Crow, J.F. & Aoki, K. (1984) Group selection for a polygenic behavioral trait: estimating the degree of population subdivision. Proc. Nat. Acad. Sci. USA, 81 , 6073-6077. Csiro (2007a) Water Availability in the Border Rivers. A report to the Australian Government from the CSIRO Murray-Darling Basin Sustainable Yields Project. p. 144. CSIRO, Australia.
181
Csiro (2007b) Water Availability in the Gwydir. A report to the Australian Government from the CSIRO Murray-Darling Basin Sustainable Yields Project. p. 148. CSIRO, Australia. Csiro (2008) Water Availability in the Moonie. A report to the Australian Government from the CSIRO Murray-Darling Basin Sustainable Yields Project. p. 79. CSIRO, Australia. Cushman, S.A., Mckelvey, K.S., Hayden, J. & Schwartz, M.K. (2006) Gene Flow in Complex Landscapes: Testing Multiple Hypotheses with Causal Modeling. The American Naturalist, 168 , 486-499. Dakin, E. & Avise, J. (2004) Microsatellite null alleles in parentage analysis. Heredity, 93 , 504-509. Davies, P.M., Harris, J., Hillman, T. & Walker, K. (2008) SRA Report 1: A Report on the Ecological Health of Rivers in the Murray – Darling Basin, 2004–2007. Prepared by the Independent Sustainable Rivers Audit Group for the Murray - Darling Basin Ministerial Council. De Lathouder, R., Jones, D. & Balcombe, S. (2009) Assessing the abundance of freshwater turtles in an Australian urban landscape. Urban Ecosystems, 12 , 215-231. Dempster, A.P., Laird, N.M. & Rubin, D.B. (1977) Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, series B, 39 , 1-38. Department of Environment and Heritage (2009) Revision of the Interim Biogeographic Regionalisation of Australia (IBRA) and the Development of Version 5.1. - Summary Report. Department of Environment and Heritage, Canberra, Australia. Department of Environment and Resources Management (2010) Refugial Waterholes Project. Department of Environment and Resources Management, Brisbane, Australia. Department of Natural Resources (1999) Overview of water Resources and Related Issues: The Moonie River catchment. Department of Natural Resources, Brisbane, Australia. Department of Natural Resources and Environment (1999) Threatened vertebrate fauna in Victoria: A systematic list of vertebrate fauna considered extinct, at risk of extinction or in major decline in Victoria. Departement of Natural Resources and Environment, Melbourne, Australia. Devillard, S., Allainé, D., Gaillard, J.-M. & Pontier, D. (2004) Does social complexity lead to sex- biased dispersal in polygynous mammals? A test on ground-dwelling sciurids. Behavioral Ecology, 15 , 83-87. Dibattista, J. (2008) Patterns of genetic variation in anthropogenically impacted populations. Conservation Genetics, 9, 141-156. Dietrich, W.F., Miller, J., Steen, R., Merchant, M.A., Damron-Boles, D., Husain, Z., Dredge, R., Daly, M.J., Ingalls, K.A., O'connor, E.T.J.A., Deangelis, M.M., Levinson, K.D.M.L., Goodman, N., Copeland, N.G., Jenkins, N.A., Hawkins, T.L., Stein, L., Page, D.C. & Lander, E.S. (1996) A comprehensive genetic map of the mouse genome. Nature, 380 , 149-152. Dobson, F.S. (1982) Competition for mates and predominant juvenile male dispersal in mammals. Animal Behaviour, 30 , 1183-1192. Donnelly, C. (2004) Sites inspection report: Turtles trapped in grids at right tower, Fairbairn Dam. Q. Unpublished Report to Environmental Protection Agency and Sunwater, Australia). Douglas, M.R., Brunner, P.C. & Douglas, M.E. (2003) Drought in an evolutionary context: molecular variability in Flannelmouth Sucker ( Catostomus latipinnis ) from the Colorado River Basin of western North America. Freshwater Biology, 48 , 1254-1273. Doupé, R.G. & Pettit, N.E. (2002) Ecological perspectives on regulation and water allocation for the Ord River, Western Australia. River Research and Applications, 18 , 307-320. Doyle, J.J. & Doyle, J.L. (1987) A rapid DNA isolation procedure for small quantities of leaf tissue. Phytochemistry Bulletin, 19 , 11-15. Dubey, S., Brown, G.P., Madsen, T. & Shine, R. (2008) Male-biased dispersal in a tropical Australian snake ( Stegonotus cucullatus , Colubridae). Molecular Ecology, 17 , 3506-3514. Dudgeon, D., Arthington, A.H., Gessner, M.O., Kawabata, Z.-I., Knowler, D.J., Lévêque, C., Naiman, R.J., Prieur-Richard, A.-H., Soto, D., Stiassny, M.L.J. & Sullivan, C.A. (2006) Freshwater biodiversity: importance, threats, status and conservation challenges. Biological Reviews, 81 , 163-182. Earl, D.A. (2011) Structure harvester v0.6.1. Available at http://taylor0.biology.ucla.edu/structureHarvester/ (accessed 09 September 2011). 182
El Mousadik, A. & Petit, R.J. (1996) High level of genetic differentiation for allelic richness among populations of the argan tree ( Argania spinosa ) endemic to Morocco. TAG Theoretical and Applied Genetics, 92 , 832-839. Ellegren, H. (2004) Microsatellites: simple sequences with complex evolution. Nature Reviews Genetics, 5, 435-445. Ellegren, H., Primmer, C.R. & Sheldon, B.C. (1995) Microsatellite 'evolution': directionally or bias in locus selection. Nature Genetics, 11 , 360-362. Engstrom, T.N., Edwards, T., Osentoski, M.F. & Myers, E.M. (2007) A Compendium of PCR Primers for mtDNA, Microsatellite, and Other Nuclear Loci for Freshwater Turtles and Tortoises. In: Advance in Turtle Genetics and Systematics. Proceedings of a Workshop on Genetics, Ethics, and Taxonomy of Freshwater Turtles and Tortoises. (Ed(s) H.B. Schaffer & N.N. Fitzsimmons & A. Georges & A. Rhodin), pp. 9-26. Chelonian Research Monographs, vol. 4. Epperson, B.K. (1993) Spatial and space-time correlations in systems of subpopulations with genetic drift and migration. Genetics, 133 , 711-727. Epperson, B.K. & Li, T.-Q. (1997) Gene Dispersal and Spatial Genetic Structure. Evolution, 51 , 672- 681. Ercolano, E. (2008) Aquatic and terrestrial habitat use of the Australian freshwater turtle, Chelodina expansa , Bucknell University, Lewisburg. Escalona, T., Engstrom, T.N., Hernandez, O.E., Bock, B.C., Vogt, R.C. & Valenzuela, N. (2009) Population genetics of the endangered South American freshwater turtle, Podocnemis unifilis , inferred from microsatellite DNA data. Conservation Genetics, 10 , 1683-1696. Estoup, A., Jarne, P. & Cornuet, J.-M. (2002) Homoplasy and mutation model at microsatellite loci and their consequences for population genetics analysis. Molecular Ecology, 11 , 1591-1604. Estoup, A., Tailliez, C., Cornuet, J.M. & Solignac, M. (1995) Size homoplasy and mutational processes of interrupted microsatellites in two bee species, Apis mellifera and Bombus terrestris (Apidae). Molecular Biology and Evolution, 12 , 1074-1084. Evanno, G., Regnaut, S. & Goudet, J. (2005) Detecting the number of clusters of individuals using the software structure: a simulation study. Molecular Ecology, 14 , 2611-2620. Excoffier, L. & Heckel, G. (2006) Computer programs for population genetics data analysis: a survival guide. Genetics, 7, 745-758. Excoffier, L. & Lischer, H.E.L. (2010) Arlequin suite ver 3.5: A new series of programs to perform population genetics analyses under Linux and Windows. Molecular Ecology Resources, 10 , 564-567. Excoffier, L., Smouse, P.E. & Quattro, J.M. (1992) Analysis of Molecular Variance Inferred From Metric Distances Among DNA Haplotypes: Application to Human Mitochondrial DNA Restriction Data. Genetics, 131 , 479-491. Ezaz, T., Valenzuela, N., Grutzner, F., Miura, I., Georges, A., Burke, R.L. & Marshall Graves, J.A. (2006) An XX/XY sex microchromosome system in a freshwater turtle, Chelodina longicollis (Testudines: Chelidae) with genetic sex determination. Chromosome Research, 14 , 139-150. Fachín-Terán, A., Vogt, R.C. & Thorbjarnarson, J.B. (2006) Seasonal Movements of Podocnemis sextuberculata (Yestudines: Podocnemididae) in the Mamirauá Sustainable Development Reserve, Amazonas, Brazil. Chelonian Conservation and Biology, 5, 18-24. Fagan, W.F. (2002) Connectivity, fragmentation and extinction risk in dendritic metapopulations Ecology, 83 , 3243-3249. Falush, D., Stephens, M. & Pritchard, J.K. (2003) Inference of Population Structure Using Multilocus Genotype Data: Linked Loci and Correlated Allele Frequencies. Genetics, 164 , 1567-1587. Faulks, L.K., Gilligan, D.M. & Beheregaray, L.B. (2010) Islands of water in a sea of dry land: hydrological regime predicts genetic diversity and dispersal in a widespread fish from Australia’s arid zone, the golden perch (Macquaria ambigua ). Molecular Ecology, 19 , 4723- 4737. Favre, L., Balloux, F., Goudet, J. & Perrin, N. (1997) Female-biased dispersal in the monogamous mammal Crocidura russula : evidence from field data and microsatellite patterns. Proceedings of the Royal Society B: Biological Sciences, 264 , 127-132. 183
