The Effects of Species Biology, Riverine Architecture and Flow Regime upon Patterns of Genetic Diversity and Gene Flow in Three Species of Northern Australian Freshwater Fish

Author Huey, Joel Anthony

Published 2008

Thesis Type Thesis (PhD Doctorate)

School School of Environment

DOI https://doi.org/10.25904/1912/2398

Copyright Statement The author owns the copyright in this thesis, unless stated otherwise.

Downloaded from http://hdl.handle.net/10072/366611

Griffith Research Online https://research-repository.griffith.edu.au

The effects of species biology, riverine architecture and flow regime upon patterns of genetic diversity and gene flow in three species of northern Australian freshwater fish.

Joel Anthony Huey, B Env Sci (Hons)

Griffith School of Environment Australian Rivers Institute Griffith University

Submitted in fulfilment of the requirements of the degree of Doctor of Philosophy

December, 2007

“Well, if I was a catfish, mama, I said, swimmin’ deep down in deep blue sea. Have these gals now, sweet mama, Sittin’ out, sittin’ out, folks, for poor me; Sittin’ out, folks, for poor me; Sittin’ out, folks, for poor me; Sittin’ out, folks, for me; Sittin’ out, folks, for me; Sittin’ out, folks, for me.”

Catfish Blues - Robert Petway (1941)

Synopsis Understanding patterns of dispersal, the movement of individuals or propagules, among populations of riverine species is imperative to their management and conservation. However, directly estimating dispersal can often be difficult. Therefore, estimates of gene flow, the movement of genes, are often used to infer dispersal among natural populations. In riverine species, gene flow is determined by species biology, riverine architecture and flow regime. While many studies investigate the role of species dispersive strategies by comparing patterns of genetic structure in different species across the same geographic range, few also attempt to investigate the role of the non-biotic influences on gene flow in a comparative manner. Instead, studies regarding landscape processes (river architecture and hydrology) are based upon observations in a single riverine environment and not compared to other catchments that may differ in riverine architecture or hydrology.

This study attempts to investigate all three factors influencing gene flow and genetic diversity using a comparative approach. This is done by contrasting two species of freshwater fish in two riverine systems that differ in their hydrological and structural makeup. By comparing patterns of genetic structure for each fish species, the role of species biology (behavioural and physical adaptations) can be explored. Then, by comparing patterns of genetic structure for each species, between riverine systems that differ in their landscape processes, the role of hydrology and riverine architecture in determining genetic structure can be explored. This study employed three different genetic markers to elucidate patterns of genetic structure and genetic diversity. These were, direct sequencing and screening of the control region of the mitochondrial DNA genome, microsatellite loci and allozymes.

The two systems used in this study were the Lake Eyre Basin of central Australia and the Gulf of Carpentaria Basin of northern Australia. The catchments of the Lake Eyre Basin are typical dryland systems, exhibiting low catchment relief, refugial waterholes, and highly variable hydrological inputs causing infrequent and widespread flooding. This contrasts with catchments in the Gulf of Carpentaria Basin, which are architecturally diverse and experience large monsoonal inputs that are seasonally predictable. It was hypothesised that the differing landscape processes (riverine architecture and flow regimes) in these two systems would differentially influence

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patterns of genetic structure in freshwater fish, generating different patterns in each system.

Three freshwater fish species were used as model taxa in this study. Neosilurus hyrtlii is an abundant and widespread freshwater catfish species, found across northern Australia, some central Australian catchments, and parts of eastern Queensland. Believed to be a strong disperser, N. hyrtlii was expected to exhibit low levels of genetic structure among populations within catchments. Alternatively, members of the genus Ambassisdae are geographically more restricted, and believed to be weaker dispersers. Two species were used in this study, Ambassis sp. and Ambassis macleayi. Ambassis sp. was used to represent the arid-zone rivers of the Lake Eyre Basin, while A. macleayi represented the Gulf of Carpentaria Basin. Both of these species were expected to exhibit higher levels of genetic structure than N. hyrtlii, based upon breeding and behavioural strategies.

Within the Lake Eyre Basin, N. hyrtlii exhibited weak genetic structure within catchments, indicative of high levels of gene flow and strong dispersal ability. This contrasted with Ambassis sp., which revealed moderate genetic structure within catchments, suggesting that this species does not exploit flood events to the same extent as N. hyrtlii. Similarly, patterns of genetic structure among catchments revealed very different evolutionary histories. While both species exhibited restricted contemporary gene flow among catchments, N. hyrtlii revealed recent divergence among catchments, with estimates overlapping with the proposed drying of Lake Eyre, approximately 60,000 years ago. In contrast, Ambassis sp. exhibited a complicated evolutionary history, with two divergent clades detected in different catchments. These clades (4.2% divergent at mtDNA) were assumed to represent separate colonisation events from the Gulf of Carpentaria.

Sampling of N. hyrtlii across the Gulf of Carpentaria Basin was poor, making estimates of genetic structure within catchments unfeasible. However, for A. macleayi, strong genetic structure was detected among populations within catchments, indicative of poor dispersal ability. Both species displayed restricted gene flow among catchments. However, again they displayed very different evolutionary histories. For N. hyrtlii, divergence among catchments was older than predictions based upon the

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presence of a large freshwater lake in the Gulf of Carpentaria during lower sea levels. This lake is believed to have been fresh until approximately 10,000 years ago. For A. macleayi, estimates of divergence between catchments were even older than that predicted for N. hyrtlii. Also, a catchment in the Gulf or Carpentaria displayed some evidence for a drainage rearrangement, with two divergent clades existing in different rivers of the same catchment.

When results were compared for N. hyrtlii and Ambassis spp., between the Lake Eyre and Gulf of Carpentaria catchments, two striking results were uncovered. Firstly, using mtDNA and microsatellites, genetic diversity negatively correlated with the coefficient of variation of the mean annual flow. This suggested that in catchments that experienced extreme flow variability, populations of N. hyrtlii were experiencing frequent population bottlenecks and local extinctions and recolonisations. This would act to reduce the effective population size (Ne), and generate much reduced genetic diversity. The same pattern was not observed in Ambassis spp., possibly due to their habit of remaining in refugial waterholes during floods, or basic differences in biology for each species in each catchment. Also, for Ambassis spp., much stronger genetic structure was detected among populations within catchments in the Gulf of Carpentaria compared to the Lake Eyre Basin. Again, this was suggested to have been due to differing landscape processes in each basin, or basic differences in biology for each species.

These results and inferences highlights the complex interaction between species biology, riverine architecture and flow regime in determining population processes in freshwater fish taxa. From a management perspective, these results also emphasize the need for comprehensive genetic surveys of riverine species and regions before developing conservation strategies for species and catchments. If the management strategy for a species is based upon research on a different species, or from the same species in a different region, erroneous assumptions may be made about species dispersal ability or effective population size, possibly having serious consequences.

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Acknowledgements

Professor Jane Hughes was a source of considerable assistance, motivation, assurance and critical assessment. Jane always managed to be available when needed for a quick chat or major disaster. Ultimately, this project would have required much more time and involved many more catastrophes without Jane’s experienced involvement.

My secondary supervisor, Dr. Andrew Baker engaged me in many awareness raising philosophical and scientific conversations. Andrew provided many comments on the multitude of drafts that I sent him, generating a final product that was better than if he hadn’t been involved. Ultimately, Andrew made me think about science, and why we do it.

My wife and best friend Simone was my unflinching support. Despite many late nights, weekends and field trips, she still gave me reassurance and encouragement. This made it possible to keep motivated when times where tough.

In the process of collecting samples, I had many people lend a helping hand. Most importantly, James Fawcett, with whom I shared my field trips, provided help, guidance, distraction, and many a laugh. He never left me behind in a waterhole, despite his constant threats. Also helping me in the field were Adam, Ben, Cathy, Erika, Harry, Jim, Kate, Ryan, Steve and Tim.

The molecular ecology lab group provided the bulk of my laboratory, statistical and theoretical assistance. These Geeks are possibly the most helpful, friendly and fun loving group of people I have met. I would like to single out Dan Schmidt, Steve Smith, Alicia Toon, Mark Ponniah, Arlene Buttwell, James Fawcett, Kate Masci, Tim Page, Rachel King, Ben Cook, Michael Arthur, David Gopurenko, Ryan Woods, Courtenay Mills, Simon Song, Mia Hillyer, Jemma Harris, Ana Dobson and Bob Bentley. Also, many members of the broader Griffith family helped on the way, namely, Sylvain Arene, Ben Stewart-Koster, Wade Hadwen, Stephen Mackay and Mark Kennard.

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The Centre for Riverine Landscapes (CRL), the Australian Rivers Institute (ARI) and the Griffith University School of Environment all supplied valuable funding and supports throughout my candidature. Petney Dickson, Lacey Shaw and Deslie Smith helped me repeatedly with a multitude of administrative and financial tasks that would have been impossible without them.

I am fortunate to have a big group of friends who where a constant source of tempting procrastination, unwinding and recharging. Alex, Yuri, Marty, Matti and the rest of the extended Granite Lakes musical family, Dave, Donna and my brothers Dan and Jarrod were all there for a cup of tea and a chat. Cheers.

Finally, I would like to thank my family. Mum, Dad, Dan, Jarrod, the Devery’s and the Huey’s all supported me through this project, even when they had no idea about what I was talking about. Also, my new family, Alison and Phil provided much support and discussion.

Thankyou.

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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 due reference is made in the thesis itself.

Joel A Huey

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Table of Contents SYNOPSIS...... I ACKNOWLEDGEMENTS...... IV TABLE OF CONTENTS ...... VII LIST OF FIGURES...... X LIST OF TABLES...... XII

1 GENERAL INTRODUCTION...... 1

1.1 GENE FLOW AND DISPERSAL ...... 1 1.2 POPULATION GENETIC MARKERS ...... 5 1.2.1 MITOCHONDRIAL DNA...... 5 1.2.2 ALLOZYMES ...... 6 1.2.3 MICROSATELLITES...... 6 1.2.4 ADVANTAGES OF USING MULTIPLE GENETIC MARKERS...... 7 1.3 GENE FLOW IN RIVERINE SPECIES ...... 8 1.3.1 RIVERINE ARCHITECTURE AND GENE FLOW...... 9 1.3.2 FLOW REGIME AND GENE FLOW ...... 10 1.3.3 SPECIES BIOLOGY AND GENE FLOW ...... 10 1.4 AUSTRALIAN RIVERINE SYSTEMS ...... 11 1.4.1 THE LAKE EYRE BASIN...... 13 1.4.2 THE GULF OF CARPENTARIA BASIN ...... 14 1.5 AUSTRALIAN FRESHWATER FISH ...... 16 1.5.1 HYRTL’S TANDAN, NEOSILURUS HYRTLII (PLOTOSIDAE) ...... 16 1.5.2 NORTHWEST GLASSFISH, AMBASSIS SP. AND MACLEAY’S GLASSFISH AMBASSIS MACLEAYI (AMBASSIDAE)...... 17 1.6 AIMS...... 17

2 GENERAL FIELD AND LABORATORY METHODOLOGIES...... 19

2.1 STUDY SPECIES ...... 19 2.2 FIELD WORK...... 20 2.2.1 STUDY SITES ...... 20 2.2.2 FIELD METHODOLOGY ...... 21 2.3 LABORATORY METHODOLOGY...... 22 2.3.1 DNA EXTRACTION ...... 22 2.3.2 MITOCHONDRIAL DNA...... 23 2.3.3 ALLOZYME ELECTROPHORESIS ...... 25 2.3.4 MICROSATELLITES...... 27 2.4 STATISTICAL METHODOLOGY...... 30 2.4.1 HARDY-WEINBERG EQUILIBRIUM ...... 30 2.4.2 GENETIC DIVERSITY...... 32 2.4.3 TESTS FOR NEUTRALITY ...... 32 2.4.4 DETECTING BOTTLENECKS IN NATURAL POPULATIONS...... 34 2.4.5 ANALYSIS OF MOLECULAR VARIANCE...... 35 2.4.6 IDENTIFYING ‘ISOLATION BY DISTANCE’ ...... 36 2.4.7 PHYLOGEOGRAPHY AND NESTED CLADE PHYLOGEOGRAPHIC ANALYSIS ...... 37 2.4.8 COALESCENT THEORY ...... 39

3 PATTERNS OF GENE FLOW IN TWO SPECIES OF FRESHWATER FISH (NEOSILURUS HYRTLII AND AMBASSIS SP.) IN THE LAKE EYRE BASIN...... 43

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3.1 INTRODUCTION ...... 43 3.2 RESULTS ...... 47 3.2.1 SAMPLING REGIME ...... 47 3.2.2 NEOSILURUS HYRTLII...... 48 3.2.3 AMBASSIS SP...... 57 3.3 DISCUSSION ...... 67 3.3.1 GENETIC DIVERSITY ...... 67 3.3.2 GENE FLOW AND GENETIC STRUCTURE...... 71 3.3.3 COMPARISON OF GENE FLOW AND GENETIC STRUCTURE IN N. HYRTLII AND AMBASSIS SP. 74 3.3.4 HISTORICAL PATTERNS OF GENE FLOW AMONG CATCHMENTS ...... 75 3.3.5 THE ROLE OF LAKE EYRE IN CONNECTING CATCHMENTS IN THE LAKE EYRE BASIN ...... 78 3.4 CONCLUSIONS ...... 80

4 PATTERNS OF GENE FLOW AND PHYLOGEOGRAPHY IN TWO SPECIES OF FRESHWATER FISH (NEOSILURUS HYRTLII AND AMBASSIS MACLEAYI) ACROSS THE GULF OF CARPENTARIA BASIN...... 82

4.1 INTRODUCTION ...... 82 4.2 RESULTS ...... 86 4.2.1 SAMPLING REGIME ...... 86 4.2.2 NEOSILURUS HYRTLII...... 87 4.2.3 AMBASSIS MACLEAYI...... 93 4.3 DISCUSSION ...... 100 4.3.1 GENETIC DIVERSITY...... 100 4.3.2 GENE FLOW AND GENETIC STRUCTURE...... 101 4.3.3 HISTORICAL PATTERNS OF GENE FLOW AMONG CATCHMENTS ...... 102 4.4 CONCLUSIONS ...... 106

5 THE EFFECT OF LANDSCAPE PROCESSES AND HISTORICAL DRAINAGE DIVISIONS UPON GENE FLOW AND GENETIC DIVERSITY IN AUSTRALIAN FRESHWATER FISH...... 108

5.1 INTRODUCTION ...... 108 5.1.1 AIMS AND HYPOTHESES...... 110 5.2 STATISTICAL METHODOLOGY ...... 111 5.3 RESULTS ...... 114 5.3.1 SAMPLING REGIME ...... 114 5.3.2 NEOSILURUS HYRTLII...... 116 5.3.3 AMBASSIS SPP...... 123 5.4 DISCUSSION ...... 125 5.4.1 HISTORICAL PATTERNS OF GENE FLOW AMONG BASINS ...... 125 5.4.2 EFFECTS OF LANDSCAPE PROCESSES UPON PATTERNS OF GENE FLOW AMONG POPULATIONS ...... 127 5.4.3 EFFECTS OF LANDSCAPE PROCESSES UPON PATTERNS OF GENETIC DIVERSITY...... 129 5.5 CONCLUSION ...... 132

6 GENERAL CONCLUSIONS...... 133

7 APPENDICES ...... 136

7.1 APPENDIX A: SPECIES LIFE HISTORY ...... 136 7.2 APPENDIX B: SAMPLE SITES...... 137 7.3 APPENDIX C: ALLELIC DIVERSITY ESTIMATES...... 139

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7.4 APPENDIX D: IM RUN INFORMATION ...... 142 7.5 APPENDIX E: MICROCHECKER RESULTS...... 143 7.6 APPENDIX F: PAIRWISE FST TABLES...... 150 7.7 APPENDIX G: ALLELE FREQUENCY FIGURES...... 158 7.8 APPENDIX H: RAW DATA ...... 161

8 REFERENCES...... 179

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List of Figures FIGURE 1.1: DRAINAGE DIVISIONS IN AUSTRALIA (FROM HTTP://WWW.BOM.GOV.AU/HYDRO/WR/BASINS/BASIN-HI_GRID.JPG) ...... 12 FIGURE 2.1: THE THREE FISH SPECIES FOCUSED UPON IN THIS STUDY. A) NEOSILURUS HYRTLII, B) AMBASSIS SP. (LAKE EYRE BASIN) AND C) AMBASSIS MACLEAYI (GULF OF CARPENTARIA BASIN). PHOTOS BY JAMES FAWCETT AND JOEL HUEY...... 19 FIGURE 2.2: SAMPLING DESIGN...... 20 FIGURE 2.3: MAP OF SAMPLING AREA SHOWING MAJOR DRAINAGES AND SITES SAMPLED IN THIS STUDY. SITES FROM THE COOPER CATCHMENT, LAKE EYRE BASIN, ARE FROM HUEY ET AL. (2006)...... 21 FIGURE 2.4: SAMPLING TECHNIQUES. A) A SET FYKE NET, FLINDERS RIVER CROSSING, GULF OF CARPENTARIA BASIN, B) CLEARING FYKE NET, LAKE CORELLA, GULF OF CARPENTARIA BASIN, C) AND D) SEINING, MAGOWRA STATION, GULF OF CARPENTARIA BASIN. ALL PHOTOS BY JAMES FAWCETT...... 22 FIGURE 2.5: DENATURING GRADIENT GEL ELECTROPHORESIS GEL. LANES 6, 9 AND 12 WERE IDENTIFIED AS DIFFERING FROM THE COMMON HAPLOTYPE AND DIRECTLY SEQUENCED...... 25 FIGURE 2.6: ALLOZYME GEL. TWO LOCI AT GLUCOSEPHOSPHATE ISOMERASE...... 26 FIGURE 2.7: MICROSATELLITE GEL, LOCUS AMB16, AMBASSIS SP.. LANES 1, 10, 19, 28 AND 32 ARE SIZE CLASS MARKERS. ALL OTHER LANES ARE INDIVIDUALS...... 30 FIGURE 2.8: HAPLOTYPE NETWORKS DEPICTING TWO GENEALOGIES EXPECTED UNDER A) POSITIVE SELECTION AND B) BALANCING SELECTION. HAPLOTYPE NETWORKS DESCRIBE THE EVOLUTIONARY RELATIONSHIPS BETWEEN HAPLOTYPES. EACH HAPLOTYPE IS DEPICTED BY A WHITE CIRCLE AND ITS RELATIVE FREQUENCY BY ITS SIZE. LINES REPRESENT BASE PAIR DIFFERENCES, WITH SMALL BLACK CIRCLES REPRESENTING EXTINCT HAPLOTYPES...... 33 FIGURE 2.9: THE ISOLATION WITH MIGRATION MODEL IS DEPICTED WITH TWO PARAMETER SETS. THE BASIC DEMOGRAPHIC PARAMETERS ARE CONSTANT EFFECTIVE POPULATION SIZES (N1, N2 AND NA), GENE FLOW RATES PER GENE COPY PER GENERATION (M1 AND M2), AND THE TIME OF POPULATION SPLITTING AT T GENERATIONS IN THE PAST. THE SECOND SET OF PARAMETERS IS SCALED BY THE NEUTRAL MUTATION RATE μ, AND IT IS THESE PARAMETERS THAT ARE ACTUALLY USED IN THE MODEL FITTING. FIGURE IS ADAPTED FROM HEY AND NIELSEN 2004...... 41 FIGURE 3.1: SAMPLING REGIME OF N. HYRTLII AND AMBASSIS SP. IN THE LAKE EYRE BASIN...... 47 FIGURE 3.2: N. HYRTLII, HAPLOTYPE NETWORK OF CONTROL REGION MTDNA VARIATION. EACH CIRCLE REPRESENTS A UNIQUE HAPLOTYPE WITH ITS EVOLUTIONARY RELATIONSHIP TO OTHER HAPLOTYPES REPRESENTED BY LINES. PIES ON THE MAP REPRESENT THE GEOGRAPHICAL DISTRIBUTION OF HAPLOTYPES...... 49 FIGURE 3.3: N. HYRTLII, POSTERIOR DISTRIBUTION OF T, THE TIME SINCE POPULATION DIVERGENCE. THE LEFT HAND AXIS IS THE RESIDENCE TIME FOR ‘COOPER V DIAMANTINA’ AND ‘COOPER V GEORGINA’. THE LEFT HAND AXIS IS THE RESIDENCE TIME FOR ‘DIAMANTINA V GEORGINA’. FOR DETAILS OF ANALYSIS SEE TEXT...... 56 FIGURE 3.4: AMBASSIS SP., HAPLOTYPE NETWORK OF CONTROL REGION MTDNA VARIATION. EACH CIRCLE REPRESENTS A UNIQUE HAPLOTYPE WITH ITS EVOLUTIONARY RELATIONSHIP TO OTHER HAPLOTYPES REPRESENTED BY LINES. PIES ON THE MAP REPRESENT THE GEOGRAPHICAL DISTRIBUTION OF HAPLOTYPES. THE DIVERGENCE BETWEEN HAPLOTYPES A AND C IS EQUAL TO 4.2% (16 BASES)...... 58 FIGURE 3.5: AMBASSIS SP., NESTING OF THE HAPLOTYPE NETWORK...... 63 FIGURE 3.6: AMBASSIS SP., POSTERIOR DISTRIBUTION OF T, THE TIME SINCE POPULATION DIVERGENCE. FOR DETAILS OF ANALYSIS SEE TEXT...... 66 FIGURE 3.7: THE HYPOTHESISED COLONISATION HISTORY OF AMBASSIS SP. EXPLAINING THE OBSERVED DATA IN THE LAKE EYRE BASIN. THE LARGE ARROWS REPRESENT POSSIBLE COLONISATION EVENTS FROM THE NORTHERN CATCHMENTS INTO THE LAKE EYRE BASIN. THE DOTTED LINE IN THE NORTHERN CATCHMENTS REPRESENTS A PREPOSED GENETIC BREAK IN THE DISTRIBUTION OF AMBASSIS SP. IN THE NORTHERN CATCHMENTS...... 77 FIGURE 4.1: SAMPLING SITES FOR N. HYRTLII AND A. MACLEAYI, GULF OF CARPENTARIA BASIN...... 86 FIGURE 4.2: N. HYRTLII, HAPLOTYPE NETWORK SHOWING CONTROL REGION MTDNA VARIATION AND GEOGRAPHIC DISTRIBUTION OF HAPLOTYPES. EACH CIRCLE

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REPRESENTS A UNIQUE HAPLOTYPE WITH ITS EVOLUTIONARY RELATIONSHIP TO OTHER HAPLOTYPES REPRESENTED BY LINES. PIES ON THE MAP REPRESENT THE GEOGRAPHICAL DISTRIBUTION OF HAPLOTYPES. WHITE HAPLOTYPES ARE SINGLETONS...... 88 FIGURE 4.3: N. HYRTLII, NESTING OF THE CONTROL REGION MTDNA HAPLOTYPE NETWORK. THE GREY CLADE IS MADE UP OF REPRESENTATIVE SAMPLES FROM THE LAKE EYRE BASIN (CHAPTER 3)...... 91 FIGURE 4.4: N. HYRTLII, POSTERIOR DISTRIBUTION OF T, THE TIME SINCE POPULATION DIVERGENCE USING CONTROL REGION MTDNA...... 92 FIGURE 4.5: A. MACLEAYI, HAPLOTYPE NETWORK SHOWING CONTROL REGION MTDNA VARIATION AND GEOGRAPHIC DISTRIBUTION OF HAPLOTYPES. EACH CIRCLE REPRESENTS A UNIQUE HAPLOTYPE WITH ITS EVOLUTIONARY RELATIONSHIP TO OTHER HAPLOTYPES REPRESENTED BY LINES. PIES ON THE MAP REPRESENT THE GEOGRAPHICAL DISTRIBUTION OF HAPLOTYPES...... 94 FIGURE 4.6: A. MACLEAYI, NESTING OF THE CONTROL REGION MTDNA HAPLOTYPE NETWORK...... 98 FIGURE 4.7: A. MACLEAYI, POSTERIOR DISTRIBUTION OF T, THE TIME SINCE POPULATION DIVERGENCE...... 99 FIGURE 5.1: LOCATIONS OF GAUGE STATIONS USED TO ESTIMATE FLOW VARIABILITY...... 113 FIGURE 5.2: SAMPLING REGIME OF N. HYRTLII, AMBASSIS SP. AND A. MACLEAYI IN THE LAKE EYRE AND GULF OF CARPENTARIA BASINS...... 114 FIGURE 5.3: N. HYRTLII, HAPLOTYPE NETWORK. HAPLOTYPES ARE DIVIDED INTO PIES REPRESENTING THE CATCHMENTS WHERE THOSE HAPLOTYPES WERE FOUND. PIES DO NOT REPRESENT RELATIVE FREQUENCIES...... 116 FIGURE 5.4: N. HYRTLII, NESTING OF THE HAPLOTYPE NETWORK...... 118 FIGURE 5.5: N. HYRTLII, GRAPH COMPARING FIXATION VALUES FOR EACH GENETIC MARKER BETWEEN THE LAKE EYRE BASIN POPULATIONS (WHITE) AND THE GULF OF CARPENTARIA BASIN POPULATIONS (BLACK)...... 120 FIGURE 5.6: N. HYRTLII, POSTERIOR DISTRIBUTION OF T, THE TIME SINCE POPULATION DIVERGENCE...... 121 FIGURE 5.7: SCATTER PLOT CORRELATING ESTIMATES OF FLOW VARIABILITY AND GENETIC DIVERSITY. EACH SAMPLED CATCHMENT IS A REPLICATE. MICROSATELLITE GENE DIVERSITY IS REPRESENTED BY GREEN, MTDNA HAPLOTYPE DIVERSITY IS REPRESENTED BY BLUE AND MTDNA NUCLEOTIDE DIVERSITY IS REPRESENTED BY RED...... 122 FIGURE 5.8: AMBASSIS SPP., GRAPH COMPARING FIXATION VALUES FOR EACH GENETIC MARKER BETWEEN THE LAKE EYRE BASIN POPULATIONS (WHITE) AND THE GULF OF CARPENTARIA BASIN POPULATIONS (BLACK). FOR MICROSATELLITE LOCI, ONLY THOSE LOCI USED IN BOTH SPECIES WERE USED TO COMPARE BETWEEN BASINS (SEE CHAPTERS 3 AND 4)...... 123 FIGURE 5.9: AMBASSIS SPP., GRAPH COMPARING FIXATION VALUES AMONG POPULATIONS WITHIN CATCHMENTS FOR MICROSATELLITES. ONLY THOSE MICROSATELLITE LOCI USED IN BOTH SPECIES WERE USED TO COMPARE BETWEEN CATCHMENTS (SEE CHAPTERS 3 AND 4)...... 124 FIGURE 5.10: AMBASSIS SPP., GRAPH COMPARING AVERAGE MICROSATELLITE GENE DIVERSITY BETWEEN BASINS (LAKE EYRE BASIN = WHITE, GULF OF CARPENTARIA BASIN = BLACK). ONLY THOSE MICROSATELLITE LOCI USED IN BOTH SPECIES WERE USED TO COMPARE BETWEEN BASINS (SEE CHAPTERS 3 AND 4). BARS REPRESENT STANDARD ERROR...... 124

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List of Tables TABLE 2.1: ALLOZYME LOCI AND CONDITIONS USED IN THIS STUDY...... 26 TABLE 2.2: MICROSATELLITE LOCI DEVELOPED IN THIS STUDY. ALLELIC RICHNESS AND NUMBER OF ALLELES CAN BE FOUND IN APPENDIX C...... 29 TABLE 3.1: SAMPLING REGIME OF N. HYRTLII AND AMBASSIS SP. IN THE LAKE EYRE BAIN...... 48 TABLE 3.2: N. HYRTLII, DISTRIBUTION OF HAPLOTYPES ACROSS SITES. HAPLOTYPE LETTERS REFER TO HAPLOTYPES SHOWN IN FIGURE 12...... 50 TABLE 3.3: N. HYRTLII, GENETIC DIVERSITY INDICES. FOR NUCLEAR MARKERS, THE EXPECTED HETEROZYGOSITY IS DISPLAYED. SIGNIFICANT DEVIATIONS FROM HWE ARE MARKED WITH AN ASTERIX (α=0.05). FOR EXPLANATIONS OF HETEROZYGOTE EXCESS OR DEFICIENCY, SEE TEXT...... 51 TABLE 3.4: N. HYRTLII, NEUTRALITY TESTS. NONE DEVIATE SIGNIFICANTLY FROM NEUTRAL EXPECTATIONS...... 52 TABLE 3.5: N. HYRTLII, RESULTS FOR BOTTLENECK. FOR EACH POPULATION WITH MORE THAN 5 INDIVIDUALS, FOR EACH MUTATION MODEL, THE RATIO OF LOCI WITH A HETEROZYGOSITY DEFICIENCY TO LOCI WITH A HETEROZYGOSITY EXCESS IS SHOWN. THE PROBABILITY THAT THERE IS AN EXCESS OF LOCI DISPLAYING A HETEROZYGOSITY EXCESS IS DISPLAYED. THE ALLELES FREQUENCY DISTRIBUTION IS ALSO DESCRIBED QUALITATIVELY. (IAM = INFINITE ALLELES MODE, TPM = TWO PHASE MODEL, SMM = STEPWISE MUTATION MODEL)...... 53 TABLE 3.6: N. HYRTLII, NESTED CLADE ANALYSIS RESULTS WITH PHYLOGEOGRAPHIC INFERENCE. SIGNIFICANTLY SMALL OF LARGE DC AND DN VALUES ARE INDICATED WITH ‘S’ AND ‘L’ RESPECTIVELY...... 54 TABLE 3.7: N. HYRTLII, MANTEL TESTS CORRELATING OVERLAND AND STREAM GEOGRAPHIC DISTANCES WITH GENETIC DISTANCES BETWEEN POPULATIONS. VALUES SHOWN ARE CORRELATION COEFFICIENTS. CORRELATION COEFFICIENTS THAT SIGNIFICANTLY DEVIATE FROM ZERO ARE INDICATED WITH AN ASTERIX (α=0.05)...... 54 TABLE 3.8: N. HYRTLII, ANALYSIS OF MOLECULAR VARIANCE RESULTS. FIXATION INDICES THAT SIGNIFICANTLY DEVIATE FROM ZERO ARE INDICATED WITH ASTERIX (* α=0.05, ** α=0.01)...... 55 TABLE 3.9: N. HYRTLII, LOCUS BY LOCUS ANALYSIS OF MOLECULAR VARIANCE RESULTS. FIXATION INDICES THAT SIGNIFICANTLY DEVIATE FROM ZERO ARE INDICATED WITH ASTERIX (* α=0.05, ** α=0.01)...... 55 TABLE 3.10: N. HYRTLII, RESULTS OF IM ANALYSIS SHOWING THE MAXIMUM LIKELIHOOD POINT ESTIMATE OF T, THE TIME SINCE POPULATION DIVERGENCE, AND THE 95% CREDIBILITY INTERVALS...... 56 TABLE 3.11: AMBASSIS SP., DISTRIBUTION OF HAPLOTYPES ACROSS SITES...... 59 TABLE 3.12: AMBASSIS SP., GENETIC DIVERSITY INDICES. FOR NUCLEAR MARKERS, THE EXPECTED HETEROZYGOSITY IS DISPLAYED. SIGNIFICANT DEVIATIONS FROM HWE ARE MARKED WITH AN ASTERIX (α=0.05). FOR EXPLANATIONS OF HETEROZYGOTE EXCESS OR DEFICIENCY, SEE TEXT...... 60 TABLE 3.13: AMBASSIS SP., NEUTRALITY TESTS. TAJIMA’S D WAS SIGNIFICANTLY GREATER THAN ZERO IN THE GEORGINA CATCHMENT...... 61 TABLE 3.14: AMBASSIS SP., RESULTS FOR BOTTLENECK. FOR EACH POPULATION WITH MORE THAN 5 INDIVIDUALS, FOR EACH MUTATION MODEL, THE RATIO OF LOCI WITH A HETEROZYGOSITY DEFICIENCY TO THE NUMBER OF LOCI WITH A HETEROZYGOSITY EXCESS IS SHOWN. THE PROBABILITY THAT THERE IS AN EXCESS OF LOCI DISPLAYING A HETEROZYGOSITY EXCESS IS DISPLAYED. THE ALLELE FREQUENCY DISTRIBUTION IS ALSO DESCRIBED QUALITATIVELY. (IAM = INFINITE ALLELES MODE, TPM = TWO PHASE MODEL, SMM = STEPWISE MUTATION MODEL)...... 62 TABLE 3.15: AMBASSIS SP., NESTED CLADE ANALYSIS RESULTS WITH PHYLOGEOGRAPHIC INFERENCE. SIGNIFICANTLY SMALL OR LARGE DC AND DN VALUES ARE INDICATED WITH ‘S’ AND ‘L’ RESPECTIVELY...... 63 TABLE 3.16: AMBASSIS SP., MANTEL TESTS CORRELATING OVERLAND AND STREAM GEOGRAPHIC DISTANCES WITH GENETIC DISTANCES BETWEEN POPULATIONS. VALUES SHOWN ARE CORRELATION COEFFICIENTS. CORRELATION COEFFICIENTS THAT SIGNIFICANTLY DEVIATE FROM ZERO ARE INDICATED WITH AN ASTERIX (α=0.05)...... 64

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TABLE 3.17: AMBASSIS SP., ANALYSIS OF MOLECULAR VARIANCE RESULTS. FIXATION INDICES THAT SIGNIFICANTLY DEVIATE FROM ZERO ARE INDICATED WITH ASTERIX (* α=0.05, ** α=0.01)...... 64 TABLE 3.18: AMBASSIS SP., LOCUS BY LOCUS ANALYSIS OF MOLECULAR VARIANCE RESULTS. FIXATION INDICES THAT SIGNIFICANTLY DEVIATE FROM ZERO ARE INDICATED WITH ASTERIX (* α=0.05, ** α=0.01)...... 65 TABLE 3.19: AMBASSIS SP., RESULTS OF IM ANALYSIS SHOWING THE MAXIMUM LIKELIHOOD POINT ESTIMATE OF T, THE TIME SINCE POPULATION DIVERGENCE, AND THE 95% CREDIBILITY INTERVALS...... 65 TABLE 4.1: SAMPLING REGIME OF N. HYRTLII AND A. MACLEAYI. IN THE GULF OF CARPENTARIA BASIN...... 87 TABLE 4.2: N. HYRTLII, DISTRIBUTION OF HAPLOTYPES ACROSS SITES IN THE GULF OF CARPENTARIA BASIN...... 89 TABLE 4.3: N. HYRTLII, GENETIC DIVERSITY INDICES. FOR NUCLEAR MARKERS, THE EXPECTED HETEROZYGOSITY IS DISPLAYED. SIGNIFICANT DEVIATIONS FROM HWE ARE MARKED WITH AN ASTERIX (α=0.05). FOR HETEROZYGOTE EXCESS OR DEFICIENCY, SEE TEXT...... 89 TABLE 4.4: N. HYRTLII, NEUTRALITY TESTS. NONE SIGNIFICANTLY DEVIATE FROM NEUTRAL EXPECTATIONS...... 90 TABLE 4.5: N. HYRTLII, RESULTS OF BOTTLENECK. FOR EACH POPULATION WITH MORE THAN 5 INDIVIDUALS, FOR EACH MUTATION MODEL) THE RATION OF LOCI WITH A HETEROZYGOSITY DEFICIENCY TO THE NUMBER OF LOCI WITH A HETEROZYGOSITY EXCESS IS SHOWN. THE PROBABILITY THAT THERE IS AN EXCESS OF LOCI DISPLAYING A HETEROZYGOSITY EXCESS IS DISPLAYED. THE ALLELE FREQUENCY DISTRIBUTION IS ALSO DESCRIBED QUALITATIVELY. (IAM = INFINITE ALLELES MODE, TPM = TWO PHASE MODEL, SMM = STEPWISE MUTATION MODEL)...... 90 TABLE 4.6: N. HYRTLII, NESTED CLADE ANALYSIS RESULTS WITH PHYLOGEOGRAPHIC INFERENCE. SIGNIFICANTLY SMALL OR LARGE DC AND DN VALUES ARE INDICATED WITH ‘S’ AND ‘L’ RESPECTIVELY...... 91 TABLE 4.7: N. HYRTLII, RESULTS OF IM ANALYSIS USING CONTROL REGION MTDNA, SHOWING THE MAXIMUM LIKELIHOOD POINT ESTIMATE OF T, THE TIME SINCE POPULATION DIVERGENCE AND THE 95% CREDIBILITY INTERVALS...... 92 TABLE 4.8: A. MACLEAYI, DISTRIBUTION OF CONTROL REGION MTDNA HAPLOTYPES ACROSS SITES IN THE GULF OF CARPENTARIA BASIN...... 94 TABLE 4.9: A. MACLEAYI, GENETIC DIVERSITY INDICES. FOR NUCLEAR MARKERS, THE EXPECTED HETEROZYGOSITY IS DISPLAYED. SIGNIFICANT DEVIATIONS FROM HWE ARE MARKED WITH AN ASTERIX (α=0.05). FOR HETEROZYGOTE EXCESS OF DEFICIENCY, SEE TEXT...... 95 TABLE 4.10: N. HYRTLII, RESULTS OF BOTTLENECK. FOR EACH POPULATION WITH MORE THAN 5 INDIVIDUALS, FOR EACH MUTATION MODEL) THE RATION OF LOCI WITH A HETEROZYGOSITY DEFICIENCY TO THE NUMBER OF LOCI WITH A HETEROZYGOSITY EXCESS IS SHOWN. THE PROBABILITY THAT THERE IS AN EXCESS OF LOCI DISPLAYING A HETEROZYGOSITY EXCESS IS DISPLAYED. THE ALLELE FREQUENCY DISTRIBUTION IS ALSO DESCRIBED QUALITATIVELY. (IAM = INFINITE ALLELES MODE, TPM = TWO PHASE MODEL, SMM = STEPWISE MUTATION MODEL)...... 95 TABLE 4.11: A. MACLEAYI, ANALYSIS OF MOLECULAR VARIANCE RESULTS INCLUDING ALL SITES ACROSS THE GULF OF CARPENTARIA. FIXATION INDICES THAT SIGNIFICANTLY DEVIATE FROM ZERO ARE INDICATED WITH AN ASTERIX (*α=0.05, **α=0.01)...... 96 TABLE 4.12: A. MACLEAYI, LOCUS BY LOCUS ANALYSIS OF MOLECULAR VARIANCE RESULTS INCLUDING ALL SITES ACROSS THE GULF OF CARPENTARIA BASIN. FIXATION INDICES THAT SIGNIFICANTLY DEVIATE FROM ZERO ARE INDICATED WITH AN ASTERIX (*α=0.05, **α=0.01)...... 96 TABLE 4.13: A. MACLEAYI, ANALYSIS OF MOLECULAR VARIANCE RESULTS EXCLUDING SITE KFC. FIXATION INDICES THAT SIGNIFICANTLY DEVIATE FROM ZERO ARE INDICATED WITH AN ASTERIX (*α=0.05, **α=0.01)...... 97 TABLE 4.14: A. MACLEAYI, LOCUS BY LOCUS ANALYSIS OF MOLECULAR VARIANCE RESULTS EXCLUDING SITE KFC. FIXATION INDICES THAT SIGNIFICANTLY DEVIATE FROM ZERO ARE INDICATED WITH AN ASTERIX (*α=0.05, **α=0.01)...... 97 TABLE 4.15: A. MACLEAYI, MANTEL TESTS CORRELATING OVERLAND AND STREAM GEOGRAPHIC DISTANCE WITH GENETIC DISTANCES BETWEEN POPULATIONS. SIGNIFICANT CORRELATIONS ARE INDICATED WITH AN ASTERIX (α=0.05)...... 97

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TABLE 4.16: A. MACLEAYI, NESTED CLADE ANALYSIS RESULTS WITH PHYLOGEOGRAPHIC INFERENCE. SIGNIFICANTLY SMALL OR LARGE DC AND DN VALUES ARE INDICATED WITH ‘S’ AND ‘L’ RESPECTIVELY...... 98 TABLE 4.17: A. MACLEAYI RESULTS OF IM ANALYSIS SHOWING THE MAXIMUM LIKELIHOOD POINT ESTIMATE OF T, THE TIME SINCE POPULATION DIVERGENCE, AND THE 95% CREDIBILITY INTERVALS...... 99 TABLE 5.1: DETAILS OF GAUGE STATIONS USED TO ESTIMATE FLOW VARIABILITY...... 113 TABLE 5.2: SAMPLING REGIME OF N. HYRTLII, AMBASSIS SP. (LAKE EYRE BASIN) AND A. MACLEAYI (GULF OF CARPENTARIA BASIN)...... 115 TABLE 5.3: N. HYRTLII, DISTRIBUTION OF HAPLOTYPES ACROSS SITES...... 117 TABLE 5.4: N. HYRTLII, NESTED CLADE ANALYSIS RESULTS WITH PHYLOGEOGRAPHIC INFERENCE. SIGNIFICANTLY SMALL OR LARGE DC AND DN VALUES ARE INDICATED WITH ‘S’ AND ‘L’ RESPECTIVELY...... 119 TABLE 5.5: N. HYRTLII, ANALYSIS OF MOLECULAR VARIANCE RESULTS. FIXATION INDICES THAT SIGNIFICANTLY DEVIATE FROM ZERO ARE INDICATED WITH AN ASTERIX (* α=0.05, ** α=0.01)...... 119 TABLE 5.6: N. HYRTLII, LOCUS BY LOCUS ANALYSIS OF MOLECULAR VARIANCE. FIXATION INDICES THAT SIGNIFICANTLY DEVIATE FROM ZERO ARE INDICATED WITH AN ASTERIX (* α=0.05, ** α=0.01)...... 120 TABLE 5.7: N. HYRTLII, RESULTS OF IM ANALYSIS SHOWING THE MAXIMUM LIKELIHOOD POINT ESTIMATE OF T, THE TIME SINCE POPULATION DIVERGENCE. THE 95% CREDIBILITY INTERVALS ARE ALSO SHOWN...... 121 TABLE 5.8: CORRELATIONS COMPARING ESTIMATES OF FLOW VARIATION AND GENETIC DIVERSITY...... 122

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1 General Introduction

1.1 Gene flow and dispersal Dispersal, the movement of individuals or propagules (Slatkin 1985), among natural populations is critical for population viability, through the recolonisation of locally extinct areas and the introduction of new genetic diversity (Moran 2002, Vrijenhoek 1998). Consequently, understanding patterns of dispersal among natural populations is essential to their short and long term conservation (Hughes 2007, Lake, et al. 2007, Montalvo, et al. 1997).

Techniques for measuring dispersal can be divided into direct and indirect methods (Slatkin 1985). Direct techniques entail observing or recapturing marked individuals, thus allowing the researcher to estimate the distance moved by individuals over a certain timeframe (e.g., mark-recapture, tagging; Bilton, et al. 2001, Bohonak 1999, Slatkin 1985). However, using direct techniques to measure dispersal can be difficult, particularly for large populations and species that are difficult to ‘mark’ or ‘tag’ (Bilton, et al. 2001). Also, estimates of dispersal based upon direct measurements may be poor, as the short-term timeframe of typical ecological projects is likely to miss rare events (e.g., mass migrations, opportunistic dispersal), which are still ecologically and evolutionarily important (Bilton, et al. 2001, Slatkin 1985, 1987). Consequently, many ‘indirect’ techniques have been developed to aid in estimating dispersal (Slatkin 1985).