Finn, D.S., Blouin, M.S. & Lytle, D.A. (2007) Population genetic structure reveals terrestrial affinities for a headwater stream insect. Freshwater Biology, 52 , 1881-1897. Fitzsimmons, N.N. & Hart, K.M. (2007) Genetic Studies of Freshwater Turtles and Tortoises: a Review of the Past 70 Years. In: Advance in Turtle Genetics and Systematics. Proceedings of a Workshop on Genetics, Ethics, and Taxonomy of Freshwater Turtles and Tortoises. (Ed(s) H.B. Schaffer & N.N. Fitzsimmons & A. Georges & A. Rhodin), pp. 27-57. Chelonian Research Monographs vol. 4. Fitzsimmons, N.N., Limpus, C.J., Norman, J.A., Goldizen, A.R., Miller, J.D. & Moritz, C. (1997) Philopatry of male marine turtles inferred from mitochondrial markers. Proceedings of the National Academy of Sciences USA, 94 , 8912-8917. Fitzsimmons, N.N., Moritz, C. & Moore, S.S. (1995) Conservation and dynamics of microsatellite loci over 300 million years of marine turtle evolution. Molecular Biology and Evolution, 12 , 432-440. Forester, A. (2008) Managing and protecting environmental water: lessons from the Gwydir for ecologically sustainable water management in the Murray Darling Basin. Environmental Planning and Law Journal, 25 , 130-153. Fortuna, M.A., Gómez-Rodríguez, C. & Bascompte, J. (2006) Spatial network structure and amphibian persistence in stochastic environments. Proceedings of the Royal Society B: Biological Sciences, 273 , 1429-1434. Frankham, R. (1995) Conservation genetics. Annual Review of Genetics, 29 , 305-323. Frankham, R. (1996) Relationship of Genetic Variation to Population Size in Wildlife. Conservation Biology, 10 , 1500-1508. Frankham, R., Ballou, J.D. & Briscoe, D.A. (2002) Introduction to conservation genetics, Cambridge University Press, Cambridge. Frantz, A.C., Cellina, S., Krier, A., Schley, L. & Burke, T. (2009) Using spatial Bayesian methods to determine the genetic structure of a continuously distributed population: clusters or isolation by distance? Journal of Applied Ecology, 46 , 493-505. Freedberg, S., Ewert, M.A., Ridenhour, B.J., Neiman, M. & Nelson, C.E. (2005) Nesting fidelity and molecular evidence for natal homing in the freshwater turtle, Graptemys kohnii . Proceedings of the Royal Society B: Biological Sciences, 272 , 1345-1350. Freedberg, S. & Wade, M.J. (2001) Cultural inheritance as a mechanism for population sex-ratio bias in reptiles. Evolution, 55 , 1049-1055. Gaffney, E.S. (1991) The fossil turtles of Australia. In: Vertebrate Paleontology of Australasia. (Ed(s) P. Vickers-Rich & J.M. Monaghan & R.F. Baird & T.H. Rich), pp. 704-720. Pioneer Design Studio University Publication Committee, Melbourne. Gaggiotti, O.E., Lange, O., Rassmann, K. & Gliddon, C. (1999) A comparison of two indirect methods for estimating average levels of gene flow using microsatellite data. Molecular Ecology, 8, 1513-1520. Gandon, S. (1999) Kin competition, the cost of inbreeding and the evolution of dispersal. Journal of Theoretical Biology, 200 , 345-364. Gehrke, P.C. & Harris, J.H. (2001) Regional-scale effects of flow regulation on lowland riverine fish communities in New South Wales, Australia. Regulated Rivers: Resource Management, 17 , 369-391. Gell, P., Tibby, J., Little, F., Baldwin, D. & Hancock, G. (2007) The impact of regulation and salinisation on floodplain lakes: the lower River Murray, Australia. Hydrobiologia, 591 , 135- 146. Georges, A. (1982) Ecological studies of Kreffts'river tortoise, Emydura krefftii (Gray), from Fraser Island, Queensland. PhD, University of Queensland, Brisbane. Georges, A. (1984) Observations on the nesting and natural incubation of the long-necked tortoise Chelodina expansa . Herpetofauna, 15 , 27-31. Georges, A. (1988) Sex determination is independent of incubation temperature in another chelid turtle, Chelodina longicollis. Copeia, 1988 , 248-254.
184
Georges, A. (1993) Setting conservation priorities for Australian freshwater turtles. In: Herpetology in Australia: A Diverse Discipline. (Ed(s) D. Lunney & D. Ayers), pp. 49-58. Royal Zoological Society of New South Wales, Mosman, NSW. Georges, A. & Adams, M. (1996) Electrophoretic delineation of species boundaries within the short- necked freshwater turtles of Australia (Testudines: Ghelidae). Zoological Journal of the Linnean Society, 118 , 241-260. Georges, A., Adams, M. & Mccord, W. (2002) Electrophoretic delineation of species boundaries within the genus Chelodina (Testudines: Chelidae) of Australia, New Guinea and Indonesia. Zoological Journal of the Linnean Society, 134 , 401-421. Georges, A., Birrell, J., Saint, K., Mccord, W.P. & Donnellan, S. (1998) A phylogeny for side-necked turtles (Chelonia: Pleurodira) based on mitochondrial and nuclear gene sequences. Biological Journal of the Linnean Society, 67 , 213-246. Georges, A., Guarino, F. & White, M. (2006) Sex-ratio bias across populations of a freshwater turtle (Testudines: Chelidae) with genotypic sex determination. Wildlife Research, 33 , 475-480. Georges, A. & Kennett, R. (1989) Dry-season distribution and ecology of Carettochelys insculpta (Chelonia: Carettochelydidae) in Kakadu National Park, northern Australia. Australian Wildlife Research, 16 , 323-335. Georges, A., Limpus, C.J. & Parmenter, J.C. (1993) Natural History of the Chelonia. In: Fauna of Australia Volume 2A. (Ed(s) C.G. Glasby & G.J.B. Ross & P.L. Beesley). AGPS, Canberra. Georges, A., Norris, R. & Wensing, L. (1986) Diet of the Freshwater Turtle Chelodina Longicollis (Testudines, Chelidae) From the Coastal Dune Lakes of the Jervis Bay Territory. Wildlife Research, 13 , 301-308. Georges, A. & Thompson, S. (2010) Diversity of Australasian freshwater turtles, with an annotated synonymy and keys to species. Zootaxa, 2496 , 1-37. Georges, A. & Thomson, S. (2006) Evolution and Zoogeography of Australian Freshwater Turtles. In: Evolution and Biogeography of Australian Vertebrates. (Ed(s) J.R. Merrick & M. Archer & G.M. Hickey & M.S.Y. Lee), pp. 291- 308. Auscipub Pty Ltd, Oatlands. Gibbons, J.W., Scott, D.E., Ryan, T.J., Buhlmann, K.A., Tuberville, T.D., Metts, B.S., Greene, J.L., Mills, T., Leiden, Y., Poppy, S. & Winne, C.T. (2000) The Global Decline of Reptiles, Deja Vu Amphibians. Bioscience, 50 , 653-666. Gibbs, J.P. & Amato, G.D. (2000) Genetics and demography in turtle conservation. In: Turtle Conservation. (Ed(s) M.W. Klemens), pp. 207-217. Smithsonian Institute Press, Washington, D.C. Glenn, T.C. & Schable, N.A. (2005) Isolating microsatellite DNA loci. Methods in Enzymology, 395 , 202-222. Goldstein, D.B. & Pollock, D.D. (1997) Launching Microsatellites: A Review of Mutation Processes and Methods of Phylogenetic Inference. Journal of Heredity, 88 , 335-342. Goode, J. (1967) Freshwater Tortoise of australia and New Guinea (in the Family Chelidae), Lansdowne, Melbourne. Goode, J. & Russell, J. (1968) Incubation of eggs of three species of Chelid tortoises and notes on their embryological development. Australian Journal of Zoology, 16 , 749-761. Goodsell, T.L. (2002) Gene flow in highly variable environments: Population structure of an Australian freshwater turtle, Emydura macquarii. Honours Thesis, University of Canberra, Canberra. Goss, K.F. (2003) Environmental flows, river salinity and biodiversity conservation: managing trade- offs in the Murray-Darling basin. Australian Journal of Botany, 51 , 619-625. Goudet, J., Perrin, N. & Waser, P. (2002) Tests for sex-biased dispersal using bi-parentally inherited genetic markers. Molecular Ecology, 11 , 1103-1114. Graham, T., Georges, A. & Mcelhinney, N. (1996) Terrestrial Orientiation by the Estern Long-necked Turtle, Chelodina longicollis , from Australia. Journal of Herpetology, 30, 467-477. Grant, E.H.C., Lowe, W.H. & Fagan, W.F. (2007) Living in the branches: population dynamics and ecological processes in dendritic networks. Ecology Letters, 10 , 165-175.