A commonly used ‘indirect’ method for measuring dispersal among natural populations is the estimation of gene flow, the movement of genes (alleles/haplotypes) among populations, or from populations into uninhabited regions (Bohonak 1999, Slatkin 1985). Dispersal leads to gene flow when migrants (or their offspring) breed with members of the new population (i.e., contribute their genes to the population gene pool). Consequently, gene flow will impact upon the population gene pool. This can be detected by sampling gene frequencies in populations, as high levels of gene flow among populations will result in alleles being shared between populations, homogenising gene frequencies (panmixia), and restricted gene flow will result in different allele frequencies in different populations (divergence) (Bilton, et al. 2001). The divergence of isolated populations results from genetic drift (the change of allele

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frequencies from one generation to the next due to the random survival of gametes), selection (the preferential survival of different alleles based on environmental pressures) and mutation (causing the accumulation of private alleles) (Bilton, et al. 2001, Bohonak 1999, Page and Holmes 1998, Slatkin 1985, 1987).

As previously mentioned, direct measurements of dispersal may miss rare dispersal events. Fortunately, gene flow studies ‘average’ gene flow over long time periods, which will incorporate rare migration events. This can be quite advantageous as some dispersal events (e.g., migration or opportunistic colonisation) may be quite infrequent but still be ecologically and evolutionarily important (Slatkin 1985, 1987). For example, in the checkerspot butterflies (Euphydryas editha), observations suggested that individuals would not disperse among adjacent populations. However, during a 25 year ecological study, one colony became extinct and was immediately recolonised by the adjacent population (Ehrlich, et al. 1975). Therefore, as most ecological studies do not have the luxury of a 25 year study period, indirect approaches are needed to identify rare dispersal events.

While gene flow studies have many advantages over direct methods for measuring dispersal, population genetic theory rests upon many assumptions. For example, when using traditional estimates of gene flow to infer dispersal among populations it is assumed that populations are in migration-drift equilibrium (Poissant, et al. 2005, Slatkin 1985, 1993). Migration is the main demographic process tempering the diverging force of genetic drift. The interaction of these two conflicting processes determines the equilibrium level of differentiation (Boileau, et al. 1992, Efremov 2004, Slatkin 1993).

Under the stepping stone model of genetic structure (Kimura 1953, Kimura and Weiss 1964), where higher levels of migration exist among adjacent sub-populations, equilibrium among sub-populations is expected to cause geographically close populations to be genetically similar compared to geographically distant populations which will be genetically divergent (Hutchison and Templeton 1999). This correlation between genetic and geographic distance is expected to take thousands of generations to develop, but will be approached faster when there is limited migration among populations (Efremov 2004).

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While identifying non-equilibrium can be easy (e.g., Slatkin 1993), finding the cause is often more challenging (Poissant, et al. 2005). For example, events causing non- equilibrium include; sudden population growth, recent high levels of gene flow and recent colonisation of new territories (Castric and Bernatchez 2003, Chenoweth and Hughes 2003, Poissant, et al. 2005). For example, in a study of a southern United States lizard species (Crotaphytus collaris collaris), Hutchison and Templeton (1999) found that populations that had recolonised regions in the Hypsithermal period were not in migration drift equilibrium. Alternatively, populations that had been stable for a longer period displayed a significant correlation between genetic and geographic distance.

Another common assumption when inferring gene flow from population genetic data is selective neutrality. The genome of an organism is made up of two types of DNA, functional, and non-functional. Functional DNA is that which codes for amino acids and proteins. In these regions, mutations that change the amino acid being coded for (non-synonymous changes) may affect the efficiency of the protein being synthesised. Therefore, non-synonymous mutations may improve or reduce the fitness (ability of an individual to reproduce) of the individual bearing that change. As such, the presence or absence of that allele in a population will not be the product of genetic drift, but will be determined by the environmental and genetic background in which it finds itself. For example, in the marine snail Littorina saxatilis, genetic variation at the Aat allozyme locus was strongly partitioned between surf and splash zones (Johannesson, et al. 1995). This strong cline in variation was maintained despite populations living in close proximity (a few meters).

Due to degeneracy in the genetic code, not all mutations in functional regions will change the amino acid being coded for (Hartl and Clark 1997). These changes, known as synonymous (or silent) changes, are selectively neutral. Non-functional regions of the genome are also selectively neutral as they do not code for amino acids or proteins. Variation at these ‘neutral’ regions is predominantly affected by genetic drift and mutations (Kimura 1968, 1983), making them ideal for population genetic studies that infer gene flow among populations. Importantly, it also allows the ‘molecular clock’ to be invoked when analysing divergence among populations.

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The molecular clock predicts that the observed mutational differences between two samples of genes is directly correlated to the time since isolation and the mutation rate (Bromham and Penny 2003, Page and Holmes 1998). This allows molecular ecologists to ‘date’ vicariant events, potentially even allowing them to identify key historical events that may have caused isolation among populations (e.g., Burridge, et al. 2006, Carini and Hughes 2004, 2006, Luttikhuizen, et al. 2003, Page, et al. 2004, Page and Hughes 2007a, b, Waters, et al. 2007).

The clock-like evolution of selectively neutral regions was identified by Kimura (1983), whereby the divergence rate was found to be directly determined by the mutation rate, independent of Ne (effective population size). According to Kimura, k (nucleotide substitution rate at a nucleotide site per year) in a diploid population of size 2N is equal to the number of new mutations arising per year (μ), multiplied the probability of fixation, u (Kimura 1983, Equations 1-4). k = 2Nμu … Equation 1 For neutral mutations, u = 1/2N … Equation 2 Therefore, k = (2N)(1/2N)μ … Equation 3 k = μ … Equation 4

While this provides a neat mathematical proof for the molecular clock, many biological criticisms have arisen. For example lineage affects, whereby different lineages appear to exhibit different mutation rates based upon changes in generation time, metabolic rate, and DNA repair efficiency, will violate the molecular clock as it assumes a stable mutation rate through time (Chao and Carr 1993, Emerson 2007, Hillis 1987, Ho, et al. 2005, Ho and Larson 2006).

The major difficulty when using the molecular clock is acquiring an appropriate mutation rate, as specific rates are rarely available for non-model taxa. Instead, mutation rates that have been calibrated for similar species and similar geographical regions are acquired from the literature. However, often there are multiple appropriate

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mutation rates published, which can vary widely. For example, when dating divergence among fish taxa at the control region of the mtDNA genome, published calibration rates range from 0.007 substitutions/site/lineage/Myr to 0.116 substitutions/site/lineage/Myr (Donaldson and Wilson 1999, Waters, et al. 2007).

1.2 Population genetic markers Many genetic markers have been developed to investigate the evolutionary history and contemporary demographic patterns of species (Sunnucks 2000). While different genetic markers are important for different evolutionary questions (Sunnucks 2000), three of the most commonly used genetic markers are mitochondrial DNA sequences, allozymes and microsatellites.

1.2.1 Mitochondrial DNA The haploid mtDNA genome differs from its nuclear counterpart, possessing only one chromosome, which is uniparentally inherited through the maternal line (Birky, et al. 1989). Therefore, as four potential nuclear DNA chromosomes can be passed to the progeny, while only one mtDNA genome (that of its mother) can be passed on by the parents, the mtDNA genome has an Ne (effective population size) one quarter that of nuclear genes (Birky, et al. 1989). Therefore, as the process of genetic drift affects smaller populations more than larger populations (Ellstrand and Elam 1993, Page and

Holmes 1998), a smaller Ne results in increased levels of genetic drift. This, along with a lack of recombination (crossing over of homologous chromosomes during meiosis), means that mtDNA possesses a higher allele extinction rate and accelerated lineage sorting rate compared to nuclear DNA (Avise 1994, Birky, et al. 1989, Zhang and Hewitt 2003). This makes mtDNA more sensitive to isolation events than nuclear DNA, causing it to be particularly useful for investigating the recent evolutionary history of populations (Avise 1994).

Mitochondrial DNA, while possessing many useful characteristics, does suffer from some shortcomings. In most population level studies only one mtDNA region is focused upon. This essentially means that the evolutionary history of that species is being looked at through the small window of a single, matrilinearly inherited locus (Zhang and Hewitt 2003). Also, even if more than one mtDNA locus is screened, they

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are all tightly linked (existing on one, non-recombining molecule), meaning that two mtDNA loci will not act as independent markers. Furthermore, a small Ne may possibly lead to oversimplification of evolutionary relationships, underestimation of genetic diversity and the concealment of some population processes (Zhang and Hewitt 2003).

1.2.2 Allozymes Allozyme electrophoresis is the technique of identifying the differential migration of proteins under an electric current (Richardson, et al. 1986). Proteins are strings of amino acids, transcribed from codons found in the genetic code in expressed regions of the genome (Richardson, et al. 1986). Mutations at the codon level can lead to different amino acids being incorporated into proteins, generating genetic diversity which can be analysed using electrophoresis (Avise 1994). When run through a gel, alleles that exhibit different electric charges (based upon differences at the protein level) will migrate at different speeds, allowing genetic variation to be identified (Avise 1994, Estoup, et al. 1998, Richardson, et al. 1986).

While allozyme electrophoresis benefits from being inexpensive and rapid, it suffers from some major weaknesses. For example, allozyme electrophoresis only detects genetic variation that will generate differences in the net charge of proteins, thus generating very low genetic diversity, making fine scale resolution of genetic structure difficult (Estoup, et al. 1998, Queller, et al. 1993). Also, because allozyme electrophoresis is concerned with genetic regions that are transcribed into amino acids and proteins they are typically under selective pressure (Estoup, et al. 1998, Johannesson, et al. 1995).

1.2.3 Microsatellites Microsatellites are tandem repeats of short DNA sequences or motifs that are found evenly distributed across the genomes of eukaryotic organisms (Handcock 1999, Queller, et al. 1993). Originally believed to be of little use for population studies, their full potential is now being realised with the advent of the polymerase chain reaction (PCR, Jarne and Lagoda 1996), and it is now appreciated that microsatellites have a number of qualities that make them powerful genetic markers. These include;

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codominance, suspected neutrality, easy scorability, and high allelic diversity (Jarne and Lagoda 1996, Queller, et al. 1993, Sunnucks 2000).

Because of their repeated motif structure, microsatellites are particularly susceptible to mutations in the DNA synthesis process (slip strand mispairing, see Eisen 1999, Ellegren 2004, Handcock 1999, Levinson and Gutman 1987). This gives them an exceptionally high mutation rate that varies between microsatellite loci, depending upon repeat length and internal structure (Eisen 1999). As a result, microsatellites are ideal genetic markers where high levels of resolution are required. As microsatellites possess higher mutation rates than coding nuclear DNA and mitochondrial DNA, they are more likely to pick up differences between populations that have only been recently isolated, and are therefore ideal for fine-scale population studies (Sunnucks 2000).

Microsatellites, like any other genetic marker, suffer from some limitations. Size homoplasy, when two alleles that are identical in state but are not identical by descent, commonly occur in microsatellites (Estoup, et al. 1995, Estoup and Cornuet 1999, Estoup, et al. 2002). This process typically suppresses the amount of observed diversity within populations, blurs the genealogical record and generates inaccurate estimates of population divergence (Adams, et al. 2004, Estoup and Cornuet 1999, Feldman, et al. 1999). Also, while microsatellites are generally assumed to be free from selective pressures (neutral), some microsatellites occur in expressed regions of the genome or can be linked to functional genes (Nielsen, et al. 2006, Schlotterer 2002). Consequently, multiple loci are required to ensure that ‘unusual’ loci can be detected (e.g., Nielsen, et al. 2006).

1.2.4 Advantages of using multiple genetic markers. This range of genetic markers gives the population geneticist the ability to investigate a wide array of evolutionary questions in a comprehensive way (Sunnucks 2000). The most obvious benefit from using multiple genetic markers is that one marker can offset the weaknesses of another marker. For example, a single mtDNA locus will identify strong structure among populations that only exchange male migrants, as the mtDNA molecule will not be passed on. This would typically lead the researcher to conclude that gene flow is restricted among populations, when a nuclear marker/s would reveal that gene flow is occurring. This approach would also have the added

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benefit of identifying a specific migration process (sex biased dispersal), allowing the researcher to further understand migration among populations.

When estimating a gene tree based upon a single gene, it is assumed that the observed gene tree reflects the true population tree or history. However, one difficulty in this assumption is that a single demographic history is capable of generating varying gene trees which may lead a researcher to draw different conclusions (Hey and Machado 2003). Typically, this cannot be overcome by sampling more individuals or more base pairs in a genetic region. However, using multiple loci is one approach that allows the researcher to observe some of the genetic stochasticity that exists in the demographic history of a species (Hey and Machado 2003).

Using multiple genetic markers also allows researchers to simultaneously identify historical and contemporary processes. This is important as historical and contemporary demographic processes all interact to generate complex patterns of genetic variation. By understanding the attributes of different genetic markers and analyses, it is possible to unravel historical and contemporary processes allowing a more comprehensive understanding of a species.

For example, Hughes and Hillyer (2006) utilised mtDNA and allozymes to elucidate historical and contemporary patterns of gene flow among and within catchments in western Queensland. In this study mtDNA and allozymes where used to estimate genetic differentiation among populations. These results where used to infer contemporary levels of gene flow among populations. To give these results context, mtDNA was also used to estimate historical connectivity among catchments. It was concluded that despite contemporary gene flow being absent among catchments, it had occurred as recently as 15,000 years ago.

1.3 Gene flow in riverine species Understanding contemporary patterns of gene flow among riverine populations is difficult owing to differences in dispersal ability among species, heterogeneous riverine structure and stochastic climatic fluctuations (Amoros and Bornette 2002, Hughes 2007, Meffe and Vrijenhoek 1988, Robinson, et al. 2002, Ward, et al. 2002). Therefore, when drawing biological conclusions from population genetic data in

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riverine systems, some understanding of riverine architecture, hydrology and species biology is required.

1.3.1 Riverine architecture and gene flow River architecture refers to the various structural features that make up the physical arrangement of riverine systems; including side arms, back waters, braided channels, oxbows, ephemeral ponds and streams, marshes, primary channels, secondary channels, altitudinal changes, waterfalls and flood plains (Amoros and Bornette 2002, Knighton and Nanson 1997). These riverine structural elements can combine in a multitude of ways to create spatially complex systems, at both large and small scales.

A popular model used to explain genetic structure in riverine systems is the ‘Stream Hierarchy Model’ (Meffe and Vrijenhoek 1988). Suggested for dendritic (tree-like) streams that exhibit varying levels of gene flow, the SHM predicts that populations found within the same branch of a river will experience higher levels of gene flow among them, compared to populations in different branches. Consequently, genetic structure is expected to be hierarchically partitioned, with higher levels of genetic divergence among sites in different branches than among sites within the same branch (Meffe and Vrijenhoek 1988). In contrast, the ‘Death Valley Model’ is expected when populations occupy isolated sections of habitat and experience no gene flow among them (Meffe and Vrijenhoek 1988). This will generate strong divergence among populations due to genetic drift, mutation and selection.

Importantly, riverine structure is not static. Instead; river capture, fluvial erosion, alluvial deposition, glacial activity and anthropogenic modifications change catchment boundaries and the nature of internal barriers to dispersal (Burridge, et al. 2006, Hurwood and Hughes 1998, Poissant, et al. 2005, Waters and Wallis 2000, Waters, et al. 2007). For example, river capture, where one river effectively steals the headwaters of another river (Bishop 1995), is frequently invoked to explain patterns of genetic structure that are not consistent with contemporary drainage patterns (e.g., Burridge, et al. 2006, Hurwood and Hughes 1998, Waters, et al. 2007).

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1.3.2 Flow regime and gene flow Flow regime is another important landscape process affecting population dynamics in freshwater fish (Amoros and Bornette 2002, Puckridge, et al. 1998, Resh, et al. 1988). The most striking example of this is drought, reducing hydrological connectivity and leaving ‘refugial’ waterholes to sustain populations until connectivity is restored (Humphries and Baldwin 2003, Magoulick and Kobza 2003, Matthews and Marsh- Matthews 2003). If the drought is severe enough, it may lead to population bottlenecks, reducing genetic diversity and altering the evolutionary trajectory of a species (e.g., Douglas, et al. 2003). Population bottlenecks occur when the effective population size (Ne) is dramatically reduced, typically through environmental disturbances or founder events, where a small number of individuals colonise a new region (Avise 1994, Leberg 1992, Ramstad, et al. 2004, Schwaegerle and Schaal

1979). When Ne is reduced, genetic diversity is expected to be reduced also, owing to the loss of random alleles in that population (Nei, et al. 1975, Ramstad, et al. 2004). From a conservation and evolutionary perspective this is critical as low diversity is expected to generate low fitness and lower adaptive potential (Lande 1988).

Floods are another important hydrological process (Resh, et al. 1988), typically producing connectivity across large geographic areas, particularly across floodplains (Balcombe, et al. 2007, Puckridge, et al. 1998). This hydrological connectivity can facilitate gene flow among otherwise isolated populations (e.g., Hanfling, et al. 2004, Huey, et al. 2006, Hughes and Hillyer 2006). Also, high hydrological inputs can generate favourable conditions for freshwater species in previously estuarine or marine environments, potentially facilitating gene flow among populations previously separated by an impassable saline environment (Pusey and Kennard 1996).

1.3.3 Species biology and gene flow The life history strategies of an organism will determine its ability to disperse through the environment, thus playing an important role in determining gene flow among populations (Bohonak 1999). For example, aquatic insects with a flying adult stage will easily disperse through the surrounding terrestrial environment, leading to high gene flow among populations, even in different catchments (Baker, et al. 2003, Bohonak 1999, Hughes, et al. 1998, Hughes 2007, Schmidt, et al. 1995). However,

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there are some notable exceptions to this generalisation (Hughes, et al. 1999a, Wishart and Hughes 2001).

Conversely, non-flying riverine invertebrates, such as crustaceans, exhibit varied dispersal abilities. Some may possess overland dispersal strategies (e.g., crawling), but not adequate larval dispersal, enabling dispersal among catchments but reducing gene flow within catchments (e.g., Ponniah and Hughes 2006). Alternatively, dispersal may be high within catchments, homogenising gene frequencies (e.g., Hughes and Hillyer 2003). Predominantly sedentary species, such as gastropods, typically display low levels of gene flow and high levels of genetic structure (e.g., Carini and Hughes 2006).

As fish are restricted to water, catchment boundaries often act as effective barriers to gene flow, causing divergence among populations (e.g., Hughes, et al. 1999b, Ikeda, et al. 2003, McGlashan and Hughes 2002, Wong, et al. 2004). However, within rivers, where hydrological connectivity can be present, high levels of contemporary gene flow are often detected (e.g., Hanfling, et al. 2004, Huey, et al. 2006, Hughes and Hillyer 2006, So, et al. 2006). Conversely, gene flow can be restricted among populations within catchments, due to either behavioural adaptations such as philopatry (e.g., Stepien and Faber 1998), or the inability to traverse in-stream barriers to dispersal (e.g., waterfalls, Carlsson, et al. 1999, Crispo, et al. 2006, McGlashan and Hughes 2000).

1.4 Australian riverine systems The Australian continent has a wide diversity of riverine systems including tropical, dryland, coastal and arid zone catchments. As can be seen in Figure 1.1, the riverine systems of Australia have been split into 245 ‘river basins’ (which I will hereafter be referring to as catchments). These are grouped into twelve ‘drainage divisions’ (which I will hereafter be referring to as ‘basins’). To understand the role of landscape processes (riverine architecture and hydrology) one must study patterns of genetic structure across landscapes exhibiting diverse structural elements and varied hydrological regimes. As the Gulf of Carpentaria and the Lake Eyre Basins cover large geographic areas and lie adjacent to each other, they provide an ideal opportunity to study gene flow and genetic diversity in different riverine systems.

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Figure 1.1: Drainage divisions in Australia (from http://www.bom.gov.au/hydro/wr/basins/basin-hi_grid.jpg)

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1.4.1 The Lake Eyre Basin The Lake Eyre Basin is a large, internally draining (endoreic) basin, covering a large part of central Australia. Found in the dry interior of the Australian continent, it exhibits many of the qualities associated with dryland rivers, such as long periods of no flow and the predominance of refugial waterholes (Knighton and Nanson 1994, 1997, 2002, Kotwicki 1986, Puckridge, et al. 2000). The gradient of the Lake Eyre Basin is quite low, for example it rarely exceeds 5.2x10-4 in the Thomson and Barcoo Rivers (Cooper catchment), and 1.7x10-4 in Cooper Creek (Knighton and Nanson 1994, Kotwicki 1986). The Cooper catchment, like other catchments in the Lake Eyre Basin, is dominated by refugial waterholes (Puckridge, et al. 2000), which can range from being small, ephemeral waterbodies, to being 20km long (Knighton and Nanson 1997, 2002).

One unique feature of the Cooper catchment, and other catchments in the Lake Eyre Basin, is their variable flow regimes (Puckridge, et al. 1998). The highly variable nature of the ENSO (El-Niño Southern Oscillation system) system largely determines hydrology in the Lake Eyre Basin, with waterholes being isolated year round and becoming hydrologically connected during flood events. These events can range from small channel flows (coinciding with monsoonal events in north Australia); to large, catchment-wide floods (caused by larger monsoonal events, occurring approximately once every 100 years, Pickup 1991).

The occurrence of large flood events, hydrologically connecting large geographic areas, provides an opportunity for riverine species to disperse among refugial waterholes, within catchments. Therefore, floods are likely to increase gene flow within catchments, homogenising gene frequencies in different populations. This has been detected in four fish species studied in the Lake Eyre Basin (Porochilus argenteus and Neosilurus hyrtlii, Huey, et al. 2006; Nematalosa erebi, Masci 2005; and Retropinna semoni, Hughes and Hillyer 2006). However, high gene flow has not been detected in all riverine species studied in the Lake Eyre Basin.

In the freshwater snail, Notopala sublineata, low dispersal capabilities were proposed to explain high levels of genetic structure across the Lake Eyre Basin, both within and among catchments (Carini and Hughes 2006). Strong genetic structuring was also

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detected in four cryptic lineages of the freshwater mussel, Velesunio spp., within and among catchments (Hughes, et al. 2004). Freshwater crustaceans, have revealed varied patterns of genetic structuring, sometimes exhibiting low divergence within catchments (e.g., Cherax destructor, Hughes and Hillyer 2003) and sometimes exhibiting strong structuring (e.g., Macrobrachium australiensis, Carini and Hughes 2004). These varied patterns of genetic structuring in different species, across the same geographic range, highlights the role of species dispersal ability in determining levels of gene flow among populations.

Catchments in the Lake Eyre Basin terminate at Lake Eyre, typically a large, dry saltpan which fills during very large flow events (Magee and Miller 1998). During the 1974 flood, some freshwater fish species were found in the lake (Nematalosa erebi, Craterocephalus eyresii and Macquaria ambigua) during which time, salt concentrations in the lake were above marine concentrations (39‰ to 300‰, Ruello 1976). Initially, this suggested that Lake Eyre may play an important role in supporting fish species and possibly act as a conduit for gene flow among catchments. However, population genetic studies since conducted have identified significant genetic structuring among catchments in the Lake Eyre Basin for fish and invertebrate species (M. australiensis, Carini and Hughes 2004; N. sublineata, Carini and Hughes 2006; and N. erebi, Masci 2005), suggesting that even if riverine species can survive in Lake Eyre during floods, they do not then disperse into adjacent catchments and contribute to the next generation.

1.4.2 The Gulf of Carpentaria Basin The Gulf of Carpentaria Basin is the tropical savannah found between Cape York Peninsula and north-east Arnhem land. The region is broadly divided into three physiographic divisions, the Isa Highlands, the Carpentaria and Inland Plains, and the Einasleigh Uplands (Perry 1964, Twidale 1964a). The Isa Highland is the mountainous high country found around Mt Isa, caused by an outcropping of igneous and metamorphic rocks (Perry 1964). This area drains into the Leichhardt and Nicholson catchments, before reaching the Gulf. The eastern part of the Gulf is primarily made up of the Einasleigh Uplands, a plateau mostly above 2000ft. Found between these two areas are the Carpentaria and Inland Plains, drained by the Flinders and Norman catchments.

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Catchments draining these different regions vary in their structural makeup. Riverine architecture in those rivers draining out of the Isa Highlands is typically strongly dendritic, with well defined and moderately-deeply incised channels. This differs from those riverine structures on the Carpentaria and Inland Plains that typically exhibit more poorly developed drainages, anastomosing channels and a more open, dendritic pattern (Perry, et al. 1964, Smart, et al. 1980).

Hydrological inputs into the region are highly seasonal, with most falling between November and April, coinciding with monsoonal events (Perry 1964, Perry, et al. 1964). Due to the lack of regular year-round water inputs into the region, most surface water is retained in waterholes during ‘dry’ periods (Twidale 1964b). Commonly found along river channels, waterholes can range from ephemeral pools, to large permanent lagoons (Twidale 1964b). It is likely that these waterholes would become hydrologically connected to each other on a yearly basis, owing to the seasonal influence of monsoonal events. However, it is also likely that the degree of connectivity, especially between very remote waterholes, would be temporally variable, owing to the influence of the El-Niño Southern Oscillation system upon monsoonal events.

Historically, catchments in the Gulf of Carpentaria Basin and the southern flowing catchments of Papua New Guinea were connected via the Lake of Carpentaria, a large freshwater inland lake (Chivas, et al. 2001, Torgersen, et al. 1983, Torgersen, et al. 1988). Chivas et al. (2001) suggests that this freshwater lake existed until approximately 9,700 years before present, when rising sea levels inundated the lake, generating the conditions seen today in the Gulf of Carpentaria.

Few studies have attempted to quantify gene flow and genetic structure among natural populations in the Gulf of Carpentaria, demonstrative of the overall lack of understanding of Australia’s tropical river systems (Hamilton and Gehrke 2005). Studies of three crustacean species, Cherax quadricarinatus (Macaranas, et al. 1995), M. rosenbergii (de Bruyn, et al. 2004) and M. australiensis (Masci 2005) all revealed significant genetic structuring among catchments, suggesting a lack of contemporary gene flow at this geographic scale. However, genetic divergence among populations

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of M. rosenbergii was shallow, suggesting recent isolation among populations in different drainages. De Bruyn et al. (2005) attributed this to the Lake of Carpentaria, which recently facilitated gene flow among catchments until the marine environment replaced the Lake and isolated catchments (Chivas, et al. 2001, Torgersen, et al. 1983).

Two fish taxa have been the focus of genetic research in the Gulf of Carpentaria Basin, Melanotaenia spp. (McGuigan, et al. 2000) and Nematalosa erebi (Masci 2005). Focusing upon Rainbowfishes, McGuigan et al. (2000) suggested that the Lake of Carpentaria may have facilitated dispersal among catchments in the Gulf of Carpentaria and Papua New Guinea. In contrast, Masci (2005) detected significant genetic structure among populations within catchments in the Gulf of Carpentaria, but not among catchments. This apparently contradictory result was attributed to non- equilibrium between migration and drift, as enough time had elapsed for genetic drift to diverge allele frequencies at the small scale, but not at the larger scale.

1.5 Australian freshwater fish

1.5.1 Hyrtl’s Tandan, Neosilurus hyrtlii (Plotosidae) The family Plotosidae is an easily identified group of catfishes (Order: Siluriformes), with all species possessing a noticeably tapered tail (Berra 1981). The most common of all eel-tailed catfishes in Australia is Neosilurus hyrtlii Steindachner 1867, considered by Unmack (1995) to be the third most widespread fish in Australia. A small fish (up to 400mm long), N. hyrtlii inhabits most of arid Australia’s dryland rivers, as well as coastal drainages in northern and eastern Australia (Allen, et al. 2002, Llewellyn and Pollard 1980, Orr and Milward 1984).

In the single study focussing on the reproductive biology of N. hyrtlii, Orr and Milward (1984) discovered that in a northern Queensland population, breeding occurred just prior to seasonal flooding events, with females producing large numbers of eggs. Other than this study of a single population, very little else is known of their reproductive biology. However, it is generally accepted by most authors on Australian fish biology and ecology that spawning most probably occurs prior to flooding in dryland populations (Allen, et al. 2002, Larson and Martin 1990, Llewellyn and Pollard 1980, Unmack 1995). This also has been supported by Huey et al. (2006), who

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reported high levels of gene flow in the Cooper catchment, suggesting flood events are exploited by N. hyrtlii, homogenising gene frequencies across the catchment.

1.5.2 Northwest Glassfish, Ambassis sp. and Macleay’s Glassfish Ambassis macleayi (Ambassidae) The family Ambassidae is comprised of 41 species from 8 genera, inhabiting the Indo- Pacific region (Allen, et al. 2002). Both freshwater and estuarine species are represented in this family, with approximately 11 species inhabiting Australia (Allen, et al. 2002). Their common name, glassfish, is derived from their semi-transparent appearance.

Ambassis sp. (Northwest Glassfish, formerly A. mulleri), is an undescribed ambassid found from the Kimberly region, extending eastward to the Leichhardt River (the Gulf of Carpentaria), and south to the Lake Eyre Basin, including Cooper Creek and the Bulloo River (Allen and Burgess 1990, Allen, et al. 2002). A widely distributed, but not well understood species, Ambassis sp. may actually represent multiple species across its distribution. Upstream movement has been observed in this species during the late wet season (Bishop, et al. 1995).

Ambassis macleayi Castelnau 1878 (Macleay’s Glassfish) is a locally abundant species, occurring across northern Australia and the trans-Fly River region of Papua New Guinea (Pusey, et al. 2004). Studies from the Alligator River and aquarium observations suggest that most recruitment occurs during the dry season in A. macleayi (Kennard 1995), with spawning occurring over aquatic vegetation (Pusey, et al. 2004). A table outlining the key differences between Ambassis spp. and N. hyrtlii can be observed in Appendix A.

1.6 Aims Using population genetic markers (mtDNA, microsatellites and allozymes), this study aims to contribute to the understanding of population genetics in Australian freshwater fish at three different levels. Firstly, this study aims to investigate the genetic structure of three species of Australian freshwater fish (N. hyrtlii, Ambassis sp. and A. macleayi), across a large portion of their natural range. Other than one study (Huey, et al. 2006), these species have not been the focus of any population genetic or

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phylogeographic research to date. This is likely to give valuable insight into the biology of these poorly understood species.

Secondly, this study aims to explore the role of species biology upon patterns of genetic structure in freshwater fish. As previously mentioned, patterns of gene flow among populations of freshwater organisms are determined by species biology, riverine architecture and hydrological inputs (Amoros and Bornette 2002, Hughes 2007, Meffe and Vrijenhoek 1988, Robinson, et al. 2002, Ward, et al. 2002). To investigate the role of species biology (physical and behavioural adaptations) upon gene flow, studies often compare patterns of genetic structure and genetic diversity in different species across the same geographic region (e.g., Kyle and Boulding 2000, Turner, et al. 2004). This study will compare two species in two basins (N. hyrtlii and Ambassis sp. in the Lake Eyre Basin; and N. hyrtlii and A. macleayi in the Gulf of Carpentaria Basin) to elucidate the role of species dispersal ability upon gene flow.

Finally, this study will investigate the role of landscape processes (riverine architecture and flow regime) upon patterns of genetic structure in Australian freshwater fish. This study will do this by comparing patterns of genetic structure and genetic diversity within basins, between the Lake Eyre and Gulf of Carpentaria Basins and identify the impact of landscape processes upon population processes.

Chapter 2 consists of a general methodology for this research, outlining sampling, laboratory and statistical approaches. These methodologies apply to all of the data chapters (3-5), however analyses specific to chapters are outlined within each. Chapter 3 investigates the patterns of genetic structure and genetic diversity in N. hyrtlii and Ambassis sp. in the Lake Eyre Basin and compares patterns of genetic structure between these species to identify the role of species biology upon gene flow. Chapter 4 investigates the patterns of genetic structure and genetic diversity in N. hyrtlii and A. macleayi in the Gulf of Carpentaria Basin and, like Chapter 3, compares patterns of genetic structure between species to identify the role of species biology upon gene flow. Chapter 5 investigates the role of landscape processes (primarily hydrology) upon patterns of genetic structure and gene flow in each basin. This is done by compiling data analysed in chapters 3 and 4 and comparing them.

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2 General Field and Laboratory Methodologies

2.1 Study Species Three species were the focus of this research; Neosilurus hyrtlii, Ambassis sp. and A. macleayi (Figure 2.1). N. hyrtlii was sampled across both the Lake Eyre and Gulf of Carpentaria Basins. Ambassis sp. was sampled across the Lake Eyre Basin and A. macleayi was sampled across the Gulf of Carpentaria Basin.

While Ambassis sp. is found in both the Lake Eyre and Gulf of Carpentaria Basins, it was only sampled in the Lake Eyre Basin for the purposes of this study. This approach was chosen as Ambassis sp. is difficult to differentiate in the field from other common Ambassis species found in the Gulf of Carpentaria Basin (e.g., the Sailfin Glassfish, A. agrammus; Elongate Glassfish, A. elongatus; Long-spined Glassfish, A. interruptus; and Vachelli’s Glassfish, A. vachellii, Allen and Burgess 1990, Allen, et al. 2002, Pusey, et al. 2004). However, A. macleayi is visually distinctive from other Ambassis species found in the Gulf of Carpentaria Basin, making field identification easier. Ambassis sp. is believed to be the only glassfish inhabiting the Lake Eyre Basin (Allen, et al. 2002), making field identification unmistakable.

Figure 2.1: The three fish species focused upon in this study. a) Neosilurus hyrtlii, b) Ambassis sp. (Lake Eyre Basin) and c) Ambassis macleayi (Gulf of Carpentaria Basin). Photos by James Fawcett and Joel Huey.

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2.2 Field Work

2.2.1 Study Sites Sites were chosen to produce a hierarchical design, with multiple sites sampled within each catchment, in multiple catchments in each basin (Figure 2.2). This would allow genetic structure to be estimated at large and small scales, within and among catchments/basins.

Figure 2.2: Sampling design.

Sampling trips were conducted over two years (2004-2005), with two trips to the Gulf of Carpentaria Basin (September 2004 and August 2005) and one trip to the Lake Eyre Basin (April 2005). Ambassis sp. and N. hyrtlii samples from the Cooper catchment (8 sites) in the Lake Eyre Basin, were collected in 2001 on an unrelated project. For N. hyrtlii, in the Cooper catchment, the data from Huey et al. (2006) was included in this project. In total, 34 and 26 sites were sampled in the Gulf of Carpentaria Basin and Lake Eyre Basin, respectively (Figure 2.3, Appendix B).

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Figure 2.3: Map of sampling area showing major drainages and sites sampled in this study. Sites from the Cooper catchment, Lake Eyre Basin, are from Huey et al. (2006).

2.2.2 Field methodology In all cases, fyke and/or seine nets were used to sample the waterhole/river (Figure 2.4). Sampling continued until 30 individuals were caught or if extensive effort obtained no more fish. Upon capture, a fin clip was taken from the tail fin of each individual, placed in a plastic bag, and immediately submerged in liquid nitrogen to preserve DNA and proteins for genetic analyses. Samples were kept in the laboratory at -80°C (Forma Scientific Enviro-Scan Bio-freezer) until required for genetic analysis. Samples were collected under a Queensland General Fisheries Permit

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(PRM03315D) and adhered to the Griffith University ethical guidelines (Permit number AES/12/04).

Figure 2.4: Sampling techniques. a) A set fyke net, Flinders River Crossing, Gulf of Carpentaria Basin, b) Clearing fyke net, Lake Corella, Gulf of Carpentaria Basin, c) and d) Seining, Magowra Station, Gulf of Carpentaria Basin. All photos by James Fawcett.

2.3 Laboratory methodology

2.3.1 DNA extraction Total genomic DNA was extracted from tissue using a modification of the CTAB/phenol-chloroform extraction procedure (Doyle and Doyle 1987). A small amount of tissue was placed in a 1.5ml tube with 700ml of 2x CTAB (50ml 1M Tris- HCl pH 8.0, 175ml 4M NaCl, 20ml 0.5M EDTA, 10g hexadecyltrimethylammonium, up to 500ml ddH2O) and 5μl of 20mg/ml proteinase K. This was vortexed and kept at 55°C overnight. Once tissue was digested, 600μl of chloroform-isoamyl (24:1) was 22

added and centrifuged for 2 minutes at 13500 rpm. The supernatant was then removed using a pipette and added to a new 1.5ml tube. This cleaning step was followed by a second cleaning step using 350μl chloroform-isoamyl and 350μl of phenol centrifuged for 5 minutes. This was followed by another chloroform-isoamyl cleaning step. The final supernatant was then added to 600μl of cold isopropanol and left at minus 80°C for at least one hour. The tube was then thawed and centrifuged for 15 minutes at 13500 rpm and aspirated making sure that the DNA pellet remained. 1000μl of 70% ethanol was then added, centrifuged for 5 minutes and aspirated. The final product was then dried in a vacuum bell, rehydrated with 100μl ddH2O and kept at 4°C until required for further analyses.

2.3.2 Mitochondrial DNA The control region of the mtDNA genome was selected for use in this study. The control region is useful for intraspecific studies owing to its higher than average mutation rate and non-coding function (Jerry and Baverstock 1998). Therefore, the control region of mtDNA genome is likely to adhere to the neutral theory of molecular evolution making it ideal for population genetic analysis (however, see Ballard and Kreitman 1995). The MT-H and PRO-L primers were taken from Palumbi (1991) and used for amplifying the control region of the mtDNA genome.

2.3.2.1 Amplification

25μl reactions contained 17.2μl ddH2O, 0.5μl template DNA, 1.0μl 10mM MT-H primer (5’- CCTGAAGTAGGAACCAGCTG -3’), 1.0μl 10mM PRO-L primer (5’- CTACCTCCAACTCCCAAAGC -3’), 0.5μL 10mM dNTP’s (Astral Scientific), 0.2μl

25mM MgCl2 (Astral Scientific), 2.5μl 10x Buffer (Astral Scientific), and 0.3μl Thermus aquaticus DNA (taq) polymerase (5 units/μl, Astral Scientific). Samples were then subjected to the following PCR protocol: an initial hold of 95°C for 4 minutes, followed by 40 cycles of 94°C for 30 seconds, 50°C for 30 seconds and 72°C for 60 seconds. This was followed by a final extension at 72°C for 5 minutes and a final hold at 4°C.

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2.3.2.2 Denaturing Gradient Gel Electrophoresis For Neosilurus hyrtlii, haplotypes were resolved using a combination of denaturing gradient gel electrophoresis (DGGE) and direct sequencing. DGGE gels were set and run using the DCode Universal Mutation Detection System (Bio-Rad). For each individual 10μl of PCR product (amplified using adapted primers with a ‘GC’ clamp) and 5μl dye (0.25ml 2% bromophenol blue, 0.25ml 2% xylene cyanol, 7ml 100% glycerol, and 0.25ml dH2O) were loaded into a 6% acrylamide gel with a 15% - 25% denaturing gradient (15ml 37.5:1 acrylamide:bis, 2ml 50xTAE (242g Tris Base,

57.1ml glacial acetic acid, 100ml 0.5M EDTA pH 8.0, filled to 1L with ddH2O ), 6.3g (15%) or 10.5g (25%) urea, 6ml (15%) or 10ml (25%) formamide filled up to 100ml with ddH2O) for 17 hours at 60 volts.

Gels were visualised using a silver staining methodology. 150ml of buffer A (15ml ethanol and 0.75ml acetic acid up to 150ml with ddH2O) was added to the gel in a tray. This was agitated and left for 3 minutes. The process was then repeated. 300ml of a silver nitrate solution (0.3g AgNO3 in 150ml ddH2O) was then added to the gel and left on an agitator for 10 minutes. The gel was then washed thoroughly, removing all excess silver. 300ml of buffer C (0.03g NaBH4 and 1.2ml formaldehyde added to

4.5g NaOH and 300ml ddH2O just before being poured over gel) was then added to the gel and left until bands on the gel could be seen. Haplotypes were then scored by eye (Figure 2.5).

The reliability of these DGGE conditions was confirmed by accurately detecting all haplotypes previously identified in Huey et al. (2006). All samples were then run against the common haplotype of that study, with samples differing from that haplotype being subjected to direct sequencing.

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Figure 2.5: Denaturing Gradient Gel Electrophoresis gel. Lanes 6, 9 and 12 were identified as differing from the common haplotype and directly sequenced.

2.3.2.3 Sequencing All mtDNA haplotype data for Ambassis sp. and macleayi were obtained using direct sequencing. For N. hyrtlii, haplotypes that were different from the common haplotype were also directly sequenced. Samples that required sequencing were first purified by adding 0.25μl exonuclease (Fermenta) and 1.0μl shrimp alkaline phosphate (SAP, 1 unit/μl, Promega Pty. Ltd.) to 5μl amplified DNA and subjected to the following protocol: 37°C for 35 minutes followed by 80°C for 20 minutes and held at 4°C until required for the next step. The final product was then diluted, depending upon the strength of the original PCR product (typically 1:10). 2μl of terminator mix (BD v. 3.1, Applied Biosystems), 2μl of 5x terminator mix buffer (Applied Biosystems),

0.32μl of 10mM PRO-L primer, 0.5μl of purified product, and 5.18μl of ddH2O was subjected to an initial hold of 96°C for 1 minute, followed by 30 cycles of 96°C for 10 seconds, 50°C for 5 seconds and 60°C for 4 minutes. This was then held at 4°C until required for further analysis. The final product was cleaned, and then sequenced on an Applied Biosystems 3130x1 Genetic Analyser at the Griffith University DNA Sequencing Facility.

2.3.3 Allozyme electrophoresis 5mg of tissue from all samples was ground in separate tubes with 60μl of grinding buffer (2.44g Trizma base, 0.375 EDTA, 5.36g of NH4Cl, 19.80g glucose, 20ml of

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0.022M NaN3 in 1l of ddH2O) and then stored at -80°C until required for further analysis. Short term storage of supernatant did not reduce enzymic activity. Populations were screened using allozyme electrophoresis on Titan III cellulose acetate plates (Helena Laboratories, Beaumont, TX, USA) in horizontal electrophoretic baths (Helena Laboratories) under optimal conditions. Staining procedures and recipes were modified from Richardson et al. (1986).

Identified in Huey et al. (2006), three polymorphic allozyme loci were used to identify izozyme variation in N. hyrtlii (Table 2.1). These were one Aspartate aminotransferase locus (AAT, EC number 2.6.1.1) and two Malate dehydrogenase loci (MDH, EC number 1.1.1.37). For Ambassis sp. and A. macleayi, 22 enzymes were screened for variation. From these, two Glucosephosphate isomerase (PGI, 5.3.1.9) loci were identified that were variable in Ambassis sp. and no variable loci were identified in A. macleayi (Table 2.1). Alleles were scored by eye and recorded for use in statistical analyses (Figure 2.6).