185
Gray, J.E. (1830) A synopsis of the species of the class Reptilia. In: The Animal Kingdom Arranged in Conformity with its Organization by the Baron Cuvier (1831). (Ed(s) E. Griffith). Whittaker, Treacher & Co., London. Gray, J.E. (1844) Catalogue of the tortoises, crocodiles and amphisbaenians in the collection of the British Museum. Edward Newman, London. Gray, J.E. (1857) Description of a new species of Chelodina from Australia. Proceedings of the Zoological Society of London, 24 , 369-371. Greenwood, P.J. (1980) Mating systems, philopatry and dispersal in birds and mammals. Animal Behaviour, 28 , 1140-1162. Greer, A.A. (2006) Encyclopedia of Australian Reptiles. In: http://www.amonline.net.au/herpetology/research/encyclopedia.pdf . (Ed(s) A.M. Online), Version date: 7 August 2006. Groombridge, J.J., Dawson, D.A., Burke, T., Prys-Jones, R., Brooke, M.D.L. & Shah, N. (2009) Evaluating the demographic history of the Seychelles kestrel ( Falco araea ): Genetic evidence for recovery from a population bottleneck following minimal conservation management. Biological Conservation, 142 , 2250-2257. Gros, A., Hovestadt, T. & Poethke, H.J. (2008) Evolution of sex-biased dispersal: The role of sex- specific dispersal costs, demographic stochasticity, and inbreeding. Ecological Modelling, 219 , 226-233. Guillot, G. (2008) Inference of structure in subdivided populations at low levels of genetic differentiation. The correlated allele frequencies model revisited. Bioinformatics, 24 , 2222- 2228. Guillot, G., Estoup, A., Mortier, F. & Cosson, J.-F. (2005a) A Spatial Statistical Model for Landscape Genetics. Genetics, 170 , 1261-1280. Guillot, G., Mortier, F. & Estoup, A. (2005b) Geneland: A computer package for landscape genetics. Molecular Ecology Notes, 5, 708-711. Guillot, G. & Santos, F. (2009) A computer program to simulate multilocus genotype data with spatially autocorrelated allele frequencies. Molecular Ecology Resources, 9, 1112-1120. Guillot, G., Santos, F. & Estoup, A. (2008) Analysing georeferenced population genetics data with Geneland: a new algorithm to deal with null alleles and a friendly graphical user interface. Bioinformatics, 24 , 1406-1407. Guillot, G., Santos, F. & Estoup, A. (2011) Population genetic analysis using R and the GENELAND program. p. 52. Manual for Program Users. Guo, S.W. & Thompson, E.A. (1992) Performing the Exact Test of Hardy-Weinberg Proportion for Multiple Alleles. Biometrics, 48 , 361-372. Hall, K.C., Baldwin, D.S., Rees, G.N. & Richardson, A.J. (2006) Distribution of inland wetlands with sulfidic sediments in the Murray-Darling Basin, Australia. Science of the Total Environment, 370 , 235-244. Hancock, M.J. (1995) The Contribution of Slippage-Like Processes to Genome Evolution. Journal of Molecular Evolution, 41 , 1038-1047. Hanski, I. (1998) Metapopulation Dynamics. Nature, 396 , 41-49. Hanski, I. (2001) Population dynamic consequences of dispersal in local populations and in metapopulations. In: Dispersal. (Ed(s) J. Clobert & E. Danchin & A.A. Dhondt & J.D. Nichols), pp. 283-298. Oxford, New York. Hardy, O.J. & Vekemans, X. (2002) SPAGeDi: a versatile computer program to analyse spatial genetic structure at the individual and population levels. Molecular Ecology Notes, 2, 618- 620. Harpending, H.C., Sherry, S.T., Rogers, A.R. & Stoneking, M. (1993) The Genetic Structure of Ancient Human Populations. Current Anthropology, 34 , 483-496. Hartl, D.L. (2000) A Primer of Populations Genetics: third edition, Sinauer Associates Inc, Sunderland MA. Hastings, A. & Harrison, S. (1994) Metapopulation Dynamics and Genetics. Annual Review of Ecology and Systematics, 25 , 167-188. 186
Hauswaldt, J.S. & Glenn, T.C. (2005) Population genetics of the diamondback terrapin ( Malaclemys terrapin ). Molecular Ecology, 14 , 723-732. Haynes, G.D., Gilligan, D.M., Grewe, P. & Nicholas, F.W. (2009) Population genetics and management units of invasive common carp Cyprinus carpio in the Murray–Darling Basin, Australia. Journal of Fish Biology, 75 , 295-320. Hazlitt, S.L., Eldridge, M.D.B. & Goldizen, A.W. (2004) Fine-scale spatial genetic correlation analyses reveal strong female philopatry within a brush-tailed rock-wallaby colony in southeast Queensland. Molecular Ecology, 13 , 3621-3632. Herron, N., Davis, R. & Jones, R. (2002) The effects of large-scale afforestation and climate change on water allocation in the Macquarie River catchment, NSW, Australia. Journal of Environmental Management, 65 , 369-381. Hess, G.R. (1996) Linking extinction to connectivity and habitat destruction in metapopulation models. The American Naturalist, 148 , 226-236. Hewitt, G.M. (2001) Speciation, hybrid zone and phylogeography-or seeing genes in space and time. Molecular Ecology, 10 , 537-549. Hey, J. & Machado, C.A. (2003) The study of structured populations - new hope for a difficult and divided science. Nature Reviews Genetics, 4, 535-543. Holsinger, K.E. & Weir, B.S. (2009) Genetics in geographically structured populations: defining, estimating and interpreting [ FST ]. Nature Reviews Genetics, 10 , 639-650. Hood, G.M. (2010) PopTools version 3.2.5. Available on the internet. URL Http://www.poptools.org . Horne, B.D., Brauman, R.J., Moore, M.J.C., Seigel, R.A. & Douglas, M.E. (2003) Reproductive and Nesting Ecology of the Yellow-Blotched Map Turtle, Graptemys flavimaculata : Implications for Conservation and Management. Copeia, 2003 , 729-738. Howeth, J.G., Mcgaugh, S.E. & Hendrickson, D.A. (2008) Contrasting demographic and genetic estimates of dispersal in the endangered Coahuilan box turtle: a contemporary approach to conservation. Molecular Ecology, 17 , 4209-4221. Hudson, R.R., Slatkin, M. & Maddison, W.P. (1992) Estimation of levels of gene flow from DNA sequence data. Genetics, 132 , 583-589. Huey, J.A., Baker, A.M. & Hughes, J.M. (2008) The effect of landscape processes upon gene flow and genetic diversity in an Australian freshwater fish, Neosilurus hyrtlii . Freshwater Biology, 53 , 1393-1408. Huey, J.A., Hughes, J.M. & Baker, A.M. (2006) Patterns of gene flow in two species of eel-tailed catfish, Neosilurus hyrtlii and Porochilus argenteus (Siluriformes: Plotosidae), in western Queensland's dryland rivers. Biological Journal of the Linnean Society, 87 , 457-467. Huey, J.A., Schmidt, D.J., Balcombe, S.R., Marshall, J.C. & Hughes, J.M. (2011) High gene flow and metapopulation dynamics detected for three species in a dryland river system. Freshwater Biology, 56 , 2378-2390. Hughes, G.N., Greaves, W.F. & Litzgus, J.D. (2009a) Nest-Site Selection by Wood Turtles (Glyptemys insculpta ) in a Thermally Limited Environment. Northeastern Naturalist, 16 , 321- 338. Hughes, J.M. (2007) Constraints on recovery: using molecular methods to study connectivity of aquatic biota in rivers and streams. Freshwater Biology, 52 , 616-631. Hughes, J.M. & Hillyer, M.J. (2006) Mitochondrial DNA and allozymes reveal high dispersal abilities and historical movement across drainage boundaries in two species of freshwater fishes from inland rivers in Queensland, Australia. Journal of Fish Biology, 68 , 270-291. Hughes, J.M., Schmidt, D.J. & Finn, D.S. (2009b) Genes in Streams: Using DNA to Understand the Movement of Freshwater Fauna and Their Riverine Habitat. Bioscience, 59 , 573-583. Hughes, L. (2003) Climate change and Australia: Trends, projections and impacts. Austral Ecology, 28 , 423-443. Hulin, V. & Guillon, J.-M. (2007) Female philopatry in a heterogeneous environment: ordinary conditions leading to extraordinary ESS sex ratios. BMC Evolutionary Biology, 7, 13-11. Humphries, P. & Baldwin, D.S. (2003) Drought and aquatic systems: an introduction. Freshwater Biology, 48 , 1141-1146. 187
Humphries, P., Serafini, L.G. & King, A.J. (2002) River regulation and fish larvae: variation through space and time. Freshwater Biology, 47 , 1307-1331. Hutchison, D.W. & Templeton, A.R. (1999) Correlation of Pairwise Genetic and Geographic Distance Measures: Inferring the Relative Influences of Gene Flow and Drift on the Distribution of Genetic Variability. Evolution, 53 , 1898-1914. Ibrahim, K.M., Nichols, R.A. & Hewitt, G.M. (1996) Spatial patterns of genetic variation generated by different forms of dispersal during range expansion. Heredity, 77 , 282-291. Jakobsson, M. & Rosenberg, N.A. (2007) CLUMPP: a cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure. Bioinformatics, 23 , 1801-1806. Jarne, P. & Lagoda, P.J.L. (1996) Microsatellites, from molecules to populations and back. Trends in Ecology & Evolution, 11 , 424-429. Jenkins, D.G., Brescacin, C.R., Duxbury, C.V., Elliott, J.A., Evans, J.A., Grablow, K.R., Hillegass, M., Lyon, B.N., Metzger, G.A., Olandese, M.L., Pepe, D., Silvers, G.A., Suresch, H.N., Thompson, T.N., Trexler, C.M., Williams, G.E., Williams, E.N. & Williams, S.E. (2007) Does size matter for dispersal distance? Global Ecology and Biogeography, 16 , 415-425. Jenkins, D.G., Carey, M., Czerniewska, J., Fletcher, J., Hether, T., Jones, A., Knight, S., Knox, J., Long, T., Mannino, M., Mcguire, M., Riffle, A., Segelsky, S., Shappell, L., Sterner, A., Strickler, T. & Tursi, R. (2010) A meta-analysis of isolation by distance: relic or reference standard for landscape genetics? Ecography, 33 , 315-320. Jensen, A. (2002) Repairing wetlands of the Lower Murray: Learning from restoration practice. Ecological Management & Restoration, 3, 5-14. Jolly, I.D., Williamson, D.R., Gilfedder, M., Walker, G.R., Morton, R., Robinson, G., Jones, H., Zhang, L., Dowling, T.I., Dyce, P., Nathan, R.J., Nandakumar, N., Clarke, R. & Mcneill, V. (2001) Historical stream salinity trends and catchment salt balances in the Murray-Darling Basin, Australia. Marine and Freshwater Research, 52 , 53-63. Judge, D. (2001) The ecology of the polytypic freshwater turtle species, Emydura macquarii macquarii. Master Thesis, University of Canberra, Canberra. Kalinowski, S.T. (2004) Counting alleles with rarefaction: private alleles and hierarchical sampling designs. Conservation Genetics, 5, 539-543. Kalinowski, S.T. (2005) HP-Rare: A computer program for performing rarefaction on measures of allelic diversity. Molecular Ecology Notes, 5, 187-189. Kawecki, T.J. (2003) Sex-biased dispersal and adaptation to marginal habitats. The American Naturalist, 162 , 415-426. Keller, L.F. & Waller, D.M. (2002) Inbreeding effects in wild populations. Trends in Ecology & Evolution, 17 , 230-241. Kelly, D.W., Muirhead, J.R., Heath, D.D. & Macisaac, H.J. (2006) Contrasting patterns in genetic diversity following multiple invasions of fresh and brackish waters. Molecular Ecology, 15 , 3641-3653. Kelly, R.P., Oliver, T.A., Sivasundar, A. & Palumbi, S.R. (2010) A method for detecting population genetic structure in diverse, high gene-flow species. Heredity, 101 , 423-436. Kennard, M.J., Arthington, A.H., Pusey, B.J. & Harch, B.D. (2005) Are alien fish a reliable indicator of river health? Freshwater Biology, 50 , 174-193. Kennett, R. & Christian, K. (1994) Metabolic depression in estivating long-neck turtles ( Chelodina rugosa ). Physiological Zoology, 67 , 1087-1102. Kennett, R., Roe, J.H., Hodges, K.M. & Georges, A. (2009) Chelodina longicollis (Shaw 1794) - eastern long-necked turtle, common long-necked turtle, common snake-necked turtle. In: Conservation Biology of Freshwater Turtles and Tortoises: A Compilation Project of the IUCN/SCC Tortoise and Freshwater Turtle Specialist Group. Chelonian Research Monographs No.5, p.031.1-031.8, doi:10.3854/crm.5.031.longicollis.v1.2009, http://www.iucn-tftsg.org/cbftt/ . (Ed(s) A.G.J. Rhodin & P.C.H. Pritchard & P.P. Van Dijk & R.A. Saumure & K.A. Buhlmann & J.B. Iverson & R.A. Mittermeier).