Table 2.1: Allozyme loci and conditions used in this study Locus EC# Buffer Run time AAT 2.6.1.1 Tris Glycene (50M) 50 min MDH 1.1.1.37 Tris Citrate (50M) 90 min PGI 5.3.1.9 Tris Glycene (50M) 50 min

Figure 2.6: Allozyme gel. Two loci at Glucosephosphate isomerase.

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2.3.4 Microsatellites One microsatellite locus used in this study (N22a) was taken from Huey et al. (2006). Otherwise, all other loci used in this study were isolated using the FIASCO (Fast Isolation by AFLP of Sequences COntaining repeats) microsatellite isolation method outlined in Zane et al. (2002) and adapted according to Hillyer et al. (2007).

Total genomic DNA (approximately 100ng), extracted using CTAB phenol/chloroform technique, was taken from five different individuals and simultaneously digested with MseI (5’-TACTCAGGACTCAT-3’) and ligated to the MseI AFLP adaptor (5’-GACGATGAGTCCTGAG-3’). Digestion/ligation reactions included 2.5μl buffer (10x Onephor All Plus), 2.5μl of 50mM DTT, 0.125μl of 10mg/ml BSA, 5μl of 5μM Adaptor, 0.5μl of 10mM ATP, 2.5μl Mse1 enzyme (10 units), 1μl of 1unit/μl ligase T4 DNA, 25-250ng of sample DNA and ddH2O up to 25μl. This was incubated at 37°C of 2-3 hours, then at 65°C for 30 minutes and stored at -4°C.

The product was then amplified using the Mse1-N primer (5’- GATGAGTCCTGAGTAAN-3’), using PCR. Reactions included 2μl of 10x buffer

(Fisher Biotec), 1.6μl of 25mM MgCl2, 0.4μl of 10mM dNTP’s, 0.1μl of 5.5 units/μl taq (Fisher Biotec), 2.3μl of 10mM Mse1-N primer, 0.5μl of digestion/ligation product, and ddH2O up to 20μl. The reaction was subjected to the following program: 25 cycles of 94°C for 30 seconds, 53°C for 60 seconds and 72°C for 60 seconds, and then held at 72°C for 5 minutes and stored at -4°C. A small amount of product was run (5 minutes at 100 volts and 45 minutes at 80 volts) on a 1% agarose gel with size class marker to ensure that product was between 200 and 1000 base pairs.

The pre-selective PCR product was hybridised with a ‘pool’ of biotinylated probes (Pool 4). Firstly, 182ml of 6x SSC, 2μl 10 SDS and 6μl 50x denhards were preheated at 62°C. 100μl of PCR product was then added to 10μl of biotinylated probes and denatured at 95°C for 5 minutes. These two solutions were mixed and incubated at 62°C for 30 minutes. Streptavidin MagneSphere Paramagnet Particles (S-PMP, Promega) were washed and added to 200μl of 6x SSC, 4μl of 50x denhards, 2μl 10% SDS and the hybridised DNA/probes and rotated for 20 minutes at room temperature.

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Hybridised DNA molecules were selectively captured using the Streptavidin MagneSphere Paramagnet. The captured product was then washed with two nonstringency washes and four stringency washes. DNA was separated from S-PMP’s by being denatured at 95°C for 5 minutes. Supernatant was then removed, added to 50μl isopropanol and 2.5μl of 3M sodium acetate and precipitated at -80°C. The product was then centrifuged and aspirated, leaving the pellet which was rehydrated in

50μl ddH2O.

A post-hybridisation PCR included 2.5μl of 10x Buffer (Fisher Biotec), 1.5μl of

25mM MgCl2, 0.4μl of 10mM dNTP’s, 0.1μl of 5.5 units/μl taq (Fisher Biotec), 2.3μl of 10mM Mse1-N primer, 1.0μl of enriched DNA, and ddH2O up to 25μl. The reaction included 25 cycles of 94°C for 30 seconds, 53°C for 60 seconds, and then held at 72°C for 5 minutes and stored at -4°C seconds.

The enriched DNA was then ligated into a vector using PGEM-t easy vector. The ligation included 10μl of 2x ligation buffer, 1.5μl of vector, 2μl ligase, 3μl or 5μl of the insert (enriched DNA), and ddH2O up to 20μl and was subjected to 16°C overnight. Ligations were used to transform competent Escherichia coli (strain JM109, Promega), using electroporation. Transformed cells were plated to LB agar plates containing ampicillin (100μg/ml) and incubated overnight at 37°C. Positive clone colonies were identified through PCR using the M13F and M13R primers (5’- GTAAAACGACGGCCAGT-3’ and 5’-CAGGAAACAGCTATGAC-3’) (Amersham Pharmacia Biotech). Reactions included 0.35μl of each primer (10mM), 0.25 of

10mM dNTP’s, 0.5μl of 50mM MgCl2, 1.25μl of 10x buffer (Austral Scientific), 1.0μl of un-biotinylated probe (pool 4, Promega), 0.05 of taq (5 units/μl, Austral

Scientific) and ddH2O up to 12.5μl. This was subjected to the following protocol: 35 cycles of 94°C for 30 seconds, 50°C for 30 seconds, and 72°C for 30 seconds, followed by a hold of 72°C for 7 minutes. Resultant positive clones were picked and cultured in TB broth. The plasmid DNA was extracted using an alkaline-lysis mini prep. The insert was sequenced with the M13F primer using the previously mentioned sequencing reaction and program.

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For N. hyrtlii, four microsatellite loci were developed that were informative and easily scored. These loci, plus N22a (Huey, et al. 2006), left five polymorphic loci that were used for statistical analyses (Table 2.2). In Ambassis sp., six microsatellite loci were successfully developed that were polymorphic and easily scored. Of these six loci, four also amplified successfully in A. macleayi.

Table 2.2: Microsatellite loci developed in this study. Allelic richness and number of alleles can be found in Appendix C. Microsatellite Locus Primer sequence (5'-3') repeat motif F TCTTGTTATGCACCCTAAGGC NH11 (TG)12 R CACACAACCTTTCTACGCTGC F CTGTCAAGGGCACAATTAGCAG NH12 (GT)36 R GCTGTAAAAACACCAGATGGG F CAGTCAGTACTCCTTTAGAGC NH16 (GT)19 R GACTCAAGCACTTTCACACTC F CAAGCTGGTATATCCAAGATC NH19 (CA)38 R TCCACTGTTGCACACATCTGC F CCCGCAATTCACATGAGAAGC AMB14 (CA)11 R CATGGCCAGATTGTCCTTACG F CTGCCTCTGTGGTGTTTGAAGC AMB16 (CA)9AG(CA)5 R GATCCATCCAGTCTGCCCTCG F CTGCAGATCAAAGCGAGAACG AMB21 (CA)12 R GAAGAGGATGAAGGAATGCCG F CAGAGAAAATCCTCAAATCCCC AMB22 (TG)12 R CTTCTCTTTGAGCTGTGGCAGG F CTCATCATCCAGCAGTGGAGG AMB24 (TG)8 R GAAGGAGGCAGGATCTATAGG F CAAATGCCATCTCGCTCCTCC AMB27 (CA)10 R CTAAGCCTCATGTGGCCCTGC

2.3.4.1 Amplification Microsatellite loci were amplified in 12.5μl PCR reactions containing 0.5μl of total genomic DNA, extracted using the aforementioned CTAB/phenol-chloroform method, 0.5μl each of 10mM forward and reverse primers, 0.25μl of 10mM dNTP’s (Astral

Scientific), 1.0μl of 25mM MgCl2 (Astral Scientific), 1.25μl of 10x Buffer (Astral Scientific), and 0.15μl of Thermus aqauticus DNA (taq) polymerase (5 units/μl, Astral Scientific). This was subjected to an initial hold of 95°C for 5 minutes, followed by 35 cycles of 95°C for 30 seconds, 50°C for 30 seconds, and 72°C for 30 seconds, with a final extension of 72°C for 5 minutes. The product was then held at 4°C until subsequent analysis.

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2.3.4.2 Screening of microsatellite loci Screening populations involved running denatured PCR product and TAMRA size markers through a 5% denaturing acrylamide gel using a GelScan 2000 rig (Corbett Research). A 1:1 PCR product : dye (5mg Bromophenol blue, 50mL Formamide, 100μl 0.5M EDTA) mix was made and denatured by holding the product at 95°C for 5 minutes and then applying to ice. The gel was run at 1200 volts and scored by eye using ONE-Dscan (version 2.03, Scanalytics Inc.). After identification of size, each sample was recorded for use in statistical analysis (Figure 2.7). Samples of N. hyrtlii from the Cooper catchment (taken from Huey, et al. 2006) were also amplified using the four newly developed microsatellite loci, and analysed.

Figure 2.7: Microsatellite gel, locus AMB16, Ambassis sp.. Lanes 1, 10, 19, 28 and 32 are size class markers. All other lanes are individuals.

2.4 Statistical Methodology

2.4.1 Hardy-Weinberg Equilibrium

When analysing a selectively neutral locus, in a randomly mating population, the sample of alleles should adhere to Hardy Weinberg Equilibrium (HWE, Lessios 1992, Wigginton, et al. 2005). HWE predicts that, under a number of assumptions, the frequency of different heterozygotes (genotypes with different alleles) and homozygotes (genotypes with the same allele) will be determined by the frequency of alleles in that population. For example, for a population with two different alleles (P and Q, the relative frequencies of which are p and q), the frequency of each

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homozygote is p2 and q2, and the frequency of the heterozygote genotype should be 2pq (see equation 5 and 6). p + q = 1 …Equation 5 p2 + 2pq + q2 = 1 …Equation 6

The assumptions of HWE include; diploidy, sexual reproduction, non-overlapping generations, random mating, large population size, no sex bias in allele frequencies, no migration, no mutation and no selection (Hartl 2000, Lessios 1992, Wigginton, et al. 2005). Common explanations for deviations from HWE include the Wahlund principle, whereby a sampled ‘population’ actually comprises of two genetically discrete populations (Johnson and Black 1984, Sinnock 1975); selection (Lessios 1992, Wigginton, et al. 2005); and null alleles, the non amplification of alleles due to mutations in the priming site, (Shaw, et al. 1999, van Oosterhout, et al. 2004).

Another common explanation for deviations from HWE, particularly in freshwater species, is the ‘patchy recruitment hypothesis’ (Bunn and Hughes 1997, Hughes, et al. 1998, Hughes, et al. 2000). Patchy recruitment occurs when recruits making up a population are the product of a few random matings. Therefore, deviations from HWE will be random across loci, populations and time (Bunn and Hughes 1997).

Using ARLEQUIN v.3.1 (Excoffier, et al. 2005), deviations from HWE were tested in the nuclear data sets. This was done within each sampled population, at each locus, using exact tests with 10,000 dememorization steps (Guo and Thompson 1992, Levene 1949). MICRO-CHECKER (van Oosterhout, et al. 2004) was used to detect anomalies in the microsatellite data set including null alleles, mis-scored loci and poorly transcribed data. MICRO-CHECKER does this by generating expected homozygote and heterozygote allele size frequencies and uses HWE to detect deviations from expectations (van Oosterhout, et al. 2004).

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2.4.2 Genetic diversity

For haploid data, genetic diversity is the probability that two randomly chosen haplotypes chosen from a sample are different. Therefore, a higher diversity index indicates a greater diversity of different haplotypes, at more similar frequencies. Nucleotide diversity is the probability that two randomly chosen homologous nucleotide sites are different.

For diploid data, genetic diversity is typically estimated as heterozygosity, the expected frequency of heterozygotes in a population (for multiple loci, average gene diversity across loci is equivalent to the probability that two randomly chosen homologous alleles are different (similar to nucleotide diversity). To estimate genetic diversity for each population in the mtDNA and nuclear DNA datasets, ARLEQUIN v3.1 (Excoffier, et al. 2005) was used.

2.4.3 Tests for Neutrality

As most gene flow inferences based on genetic structure assume selective neutrality, neutrality tests were calculated to identify the effects of selection and population size fluctuations upon the mtDNA data set, prior to subsequent analysis. A common test for selective neutrality is Tajima’s D (Tajima 1989b), which can potentially identify two major forms of selection; positive selection (where particular mutations are selectively advantageous and all other mutants are removed from the population) and balancing selection (the maintenance of artificially high diversity). Tajima’s D estimates theta (θ), based on the total number of pairwise differences (θπ) and the number of segregating sites (θS) and then compares these different measures of diversity (Page and Holmes 1998, Tajima 1989b). Under positive selection (or genetic hitchhiking), a single mutant will be swept to fixation, and then after a number of generations, changes at synonymous sites (sites not under selection) will generate a number of closely related haplotypes, each at low frequencies (Figure 2.8a). In this scenario, the number of segregating sites will be high compared to the number of pairwise differences, which after following the equations outlined in Tajima (1989b), generates a negative D value. Alternatively, balancing selection will maintain a number of different haplotypes, which may be evolutionary divergent, at similar frequencies (Figure 2.8b). This generates a positive D value.

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Figure 2.8: Haplotype networks depicting two genealogies expected under a) positive selection and b) balancing selection. Haplotype networks describe the evolutionary relationships between haplotypes. Each haplotype is depicted by a white circle and its relative frequency by its size. Lines represent base pair differences, with small black circles representing extinct haplotypes.

From a population genetic point of view, Tajima’s D is also useful for determining demographic events such as population bottlenecks and population expansions (Tajima 1989a). Under a population bottleneck, alleles at low frequencies will be lost (or will have a higher probability of being lost than other alleles) generating a haplotype network similar to that seen in Figure 2.8b. Alternatively, a population expansion that follows a bottleneck is likely to generate a ‘star shaped phylogeny’, where one or two haplotypes are in high frequencies with many newly derived haplotypes radiating outwards (Figure 2.8a).

While neutrality tests are useful for detecting the effects of both selection and demographic processes upon molecular data, this also makes it difficult to differentiate between molecular and demographic causes for deviations from expectations. However, different neutrality tests are more sensitive to particular phenomena, such as selection or population expansions (Fu 1997, Peck and Congdon 2004). For example, Peck and Congdon (2004) differentiated between population growth (or a range expansion) and background selection by using Fu’s FS and Fu and

Li’s F* and D* statistics. Typically, a range expansion will generate a significant FS and non-significant D* and F* statistics, while background selection will generate the

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reverse pattern (Fu 1997, Peck and Congdon 2004). Therefore, when a suite of neutrality tests are used together, one can distinguish between alternative explanations for deviations from neutrality.

In this study, estimates of Tajima’s D (Tajima 1989b), Fu and Li’s D* and F* (Fu and

Li 1993), Fu’s FS (Fu 1997), and the R2 statistic (Ramos-Onsins and Rozas 2002) were generated using DnaSP v.4.1.9 (Rozas, et al. 2003), and statistically tested using a coalescent simulation. Simulation tests by Ramos-Onsins and Rozas 2002 suggest that statistical tests that use information from the distribution of the pairwise sequence differences (or mismatch distribution) are very conservative. Therefore, this approach to test for population size fluctuations was not utilised for this study in preference to more powerful tests such as Fu’s FS (Fu 1997) and the R2 statistic (Ramos-Onsins and Rozas 2002). Tests were conducted on each sampled catchment.

2.4.4 Detecting bottlenecks in natural populations Bottlenecks occur when the effective number of breeders in a population (effective population size, Ne) is dramatically reduced (Nei, et al. 1975) The detection of these demographic events is important when interpreting population genetic data, and is of relevance to population conservation as bottlenecks can increase inbreeding depression, cause a loss of genetic diversity, and reduce adaptive potential (Amos and Balmford 2001, Keller, et al. 2001, Keller and Waller 2002, Nei, et al. 1975). When

Ne is reduced, heterozygosity and the number of alleles is reduced (Cornuet and Luikart 1996). However, after a bottleneck, allelic diversity is lost faster than heterozygosity. Therefore, one can compare allelic diversity and the expected heterozygosity based upon the number of alleles and determine if the number of alleles has been reduced faster than heterozygosity (Cornuet and Luikart 1996, Luikart and Cornuet 1998, Maruyama and Fuerst 1985, Piry, et al. 1999).

The program BOTTLENECK (Piry, et al. 1999) was used in this study to detect bottlenecks using microsatellite allele frequency data. BOTTLENECK (Piry, et al. 1999) uses the total number of alleles to estimate the expected heterozygosity based upon three mutation models, the infinite alleles model (IAM), the two-phase model (TPM) and the stepwise mutation model (SMM). The previously mentioned relationship between the number of alleles and heterozygosity after a bottleneck, was originally

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only detected for the IAM and may not be detected in loci adhering to the strict SMM (Maruyama and Fuerst 1985, Piry, et al. 1999). However, if the locus deviates only slightly from the SMM, the locus will exhibit a heterozygosity excess as the result of a bottleneck (Piry, et al. 1999). Therefore, the expected heterozygosity was also calculated assuming the TPM, which is intermediate between the IAM and SMM.

BOTTLENECK (Piry, et al. 1999) statistically tests the relationship between observed and expected heterozygosity using a Sign test, Wilcoxon sign-rank test or a standardised differences test, using each locus as a replicate (Piry, et al. 1999). For this study, the Wilcoxon signed rank test was used as it provides higher power than the other tests, requiring as few as four polymorphic loci and 15 individuals (Cornuet and Luikart 1996, Piry, et al. 1999). BOTTLENECK (Piry, et al. 1999) uses a quantitative (mentioned above) and a qualitative approach when identifying bottlenecks. Using simulations Luikart et al. (1998) found that populations that had recently (a few generations) experienced a population bottleneck would exhibit a mode shift in the allele frequency distribution. In this study, BOTTLENECK (Piry, et al. 1999) was used to calculate the allele frequency distribution and qualitatively identify mode shifts.

2.4.5 Analysis of Molecular Variance

When populations are isolated, genetic drift and the accumulation of unique mutations generates divergences in gene frequencies. These differences in gene frequencies can be summarised using FST (Wright 1951), one of the earliest tests for population divergence. FST partitions genetic variation into two groups, the heterozygosity that exists within populations (ĤS) and the total heterozygosity (HT). Therefore, FST is the amount of genetic variation that exists between populations, proportional to the total variation (Equation 7).

FST = (HT-ĤS)/HT …Equation 7

FST as described by Wright (1951) is rarely used in population genetic analyses, but has been refined in many ways to account for variation in sample sizes, number of

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loci, etc… (e.g., Gaggiotti and Excoffier 2000, Nei and Li 1979, Raymond and Rousset 1995, Reynolds, et al. 1983, Slatkin 1995, Weir and Cockerham 1984).

When sampling a number of putative populations, a researcher will typically have a preconceived hypothesis about the nature of genetic structure among those populations. Using a riverine example, the Stream Hierarchy Model (Meffe and Vrijenhoek 1988) predicts that population structure will reflect the dendritic (tree-like) nature of riverine systems. Analysis of Molecular Variance (AMOVA) is designed to test such hypotheses by partitioning genetic structure ‘among groups’, ‘among sites, within groups’ and ‘within sites’ (Excoffier, et al. 1992). AMOVA, then calculates fixation indices, analogous to Wright’s FST, for each hierarchical level (FCT = among groups, FSC = among sites within groups, and FST among all sites), which are then tested for significant deviations from zero using permutation, χ2 and exact tests.

AMOVA, generated using ARLEQUIN v3.1 (Excoffier, et al. 2005), was used to partition genetic variation and estimate fixation values for each genetic marker at three hierarchical levels, ‘among catchments’, ‘among sites within catchments’ and ‘within sites’. For AMOVA calculations using mtDNA sequence data, the FST analogue, ΦST was used. ΦST utilises sequence divergence among haplotypes when partitioning variation (Excoffier, et al. 1992). For nuclear data sets the FST analogue, θ was used (Weir and Cockerham 1984). Using ARLEQUIN v3.1 (Excoffier, et al. 2005), pairwise

FST tables were calculated comparing all sampled populations. For mtDNA data these were calculated using ‘pairwise differences’, which includes sequence divergence in the calculation of FST.

2.4.6 Identifying ‘Isolation by Distance’

Isolation by Distance (IBD) is a model explaining divergence among adjacent and distant populations. IBD occurs when the dispersal distance of individuals (or their propagules) is less than the entire distribution of the species, and enough time has elapsed to reach migration - drift equilibrium (Slatkin 1993, Wright 1943). Under this model, local genetic differences accumulate in populations and cannot disperse between distant populations, but can occasionally reach closer populations. Therefore, adjacent sites will be more genetically similar than distant sites.

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This pattern can be detected by correlating genetic and geographic distances using Mantel tests (Bohonak 1999, 2002). However, it needs to be remembered that a significant correlation between genetic and geographic distance does not ‘prove’ IBD and exclude other explanations. Instead, the nature of the sampling design needs to be taken into account. For example, if a number of sites are found in close proximity and a single site is located on the other side of a plausible barrier to gene flow, it is possible that a significant correlation between genetic and geographic distance is caused by this barrier to gene flow, instead of IBD.

Using all genetic markers, Mantel tests were calculated using ARLEQUIN v.3.1 (Excoffier, et al. 2005), to test for correlations between geographic and genetic distances. A significant correlation between geographic and genetic distance can be suggestive of Isolation by Distance (Wright 1943). In all cases, Slatkin’s Linearized

FST was used (FST/(1-FST), Slatkin 1995), calculated using ARLEQUIN v.3.1 (Excoffier, et al. 2005). In all cases 1000 permutations where used to tests for significance. These tests were calculated among and within catchments. In an attempt to identify the relative importance of floodplain versus channel movement, Mantel tests were calculated separately for stream distance and overland distance. Stream distances between catchments were calculated via Lake Eyre in the Lake Eyre Basin and the Gulf of Carpentaria in the Gulf of Carpentaria Basin.

2.4.7 Phylogeography and Nested Clade Phylogeographic Analysis

Nested Clade Analysis (NCA, or more recently Nested Clade Phylogeographical Analysis, NCPA, Templeton, et al. 1995, Templeton 1998, 2004) takes the haplotype genealogy and divides it into evolutionary ‘slices’, with closely related haplotypes representing recent relationships and distantly related haplotypes representing more ancient evolutionary timescales. The haplotype genealogy is visualised using a network (see Clement, et al. 2000), where each haplotype is represented by a node and the evolutionary relationships between haplotypes are indicated by connecting lines. Nesting of the haplotype network proceeds from the tip haplotypes and progresses inwards, grouping haplotypes that are one base pair divergent, until all haplotypes are

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grouped together into 1-step clades. Ambiguous nestings, where a haplotype/clade is stranded and can be nested in one of two clades, are resolved by grouping the ambiguous haplotype with the clade with the lowest sample size, thus increasing statistical power (Templeton and Sing 1993). Once the 1-step clades are nested, the 1- step clades are treated as if they were haplotypes, and are nested accordingly until a single clade, including all haplotypes, is reached (Templeton, et al. 1995, Templeton 1998, 2004).

The nested clades are then analysed using a pseudo-statistical approach, whereby the analysis first identifies a significant association between geography and haplotypes/clades using a χ2 test (statistical step), and then uses a ‘key’ to identify possible demographic processes explaining the patterns (inferential step). NCA does this by calculating descriptive statistics for each haplotype/clade. The first is the clade distance (DC) value, which is the average distance of all individuals in a clade, from the geographic centre of that clade (Posada, et al. 2000, Templeton, et al. 1995). This roughly equates to an estimate of how dispersed individuals in a clade are. The second statistic is the nested clade distance (DN), which is the average distance of all individuals in a clade from the geographic centre of the nesting clade, an estimate of how far individuals from one clade are from individuals in other clades (Posada, et al. 2000, Templeton, et al. 1995). The clade and nested clade distances are calculated for each haplotype/clade, and the interior-tip comparison, which is the average interior distance, minus the average tip distance (Posada, et al. 2000). These statistics are then tested for deviations from random expectations using a permutation test.

Significantly small or large DC and DN values can then be used to formulate phylogeographic inferences. Templeton et al. (1995) outlines three major phylogeographic inferences; restricted gene flow, range expansion and allopatric fragmentation. Restricted gene flow is typically identified when tip clade distances are significantly small and, but not necessarily, when interior clade distances are significantly large. Therefore, when tip clades are restricted to particular sites, it suggests that they have recently evolved and have not yet spread to other sites, indicative of restricted gene flow. Range expansion, when a clade extends its natural range, is detected when clade distances and nested clade distances are significantly large. However, contiguous and long distance range expansion will produce subtly

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different patterns (see Templeton, et al. 1995). Finally, allopatric fragmentation, when populations have evolved and diverged in isolation, is detected when the clade distances at higher clade levels are significantly small. This may coincide with rapid increases in the nested clade distances whilst clade distances remain restricted.

In this study, Nested Clade Analysis (NCA, Templeton, et al. 1995, Templeton 1998, 2004) was used to identify if there was any geographic association between haplotypes or clades, and then to identify potential demographic processes that may explain these patterns. To do this, the mtDNA haplotype network was first nested 2 according to the rules in Templeton (2004). χ , DC and DN values were then calculated using GEODIS v2.5 (Posada, et al. 2000) and interpreted using the key found in Templeton (2004).

NCA, whilst generating much interest and support when it was first utilised, has received much criticism in recent times. The main criticism of NCA derives from its pseudo-statistical approach when assigning phylogeographical inferences, post hoc (Knowles and Maddison 2002, Knowles 2004). Although NCA uses a statistical 2 approach when generating χ , DC and DN values, it does not generate any statistical ‘confidence’ or estimates of error when assigning inferences (Knowles and Maddison 2002, for an exception see Moya, et al. 2007). Therefore, one cannot be sure about how well a given inference fits the data, or even if an alternative explanation is equally valid. Consequently, the field of Statistical Phylogeography has appeared, which attempts to test specific, predetermined hypotheses using computationally intensive models that accommodate the stochasticity of the genealogical record (Knowles and Maddison 2002, Knowles 2004). Despite these criticisms, NCA remains a commonly used tool in phylogeography, particularly as an exploratory tool (Templeton 2004).

2.4.8 Coalescent Theory

Coalescent theory, initially described by Kingman (1982), has moved from an obscure concept to a central theory in population genetics (Hein, et al. 2005). The Coalescent is effectively a model predicting the random processes of genetic drift, mutation and recombination and can be used to simulate genetic variation under certain

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evolutionary conditions (Rosenberg and Nordborg 2002). Using the coalescent, the genealogy of a known sample of homologous sequences can be simulated going backwards in time until the most recent common ancestor is found (MRCA), and then mutations can be added to the tree branches going forwards (Fu and Li 1999, Rosenberg and Nordborg 2002). This can be done repeatedly, generating a simulated data set which can be used to test a range of evolutionary questions (Fu and Li 1999, Rosenberg and Nordborg 2002).

Most importantly, as previously mentioned, the coalescent provides an opportunity to consider sampled genealogies as exactly that, samples. Therefore, they are not a perfect representation of evolutionary history, generated by a deterministic process. Instead, genealogies can be considered as the product of a stochastic process, whereby the observed tree is merely one of many that could have been generated under the same conditions (e.g., Carstens and Knowles 2007, Moya, et al. 2007, Spellman and Klicka 2006).

Using the mtDNA data set, the program IM (Hey and Nielsen 2004) was used to estimate t, the time in years since population divergence among catchments. IM (Hey and Nielsen 2004) uses Bayesian methodologies and MCMC algorithms to simultaneously estimate the constant effective population sizes for two populations

(θ1, θ2), the ancestral constant effective population size prior to the population split

(θA), gene flow rates per gene copy per generation (m1, m2) and the number of generations in the past that the populations split as a function of the mutation rate (µ) (See Figure 2.9).

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Figure 2.9: The isolation with migration model is depicted with two parameter sets. The basic demographic parameters are constant effective population sizes (N1, N2 and NA), gene flow rates per gene copy per generation (m1 and m2), and the time of population splitting at t generations in the past. The second set of parameters is scaled by the neutral mutation rate μ, and it is these parameters that are actually used in the model fitting. Figure is adapted from Hey and Nielsen 2004.

To simplify the model it was assumed that no migration had occurred among populations since they split. It was assumed that the saline environments of Lake Eyre and the Gulf of Carpentaria, and the boundary between the two basins would make migration among populations in different catchments/basins very rare. For each analysis, populations within each catchment/basin where pooled to achieve higher sample sizes. Prior distributions for the parameters were specific to each pairwise comparison and multiple runs of each comparison were made to ensure that the best estimate of t was attained. For each comparison 10,000,000 steps were used in the chain, with a burn in of 100,000 steps. Once the posterior distribution of t was obtained, the bin that that yielded the highest residence time was used as the point estimate of t. An estimate of credibility was obtained by taking the 95% intervals from the posterior distribution.

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To convert the estimate of mutational time since population divergence (t) to years (t) we applied the equation used in Hey & Nielsen (2004), t = tμ (t=t/μ), where μ is the number of mutations per locus per generation. A divergence rate of 3.6% per million generations was used (Donaldson and Wilson 1999), based upon Snook (Centropomus) assumed to be separated by the Isthmus of Panama 3.5 million years ago. Research suggests that N. hyrtlii and members of the genus Ambassis become sexually mature in one year (Pusey, et al. 2004), allowing estimates of generations since population divergence to be converted to years since population divergence. IM run command lines, update rates and ESS scores can be found in Appendix D.

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3 Patterns of gene flow in two species of freshwater fish (Neosilurus hyrtlii and Ambassis sp.) in the Lake Eyre Basin.

3.1 Introduction The dryland rivers of central Australia are characterised by anastomosing, shallow channels, low catchment gradients and extreme flow variability (Knighton and Nanson 1994, Kotwicki 1986, Puckridge, et al. 1998, Puckridge 1999, Puckridge, et al. 2000). Typically, these systems experience year round low flow, with small channel flows coinciding with monsoonal inputs in the tropical catchments of northern Australia (Puckridge, et al. 1998). During low-flow periods, riverine biota are supported by refugial waterholes, restricting the movement of obligate freshwater species across catchments. However, during large monsoonal events in northern Australia, channel flows may break river banks and, owing to the low catchment gradients, cover large geographic areas with water (Pickup 1991). These brief periods of hydrological connectivity may facilitate gene flow among populations within catchments.

Previous population genetic research in the Lake Eyre Basin suggests that for various invertebrate species (Notopala sublineata, snail, Carini and Hughes 2006; Macrobrachium australiensis, prawn, Carini and Hughes 2004; and Velisunio spp., mussel, Hughes, et al. 2004), flood events do not enable widespread gene flow among populations within catchments. In these species it was inferred that while widespread connectivity among waterholes was possible, individuals largely remained in their refugial waterhole. However, in some fish species, low levels of genetic structure have been detected (Huey, et al. 2006, Hughes and Hillyer 2006), suggesting that these species are quick to exploit floodplain environments during floods. This suggestion has been supported by ecological studies finding high fish production on the floodplain during a flood on Cooper Creek (Balcombe, et al. 2007). This highlights the role of life history in determining gene flow among populations, with low gene flow inferred for species that are largely sedentary (e.g., snails and mussels) and high gene flow detected for highly mobile species (e.g., fish).

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Catchments in the Lake Eyre Basin terminate at Lake Eyre, a large saltpan that infrequently becomes inundated during extreme floods in the Georgina, Diamantina and Cooper catchments (Kotwicki 1986, Magee and Miller 1998). These flood events may create conditions in Lake Eyre that are tolerable for freshwater fish and allow gene flow among catchments. During 1974/75 Lake Eyre filled and supported some freshwater fish species (Nematalosa erebi, Craterocephalus eyresii and Macquaria ambigua), despite salt concentrations above marine conditions (>39‰, Ruello 1976). However, phylogeographic research of N. erebi in the Lake Eyre Basin by Masci et al. (in review) revealed significant genetic structure among catchments, indicative of historical isolation. This suggests that Lake Eyre does not facilitate contemporary gene flow among catchments for these species, despite being viable habitat for some species when it fills.

The historical role of Lake Eyre in connecting catchments in the Lake Eyre Basin is not well understood. According to Kotwicki and Allan (1998), Lake Eyre assumed its present position approximately 21 million years ago (Early Miocene) whilst still a large freshwater lake, and entered the Holocene Epoch (10 thousand years ago) as a dry saltpan subject to occasional flooding. Magee and Miller (1998) have suggested that the drying process may have begun as early as 60,000 years ago, with Lake Eyre shifting from a permanently wet, surface water dominated system, to a groundwater dominated system. This then led to the formation of a salt crust (Magee and Miller 1998), presumably increasing the salinity of the lake when it did fill.

Neosilurus hyrtlii (Hyrtl’s Tandan) is a widespread fish species common in the catchments of the Lake Eyre Basin. Breeding experiments and anecdotal evidence suggest that in eastern and northern Australia, this species spawns prior to the summer wet season (Orr and Milward 1984, Pusey, et al. 2004). In the Lake Eyre Basin where floods generate high hydrological connectivity, this breeding strategy would potentially allow widespread movement of larvae and juveniles. This hypothesis is supported by a study in the Cooper catchment which found panmixia across the catchment in N. hyrtlii and a closely related plotosid, Porochilus argenteus (Huey, et al. 2006).

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Ambassis sp. (Northwest Glassfish, formerly A. mulleri) is a poorly understood species inhabiting the Lake Eyre Basin and parts of northern Australia (Allen, et al. 2002). Very little research has been conducted on this species and little is known about its reproductive biology or movement patterns. Other species of Ambassis exhibit varied breeding strategies; ranging from year-round spawning, with a majority of recruitment occurring during the dry season (A. macleayi, Kennard 1995, Pusey, et al. 2004) to wet season spawning (A. agrammus, Bishop, et al. 2001). Balcombe et al. (2007) found low densities of juvenile and larval Ambassis sp. when sampling during a flood on Cooper Creek, suggesting that perhaps spawning in Ambassis sp. does not coincide with floods.

If spawning in Ambassis sp. does not coincide with periods of highest connectivity among waterholes, only adults would be present and able to exploit hydrological connectivity among populations. This is contrasted with N. hyrtlii, which would have just spawned and would be in massive abundances on the floodplain (as observed by Balcombe, et al. 2007), thus making individuals more likely to end up in a different waterhole from their natal site. Therefore, one would expect higher gene flow among waterholes for N. hyrtlii compared to Ambassis sp..

This study aims to explore the contemporary and historical patterns of gene flow in Neosilurus hyrtlii and Ambassis sp. across three catchments in the Lake Eyre Basin, (the Cooper, Diamantina and Georgina catchments). By using three different molecular markers (control region mtDNA, microsatellites and allozymes), it is hoped that the patterns of gene flow and historical connectivity can be elucidated at large (among catchments) and small (within catchments) spatial scales and temporal scales.

Using the aforementioned markers, it is predicted that; 1. Due to intermittent periods of high hydrological connectivity among waterholes during flood events and previous research in the Lake Eyre Basin suggesting that fish are able to disperse widely during these periods, patterns of genetic structuring within catchments will be low for both fish species, indicative of contemporary gene flow. 2. As spawning in N. hyrtlii is expected to coincide with flood events and spawning in Ambassis sp. may not, genetic structuring, particularly within

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catchments, is expected to be weaker in N. hyrtlii than Ambassis sp.. This will be indicative of higher contemporary gene flow in N. hyrtlii, compared to Ambassis sp. 3. Previous population genetic research in the Lake Eyre Basin has found no evidence for contemporary gene flow among catchments, suggestive of historical isolation. Therefore, in N. hyrtlii and Ambassis sp., significant genetic structure will be detected among catchments. 4. The drying of Lake Eyre (60 thousand year ago) is expected to be the most recent period of connectivity among catchments in the Lake Eyre Basin. Therefore, divergence among populations in catchments that are connected via Lake Eyre will corroborate with the estimated drying of Lake Eyre.

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3.2 Results

3.2.1 Sampling Regime Samples were successfully sampled in the three major catchments in the Lake Eyre Basin, the Cooper, Diamantina and Georgina (Figure 3.1, Table 3.1). A total of 341 N. hyrtlii individuals were sampled across the basin (68, 63 and 210 in the Georgina, Diamantina and Cooper, respectively). For Ambassis sp., 286 individuals were sampled (147, 9 and 130 in the Georgina, Diamantina and Cooper, respectively).

Figure 3.1: Sampling regime of N. hyrtlii and Ambassis sp. in the Lake Eyre Basin.

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Table 3.1: Sampling regime of N. hyrtlii and Ambassis sp. in the Lake Eyre Bain. Neosilurus hyrtlii Ambassis sp. mtDNA nuDNA mtDNA nuDNA Site Site sample sample sample sample Code size size size size Georgina catchment Boulia BO 19 19 10 30 Wirrilyerna WY 6 6 10 13 Rocklands RL 31 31 10 30 Glenorminston 1 GO1 - - 10 30 Glenorminston 2 GO2 5 5 10 29 Bulla Bulla WH BB 7 7 - - Eyre Creek EC - - 10 15 Diamantina catchment Birdsville 3 B3 20 20 - - Monkira 1 MK1 15 15 - - Monkira 3 MK3 16 16 - - Diamantina Lakes 1 DL1 6 6 - - Diamantina Lakes 2 DL2 6 6 9 9 Cooper catchment Murken MR 10 30 8 8 Glen Murken GM 10 30 10 30 Homestead HS 12 30 7 7 One Mile OM 12 25 10 23 Top TP 10 30 5 5 Waterloo WL 10 20 7 7 Tanbar TB 5 5 9 20 Yalangah YG 10 10 10 20

3.2.2 Neosilurus hyrtlii Using SEQUENCHER, a 393bp fragment of the control region was aligned and used for further analysis. Screening of mtDNA variation revealed 8 haplotypes (Figure 3.2, and Table 3.2). MtDNA genetic diversity ranged from 0.00 to 0.60. Nucleotide diversity ranged from 0.00 to 0.00153 (Table 3.3). No neutrality test significantly deviated from neutral expectations (Table 3.4). TCS (Clement, et al. 2000) arranged haplotypes in a ‘star-like’ phylogeny, with one internal haplotype, ‘W’, existing in all populations (Figure 3.2). All other haplotypes were one base pair different from this internal haplotype with tip haplotypes restricted to one or two catchments.

Using MICRO-CHECKER (van Oosterhout, et al. 2004), some populations at some loci revealed evidence for null alleles (see appendix E). Overall, only four out of 144 HWE tests were found to significantly deviate from expectations (Table 3.3). HWE tests that significantly deviated from expectations all showed a heterozygote deficiency except for population WL at locus NH12, which showed a heterozygote excess.

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Figure 3.2: N. hyrtlii, haplotype network of control region mtDNA variation. Each circle represents a unique haplotype with its evolutionary relationship to other haplotypes represented by lines. Pies on the map represent the geographical distribution of haplotypes.

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Table 3.2: N. hyrtlii, distribution of haplotypes across sites. Haplotype letters refer to haplotypes shown in Figure 12. Haplotypes Catchment Population W X Y Z AA AB AC AD BO 16 1 1 1 WY 4 2 RL 27 3 1 GO2 5 Georgina catchment BB 6 1 B3 16 4 MK1 12 3 MK3 10 5 1 DL1 3 3 catchment Diamantina Diamantina DL2 5 1 MR 9 1 GM 10 HS 10 2 OM 9 1 2 TP 9 1 WL 9 1 TB 5 Cooper catchment catchment Cooper YG 8 1 1 TOTAL 173 2 5 1 20 1 5 3

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Table 3.3: N. hyrtlii, genetic diversity indices. For nuclear markers, the expected heterozygosity is displayed. Significant deviations from HWE are marked with an asterix (α=0.05). For explanations of heterozygote excess or deficiency, see text. control region microsatellites allozymes Expected gene nucleotide gene Expected Heterozygosity gene Catchment Site Heterozygosity diversity diversity diversity diversity N22a NH16 NH11 NH19 NH12 AAT MDH-1 MDH-2 BO 0.30 0.00080 0.84 0.87 0.78 0.78 0.90 0.88 0.15 0.46 0.00 0.00 WY 0.53 0.00136 0.82 0.85* 0.83 0.79 0.91 0.73 0.18 0.53 0.00 0.00 RL 0.24 0.00062 0.81 0.86 0.62 0.78 0.90 0.87 0.16 0.48 0.00 0.00 GO2 0.00 0.00000 0.73 0.56 0.36 0.89 0.93 0.91 0.16 0.47 0.00 0.00 Georgina catchment BB 0.29 0.00073 0.75 0.79 0.60 0.73 0.89 0.94 0.18 0.53 0.00 0.00 B3 0.34 0.00086 0.81 0.94 0.78 0.47 0.93 0.94 0.17 0.50 0.00 0.00 MK1 0.34 0.00087 0.80 0.88 0.71 0.59 0.88 0.94 0.15 0.46 0.00 0.00 MK3 0.54 0.00148 0.85 0.90 0.77 0.73 0.93 0.92 0.17 0.51 0.00 0.00 DL1 0.60 0.00153 0.78 0.88 0.85 0.32 0.89 0.95 0.18 0.55 0.00 0.00 catchment Diamantina Diamantina DL2 0.33 0.00085 0.78 0.91 0.79 0.44 0.88 0.91 0.00 0.00 0.00 0.00 MR 0.20 0.00051 0.65 0.68* 0.44 0.35 0.91 0.88 0.02 0.03 0.03 0.00 GM 0.00 0.00000 0.64 0.77 0.35 0.40 0.85 0.84 0.02 0.03 0.03 0.00 HS 0.30 0.00077 0.69 0.77 0.40 0.51 0.90 0.86 0.07 0.07 0.16 0.00 OM 0.44 0.00120 0.67 0.71* 0.40 0.49 0.90 0.86 0.06 0.12 0.08 0.00 TP 0.20 0.00051 0.65 0.65 0.35 0.49 0.91 0.85 0.05 0.10 0.07 0.00 WL 0.20 0.00051 0.69 0.70 0.43 0.50 0.93 0.89* 0.05 0.05 0.10 0.00

Cooper catchment catchment Cooper TB 0.00 0.00000 0.57 0.38 0.20 0.51 0.87 0.89 0.00 0.00 0.00 0.00 YG 0.38 0.00102 0.67 0.74 0.36 0.51 0.93 0.79 0.03 0.00 0.10 0.00

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Table 3.4: N. hyrtlii, neutrality tests. None deviate significantly from neutral expectations.

Tajima's D Fu & Li's D* Fu & Li's F* Fu's FS R2

Georgina catchment -1.07 -0.49 -0.78 -1.98 0.06

Diamantina catchment -0.63 -1.82 -1.7 -1.02 0.08

Cooper catchment -1.12 0.85 0.27 -2.22 0.04

The program BOTTLENECK (Piry, et al. 1999) detected some evidence for population bottlenecks in the N. hyrtlii microsatellite dataset (Table 3.5). For populations BO, RL and MK3 (Georgina and Diamantina catchments), there were more loci with a lower than expected observed heterozygosity, than expected at random assuming an infinite alleles mutation model. However, all populations displayed L-shaped allele frequency distributions, which is not expected after a population bottleneck (Piry, et al. 1999).

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Table 3.5: N. hyrtlii, results for BOTTLENECK. For each population with more than 5 individuals, for each mutation model, the ratio of loci with a heterozygosity deficiency to loci with a heterozygosity excess is shown. The probability that there is an excess of loci displaying a heterozygosity excess is displayed. The alleles frequency distribution is also described qualitatively. (IAM = infinite alleles mode, TPM = two phase model, SMM = stepwise mutation model). Catchment Georgina Diamantina Population BO RL B3 MK1 MK3 n 19 31 20 15 16 H /H 0/5 0/5 1/4 3/2 0/5 IAM def exc p (Hexc) 0.016 0.016 0.313 0.688 0.016

TPM (95% Hdef/Hexc 3/2 5/0 3/2 3/2 2/3 SMM) p (Hexc) 0.594 1.000 0.891 0.953 0.40625 H /H 4/1 5/0 4/1 4/1 2/3 SMM def exc p (Hexc) 0.984 1.000 0.953 0.969 0.688 Allele frequency Approx. L- Approx. Approx. L- Approx. L- Approx. L- distribution shaped L-shaped shaped shaped shaped Catchment Cooper Population MR GM HS OM TP WL YG n 30 30 30 25 30 20 10 H /H 3/2 2/3 2/3 4/1 2/3 3/2 2/3 IAM def exc p (Hexc) 0.406 0.5 0.109 0.891 0.109 0.406 0.594

TPM (95% Hdef/Hexc 4/1 4/1 5/0 4/1 3/2 3/2 4/1 SMM) p (Hexc) 0.953 0.96875 1.000 0.984 0.953 0.953 0.953 H /H 4/1 4/1 5/0 4/1 4/1 3/2 4/1 SMM def exc p (Hexc) 0.984 0.984 1.000 0.984 0.984 0.953 0.953 Allele frequency Approx. L- Approx. L- Approx. Approx. Approx. Approx. Approx. L- distribution shaped shaped L-shaped L-shaped L-shaped L-shaped shaped

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Nesting of the mtDNA haplotype network comprised only one nesting level encompassing the entire network. Nested Clade Analysis concluded that the geographic distribution of haplotypes was structured, and as such rejected the null hypothesis of panmixia (Table 3.6). The inference key suggested that ‘restricted gene flow with isolation by distance’ was responsible for the observed distribution of haplotypes. Mantel tests, correlating genetic and geographic distance, indicated only one significant correlation (Table 3.7), between microsatellite genetic distance and stream geographic distance, over the entire Lake Eyre Basin.

Table 3.6: N. hyrtlii, Nested Clade Analysis results with phylogeographic inference. Significantly small of large DC and DN values are indicated with ‘S’ and ‘L’ respectively.

Clade Haplotype DC DN Conclusion W 214.73L 214.88L X 112.03 201.89 Y 42.73S 227.05 Restricted gene Total Cladogram Z 0.00 200.96 flow with isolation 2 χ p-value = AA 141.60S 150.56S by distance 0.034 (inference key; AB 0.00 109.17 1,2,3,4) AC 83.00S 195.21 AD 87.63 241.75 I-T 108.03S 37.54S

Table 3.7: N. hyrtlii, Mantel tests correlating overland and stream geographic distances with genetic distances between populations. Values shown are correlation coefficients. Correlation coefficients that significantly deviate from zero are indicated with an asterix (α=0.05). control region microsatellites allozymes LEB 0.10 0.58* -0.09 Georgina -0.24 0.12 0.00 Diamantina -0.28 0.00 -0.22 Stream distances distances Cooper 0.07 0.08 -0.16 LEB 0.02 -0.03 -0.12 Georgina -0.24 -0.01 0.00 Diamantina -0.30 0.00 -0.26 Overland Distances Distances Cooper 0.04 0.07 -0.17

Analysis of molecular variance also rejected panmixia (Tables 3.8 and 3.9). Significant structure was identified among catchments using all genetic markers

(mtDNA ΦCT=0.09, microsatellites FCT=0.11, allozyme FCT=0.43) and among populations, within catchments using microsatellites (FSC=0.01). Significant structure was also detected between the Diamantina and Georgina catchments (mtDNA

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ΦCT=0.05, microsatellites FCT=0.04, allozyme FCT=0.11). For pairwise ΦST and FST values, see Appendix F.

Table 3.8: N. hyrtlii, Analysis of Molecular Variance results. Fixation indices that significantly deviate from zero are indicated with asterix (* α=0.05, ** α=0.01). control region microsatellites allozymes Source % % % Φ F F Variation ST Variation ST Variation ST

Among all catchments, 8.94 0.09** 11.43 0.11** 43.38 0.43** Lake Eyre Basin

Among populations, 0.73 0.01 0.64 0.01** 0.48 0.01 within catchments

Within populations 90.33 0.10** 87.93 0.12** 56.14 0.44**

Between the Diamantina 4.70 0.05* 4.26 0.04** 11.02 0.11** and Georgina catchments

Among populations, 0.34 0.00 0.74 0.01** 0.88 0.01 within catchments

Among populations, within the Georgina 0.02 0.01 -0.04 catchment Among populations, within the Diamantina 0.02 0.01* 0.05 catchment Among populations, within the Cooper -0.01 0.01* -0.01 catchment

Table 3.9: N. hyrtlii, locus by locus Analysis of Molecular Variance results. Fixation indices that significantly deviate from zero are indicated with asterix (* α=0.05, ** α=0.01). Among Within Among Among catchments populations, within populations populations catchments % % F F % Variation F Variation CT Variation SC ST N22a 13.11 0.13** 1.22 0.01** 85.67 0.14** NH16 21.41 0.21** 0.11 0.00 78.47 0.22** NH11 16.50 0.16** 0.44 0.01 83.06 0.17** NH19 3.38 0.03** 1.40 0.01** 95.22 0.05** NH12 7.31 0.07** -0.15 0.00 92.84 0.07** AAT 47.48 0.47** 0.64 0.01 51.88 0.48** MDH-1 2.58 0.03** -1.15 -0.01 98.57 0.01

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Using a mutation rate of 7.2 x 10-6 mutations per locus per year, estimates of t, the time since population divergence among catchments in the Lake Eyre Basin were low (Table 3.10, Figure 3.3). Point estimates for each pairwise comparison ranged from 1.7 thousand years ago (Georgina-Diamantina) to 23 thousand years ago (Cooper- Diamantina). However, point estimates had an upper credibility interval up to an order of magnitude greater (8 thousand to 210 thousand years ago).

Table 3.10: N. hyrtlii, results of IM analysis showing the maximum likelihood point estimate of t, the time since population divergence, and the 95% credibility intervals. MLE t (years) 0.025 CI 0.975 CI Cooper v Diamantina catchment 22,887 12,324 210,915 Cooper v Georgina catchment 17,606 10,563 164,085 Diamantina v Georgina catchment 1,761 1,056 8,803

0.04 0.18 Cooper vs. Diamantina 0.035 Cooper vs. Georgina 0.16 Diamantina vs. Georgina 0.14 0.03

0.12 0.025 0.1 0.02 0.08 0.015 0.06

0.01 0.04 residence time (diamantina vs. georgina) residence time (cooper vs. diamantina, georgina) 0.005 0.02

0 0 0 25000 50000 75000 100000 125000 150000 time since population divergence (years)

Figure 3.3: N. hyrtlii, posterior distribution of t, the time since population divergence. The left hand axis is the residence time for ‘Cooper v Diamantina’ and ‘Cooper v Georgina’. The left hand axis is the residence time for ‘Diamantina v Georgina’. For details of analysis see text.

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3.2.3 Ambassis sp. Screening of a 378bp fragment of the mtDNA control region in Ambassis sp. revealed eight haplotypes (Figure 3.4, Table 3.11), of which none were shared between catchments. The program TCS (Clement, et al. 2000) was unable to ascertain the evolutionary relationship between the ‘Cooper/Diamantina’ and ‘Georgina’ clades as it was too deep. Divergence between the ‘Georgina’ and ‘Cooper/Diamantina’ clades was approximately 4.2 percent (16 different base pairs between the internal haplotypes ‘A’ and ‘C’).

Control region genetic diversity varied between 0.00 and 0.71. Nucleotide diversity varied between 0.00 and 0.00282 (Table 3.12). Neutrality tests were all non- significant except for Tajima’s D in the Georgina catchment which was significantly greater than zero (Table 3.13).

Using MICRO-CHECKER (van Oosterhout, et al. 2004), null alleles were detected in one population at one locus in the Ambassis sp. data set (Appendix D). Tests for HWE revealed four tests that deviated from expectations in the microsatellite data set and one that deviated in the allozyme data set (Table 3.12). All tests that deviated from HWE suffered from heterozygote deficiency. Microsatellite gene diversity varied between 0.29 and 0.51. Allozyme genetic diversity varied between 0.00 and 0.36 (Table 3.12).

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Figure 3.4: Ambassis sp., haplotype network of control region mtDNA variation. Each circle represents a unique haplotype with its evolutionary relationship to other haplotypes represented by lines. Pies on the map represent the geographical distribution of haplotypes. The divergence between haplotypes A and C is equal to 4.2% (16 bases).

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Table 3.11: Ambassis sp., distribution of haplotypes across sites. Haplotypes Catchment Population A B C D E F G H BO 9 1 WY 5 5 RL 8 2 GO1 10 Georgina catchment GO2 9 1 EC 8 2 Diamantina DL2 9 MR 8 GM 9 1 HS 5 2 OM 10 TP 4 1 WL 6 1 TB 9 Cooper catchment catchment Cooper YG 5 1 3 1 TOTAL 49 11 56 1 4 2 3 9

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Table 3.12: Ambassis sp., genetic diversity indices. For nuclear markers, the expected heterozygosity is displayed. Significant deviations from HWE are marked with an asterix (α=0.05). For explanations of heterozygote excess or deficiency, see text. control region microsatellites allozymes Expected gene nucleotide gene Expected Heterozygosity gene Catchment Site Heterozygosity diversity diversity diversity diversity AMB14 AMB16 AMB21 AMB22 AMB24 AMB27 PGI-1 PGI-2 BO 0.20 0.00053 0.42 0.74 0.55 0.42 0.00 0.03 0.76 0.24 0.00 0.49 WY 0.56 0.00147 0.43 0.74 0.51 0.53 0.00 0.00 0.78 0.25 0.00 0.51 RL 0.36 0.00094 0.43 0.65 0.64 0.63 0.03 0.00 0.63 0.22 0.00 0.44 GO1 0.00 0.00000 0.40 0.73 0.55 0.41 0.00 0.00 0.71 0.25 0.00 0.50 Georgina catchment GO2 0.20 0.00053 0.40 0.64 0.53 0.55 0.00 0.00 0.71 0.22 0.00 0.44 EC 0.36 0.00094 0.37 0.57 0.58 0.49 0.00 0.00 0.59 0.25 0.00 0.51 Diamantina DL2 0.00 0.00000 0.29 0.21 0.00 0.00 0.21 0.50 0.80* 0.00 0.00 0.00 MR 0.00 0.00000 0.39 0.72 0.13 0.65 0.46 0.13 0.24 0.12 0.00 0.23 GM 0.20 0.00053 0.46 0.64* 0.03 0.79 0.51 0.51* 0.30 0.32 0.43 0.21 HS 0.40 0.00106 0.32 0.53 0.00 0.78 0.26 0.36 0.00 0.32 0.27 0.36 OM 0.00 0.00000 0.47 0.58 0.20 0.74 0.45 0.51 0.35 0.24 0.40* 0.09 TP 0.48 0.00126 0.43 0.56 0.00 0.73 0.47 0.60 0.20 0.18 0.36 0.00 WL 0.29 0.00076 0.51 0.49 0.00 0.78 0.53 0.75 0.49 0.14 0.27 0.00

Cooper catchment catchment Cooper TB 0.00 0.00000 0.46 0.66 0.00 0.77 0.49 0.47 0.35 0.32 0.38 0.26 YG 0.71 0.00282 0.47 0.59 0.10 0.79 0.50 0.44 0.38* 0.36 0.43 0.28

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Table 3.13: Ambassis sp., neutrality tests. Tajima’s D was significantly greater than zero in the Georgina catchment.

Tajima's D Fu & Li's D* Fu & Li's F* Fu's FS R2

Georgina catchment 3.48** 0.53 0.62 1.06 0.15

Cooper catchment -1.18 -0.17 -0.57 -2.58 0.05

For Ambassis sp., the program BOTTLENECK (Piry, et al. 1999) detected some evidence for population bottlenecks in the microsatellite dataset (Table 3.14). The population WY (Georgina catchment), displayed evidence for a population bottleneck assuming infinite alleles, two-phase and stepwise mutation models. Populations RL, GO1 (Georgina catchment) and TB (Cooper catchment) had more loci with a lower than expected observed heterozygosity, than expected at random, assuming an infinite alleles mutation model. Also, four (out of six) populations in the Georgina catchment displayed shifted modes in their allele frequency distributions, suggestive of population bottlenecks.

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Table 3.14: Ambassis sp., results for BOTTLENECK. For each population with more than 5 individuals, for each mutation model, the ratio of loci with a heterozygosity deficiency to the number of loci with a heterozygosity excess is shown. The probability that there is an excess of loci displaying a heterozygosity excess is displayed. The allele frequency distribution is also described qualitatively. (IAM = infinite alleles mode, TPM = two phase model, SMM = stepwise mutation model). Catchment Georgina Cooper Population BO WY RL GO1 GO2 EC GM OM YG TB n 30 13 30 30 29 15 30 23 30 20 H /H 2/3 0/4 1/4 0/4 1/3 1/3 2/4 2/4 3/3 1/4 IAM def exc p (Hexc) 0.078 0.031 0.047 0.031 0.063 0.063 0.344 0.344 0.344 0.031

TPM (95% Hdef/Hexc 2/3 0/4 1/4 1/3 2/2 3/1 3/3 3/3 3/3 3/2 SMM) p (Hexc) 0.688 0.031 0.313 0.063 0.563 0.906 0.656 0.719 0.781 0.406 H /H 2/3 0/4 1/4 1/3 2/2 3/1 3/3 3/3 3/3 3/2 SMM def exc p (Hexc) 0.688 0.031 0.313 0.094 0.906 0.938 0.719 0.719 0.922 0.594 Approx. Approx. Approx. Approx. Approx. Approx. Allele frequency L- Shifted Shifted Shifted L- Shifted L- L- L- L- distribution shaped mode mode mode shaped mode shaped shaped shaped shaped

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Nesting of the haplotype network identified four 1-step clades, two 2-step clades, and the total cladogram (Figure 3.5). GEODIS v2.5 (Posada, et al. 2000) identified three clades that significantly deviated from panmixia (Table 3.15). The inference key (Templeton 2004) suggested that in Clade 1-2 (Cooper catchment), ‘Contiguous Range Expansion’ explained the geographic distribution of haplotypes. ‘Allopatric fragmentation’ was suggested for clade 2-1 (Cooper-Diamantina) and the total cladogram.

Figure 3.5: Ambassis sp., nesting of the haplotype network.

Table 3.15: Ambassis sp., Nested Clade Analysis results with phylogeographic inference. Significantly small or large DC and DN values are indicated with ‘S’ and ‘L’ respectively.

Clade Haplotype DC DN Conclusion C 75.63 75.55 Clade 1-2 Contiguous Range D 0.00 97.20 2 p-value = Expansion (inference χ L 0.032 E 111.15 114.54 key; 1, 2, 11, 12) I-T -13.29S -35.52S S S Clade 2-1 1-2 78.49 88.65 Allopatric χ2 p-value = 1-3 72.79 90.02 Fragmentation 0.000 (inference key; 1, 19) 1-4 0.00S 228.12L Total Cladogram 2-1 104.24S 204.06S Allopatric χ2 p-value = Fragmentation 0.000 2-2 135.31S 289.01L (inference key; 1, 19)

Mantel tests, correlating genetic and geographic distances, revealed six significant comparisons (Table 3.16). Over the entire basin, stream distance positively correlated with genetic distance using all markers. Also, using microsatellites, overland distance positively correlated with genetic distance over the entire basin. Within the Georgina

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catchment, microsatellite genetic distance positively correlated with overland and stream distance.

Table 3.16: Ambassis sp., Mantel tests correlating overland and stream geographic distances with genetic distances between populations. Values shown are correlation coefficients. Correlation coefficients that significantly deviate from zero are indicated with an asterix (α=0.05). control region microsatellites allozymes LEB 0.26* 0.80* 0.48* Georgina -0.41 0.71* 0.28 Stream

distances distances Cooper -0.12 0.24 -0.12 LEB 0.16 0.66* 0.22 Georgina -0.41 0.96* 0.28 Overland Distances Distances Cooper -0.13 0.26 -0.08

Using all genetic markers, Analysis of Molecular Variance revealed significant genetic structure among catchments (Table 3.17 and 3.18, mtDNA ΦST=0.98, microsatellite FST=0.49, allozyme FST=0.61). Significant genetic structure was also detected among populations within catchments using all markers (mtDNA ΦST=0.10, microsatellite FST=0.03, allozyme FST=0.03). For pairwise ΦST and FST values, see Appendix F.

Table 3.17: Ambassis sp., Analysis of Molecular Variance results. Fixation indices that significantly deviate from zero are indicated with asterix (* α=0.05, ** α=0.01). control region microsatellites allozymes Source % % % Φ F F Variation ST Variation ST Variation ST Among all catchments, Lake 97.81 0.98** 49.05 0.49** 60.94 0.61** Eyre Basin Among populations, within 0.21 0.10* 1.46 0.03** 1.27 0.03** catchments

Within populations 1.98 0.98** 49.48 0.51** 37.78 0.62**

Among populations, within 0.07* 0.01* 0.00 the Cooper catchment Among populations, within 0.10 0.01** 0.06** the Georgina catchment

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Table 3.18: Ambassis sp., locus by locus Analysis of Molecular Variance results. Fixation indices that significantly deviate from zero are indicated with asterix (* α=0.05, ** α=0.01). Among Within Among Among catchments populations, within populations populations catchments % % F F % Variation F Variation CT Variation SC ST AMB14 28.89 0.29** 1.73 0.02** 69.38 0.31** AMB16 66.88 0.67** 0.14 0.00 32.99 0.67** AMB21 34.11 0.34** 2.35 0.04** 63.54 0.36** AMB22 39.03 0.39** 0.74 0.01 60.23 0.40** AMB24 75.84 0.76** 1.43 0.06** 22.73 0.77** AMB27 41.51 0.42** 2.02 0.03** 56.46 0.44** PGI-1 79.49 0.79** 0.07 0.00 20.44 0.80** PGI-2 31.20 0.31** 3.19 0.05** 65.61 0.34**

Using a mutation rate of 6.8 x 10-6 mutations per locus per year, pairwise point estimates for time since divergence of populations between each catchment varied between 140 to 910 thousand years before present (Table 3.19, Figure 3.6). The upper credibility intervals were 2-3 times greater than the point estimates of the time since population divergence.

Table 3.19: Ambassis sp., results of IM analysis showing the maximum likelihood point estimate of t, the time since population divergence, and the 95% credibility intervals. MLE t (years) 0.025 CI 0.975 CI Cooper v Diamantina catchment 139,706 72,059 466,176 Cooper v Georgina catchment 813,235 192,647 1,454,412 Diamantina v Georgina catchment 910,294 186,765 1,557,353

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0.0035 0.014 Cooper vs. Georgina Diamantina vs. Georgina 0.003 0.012 Cooper vs. Diamantina

0.0025 0.01

0.002 0.008

0.0015 0.006

0.001 0.004 residence time (cooper vs. diamantina)

residence time vs. (georgina cooper, diamantina) 0.0005 0.002

0 0 0 500000 1000000 1500000 2000000 time since population divergence (years)

Figure 3.6: Ambassis sp., posterior distribution of t, the time since population divergence. For details of analysis see text.

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3.3 Discussion

3.3.1 Genetic Diversity In the microsatellite data set, null alleles were identified in N. hyrtlii populations at some loci. Null alleles will typically lead to an underestimation of observed heterozygosity and deviations from HWE, as a certain proportion of heterozygotes will be scored as homozygotes (van Oosterhout, et al. 2004). However, null alleles were only detected in a minority of sites, at some loci. This suggests that null alleles were in very low frequencies or that they were identified merely as the result of random fluctuations in genotype frequencies.

Deviations from HWE can be indicative of selection, non-random mating, poor scoring or mis-transcribed data (Lessios 1992, van Oosterhout, et al. 2004, Wigginton, et al. 2005). In all cases where deviations from HWE were detected, the original gels were re-read to ensure that the data were correctly scored and transcribed. Selection is an unlikely cause for deviations from HWE in the microsatellite data set, as microsatellite loci are typically free of selective constraints (Jarne and Lagoda 1996, Queller, et al. 1993, Sunnucks 2000). Nevertheless, microsatellites can be linked to functional genes and be affected by genetic hitchhiking (Maynard Smith and Haigh 1974). However, deviations from HWE were not consistent across populations at a locus, typically seen when selection is affecting the dataset. This suggests that selection is not a likely explanation for deviations from HWE.

Two biological explanations for deviations in HWE are the Wahlund effect, where two non-interbreeding cohorts are sampled in the same site (Johnson and Black 1984, Sinnock 1975) and the ‘patchy recruitment hypothesis’, where sampled individuals are from only a few matings (Bunn and Hughes 1997, Hughes, et al. 1998). The Wahlund effect is expected to generate a heterozygote deficiency at all loci in a population, which was not observed in either species in any population. This suggests that the Wahlund effect is an unlikely explanation for deviation from HWE.

Patchy recruitment, however, will generate random deviations from HWE across loci and populations (Bunn and Hughes 1997, Hughes, et al. 1998), as observed in this

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dataset. Also, according to Bunn and Hughes (1997), the ‘patchy recruitment hypothesis’ predicts that genetic divergences within streams will not correlate with geographic distances between sites. This was tested in the N. hyrtlii and Ambassis sp. datasets using Mantel tests, which concluded that there was no correlation between geographic and genetic distance in any catchment, except for Ambassis sp. in the Georgina catchment using microsatellite data. Finally, the ‘patchy recruitment hypothesis’ predicts that patterns of genetic differentiation between reaches will differ between loci as the alleles that comprise a site at any locus will be the product of the individuals that happen to breed there, not genetic drift which will cause divergence to be consistent across loci. (Bunn and Hughes 1997). This was observed in the locus by locus AMOVA results for both species, in which some loci showed significant structure within catchments and others did not. This is further support for the ‘patchy recruitment hypothesis’ in N. hyrtlii and Ambassis sp. in the Lake Eyre Basin.

Patchy recruitment was originally proposed to explain unusual patterns of genetic diversity and structure in flying insects (Bunn and Hughes 1997, Hughes, et al. 1998). It occurs when a few matings and chance oviposition by a few females dominates the recruits of the next generation. For freshwater fish, patchy recruitment could occur if, following a large flood and the subsequent receding of flood waters, the individuals that manage to recolonise the refugia are few and are highly fecund. Thus, a few matings will dominate the gene pool of the subsequent generation, producing random deviations from HWE, inconsistent levels of genetic divergence between sites, across loci and no pattern of isolation by distance (Bunn and Hughes 1997, Hughes, et al. 1998). Importantly, to test this hypothesis, it is recommended by Bunn and Hughes (1997), that temporal sampling should be carried out, as the observed patterns should differ between sampling occasions. Unfortunately, the sampling design of this study did not entail temporal sampling and could not be tested. Despite this, the ‘patchy recruitment hypothesis’ does appear to describe patterns of genetic diversity and gene flow in these species. However, more explicit testing of the ‘patchy recruitment hypothesis’ and possible alternative explanations is required to appropriately identify the processes generating the observed data.

For N. hyrtlii, control region mtDNA diversity was low (gene diversity = 0.0 to 0.6, nucleotide diversity =0.0 to 0.00153). Similarly, Ambassis sp. revealed low mtDNA

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diversity (gene diversity = 0.00 to 0.71, nucleotide diversity = 0.00 to 0.00282). These estimates are much smaller than estimates derived from other fish studies using control region mtDNA (e.g., Hughes, et al. 1999b, Ikeda, et al. 2003, Krieg, et al. 2000, Stepien and Faber 1998, Takagi, et al. 2006). Three possible processes can explain low genetic diversity; selection, population bottlenecks or low but stable effective population size.

The control region of the mtDNA genome is non-coding (Ballard and Kreitman 1995), and as such, should be free from direct selection. However, when a functional gene is positively selected and becomes fixed, linked genetic regions may be ‘swept’ to fixation as well, via the process of ‘genetic hitchhiking’ (Fay and Wu 2000, Maynard Smith and Haigh 1974). This may generate low genetic diversity in linked genes as only those alleles/haplotypes that were linked to the original mutant will remain in the population. Recombination, the process whereby genetic regions are swapped between homologous chromosomes during meiosis, tempers genetic hitchhiking, as recombination will reduce linkage between adjacent genes (Maynard Smith and Haigh 1974). However, the mtDNA genome is particularly susceptible to genetic hitchhiking as it is a single, non-recombining molecule (Ballard and Kreitman 1995). Despite this, neutrality tests failed to find evidence for deviations from neutrality (except for Ambassis sp. in the Georgina catchment), suggesting that it is an unlikely explanation for low diversity in the mtDNA data set.

Population bottlenecks, where the number of individuals in a population is drastically reduced, and founder events, where a small population colonises a new region, will generate low genetic diversity within populations through the random removal of different alleles/haplotypes (Amos and Balmford 2001, Keller, et al. 2001, Keller and Waller 2002, Nei, et al. 1975). Population bottlenecks and founder events have been invoked to explain low genetic diversity in many freshwater fish species (e.g., Douglas, et al. 2003, Laroche and Durand 2004) making this an plausible explanation for low genetic diversity in the control region dataset.

However, low genetic diversity is not just caused by sudden reductions in Ne (bottlenecks). In a single, isolated population there should be a positive relationship between neutral genetic diversity and Ne (Crow and Kimura 1970). Therefore, smaller

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populations are suspected to possess lower genetic diversity than larger populations. Studies of the European Bullhead have invoked patch size (analogous to population size) to explain low genetic diversity, suggesting that population sizes rather than bottlenecks explained low microsatellite and allozyme variation (Hanfling and Brandl 1998, Hanfling, et al. 2002, Knaepkens, et al. 2004). Therefore, small population sizes in N. hyrtlii and Ambassis sp. may explain the observed low diversity in the mtDNA dataset.

As neutrality tests failed to identify evidence for selective pressures or population bottlenecks in the N. hyrtlii data set across the Lake Eyre Basin and in the Ambassis sp. data set in the Cooper and Georgina catchments, low population sizes appear to be a more plausible explanation for low mtDNA diversity. This is also supported by previous studies on fish populations in the Lake Eyre Basin, which have detected low diversity in mtDNA datasets (e.g., Huey, et al. 2006, Hughes and Hillyer 2006, Masci 2005) suggesting that populations of freshwater fish in the Lake Eyre Basin have low effective population sizes.

While neutrality tests failed to identify selective or demographic pressures in the N. hyrtlii data set, for Ambassis sp., a significantly positive Tajima’s D rejected neutrality in the Georgina catchment. This result can be caused by two alternate processes; positive selection is maintaining particular haplotypes in the population and removing other deleterious haplotypes, or a population bottleneck has reduced Ne, removing random alleles from the population (Tajima 1989a, b).

Using BOTTLENECK (Piry, et al. 1999), for Ambassis sp. in the Georgina catchment, three (out of six) populations showed significantly greater observed heterozygosity than expected under the IAM. The other three populations were also very close to significant (p = 0.063-0.078). This result is expected soon after a population bottleneck (Cornuet and Luikart 1996, Luikart and Cornuet 1998, Piry, et al. 1999). Also, four (out of six) populations exhibited a mode shift in the allele frequency distribution, indicative of a recent population bottleneck (Luikart, et al. 1998, Piry, et al. 1999). The concordance between mtDNA (Tajima’s D, Tajima 1989a, b) and microsatellite results (BOTTLENECK, Piry, et al. 1999) is strong evidence that a

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bottleneck has recently reduced Ne in populations of Ambassis sp. in the Georgina catchment.

3.3.2 Gene flow and genetic structure As hypothesised, for N. hyrtlii, genetic structure within catchments was low. Within catchments, for mtDNA and allozymes, no significant structure was detected and microsatellite variation, while significant, was weak (FST=0.01). This suggests that high levels of contemporary gene flow exist among populations of N. hyrtlii within catchments in the Lake Eyre Basin. This supports suggestions that N. hyrtlii is an efficient disperser, capable of exploiting high flow events and dispersing among distant populations. This low level of genetic structure mirrors that observed in the previous study of N. hyrtlii in the Cooper catchment (Huey, et al. 2006).

In contrast, for Ambassis sp., significant genetic structure was detected among populations within catchments using all genetic markers. However, it was also weak

(ΦST = 0.10, microsatellite and allozyme FST = 0.03). This is suggestive of some restriction to gene flow among populations, within catchments. The level of genetic structure observed in Ambassis sp. is similar to that observed in two freshwater fish studied in the Lake Eyre Basin; Porochilus argenteus (Huey, et al. 2006) and Nematalosa erebi (Hughes and Hillyer 2006, Masci 2005). Therefore, while floods may provide high levels of hydrological connectivity among waterholes, some fish species, such as Ambassis sp., do not disperse freely during these events.

While patterns of genetic structure in N. hyrtlii were suggestive of high levels of gene flow within catchments, the same pattern did not extend to patterns of genetic structure among catchments. For both N. hyrtlii and Ambassis sp., significant genetic structure was detected among catchments, using all genetic markers. Importantly, for both species, the fixation indices among catchments were greater than the fixation value among populations within catchments, suggesting greater gene flow among populations within catchments than among populations in different catchments. This pattern of genetic structure is predicted by the Stream Hierarchy Model (SHM, Meffe and Vrijenhoek 1988), whereby greater gene flow will occur among sites in the same branch than among sites in different branches.

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However, even though genetic structure in the Lake Eyre Basin appears to be well explained by the Stream Hierarchy Model, microsatellite loci in N. hyrtlii, and all loci in Ambassis sp. suggest a correlation between genetic and geographic distance, at the broadest geographical scale. This is indicative of ‘Isolation by Distance’, the accumulation of local genetic differences under geographically restricted dispersal, where more distant populations are assumed to be more divergent than adjacent populations (Slatkin 1993, Wright 1943). While these two models (SHM and IBD) can be difficult to differentiate (sites in different reaches will typically be more geographically distant than sites in the same reach), the analysed data can shed some light on the more likely explanation.

Under long term migration-drift equilibrium and dispersal distances less than the total study area, ‘Isolation by Distance’ will be simultaneously observed at large and small scales (Chenoweth and Hughes 2003, Crow and Aoki 1984). Also, after a migration- drift equilibrium disturbing event, ‘isolation by distance’ will return to small scales first, with the historical colonisation signal persisting at the larger scales (Chenoweth and Hughes 2003, Crow and Aoki 1984). Here, the reverse pattern is observed, with a significant correlation between genetic and geographic distance at the large scale, but not the small scale (excluding Ambassis sp. in the Georgina catchment using microsatellites, which is considered later). This is unlikely under ‘Isolation by Distance’ suggesting that the Stream Hierarchy Model is a more appropriate model to describe the observed variation at the broader geographic scale.

Restricted gene flow among catchments in the Lake Eyre Basin suggests two things. First, that Lake Eyre does not facilitate extensive gene flow for these species, even during high flow events where the Lake can fill and support freshwater fish species (Kotwicki 1986, Ruello 1976). . Second, an inhospitable Lake Eyre is not the only cause of genetic structure among catchments as evidence for restricted gene flow was also detected between the Diamantina and Georgina catchments, which coalesce before they reach the saline environments of Lake Eyre. This suggests that, even if Lake Eyre was hospitable to N. erebi, gene flow still may not occur among catchments in the Lake Eyre Basin owing to the isolating nature of catchment boundaries in this system. These results mirror those from the only other fish species studied across the Cooper, Diamantina and Georgina catchments in the Lake Eyre Basin. In a study of

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Nematalosa erebi (bony bream) significant genetic structure was detected among catchments in the Lake Eyre Basin (Masci 2005), despite evidence for this species surviving in Lake Eyre during floods (Ruello 1976).

Genetic divergence among catchments was detected for all genetic markers in N. hyrtlii, however FCT values were much greater for the locus Aat (0.47) compared to other nuclear loci (0.03-0.21). Allozyme loci are susceptible to selective forces as they are protein encoding genes. Therefore, this disparity may owe to selective differences between catchments. The Aat locus (aspartate aminotransferase) has been identified in numerous studies as being affected by selection in gastropod species (Armbruster 2001, Johannesson, et al. 1995, Panova and Johannesson 2004, Tatarenkov and Johannesson 1994). In these studies, different Aat alleles dominated different microhabitats (e.g., open/exposed vs. moist/shady, upper shore vs. lower shore, splash zone vs. surf zone). It is clear that the abnormally high divergence among catchments for Aat could be explained by selection. However, more specific research would be required to identify the selective mechanisms which would generate such patterns.

While low FST values are indicative of high levels of contemporary gene flow, this conclusion assumes that populations are in migration-drift equilibrium (Wright 1943, 1951). Migration-drift equilibrium is commonly accompanied by a pattern of ‘isolation by distance’ (Crow and Aoki 1984). ‘Isolation by distance’ was tested using mantel tests, which failed to identify a significant correlation between genetic distance and geographic distance within catchments (excluding Ambassis sp. in the Georgina catchment using microsatellites). This suggests that populations within catchments may not be in migration-drift equilibrium and the conclusion that populations are experiencing high levels of gene flow may be flawed. However, the as populations of Ambassis sp. were in equilibrium in the Georgina catchment it is likely that populations are in migration-drift equilibrium. Therefore, FST values for Ambassis sp. in the Georgina catchment should be reflective of the gene flow that is occurring among populations.

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3.3.3 Comparison of gene flow and genetic structure in N. hyrtlii and Ambassis sp. It was hypothesised that, owing to different spawning and behavioural strategies, N. hyrtlii would exhibit weaker genetic structure than Ambassis sp.. Overall, populations of N. hyrtlii did exhibit lower levels of genetic structure among populations within and among catchments. This suggests that N. hyrtlii is a more proficient disperser than Ambassis sp., capable of exploiting widespread hydrological connectivity during flood events. As previously mentioned, this difference in genetic structure in each species is likely to result from different spawning and behavioural strategies.

Research on N. hyrtlii reproduction suggests that spawning in this species occurs during rises in water levels, coinciding with flood events (Orr and Milward 1984). Orr and Milward (1984) suggest that this is an ideal strategy in a unpredictable aquatic environment, with higher water levels providing the best environment for growth and survival of young fish. In the dryland catchments of western Queensland, with low catchment gradients and extreme flow events, this strategy will also produce ideal conditions for widespread dispersal as larval densities will be highest during the period of highest hydrological connectivity. This will lead to high gene flow, suppressing genetic drift and homogenising gene frequencies.

While little is known about the reproductive strategies of Ambassis sp., it was suggested that spawning in Ambassis sp. did not coincide with flood events. This was hypothesised as fish floodplain production research on Cooper Creek found very low densities of Ambassis sp. adults, juveniles and larvae on the floodplain, compared to that observed in waterholes (Balcombe, et al. 2007). If so, high larval and juvenile densities would not coincide with high hydrological connectivity during floods. This would generate low gene flow among populations in separate waterholes, leading to divergence via genetic drift and mutation.

This hypothesis appears to be corroborated by the data; however other biological possibilities may explain the results. For example, if spawning did coincide with flood events, low levels of gene flow may still occur if adults/juveniles/larvae did not leave the refugial waterhole. If this was the case, gene flow would not be high, and allele frequencies in populations would still diverge. Therefore, to understand the biological causes of restricted gene flow among populations of Ambassis sp. within catchments, 74

more physiological, ecological and behavioural research needs to be conducted on this species.

3.3.4 Historical patterns of gene flow among catchments For N. hyrtlii, significant genetic structure among catchments suggests a lack of contemporary gene flow among catchments. However, because mtDNA data is particularly susceptible to divergence (owing to a smaller Ne than nuclear DNA, Birky, et al. 1989), and will reach reciprocal monophyly faster than nuclear DNA markers (Avise 1994, 2000), shared mtDNA haplotypes in each catchment suggests recent divergence. This was reflected by pairwise point estimates of time since population divergence ranging from 1.7 to 22.9 thousand years before present.

In contrast, Ambassis sp., shared no mtDNA haplotypes among catchments, suggesting a more ancient divergence among populations than that observed for N. hyrtlii. As expected, this was reflected by pairwise point estimates of time since population divergence ranging between 139.7 and 910.3 thousands of years before present.

For N. hyrtlii, the shortest divergence time was between the Diamantina and Georgina catchments (1,700 years ago). This is not surprising as the Diamantina and Georgina catchments share a confluence before reaching Lake Eyre, suggesting that they may share a more recent historical connectivity that catchments either side of Lake Eyre. However, a very different pattern was observed in Ambassis sp., with haplotypes from the Georgina catchment forming a clade, 4.2% divergent from the Diamantina/Cooper clade. This suggests that populations in the Diamantina catchment, despite being more closely connected to the Georgina than the Cooper, were more recently connected to the Cooper catchment than the Georgina catchment. This large divergence between the Georgina catchment and the Diamantina/Cooper catchments has not been observed in any other species studied across the Lake Eyre Basin (Carini and Hughes 2004, 2006, Hughes, et al. 2004, Masci 2005).

It is possible that the 4.2% divergent clades in the Lake Eyre Basin represent separate species of Ambassis. However, the biological species concept could not be tested as both clades were not sampled sympatrically. Therefore, reproductive isolation could

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not be tested using nuclear markers, a common method for identifying reproductive isolation (e.g., Lavery and Shaklee 1991). It is possible that both clades do exist sympatrically, particularly in the Diamantina catchment which was not sampled as successfully as the Cooper and Georgina catchments. Therefore, further analysis of this genetic break would entail a more thorough sampling effort of the Diamantina catchment. This would also aid in identifying possible barriers to gene flow which may have caused isolation between the Georgina catchment and the rest of the Lake Eyre basin.

While it is possible that divergence has occurred between the ‘Georgina’ and ‘Cooper/Diamantina’ clades in situ, it is also possible that each clade actually represents an independent colonisation event from northern catchments. Under this scenario, populations of Ambassis sp. (which are also found across northern Australia, Allen, et al. 2002), may have been isolated either side of a barrier to gene flow in northern Australia, generating a deep divergence between two clades. Then, two independent founder events may have occurred, from one clade into the Georgina catchment, and from a different clade into the Diamantina and Cooper catchments. This hypothesis could be tested by sampling across the entire known range of Ambassis sp., in an attempt to find evidence for either clade in northern Australia. Unfortunately, Ambassis sp. was not sampled in the Gulf of Carpentaria Basin.

The hypothesis that the Lake Eyre Basin was colonised by two divergent lineages during independent events is also supported by the geomorphological history of the Gulf of Carpentaria - Lake Eyre Basin divide. Evidence exists for river capture events from the Cooper catchment (Lake Eyre Basin) into the Flinders catchment (Gulf of Carpentaria Basin) and from the Flinders catchment into the Diamantina catchment (Lake Eyre Basin, Coventry, et al. 1985, Twidale 1966, Unmack 2001). As the Flinders catchment runs adjacent to the headwaters of the Cooper and Diamantina catchments, historical evidence for river capture events across this boundary lends support to the theory that these catchments in the Lake Eyre Basin were colonised from the same catchment (the Flinders) and as such, the same clade. Then, with time these populations in the Cooper and Diamantina catchments have diverged in isolation, accounting for the two base pair divergence in the mtDNA network. In contrast, the headwaters of the Georgina catchment are adjacent to the headwaters of

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the Gregory River (a subcatchment of the Nicholson catchment). If populations of Ambassis sp. from the Gregory River were genetically divergent from populations in the Flinders catchment, and a colonisation event occurred from the Gregory River into the Georgina Catchment, the pattern observed in this study could result (Figure 3.7). Unfortunately, no geological records could be found that would support two separate colonisation events into the Lake Eyre Basin.

Figure 3.7: The hypothesised colonisation history of Ambassis sp. explaining the observed data in the Lake Eyre Basin. The large arrows represent possible colonisation events from the northern catchments into the Lake Eyre Basin. The dotted line in the northern catchments represents a preposed genetic break in the distribution of Ambassis sp. in the northern catchments.

Also, two very different patterns of genetic structure for Ambassis sp. in the Cooper and Georgina catchments lends support to this hypothesised colonisation history. Detection of isolation by distance in the Georgina catchment, and not in the Cooper catchment, suggests that it has been present in the Georgina catchment long enough for migration - drift equilibrium to occur. As this pattern was not detected in the Cooper catchment, it suggests that perhaps migration - drift equilibrium has not yet

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been reached in this system. This suggests that Ambassis sp. has been present in the Georgina catchment longer than it has been present in the Cooper catchment.

Furthermore, in the Cooper catchment, control region mtDNA data suggests that the range of Ambassis sp. has been slowly spreading across the Cooper catchment. This implies a recent colonisation into the Cooper catchment, which could not have occurred from the Georgina catchment, at it is genetically divergent. Therefore, it is feasible that Ambassis sp. colonised the Lake Eyre Basin via two (or more) independent events, from already genetically divergent populations.

3.3.5 The Role of Lake Eyre in connecting catchments in the Lake Eyre Basin It was hypothesised that divergence among populations on either side of Lake Eyre will reflect the drying of the Lake, approximately 60 thousand years ago (Magee and Miller 1998). For N. hyrtlii, point estimates for time since population divergence either side of Lake Eyre were smaller than the predicted time since the Lake became inhospitable (17.6 and 22.9 thousand years). For Ambassis sp., the point estimate was older than the predicted 60 thousand years (139 and 813 thousand years). Therefore, superficially, it would appear that the drying of Lake Eyre does not explain the divergence among populations in catchments either side of Lake Eyre.

However, for N. hyrtlii, the upper 95% credibility interval of the posterior distribution of ‘time since divergence’ did overlap with the estimated time that Lake Eyre dried (211 and 164 thousand years ago). Therefore, we cannot reject the hypothesis that the drying of Lake Eyre caused the subsequent divergence of populations of N. hyrtlii either side of Lake Eyre. In contrast, for Ambassis sp., the 95% credibility intervals for time since population divergence still predate 60 thousand years ago (lower 95% CI Diamantina vs. Cooper ≈ 72,000 years, Cooper vs. Georgina ≈ 193,000 years, Diamantina vs. Georgina ≈ 187,000 years).

If it is assumed that at least two independent colonisation events into the Lake Eyre Basin have occurred (as previously discussed), we can ignore the large divergence between the Cooper/Diamantina and Georgina catchments, as it is possible that this vicariant event did not occur in situ. This leaves the divergence between the Cooper

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and Diamantina catchments (72, 000 - 466,000 years ago), which is also older than that predicted by the drying of Lake Eyre (60,000 years ago). This may be due to populations either side of Lake Eyre diverging before Lake Eyre became saline, or an inappropriate mutation rate.

While the molecular clock is a popular tool in molecular ecology, there is still debate about its appropriateness for dating vicariant events and whether ‘clock-like’ evolution will be consistently observed in all datasets. For example, the molecular clock holds that, as long as the mutation rate remains constant, populations will diverge in a clock like manner (Kimura 1983). However, recently Ho et al. (2005) has proposed that the rate of divergence does not remain constant. Instead, the divergence rate is “time dependent”, starting with a very fast rate soon after populations split and slowing down over time. While it has been well understood that divergence rates are very high at the pedigree level (Ballard and Dean 2001, Parsons, et al. 1997), Ho et al. (2005) suggests that faster rates may not slow down until approximately 2 million years after an isolation event (also see Burridge, et al. 2006, Waters, et al. 2007).