188
Kennett, R. & Russell-Smith, J. (1993) Seed dispersal by freshwater turtles in northern Australia. In: Herpetology in Australia: A Diverse Discipline. (Ed(s) D. Lunney & D. Ayers), pp. 69-70. Royal Zoological Socitey of New South Wales, Mossman, NSW. Kennett, R. & Tory, O. (1996) The diet of two freshwater turtles. Chelodina rugosa and Elseya dentata (Testudines: Chelidae) from the wet-dry tropics of northern Australia. Copeia, 1996 , 409-419. Kennett, R.M. & Georges, A. (1990) Habitat utilisation and its relationship to growth and reproduction of the eastern long-necked turtle, Chelodina longicollis (Testudinata: Chelidae), from Australia. Herpetologica, 46 , 22-33. Kershaw, P., Moss, P. & Van Der Kaars, S. (2003) Causes and consequences of long-term climatic variability on the Australian continent. Freshwater Biology, 48 , 1274-1283. Kimmons, J.B. & Moll, D. (2010) Seed Dispersal by Red-Eared Sliders ( Trachemys scripta elegans ) and Common Snapping Turtles ( Chelydra serpentina ). Chelonian Conservation and Biology, 9, 289-294. Kimura, M. & Weiss, G.H. (1964) The stepping stone model of populations structure and the decrease of genetic correlation with distance. Genetics, 49 , 561-576. King, A., Tonkin, Z. & Mahoney, J. (2009) Environmental flow enhances native fish spawning and recruitment in the Murray River, Australia. River Research and Applications, 25 , 1205-1218. Kingsford, R.T. (1999) Managing the water of the Border Rivers in Australia: irrigation, Government and the wetland environment. Wetlands Ecology and Management, 7, 25-35. Kingsford, R.T. (2000) Ecological impacts of dams, water diversions and river management on floodplain wetlands in Australia. Austral Ecology, 25 , 109-127. Kingsford, R.T. (2011) Conservation management of rivers and wetlands under climate change: a synthesis. Marine and Freshwater Research, 62 , 217-222. Kingsford, R.T. & Auld, K.M. (2005) Waterbird breeding and environmental flow management in the Macquarie Marches, arid Australia. Rivers Research and Applications, 21 , 187-200. Kingsford, R.T., Georges, A. & Unmack, P.J. (2006) Vertebrates of desert rivers: meeting the challenges of temporal and spatial unpredictability. In: Ecology of Desert Rivers. (Ed(s) R.T. Kingsford), pp. 154 - 202. Cambridge University Press, Melbourne. Kingsford, R.T., Lemly, A.D. & Thompson, J.R. (2006) Impact of dams, river management and diversions on desert rivers. In: Ecology of Desert Rivers. (Ed(s) R. Kingsford), pp. 203-247. Cambridge Univeristy Press, Cambridge. Kingsford, R.T. & Thomas, R.F. (2004) Destruction of Wetlands and Waterbird Populations by Dams and Irrigation on the Murrumbidgee River in Arid Australia. Environmental Management, 34 , 383-396. Kingsford, R.T., Walker, K.F., Lester, R.E., Young, W.J., Fairweather, P.G., Sammut, J. & Geddes, M.C. (2011) A Ramsar wetland in crisis - the Coorong, Lower Lakes and Murray Mouth, Australia. Marine and Freshwater Research, 62 , 255-265. Klemens, M.W. (2000) Turtle Conservation, Smithsonian Institution Press Washington, D. C. . Knight, M.E., Oppen, M.J.H.V., Smith, H.L., Rico, C., Hewitt, G.M. & Turner, G.F. (1999) Evidence for male-biased dispersal in Lake Malawi cichlids from microsatellites. Molecular Ecology, 8, 1521-1527. Knighton, A.D. & Nanson, G.C. (1994) Waterholes and their significance in the anastomosing channel system of Cooper Creek, Australia. Geomorphology, 9, 311-324. Koizumi, I. (2011) Integration of ecology, demography and genetics to reveal population structure and persistence: a mini review and case study of stream-dwelling Dolly Varden. Ecology of Freshwater Fish, 20 , 352-363. Koizumi, I., Yamamoto, S. & Maekawa, K. (2006) Decomposed pairwise regression analysis of genetic and geographic distances reveals a metapopulation structure of stream-dwelling Dolly Varden charr. Molecular Ecology, 15 , 3175-3189. Kolbe, J.J. & Janzen, F.J. (2002) Impact of Nest-Site Selection on Nest Success and Nest Temperature in Natural and Disturbed Habitats. Ecology, 83 , 269-281.
189
Kuo, C.-H. & Janzen, F.J. (2004) Genetic Effects of a Persistent Bottleneck on a Natural Population of Ornate Box Turtles ( Terrapene ornata ). Conservation Genetics, 5, 425-425-437. Lacy, R.C. (1987) Loss of Genetic Diversity from Managed Populations: Interacting Effects of Drift, Mutation, Immigration, Selection, and Population Subdivision. Conservation Biology, 1, 143- 158. Lake, P.S. (2003) Ecological effects of perturbation by drought in flowing waters. Freshwater Biology, 48 , 1161-1172. Lamb, T. & Lydeard, C. (1994) A Molecular Phylogeny of the Gopher Tortoises, with Comments on Familial Relationships within the Testudinoidea. Molecular Phylogenetics and Evolution, 3, 283-291. Lambert, D.M., Ritchie, P.A., Millar, C.D., Holland, B., Drummond, A.J. & Baroni, C. (2002) Rates of Evolution in Ancient DNA from Adelie Penguins. Science, 295, 2270-2273. Landcaster, J. & Belyea, L.R. (1997) Nested hierarchies and scale-dependence of mechanisms of flow refugium use. Journal of the North American Benthological Society, 16 , 221-238. Lande, R. (1988) Genetics and demography in biological conservation. Science, 241 , 1455-1460. Lane, A. & Shine, R. (2011) Intraspecific variation in the direction and degree of sex-biased dispersal among sea-snake populations. Molecular Ecology, 20 , 1870-1876. Larned, S.T., Datry, T., Arscott, D.B. & Tockner, K. (2010) Emerging concepts in temporary-river ecology. Freshwater Biology, 55 , 717-738. Latch, E.K., Scognamillo, D.G., Fike, J.A., Chamberlain, M.J. & Rhodes Jr, O.E. (2008) Deciphering Ecological Barriers to North American River Otter (Lontra canadensis ) Gene Flow in the Louisiana Landscape. Journal of Heredity, 99 , 265-274. Law, B., Buckleton, J.S., Triggs, C.M. & Weir, B.S. (2003) Effects of Population Structure and Admixture on Exact Tests for Association Between Loci. Genetics, 164 , 381-387. Lawson Handley, L.J. & Perrin, N. (2007) Advances in our understanding of mammalian sex-biased dispersal. Molecular Ecology, 16 , 1559-1578. Legendre, P. (2000) Comparison of permutation methods for the partial correlation and partial mantel tests. Journal of Statistical Computation and Simulation, 67 , 37-73. Legler, J.M. (1960) A simple and inexpensive device for trapping aquatic turtles. Proceedings, Utah Academy of Sciences, Arts and Letters, 37 , 63-66. Legler, J.M. (1976) Feeding habits of some Australian short-necked tortoises. The Victorian Naturalist, 93 , 40-43. Legler, J.M. (1977) Stomach Flushing: A Technique for Chelonian Dietary Studies. Herpetologica, 33 , 281-284. Legler, J.M. (1978) Observations on behaviour and ecology in an Australian turtle, Chelodina expansa (Testudines : Chelidae). Canadian Journal of Zoology, 56 , 2449-2453. Legler, J.M. (1985) Australian Chelid Turtles: Reproductive Patterns in Wide-Ranging taxa. In: Biology of Australasian Frogs and Reptiles. (Ed(s) G. Grigg & R. Shine & H. Ehmann), pp. 117-123. Surrey Beatty & sons Pty Limited, Canberra. Legler, J.M. & Georges, A. (1993) Family Chelidae. In: Fauna of Australia Volume 2A. (Ed(s) C.G. Glasby & G.J.B. Ross & P.L. Beesley). AGPS, Canberra. Leigh, C. & Sheldon, F. (2008) Hydrological changes and ecological impacts associated with water resource development in large floodplain rivers in the Australian tropics. River Research and Applications, 24 , 1251-1270. Leigh, C., Sheldon, F., Kingsford, R.T. & Arthington, A.H. (2010) Sequential floods drive 'booms' and wetland persistence in dryland rivers: a synthesis. Marine and Freshwater Research, 61 , 896-908. Li, Y.-C., Korol, A.B., Fahima, T., Beiles, A. & Nevo, E. (2002) Microsatellites: genomic distribution, putative functions and mutational mechanisms: a review. Molecular Ecology, 11 , 2453-2465. Limpus, D.J., Hodge, W.J. & Limpus, C.J. (2006) Impacts of Dams and Weirs on Freshwater Turtles: Fairbairn Dam, March 2006. In: Conservation technical and data support. (Ed(s) M. Jones). The State of Queensland. Environmental Protection Agency. 190
Lind, P.R., Robson, B.J. & Mitchell, B.D. (2006) The influence of reduced flow during a drought on patterns of variation in macroinvertebrate assemblages across a spatial hierarchy in two lowland rivers. Freshwater Biology, 51 , 2282-2295. Lintermans, M. (1997) The lake Burley Griffin fishery: 1997 sampling report. Consultancy report of the National Capital Authority. Lohmueller, K.E., Bustamante, C.D. & Clark, A.G. (2010) The Effect of Recent Admixture on Inference of Ancient Human Population History. Genetics, 185 , 611-622. Lowe, W.H. (2002) Landscape-Scale Spatial Population Dynamics in Human-Impacted Stream Systems. Environmental Management, 30 , 225-233. Lowe, W.H. (2003) Linking dispersal to local population dynamics: A case study using a headwater salamander system. Ecology, 84 , 2145-2154. Lowe, W.H. & Allendorf, F.W. (2010) What can genetics tell us about population connectivity? Molecular Ecology, 19 , 3038-3051. Lowe, W.H., Likens, G.E. & Power, M.E. (2006) Linking Scales in Stream Ecology. Bioscience, 56 , 591-597. Lynch, M. & Ritland, K. (1999) Estimation of pairwise relatedness with molecular markers. Genetics, 152 , 1753-1766. Macculloch, R.D. & Secoy, D.M. (1983) Movement in a River Population of Chrysemys picta bellii in Southern Saskatchewan. Journal of Herpetology, 17 , 283-285. Mackay, N. & Eastburn, D. (1990) The Murray. Murray Darling Basin Commission, Canberra. Magoulick, D.D. & Kobza, R.M. (2003) The role of refugia for fishes during drought: a review and synthesis. Freshwater Biology, 48 , 1186-1198. Maheshwari, B.L., Walker, K.F. & Mcmahon, T.A. (1995) Effects of regulation on the flow regime of the river Murray, Australia. Regulated Rivers: Research & Management, 10 , 15-38. Manel, S., Gaggiotti, O.E. & Waples, R.S. (2005) Assignment methods: matching biological questions with appropriate techniques. Trends in Ecology & Evolution, 20 , 136-142. Manel, S., Schwartz, M.K., Luikart, G. & Taberlet, P. (2003) Landscape genetics: combining landscape ecology and population genetics. Trends in Ecology & Evolution, 18 , 189-197. Mank, J.E. & Avise, J.C. (2004) Individual organisms as units of analysis: Bayesian-clustering alternatives in population genetics. Genetical Research, 84 , 135-143. Mantel, N. (1967) The detection of disease clustering and a generalized regression approach. Cancer Research, 27 , 209-220. Martin-Smith, K.M. & Laird, L.M. (1998) Depauperate freshwater fish communities in Sabah: the role of barriers to movement and habitat quality. Journal of Fish Biology, 53 , 331-344. Martinez, P.A., Ezaz, T., Valenzuela, N., Georges, A. & Marshall Graves, J.A. (2008) An XX/XY heteromorphic sex chromosome system in the Australian chelid turtle Emydura macquarii: A new piece in the puzzle of sex chromosome evolution in turtles. Chromosome Research, 16 , 815-825. Mccauley, D.E. (1991) Genetic consequences of local population extinction and recolonization. Trends in Ecology & Evolution, 6, 5-8. Mccauley, D.E. (1993) Genetic consequences of extinction and recolonization in fragmented habitats. In: Biotic interactions and global change. (Ed(s) P.M. Kareiva & J.G. Kingsolver & R.B. Huey). Sinauer, Sunderland, MA. Mclean, A.J., Schmidt, D.J. & Hughes, J.M. (2008) Do lowland habitats represent barriers to dispersal for a rainforest mayfly, Bungona narilla , in south-east Queensland? Marine and Freshwater Research, 59 , 761-771 Mcmahon, T.A. & Finlayson, B.L. (2003) Droughts and anti-droughts: the low flow hydrology of Australian rivers. Freshwater Biology, 48 , 1147-1160. Mdbc (2008) Annual Report 2007-2008. N.S.W. To the Parliaments of the Australian Government, Victoria, South Australia and Queensland, the Legislative Assembly of the Australian Capital Territory, and the Australian Community). Meathrel, C.E., Suter, P.J. & Radford, N.M. (2002) Niche Segregation Between Three Species of Freshwater Turtle in a Large Billabong During Flood. The Victorian Naturalist, 119 , 160-173. 191
Meathrel, C.E., Suter, P.J. & Reid, S. (2004) Habitat and dietary preferences if freshwater turtles in ephemeral billabongs on the Ovens River, north-east Victoria. The Victorian Naturalist, 121 , 4-14. Meffe, G.K. & Vrijenhoek, R.C. (1988) Conservation Genetics in the Management of Desert Fishes. Conservation Biology, 2, 157-169. Metzgar, D., Bytof, J. & Wills, C. (2000) Selection against frameshift mutations limits microsatellite expansion in coding DNA. Genome Research, 10 , 72-80. Milligan, B.G. (2003) Maximum-likelihood estimation of relatedness. Genetics, 163 , 1153-1167. Missiaggia, A. & Grattapaglia, D. (2006) Plant microsatellite genotyping with 4-color fluorescent detection using multiple-tailed primers. Genetic and Molecular Research, 1, 72-78. Mockford, S.W., Mceachern, L., Herman, T.B., Snyder, M. & Wright, J.M. (2005) Population genetic structure of a disjunct population of Blanding's turtle ( Emydoidea blandingii) in Nova Scotia, Canada. Biological Conservation, 123 , 373-380. Moll, D. & Moll, E.O. (2004) The Ecology, Exploitation, and Conservation of River Turtles. p. 393. Oxford University Press, New York. Morgan, T.J., Garland, T., Jr., Irwin, B.L., Swallow, J.G. & Carter, P.A. (2003) The Mode of Evolution of Molecular Markers in Populations of House Mice Under Artificial Selection for Locomotor Behavior. Journal of Heredity, 94 , 236-242. Morita, K. & Yamamoto, S. (2002) Effects of Habitat Fragmentation by Damming on the Persistence of Stream-Dwelling Charr Populations. Conservation Biology, 16 , 1318-1323. Moritz, C. (1994) Defining 'Evolutionary Significant Units' for conservation. Trends in Ecology & Evolution, 9, 373-375. Moritz, C. (2002) Strategies to Protect Biological Diversity and the Evolutionary Process That Sustain It. Systematic Biology, 51 , 238-254. Morreale, S.J., Gibbons, J.W. & Congdon, J.D. (1984) Significance of activity and movement in the yellow-bellied slider turtle ( Pseudemys scripta ). Canadian Journal of Zoology, 62 , 1038 - 1042. Morton, S.R., Stafford Smith, D.M., Friedel, M.H., Griffin, G.F. & Pickup, G. (1995) The stewardship of arid Australia: Ecology and landscape management. Journal of Environmental Management, 43 , 195-217. Motro, U. (1991) Avoiding Inbreeding and Sibling Competition: The Evolution of Sexual Dimorphism for Dispersal. The American Naturalist, 137 , 108-115. Murphy, J.B. & Lamoreaux, W.E. (1978) Mating Behavior in Three Australian Chelid Turtles (Testudines: Pleurodira: Chelidae). Herpetologica, 34 , 398-405. Narum, S.R. (2006) Beyond Bonferroni: Less conservative analyses for conservation genetics. Conservation Genetics, 7, 783-787. Nei, M. (1972) Genetic distance between populations. The American Naturalist, 106 , 283-292. Neigel, J.E. (1997) A Comparison of Alternative Strategies for Estimating Gene Flow from Genetic Markers. Annual Review of Ecology and Systematics, 28 , 105-128. Neigel, J.E. (2002) Is FST obsolete? Conservation Genetics, 3, 167-168. Neuhauser, C. (2007) Mathematical models in population genetics. In: Handbook of Statistical Genetics, Third Edition. (Ed(s) D.J. Balding & M. Bishop & C. Cannings). John Wiley & Sons, Ltd. O'brien, P.E. & Burne, R.V. (1994) The Great Cumbung Swamp-Terminus of the low-gradient Lachland River, Eastern Australia. AGSO Journal of Australian Geology & Geophysics, 15 , 223-233. Olivieri, I., Michalakis, Y. & Gouyon, P.-H. (1995) Metapopulation Genetics and the Evolution of Dispersal. The American Naturalist, 146 , 202-228. Pages, R.D.M. & Holmes, E.C. (1998) Molecular Evolution: A Phylogenetic Approach, Blackwell Publishing, Oxford. Palsboll, P.J., Bérubé, M. & Allendorf, F.W. (2007) Identification of management units using population genetic data. Trends in Ecology & Evolution, 22 , 11-16.
192
Pannell, J.R. & Charlesworth, B. (1999) Neutral genetic diversity in a metapopulation with recurrent local extinction and recolonization. Evolution, 53 , 664-676. Parmenter, J.C. (1976) The natural history of the Australian freshwater turtle Chelodina longicollis Shaw (Testudinata:Chelidae). Ph.D. Thesis, University of New England, Armidale. Parmenter, J.C. (1985) Reproduction And Survivorship of Chelodia longicollis (TESTUDINATA: Chelidae). In: Biology of Australasian Frogs And Reptiles. (Ed(s) G. Grigg & R. Shine & H. Ehmann), pp. 53-61. Surrey Beatty & Sons Pty Limited, Canberra. Parsons, T.J., Muniec, D.S., Sullivan, K., Woodyatt, N., Alliston-Greiner, R., Wilson, M.R., Berry, D.L., Holland, K.A., Weedn, V.W., Gill, P. & Holland, M.M. (1997) A high observed substitution rate in the human mitochondrial DNA control region. Nature Genetics, 15 , 363- 368. Peakall, R., Ruibal, M. & Lindenmayer, D.B. (2003) Spatial autocorrelation analysis offers new insight into gene flow in the Australian bush rats, Rattus fuscipes . Evolution, 57 , 1182-1195. Peakall, R., Smouse, P.E. & Huff, D.R. (1995) Evolutionary implications of allozymes and RAPD variation in diploid populations of dioecious buffalograss Buchloe dactyloide s. Molecular Ecology, 4, 135-147. Pearse, D.E., Arndt, A.D., Valenzuela, N., Miller, B.A., Cantarelli, V. & Sites Jr, J.W. (2006) Estimating population structure under nonequilibrium conditions in a conservation context: continent-wide population genetics of the giant Amazon river turtle, Podocnemis expansa (Chelonia; Podocnemididae). Molecular Ecology, 15 , 985-1006. Pearse, D.E. & Avise, J.C. (2001) Turtle Mating Systems: Behavior, Sperm Storage, and Genetic Paternity. Journal of Heredity, 92 , 206-211. Pearse, D.E., Janzen, F.J. & Avise, J.C. (2001) Genetic markers substantiate long-term storage and utilization of sperm by female painted turtles. Heredity, 86 , 378-384. Pemberton, J.M., Coltman, D.W., Coulson, T.N. & Slate, J. (1999) Using microsatellite to measure the fitness consequences of inbreeding and outbreeding. In: Microsatellites: Evolution and Applications. (Ed(s) D.B. Goldstein & C. Schlotterer), pp. 151-164. Oxford University Press, New York. Perrin, C. & Roy, M.S. (2000) Rapid and efficient identification of microsatellite loci from the sea urchin, Evechinus chloroticus . Molecular Ecology, 9, 2220-2222. Perrin, N. & Goudet, J. (2001) Inbreeding, kinship, and the evolution of natal dispersal. In: Dispersal. (Ed(s) J. Clobert & E. Danchin & A.A. Dhondt & J.D. Nichols), pp. 123-142. Oxford, New York. Perrin, N. & Mazalov, V. (1999) Dispersal and Inbreeding Avoidance. pp. 282-292. The University of Chicago Press for The American Society of Naturalists. Petit, R.M.J., Mousadik, A.E. & Pons, O. (1998) Identifying Populations for Conservation on the Basis of Genetic Markers. Conservation Biology, 12 , 844-855. Phillott, A.D. & Parmenter, J.C. (2000) Spotted barramundi predation upon Emydura Krefftii and Chelodina expansa hatchlings in central Qeensland. Herpetofauna, 30 , 22-25. Pittock, J. & Finlayson, C.M. (2011) Australia's Murray-Darling Basin: freshwater ecosystem conservation options in an era of climate change. Marine and Freshwater Research, 62 , 232- 243. Ponniah, M. & Hughes, J.M. (2004) The evolution of Queensland spiny mountain crayfish of the genus Euastacus. I. Testing vicariance and dispersal with interspecific mitochondrial DNA. Evolution, 58 , 1073-1085. Preston, B.L. & Jones, R.N. (2008) A national assessment of the sensitivity of Australian runoff to climate change. Atmospheric Science Letters, 9, 202-208. Price, J., Gross, C.L. & Whalley, W. (2010) Prolonged summer flooding switched dominance from the invasive weed Lippia ( Phyla canescens ) to native species in one small, ephemeral wetland. Ecological Management & Restoration, 11 , 61-63. Pritchard, J.K., Stephens, M. & Donnelly, P. (2000) Inference of Population Structure Using Multilocus Genotype Data. Genetics, 155 , 945-959.