This suggests that using calibration rates based upon speciation events may not be appropriate for dating intraspecific vicariance. For example, in this study, a divergence rate of 3.6% per site per million years was used, which is based upon divergence between different species of Snook (Percoidei: Centropomidae) isolated by the Isthmus of Panama approximately 3.5 million years ago (Donaldson and Wilson 1999). However, estimates of sequence divergence for the control region, based upon intraspecific divergence, have been recorded as high as 23.3% per million years (Burridge, et al. 2006). If a faster rate is more appropriate for the time scale considered in this study, the estimate of time since population divergence between catchments would be more recent. For Ambassis sp., this may align estimates of time since population divergence with the estimated time that Lake Eyre dried. Despite this, recent reanalysis of the data set found in Ho et al. (Ho, et al. 2005) finds no evidence for the ‘time-dependent molecular clock’ (Emerson 2007).

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3.4 Conclusions Populations of N. hyrtlii and Ambassis sp. exhibit very different contemporary patterns of gene flow and evolutionary histories. It is likely that spawning behaviour and historical colonisation patterns have shaped these contrasting patterns of genetic structure in each species.

For N. hyrtlii, flood induced spawning facilitates widespread dispersal and gene flow, homogenising gene frequencies. In contrast, Ambassis sp. is probably a dry season spawner, reducing the opportunity for larvae to disperse to new waterholes during flood events. This restricts gene flow, causing genetic structure among populations. Both species have also undergone different evolutionary histories. N. hyrtlii appears to have had a simple evolutionary history in the Lake Eyre Basin, with genetic structure among catchments consistent with channel architecture and estimates of divergence overlapping with predicted dates for the drying of Lake Eyre. Alternatively, for Ambassis sp., multiple colonisation events from the Gulf of Carpentaria Basin, coupled with different population processes in each catchment has generated a complicated pattern requiring further sampling to resolve fully.

This result highlights the importance of organismal biology in determining levels of gene flow among populations in riverine species and aiding in the conservation of freshwater taxa. From a management point of view, restricted gene flow in Ambassis sp. provides a challenge for restoration projects in the Lake Eyre Basin, as individuals may not be able to easily disperse to restored habitat (Hughes 2007). Also, the presence of divergent clades (which constitute separate Evolutionarily Significant Units, ESU’s, Moritz 1994), makes the translocation of individuals from one catchment to another a contentious issue (e.g., Hughes, et al. 2003)

These results for Ambassis sp. also have important implications for other closely related Ambassis taxa. Ambassis agassizii is a closely related glassfish from the Murray-Darling Basin and coastal catchments on the eastern seaboard (Allen and Burgess 1990, Allen, et al. 2002). A. agassizii is of special interest as it has undergone recent declines in the Murray-Darling Basin, becoming extinct in South Australia and Victoria, and attracting conservation attention in New South Wales (Pusey, et al. 2004). If the results found for Ambassis sp. in this study apply to A. agassizii,

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management approaches for A. agassizii need to take into account poor dispersal ability and therefore, low recolonisation potential.

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4 Patterns of gene flow and phylogeography in two species of freshwater fish (Neosilurus hyrtlii and Ambassis macleayi) across the Gulf of Carpentaria Basin.

4.1 Introduction The Gulf of Carpentaria Basin drains the tropical savannah that lies between Cape York and north-east Arnhem land. This landscape is physically diverse, ranging from upland, igneous regions to alluvial, lowland plains (Perry, et al. 1964, Smart, et al. 1980). As a result, catchments in the Gulf of Carpentaria Basin are architecturally diverse. For example, the Leichhardt, Nicholson and Mitchell catchments all flow out of rocky, upland regions, generating deeply incised channels with gorges and waterfalls. Alternatively, the Flinders and Norman catchments drain the Carpentaria Plains generating typical lowland systems with low catchment gradients and broad alluvial floodplains.

The Gulf of Carpentaria Basin, like all of northern Australia, is characterised by strongly seasonal rainfall patterns, with hydrological inputs dominated by the summer monsoon (Hall 1984, Perry 1964, Perry, et al. 1964). In the dry season, catchments typically consist of disconnected waterholes, excluding the Gregory catchment (a sub- catchment of the Nicholson) which is perennially fed by freshwater springs (Unmack 2001). However, during the wet season, massive quantities of rain cause riverbanks to overflow, hydrologically connecting large geographic areas, particularly in lowland areas with floodplains. This hydrological connectivity may be exploited by freshwater fish species, generating high levels of gene flow among populations within catchments.

However, in a population genetic study of the freshwater fish Nematalosa erebi, across the Gulf of Carpentaria, evidence was found for restricted gene flow among populations within catchments (Masci 2005). This suggests that for some species, wide spread hydrological connectivity may not generate wide spread gene flow among populations within catchments.

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All catchments in the Gulf of Carpentaria Basin flow north into the Gulf of Carpentaria, a large marine environment bordered by Cape York Peninsula to the east, Papua New Guinea to the north and north-eastern Arnhem land to the west. While it is unlikely that this marine environment is a conduit for gene flow in freshwater fish, flood plumes and flooded coastal regions have been invoked to explain inter-basin dispersal in some freshwater fish species, in other catchments (e.g., Hurwood and Hughes 1998). Also, in areas where catchment gradients are low, large monsoons may generate hydrological connectivity between adjacent catchments across low-lying delta regions. Anecdotal satellite evidence suggests that during monsoons that coincide with tropical cyclones, hydrological connectivity does indeed occur across catchment boundaries (L. Lymburner, pers. comm).

Historically, during lower sea levels in the Quaternary, the Gulf of Carpentaria was isolated from the surrounding marine environment, generating the Lake of Carpentaria, a large freshwater lake covering more than 29,000 km2 (Chivas, et al. 2001, Torgersen, et al. 1983, Torgersen, et al. 1988). Approximately 10,000 years ago (Chivas, et al. 2001), a final marine transgression occurred across the Arafura Sill, generating increasingly more saline conditions until a fully marine Gulf of Carpentaria remained, approximately 8 thousand years ago (Torgersen, et al. 1988). Before the final marine transgression 10 thousand years ago, the Lake of Carpentaria was close to full and surrounded by grassland, covering 600 x 300 km and reached depths of 15m (Chivas, et al. 2001). This lake may have provided hydrological connectivity among populations of freshwater fish in different catchments, potentially facilitating gene flow as recently as 10 thousand years ago.

In a study of the giant freshwater prawn, Macrobrachium rosenbergii, across northern Australia, evidence was found for the Lake of Carpentaria facilitating historical gene flow among catchments in the Gulf of Carpentaria Basin (de Bruyn, et al. 2004). Also, a study of Rainbowfish species, McGuigan (2000), found some evidence for the Lake of Carpentaria Basin facilitating dispersal of Rainbowfishes between Australia and Papua New Guinea. However, in the only other study of a freshwater fish species in the Gulf of Carpentaria Basin, Masci (2005) found no evidence for restricted gene flow among populations among catchments, possibly suggesting contemporary gene flow among catchments via flood plumes or hydrological connectivity across

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catchment boundaries. However, considering the aforementioned detection of restricted gene flow among populations within catchments, this result is unusual. Logically, restricted gene flow at the fine spatial scale should also correspond with restricted gene flow at the large spatial scale. Masci (2005) concluded that this was due to a lack of migration-drift equilibrium at the larger spatial scale, possible due to recent increases in population size.

Neosilurus hyrtlii is a widespread fish species with a range that extends across northern Australia (Allen, et al. 2002, Llewellyn and Pollard 1980, Pusey, et al. 2004, Unmack 1995). Breeding experiments and observations suggest that in eastern and northern Australia, this species spawns prior to the summer wet season (Orr and Milward 1984, Pusey, et al. 2004). In the seasonally predictable catchments of the Gulf of Carpentaria, this strategy is likely to generate a high abundance of larvae and juveniles during the periods of highest hydrological connectivity. Therefore, patterns of genetic structure in this species are likely to reflect the extent of hydrological connectivity during monsoonal events.

Alternatively, in Ambassis macleayi, a small glassfish found across the Northern Territory and northern Queensland, the vast majority of recruitment occurs in the dry season (Kennard 1995, Pusey, et al. 2004). This would decrease opportunities for gene flow as high larval and juvenile densities would not coincide with periods of high hydrological connectivity. In some closely related Ambassis, upstream migration has been observed in the late wet season, possibly in response to availability and quality of habitat (Bishop, et al. 1995). This suggests that A. macleayi may still disperse between populations within catchments, perhaps from floodplain habitats into refugial waterholes as conditions worsen.

This chapter aims to explore the population genetic and phylogeographic structure of Neosilurus hyrtlii and Ambassis macleayi across catchments in the Gulf of Carpentaria Basin. By using three different molecular markers (control region mtDNA, microsatellites and allozymes) and a range of statistical methodologies, it is hoped that the patterns of gene flow and historical connectivity can be elucidated at large (among catchments) and small (within catchment) spatial scales.

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Using the aforementioned markers, this study predicts that; 1. Due to large flood events during the monsoon season, high levels of gene flow will exist among populations, within catchments. Therefore, patterns of genetic structuring within catchments will be weak in both fish species, indicative of contemporary gene flow. 2. While high levels of gene flow are expected to homogenise gene frequencies, generating weak genetic structure among populations within catchments, dry- season spawning is expected to generate stronger genetic structuring in A. macleayi compared to the wet season spawning of N. hyrtlii,. 3. As previous genetic research on fish and crustaceans has detected restricted gene flow among catchments, catchment boundaries are expected to isolate populations. Therefore, genetic divergence is expected among catchments, indicative of restricted contemporary gene flow. 4. Furthermore, divergence between populations in different catchments will correlate with the estimated marine incursion into the Lake of Carpentaria, approximately 10 thousand years ago.

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4.2 Results

4.2.1 Sampling Regime Samples of N. hyrtlii were taken from four catchments (Figure 4.1, Table 4.1); the Nicholson (n=10), the Leichhardt (n=25), the Norman (n=1) and the Mitchell (n=31). Unfortunately, for N. hyrtlii, despite extensive sampling effort, only one site per catchment yielded multiple individuals. This meant that, for N. hyrtlii, estimates of genetic structure within catchments were not possible. Sampling of A. macleayi was more successful with 140 individuals being taken from three catchments (Nicholson, n=72; Leichhardt, n=88; Norman, n=10).

Figure 4.1: Sampling sites for N. hyrtlii and A. macleayi, Gulf of Carpentaria Basin.

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Table 4.1: Sampling regime of N. hyrtlii and A. macleayi. in the Gulf of Carpentaria Basin. Neosilurus hyrtlii Ambassis macleayi mtDNA nuDNA mtDNA nuDNA Site sample sample sample sample Code size size size size Nicholson catchment Gregory Downs 1 GD1 - - 9 30 Adel's Grove AG - - 8 12 Kingfisher Camp KFC 10 10 10 30 Leichhardt catchment East Leichhardt Dam LD 1 - 8 30 Moondarra Dam MO - - 10 12 Lake Julius JU 2 - - - Augustus Downs AU - - 8 8 Floraville Station FL - - 9 30 Nardoo Station NA - - 8 8 Mellish Park Station MP 23 23 - - Norman catchment Lake Belmore LB 1 - - - Iffley Station IF - - 7 10 Mitchell catchment Elizebeth Creek EL 30 30 - - Lynd Junction LJ 1 - - -

4.2.2 Neosilurus hyrtlii Using SEQUENCHER, a 393bp fragment of the mtDNA control region was aligned and used for further analyses. Screening of the control region revealed 22 unique haplotypes from 67 individuals (Figure 4.2, Table 4.2). Accordingly, mtDNA haplotype and nucleotide diversity has high, ranging from 0.77 to 0.84 and 0.00414 to 0.00944 respectively (Table 4.3). Evolutionary relationships between haplotypes revealed a complicated network with many missing haplotypes (Figure 4.2, Table 4.2). Neutrality tests suggested that no populations deviated significantly from neutral expectations (Table 4.4).

Using MICRO-CHECKER (van Oosterhout, et al. 2004), one population at one locus was identified as having possible null alleles (Appendix E). Four tests for deviations from HWE were found to be significant (2 microsatellite, 2 allozyme, Table 4.3), with all of these tests showing heterozygote deficiency. Microsatellite gene diversity ranged from 0.84 to 0.91, and allozyme gene diversity ranged from 0.09 to 0.36. Using the program BOTTLENECK, Microsatellite data showed significant evidence for population bottlenecks in all catchments assuming the infinite alleles mutation model (Table 4.5).

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However, all populations showed an approximately L-shaped allele frequency distribution.

Figure 4.2: N. hyrtlii, haplotype network showing control region mtDNA variation and geographic distribution of haplotypes. Each circle represents a unique haplotype with its evolutionary relationship to other haplotypes represented by lines. Pies on the map represent the geographical distribution of haplotypes. White haplotypes are singletons.

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Table 4.2: N. hyrtlii, distribution of haplotypes across sites in the Gulf of Carpentaria Basin. Haplotypes Catchment Population A B C D E F G H I J K L M N O P Q R S T U V Nicholson KFC 2 1 1 1 4 1 LD 1 Leichhardt JU 1 1 MP 2 2 1 5 10 2 1 Norman LB 1 EL 1 1 3 11 1 1 1 1 3 1 5 1 Mitchell LJ 1

Table 4.3: N. hyrtlii, genetic diversity indices. For nuclear markers, the expected heterozygosity is displayed. Significant deviations from HWE are marked with an asterix (α=0.05). For heterozygote excess or deficiency, see text. control region microsatellites allozymes gene nucleotide gene Expected Heterozygosity gene Expected Heterozygosity Catchment Site diversity diversity diversity N22a NH16 NH11 NH19 NH12 diversity AAT MDH-1 MDH-2 Nicholson KFC 0.84 0.00944 0.91 0.93 0.84 0.92* 0.92 0.94 0.36 0.61* 0.00 0.48 Leichhardt MP 0.77 0.00414 0.84 0.86 0.73 0.87 0.80 0.92 0.11 0.20* 0.00 0.12 Mitchell EL 0.84 0.00932 0.90 0.92* 0.85 0.87 0.90 0.94 0.09 0.29 0.00 0.00

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Table 4.4: N. hyrtlii, Neutrality tests. None significantly deviate from neutral expectations.

Catchment Tajima's D Fu & Li's D* Fu & Li's F* Fu's FS R2 Nicholson 0.73 0.22 0.39 -0.31 0.18 Leichhardt -0.45 -0.66 -0.7 -1.56 0.13 Mitchell 0.12 -1.39 -1.08 -2.26 0.12

Table 4.5: N. hyrtlii, results of BOTTLENECK. For each population with more than 5 individuals, for each mutation model) the ration of loci with a heterozygosity deficiency to the number of loci with a heterozygosity excess is shown. The probability that there is an excess of loci displaying a heterozygosity excess is displayed. The allele frequency distribution is also described qualitatively. (IAM = infinite alleles mode, TPM = two phase model, SMM = stepwise mutation model). Nicholson Leichhardt Mitchell n 10 23 30 H /H 1/4 1/4 0/5 IAM def exc p (Hexc) 0.047 0.031 0.016

TPM (95% Hdef/Hexc 1/4 4/1 3/2 SMM) p (Hexc) 0.313 0.922 0.922 H /H 1/4 4/1 4/1 SMM def exc p (Hexc) 0.313 0.969 0.984 Allele frequency Approximately L- Approximately L- Approximately L- distribution shaped shaped shaped

For N. hyrtlii, there were four levels in the nested haplotype network (Figure 4.3). Nesting of the haplotype network proceeded as if haplotypes from the Lake Eyre Basin were included (grey clade, for results see Chapter 3). For this chapter, only those clades in the Gulf of Carpentaria Basin are discussed. In the Gulf of Carpentaria Basin, NCA revealed two clades for which there was a significant deviation from random expectations of geographic distribution (Table 4.6). Using the inference key found in (Templeton 2004), Clades 2-2 and 3-1 both revealed evidence for ‘Restricted Gene Flow, with Isolation by Distance’.

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Figure 4.3: N. hyrtlii, nesting of the control region mtDNA haplotype network. The grey clade is made up of representative samples from the Lake Eyre Basin (Chapter 3).

Table 4.6: N. hyrtlii, nested clade analysis results with phylogeographic inference. Significantly small or large DC and DN values are indicated with ‘S’ and ‘L’ respectively. Nested Clade Clade DC DN Conclusion Clade 2-2 1-3 (I) 305.30L 305.95L RGF w/ IBD 2 χ p-value = 1-4 (T) 0.00S 166.90S (Inference Key; 0.000 1,2,3,4) I-T 305.30L 139.05L 2-1 (T) 108.07S 252.18 Clade 3-1 2-2 (I) 258.43 278.69 RGF w/ IBD χ2 p-value = (Inference Key; 0.002 2-3 (I) 130.33 244.99 1,2,3,4) I-T 113.76L 16.88

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Mantel tests and AMOVA were not conducted as the number of sites sampled was not considered sufficient. Global FST values were significant among sites across the Gulf of Carpentaria (Φ=0.25, p=<0.01, microsatellite FST=0.05, p=<0.01, allozyme

FST=0.13, p=<0.01, for pairwise FST tables see Appendix F). Using control region mtDNA and a mutation rate of 7.1 x 10-6 mutations per locus per year, pairwise point estimates of t, the time since population divergence among catchments in the Gulf of Carpentaria Basin, ranged between 25.8 to 97.9 thousand years ago (Table 4.7). The posterior distributions of t were all largely overlapping (Figure 4.4).

Table 4.7: N. hyrtlii, results of IM analysis using control region mtDNA, showing the maximum likelihood point estimate of t, the time since population divergence and the 95% credibility intervals. MLE t 0.025 CI 0.975 CI Mitchell vs. Leichhardt 56,408 25,986 149,789 Mitchell vs. Nicholson 25,775 2,394 71,972 Leichhardt vs. Nicholson 97,887 41,549 254,225

0.008 0.014 Mitchell vs Leichhardt 0.007 0.012 Mitchell vs Nicholson 0.006 Leichhardt vs Nicholson 0.01 0.005 0.008 0.004 0.006 0.003 Nicholson) 0.004 0.002

Leichhardt, Nicholson) Leichhardt, 0.001 0.002 residence time (Mitchell vs vs (Mitchell time residence residence time (Leichhardt vs vs (Leichhardt time residence 0 0 0 100000 200000 300000 years since population divergence (years)

Figure 4.4: N. hyrtlii, posterior distribution of t, the time since population divergence using control region mtDNA.

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4.2.3 Ambassis macleayi Using SEQUENCHER, a 396 bp fragment of the mtDNA control region was aligned and used for further analysis. Screening of the control region revealed only four haplotypes, each restricted to a single drainage (Table 4.8, Figure 4.5). As each site contained only a single haplotype, haplotype and nucleotide diversity was zero for every population. The evolutionary relationship between haplotypes suggested that three of the four haplotypes were all one base pair divergent from an extinct or non- sampled haplotype. The haplotype from one site (KFC), on the Nicholson River was five base pairs divergent from the closest, extant haplotype. As all drainages were fixed for a single haplotype, neutrality tests were not performed.

MICRO-CHECKER (van Oosterhout, et al. 2004) identified some evidence for null alleles (Appendix E). Tests for HWE revealed four tests where populations significantly deviated from HWE at a locus (Table 4.9). All significant tests suffered from heterozygote deficiency, except for population IF at locus AMB22, which suffered from heterozygote excess. Microsatellite gene diversity ranged from 0.22 to 0.58. Screening of allozyme loci revealed no loci that were polymorphic. As such, allozymes were not analysed for A. macleayi. Using BOTTLENECK (van Oosterhout, et al. 2004), microsatellite data revealed no quantitative evidence for a recent population bottleneck (Table 4.10). However, one population, FL, showed a shifted allele distribution mode.

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Figure 4.5: A. macleayi, haplotype network showing control region mtDNA variation and geographic distribution of haplotypes. Each circle represents a unique haplotype with its evolutionary relationship to other haplotypes represented by lines. Pies on the map represent the geographical distribution of haplotypes.

Table 4.8: A. macleayi, distribution of control region mtDNA haplotypes across sites in the Gulf of Carpentaria Basin. Haplotypes Catchment Population A B C D GD1 9 Nicholson AG 8 KFC 10 LD 8 MO 10 Leichhardt AU 8 FL 9 NA 8 Norman IF 7

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Table 4.9: A. macleayi, genetic diversity indices. For nuclear markers, the expected heterozygosity is displayed. Significant deviations from HWE are marked with an asterix (α=0.05). For heterozygote excess of deficiency, see text. control region microsatellites gene nucleotide gene Expected Heterozygosity Catchment Site diversity diversity diversity AMB14 AMB16 AMB21 AMB22 GD1 0.00 0.00 0.52 0.34 0.10 0.85 0.79 Nicholson AG 0.00 0.00 0.57 0.74 0.00 0.73* 0.80 KFC 0.00 0.00 0.42 0.27 0.00 0.70 0.71* LD 0.00 0.00 0.22 0.51* 0.00 0.13 0.25 MO 0.00 0.00 0.29 0.52 0.00 0.08 0.54 Leichhardt AU 0.00 0.00 0.46 0.66 0.33 0.23 0.61 FL 0.00 0.00 0.53 0.74 0.07 0.62 0.71 NA 0.00 0.00 0.58 0.68 0.46 0.59 0.56 Norman IF 0.00 0.00 0.57 0.27 0.36 0.78 0.85*

Table 4.10: N. hyrtlii, results of BOTTLENECK. For each population with more than 5 individuals, for each mutation model) the ration of loci with a heterozygosity deficiency to the number of loci with a heterozygosity excess is shown. The probability that there is an excess of loci displaying a heterozygosity excess is displayed. The allele frequency distribution is also described qualitatively. (IAM = infinite alleles mode, TPM = two phase model, SMM = stepwise mutation model). Pop LD FL MO GD1 KFC AG IF n 30 30 12 30 30 12 10 H /H 2/1 1/3 1/2 2/2 1/2 0/3 1/3 IAM def exc p (Hexc) 0.875 0.094 0.188 0.563 0.813 0.063 0.438

TPM (95% Hdef/Hexc 2/1 2/2 1/2 4/0 2/1 2/1 4/0 SMM) p (Hexc) 0.125 0.438 0.188 1.000 0.875 0.875 1.000 H /H 2/1 2/2 1/2 4/0 2/1 2/1 4/0 SMM def exc p (Hexc) 0.938 0.563 0.813 1.000 0.938 0.938 1.000 Allele frequency Approx. L- Shifted Approx. L- Approx. L- Approx. L- Approx. L- Approx. L- distribution shaped mode shaped shaped shaped shaped shaped

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Analysis of Molecular Variance was carried out on two separate data sets. The first included the site KFC in the Nicholson catchment, which was divergent at the mtDNA control region (Table 4.11 and 4.12). Then the same analyses were conducted excluding that site (Table 4.13 and 4.14). AMOVA revealed significant genetic structure among catchments, with both mtDNA and microsatellite loci (mtDNA

ΦCT=0.51, microsatellite FCT=0.14). Both markers also detected genetic structure among populations, within catchments (mtDNA ΦSC=1.00, microsatellite FSC=0.19). However this pattern was not detected using mtDNA once site KFC was removed from the data set (mtDNA ΦSC=0.00, microsatellite FSC=0.11). Even after removing KFC from the data set, significant genetic structure was detected among populations, within catchments using microsatellites (FST=0.11). For pairwise FST tables, see Appendix F.

Table 4.11: A. macleayi, Analysis of Molecular Variance results including all sites across the Gulf of Carpentaria. Fixation indices that significantly deviate from zero are indicated with an asterix (*α=0.05, **α=0.01). control region microsatellites Source % % Φ F Variation ST Variation ST

Among all catchments 51.14 0.51* 14.08 0.14**

Among populations, 48.86 1.00** 16.01 0.19** within catchments

Within populations 0.00 1.00** 69.90 0.30**

Among populations, within the Leichhardt - 0.13** catchment

Table 4.12: A. macleayi, locus by locus Analysis of Molecular Variance results including all sites across the Gulf of Carpentaria Basin. Fixation indices that significantly deviate from zero are indicated with an asterix (*α=0.05, **α=0.01). Among Within Among Among catchments populations, within populations populations catchments % % Locus F F % Variation F Variation CT Variation SC ST AMB14 -2.56 -0.03 30.06 0.29** 72.50 0.28** AMB16 -2.98 -0.03 17.26 0.17* 85.72 0.14** AMB21 27.44 0.27* 11.23 0.15** 61.34 0.39** AMB22 15.75 0.16* 9.03 0.11** 75.22 0.25**

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Table 4.13: A. macleayi, Analysis of Molecular Variance results excluding site KFC. Fixation indices that significantly deviate from zero are indicated with an asterix (*α=0.05, **α=0.01). control region microsatellites Source % % Φ F Variation ST Variation ST Among all catchments 100 1.00** 20.78 0.21** Among populations, 0.00 0.00 8.75 0.11** within catchments Within populations 0.00 1.00** 70.47 0.30**

Table 4.14: A. macleayi, locus by locus Analysis of Molecular Variance results excluding site KFC. Fixation indices that significantly deviate from zero are indicated with an asterix (*α=0.05, **α=0.01). Among Among catchments populations, within Within populations catchments % % % Locus F F F Variation CT Variation SC Variation ST AMB14 22.57 0.23* 7.89 0.10** 69.53 0.30** AMB16 -6.17 -0.06 18.55 0.17** 87.62 0.12** AMB21 24.63 0.25* 11.86 0.16** 63.51 0.36** AMB22 18.43 0.18 5.35 0.07** 76.22 0.24**

Mantel tests, correlating microsatellite genetic and geographic distance, were conducted among all sites, using stream and overland distance, and among sites within the Leichhardt catchment. A lack of genetic diversity at the control region did not allow this analysis to be conducted using mtDNA data. Only two significant correlations were revealed (Table 4.15). Using stream and overland distances, genetic and geographic distances correlated among all sites. The same pattern was not detected within the Leichhardt catchment.

Table 4.15: A. macleayi, Mantel tests correlating overland and stream geographic distance with genetic distances between populations. Significant correlations are indicated with an asterix (α=0.05). microsatellites Gulf of Carpentaria 0.65* Stream Distances Leichhardt catchment 0.52 Gulf of Carpentaria 0.61* Overland Distance Leichhardt catchment 0.58

Nesting of the control region haplotype network identified three nesting levels (Figure 4.6). Using v. 2.5 (Posada, et al. 2000) and the inference key found in Templeton (2004), ‘Allopatric Fragmentation’ was detected at clade 1-1, 2-1 and the Total Cladogram (Table 4.16).

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Figure 4.6: A. macleayi, nesting of the control region mtDNA haplotype network.

Table 4.16: A. macleayi, nested clade analysis results with phylogeographic inference. Significantly small or large DC and DN values are indicated with ‘S’ and ‘L’ respectively. Nested Clade Clade DC DN Conclusion 1-1 A 116.43 115.58 Allopatric 2 χ p-value = B 39.00S 95.60 Fragmentation 0.000 (Inference Key; 1, 19) I-T N/A N/A 1-1 109.87S 110.09S 2-1 S L Allopatric χ2 p-value = 1-2 0.00 157.55 Fragmentation 0.000 - (Inference Key; 1, 19) I-T 109.87S 47.46L S Total Cladogram 2-1 116.02 112.77 Allopatric 2 χ p-value = 2-2 0.00S 182.71L Fragmentation 0.000 (Inference Key; 1, 19) I-T N/A N/A

Using control region mtDNA, point estimates of t, the time since population divergence, were calculated for each pairwise comparison between catchments. However, the Nicholson catchment was divided into two subcatchments, the Nicholson River proper (represented by site KFC) and the Gregory River (represented by sites AG and GD1). This generated six pairwise combinations (Table 4.17). Two ‘groups’ of estimates were revealed, those that were catchments compared with the Nicholson River which ranged from 266.5 to 378.8 thousand years ago, and the rest which ranged from 109.4 to 120.6 thousand years ago. Graphical representation of the posterior distributions revealed largely overlapping distributions (Figure 4.7).

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Table 4.17: A. macleayi results of IM analysis showing the maximum likelihood point estimate of t, the time since population divergence, and the 95% credibility intervals. MLE t 0.025 CI 0.975 CI Nicholson vs. Gregory 361,953 70,146 934,343 Nicholson vs. Leichhardt 378,788 70,146 934,343 Nicholson vs. Norman 266,554 53,311 765,993 Gregory vs. Leichhardt 109,428 30,864 485,410 Gregory vs. Norman 115,039 30,864 491,021 Leichhardt vs. Norman 120,651 30,864 491,021

0.025 0.014 Gregory vs Norman Gregory vs Leichhardt 0.012 0.02 Leichhardt vs Norman Nicholson vs Gregory 0.01 Nicholson vs Leichhardt 0.015 Nicholson vs Norman 0.008

0.006 0.01 Leichhardt) 0.004 0.005 0.002 Leichhardt vs Norman and residence time (Gregory vs Norman, residence time (Nicholson vs rest) 0 0 0 200000 400000 600000 800000 1000000 time since population divergence (years)

Figure 4.7: A. macleayi, posterior distribution of t, the time since population divergence.

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4.3 Discussion

4.3.1 Genetic diversity For nuclear loci, some populations at some loci significantly deviated from HWE. Four tests were found to deviate from HWE for both N. hyrtlii and A. macleayi, out of 24 and 36 tests, respectively. Significant tests were not consistent across loci or populations, and MICRO-CHECKER (van Oosterhout, et al. 2004) showed no evidence for null alleles or mis-scored data. Therefore, it is unlikely that deviations from HWE are the product of selection or population specific processes (e.g., the Wahlund effect). As the frequency of significant HWE tests was low (N. hyrtlii = 0.16, A. macleayi = 0.11), it is assumed that deviations from HWE are a result of sampling and random fluctuations in allele frequencies and were not caused by population processes (e.g., non-random mating) or non-informative loci.

For N. hyrtlii, control region variation was high (haplotype diversity = 0.77-0.84). This is typical of the control region, which is non-coding and therefore free of selective constraint (Ballard and Kreitman 1995). This generates a mutation rate much faster than that seen across the rest of the mtDNA genome, producing high genetic variation (e.g., Hughes, et al. 1999b, Ikeda, et al. 2003, Krieg, et al. 2000, Stepien and Faber 1998, Takagi, et al. 2006). Conversely, for A. macleayi, control region variation was extremely low, with each site fixed for a single haplotype. In addition, every site in the Leichhardt catchment and both sites in the Gregory River (sub-catchment of the Nicholson), were fixed for single haplotypes.

Low genetic diversity can be caused by selection or population processes such as historical bottlenecks and low Ne (Nei, et al. 1975). Population bottlenecks will typically affect loci across the genome, and can therefore be verified by investigating different loci (Piry, et al. 1999). Microsatellite loci for A. macleayi revealed no evidence for a population bottleneck, suggesting that a population bottleneck does not explain the low variation at the control region of the mtDNA genome. However, the program BOTTLENECK (Piry, et al. 1999), which was used to detect population bottlenecks in the microsatellite data set, suffers from low power when few loci are used (Cornuet and Luikart 1996, Luikart and Cornuet 1998). Also, this methodology

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is only useful for detecting population bottlenecks in the recent past as opposed to historical bottlenecks. Therefore, while the microsatellite data may not suggest a population bottleneck, which could be used to explain low mtDNA diversity, the low power of BOTTLENECK (Piry, et al. 1999) with four loci, and differing temporal scales for each marker, makes it difficult to confirm or reject a bottleneck in A. macleayi.

Neutrality tests, which can be used to identify selection in the mtDNA data set, were not conducted due to a lack of diversity within sites. Therefore, it is impossible to identify if selection can explain low diversity in the mtDNA data set. However, as the control region of the mtDNA genome is non-coding, it is assumed to be free of selective constraint (Ballard and Kreitman 1995, Ballard and Dean 2001). Therefore, assuming selection on a linked gene has not caused genetic hitchhiking (Maynard Smith and Haigh 1974), selection is an unlikely cause for low diversity in this data set. However, as no better explanation can be invoked to explain low diversity in this species, selection must be considered a possibility, along with population bottlenecks and low Ne.

4.3.2 Gene flow and genetic structure For A. macleayi, significant genetic structure was detected among sites within catchments using microsatellites. Moreover, this pattern persisted after removing the divergent population in the Nicholson River (KFC) from the analysis. Within the

Leichhardt catchment, using microsatellites, the estimate of FST was equal to 0.13, suggesting restricted gene flow among sites within this catchment. Furthermore, this pattern was not being caused by a minority of sites; instead, most sites in the

Leichhardt catchment were significantly different from each other (see pairwise FST tables, Appendix B). This indicates that, despite monsoonal events causing widespread flooding and hydrological connectivity across this catchment, populations of A. macleayi are genetically discrete and contemporary gene flow rarely occurs among them.

Breeding behaviour in A. macleayi is a likely explanation for low levels of gene flow among populations within the Leichhardt catchment. Studies of A. macleayi suggest that a majority of recruitment occurs in the dry season (Kennard 1995, Pusey, et al. 2004). At this time, hydrological inputs are minimal and populations would be

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residing in disconnected refugia, unable to move between adjacent populations. This finding contradicts the suggestion that A. macleayi migrates upstream during the early wet season in response to changes in habitat availability (Bishop, et al. 1995), as migrations of this nature would be expected to homogenise gene frequencies. Instead, populations of A. macleayi remain in refugial waterholes during the wet season.

As restricted gene flow was detected within the Leichhardt catchment using microsatellites, a pattern of ‘Isolation by Distance’ (Wright 1943) might be expected, particularly in the Leichhardt catchment where the sampling design resembled a linear transect, spanning the length of the main river channel. However, Mantel tests, correlating genetic and geographic distance within the Leichhardt catchment, did not support this hypothesis. This suggests that populations are not even experiencing low levels of gene flow among adjacent populations, which is expected to generate a correlation between genetic and geographic distance (Wright 1943).

For A. macleayi, if the divergent site KFC is ignored, genetic structure across the Gulf of Carpentaria Basin can be described by the Stream Hierarchy Model (SHM, Meffe and Vrijenhoek 1988), whereby less genetic divergence is observed among sites within a branch/river/catchment than among them. This suggests that no contemporary gene flow is occurring among basins, despite large flow events during the monsoon season which can hydrologically connect different catchments (L. Lymburner, pers. comm.). However, considering that restricted gene flow was detected among populations within the Leichhardt catchment, it is not surprising that restricted gene flow was also detected among catchments.

Unfortunately, for N. hyrtlii, the Stream Hierarchy Model (Meffe and Vrijenhoek 1988) could not be tested due to poor sampling success. However, significant structure was detected among catchments. This suggests that catchment boundaries and the marine environment are restricting gene flow among populations in different catchments.

4.3.3 Historical patterns of gene flow among catchments For N. hyrtlii, significant genetic structure among sites in different catchments suggests restricted contemporary gene flow among catchments. However, mtDNA

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variation suggests recent isolation owing to many shared haplotypes among catchments. This indicates that sufficient time has not yet elapsed for genetic drift to remove shared haplotypes (Avise, et al. 1987, Avise 2000). Also, the mtDNA network does not reveal a clear phylogeographic pattern with a few ancestral (internal) haplotypes shared between catchments and more recent (tip) haplotypes restricted to particular catchments (Avise 2000). Instead, haplotypes at either end of the network can be found in the same site, tip haplotypes can be found in different catchments and internal haplotypes are rare or missing from the network.

For N. hyrtlii, pairwise point estimates of time since population divergence between catchments were low, ranging between 25-97 thousand years ago. These estimates appear to be quite different, suggesting that perhaps different vicariant events may have generated the observed divergence among catchments. However, using control region mtDNA, the 95% credibility intervals of all the posterior distributions of t were overlapping. Therefore, a single isolation event explaining divergence among catchments in the Gulf of Carpentaria Basin cannot be rejected.

The drying of Lake Carpentaria is the most likely event that would have produced isolation among catchments, which has been invoked to explain similar divergences among catchments for the giant freshwater prawn, Macrobrachium rosenbergii (de Bruyn, et al. 2004). However, the estimated date that the Lake of Carpentaria became saline is more recent than the estimated times since population divergence for N. hyrtlii (approximately 10,000 years ago, Chivas, et al. 2001, Torgersen, et al. 1988). The only pairwise estimate of time since population divergence that does have 95% credibility intervals that overlap with this estimate is the Mitchell catchment vs. the Nicholson catchment (2-71 thousand years ago). However, if we assume that a single isolating event produced the observed divergences among catchments, one must conclude that isolation between catchments in the Gulf of Carpentaria predates estimates for the salinisation Gulf/Lake of Carpentaria.

This observed disparity may be explained in three ways. First, the observed mtDNA variation is not a representative sample from these catchments and has accordingly produced inaccurate estimates of time since population divergence. Second, population divergence did precede the estimate of the final marine transgression into

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the Lake of Carpentaria. This is possible if the Lake of Carpentaria was becoming saline long before the estimated time of the final marine transgression, or if isolation between populations had already begun in each catchment before 10 thousand years ago.

Finally, an inaccurate mutation rate may have generated this disparity. As previously mention (Chapter 3), recent research has suggested that mutation rates calibrated using vicariant events older than 2 million years, may be inappropriate for timing vicariant events younger than 2 million years (Burridge, et al. 2006, Ho, et al. 2005, Howell, et al. 2003, Waters, et al. 2007). If a faster mutation rate was more accurate for N. hyrtlii divergence in the Gulf of Carpentaria Basin, estimated divergence times would be more recent, perhaps supporting the hypothesis that the marine transgression into the Lake of Carpentaria caused isolation and subsequent divergence of populations in different catchments.

For A. macleayi, catchments in the Gulf of Carpentaria were fixed for a single unique haplotype, with two to five base pairs separating each haplotype. This immediately suggests an ‘old’ vicariant event, much older than that observed in N. hyrtlii where sharing of haplotypes persisted. Also, Nested Clade Analysis supported the conclusion of allopatric fragmentation for A. macleayi. Estimates of time since population divergence could be split into two groups, those compared to the divergent population in the Nicholson River, and the rest. Point estimates between the Nicholson River and the rest of the Gulf of Carpentaria basin ranged between 266-378 thousand years ago, much older than estimates for N. hyrtlii. Other point estimates ranged between 109- 120 thousand years ago. The 95% credibility intervals were all older than the predicted time that the Lake of Carpentaria became saline, approximately 10 thousand years ago. Therefore, populations of A. macleayi in each catchment apparently became isolated much earlier, most likely caused by a different historical vicariant event. It is possible that, as restricted gene flow among populations of A. macleayi was detected in the Leichhardt catchment, the Lake of Carpentaria may not have provided a conduit for gene flow, even when it was non-saline.

The presence of two distinct ‘groups’ of pairwise estimates for time since population divergence suggests that two different vicariant events have occurred, one explaining

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divergence between the Nicholson River and the other sampled catchments, and another explaining divergence between the Gregory River, Leichhardt catchment and the Norman catchment. However, for A. macleayi, using control region mtDNA, the 95% credibility intervals of the posterior distributions overlap. This suggests that a single vicariant event explaining the observed divergences among catchments cannot be rejected.

Interestingly, estimates of divergence among catchments for each species are quite different. While still overlapping using the 95% credibility intervals, point estimates for N. hyrtlii are consistently younger than that observed in A. macleayi. Therefore, as point estimates for population divergence are consistently older in A. macleayi compared to N. hyrtlii, and the posterior distributions of the time since population divergence are almost non-overlapping, it is likely that different vicariant events isolated catchments for each species. The dispersal ability of each species may explain how a vicariant event could cause one species to begin to diverge, while at the same time not impeding the dispersal of another species.

As originally hypothesised, N. hyrtlii is expected to be a good disperser. While this was not tested within catchments in the Gulf of Carpentaria, high levels of gene flow were detected in the Lake Eyre Basin (Chapter 3) and in the Cooper catchment in a previous study (Huey, et al. 2006). Alternatively, for A. macleayi, restricted gene flow was detected in the Leichhardt catchment, indicative of poor dispersal ability. Therefore, even if a vicariant event initially restricted the dispersal of A. macleayi, it may have not been sufficient to restrict the movement of N. hyrtlii, which is a much more proficient disperser. This may explain how divergence among catchments could have begun in A. macleayi, and not in N. hyrtlii.

For A. macleayi, deep divergence was observed among sub-catchments/rivers in the Nicholson catchment. Interestingly, this divergence among rivers within the Nicholson catchment is as old, or older than that observed among catchments. As patterns of genetic structure reflect historical rather than contemporary processes, the observed patterns of genetic divergence among populations in the Nicholson catchment may actually reflect historical drainage arrangements.

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Drainage rearrangements are frequently invoked to explain high levels of divergence among fish populations within catchments compared to among catchments (e.g., McGlashan and Hughes 2000, 2001, Poissant, et al. 2005, Waters and Wallis 2000). Drainage rearrangements can be caused by river capture, where one river ‘steals’ the headwaters of another via unequal rates of erosion or uplift (Bishop 1995, Waters and Wallis 2000). In the case of the Nicholson catchment, it is possible that the Nicholson and Gregory Rivers’ historically reached the Gulf/Lake of Carpentaria independently, and populations in each were diverging in isolation. Then, after populations in either catchment had diverged, drainage rearrangements caused the rivers to merge before reaching the Gulf of Carpentaria and restricted gene flow has caused each river to remain fixed for different haplotypes.

4.4 Conclusions Unfortunately, for N. hyrtlii, patterns of genetic structure within catchments were not tested owing to limited samples. However, if we assume that dispersal ability for N. hyrtlii in the Gulf of Carpentaria Basin is similar to that inferred from genetic structure in the Lake Eyre Basin (Chapter 3), it is apparent that N. hyrtlii is a more proficient disperser than A. macleayi. This was supported by estimates of divergence among catchments, which was uniformly older for A. macleayi compared to N. hyrtlii. This highlights the role of organismal biology in determining the levels of gene flow among populations, both within and among catchments.

At present, the Gulf of Carpentaria represents a relatively undeveloped, poorly studied water resource on the Australian continent (Hamilton and Gehrke 2005). However, this is likely to change as droughts and human population growth makes water security an important political issue. Consequently, the results from this research have important implications for conservation and management of riverine species in this area. While it may be logical to assume that widespread hydrological connectivity during the summer monsoon will allow species to recolonise disturbed habitats, this research suggests that some species, such as A. macleayi, may not exploit hydrological connectivity. Therefore, management approaches may need to include translocation strategies for species that do not disperse during flood events.

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However, translocation exercises need to include an understanding of the natural genetic variation to minimise the loss of unique genetic diversity and the hybridisation of divergent lineages (Cross 2000, Hughes, et al. 2003). For example, the observed fixed mtDNA and divergent nuDNA frequencies for A. macleayi in different catchments suggests that these populations represent separate Evolutionarily Significant Units (ESU’s, Moritz 1994). Therefore, they should be managed separately and not translocated between catchments. Overall the identification of these ESU’s highlights the need for more genetic surveys of this region to identify other genetically discrete populations.

Low genetic diversity in populations of A. macleayi poses another conservation management concern. It is generally understood that genetic diversity is essential to the long term viability of populations, owing to its importance in providing evolutionary potential and maintaining genetic fitness (Reed and Frankham 2003, Vrijenhoek 1998). For example, in a study of an endangered freshwater fish, the Sonoran topminnow, Poeciliopsis occidentalis, Quattro and Vrijenhoek (1989) found that survival, growth, early fecundity and developmental stability were all poorest in fish from a population with no electrophoretically detectable variation. Further investigation of A. macleayi may also reveal important correlations between life history traits and genetic diversity. This would also make carefully planned translocations a useful management strategy for maintaining population viability (e.g., Vrijenhoek, et al. 1985).