193
Pritchard, J.K., Wen, X. & Falush, D. (2007) Documentation for structure software: version 2.2. pp. 1-36, University of Chicago, Chicago. Pritchard, P.C.H. (1984) Piscivory in turtles, and evolution of the long-necked Chelidae. Symposia of the Zoological Society of London, 52 , 87-110. Prugnolle, F. & De Meeus, T. (2002) Inferring sex-biased dispersal from population genetic tools: a review. Heredity, 88 , 161-165. Puckridge, J.T., Sheldon, F., Walker, K.F. & Boulton, A.J. (1998) Flow variability and the ecology of large rivers. Marine and Freshwater Research, 49 , 55-72. Puckridge, J.T., Walker, K.F. & Costelloe, J.F. (2000) Hydrological persistence and the ecology of dryland rivers. Regulated Rivers: Research & Management, 16 , 385-402. Pusey, A.E. (1987) Sex-biased dispersal and inbreeding avoidance in birds and mammals. Trends in Ecology & Evolution, 2, 295-299. Queller, D.C. & Goodnight, K.F. (1989) Estimating Relatedness Using Genetic Markers. Evolution, 43 , 258-275. Quilty, P.G. (1994) The background: 144 million years of paleoclimate and paleogeography. In: History of the Australian Vegetation, Cretaceous to present. (Ed(s) R. Hill), pp. 14-44. Cambridge University Press, Cambridge. Raymond, M. & Rousset, F. (1995) An exact test for population differentiation. Evolution, 49 , 1280- 1284. Rayner, T.S., Jenkins, K.M. & Kingsford, R.T. (2009) Small environmental flows, drought and the role of refugia for freshwater fish in the Macquarie Marshes, arid Australia. Ecohydrology, 2, 440-453. Real, K., Schmidt, D.J. & Hughes, J.M. (2009) Mogurnda adspersa microsatellite markers: multiplexing and multi-tailed primer tagging. Conservation Genetics Resources, 1, 411-414. Reed, D.H. & Frankham, R. (2003) Correlation between Fitness and Genetic Diversity. Conservation Biology, 17 , 230-237. Rees, M., Roe, J.H. & Georges, A. (2009) Life in the suburbs: Behavior and survival of a freshwater turtle in response to drought and urbanization. Biological Conservation, 142 , 3172-3181. Reid, M.A. & Brooks, J.J. (2000) Detecting effects of environmental water allocations in wetlands of the Murray-Darling Basin, Australia. Regulated Rivers: Research & Management, 16 , 479- 496. Reinhard, W. (2008) The SANDOZ catastrophe and the consequence for the River Rhine. In: Risk Assessment as a Basis for the Forecast and Prevention of Catastrophies. (Ed(s) I. Apostol & W.G. Coldewey & D.L. Barry & D. Reimer), pp. 113-121. IOS Press, Amsterdam, Netherlands. Reinhold, K. (1998) Nest-site philopatry and selection for environmental sex determination. Evolutionary Ecology, 12 , 245-250. Ren, S., Kingsford, R.T. & Thomas, R.F. (2011) Modelling flow to and inundation of the Macquarie Marshes in arid Australia. Environmetrics, 21 , 549-561. Ricciardi, A. & Rasmussen, J.B. (1999) Extinction Rates of North American freshwater fauna. Conservation Biology, 11 , 1081-1093. Rice, W.R. (1989) Analyzing Tables of Statistical Tests. Evolution, 43 , 223-225. Richter, B.D., Braun, D.P., Mendelson, M.A. & Master, L.L. (1997) Threats to Imperiled Freshwater Fauna. Conservation Biology, 11 , 1081-1093. Rioux Paquette, S., Louis, E.E. & Lapointe, F.-J. (2010) Microsatellite Analyses Provide Evidence of Male-Biased Dispersal in the Radiated Tortoise Astrochelys radiata (Chelonia: Testudinidae). Journal of Heredity, 101 , 403-412. Roberts, M.A., Schwartz, T.S. & Karl, S.A. (2004) Global Population Genetic Structure and Male- Mediated Gene Flow in the Green Sea Turtle ( Chelonia mydas ): Analysis of Microsatellite Loci. Genetics, 166 , 1857-1870. Robinson, C.T., Tockner, K. & Ward, J.V. (2002) The fauna of dynamic riverine landscapes. Freshwater Biology, 47 , 661-677.
194
Roe, J.H. (2007) The terrestrial Ecology of a Freshwater Turtle, Chelodina longicollis, in Booderee National Park, Australia. PhD Thesis, University of Canberra, Canberra. Roe, J.H. & Georges, A. (2007) Heterogeneous wetland complexes, buffer zones, and travel corridors: landscape management for freshwater reptiles. Biological Conservation, 135 , 67-76. Roe, J.H. & Georges, A. (2008a) Energy and Water Flux during Terrestrial Estivation and Overland Movement in a Freshwater Turtle. Physiological and Biochemical Zoology, 81 , 570-583. Roe, J.H. & Georges, A. (2008b) Maintenance of variable response for coping with wetland drying in freshwater turtles. Ecology, 89 , 485-494. Roe, J.H. & Georges, A. (2008c) Terrestrial activity, movement and spatial ecology of an Australian freshwater turtle, Chelodina longicollis , in a temporally dynamic wetland system. Austral Ecology, 33 , 1045-1056. Roe, J.H. & Georges, A. (2009) Responses of freshwater turtles to drought: the past, present and implications for future climate change in Australia. In: Meltdown: climate change, natural disasters, and other catastrophes - fears and concerns for the future. (Ed(s) K. Grow), pp. 175-190. Natural Disaster Research, Prediction and Mitigation Series. Nova Science Publishers, New York. Rogers, L.J. (1966) The nitrogen excretion of Chelodina longicollis under conditions of hydration and dehydration. Comparative Biochemistry and Physiology, 18 , 249-260. Roshier, D.A., Whetton, P.H., Allan, R.J. & Robertson, A.I. (2001) Distribution and persistence of temporary wetland habitats in arid Australia in relation to climate. Austral Ecology, 26 , 371- 384. Rourke, M.L., Mcpartlan, H.C., Ingram, B.A. & Taylor, A.C. (2011) Variable stocking effect and endemic population genetic structure in Murray cod Maccullochella peelii . Journal of Fish Biology, 79 , 155-177. Rousset, F. (1997) Genetic Differentiation and Estimation of Gene Flow from F-Statistics Under Isolation by Distance. Genetics, 145 , 1219-1228. Rousset, F. (2000) Genetic differentiation between individuals. Journal of Evolutionary Biology, 13 , 58-62. Rousset, F. (2008) Genepop'007: a complete reimplementation of the Genepop software for Windows and Linux. Molecular Ecology Resources, 8, 103-106. Rowe, C.L. (2008) "The Calamity of So Long Life": Life Histories, Contaminants, and Potential Emerging Threats to Long-lived Vertebrates. Bioscience, 58 , 623-631. Rowe, G. & Beebee, T.J.C. (2007) Defining population boundaries: use of three Bayesian approaches with microsatellite data from British natterjack toads ( Bufo calamita ). Molecular Ecology, 16 , 785-796. Rubinsztein, D.C., Amos, W., Leggo, J., Goodburn, S., Jain, S., Li, S.-H., Margolis, R.L., Ross, C.A. & Ferguson-Smith, M.A. (1995) Microsatellite evolution - evidence for directionality and variation in rate between species. Nature Genetics, 10 , 337-343. Ruzzante, D.E. (1998) A comparison of several measures of genetic distance and population structure with microsatellite data: bias and sampling variance. Canadian Journal of Fisheries and Aquatic Sciences, 55 , 1-14. Safner, T., Miller, M.P., Mcrae, B.H., Fortin, M.-J. & Manel, S. (2011) Comparison of Bayesian CLustering and Edge Detection Methods for Inferring Boundaries in Landscape Genetics. International Journal of Molecular Sciences, 12 , 865-889. Sajwaj, T.D., Piepgras, S.A. & Wlang, J.W. (1998) Blanding's Turtle ( Emydoidea blandingii ) at Camp Ripley. Report submitted to the Nongame Wildlife Program. p. 185. Minnesota Department of Natural Resources. Sakaguchi, S., Takeuchi, Y., Yamasaki, M., Sakurai, S. & Isaqi, Y. (2011) Lineage admixture during postglacial range expansion is responsible for the increased gene diversity of Kalopanax septembolus in a recently colonised territory. Heredity, 107 , 338-348. Saunders, D.L., Meeuwig, J.J. & Vincent, A.C.J. (2002) Freshwater Protected Areas: Strategies for Conservation. Conservation Biology, 16 , 30-41. Schlotterer, C. (2000) Evolutionary dynamics of microsatellites DNA. Chromosoma, 109 , 365-371. 195
Schmidt, S.K., Hughes, J.M. & Bunn, S.E. (1995) Gene flow among conspecific populations of Baetis sp . (Ephemeroptera): adult flight and larval drift. Journal of the North American Benthological Society, 14 , 147-157. Schooley, R.L. & Wiens, J.A. (2003) Finding habitat patches and directional connectivity. OIKOS, 102 , 559-570. Schuelke, M. (2000) An economic method for the fluorescent labelling of PCR fragments. Nature Biotechnology, 18 , 233-234. Schultheis, A.S. & Hughes, J.M. (2005) Spatial pattern of genetic structure among populations of a stone-cased caddis (Trichoptera: Tasimiidae) in south-east Queensland, Australia. Freshwater Biology, 50 , 2002-2010. Schwartz, M.K. & Mckelvey, K.S. (2008) Why sampling scheme matters: the effect of sampling scheme on landscape genetic results. Conservation Genetics, 10 , 441-452. Schweiger, O., Frenzel, M. & Durka, W. (2004) Spatial genetic structure in a metapopulation of the land snail Cepaea nemoralis (Gastropoda: Helicidae). Molecular Ecology, 13 , 3645-3655. Scribner, K.T., Blanchong, J.A., Bruggeman, D.J., Epperson, B.K. & Et Al. (2005) Geographical Genetics: Conceptual Foundations And Empirical Applications of Spatial Genetic Data in Wildlife Management. Journal of Wildlife Management, 69 , 1434. Scribner, K.T. & Chesser, R.K. (2001) Group-Structured Genetic Models in Analyses of the Population and Behavioral Ecology of Poikilothermic Vertebrates. Journal of Heredity, 92 , 180-189. Scribner, K.T., Congdon, J.D., Chesser, R.K. & Smith, M.H. (1993) Annual differences in female reproductive success affect spatial and cohort-specific genotypic heterogeneity in painted turtles. Evolution, 47 , 1360-1373. Seddon, J.M., Georges, A., Baverstock, P.R. & Mccord, W. (1997) Phylogenetic Relationships of Chelid Turtles (Pleurodira: Chelidae) Based on Mitochondrial 12S rRNA Gene Sequence Variation. Molecular Phylogenetics and Evolution, 7, 55-61. Selkoe, K.A. & Toonen, R.J. (2006) Microsatellites for ecologists: a practical guide to using and evaluating microsatellite markers. Ecology Letters, 9, 615-629. Shaw, G. (1794) Zoology of New Holland., British Museum of Natural History, London. Sheldon, F., Bunn, S.E., Hughes, J.M., Arthington, A.H., Balcombe, S.R. & Fellows, C.S. (2010) Ecological roles and threats to aquatic refugia in arid landscapes: dryland river waterholes. Marine and Freshwater Research, 61 , 885-895. Sheridan, C.M., Spotila, J.R., Bien, W.F. & Avery, H.W. (2010) Sex-biased dispersal and natal philopatry in the diamondback terrapin, Malaclemys terrapin . Molecular Ecology, 19 , 5497- 5510. Sinnock, P. (1975) The Wahlund Effect for the two-locus model. The American Naturalist, 109 , 565- 570. Slatkin, M. (1985) Gene Flow in Natural Populations. Annual Review of Ecology and Systematics, 16 , 393-430. Slatkin, M. (1987) Gene flow and the geographic structure of natural populations. Science, 236 , 787- 799. Slatkin, M. (1993) Isolation by distance in equilibrium and non-equilibrium populations. Evolution, 47 , 264-280. Slatkin, M. (1994) Linkage Disequilibrium in Growing and Stable Populations. Genetics, 137 , 331- 336. Slatkin, M. (1995) A measure of population subdivision based on microsatellite allele frequencies. Genetics, 139 , 457-462. Slatkin, M. & Arter, H.E. (1991) Spatial Autocorrelation Methods in Population Genetics. The American Naturalist, 138 , 499-517. Smouse, P.E. & Peakall, R. (1999) Spatial autocorrelation analysis of individual multiallele and multilocus genetic structure. Heredity, 82 , 561-573. Smouse, P.E., Peakall, R. & Gonzales, E. (2008) A heterogeneity test for fine-scale genetic structure. Molecular Ecology, 17 , 3389-3400. 196
Sokal, R.R. (1979) Ecological parameters inferred from spatial correlograms. In: Contemporary Quantitative Ecology and Related Ecometrics. (Ed(s) G.P. Patil & M.L. Rosenzweig), pp. 167-196. International Cooperative Publishing House, Fairland, Maryland. Sokal, R.R. & Oden, N.L. (1991) Spatial Autocorrelation Analysis as an Inferential Tool in Population Genetics. The American Naturalist, 138 , 518-521. Sokal, R.R. & Wartenberg, D.E. (1983) A test of spatial autocorrelation analysis using an isolation- by-distance model. Genetics, 105 , 219-237. Souza, F.L. & Abe, A.S. (1997) Population structure, activity, and conservation of the neotropical freshwater turtle, Hydromedusa maximiliani , in Brazil. Chelonian Conservation and Biology, 1, 521-525. Souza, F.L. & Abe, A.S. (2000) Feeding ecology, density and biomass of the freshwater turtle, Phrynops geoffroanus , inhabiting a polluted urban river in south-eastern Brazil. Journal of the Zoological Society of London, 252 , 437-446. Souza, F.L., Cunha, A.F., Oliveira, M.A., Pereira, G.A.G. & Dos Reis, S.F. (2002a) Estimating dispersal and gene flow in the neotropical freshwater turtle Hydromedusa maximiliani (Chelidae) by combining ecological and genetic methods. Genetics and Molecular Biology, 25 , 151-155. Souza, F.L., Cunha, A.F., Oliveira, M.A., Pereira, G.A.G., Pinheiro, H.P. & Dos Reis, S.F. (2002b) Partitioning of molecular variation at local spatial scales in the vulnerable neotropical freshwater turtle, Hydromedusa maximiliani (Testudines, Chelidae): implications for the conservation of aquatic organisms in natural hierarchical systems. Biological Conservation, 104 , 119-126. Spencer, R.-J. (2002a) Experimentally testing nest-site selection: fitness trade-offs and predation risk in turtles. Ecology, 83 , 2136-2144. Spencer, R.-J. (2002b) Growth patterns of two widely distributed freshwater turtles and a comparison of common methods used to estimate age. Australian Journal of Zoology, 50 , 477-490. Spencer, R.-J., Janzen, F.J. & Thompson, M.B. (2006) Counterintuitive density-dependent growth in a long-lived vertebrate after removal of nest predators. Ecology, 87 , 3109-3118. Spencer, R.-J. & Thompson, M.B. (2003) The significance of predation in nest site selection of turtles: an experimental consideration of macro- and microhabitat preferences. OIKOS, 102 , 592-600. Spencer, R.-J. & Thompson, M.B. (2005) Experimental Analysis of the Impact of Foxes on Freswater Turtles Populations. Conservation Biology, 19 , 845-854. Spencer, R.-J., Thompson, M.B. & D. Hume, I. (1998) The diet and digestive energetics of an Australian short-necked turtle, Emydura macquarii . Comparative Biochemistry and Physiology - Part A: Molecular & Integrative Physiology, 121 , 341-349. Spielman, D., Brook, B.W., Briscoe, D.A. & Frankham, R. (2004) Does Inbreeding and Loss of Genetic Diversity Decrease Disease Resistance? Conservation Genetics, 5, 439-448. Spinks, P.Q., Bradley Shaffer, H., Iverson, J.B. & Mccord, W.P. (2004) Phylogenetic hypotheses for the turtle family Geoemydidae. Molecular Phylogenetics and Evolution, 32 , 164-182. Stanley, E., Fisher, S. & Grimm, N. (1997) Ecosystem expansion and contraction in streams. Bioscience, 47 , 427-435. Steen, D.A., Aresco, M.J., Beilke, S.G., Compton, B.W., Condon, E.P., Kenneth Dodd, C., Forrester, H., Gibbons, J.W., Greene, J.L., Johnson, G., Langen, T.A., Oldham, M.J., Oxier, D.N., Saumure, R.A., Schueler, F.W., Sleeman, J.M., Smith, L.L., Tucker, J.K. & Gibbs, J.P. (2006) Relative vulnerability of female turtles to road mortality. Animal Conservation, 9, 269-273. Steinberg, E.K. & Jordan, C.E. (1997) Using molecular genetics to learn about the ecology of threatened species: the allure and illusion of measuring genetic structure in natural populations. In: Conservation Biology for the Coming Decade. (Ed(s) P.L. Fielder & P.M. Kareiva), pp. 440-460. Chapman and Hall, New York. Sternberg, D., Balcombe, S., Marshall, J. & Lobegeiger, J. (2008) Food resource variability in an Australian dryland river: evidence from the diet of two generalist native fish species. Marine and Freshwater Research, 59 , 137-144. 197
Stott, P. (1987) Terrestrial Movements of the Freshwater Tortoise Chelodina Longicollis Shaw as Monitored With a Spool Tracking Device. Wildlife Research, 14 , 559-567. Stuart, S.N., Chanson, J.S., Cox, N.A., Young, B.E., Rodrigues, A.S.L., Fischman, D.L. & Waller, R.W. (2004) Status and Trends of Amphibian Declines and Extinctions Worldwide. Science, 306 , 1783-1786. Sunnucks, P. (2000) Efficient genetic markers for population biology. Trends in Ecology & Evolution, 15 , 199-203. Switzer, P.V. (1993) Site fidelity in predictable and unpredictable habitats. Evolutionary Ecology, 7, 533-555. Taberlet, P., Griffin, S., Goossens, B., Questiau, S., Manceau, V., Escaravage, N., Waits, L.P. & Bouvet, J. (1996) Reliable Genotyping of Samples with Very Low DNA Quantities Using PCR. Nucleic Acids Research, 24 , 3189-3194. Tessier, N., Rioux Paquette, S. & Lapointe, F.-J. (2005) Conservation genetic of the wood turtle (Glyptemys insculpta ) in Quebec, Canada. Canadian Journal of Zoology, 83 , 765-772. Thompson, M.B. (1983) Population of the Murray River tortoise Emydura (Chelodina): the effect of egg predation by the red fox, Vulpes vulpes . Australian Wildlife Research, 10 , 363-371. Thompson, M.B. (1993) Hypothetical considerations of the biomass of chelid tortoises in the River Murray and the possible influences of predation by introduced foxes. In: Herpetology in Australia: A Diverse Discipline. (Ed(s) D. Lunney & D. Ayers), pp. 219-224. Royal Zoological Society of New South Wales, Mosman, NSW. Thompson, S., Hamman, M., Latta, C. & Latta, G. (2006) The Environmental Impacts of Dams on the regionally Endemic Turtles of the Mary River. In: Unpublished report commissioned by Save the Mary River Coordinating Group Inc. (Ed(s). Thoms, M.C. & Sheldon, F. (2000a) Lowland rivers: an Australian introduction. Regulated Rivers: Research & Management, 16 , 375-383. Thoms, M.C. & Sheldon, F. (2000b) Water resource development and hydrological change in a large dryland river: the Barwon - Darling River, Australia. Journal of Hydrology, 228 , 10-21. Thoms, M.C., Southwell, M. & Mcginness, H.M. (2005) Floodplain-river ecosystems: Fragmentation and water resources development. Geomorphology, 71 , 126-138. Thomson, R.C., Shedlock, A.M., Edwards, S.V. & Shaffer, H.B. (2008) Developing markers for multilocus phylogenetics in non-model organisms: A test case with turtles. Molecular Phylogenetics and Evolution, 49 , 514-525. Todd, C.R., Ryan, T., Nicol, S.J. & Bearlin, A.R. (2005) The impact of cold water releases on the critical period of post-spawning survival and its implications for Murray cod ( Maccullochella peelii peelii ): a case study of the Mitta Mitta River, southeastern Australia. River Research and Applications, 21 , 1035-1052. Tóth, G., Gáspári, Z. & Jurka, J. (2000) Microsatellites in Different Eukaryotic Genomes: Survey and Analysis. Genome Research, 10 , 967-981. Tucker, A.D. (1999) Cumulative Effects of Dams and Weirs of Freshwater Turtles: Fitzroy, Kolan, Burnett and Mary Catchments. In: Unpublished report of the Qld Department of Natural Resources. (Ed(s). Queensland Parks and Wildlife Service. Tucker, A.D., Limpus, C.J., Priest, T.E., Cay, J., Glen, C. & Guarino, E. (2001) Home ranges of Fitzroy River turtles ( Rheodytes leukops ) overlap riffle zones: potential concerns related to river regulation. Biological Conservation, 102 , 171-181. Tucker, A.D., Mccallum, H.I., Limpus, C.J. & Mcdonald, K.R. (1998) Sex-biased dispersal in a long- lived polygynous reptile ( Crocodylus johnstoni ). Behavioral Ecology and Sociobiology, 44 , 85-90. Unmack, P.J. (2001) Fish persistence and fluvial geomorphology in central Australia. Journal of Arid Environments, 49 , 653-669. Valenzuela, N. (2000) Multiple paternity in side-neck turtles Podocnemis expansa : evidence from microsatellite DNA data. Molecular Ecology, 9, 99-105. Vallone, P.M. & Butler, J.M. (2004) AutoDimer: a screening tool for primer-dimer and hairpin structures. BioTechniques, 372 , 226-231. 198