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5 The effect of landscape processes and historical drainage divisions upon gene flow and genetic diversity in Australian freshwater fish

5.1 Introduction Flow regime is an important landscape process affecting population dynamics in freshwater fish (Amoros and Bornette 2002, Puckridge, et al. 1998, Resh, et al. 1988). The most striking example of this is drought, reducing hydrological connectivity and leaving ‘refugia’ to sustain populations until connectivity is restored (Humphries and Baldwin 2003, Magoulick and Kobza 2003, Matthews and Marsh-Matthews 2003). If the drought is severe enough, it may lead to population bottlenecks, reducing genetic diversity and altering the evolutionary trajectory of a species (Douglas, et al. 2003). Floods are another important hydrological process (Resh, et al. 1988), typically producing connectivity across large geographic areas, particularly in floodplain dominated catchments (Balcombe, et al. 2007, Puckridge, et al. 1998). This hydrological connectivity can facilitate gene flow among otherwise isolated populations (Hanfling, et al. 2004, Huey, et al. 2006, Hughes and Hillyer 2006).

One strategy to survive in hydrologically variable systems (i.e., systems subjected to frequent and unpredictable droughts and floods) is to be able to quickly exploit connectivity events (Glover 1982). Therefore, some fish species found in such environs are expected to be good dispersers and experience complex demographic histories, dominated by extinction and colonization events, population expansions and bottlenecks (Matthews and Marsh-Matthews 2003). Over time, this is expected to generate low genetic diversity (owing to low population sizes and frequent bottlenecks), and low divergence among populations as individuals frequently disperse among populations, homogenising gene frequencies (Huey, et al. 2006, Hughes and Hillyer 2006).

Another major factor affecting gene flow, connectivity, and genetic diversity in freshwater fish is the physical nature of the riverine landscape (Hughes 2007, Robinson, et al. 2002, Ward, et al. 2002). While water may provide the medium through which fish can disperse, riverine architecture will affect the ability of water to reach isolated refugia, and will determine the persistence of high-flow events (Ward,

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et al. 2002). Typically, floodplains facilitate widespread hydrological connectivity, allowing high levels of gene flow in those species that can exploit them (e.g. Balcombe, et al. 2007, Huey, et al. 2006). Alternatively, deeply incised channels will be affected differently, not overflowing and flooding as easily, leading to isolation and subsequent divergence, particularly among headwaters (McGlashan and Hughes 2000, 2002).

The tropical and arid zone rivers of northern and central Australia provide an ideal system to investigate the impact of hydrology and riverine architecture upon the population dynamics of freshwater fish. Because monsoonal climatic systems dominate northern Australia (Perry 1964, Perry, et al. 1964), hydrological variability is seasonally predictable. Also, the rivers of northern Australia exhibit varied riverine architecture ranging from anastomosing, floodplain dominated systems to steep, dendritic systems (Perry, et al. 1964, Smart, et al. 1980). In contrast, the arid-zone, ‘dryland’ rivers of central Australia experience high hydrological variability, have extremely low catchment gradients, and are dominated by infrequently connected refugial waterholes (Knighton and Nanson 1994, 1997, 2002, Kotwicki 1986, Puckridge, et al. 2000).

Historical connectivity among these basins is not well studied. Some evidence exists for a river capture event, from the Lake Eyre Basin (Prairie Ck), into the Flinders River (Gulf of Carpentaria Basin) between 3.3 and 5.5mya (Coventry, et al. 1985). Also, the Selwyn Upwarp is believed to have rearranged the headwaters of the Flinders River, south, into the Diamantina River (Lake Eyre Basin, Twidale 1966). This event has not been accurately dated, but is believed to have occurred between 5 million and 10 thousand years ago (late Pleistocene) (Unmack 2001). Masci et al. (in review) used the molecular clock to estimate the time since population divergence for a freshwater fish species (Bony Bream, Nematalosa erebi) across this basin divide. For N. erebi, using the ATPase mtDNA gene, two vicariant events were identified (160 and 350 thousand years ago).

These contrasting basins provide an opportunity to study a single species (or closely related species) over a large geographic area, exhibiting different geological histories and climatic conditions. Therefore, processes arising from non-biotic sources (e.g.,

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riverine structure and hydrological variability) that affect gene flow and genetic diversity in freshwater fish species can be identified and explored.

5.1.1 Aims and Hypotheses Using N. hyrtlii (across both basins), Ambassis sp. (in the Lake Eyre Basin) and Ambassis macleayi (Gulf of Carpentaria Basin) as model taxa, this chapter aims to explore the relative roles of landscape processes upon gene flow and genetic diversity in Australian freshwater fish. It will also explore the historical patterns of gene flow across the Lake Eyre – Gulf of Carpentaria basin divide. Using three different molecular markers (mtDNA, microsatellites and allozyme electrophoresis), this study predicts that; 1. As drainage rearrangements between the Gulf of Carpentaria Basin and Lake Eyre Basin are likely to represent the most recent gene flow events between these basins, genetic divergence for N. hyrtlii between these basins will correlate with estimates of drainage rearrangements. These estimates are between 10 thousand and 5 million years ago. 2. More specifically, estimates of divergence among basins for N. hyrtlii will be similar to that detected by Masci et al. (Masci, et al. in review), approximately 160,000 and 350,000 years ago 3. As the landscape processes (geomorphology and hydrological inputs) in each basin are different, markedly different patterns of genetic structure and genetic diversity will be observed in each basin.

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5.2 Statistical methodology Large portions of the discussion in this chapter will make reference to statistical methods from previous chapters (Chapters 3 and 4). For specific outlines of analytical approaches see the relevant chapters.

For N. hyrtlii, Nested Clade Analysis (NCA, Templeton, et al. 1995, Templeton 1998, 2004) was used to identify if there was any geographic association between haplotypes or clades, and then to identify potential demographic processes that may explain these patterns. To do this, the mtDNA haplotype network was first nested 2 according to the rules in Templeton (2004). χ , DC and DN values were then calculated using GEODIS v2.5 (Posada, et al. 2000) and interpreted using the key found in Templeton (2004). This analysis includes the clades already discussed in chapters 3 and 4. However, clades that contained haplotypes from different basins were not discussed in those chapters as they were not relevant to phylogeographic patterns within each basin. Those clades will be shown and discussed in this chapter.

For N. hyrtlii, using all molecular markers, Analysis of Molecular Variance (AMOVA) was used to partition genetic variation among hierarchical levels; ‘among basins’, ‘among populations, within basins’ and ‘within populations’. AMOVA was calculated using ARLEQUIN v3.1 (Excoffier, et al. 2005). Fixation values were calculated for these hierarchical levels (ΦST for mtDNA data, θ for nuclear data, Excoffier, et al. 1992, Weir and Cockerham 1984).

For N. hyrtlii, The program IM (Isolation-Migration, Hey and Nielsen 2004) was used to estimate t, the time since population divergence between basins. To simplify the model it was assumed that no migration had occurred among populations since they split. Prior distributions for the parameters were specific to each pairwise comparison and multiple runs of each comparison were made to ensure that the best estimate of t was obtained. For each comparison 10,000,000 steps were used in the chain, with a burn in of 100,000 steps. Once the posterior distribution of t was obtained, the bin that yielded the highest residence time was used as the point estimate of t. An estimate of credibility was obtained by taking the 95% intervals from the posterior distribution.

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To convert the estimate of mutational time since population divergence (t) to years (t), the equation used in Hey & Nielsen (2004) was used, t = tμ (t=t/μ), where μ is the number of mutations per locus per generation. A divergence rate of 3.6% per million generations was used (Donaldson and Wilson 1999), based upon Snook (Centropomus) assumed to be separated by the Isthmus of Panama 3.5 million years ago. Research suggests that N. hyrtlii and members of the genus Ambassis become sexually mature in one year (Pusey, et al. 2004), allowing estimates of generations since population divergence to be converted to years since population divergence.

In an attempt to identify the impact of hydrology upon genetic diversity in N. hyrtlii, flow variability and genetic variation was compared. A 15 year period (1972-1987) was identified from a gauge station in each catchment and used to calculate the coefficient of variation of the mean annual flow for each catchment. The gauge stations chosen were all on the main river of the catchment and were thus deemed to be representative of the whole catchment and comparable across catchments (gauge station numbers; 919011A, 913004A, 912107A, 003101A, 002101B, 001203A, Table 5.1, Figure 5.1). Using ARLEQUIN v.3.1 (Excoffier, et al. 2005), the genetic diversity of each catchment was calculated by pooling sites within catchments and calculating mtDNA gene diversity, mtDNA nucleotide diversity, microsatellite gene diversity and allozyme gene diversity. Using the statistical package SPSS v11.0, correlations between genetic diversity and flow variability were statistically tested using Spearman’s rank correlation.

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Table 5.1: Details of gauge stations used to estimate flow variability. Catchment Gauge Station Station Name Latitude Longitude Mitchell 919011A Gamboola 16° 32.20" S 143° 40.60" E Leichhardt 913004A Kajabbi 20° 04.50" S 139° 56.40" E Nicholson 912107A Connolly’s Hole 17° 53.10" S 138° 15.90" E Cooper 003101A Currareva 25° 19.60" S 142° 43.90" E Diamantina 002101B Birdsville 25° 54.20" S 139° 22.50" E Georgina 001203A Roxborough Downs 22° 30.80" S 138° 50.50" E

Figure 5.1: Locations of gauge stations used to estimate flow variability.

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5.3 Results

5.3.1 Sampling Regime Samples were taken from 34 sites, across 7 catchments, in two basins (Figure 5.2, Table 5.2). Note that for the Lake Eyre Basin, Ambassis samples are Ambassis sp., and for the Gulf of Carpentaria Basin, Ambassis samples are A. macleayi.

Figure 5.2: Sampling regime of N. hyrtlii, Ambassis sp. and A. macleayi in the Lake Eyre and Gulf of Carpentaria Basins.

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Table 5.2: Sampling regime of N. hyrtlii, Ambassis sp. (Lake Eyre Basin) and A. macleayi (Gulf of Carpentaria Basin). Neosilurus hyrtlii Ambassis spp. mtDNA nuDNA mtDNA nuDNA Site Site sample sample sample sample Code size size size size Georgina catchment Boulia BO 19 19 10 30 Wirrilyerna WY 6 6 10 13 Rocklands RL 31 31 10 30 Glenorminston 1 GO1 - - 10 30 Glenorminston 2 GO2 5 5 10 29 Bulla Bulla WH BB 7 7 - - Eyre Creek EC - - 10 15 Diamantina catchment Birdsville 3 B3 20 20 - - Monkira 1 MK1 15 15 - - Monkira 3 MK3 16 16 - - Diamantina Lakes 1 DL1 6 6 - - Diamantina Lakes 2 DL2 6 6 9 9 Cooper catchment Murken MR 10 30 8 8 Glen Murken GM 10 30 10 30 Homestead HS 12 30 7 7 One Mile OM 12 25 10 23 Top TP 10 30 5 5 Waterloo WL 10 20 7 7 Tanbar TB 5 5 9 30 Yalangah YG 10 10 10 20 Nicholson catchment Gregory Downs 1 GD1 - - 9 30 Adel's Grove AG - - 8 12 Kingfisher Camp KFC 10 10 10 30 Leichhardt catchment East Leichhardt Dam LD 1 - 8 30 Moondarra Dam MO - - 10 12 Lake Julius JU 1 - - - Augustus Downs AU - - 8 8 Floraville Station FL - - 9 30 Nardoo Station NA - - 8 8 Mellish Park Station MP 23 23 - - Norman catchment Lake Belmore LB 1 - - - Iffley Station IF - - 7 10 Mitchell catchment Elizebeth Creek EL 30 30 - - Lynd Junction LJ 1 - - -

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5.3.2 Neosilurus hyrtlii Using SEQUENCHER, a 393 bp fragment of the control region was aligned and used for further analysis. Screening of the mtDNA variation revealed 30 haplotypes (Figure 5.3, Table 5.3, GenBank accession numbers EU099846-EU099871, and AK648969- AK648972). Eight of these haplotypes were found exclusively in the Lake Eyre Basin, with 22 found exclusively in the Gulf of Carpentaria Basin.

Figure 5.3: N. hyrtlii, haplotype network. Haplotypes are divided into pies representing the catchments where those haplotypes were found. Pies do not represent relative frequencies.

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Table 5.3: N. hyrtlii, distribution of haplotypes across sites. Haplotypes Basin Catchment Site A B C D E F G H I J K L M N O P Q R S T U V W X Y Z AA AB AC AD

Mitchell EL 1 1 3 11 1 1 1 1 3 1 5 1 LJ 1 MP 2 2 1 5 10 2 1 Leichhardt LD 1 JU 1 1 Nicholson KFC 2 1 1 1 4 1

Gulf of Carpentaria Norman LB 1 MR 9 1 GM 10 HS 10 2 OM 9 1 2 Cooper TP 9 1 WL 9 1 TB 5 YG 8 1 1 B3 16 4 MK1 12 3

Lake Eyre Diamantina MK3 10 5 1 DL1 3 3 DL2 5 1 BO 16 1 1 1 WY 4 2 Georgina RL 27 3 1 GO2 5 BB 6 1 TOTAL 1 1 2 2 1 5 1 14 15 1 1 1 2 1 1 1 5 1 1 9 1 1 173 2 5 1 20 1 5 3

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Nesting of the mtDNA haplotype network created four nesting levels (Figure 5.4). Analysis of the nested clades revealed five clades that rejected the null hypothesis of no geographic association of haplotype/clades (Table 5.4). These clades revealed ‘restricted gene flow with isolation by distance’, except Clade 2-5, which could not be resolved due to inadequate sampling, and the Total Cladogram, which reached an inconclusive outcome as tip and interior clades were not assigned.

Figure 5.4: N. hyrtlii, nesting of the haplotype network.

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Table 5.4: N. hyrtlii, nested clade analysis results with phylogeographic inference. Significantly small or large DC and DN values are indicated with ‘S’ and ‘L’ respectively. Nested Clade Clade DC DN Conclusion Clade 2-2 1-3 305.30L 305.95L RGF w/ IBD (Inference χ2 p-value = S S 1-4 0.00 166.90 Key; 1,2,3,4) 0.000 I-T 305.30L 139.05L L Clade 2-5 1-13 0.00 869.04 Inadequate sampling χ2 p-value = 1-14 209.01S 209.09S (Inference Key; 1, 19, 0.003 I-T -209.01L 686.95L 20) 2-1 108.07S 252.18 Clade 3-1 2-2 258.43 278.69 RGF w/ IBD (Inference χ2 p-value = Key; 1,2,3,4) 0.002 2-3 130.33 244.99 I-T 113.76L 16.88 L Clade 3-2 2-4 227.38 762.97 RGF w/ IBD (Inference χ2 p-value = S S 2-5 210.36 213.19 Key; 1,2,3,4) 0.000 I-T 17.02 549.79L S L Total Cladogram 3-1 254.40 497.03 Inconclusive outcome χ2 p-value = S S 3-2 237.75 294.15 (Inference Key; 1, 2) 0.000 I-T N/A N/A

AMOVA found significant genetic structure at all levels of the hierarchy (Table 5.5). Control region variation was predominantly partitioned ‘among basins’ (85%). In contrast, much less variation was partitioned ‘among basins’ for microsatellites and allozymes (9% and 45%, respectively). For all markers, more structure was detected

‘among basins’, than ‘among populations, within basins’ (mtDNA ΦCT=0.85,

ΦSC=0.14; microsatellites FCT=0.09, FSC=0.08; allozymes FCT=0.45, FSC=0.30). Locus by locus AMOVA revealed some variation in the amount of structure detected at each hierarchical level for each locus (Table 5.6).

Table 5.5: N. hyrtlii, Analysis of Molecular Variance results. Fixation indices that significantly deviate from zero are indicated with an asterix (* α=0.05, ** α=0.01). Control region Microsatellites Allozymes Source % % % Φ F F Variation ST Variation ST Variation ST

Among Basins 84.77 0.85** 9.09 0.09** 45.01 0.45** Among Populations 2.16 0.14** 6.88 0.08** 16.24 0.30** within Basins Within 13.07 0.87** 84.03 0.16** 38.75 0.61** Populations

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Table 5.6: N. hyrtlii, locus by locus Analysis of Molecular Variance. Fixation indices that significantly deviate from zero are indicated with an asterix (* α=0.05, ** α=0.01). Among populations, Among Basins Within Populations within basins

% variation FCT % variation FSC % variation FST N22a 3.40 0.03 9.02 0.09** 87.58 0.12** NH16 23.20 0.23** 9.50 0.12** 67.30 0.33** NH11 18.68 0.19** 8.16 0.10** 73.17 0.27** NH19 2.24 0.02* 4.08 0.04** 93.69 0.06** NH12 0.36 0.00 4.17 0.04** 95.47 0.05** AAT 48.55 0.49** 17.03 0.33** 34.42 0.66** MDH-1 0.74 0.01 0.70 0.01 98.56 0.01 MDH-2 16.60 0.17 16.74 0.20** 66.67 0.33**

Figure 5.5 graphically compares the global ΦST (mtDNA) and FST (nuDNA) values within each basin using each genetic marker (see Chapters 3 and 4 for more detail). As can be seen there is no consistent pattern across markers. The mtDNA data suggests much stronger genetic divergence among populations in the Gulf of Carpentaria basin compared to the Lake Eyre Basin. Conversely, microsatellite and allozyme loci indicate the opposite pattern.

0.5 0.45 0.4 0.35 0.3 0.25 0.2

Fixation value Fixation 0.15 0.1 0.05 0 mtDNA microsatellites allozymes Genetic marker

Figure 5.5: N. hyrtlii, graph comparing fixation values for each genetic marker between the Lake Eyre Basin populations (white) and the Gulf of Carpentaria Basin populations (black).

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Using a mutation rate of 7.2 x 10-6, the time since population divergence between basins was estimated at 193.7 thousand years ago (Table 5.7, Figure 5.6). The 95% credibility intervals ranged from 95 to 563 thousand years ago.

Table 5.7: N. hyrtlii, results of IM analysis showing the maximum likelihood point estimate of t, the time since population divergence. The 95% credibility intervals are also shown. MLE t (years) 0.025 CI 0.0975 CI Gulf of Carpentaria vs. Lake Eyre Basin 193,750 95,139 563,194

0.006

0.005

0.004

0.003

0.002 residence time

0.001

0 0 100000 200000 300000 400000 500000 600000 700000 time since population divergence

Figure 5.6: N. hyrtlii, posterior distribution of t, the time since population divergence.

To identify the role of hydrological variability upon genetic diversity in the two basins, the CV of mean annual flow was correlated with four estimates of genetic diversity; mtDNA haplotype diversity, mtDNA nucleotide diversity, microsatellite gene diversity and allozyme gene diversity. Using a Spearman’s Rank Correlation, there were significantly negative correlations for mtDNA haplotype diversity, mtDNA nucleotide diversity and microsatellite gene diversity with the CV of mean annual flow (p = 0.005, Table 5.8, Figure 5.7). The correlation between allozyme gene diversity and CV of mean annual flow did not yield a significant result (p=0.827).

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Table 5.8: Correlations comparing estimates of flow variation and genetic diversity. Variate 1 Variate 2 Rho p-value CV Mean Annual Flow mtDNA haplotype diversity -0.94 0.005 mtDNA nucleotide diversity -0.94 0.005 microsatellite gene diversity -0.94 0.005 allozyme gene diversity 0.12 0.827

1 0.012

0.9 0.01 0.8

0.7 0.008 0.6

0.5 0.006

diversity 0.4 0.004 0.3 mtDNA nuceotide diversity nuceotide mtDNA 0.2

mtDNA and microsatellite genetic genetic and microsatellite mtDNA 0.002 0.1

0 0 1 1.2 1.4 1.6 1.8 CV mean annual flow

Figure 5.7: Scatter plot correlating estimates of flow variability and genetic diversity. Each sampled catchment is a replicate. Microsatellite gene diversity is represented by green, mtDNA haplotype diversity is represented by blue and mtDNA nucleotide diversity is represented by red.

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5.3.3 Ambassis spp. The following figures are summaries of data found in Chapters 3 and 4. Figure 5.8, compares the fixation values for mtDNA and microsatellite loci between the Lake Eyre and Gulf of Carpentaria Basins. It is evident from this figure that similar levels of genetic structure among populations exist in each basin. Figure 5.9, breaks this genetic structure down into the three catchments that were well sampled (Cooper, Georgina and Leichhardt catchments) and displays the genetic structure among populations, within catchments. The Cooper and Georgina catchments are found in the Lake Eyre Basin and the Leichhardt catchment is found in the Gulf of Carpentaria Basin. Genetic structure among populations, within catchments was much weaker in the Lake Eyre Basin, than in the Gulf of Carpentaria Basin. Also, there appears to be no difference in genetic diversity between the basins (Figure 5.10).

1.20

1.00

0.80

0.60

0.40 Fixation value Fixation

0.20

0.00 mtDNA microsatellites Genetic marker

Figure 5.8: Ambassis spp., graph comparing fixation values for each genetic marker between the Lake Eyre Basin populations (white) and the Gulf of Carpentaria Basin populations (black). For microsatellite loci, only those loci used in both species were used to compare between basins (See Chapters 3 and 4).

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0.14

0.12

0.1

0.08

0.06

Fixation values 0.04

0.02

0 Cooper (LEB) Georgina (LEB) Leichhardt (GoCB) Catchments

Figure 5.9: Ambassis spp., graph comparing fixation values among populations within catchments for microsatellites. Only those microsatellite loci used in both species were used to compare between catchments (See Chapters 3 and 4).

0.6 y 0.5

0.4

0.3

0.2

0.1 Microsatellite diversti gene 0 Basin

Figure 5.10: Ambassis spp., graph comparing average microsatellite gene diversity between basins (Lake Eyre Basin = White, Gulf of Carpentaria Basin = Black). Only those microsatellite loci used in both species were used to compare between basins (See Chapters 3 and 4). Bars represent standard error.

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5.4 Discussion

5.4.1 Historical patterns of gene flow among basins

NCPA identified three clades that significantly deviated from random expectations and included haplotypes from both basins. Clades 2-5 and the total cladogram both revealed inconclusive outcomes. However, clade 3-2 suggested that restricted gene flow with isolation by distance explained the observed distribution of haplotypes. The haplotypes in this clade included samples from all catchments in the Lake Eyre Basin, and the Mitchell, Leichhardt and Nicholson catchments in the Gulf of Carpentaria

Basin. This result concurs with the significant FCT values using all markers, suggesting restricted gene flow among catchments.

Using all genetic markers, AMOVA results indicate that gene flow among populations of N. hyrtlii between basins is restricted. However, the FCT value for the microsatellite dataset (0.09) was much lower than that observed for mtDNA (0.85) and the allozymes (0.45). This disparity is likely to owe to three processes. Firstly, mtDNA has a fourfold smaller effective population size than nuclear DNA as it is matrilinearly inherited and haploid (Birky, et al. 1989). Therefore, as genetic drift affects smaller populations more than larger populations, mtDNA datasets are expected to exhibit greater divergence than nuclear datasets. Secondly, homoplasy, where two alleles are identical in state but not by inheritance, is common in microsatellites (Estoup, et al. 1995, Estoup and Cornuet 1999, Estoup, et al. 2002). Homoplasy typically suppresses genetic divergence among populations estimated by FST. Finally, the higher mutation rate of microsatellite DNA means that there is more total variation, thus generating smaller FST values.

Using the program IM (Hey and Nielsen 2004), populations of N. hyrtlii in the Lake Eyre and Gulf of Carpentaria Basins were apparently isolated approximately 200,000 years ago. However, it is plausible that the true value lies between 100ka and 550ka (late Pleistocene) as the 95% credibility intervals cover this range. This estimate overlaps with geomorphological research that suggests drainage rearrangements

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caused river capture events between the Lake Eyre and Gulf of Carpentaria Basins between 5.5mya and 10ka (Coventry, et al. 1985, Twidale 1966, Unmack 2001).

However, it is important to note that the estimates of these geomorphological events have large ranges (the Selwyn Upwarp event, 4.99 million years and the Flinders Debunking event, 2.2 million years). Also, the population genetic estimate of time since population divergence has large credibility intervals (450ka). This makes the rejection of any hypothesised isolating event difficult, as there is a high likelihood that the geomorphological and genetic estimates will overlap. Therefore, while these genetic and geomorphological estimates do overlap, this is not conclusive evidence that the Selwyn Upwarp, 5 million to 10 thousand years ago (Twidale 1966), or the Flinders River debunking, 5.5 to 3.3mya (Coventry, et al. 1985), did facilitate gene flow between populations in either basin. Instead, a multitude of other geomorphological or hydrological events, which have not yet been documented, may have generated the observed divergence.

Nevertheless, the estimate of time since population divergence for N. hyrtlii also overlaps with the only other fish species to be studied across this drainage divide. For Nematalosa erebi, two isolation events were detected, dated at approximately 160 thousand and 350 thousand years ago (Masci, et al. in review). This suggests that perhaps an event that caused vicariance in N. erebi, also generated the divergence in N. hyrtlii.

While the Lake Eyre - Gulf of Carpentaria drainage divide has not been studied extensively using population genetic techniques, substantial research has been focused upon the Lake Eyre - Murray Darling Basin drainage division, found south of the Lake Eyre Basin (Carini and Hughes 2004, Cook, et al. 2002, Huey, et al. 2006, Hughes and Hillyer 2003, Hughes, et al. 2004, Hughes and Hillyer 2006).

Estimates of population divergence between the Lake Eyre and Murray Darling Basins, for freshwater fish, vary between the late Pleistocene (40, 000 years, N. hyrtlii, Huey, et al. 2006) and early Pleistocene (1.5 million years, Retropinna semoni, Hughes and Hillyer 2006). For N. erebi, the only species previously studied across the Lake Eyre - Gulf of Carpentaria divide, the estimate for population divergence

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between the Lake Eyre - Murray Darling Basin divide was approximately 150,000 years ago (Hughes and Hillyer 2006), much more than that observed for N. hyrtlii. Therefore, while it is unlikely that the same vicariant event isolated populations of N. hyrtlii and N. erebi either side of the Lake Eyre - Murray Darling Basin divide, the same conclusion does not apply across the Lake Eyre - Gulf of Carpentaria Basin divide.

5.4.2 Effects of landscape processes upon patterns of gene flow among populations Unfortunately, for N. hyrtlii, poor sampling in the Gulf of Carpentaria Basin renders direct comparisons of ‘within catchment’ genetic structure between the two basins impractical. However, for the Ambassis species, adequate sampling occurred within catchments to permit comparisons. Comparing genetic structure among populations within catchments in each basin, so that only microsatellite loci used in both species are compared, it can be seen that FST values are an order of magnitude greater in the Gulf of Carpentaria Basin, compared to the Lake Eyre Basin. This suggests that, despite the large hydrological inputs during the summer monsoon, less gene flow is occurring among populations within catchments in the Gulf of Carpentaria Basin, than observed in the Lake Eyre Basin. This is also despite a smaller geographic area sampled in the Gulf of Carpentaria Basin (280.93 km between most distant sites, within catchments) compared to the Lake Eyre Basin (688.74 km between most distant sites, within catchments).

However, as each basin is represented by different species, different patterns in each basin may be the product of biological differences between species, not landscape processes. For example, studies of A. macleayi (the Gulf of Carpentaria Basin species) suggest that it is a dry season spawner (Kennard 1995, Pusey, et al. 2004). Alternatively, the spawning behaviour of Ambassis sp. (the Lake Eyre Basin species) is not well understood and a great diversity of breeding strategies exist within the genus (Bishop, et al. 2001, Kennard 1995, Pusey, et al. 2004). If Ambassis sp. was a year round or flood-induced spawner this would generate weak genetic structure, whereas the dry season spawning of A. macleayi would generate stronger genetic structure.

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However, in a study of a freshwater fish (N. erebi) in the Lake Eyre and Gulf of Carpentaria Basins, mtDNA variation revealed significant structure ‘among sites, within catchments’ in the Gulf of Carpentaria Basin, which was not detected ‘among sites, within catchments’ in the Lake Eyre Basin (Masci 2005). This suggests that landscape processes may explain the observed differences in genetic structure within catchments, in each basin for Ambassis sp. and A. macleayi.

For Ambassis spp., different historical processes will have generated the observed patterns of divergence between catchments in each basin. However, a comparable pattern was observed in each basin. For Ambassis sp., in the Lake Eyre Basin, two divergent clades were detected, differing by 4.2% at the control region. This pattern was unique in the Lake Eyre Basin and has not been detected in other species studied in the area (Carini and Hughes 2004, 2006, Huey, et al. 2006, Hughes and Hillyer 2003, Hughes, et al. 2004, Hughes and Hillyer 2006). Likewise, for A. macleayi, in the Gulf of Carpentaria Basin, a single site was sampled that was 5 bp (approximately 1%) divergent from the nearest haplotype. This pattern was not observed in N. hyrtlii in this study, or in previous studies on other taxa (de Bruyn, et al. 2004, Masci, et al. in review).

It was concluded that the divergence observed in Ambassis sp. in the Lake Eyre Basin did not occur in situ, but represented independent colonisation events from divergent clades in the Gulf of Carpentaria Basin (see Chapter 3). If this is the case, it is possible that the divergences observed in Ambassis sp. in the Lake Eyre Basin and in A. macleayi in the Gulf of Carpentaria Basin were caused by the same vicariant event in the Gulf of Carpentaria Basin. The estimated times for divergence between the clades in each species are different but have overlapping 95% credibility intervals making this conclusion plausible (Ambassis sp. ≈187,000-1,557,000 years, A. macleayi ≈ 53,000-934,000 years).

For N. hyrtlii, historical patterns of connectivity between pairs of catchments in each basin were not concordant. For N. hyrtlii, divergence times ranged from 1.7 to 22.8 thousand years ago in the Gulf of Carpentaria Basin and 25.7 to 97.8 thousand years ago in the Lake Eyre Basin. This difference highlights the different historical processes that likely gave rise to divergence among catchments, such as the drying of

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Lake Eyre and the marine transgression into the Lake of Carpentaria (Chivas, et al. 2001, Magee and Miller 1998, Torgersen, et al. 1988).

5.4.3 Effects of landscape processes upon patterns of genetic diversity For N. hyrtlii, control region mtDNA diversity confirmed a clear difference in diversity between the Gulf of Carpentaria Basin and the Lake Eyre Basin. The much higher genetic diversity in the Gulf of Carpentaria Basin compared to the Lake Eyre Basin was mirrored by microsatellite loci, but not allozyme loci. This is most likely due to the lower mutation rate in allozymes compared to microsatellites and control region mtDNA (Estoup, et al. 1998, Queller, et al. 1993, Sunnucks 2000), generating very low diversity in allozymes over the entire study area.

The much higher diversity in the Gulf of Carpentaria Basin compared to the Lake Eyre Basin for mtDNA and microsatellite data may be explained by two different processes; a recent founder event from the Gulf of Carpentaria Basin into the Lake Eyre Basin, or different effective population sizes in each basin.

Founder events, where a small number of individuals colonise a new region, typically generate population bottlenecks, reducing genetic diversity (Boileau, et al. 1992, Schwaegerle and Schaal 1979). If a colonization event occurred from the Gulf of Carpentaria Basin into the Lake Eyre Basin, followed by a population and range expansion throughout the entire Lake Eyre Basin, low diversity estimates would be observed. As the Lake Eyre Basin lies at the southern end of the natural distribution of N. hyrtlii, it is possible that N. hyrtlii migrated south from the Gulf of Carpentaria Basin, a region of high plotosid species diversity (Allen, et al. 2002, Pusey, et al. 2004). Castric & Bernatchez (2003) found similar patterns of reduced diversity at the periphery of the natural range of the anadromous Brook Charr (Salvelinus fontinalis). This was attributed to recent range expansions into the new region, reducing genetic diversity.

However, a founder event into a new region followed by a population expansion will leave a signature upon population genetic data, which can be tested using neutrality tests (Fu 1997, Peck and Congdon 2004, Ramos-Onsins and Rozas 2002, Tajima

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1989a, b). For N. hyrtlii, all five neutrality tests revealed no evidence supporting a recent population expansion or non-neutral evolution within catchments in the Lake Eyre Basin (Chapter 3). Also, a recent founder event and subsequent range expansion across the Lake Eyre Basin will generate low divergence across the Gulf of Carpentaria - Lake Eyre Basin boundary, and among catchments within the Lake Eyre Basin. Estimates suggest that the Gulf of Carpentaria and Lake Eyre Basins diverged approximately 200,000 years ago and that populations in the Lake Eyre Basin have been separated for at least 12,000 years (Chapter 3). This divergence in gene frequencies is unlikely to have occurred if a founder event and large range expansion have recently occurred across the Lake Eyre Basin.

In a single, isolated population, there should be a positive relationship between genetic diversity and effective population size (Crow and Kimura 1970). Therefore, low genetic diversity may be caused by low effective population size (Ne). Small population sizes have been invoked to explain low genetic diversity in some populations of the freshwater fish, the European Bullhead (Hanfling and Brandl 1998, Hanfling, et al. 2002, Knaepkens, et al. 2004). Using microsatellite and allozyme variation, patch size (spatial extent of the population, analogous to population size) positively correlated with genetic diversity, suggesting that differences in genetic diversity could be explained by contemporary population sizes, not historical bottlenecks or founder events (Hanfling and Brandl 1998, Hanfling, et al. 2002).

The boom-bust cycle has often been invoked for explaining population dynamics in the dryland rivers of western Queensland (Arthington, et al. 2005, Balcombe, et al. 2005, Balcombe, et al. 2007, Kingsford, et al. 1999), with isolated populations being sustained by refugial waterholes for long periods before exploiting floodplain resources during infrequent and unpredictable flood events (Balcombe, et al. 2005). These floods lead to huge, but short lived ‘booms’ in fish densities, with floodwaters soon receding and returning to pre-flood conditions (‘bust’). This hydrologically dependent cycle has the potential to dramatically reduce Ne, through repeated bottlenecks, local extinctions and recolonisation events. This pattern is quite different from that experienced by populations in the Gulf of Carpentaria Basin, which have seasonally predictable flow regimes enabling long term population size stability resulting in higher genetic diversity.

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The ‘hydrologically dependant effective population size hypothesis’ is supported by significantly negative correlations between genetic diversity and flow variability. For both microsatellites and mtDNA, higher genetic diversity was observed in those catchments that had more stable flow regimes compared to those catchments that had high flow variability. Therefore, the greater the flow variability, the more often a population will be disturbed, causing local extinctions and bottlenecks, thus reducing genetic diversity.

The same pattern was not observed for the two Ambassis taxa studied in this project. Instead, extremely low mtDNA diversity was observed in the Gulf of Carpentaria in A. macleayi, and moderate haplotype diversity was observed in Ambassis sp. in the Lake Eyre Basin. Also, average microsatellite gene diversity, for the four loci used in both species, was comparable, averaging 0.43 for the Lake Eyre Basin and 0.46 for the Gulf of Carpentaria Basin. This suggests that hydrology is not affecting genetic diversity in the Ambassis species in the same way as N. hyrtlii. This may be due to the poor dispersal ability of Ambassis spp., causing individuals to remain in refugial waterholes during flow events, making them less susceptible to the subsequent bottlenecks when floodwaters recede. However, as the Ambassis taxa sampled in each basin were different species, the patterns of genetic diversity may be the product of biological differences between the species, rather than differences in landscape processes in each basin.

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5.5 Conclusion For N. hyrtlii, using microsatellites and mtDNA, much higher genetic diversity was observed in the Gulf of Carpentaria Basin compared to the Lake Eyre Basin. It was hypothesised that this resulted from differing landscape processes, namely hydrological variability, in each basin, maintaining larger population sizes in the Gulf of Carpentaria, thus generating higher genetic diversity. This was also supported by hydrological variability data from the sampled catchments, which negatively correlated with genetic diversity. This suggests that the extreme hydrological variability of the Lake Eyre Basin supports much smaller population sizes than catchments in the Gulf of Carpentaria.

Levels of genetic diversity and the patterns of gene flow among populations of riverine species are determined by species biology, riverine architecture and flow regime (Amoros and Bornette 2002, Hughes 2007, Meffe and Vrijenhoek 1988, Robinson, et al. 2002, Ward, et al. 2002). The presence of a strong correlation between flow variability and genetic diversity in N. hyrtlii illustrates this point. Also, very different patterns of genetic structure were detected within catchments in each basin for Ambassis spp.. This may also provide evidence for the relationship between gene flow and landscape processes. However, as different species were used as model taxa in each basin, possible differences based on species biology cannot be ignored.

Overall, this highlights the importance of landscape processes when considering the population processes of freshwater fish species. For example, from a management perspective, low diversity and low effective population sizes in the Lake Eyre Basin may not be obvious considering the frequent booms in population size during flood events. However, this is an important consideration as populations with low diversity are susceptible to extinction due to low adaptive potential and potential inbreeding depression (Amos and Balmford 2001). Therefore, the apparent resilience of N. hyrtlii populations in the Lake Eyre Basin may be unfounded as rapid catchment wide changes may occur too quickly for populations to adapt, leading to local extinctions.

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6 General Conclusions The initial aim of this project was to explore the relative roles of species biology, riverine architecture and flow regime upon patterns of genetic structure in northern Australian freshwater fish taxa. Using two different freshwater fish taxa (Neosilurus hyrtlii and Ambassis spp.), studied across two basins (the Lake Eyre and Gulf of Carpentaria Basins), evidence was found for all three factors influencing patterns of gene flow and genetic diversity.

In both basins, genetic structure among populations within catchments was stronger in Ambassis spp., than in N. hyrtlii. This fitted predictions based upon the different biological attributes of each species. For example, N. hyrtlii is believed to be a flood induced spawner, which is expected to generate high larval densities during periods of high hydrological connectivity (Orr and Milward 1984, Pusey, et al. 2004). Conversely, Ambassis spp. exhibit varied breeding strategies, such as year round, dry season and wet season spawning (Pusey, et al. 2004). As well as different observed levels of genetic structure, different species also exhibited different patterns of genetic structure in the same catchments. In the Georgina catchment, Lake Eyre Basin, isolation by distance was detected for Ambassis sp., and not in N. hyrtlii. Also, in the Cooper catchment in the Lake Eyre Basin, evidence for contiguous range expansion was detected for Ambassis sp. and not N. hyrtlii. These results (the consistently stronger genetic structure in Ambassis spp. compared to N. hyrtlii and different patterns of genetic structure among populations within catchments) highlight the role of species biology in determining levels of gene flow among populations.

Differences between species were not restricted to differences in contemporary levels of gene flow. Very different evolutionary histories were also observed for each species, in both basins. For example, in the Lake Eyre Basin, N. hyrtlii displayed a simple evolutionary history, with divergence among catchments reflective of the arrangement of catchments with respect to Lake Eyre. Alternatively, for Ambassis sp., divergent clades were detected that did not corroborate with expectations based upon the arrangement of catchments. It was concluded that these divergent clades (4.2% divergent at control region mtDNA) represented two independent colonisations into the Lake Eyre Basin, from the Gulf of Carpentaria Basin. In the Gulf of Carpentaria Basin, very different evolutionary histories were also detected. For A. macleayi, each 133

sampled catchment/sub-catchment was fixed for a separate haplotype, indicative of an old vicariance between catchments. This was supported by point estimates of time since population divergence ranging between 115,000 and 379,000 years ago. Alternatively, for N. hyrtlii, divergence among catchments was estimated to have been much more recent, with point estimates for time since population divergence ranging between 25,000 and 97,000 years ago.

In this project, riverine architecture was identified as an important factor influencing genetic structure among populations, within and among catchments. The results supported this expectation, as patterns of genetic structure across basins and catchments were mostly determined by the arrangements of channels. For example, for N. hyrtlii, in the Lake Eyre Basin, genetic structure among and within catchments was explained by the Stream Hierarchy Model (Meffe and Vrijenhoek 1988), whereby greater genetic divergence was observed among catchments than within catchments. Also, even though genetic structure for Ambassis sp. did not strictly match drainage arrangements, catchment boundaries were still restricting gene flow, generating more gene flow among populations within catchments than among catchments. In the Gulf of Carpentaria Basin the same pattern was observed, with catchment boundaries restricting gene flow in A. macleayi (which also adhered to the SHM) and N. hyrtlii (which could not be tested for the SHM).

For A. macleayi, in the Gulf of Carpentaria Basin, some evidence was found for a drainage rearrangement. In the Nicholson catchment, two sub-catchments/rivers were fixed for different haplotypes, approximately one percent divergent using control region mtDNA. This suggested that historically these rivers flowed into the Gulf of Carpentaria independently and populations of A. macleayi were diverging in isolation. Then, a drainage rearrangement (for which there is no geological evidence in the literature) caused these rivers to coalesce before flowing reaching the Gulf and restricted gene flow has ensured that both clades haven’t yet mixed. This result, as well as the observed role of catchment boundaries in restricting gene flow among catchments, emphasizes the importance of riverine architecture, both contemporaneously and historically, in determining gene flow among populations in freshwater fish.

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The role of hydrology in determining patterns of gene flow was difficult to test in this study due to poor sampling of N. hyrtlii in the Gulf of Carpentaria and the use of two different species of Ambassis in each basin. This made a comparative approach difficult. However, identifying the effect of hydrological variability upon genetic diversity was possible. For N. hyrtlii, much higher genetic diversity was found in the Gulf of Carpentaria Basin compared to the Lake Eyre Basin. It was proposed that this resulted from the higher hydrological variability within the Lake Eyre Basin compared to the Gulf of Carpentaria Basin. This hypothesis was supported by significantly negative correlations between genetic diversity (for mtDNA and microsatellites) and the CV of mean annual flow, measured for each catchment over a 15 year period.

The ‘hydrologically dependant effective population size hypothesis’ predicts that populations that are found in highly hydrologically variable catchments/branches, will possess lower genetic diversity that those populations found in hydrologically predictable environments. This is because populations in variable catchments will be repeatedly disturbed causing local bottlenecks, extinctions and recolonisations. These processes will act to reduce the effective population size of populations, thus reducing genetic diversity.

In conclusion, the data analysed in this project can not be explained by any single process/event. Instead species biology, riverine architecture, hydrology and historical connectivity have all interacted to generate complicated patterns of genetic structure and genetic diversity. Therefore, projects that hope to conserve populations of riverine species need to be able to account for all of these processes when managing natural populations.