Van De Casteele, T., Galbusera, P. & Matthysen, E. (2001) A comparison of microsatellite-based pairwise relatedness estimators. Molecular Ecology, 10 , 1539-1549. Van Oosterhout, C., Hutchinson, W.F., Wills, D.P.M. & Shipley, P. (2004) MICRO-CHECKER: software for identifying and correcting genotyping errors in microsatellite data. Molecular Ecology Notes, 4, 535-538. Van Vliet, M.T.H. & Zwolsman, J.J.G. (2008) Impact of summer droughts on the water quality of the Meuse river. Journal of Hydrology, 353 , 1-17. Vance-Borland, K., Roux, D., Nel, J. & Pressey, B. (2008) From the Mountain to the sea: Where is Freshwater Conservation in the SCB Agenda? Conservation Biology, 22 , 505-507. Vaughn, C.C. & Taylor, C.M. (1999) Impoundments and the Decline of Freshwater Mussels: a Case Study of an Extinction Gradient. Conservation Biology, 13 , 912-920. Velo-Anton, G., Becker, C.G. & Cordero-Rivera, A. (2010) Turtle Carapace Anomalies: The Roles of Genetic Diversity and Environment. PLoS ONE, 6, e18714. Verhoeven, K.J.F., Macel, M., Wolfe, L.M. & Biere, A. (2011) Population admixture, biological invasions and the balance between local adaptation and inbreeding depression. Proceedings of the Royal Society B: Biological Sciences, 278 , 2-8. Vigilant, L., Stoneking, M., Harpending, H.C., Hawkes, K. & Wilson, A.C. (1991) African populations and the evolution of human mitochondrial DNA. Science, 253 , 1503-1507. Vorosmarty, C.J., Mcintyre, P.B., Gessner, M.O., Dudgeon, D., Prusevich, A., Green, P., Glidden, S., Bunn, S.E., Sullivan, C.A., Liermann, C.R. & Davies, P.M. (2010) Global threats to human water security and river biodiversity. Nature, 467 , 555-561. Wagner, A., Creel, S. & Kalinowski, S. (2006) Estimating relatedness and relationships using microsatellite loci with null alleles. Heredity, 97 , 336-345. Walker, K.F. (1992) A semi-arid lowland river: the River Murray, Australia. In: The Rivers Handbook. (Ed(s) P.A. Calow & G.E. Petts), pp. 472-492. Blackwell Scientific, Oxford. Walker, K.F. (2006) Serial weirs, cumulative effects: the Lower River Murray, Australia. In: Ecology of Desert Rivers. (Ed(s) R. Kingsford), pp. 248-279. Cambridge University Press, Cambridge. Walker, K.F., Boulton, A.J., Thomas, M.C. & Sheldon, F. (1994) Effects of water-level changes induced by weirs on the distribution of littoral plants along the River Murray, South Australia. Marine and Freshwater Research, 45 , 1421-1438. Walker, K.F., Sheldon, F. & Puckridge, J.T. (1995) A perspective on dryland river ecosystems. Regulated Rivers: Research & Management, 11 , 85-104. Walker, K.F. & Thoms, M.C. (1993) Environmental effects of flow regulation on the Lower River Murray, South Australia. Regulated Rivers: Research & Management, 8, 103-119. Walter, R. & Epperson, B.K. (2001) Geographic pattern of genetic variation in Pinus resinosa : area of greatest diversity is not the origin of postglacial populations. Molecular Ecology, 10 , 103- 111. Wang, J. (2002) An Estimator for Pairwise Relatedness Using Molecular Markers. Genetics, 160 , 1203-1215. Wang, J. (2007) Triadic IBD coefficients and applications to estimating pairwise relatedness. Genetical Research, 89 , 135-153. Wang, J. (2011) coancestry: a program for simulating, estimating and analysing relatedness and inbreeding coefficients. Molecular Ecology Resources, 11 , 141-145. Waples, R.S. & Gaggiotti, O. (2006) What is a population? An empirical evaluation of some genetic methods for identifying the number of gene pools and their degree of connectivity. Molecular Ecology, 15 , 1419-1439. Ward, J.V., Tockner, K. & Schiemer, F. (1999) Biodiveristy of floodplain river cosystems: ecotone and connectivity. Regulated Rivers: Research & Management, 15 , 125-139. Wassens, S., Hall, A., Osborne, W. & Watts, R.J. (2010) Habitat characteristics predict occupancy patterns of the endangered amphibian Litoria raniformis in flow-regulated flood plain wetlands. Austral Ecology, 35 , 944-955. Wattier, R., Engel, C.R., Saumitou-Laprade, P. & Valero, M. (1998) Short allele dominance as a source of heterozygote deficiency at microsatellite loci: experimental evidence at the 199
dinucleotide locus Gv1CT in Gracilaria gracilis (Rodophyta). Molecular Ecology, 7, 1569- 1573. Weir, B.S. (1996) Genetic data analysis II: methods for discrete population genetic data, Sinauer Associates, Sunderland, Massachusetts. Weir, B.S. & Cockerham, C.C. (1984) Estimating F-Statistics for the Analysis of Population Structure. Evolution, 38 , 1358-1370. Weissenbach, J., Gyapay, G., Dib, C., Vignal, A., Morissette, J., Millasseau, P., Vaysseix, G. & Lathrop, M. (1992) A second-generation linkage map of the human genome. Nature, 359 , 794-801. White, M. (2002) The Cooper Creek Turtle Persisting Under Pressure: A Study In Arid Australia. Honors Thesis, University of Canberra, Canberra. Whitlock, M.C. (2001) Dispersal and the genetic properties of metapopulations. In: Dispersal. (Ed(s) J. Clobert & E. Danchin & A.A. Dhondt & J.D. Nichols), pp. 273-282. Oxford, New York. Whitlock, M.C. & Mccauley, D.E. (1990) Some population genetic consequences of colony formation and extinction: Genetic correlations within founding groups. Evolution, 44 , 1717-1725. Whitlock, M.C. & Mccauley, D.E. (1999) Indirect measures of gene flow and migration: F ST ≠ 1 / (4Nm+1). Heredity, 82 , 117-126. Whittington, J., Bunn, S.E., Cullen, P., Jones, G., Thoms, M.C., Quinn, G. & Walker, K. (2002) Ecological Assessment of Flow Management Scenarios for the Lower Balonne. In: Report to Queensland Department of Natural Resrouces and Mines. (Ed(s). CRC for Freshwater Ecology, Canberra. Wiens, J.A. (1997) Metapopulation dynamics and landscape ecology. In: Metapopulation Biology: Ecology, Genetics, and Evolution. (Ed(s) I.A. Hanski & M.E. Gilpin), pp. 43-60. Academic Press, San Diego, CA. Wiens, J.A. (2001) The landscape context of dispersal. In: Dispersal. (Ed(s) J. Clobert & E. Danchin & A.A. Dhondt & J.D. Nichols), pp. 96-109. Oxford University Press, New York. Wiens, J.A. (2002) Riverine landscapes: taking landscape ecology into the water. Freshwater Biology, 47 , 501-515. Wigginton, J.E., Cutler, D.J. & Abecasis, G.R. (2005) A Note on Exact Tests of Hardy-Weinberg Equilibrium. The American Journal of Human Genetics, 76 , 887-893. Willi, Y., Van Buskirk, J., Schmid, B. & Fischer, M. (2007) Genetic isolation of fragmented populations is exacerbated by drift and selection. Journal of Evolutionary Biology, 20 , 534- 542. Wilson, G., Spencer, J. & Heagney, E. (2010) Responses of fish and waterbirds to flow variability in the Gwydir Wetlands. In: Ecosystem response modelling in the Murray-Darling Basin. (Ed(s) N. Saintilan & I. Overton), pp. 103-118. CSIRO Publishing, Melbourne. Woinarski, J.C.Z. (1993) The status of herpetofauna and herpetology in the Northern Territory. In: Herpetology in Australia: A Diverse Discipline. (Ed(s) D. Lunney & D. Ayers), pp. 81-106. Transaction of the Royal Zoological Society of New South Wales, Mosman. Wright, S. (1943) Isolation by distance. Genetics, 28 , 114-138. Yamamoto, S., Morita, K., Koizumi, I. & Maekawa, K. (2004) Genetic Differentiation of White- Spotted Charr ( Salvelinus leucomaenis ) Populations After Habitat Fragmentation: Spatial– Temporal Changes in Gene Frequencies. Conservation Genetics, 5, 529-538. Young, W.J. & Kingsford, R.T. (2006) Flow variability in large unregulated dryland rivers. In: Ecology of Desert Rivers. (Ed(s) R.T. Kingsford), pp. 11-46. Cambridge University Press, Cambridge. Zar, J.H. (1999) Biostatistical Analysis, Prentice Hall, Upper Saddle River, New Jersey 07458. Zenger, K.R., Eldridge, M.D.B. & Cooper, D.W. (2003) Intraspecific variation, sex-biased dispersal and phylogeography of the eastern grey kangaroo ( Macropus giganteus ). Heredity, 91 , 153- 162.
200