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7 Appendices

7.1 Appendix A: Species Life History Information taken from Pusey et al., (2004), and references within. Trait Neosilurus hyrtlii Ambassis genus Age at sexual 12 month (?) 3 months - 12 months maturity Early wet season (A.agrammus), elevated GSI between September Peak spawning and December (A. agassizii), mature Start of wet season activity fish present all year, small peak in mean GSI in early wet season (A. macleayi)

Temperature and flooding (?, A. Inducement to Rising water levels agrammus), increase in day length spawning and temperature (A. agassizii)

312-2905 eggs (A. agrammus), 149- 3630 eggs in one female Fecundity 1574 (A. agassizii) and 320-2360 (A. 205mm TL macleayi)

Unknown, may be several At night up to four nights in a row (A. events in one season but agrammus), several days (A. Frequency of unknown whether single agassizii) and spawns in batches of spawning individuals spawns more than several hundred eggs each day over once a seven day period (A. macleayi)

Spawning Upstream Downstream (A. agrammus) migration

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7.2 Appendix B: Sample sites Lake Eyre Basin sample sites Lake Eyre Basin Site Site Code Latitude (S) Longitude (E) Georgina catchment Boulia BO 22 54.569 139 55.113 Wirrilyerna WY 23 04.304 139 30.744 Rocklands RL 19 51.623 138 05.978 Glenorminston 1 GO1 22 45.143 138 37.301 Glenorminston 2 GO2 22 54.997 138 48.229 Glenorminston 3 GO3 22 54.755 138 52.251 Bulla Bulla WH BB 22 55.025 140 26.434 Eyre Creek EC 24 55.008 139 38.937 Diamantina catchment Birdsville 1 B1 25 54.561 139 21.847 Birdsville 2 B2 25 55.191 139 21.246 Birdsville 3 B3 25 54.590 139 22.104 Monkira 1 MK1 24 49.650 140 37.206 Monkira 2 MK2 24 51.122 140 37.026 Monkira 3 MK3 24 58.151 140 23.428 Diamantina L 1 DL1 23 42.374 141 05.750 Diamantina L 2 DL2 23 40.346 140 59.183 Old Cork WH OC 22 55.467 141 52.350 Diomedes WH DI 22 51.466 141 57.100 Cooper catchment Murken MR 25 25.776 142 43.965 Glen Murken GM 25 26.868 142 40.718 Homestead HS 25 48.463 143 02.603 One Mile OM 25 50.700 143 03.120 Top TP 24 11.043 143 21.093 Waterloo WL 24 13.637 143 17.388 Tanbar TB 25 50.222 141 55.000 Yalangah YG 25 51.227 141 58.420

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Gulf of Carpentaria Basin sample sites Gulf of Carpentaria Basin Latitude (S) Longitude (E) Nicholson catchment Gregory Downs 1 GD1 18 31.824 139 15.121 Gregory Downs 2 GD2 18 30.072 139 17.331 Gregory Downs 3 GD3 18 21.789 139 16.636 Gregory Downs 4 GD4 18 23.770 139 14.348 Adel's Grove 2 AG2 18 40.325 138 32.920 Riversleigh 1 RI 19 01.116 138 43.529 Adel's Grove AG 18 41.365 138 31.810 Kingfisher Camp KFC 17 52.481 138 16.978 Leichhardt catchment East Leichhardt Dam LD 20 47.319 139 47.521 Moondarra Dam MO 20 36.061 139 33.170 Lake Julius JU 20 07.751 139 43.416 Augustus Downs AD 18 31.850 139 51.112 Floraville Station FL 18 14.068 139 52.711 Nardoo Station NA 18 47.374 139 43.000 Mellish Park Station MP 18 52.261 139 21.568 Flinders catchment Cowen Downs 1 CD1 18 59.318 140 35.997 Cowen Downs 2 CD2 18 58.511 140 34.458 Magowra 1 MA1 17 37.822 140 43.862 Magowra 2 MA2 17 50.765 140 43.397 Flinders River Crossing FRC 18 09.640 140 51.305 Milgara MI 18 14.904 140 52.796 Cloncurry River CLR 20 42.154 140 29.473 Chinaman's Bend Dam CBD 20 42.944 140 28.514 Lake Corella LC 20 50.140 140 02.209 Woondoola Station 18 36.743 140 58.695 Norman catchment Lake Belmore LB 18 10.592 142 16.019 Belmore Creek BC 18 09.999 142 12.995 Yappar River Station YA 18 25.910 141 16.230 Iffley Station IF 18 51.215 141 11.498 Gilbert catchment Gilbert River GR 17 10.113 141 46.100 Staaten catchment Staaten River STA 16 31.970 142 03.378 Mitchell catchment Walsh River WA 16 59.419 144 17.960 Elizebeth Creek EL 16 40.187 144 01.576 Lynd Junction LJ 16 27.842 143 18.384

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7.3 Appendix C: Allelic Diversity estimates Neosilurus hyrtlii alleles per locus MDH- MDH- Population n N22a NH16 NH11 NH19 NH12 AAT 1 2 MR 30 8 4 3 17 10 2 2 1 GM 30 9 4 2 17 11 2 2 1 HS 30 8 4 4 19 11 2 2 1 OM 25 8 4 5 17 10 2 2 1 TP 30 8 3 3 18 10 2 2 1 WL 20 8 4 4 16 10 2 2 1 TB 5 3 2 3 5 6 1 1 1 YG 10 7 4 2 12 7 1 2 1 B3 20 17 8 6 17 15 2 1 1 MK1 15 10 8 8 12 14 2 1 1 MK3 16 11 6 7 15 13 2 1 1 DL1 6 7 6 3 7 9 2 1 1 DL2 6 8 5 3 7 8 1 1 1 BO 19 10 7 7 13 11 2 1 1 WY 6 6 5 5 8 5 2 1 1 RL 31 12 5 8 16 12 2 1 1 GO2 5 2 2 6 8 7 2 1 1 BB 7 5 3 5 7 8 2 1 1 EL 30 17 13 13 15 20 2 1 1 MP 23 11 7 9 10 16 3 1 2 KFC 10 11 9 9 10 12 4 1 2 Mean 8.86 5.38 5.48 12.67 10.71 2.00 1.33 1.10 s.d. 3.62 2.55 2.75 4.31 3.48 0.62 0.47 0.29 Total 38 18 21 43 30 6 2 2

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Ambassis sp. alleles per locus PGI- PGI- Population n AMB14 AMB16 AMB21 AMB22 AMB24 AMB27 1 2 MR 8 4 2 5 2 2 3 1 2 GM 30 3 2 7 2 4 5 3 2 OM 7 3 2 8 2 3 6 3 2 TP 23 2 1 4 2 3 2 2 1 HS 5 2 1 5 2 2 1 3 2 WL 7 2 1 5 2 4 4 3 1 YG 30 3 2 7 3 4 5 3 2 TB 20 3 1 8 2 3 3 3 2 DL2 9 2 1 1 2 2 6 1 1 EC 15 4 3 4 1 1 4 1 2 RL 30 4 3 4 2 1 3 1 2 BO 30 5 3 4 1 2 6 1 2 WY 13 4 2 3 1 1 6 1 2 GO1 30 5 3 3 1 1 5 1 2 GO2 29 5 3 5 1 1 5 1 2 Mean 3.40 2.00 4.87 1.73 2.27 4.27 1.87 1.80 s.d. 1.08 0.82 1.89 0.57 1.12 1.53 0.96 0.40 Total 8 5 11 5 5 14 3 2

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Ambassis macleayi alleles per locus Population n AMB14 AMB16 AMB21 AMB22 AU 8 3 2 2 5 NA 8 3 2 4 3 LD 30 3 1 3 3 FL 30 4 2 4 6 MO 12 2 1 2 3 GD1 30 4 2 11 10 KFC 30 4 1 6 5 AG 12 5 1 6 7 IF 10 2 4 6 8 Mean 3.20 1.80 4.50 5.40 s.d. 0.98 0.87 2.77 2.25 Total 7 5 14 15

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7.4 Appendix D: IM Run Information

Update rates ESS values Comparison command string q1 q2 qA t q1 q2 qA t -l 10000000 -b 100000 - Cooper vs. Georgina m1 0 -m2 0 -q1 30 -q2 30 27.32 29.7 27.31 13.65 116 343 190 59 -qa 20 -t 3 -l 10000000 -b 100000 - Cooper vs. Diamantina m1 0 -m2 0 -q1 30 -q2 30 21.33 23.31 36.11 14.9 117 224 191 57 -qa 75 -t 5 -l 10000000 -b 100000 - Diamantina vs. Georgina m1 0 -m2 0 -q1 100 -q2 69.36 67.4 11.67 1.41 762 592 307 91 100 -qa 40 -t 5 -l 10000000 -b 100000 - Mitchell vs. Nicholson m1 0 -m2 0 -q1 100 -q2 95.87 97.69 47.87 22.75 953 1099 133 87 100 -qa 100 -t 2 -l 10000000 -b 100000 - Leichhardt vs. Nicholson m1 0 -m2 0 -q1 40 -q2 40 46.44 89.22 61.24 20.16 4213 3861 6041 1122 -qa 200 -t 10 -l 10000000 -b 100000 - Mitchell vs. Leichhardt m1 0 -m2 0 -q1 200 -q2 83.48 58.14 49.14 29.59 80 475 503 123 100 -qa 100 -t 3 -l 10000000 -b 100000 - Gulf of Carpentaria vs. Lake m1 0 -m2 0 -q1 20 -q2 8.19 63.64 73.01 17.78 47 55 800 25 Eyre 120 -qa 100 -t 10

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7.5 Appendix E: Microchecker Results Neosilurus hyrtlii: Probability of scoring observed large Homozygote error due null Pop Locus homozygote H H allele E O excess to alleles class dropout stuttering frequency N22a N/A 9.82 14 Yes No No Yes NH16 N/A 17.12 18 No No No No MR NH11 N/A 19.62 22 No No No No NH19 > 0.05 3.30 5 No No No No NH12 > 0.05 4.00 3 No No No No N22a > 0.05 7.28 5 No No No No NH16 N/A 19.72 21 No No No No GM NH11 N/A 18.27 20 No No No No NH19 > 0.05 4.87 5 No No No No NH12 > 0.05 5.10 8 No No No No N22a > 0.05 7.17 7 No No No No NH16 N/A 18.28 20 No No No No HS NH11 N/A 14.85 14 No No No No NH19 > 0.05 3.47 4 No No No No NH12 > 0.05 4.65 3 No No No No N22a N/A 7.72 11 No No No No NH16 N/A 15.10 15 No No No No OM NH11 N/A 13.06 12 No No No No NH19 > 0.05 2.96 6 Yes No No Yes NH12 > 0.05 3.82 4 No No No No N22a N/A 10.72 11 No No No No NH16 N/A 19.82 21 No No No No TP NH11 N/A 15.45 12 No No No No NH19 > 0.05 3.03 2 No No No No NH12 > 0.05 4.93 3 No No No No N22a N/A 6.28 7 No No No No NH16 N/A 11.70 14 No No No No WL NH11 N/A 10.27 11 No No No No NH19 > 0.05 1.80 2 No No No No NH12 > 0.05 2.63 1 No No No No N22a N/A 3.30 3 No No No No NH16 N/A 4.10 4 No No No No TB NH11 N/A 2.70 2 No No No No NH19 > 0.05 1.10 0 No No No No NH12 > 0.05 1.00 1 No No No No N22a > 0.05 2.95 2 No No No No NH16 N/A 6.55 6 No No No No YG NH11 N/A 5.20 6 No No No No NH19 > 0.05 1.15 1 No No No No NH12 > 0.05 2.45 3 No No No No

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Neosilurus hyrtlii Probability of scoring observed large Homozygote error due null Pop Locus homozygote H H allele E O excess to alleles class dropout stuttering frequency N22a > 0.05 1.73 2 No No No No NH16 > 0.05 4.78 7 No No No No B3 NH11 N/A 10.88 11 No No No No NH19 > 0.05 1.80 1 No No No No NH12 > 0.05 1.72 0 No No No No N22a > 0.05 2.23 2 No No No No NH16 > 0.05 4.63 5 No No No No MK1 NH11 N/A 6.43 6 No No No No NH19 > 0.05 2.23 2 No No No No NH12 > 0.05 1.30 0 No No No No N22a > 0.05 2.06 2 No No No No NH16 > 0.05 4.00 2 No No No No MK3 NH11 > 0.05 4.63 6 No No No No NH19 > 0.05 1.59 3 No No No No NH12 > 0.05 1.78 3 No No No No N22a > 0.05 1.17 0 No No No No NH16 > 0.05 1.33 0 No No No No DL1 NH11 N/A 4.25 4 No No No No NH19 > 0.05 1.08 2 No No No No NH12 > 0.05 0.75 1 No No No No N22a > 0.05 1.00 1 No No No No NH16 > 0.05 1.67 1 No No No No DL2 NH11 N/A 3.58 4 No No No No NH19 > 0.05 1.17 1 No No No No NH12 > 0.05 1.00 1 No No No No N22a > 0.05 2.87 3 No No No No NH16 > 0.05 4.61 3 No No No No BO NH11 > 0.05 4.50 3 No No No No NH19 > 0.05 2.39 3 No No No No NH12 > 0.05 2.76 1 No No No No N22a > 0.05 1.33 1 No No No No NH16 > 0.05 1.42 1 No No No No WY NH11 > 0.05 1.67 1 No No No No NH19 > 0.05 1.00 0 No No No No NH12 > 0.05 2.00 3 No No No No N22a > 0.05 4.85 6 No No No No NH16 N/A 12.11 11 No No No No RL NH11 > 0.05 7.08 4 No No No No NH19 > 0.05 3.63 1 No No No No NH12 > 0.05 4.48 4 No No No No N22a > 0.05 2.50 4 No No No No NH16 N/A 3.40 3 No No No No GO2 NH11 < 0.025 1.00 2 No No No No NH19 N/A 0.80 0 No No No No NH12 > 0.05 0.90 1 No No No No

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Neosilurus hyrtlii Probability of scoring observed large Homozygote error due null Pop Locus homozygote H H allele E O excess to alleles class dropout stuttering frequency N22a > 0.05 1.67 1 No No No No NH16 N/A 3.07 2 No No No No BB NH11 > 0.05 2.00 1 No No No No NH19 > 0.05 1.21 1 No No No No NH12 > 0.05 0.83 0 No No No No N22a > 0.05 2.90 4 No No No No NH16 > 0.05 4.88 5 No No No No EL NH11 > 0.05 4.47 9 Yes No No Yes NH19 > 0.05 3.42 6 No No No No NH12 > 0.05 2.13 3 No No No No N22a > 0.05 3.63 5 No No No No NH16 > 0.05 6.50 6 No No No No MP NH11 > 0.05 3.52 6 No No No No NH19 > 0.05 5.07 6 No No No No NH12 > 0.05 2.28 3 No No No No N22a > 0.05 1.15 0 No No No No NH16 < 0.05 2.05 4 No No No No KFC NH11 < 0.01 1.30 5 Yes No No Yes NH19 > 0.05 1.30 1 No No No No NH12 > 0.05 1.05 1 No No No No

145

Ambassis sp. Probability scoring of observed large Homozygote error due Pop Locus homozygote H H allele null alleles E O excess to class dropout stuttering frequency AMB14 > 0.05 2.63 1 No No No No AMB16 > 0.05 7.06 7 No No No No MR AMB21 N/A 3.13 1 No No No No AMB22 N/A 4.56 7 No No No No AMB24 N/A 7.06 7 No No No No AMB27 N/A 6.19 6 No No No No AMB14 > 0.05 11.25 12 No No No No AMB16 N/A 29.02 29 No No No No AMB21 > 0.05 6.65 6 No No No No GM AMB22 > 0.05 15.00 16 No No No No AMB24 N/A 15.97 25 No Yes No No AMB27 N/A 21.12 24 No No No No AMB14 N/A 9.96 9 No No No No AMB16 N/A 18.54 20 No No No No AMB21 > 0.05 6.28 9 No No No No OM AMB22 N/A 12.89 10 No No No No AMB24 N/A 11.63 15 No No No No AMB27 N/A 15.09 15 No No No No AMB14 N/A 2.50 2 No No No No AMB16 N/A 2.00 2 No No No No AMB21 > 0.05 1.70 3 No No No No TP AMB22 N/A 2.90 2 No No No No AMB24 N/A 2.30 3 No No No No AMB27 N/A 4.10 4 No No No No AMB14 N/A 3.57 5 No No No No AMB16 N/A 7.00 7 No No No No AMB21 > 0.05 1.93 1 No No No No HS AMB22 N/A 5.29 5 No No No No AMB24 N/A 4.64 6 No No No No AMB27 N/A 7.00 7 No No No No AMB14 N/A 3.79 4 No No No No AMB16 N/A 7.00 7 No No No No AMB21 > 0.05 1.93 3 No No No No WL AMB22 N/A 3.57 5 No No No No AMB24 > 0.05 1.86 2 No No No No AMB27 N/A 3.79 5 No No No No AMB14 N/A 12.45 13 No No No No AMB16 N/A 27.15 29 No No No No AMB21 > 0.05 6.67 5 No No No No YG AMB22 N/A 15.22 16 No No No No AMB24 N/A 17.12 20 No No No No AMB27 N/A 18.83 23 Yes No No Yes

146

Ambassis sp. Probability scoring of observed large Homozygote error due Pop Locus homozygote H H allele null alleles E O excess to class dropout stuttering frequency AMB14 > 0.05 7.20 7 No No No No AMB16 N/A 20.00 20 No No No No TB AMB21 > 0.05 4.97 7 No No No No AMB22 N/A 10.40 10 No No No No AMB24 N/A 10.93 14 No No No No AMB27 N/A 13.23 14 No No No No AMB14 N/A 7.22 7 No No No No AMB16 N/A 9.00 9 No No No No AMB21 N/A 9.00 9 No No No No DL2 AMB22 N/A 7.22 7 No No No No AMB24 N/A 4.72 6 No No No No AMB27 > 0.05 2.22 4 No No No No AMB14 N/A 6.73 6 No No No No AMB16 N/A 6.63 8 No No No No AMB21 N/A 7.93 9 No No No No EC AMB22 N/A 15.00 15 No No No No AMB24 N/A 15.00 15 No No No No AMB27 N/A 6.50 3 No No No No AMB14 N/A 10.78 12 No No No No AMB16 > 0.05 11.05 13 No No No No AMB21 > 0.05 11.38 10 No No No No RL AMB22 N/A 29.02 29 No No No No AMB24 N/A 30.00 30 No No No No AMB27 > 0.05 11.32 8 No No No No AMB14 > 0.05 8.20 10 No No No No AMB16 N/A 13.75 15 No No No No AMB21 N/A 17.72 16 No No No No BO AMB22 N/A 30.00 30 No No No No AMB24 N/A 29.02 29 No No No No AMB27 > 0.05 7.63 6 No No No No AMB14 > 0.05 3.73 7 No No No No AMB16 N/A 6.65 8 No No No No AMB21 N/A 6.35 6 No No No No WY AMB22 N/A 13.00 13 No No No No AMB24 N/A 13.00 13 No No No No AMB27 > 0.05 3.23 3 No No No No AMB14 > 0.05 8.48 9 No No No No AMB16 N/A 13.72 15 No No No No AMB21 N/A 17.85 20 No No No No GO1 AMB22 N/A 30.00 30 No No No No AMB24 N/A 30.00 30 No No No No AMB27 > 0.05 9.00 12 No No No No

147

Ambassis sp. Probability scoring of observed large Homozygote error due Pop Locus homozygote H H allele null alleles E O excess to class dropout stuttering frequency AMB14 N/A 10.71 12 No No No No AMB16 N/A 14.02 10 No No No No AMB21 N/A 13.41 16 No No No No GO2 AMB22 N/A 29.00 29 No No No No AMB24 N/A 29.00 29 No No No No AMB27 > 0.05 8.86 3 No No No No

148

Ambassis macleayi Probability scoring of observed large Homozygote error due null Pop Locus homozygote H H allele E O excess to alleles class dropout stuttering frequency AMB14 > 0.05 3.06 3 No No No No AU AMB16 N/A 5.56 5 No No No No AMB21 N/A 6.25 6 No No No No AMB22 N/A 3.44 3 No No No No AMB14 > 0.05 2.94 3 No No No No AMB16 N/A 4.56 3 No No No No NA AMB21 N/A 3.56 3 No No No No AMB22 N/A 3.36 3 No No No No AMB14 N/A 14.87 18 No No No No AMB16 N/A 30.00 30 No No No No LD AMB21 N/A 26.22 26 No No No No AMB22 N/A 21.90 23 No No No No AMB14 > 0.05 8.28 11 No No No No AMB16 N/A 28.07 28 No No No No FL AMB21 > 0.05 11.67 16 No No No No AMB22 > 0.05 9.15 11 No No No No AMB14 N/A 6.04 5 No No No No AMB16 N/A 12.00 12 No No No No MO AMB21 N/A 11.04 11 No No No No AMB22 N/A 5.79 6 No No No No AMB14 N/A 20.05 22 No No No No AMB16 N/A 27.15 27 No No No No GD1 AMB21 > 0.05 4.92 5 No No No No AMB22 > 0.05 6.67 3 No No No No AMB14 N/A 21.95 21 No No No No AMB16 N/A 30.00 30 No No No No KFC AMB21 > 0.05 9.25 7 No No No No AMB22 < 0.025 9.13 16 Yes No No Yes AMB14 N/A 21.95 21 No No No No AMB16 N/A 30.00 30 No No No No AG AMB21 > 0.05 9.25 7 No No No No AMB22 < 0.025 9.13 16 Yes No No Yes AMB14 > 0.05 3.50 4 No No No No AMB16 N/A 12.00 12 No No No No IF AMB21 < 0.025 3.63 7 Yes No No Yes AMB22 > 0.05 2.75 2 No No No No

149

7.6 Appendix F: Pairwise FST tables

N. hyrtlii, pairwise ΦST tables, Lake Eyre Basin, using control region mtDNA. Significant values are in bold (α=0.05). MR GM HS OM TP WL TB YG B3 MK1 MK3 DL1 DL2 BO WY RL GO2 BB MR 0.00 GM 0.00 0.00 HS -0.08 0.07 0.00 OM -0.01 0.04 0.01 0.00 TP 0.00 0.00 0.05 -0.06 0.00 WL 0.00 0.00 0.05 0.04 0.00 0.00 TB -0.08 0.00 -0.02 -0.04 -0.08 -0.08 0.00 YG -0.07 0.00 -0.05 0.00 0.00 -0.07 -0.08 0.00 B3 0.09 0.09 0.12 0.11 0.09 0.09 0.02 0.09 0.00 MK1 0.08 0.10 0.12 0.10 0.08 0.08 0.02 0.07 -0.06 0.00 MK3 0.15 0.16 0.17 0.15 0.15 0.15 0.08 0.13 -0.02 -0.03 0.00 DL1 0.38 0.51 0.37 0.30 0.38 0.38 0.36 0.31 0.11 0.09 -0.05 0.00 DL2 0.02 0.09 0.05 0.02 0.02 0.02 -0.03 -0.01 0.09 0.08 0.12 0.30 0.00 BO -0.01 -0.04 0.05 0.04 -0.01 -0.01 -0.10 0.01 0.03 0.02 0.11 0.30 -0.05 0.00 WY 0.20 0.31 0.20 0.15 0.20 0.20 0.16 0.14 0.23 0.21 0.21 0.32 -0.11 0.11 0.00 RL 0.02 -0.01 0.08 0.08 0.02 0.02 -0.07 0.04 0.00 -0.01 0.09 0.31 0.01 -0.02 0.20 0.00 GO2 -0.08 0.00 -0.02 -0.04 -0.08 -0.08 0.00 -0.08 0.02 0.02 0.08 0.36 -0.03 -0.10 0.16 -0.07 0.00 BB 0.01 0.05 0.05 0.02 0.01 0.01 -0.06 -0.01 -0.09 -0.10 -0.04 0.13 0.00 -0.06 0.15 -0.08 -0.06 0.00

150

N. hyrtlii, pairwise FST tables, Lake Eyre Basin, using microsatellites. Significant values are in bold (α=0.05). MR GM HS OM TP WL TB YG B3 MK1 MK3 DL1 DL2 BO WY RL GO2 BB MR 0.000 GM 0.001 0.000 HS 0.000 0.003 0.000 OM -0.001 0.002 0.000 0.000 TP 0.002 -0.002 0.003 0.002 0.000 WL -0.002 -0.001 0.000 0.001 0.001 0.000 TB 0.010 0.005 0.012 0.005 0.006 0.011 0.000 YG 0.002 -0.001 0.003 0.003 0.000 -0.001 0.008 0.000 B3 0.000 0.002 0.002 0.001 0.003 0.001 0.010 0.003 0.000 MK1 0.000 0.002 0.002 0.001 0.003 0.001 0.010 0.003 0.000 0.000 MK3 0.000 0.002 0.002 0.001 0.003 0.001 0.010 0.003 0.000 0.000 0.000 DL1 -0.001 0.002 0.002 0.001 0.003 0.001 0.011 0.003 0.000 0.000 0.000 0.000 DL2 0.000 0.002 0.002 0.001 0.003 0.001 0.011 0.003 0.000 0.000 0.000 0.000 0.000 BO 0.000 0.002 0.002 0.001 0.003 0.001 0.010 0.003 0.000 0.000 0.000 0.000 0.000 0.000 WY 0.000 0.002 0.002 0.001 0.003 0.001 0.011 0.003 0.000 0.000 0.000 0.000 0.000 -0.004 0.000 RL 0.000 0.002 0.002 0.001 0.003 0.001 0.010 0.003 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 GO2 0.000 0.002 0.002 0.001 0.003 0.001 0.011 0.003 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 BB -0.002 -0.017 -0.012 -0.003 -0.011 -0.005 0.016 0.008 0.002 0.005 0.005 0.006 0.006 0.005 -0.006 0.005 0.006 0.000

151

N. hyrtlii, pairwise FST tables, Lake Eyre Basin, using allozymes. Significant values are in bold (α=0.05). MR GM HS OM TP WL TB YG B3 MK1 MK3 DL1 DL2 BO WY RL GO2 BB MR 0.00 GM -0.01 0.00 HS 0.02 0.01 0.00 OM 0.04 0.01 0.00 0.00 TP 0.03 0.00 0.00 -0.02 0.00 WL 0.02 -0.01 -0.02 -0.02 -0.02 0.00 TB -0.05 -0.05 -0.02 -0.01 -0.02 -0.03 0.00 YG 0.01 -0.02 -0.03 -0.02 -0.02 -0.03 -0.04 0.00 B3 0.44 0.39 0.33 0.26 0.29 0.29 0.28 0.29 0.00 MK1 0.37 0.32 0.24 0.17 0.20 0.21 0.20 0.21 -0.01 0.00 MK3 0.48 0.43 0.35 0.28 0.32 0.32 0.30 0.31 -0.03 -0.01 0.00 DL1 0.67 0.61 0.48 0.39 0.45 0.45 0.43 0.44 -0.05 0.00 -0.05 0.00 DL2 -0.04 -0.04 -0.01 0.00 -0.01 -0.02 0.00 -0.03 0.29 0.22 0.31 0.45 0.00 BO 0.68 0.64 0.58 0.52 0.56 0.55 0.52 0.53 0.08 0.17 0.07 0.00 0.54 0.00 WY 0.74 0.69 0.57 0.49 0.54 0.54 0.52 0.53 0.00 0.07 -0.02 -0.08 0.55 -0.05 0.00 RL 0.58 0.55 0.50 0.44 0.48 0.47 0.46 0.46 0.05 0.12 0.04 -0.02 0.47 -0.02 -0.05 0.00 GO2 0.84 0.79 0.68 0.62 0.66 0.67 0.67 0.67 0.08 0.18 0.07 -0.01 0.69 -0.06 -0.07 -0.04 0.00 BB 0.72 0.66 0.55 0.47 0.52 0.52 0.49 0.51 -0.01 0.06 -0.02 -0.07 0.52 -0.03 -0.08 -0.04 -0.06 0.00

152

Ambassis sp., pairwise ΦST tables, Lake Eyre Basin, using control region mtDNA. Significant values are in bold (α=0.05). MR GM OM TP HS WL YG TB DL2 EC RL BO WY GO1 GO2 MR 0.00 GM -0.02 0.00 OM 0.00 0.00 0.00 TP 0.19 0.15 0.23 0.00 HS 0.10 0.04 0.15 0.09 0.00 WL 0.02 -0.13 0.05 0.11 0.01 0.00 YG 0.08 0.07 0.11 0.05 -0.12 0.03 0.00 TB 0.00 -0.01 0.00 0.21 0.13 0.04 0.10 0.00 DL2 1.00 0.95 1.00 0.91 0.94 0.94 0.77 1.00 0.00 EC 0.99 0.98 0.99 0.97 0.98 0.98 0.95 0.99 0.99 0.00 RL 0.99 0.98 0.99 0.97 0.98 0.98 0.95 0.99 0.99 -0.11 0.00 BO 0.99 0.99 0.99 0.98 0.98 0.98 0.96 0.99 0.99 -0.07 -0.07 0.00 WY 0.98 0.98 0.98 0.97 0.97 0.97 0.95 0.98 0.98 0.09 0.09 0.24 0.00 GO1 1.00 0.99 1.00 0.99 0.99 0.99 0.96 1.00 1.00 0.11 0.11 0.00 0.44 0.00 GO2 0.99 0.99 0.99 0.98 0.98 0.98 0.96 0.99 0.99 -0.07 -0.07 -0.11 0.24 0.00 0.00

153

Ambassis sp., pairwise FST tables, Lake Eyre Basin, using microsatellites. Significant values are in bold (α=0.05). MR GM OM TP HS WL YG TB DL2 EC RL BO WY GO1 GO2 MR 0.000 GM 0.033 0.000 OM 0.025 0.003 0.000 TP 0.053 0.008 -0.004 0.000 HS 0.047 0.016 -0.001 -0.017 0.000 WL 0.007 0.000 -0.003 -0.005 0.007 0.000 YG 0.025 0.002 0.002 0.001 0.003 -0.003 0.000 TB 0.044 0.007 -0.002 -0.001 -0.002 0.009 -0.001 0.000 DL2 0.062 0.037 0.022 0.038 0.043 0.042 0.040 0.034 0.000 EC 0.068 0.030 0.030 0.035 0.045 0.029 0.030 0.033 0.058 0.000 RL 0.051 0.015 0.015 0.019 0.029 0.014 0.015 0.018 0.042 0.006 0.000 BO 0.052 0.016 0.017 0.020 0.030 0.015 0.016 0.020 0.044 0.009 0.006 0.000 WY 0.057 0.019 0.019 0.024 0.034 0.018 0.019 0.023 0.048 0.020 0.016 -0.002 0.000 GO1 0.053 0.017 0.017 0.021 0.031 0.015 0.017 0.020 0.044 0.008 0.003 0.005 0.004 0.000 GO2 0.061 0.024 0.024 0.029 0.039 0.023 0.024 0.027 0.052 0.017 0.009 0.009 0.005 0.000 0.000

154

Ambassis sp., pairwise FST tables, Lake Eyre Basin, using allozymes. Significant values are in bold (α=0.05). MR GM OM TP HS WL YG TB DL2 EC RL BO WY GO1 GO2 MR 0.00 GM 0.06 0.00 OM 0.08 -0.01 0.00 TP 0.11 -0.03 -0.05 0.00 HS -0.01 -0.01 0.03 0.03 0.00 WL 0.05 0.00 -0.02 -0.05 0.03 0.00 YG 0.05 -0.01 0.01 0.01 -0.02 0.02 0.00 TB 0.03 -0.02 0.00 -0.02 -0.03 0.00 -0.01 0.00 DL2 0.94 0.73 0.81 0.93 0.84 0.94 0.71 0.75 0.00 EC 0.72 0.54 0.61 0.63 0.60 0.69 0.53 0.55 0.49 0.00 RL 0.76 0.63 0.69 0.72 0.68 0.75 0.62 0.64 0.20 0.10 0.00 BO 0.71 0.55 0.61 0.62 0.61 0.68 0.54 0.57 0.48 -0.02 0.14 0.00 WY 0.73 0.53 0.61 0.63 0.60 0.69 0.52 0.55 0.51 -0.04 0.11 -0.03 0.00 GO1 0.71 0.55 0.61 0.62 0.61 0.68 0.55 0.57 0.44 -0.03 0.10 -0.01 -0.03 0.00 GO2 0.77 0.63 0.70 0.72 0.68 0.76 0.62 0.65 0.20 0.11 -0.02 0.14 0.11 0.11 0.00

155

N. hyrtlii, pairwise ΦST tables, Gulf of Carpentaria Basin, using control region mtDNA. Significant values are in bold (α=0.05). EL MP KFC EL 0.00 MP 0.24 0.00 KFC 0.06 0.50 0.00

N. hyrtlii, pairwise FST tables, Gulf of Carpentaria Basin, using microsatellites. Significant values are in bold (α=0.05). EL MP KFC EL 0.000 MP 0.001 0.000 KFC 0.000 0.003 0.000

N. hyrtlii, pairwise FST tables, Gulf of Carpentaria Basin, using allozymes. Significant values are in bold (α=0.05). EL MP KFC EL 0.00 MP 0.01 0.00 KFC 0.18 0.18 0.00

156

Ambassis sp., pairwise ΦST tables, Gulf of Carpentaria Basin, using control region mtDNA. Significant values are in bold (α=0.05). AU NA LD FL MO GD1 KFC AG IF AU 0.00 NA 0.00 0.00 LD 0.00 0.00 0.00 FL 0.00 0.00 0.00 0.00 MO 0.00 0.00 0.00 0.00 0.00 GD1 1.00 1.00 1.00 1.00 1.00 0.00 KFC 1.00 1.00 1.00 1.00 1.00 1.00 0.00 AG 1.00 1.00 1.00 1.00 1.00 0.00 1.00 0.00 IF 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.00

Ambassis sp., pairwise FST tables, Gulf of Carpentaria Basin, using microsatellites. Significant values are in bold (α=0.05). AU NA LD FL MO GD1 KFC AG IF AU 0.000 NA -0.012 0.000 LD 0.043 0.192 0.000 FL 0.075 0.060 0.180 0.000 MO 0.038 0.169 0.066 0.152 0.000 GD1 0.254 0.161 0.420 0.139 0.358 0.000 KFC 0.335 0.286 0.438 0.211 0.401 0.318 0.000 AG 0.261 0.196 0.465 0.115 0.363 0.083 0.218 0.000 IF 0.263 0.224 0.448 0.203 0.318 0.225 0.352 0.164 0.000

157

7.7 Appendix G: Allele frequency figures N. hyrtlii, Lake Eyre Basin, allele frequencies for nuclear data (microsatellites, histograms; allozymes, pie diagrams). The x-axis of the histograms represents the relative frequency of each allele, in order of size class. Relative frequencies are not comparable across graphs as axes are not standardised. Pieces of pie represent the relative frequency of each allele.

158

Ambassis sp., Lake Eyre Basin, allele frequencies for nuclear data (microsatellites, histograms; allozymes, pie diagrams). The x-axis of the histograms represents the relative frequency of each allele, in order of size class. Relative frequencies are not comparable across graphs as axes are not standardised. Pieces of pie represent the relative frequency of each allele.

159

A. macleayi, Gulf of Carpentaria Basin, allele frequencies for microsatellites. The x- axis of the histograms represents the relative frequency of each allele, in order of size class. Relative frequencies are not comparable across graphs as axes are not standardised.

160

7.8 Appendix H: Raw Data The tables below display the allele values for all nuclear loci. Neosilurus hyrtlii Allozyme loci Microsatellite loci Sample Aat Mdh-1 Mdh-2 N22a NH 16 NH 11 NH 19 NH 12 MR1 3 3 3333151169125121114114 171 183 137 133 MR2 3 3 3333151151121121114114 217 195 145 131 MR3 3 3 3333151151121121114114 179 179 131 129 MR4 3 3 3333169171121121114114 177 177 129 125 MR5 3 3 3333151151123117114114 181 179 133 127 MR6 3 3 3333147173125117114114 185 181 137 131 MR7 3 3 3333151151121121112110 177 195 137 131 MR8 3 3 3333169151121121114112 179 145 135 131 MR9 3 3 3333171155121117114112 177 211 135 135 MR10 3 3 3333167149121117114112 177 193 137 133 MR11 3 3 3333151151121121114114 177 199 139 131 MR12 3 3 3333173151121121112112 177 183 131 129 MR13 3 3 3333151151121121112114 185 175 135 137 MR14 2 3 4333151151123117114112 177 177 139 133 MR15 3 3 3333151151123121112112 177 195 145 127 MR16 3 3 3333151151121121114114 177 193 131 127 MR17 3 3 3333151167121121114114 177 179 135 129 MR18 3 3 3333151151121121114114 211 179 133 133 MR19 3 3 3333167169121121114114 195 215 133 137 MR20 3 3 3333151169121121114114 175 175 129 123 MR21 3 3 3333151151121117114114 177 171 135 131 MR22 3 3 3333151149121117114114 197 181 137 129 MR23 3 3 3333169171121121114114 177 183 137 133 MR24 3 3 3333149169125117114112 171 201 139 137 MR25 3 3 3333167173121121114114 217 181 137 125 MR26 3 3 3333151167121121114112 193 201 137 135 MR27 3 3 3333151167121117114114 181 181 139 129 MR28 3 3 3333151151121117114114 179 211 139 137 MR29 3 3 3333149149121121114114 179 181 131 129 MR30 3 3 3333149149121121114114 195 223 127 127 GM1 3 3 3333149167121121114114 193 179 135 133 GM2 3 3 3333151151121121114112 179 121 143 127 GM3 3 3 3333149167117117114114 201 197 129 137 GM4 3 3 3333151151121121114114 197 179 137 137 GM5 3 3 3333151153121121114114 187 181 133 129 GM6 3 3 3333151165121121114114 181 185 135 131 GM7 3 3 3333151165123121114114 181 215 133 133 GM8 3 3 3333151169121121114114 193 201 129 125 GM9 3 3 3333167165121121114114 181 179 131 129 GM10 3 3 3333151169121121114114 201 121 133 131 GM11 3 3 3333151165121121114112 207 193 137 131 GM12 3 3 3333151149121121114112 179 179 135 135 GM13 3 3 3333151163121119114112 179 179 135 135 GM14 3 3 3333151165121119114112 219 183 137 135 GM15 3 3 3333151151117121114112 117 205 141 137 GM16 3 3 3333165169121121114114 211 179 137 135 161

GM17 3 3 3333169171121121114114 181 179 135 123 GM18 3 3 3333151169121121114114 181 179 137 135 GM19 3 3 3333151149121117114112 181 181 137 137 GM20 3 3 3333165165121121114114 179 179 139 137 GM21 3 3 3333151149123121112112 181 197 137 135 GM22 3 3 4333151151121117114114 181 179 123 135 GM23 3 3 3333151167121117114114 179 181 135 129 GM24 3 3 3333151167121121114112 179 179 137 125 GM25 2 3 3333149163121121112112 183 171 137 137 GM26 3 3 3333169171121121114112 201 193 137 137 GM27 3 3 3333151173121121112112 181 185 133 129 GM28 3 3 3333151173121121114112 181 171 133 127 GM29 3 3 3333151167121121114114 181 177 131 127 GM30 3 3 3333151169123117114114 181 171 131 131 HS1 3 3 3333151151121123114114 213 181 135 127 HS2 3 3 3333151167121121114112 117 215 143 135 HS3 3 3 3333151169121121114112 181 117 139 137 HS4 3 3 3333151153121121114114 181 171 135 131 HS5 2 3 4333171167121117112112 181 117 141 125 HS6 2 3 4333151171121121114114 181 171 143 135 HS7 3 3 3333151151121123114112 211 181 133 129 HS8 3 3 3333151155121121116114 181 197 139 123 HS9 3 3 3333151149117117114114 181 171 142 137 HS10 3 3 3333169171121125114114 193 217 137 131 HS11 3 3 3333151173121121114114 171 171 137 131 HS12 3 3 3333151169121117114112 177 181 137 135 HS13 3 3 3333151149121121114112 177 177 139 133 HS14 3 3 4333151169121121114114 181 181 135 131 HS15 3 3 3333151167121121112112 201 215 137 133 HS16 3 3 3333151153121121114112 217 179 131 123 HS17 3 3 3333151169121123114112 171 193 135 135 HS18 3 3 4333151149121123114114 177 187 135 133 HS19 3 3 3333149171121123114112 193 179 137 131 HS20 3 3 3333169171121121114112 181 183 139 131 HS21 3 3 3333149167117117112112 183 197 137 133 HS22 3 3 3333167169121117112112 177 171 143 139 HS23 3 3 3333151167121121114114 177 191 137 135 HS24 3 3 4333151151121121114112 177 207 135 131 HS25 3 3 3333151151121121114110 219 177 137 137 HS26 3 3 3333149171125121114112 183 193 135 135 HS27 3 3 3333151149121121114112 177 177 137 133 HS28 3 3 3333167167121121114112 195 197 135 129 HS29 3 3 3333167167121121114114 177 171 137 129 HS30 3 3 3333167167121121114112 185 177 137 133 OM1 3 3 3333151151121117114114 185 179 139 127 OM2 3 3 3333171167121117112112 181 181 135 133 OM3 3 3 3333151171121121116114 203 179 145 129 OM4 3 3 3333151151121121114114 179 185 139 131 OM5 3 3 3333151171123123114112 195 179 133 127 OM6 3 3 3333151155121121114112 193 199 131 125 OM7 3 3 4333149149123121114112 195 177 135 131 OM8 3 3 4333151169121121114112 177 181 137 135

162

OM9 3 3 3333151151121117114114 117 117 139 137 OM10 3 3 3333151171121117114114 175 187 137 131 OM11 3 3 3333151151121117114112 179 179 137 133 OM12 2 2 3333177177137121142134 195 181 121 121 OM13 3 3 3333155171121121114112 193 205 137 133 OM14 3 3 3333151151121121114112 201 213 139 135 OM15 3 3 3333149169121117114112 177 177 137 137 OM16 3 3 3333151155121121114114 177 171 133 129 OM17 3 3 3333151151121121114114 183 193 137 133 OM18 3 3 3333151151121121114114 199 177 133 129 OM19 3 3 3333151151121123114114 217 175 133 127 OM20 3 3 3333151165121121114114 217 177 137 137 OM21 3 3 3333165167121121114114 177 177 137 137 OM22 3 3 3333151151121117116114 179 179 137 135 OM23 3 3 3333167171121121114112 177 193 145 135 OM24 2 3 3333151167121121114114 177 179 135 129 OM25 3 3 3333149155121121114112 177 213 137 127 TP1 3 3 3333151155121121114114 179 211 137 131 TP2 3 3 3333151173121121114112 171 215 143 137 TP3 3 3 3333149161123117114112 171 179 139 133 TP4 3 3 3333149171121121112112 185 181 137 133 TP5 3 3 3333151167121121114112 181 181 137 133 TP6 3 3 3333151151121117114112 195 171 135 127 TP7 2 3 3333151167121121114114 179 197 135 131 TP8 2 3 3333151151121123114114 219 195 137 129 TP9 3 3 3333155167121121114112 197 203 137 125 TP10 3 3 3333151151121121114114 181 179 135 135 TP11 3 3 3333151151121121114112 199 195 143 135 TP12 3 3 3333151151121121114114 201 211 137 131 TP13 3 3 3333173151121121114112 181 175 135 131 TP14 3 3 3333151151121121114112 179 193 137 133 TP15 3 3 4333169151121121114112 201 203 137 129 TP16 3 3 3333167169121123114112 183 181 135 127 TP17 2 3 3333173151121123114112 183 221 135 131 TP18 3 3 3333173155121117114114 207 219 141 137 TP19 3 3 3333151151123123112112 181 219 141 139 TP20 3 3 3333171151121117114114 193 221 133 129 TP21 3 3 3333151151121121114112 181 179 137 131 TP22 3 3 3333169151121123114114 179 187 135 129 TP23 3 3 3333167151121121114112 183 221 137 137 TP24 3 3 3333151151121121114112 179 179 139 137 TP25 3 3 3333151151121121114112 179 193 135 135 TP26 3 3 4333167151121121114112 183 179 135 127 TP27 3 3 3333151151121117114112 181 183 139 137 TP28 3 3 3333167155121121114114 193 197 135 133 TP29 3 3 3333167155121121114114 193 197 135 133 TP30 3 3 3333169151121121116112 181 179 143 137 WL1 3 3 3333151167121121114112 181 183 139 127 WL2 3 3 4333165165121121114112 211 197 137 137 WL3 3 3 3333149167117117114114 197 209 137 135 WL4 3 3 3333151151121121114112 195 179 137 129 WL5 3 3 3333149169121125114114 211 171 127 125

163

WL6 2 3 3333151151121121114114 209 175 135 129 WL7 3 3 3333151149123121114114 201 177 127 123 WL8 3 3 3333151149121121114112 201 195 133 127 WL9 3 3 3333151173121121114114 181 193 139 131 WL10 3 3 3333151151123123114114 181 221 133 125 WL11 3 3 3333169171121121114114 205 215 137 135 WL12 3 3 3333151173121117114112 177 181 135 137 WL13 3 3 3333151151121121116112 177 183 137 135 WL14 3 3 3333151167125121112112 183 185 137 133 WL15 3 3 3333151165121121114114 179 179 141 135 WL16 3 3 4333151171121121114110 179 171 135 129 WL17 3 3 3333151151121121114112 179 177 127 123 WL18 3 3 3333165167121117114114 179 221 133 127 WL19 3 3 3333151155121121114112 177 177 139 131 WL20 3 3 3333151151121123112112 181 211 133 125 TB1 3 3 3333151151121121114112 179 209 137 131 TB2 3 3 3333151155121121114116 183 201 125 125 TB3 3 3 3333151151121121114114 179 177 137 133 TB4 3 3 3333151151121117112114 209 183 135 137 TB5 3 3 3333151167121121114114 183 201 139 133 YG1 3 3 3333149167121121112112 199 175 135 135 YG2 3 3 3333151171121121114114 179 223 135 135 YG3 3 3 3333151169121121114112 171 211 135 133 YG4 3 3 3333151151121123114114 183 209 133 133 YG5 3 3 3333151165121121114114 171 199 139 135 YG6 3 3 3333151171121121114112 177 197 139 133 YG7 3 3 4333155165121117112112 179 179 133 131 YG8 3 3 3333151149121123114112 179 217 135 129 YG9 3 3 3333151167121121114114 199 205 141 123 YG10 3 3 3333151151121119114112 183 171 139 133 B3-1 2 2 3333169155121121124114 173 211 159 135 B3-2 2 3 3333165165121117114114 183 179 139 135 B3-3 3 3 3333171165139117124114 177 193 139 123 B3-4 3 3 3333177225139117124106 177 225 131 113 B3-5 2 3 3333181195117117114114 181 195 127 113 B3-6 2 3 3333187189121121130114 187 189 139 133 B3-7 3 3 3333179187121117128114 179 187 131 127 B3-8 2 2 3333183179123117114132 183 179 123 113 B3-9 3 3 3333183225121111130106 183 225 139 133 B3-10 3 3 3333181233121121114114 181 233 129 119 B3-11 2 2 3333185171117117114114 185 171 127 117 B3-12 2 2 3333187189121121114106 187 189 131 127 B3-13 2 2 3333183219117109130114 183 219 123 119 B3-14 3 3 3333187171117113114114 187 171 131 113 B3-15 3 3 3333185227117113114114 185 227 157 117 B3-16 2 3 3333181171139109114114 181 171 133 115 B3-17 2 3 3333183205121121114114 183 205 123 121 B3-18 3 3 3333187177139125114114 187 177 123 117 B3-19 2 3 3333187187113109114114 187 187 135 121 B3-20 2 3 3333183233117109114114 183 233 131 125 MK1-1 2 3 3333167155121135114106 185 179 123 119 MK1-2 2 3 3333155159121121130124 183 177 131 117

164

MK1-3 2 3 3333171167121119130114 183 187 125 121 MK1-4 2 3 3333169171125117130114 183 171 139 125 MK1-5 2 3 3333169165121121114114 171 207 135 127 MK1-6 2 3 3333171165121111114114 175 135 139 113 MK1-7 3 3 3333167167121121130114 183 189 129 119 MK1-8 3 3 3333175169117117114114 175 175 131 123 MK1-9 3 3 3333171171117139114140 183 233 159 127 MK1-10 3 3 3333165163117139116114 183 171 135 127 MK1-11 3 3 3333167165121121128114 177 193 119 117 MK1-12 2 3 3333173169139121114114 183 187 125 115 MK1-13 2 2 3333165163121113126106 189 177 135 123 MK1-14 3 3 3333165147121111114114 187 177 129 125 MK1-15 2 3 3333169167121117114114 183 183 139 133 MK3-1 2 3 3333165147121121114114 183 221 133 119 MK3-2 2 3 3333171165121117132114 177 171 123 123 MK3-3 2 3 3333163155139117114106 217 171 139 117 MK3-4 3 3 3333169153123121114114 235 171 121 117 MK3-5 2 3 3333159153117109114114 177 177 131 119 MK3-6 2 3 3333169155123109130114 235 177 135 135 MK3-7 2 2 3333171167139109130106 185 185 125 123 MK3-8 2 3 3333169163121121114114 233 233 133 127 MK3-9 2 2 3333163153139121132130 205 233 117 117 MK3-10 2 3 3333169169123121130134 175 193 135 123 MK3-11 2 2 3333167151121117124114 183 177 145 133 MK3-12 2 3 3333165165121117130106 177 175 137 135 MK3-13 3 3 3333165159121109130140 189 175 129 127 MK3-14 3 3 3333169165121113140130 171 225 135 127 MK3-15 3 3 3333169155123121130130 185 175 123 119 MK3-16 3 3 3333175167123117114114 181 187 133 119 DL1-1 2 3 3333169151123121114114 187 191 123 121 DL1-2 2 2 3333169165121117114114 171 205 133 129 DL1-3 2 3 3333163161135117132114 177 177 135 117 DL1-4 2 3 3333171169121109114114 177 173 125 125 DL1-5 3 3 3333169167117109130114 181 171 117 113 DL1-6 2 3 3333165167139117114114 181 181 131 123 DL2-1 3 3 3333169151121117132130 183 177 131 125 DL2-2 3 3 3333157137121121114114 183 193 123 117 DL2-3 3 3 3333169167139117114114 175 189 129 117 DL2-4 3 3 3333171155117109130114 177 173 115 113 DL2-5 3 3 3333169165123117114114 183 183 123 123 DL2-6 3 3 3333155155123117114114 171 193 119 117 BO1 2 3 3333155173117109106106 181 185 113 117 BO2 2 3 3333153169117111130106 173 215 123 121 BO3 2 2 3333159171111121126114 173 173 123 123 BO4 2 3 3333169167121117130114 185 215 121 117 BO5 2 2 3333169171123117130114 173 173 125 123 BO6 2 2 3333169153121117124106 173 185 129 121 BO7 3 3 3333169173121109114106 183 215 121 117 BO8 2 2 3333169159121109130130 173 185 127 121 BO9 3 3 3333155169117117114106 177 205 127 123 BO10 2 3 3333169159117111114106 185 187 129 121 BO11 2 3 3333153153117109106106 177 175 123 117

165

BO12 2 3 3333151167121117114106 179 179 153 119 BO13 2 2 3333169171139123132130 183 177 125 119 BO14 2 3 3333171161121117126114 183 179 131 123 BO15 2 3 3333167165121111114106 177 217 125 123 BO16 2 2 3333167167109109124114 173 177 153 121 BO17 2 3 3333153161117117140130 173 179 123 119 BO18 2 2 3333161165117115130114 221 209 125 117 BO19 2 2 3333169169117115132106 175 183 135 121 WY1 2 3 3333159159123117130114 173 187 123 123 WY2 2 2 3333183181127127106106 169 153 93 93 WY3 3 3 3333183185127121104104 159 159 95 95 WY4 2 2 3333169167123117114106 179 177 133 121 WY5 3 3 3333169167121111124114 181 179 121 119 WY6 2 2 3333169157121111130114 185 179 121 121 WY7 2 3 3333169167121109132114 211 177 121 121 WY8 2 3 3333163155117117132132 219 177 135 123 RL1 2 3 3333171155117111106106 173 187 129 123 RL2 2 3 3333171149121117106106 169 153 121 117 RL3 2 2 3333169165121109132106 177 205 129 121 RL4 2 2 3333153155117109132106 177 173 121 121 RL5 3 3 3333169159121117132106 173 185 129 127 RL6 2 2 3333159167121109114114 185 193 121 119 RL7 2 3 3333165163121117126114 173 211 127 125 RL8 2 2 3333159143117109132114 185 173 121 125 RL9 3 3 3333169169121109140106 205 173 121 125 RL10 2 3 3333169167117117132106 185 187 133 123 RL11 2 2 3333167167117117114106 205 177 135 117 RL12 2 3 3333159171117109132114 205 185 121 117 RL13 2 2 3333169167117109114106 217 173 129 117 RL14 3 3 3333165151121117146126 175 205 135 123 RL15 2 2 3333169163117117114114 173 185 153 151 RL16 2 3 3333169167117117126114 177 205 127 121 RL17 3 3 3333169167117117114106 179 205 127 125 RL18 2 2 3333165165117117130106 173 211 123 123 RL19 2 2 3333165165121117132130 173 179 123 123 RL20 3 3 3333167155121117130106 173 181 123 117 RL21 2 2 3333169169111111132114 173 221 121 117 RL22 2 3 3333169155117117130106 185 211 133 119 RL23 2 2 3333167159121117132114 179 215 117 117 RL24 3 3 3333167171117111114106 175 173 123 121 RL25 2 3 3333169169117109124106 177 187 121 117 RL26 2 2 3333159145117121132114 177 205 123 119 RL27 3 3 3333159169121117114106 177 185 135 117 RL28 2 2 3333169167117117114106 183 185 125 121 RL29 2 3 3333169153117117130125 179 179 121 117 RL30 2 3 3333171165121121146130 177 221 135 123 RL31 2 2 3333167149123117114106 187 211 131 121 GO2-1 2 2 3333171169121117114114 185 173 123 119 GO2-2 2 2 3333171171117117126106 227 187 125 125 GO2-3 2 2 3333169169117117106134 193 173 127 121 GO2-4 2 3 3333177179121127106106 153 153 93 93 GO2-5 3 3 3333171171117117130130 175 211 133 123

166

GO2-6 2 3 3333169169121117132106 173 219 123 117 BB1 2 3 3333167167121117140106 185 173 157 117 BB2 2 3 3333- - 121121- - 181 181 - - BB3 2 3 3333169159117117114106 173 179 121 113 BB4 2 3 3333167165121117114106 173 177 125 119 BB5 2 3 3333171165121109130114 183 185 133 129 BB6 2 2 3333169167121117114114 173 177 125 119 BB7 2 3 3333167165121117132114 225 183 129 121 EL1 2 3 3333167167109109126114 175 175 127 119 EL2 2 3 3333155129111109124122 201 185 133 119 EL3 2 3 3333173159111105130126 181 181 127 111 EL4 2 2 3333167171113109132124 179 175 131 121 EL5 2 2 3333167175117101124124 177 163 121 121 EL6 2 2 3333167165125113126126 213 179 137 119 EL7 2 2 3333161155119109126126 177 173 131 121 EL8 2 2 3333175173119111124112 161 175 135 121 EL9 2 2 3333167141117113124138 173 171 103 139 EL10 2 2 3333169185115111122122 175 165 135 113 EL11 2 3 3333175167137115124122 175 173 121 137 EL12 2 3 3333175167115117122104 177 181 135 141 EL13 2 3 3333157161109111126124 185 173 141 137 EL14 2 2 3333129163121113126112 175 185 117 105 EL15 2 2 3333193169119119132132 181 163 121 117 EL16 2 2 3333139173109109132132 181 163 129 119 EL17 2 3 3333157161121101124122 175 185 135 125 EL18 2 2 3333161175121109134130 181 181 139 129 EL19 2 2 3333155161121109124124 175 171 123 113 EL20 2 2 3333165177121105132112 177 171 139 113 EL21 2 2 3333165167115109124124 175 181 125 125 EL22 2 2 3333165139121109124118 201 177 141 139 EL23 2 3 3333165165109107128104 163 163 141 119 EL24 2 2 3333159177109109132104 173 173 135 125 EL25 2 2 3333129129121109134120 173 169 123 119 EL26 2 3 3333161155111107120104 177 167 137 137 EL27 2 2 3333165167109109124104 173 173 117 97 EL28 2 3 3333175175117109124124 181 163 143 97 EL29 2 2 3333161159111109126122 177 159 117 115 EL30 - - 3333175161115123112104 181 179 121 113 LJ1 2 3 3333------JU1 1 1 3333171153115115126124 179 165 163 145 JU2 1 1 3333161175109109130124 173 177 147 131 MP1 2 2 3333173151109109128128 169 169 143 143 MP2 2 2 3333171151113103142128 171 177 151 133 MP3 2 2 3333171171111123130128 173 169 133 129 MP4 2 2 3333173151111103128126 169 187 143 133 MP5 2 2 3323171171109123142126 179 169 131 129 MP6 2 3 3333201155113109130120 175 187 135 119 MP7 2 2 3333167157123109142128 175 177 127 127 MP8 2 2 3333151171115113150114 179 169 147 137 MP9 2 2 3333173153109109114114 169 175 141 123 MP10 2 2 3333171151109113142124 175 175 157 141 MP11 2 2 3333171201111115124124 171 177 141 133

167

MP12 2 2 3333171167109109120114 169 169 141 149 MP13 4 4 3323171173123123126124 169 169 143 141 MP14 2 2 3323201159109107130120 179 187 131 125 MP15 2 3 3333173175109103120115 169 175 147 139 MP16 2 2 3333201201109103126114 169 169 141 131 MP17 2 2 3333171171109123124124 175 213 133 131 MP18 2 2 3333173201113109124116 169 175 143 127 MP19 2 3 3333167203109109130126 183 179 141 141 MP20 2 2 3333171203113109128128 169 169 141 135 MP21 2 2 3333201159111109128128 175 169 125 119 MP22 2 2 3333173159109109124114 181 179 129 119 MP23 2 2 3333159159109115128114 187 183 143 119 LD3 1 1 3322173171123109130128 175 175 139 135 KFC 1 2 2 3323189153109111122114 189 135 137 133 KFC 2 2 2 3333167143109117126126 189 135 133 121 KFC 5 2 3 3333155161113113126106 169 209 135 133 KFC 6 2 2 3323189155123109142114 187 171 127 123 KFC 7 2 3 3323169155113119122114 159 135 143 137 KFC 8 3 3 3333153157113109116116 169 215 131 121 KFC 9 2 2 3333167161109109150106 187 169 137 135 KFC 10 2 2 3333167155113113124124 169 159 141 103 KFC 11 0 1 3322187181127143104104 143 143 93 93 KFC 12 1 1 3322187183121121106106 167 135 93 91

168

Ambassis sp. Allozyme loci Microsatellite loci Sample PGI - 1 PGI - 2 AMB 14 AMB 16 AMB 21 AMB 22 AMB 24 AMB 27 MR1 2 2 2 2 141 143 111 111 152 158 185 185 153 153 203 203 MR2 2 2 2 2 141 141 111 111 152 146 185 179 153 153 203 203 MR3 2 2 2 2 141 143 111 111 152 146 185 185 153 153 203 203 MR4 2 2 1 2 141 149 111 111 152 146 185 185 153 153 203 197 MR5 2 2 2 2 159 143 111 111 152 146 185 185 153 153 203 203 MR6 2 2 2 2 141 143 111 111 152 156 179 179 153 153 203 209 MR8 2 2 2 2 141 149 111 135 152 152 185 185 153 151 203 203 MR9 2 2 1 2 149 143 111 111 152 162 179 179 153 153 203 203 GM1 2 2 2 2 143 141 111 111 162 156 185 179 153 153 203 203 GM2 1 2 2 2 143 141 111 111 162 146 185 179 151 151 203 203 GM3 2 2 2 2 143 149 111 111 146 146 185 185 153 153 203 203 GM4 2 2 2 2 141 149 111 111 152 152 185 185 151 151 203 203 GM5 2 2 2 2 143 149 111 111 152 158 179 185 153 151 203 203 GM6 1 2 2 2 141 141 111 111 152 146 185 185 151 151 203 203 GM7 1 2 2 2 143 149 111 111 162 162 185 179 151 151 203 203 GM8 1 2 2 2 143 149 111 111 162 152 185 179 153 153 203 203 GM9 2 2 2 2 143 149 111 111 158 158 179 179 153 153 203 203 GM10 2 2 1 2 143 149 111 111 158 144 179 179 153 153 203 195 GM11 2 2 2 2 141 141 111 111 146 146 185 179 151 151 203 203 GM12 2 2 2 2 141 141 111 111 162 158 185 185 153 153 203 203 GM13 1 3 2 2 141 141 111 111 162 162 185 179 153 133 203 203 GM14 1 2 2 2 141 143 111 135 146 152 179 179 153 153 203 203 GM15 2 2 2 2 143 149 111 111 158 152 185 185 151 153 203 203 GM16 2 2 2 2 141 143 111 111 162 158 185 179 153 153 221 221 GM17 2 2 1 2 141 149 111 111 162 156 179 179 153 153 209 209 GM18 1 2 2 2 149 149 111 111 152 162 185 179 153 153 203 203 GM19 1 2 2 2 141 141 111 111 146 158 185 185 153 153 203 203 GM20 2 3 2 2 141 141 111 111 158 152 185 179 151 153 203 203 GM21 1 2 1 2 149 149 111 111 152 146 185 185 153 153 203 221 GM22 2 3 2 2 141 141 111 111 152 162 179 179 153 153 203 209 GM23 2 3 2 2 141 143 111 111 146 162 179 179 151 153 203 203 GM24 1 2 1 2 149 143 111 111 146 162 179 179 151 153 203 197 GM25 2 2 2 2 143 141 111 111 152 158 185 179 151 151 221 203 GM26 2 2 2 2 141 141 111 111 152 160 185 179 153 149 203 203 GM27 1 2 1 2 141 143 111 111 162 144 185 179 153 133 203 203 GM28 2 2 1 2 141 141 111 111 152 146 179 179 151 151 203 203 GM29 2 3 2 2 141 149 111 111 152 162 185 179 153 153 203 203 GM30 2 2 1 2 141 141 111 111 152 162 185 185 153 153 209 203 OM1 2 2 2 2 141 143 111 111 146 140 185 185 153 151 203 203 OM2 2 2 2 2 141 141 111 111 146 158 185 179 151 151 203 203 OM3 2 2 2 2 143 143 111 111 146 162 185 179 153 153 203 221 OM4 2 2 2 2 149 141 111 111 146 146 185 179 153 151 203 265 OM5 1 2 2 2 141 143 111 111 156 158 185 185 153 153 203 265 OM6 1 2 2 2 141 143 111 111 146 146 185 185 151 133 203 221 OM7 1 2 2 2 141 149 111 111 146 146 185 179 153 153 203 203 OM8 2 2 2 2 143 143 111 135 146 146 185 185 153 153 203 203 OM9 2 2 2 2 143 143 111 111 152 156 185 179 153 153 203 203 OM10 2 2 2 2 143 143 111 111 162 146 185 185 153 153 203 203

169

OM11 2 2 1 2 141 143 111 111 162 160 185 179 153 153 203 203 OM12 1 3 2 2 141 143 111 111 146 146 185 179 153 151 203 203 OM13 2 2 2 2 141 143 111 135 152 162 185 185 153 151 203 203 OM14 2 2 2 2 141 141 111 111 152 144 185 185 151 153 203 203 OM15 1 3 2 2 141 141 111 111 162 152 185 179 153 153 209 197 OM16 1 3 2 2 141 143 135 135 140 162 185 185 151 153 203 203 OM17 2 2 2 2 143 143 111 111 146 146 185 179 153 153 203 203 OM18 2 2 2 2 149 143 111 111 152 162 185 179 151 151 203 221 OM19 2 2 2 2 149 143 111 111 162 146 179 179 153 153 203 209 OM20 2 2 1 2 143 143 111 111 146 146 185 185 153 133 203 203 OM21 2 2 2 2 143 141 111 111 146 152 185 179 151 151 203 203 OM22 1 2 2 2 141 143 111 135 152 152 185 179 153 153 203 203 OM23 1 2 2 2 141 143 111 111 162 162 185 179 151 151 203 213 TP1 2 2 2 2 141 143 111 111 146 162 185 179 153 153 203 203 TP2 2 2 2 2 141 143 111 111 146 146 185 185 151 151 203 207 TP3 2 2 2 2 141 141 111 111 144 162 185 179 151 133 203 203 TP4 1 2 2 2 143 143 111 111 140 140 185 179 151 151 203 203 TP5 1 2 2 2 141 143 111 111 146 146 185 185 153 151 203 203 HS1 2 2 1 2 141 141 111 111 146 162 185 179 153 153 203 203 HS2 2 2 1 2 143 143 111 111 162 158 185 185 153 153 203 203 HS3 2 3 2 2 141 143 111 111 146 146 185 185 153 153 203 203 HS4 2 2 2 2 141 143 111 111 146 156 185 185 151 151 203 203 HS5 2 2 2 2 141 141 111 111 146 158 185 185 153 151 203 203 HS6 1 2 1 2 143 143 111 111 152 158 185 185 153 153 203 203 HS7 2 2 2 2 141 141 111 111 156 146 185 179 153 153 203 203 WL1 1 2 2 2 143 141 111 111 162 158 179 179 133 131 203 201 WL2 2 2 2 2 141 141 111 111 146 146 179 179 151 133 203 203 WL3 2 3 2 2 143 141 111 111 162 158 185 185 151 133 197 197 WL4 2 2 2 2 143 141 111 111 146 146 185 185 151 133 203 203 WL5 2 2 2 2 141 141 111 111 146 144 185 179 153 133 203 203 WL6 2 2 2 2 141 141 111 111 152 152 185 185 153 153 203 203 WL7 2 2 2 2 143 143 111 111 162 146 185 179 153 153 203 209 YG1 2 3 2 2 141 141 111 111 158 152 185 179 153 153 203 203 YG2 2 3 2 2 141 141 111 111 158 146 179 179 153 153 203 203 YG3 1 2 2 2 149 143 111 111 146 140 185 179 153 151 203 203 YG4 2 2 1 2 141 149 111 111 152 146 185 185 153 153 203 203 YG5 1 2 2 2 143 143 111 111 162 152 185 179 151 153 203 203 YG6 2 2 1 2 141 141 111 111 152 146 185 185 153 151 203 203 YG7 1 2 1 2 143 141 111 111 152 146 185 185 153 153 203 203 YG8 1 2 1 2 143 141 111 111 152 160 179 179 153 133 203 203 YG9 2 2 2 2 149 141 111 111 152 160 185 185 133 133 203 203 YG10 2 2 2 2 143 143 111 111 162 146 185 179 153 153 203 203 YG11 2 3 2 2 141 141 111 111 152 162 179 179 153 151 203 203 YG12 2 3 2 2 149 141 111 111 152 152 185 185 151 151 203 203 YG13 2 3 1 2 149 143 111 135 146 156 185 185 153 151 203 209 YG14 2 2 2 2 141 141 111 111 152 160 179 179 153 153 203 209 YG15 2 2 2 2 141 141 111 111 158 152 185 185 151 153 203 221 YG16 2 3 1 2 141 143 111 111 146 158 185 179 153 153 197 197 YG17 2 2 2 2 141 143 111 111 146 146 185 179 153 153 203 221 YG18 1 3 2 2 141 149 111 111 146 152 185 179 153 151 203 221 YG19 1 2 2 2 141 143 111 111 162 162 185 179 153 153 221 221 YG20 2 3 1 2 141 141 111 111 152 152 185 179 153 149 203 203

170

YG21 2 2 2 2 141 143 111 111 152 158 185 185 153 153 203 203 YG22 2 2 1 2 141 141 111 111 140 152 185 179 153 133 203 203 YG23 2 2 2 2 141 143 111 111 146 158 185 179 153 153 203 203 YG24 2 3 2 2 141 141 111 111 140 140 185 185 133 133 203 203 YG25 2 2 1 2 149 149 135 135 152 146 185 185 153 153 203 203 YG26 2 2 2 2 149 141 111 111 152 162 185 179 153 153 197 197 YG27 2 2 2 2 143 143 111 111 152 158 185 179 153 153 203 203 YG28 2 2 2 2 143 141 111 111 140 146 179 179 153 153 203 209 YG29 1 2 2 2 143 141 111 111 152 162 185 181 153 153 203 203 YG30 2 2 1 2 143 141 111 111 162 158 185 185 153 153 205 203 TB1 2 2 2 2 141 141 111 111 156 146 185 185 153 153 203 203 TB2 2 2 2 2 141 143 111 111 146 146 185 185 153 153 203 203 TB3 2 2 2 2 149 143 111 111 146 152 185 185 153 133 203 203 TB4 2 2 2 2 149 143 111 111 146 156 185 179 153 153 203 203 TB5 1 2 1 2 143 141 111 111 146 146 185 179 153 151 203 209 TB6 1 2 2 2 141 141 111 111 146 146 185 179 151 151 203 221 TB7 2 2 1 2 149 143 111 111 146 146 185 179 153 153 203 209 TB8 2 2 1 2 141 141 111 111 146 160 185 185 153 153 203 203 TB9 2 2 2 2 143 143 111 111 160 158 185 179 153 153 203 203 TB10 2 2 2 2 141 149 111 111 144 158 179 179 151 151 203 203 TB11 2 2 2 2 143 141 111 111 152 152 185 185 153 151 203 203 TB12 1 2 1 2 143 149 111 111 152 156 185 185 153 153 203 209 TB13 1 2 2 2 143 143 111 111 158 146 185 179 153 153 203 203 TB14 1 3 1 2 141 143 111 111 140 152 179 179 153 133 203 221 TB15 2 3 1 2 141 141 111 111 158 146 179 179 153 153 203 203 TB16 2 3 2 2 149 143 111 111 146 152 185 185 153 151 203 221 TB17 2 2 2 2 149 143 111 111 146 162 185 179 153 153 203 203 TB18 1 2 2 2 141 141 111 111 146 158 185 179 151 151 203 203 TB19 2 2 2 2 141 143 111 111 158 158 185 179 153 133 203 203 TB20 2 2 2 2 143 149 111 111 162 162 185 179 153 153 221 221 DL2-1 1 1 1 1 143 143 111 111 146 146 185 185 153 151 211 293 DL2-2 1 1 1 1 141 143 111 111 146 146 185 185 151 153 203 203 DL2-3 1 1 1 1 143 143 111 111 146 146 185 185 153 153 203 203 DL2-4 1 1 1 1 143 143 111 111 146 146 185 185 151 151 211 207 DL2-5 1 1 1 1 143 143 111 111 146 146 185 185 153 153 293 293 DL2-6 1 1 1 1 143 143 111 111 146 146 185 187 153 153 211 207 DL2-7 1 1 1 1 141 143 111 111 146 146 185 185 153 151 293 287 DL2-8 1 1 1 1 143 143 111 111 146 146 185 187 151 151 293 291 DL2-9 1 1 1 1 143 143 111 111 146 146 185 185 153 153 293 293 EC1 1 1 1 2 147 147 109 109 132 128 185 185 131 131 209 197 EC2 1 1 1 2 147 161 109 127 128 128 185 185 131 131 209 197 EC3 1 1 1 2 147 147 109 127 128 154 185 185 131 131 209 209 EC4 1 1 1 2 147 143 127 127 128 128 185 185 131 131 209 209 EC5 1 1 1 2 147 147 113 113 154 154 185 185 131 131 207 209 EC6 1 1 2 2 147 147 113 127 128 128 185 185 131 131 209 197 EC7 1 1 1 2 147 147 109 109 154 128 185 185 131 131 209 209 EC8 1 1 1 1 161 147 109 127 128 128 185 185 131 131 209 207 EC9 1 1 1 2 147 143 127 127 128 128 185 185 131 131 209 197 EC10 1 1 1 2 147 143 127 127 128 128 185 185 131 131 209 197 EC11 1 1 1 2 143 159 127 127 132 154 185 185 131 131 209 203 EC12 1 1 2 2 147 159 127 109 128 128 185 185 131 131 209 207 EC13 1 1 2 2 147 147 127 109 128 128 185 185 131 131 209 207

171

EC14 1 1 1 2 143 161 127 127 128 132 185 185 131 131 209 197 EC15 1 1 1 2 147 159 127 109 128 146 185 185 131 131 209 197 RL1 1 1 1 2 147 159 127 109 128 128 185 185 131 131 197 207 RL2 1 1 1 1 159 159 109 127 128 154 185 185 131 131 209 197 RL3 1 1 1 1 161 147 113 113 128 154 185 185 131 131 209 207 RL4 1 1 1 1 161 161 109 127 128 128 185 185 131 131 209 197 RL5 1 1 1 2 147 147 127 127 154 146 185 185 131 131 209 209 RL6 1 1 1 2 147 143 127 127 128 154 185 185 131 131 209 197 RL7 1 1 1 2 147 159 127 109 128 154 185 185 131 131 197 197 RL8 1 1 1 1 147 143 109 109 128 154 185 185 131 131 209 197 RL9 1 1 1 1 147 143 109 109 132 132 185 185 131 131 197 197 RL10 1 1 1 2 147 147 113 127 128 154 185 185 131 131 197 197 RL11 1 1 1 1 147 147 127 127 132 154 185 185 131 131 209 197 RL12 1 1 1 2 147 147 109 109 154 154 185 185 131 131 207 197 RL13 1 1 1 2 147 143 109 127 128 128 185 185 131 131 209 197 RL14 1 1 1 1 161 147 127 109 128 132 185 185 131 131 209 197 RL15 1 1 1 1 147 147 113 109 128 132 185 185 131 131 197 197 RL16 1 1 1 2 147 161 109 109 128 154 185 185 131 131 209 197 RL17 1 1 1 1 147 143 109 127 132 154 185 159 131 131 209 207 RL18 1 1 1 2 143 161 113 109 154 154 185 185 131 131 209 207 RL19 1 1 1 1 147 147 109 113 154 128 185 185 131 131 209 207 RL20 1 1 1 2 147 161 127 127 128 132 185 185 131 131 209 207 RL21 1 1 1 2 161 161 113 113 154 128 185 185 131 131 209 197 RL22 1 1 2 2 147 143 109 127 154 132 185 185 131 131 197 197 RL23 1 1 1 2 143 143 109 127 128 128 185 185 131 131 209 207 RL24 1 1 1 2 161 143 127 127 128 154 185 185 131 131 209 209 RL25 1 1 1 1 161 143 113 127 128 154 185 185 131 131 209 197 RL26 1 1 1 1 147 143 113 113 128 128 185 185 131 131 209 207 RL27 1 1 1 2 147 147 109 127 128 154 185 185 131 131 209 197 RL28 1 1 1 1 147 147 127 127 154 154 185 185 131 131 197 197 RL29 1 1 1 2 147 143 127 109 128 154 185 185 131 131 209 197 RL30 1 1 2 2 147 143 127 109 154 154 185 185 131 131 207 197 BO1 1 1 1 2 159 159 113 127 128 128 185 185 131 131 207 203 BO2 1 1 1 2 143 159 109 127 128 132 185 185 131 131 209 197 BO3 1 1 1 1 143 159 127 109 154 128 185 185 131 131 207 207 BO4 1 1 1 2 147 147 127 109 154 128 185 185 131 153 209 197 BO5 1 1 2 2 143 159 127 127 146 154 185 185 131 131 207 207 BO6 1 1 2 2 147 159 127 127 128 128 185 185 131 131 209 207 BO7 1 1 2 2 147 159 109 109 128 146 185 185 131 131 207 205 BO8 1 1 2 2 161 143 109 109 128 128 185 185 131 131 209 203 BO9 1 1 2 2 147 143 127 109 128 128 185 185 131 131 207 205 BO10 1 1 2 2 147 159 113 127 128 128 185 185 131 131 207 207 BO11 1 1 1 2 143 165 113 127 128 132 185 185 131 131 209 207 BO12 1 1 1 1 147 159 127 127 128 132 185 185 131 131 207 205 BO13 1 1 2 2 143 161 109 127 128 128 185 185 131 131 203 209 BO14 1 1 2 2 147 159 109 109 128 128 185 185 131 131 197 195 BO15 1 1 2 2 143 159 109 127 128 154 185 185 131 131 207 207 BO16 1 1 2 2 159 159 109 127 128 128 185 185 131 131 207 205 BO17 1 1 1 2 159 161 109 109 128 128 185 185 131 131 207 205 BO18 1 1 1 2 147 147 127 127 128 154 185 185 131 131 207 197 BO19 1 1 1 2 143 161 127 127 128 128 185 185 131 131 209 197 BO20 1 1 1 2 147 147 127 127 128 128 185 185 131 131 209 209

172

BO21 1 1 1 2 159 159 127 127 128 154 185 185 131 131 207 203 BO22 1 1 1 2 143 143 113 113 128 128 185 185 131 131 207 203 BO23 1 1 1 1 161 159 127 127 128 154 185 185 131 131 209 197 BO24 1 1 2 2 147 147 127 127 128 128 185 185 131 131 207 209 BO25 1 1 1 2 147 147 127 109 154 128 185 185 131 131 197 195 BO26 1 1 1 1 147 159 127 127 128 154 185 185 131 131 207 205 BO27 1 1 1 2 143 147 127 109 128 128 185 185 131 131 207 205 BO28 1 1 1 1 143 143 127 109 128 132 185 185 131 131 207 207 BO29 1 1 1 2 159 161 127 109 128 128 185 185 131 131 203 197 BO30 1 1 1 2 147 143 127 109 128 128 185 185 131 131 207 197 WY1 1 1 1 2 147 147 127 127 128 132 185 185 131 131 209 197 WY2 1 1 1 2 147 159 127 109 128 154 185 185 131 131 207 203 WY3 1 1 2 2 147 147 127 127 128 128 185 185 131 131 207 207 WY4 1 1 1 2 159 159 127 109 128 128 185 185 131 131 209 203 WY5 1 1 1 1 161 161 127 109 128 154 185 185 131 131 207 197 WY6 1 1 2 2 159 147 127 109 154 154 185 185 131 131 209 209 WY7 1 1 1 1 161 143 109 109 128 128 185 185 131 131 209 197 WY8 1 1 1 1 143 143 127 127 128 128 185 185 131 131 207 203 WY9 1 1 2 2 159 159 127 127 128 128 185 185 131 131 207 203 WY10 1 1 1 2 147 143 109 109 128 154 185 185 131 131 203 207 WY11 1 1 1 2 159 159 109 109 128 132 185 185 131 131 205 207 WY12 1 1 2 2 147 159 127 109 128 132 185 185 131 131 207 207 WY13 1 1 2 2 161 147 127 127 128 132 185 185 131 131 201 197 GO1-1 1 1 2 2 147 147 109 109 128 128 185 185 131 131 203 209 GO1-2 1 1 1 2 147 147 127 127 128 154 185 185 131 131 209 209 GO1-3 1 1 1 2 147 159 109 109 128 128 185 185 131 131 209 197 GO1-4 1 1 1 2 147 159 109 127 128 128 185 185 131 131 209 197 GO1-5 1 1 1 2 147 147 127 109 132 154 185 185 131 131 209 197 GO1-6 1 1 1 2 147 143 109 109 128 128 185 185 131 131 209 207 GO1-7 1 1 2 2 161 161 113 127 128 154 185 185 131 131 207 197 GO1-8 1 1 2 2 161 147 109 127 128 128 185 185 131 131 207 205 GO1-9 1 1 1 1 161 147 127 127 128 154 185 185 131 131 207 205 GO1-10 1 1 1 2 161 147 113 127 128 128 185 185 131 131 207 207 GO1-11 1 1 1 2 143 161 109 109 128 128 185 185 131 131 207 207 GO1-12 1 1 2 2 147 147 109 127 128 154 185 185 131 131 207 207 GO1-13 1 1 1 2 161 159 109 127 128 154 185 185 131 131 203 207 GO1-14 1 1 1 1 161 159 127 127 128 128 185 185 131 131 203 209 GO1-15 1 1 1 2 161 159 109 127 128 128 185 185 131 131 209 197 GO1-16 1 1 2 2 161 161 109 127 128 132 185 185 131 131 207 207 GO1-17 1 1 2 2 161 159 127 109 132 132 185 185 131 131 207 209 GO1-18 1 1 2 2 161 143 109 127 128 128 185 185 131 131 209 209 GO1-19 1 1 1 1 147 143 109 127 128 128 185 185 131 131 207 207 GO1-20 1 1 1 2 147 163 109 109 128 154 185 185 131 131 197 197 GO1-21 1 1 1 2 159 159 109 109 128 128 185 185 131 131 207 209 GO1-22 1 1 1 2 161 147 109 113 128 128 185 185 131 131 209 203 GO1-23 1 1 1 2 159 147 109 109 128 128 185 185 131 131 209 209 GO1-24 1 1 2 2 147 143 109 109 128 128 185 185 131 131 209 197 GO1-25 1 1 1 2 161 143 109 127 128 154 185 185 131 131 207 209 GO1-26 1 1 1 2 147 143 109 127 132 128 185 185 131 131 209 203 GO1-27 1 1 1 1 161 161 109 109 128 154 185 185 131 131 209 209 GO1-28 1 1 1 1 159 143 127 127 128 132 185 185 131 131 207 197 GO1-29 1 1 2 2 147 161 127 127 128 128 185 185 131 131 209 209

173

GO1-30 1 1 1 2 147 147 127 127 128 128 185 185 131 131 207 207 GO2-1 1 1 1 1 147 159 109 127 128 128 185 185 131 131 209 197 GO2-2 1 1 1 2 147 159 109 109 128 128 185 185 131 131 207 207 GO2-3 1 1 1 1 147 147 113 127 128 128 185 185 131 131 209 207 GO2-4 1 1 1 1 147 159 109 109 128 128 185 185 131 131 203 197 GO2-5 1 1 1 2 161 147 127 109 128 146 185 185 131 131 209 203 GO2-6 1 1 1 2 159 159 127 127 128 128 185 185 131 131 207 197 GO2-7 1 1 1 2 147 143 109 109 128 132 185 185 131 131 209 209 GO2-8 1 1 1 2 165 147 109 127 128 154 185 185 131 131 207 209 GO2-9 1 1 1 1 147 147 109 127 154 132 185 185 131 131 209 197 GO2-10 1 1 1 1 147 147 109 127 132 132 185 185 131 131 209 197 GO2-11 1 1 1 1 147 143 109 127 128 128 185 185 131 131 209 207 GO2-12 1 1 1 2 143 159 109 127 128 128 185 185 131 131 209 207 GO2-13 1 1 1 2 147 147 109 127 128 128 185 185 131 131 209 207 GO2-14 1 1 1 2 147 159 109 109 128 128 185 185 131 131 209 203 GO2-15 1 1 1 1 147 161 109 127 128 162 185 185 131 131 209 207 GO2-16 1 1 1 1 147 147 109 127 128 128 185 185 131 131 203 197 GO2-17 1 1 1 1 143 159 127 127 128 128 185 185 131 131 209 197 GO2-18 1 1 2 2 143 159 109 127 128 132 185 185 131 131 209 207 GO2-19 1 1 1 2 147 147 127 127 154 154 185 185 131 131 209 207 GO2-20 1 1 2 2 147 143 109 109 154 132 185 185 131 131 207 205 GO2-21 1 1 1 2 147 147 109 127 128 128 185 185 131 131 209 207 GO2-22 1 1 1 2 147 147 127 127 128 154 185 185 131 131 207 207 GO2-23 1 1 1 2 147 159 109 127 154 128 185 185 131 131 209 207 GO2-24 1 1 1 1 143 159 127 109 154 128 185 185 131 131 209 197 GO2-25 1 1 1 1 147 147 127 127 154 128 185 185 131 131 209 207 GO2-26 1 1 1 1 147 143 109 127 128 128 185 185 131 131 209 207 GO2-27 1 1 1 2 159 143 109 127 128 128 185 185 131 131 209 207 GO2-28 1 1 1 2 159 159 109 127 132 154 185 185 131 131 207 197 GO2-29 1 1 1 1 159 159 109 127 154 132 185 185 131 131 197 197

174

Ambassis macleayi Microsatellite locus Sample AMB 14 AMB 16 AMB 21 AMB 22 AU1 151 151 96 94 128 128 185 185 AU2 147 147 94 94 128 128 185 179 AU3 151 145 94 94 128 126 175 189 AU4 151 145 94 94 128 128 185 185 AU5 151 145 94 94 128 126 185 189 AU6 151 151 94 96 128 128 185 185 AU7 147 145 94 94 128 128 185 187 AU8 151 145 94 96 128 128 185 179 NA1 151 145 94 96 128 128 185 185 NA2 145 147 94 96 128 128 185 185 NA3 151 151 94 96 128 126 185 179 NA4 151 147 94 96 128 130 187 179 NA5 147 147 94 94 128 126 185 185 NA6 151 151 94 96 130 140 - - NA7 151 147 94 94 128 130 185 179 NA8 147 145 94 94 128 128 185 187 LD1 145 145 94 94 128 128 185 185 LD2 151 145 94 94 128 128 185 185 LD3 151 151 94 94 128 128 185 185 LD4 151 151 94 94 128 128 185 189 LD5 151 145 94 94 128 128 185 189 LD6 151 151 94 94 128 128 185 185 LD7 151 151 94 94 128 128 185 185 LD8 151 145 94 94 128 128 185 185 LD9 151 151 94 94 128 128 185 185 LD10 145 145 94 94 128 128 185 185 LD11 145 151 94 94 128 128 185 185 LD12 151 151 94 94 128 128 185 189 LD13 151 151 94 94 128 128 185 185 LD14 151 145 94 94 128 128 185 187 LD15 145 145 94 94 128 128 185 185 LD16 151 145 94 94 128 128 189 189 LD17 147 147 94 94 128 136 185 185 LD18 151 151 94 94 128 128 185 185 LD19 145 145 94 94 128 128 185 185 LD20 151 145 94 94 128 126 185 185 LD21 151 151 94 94 128 128 185 185 LD22 151 145 94 94 128 128 185 185 LD23 151 145 94 94 128 128 185 185 LD24 151 145 94 94 128 128 185 185 LD25 151 151 94 94 128 126 185 187 LD26 151 145 94 94 128 126 185 185 LD27 151 151 94 94 128 128 - - LD28 151 145 94 94 128 128 185 185 LD29 145 145 94 94 128 128 185 185 LD30 151 151 94 94 128 128 185 189 FL1 153 147 94 94 128 126 185 185 FL2 147 147 94 94 128 128 191 185 FL3 145 145 94 94 134 134 191 185 FL4 151 145 94 94 128 126 189 185

175

FL5 153 147 94 94 134 134 189 191 FL6 151 145 94 94 134 126 185 185 FL7 151 151 94 94 134 128 185 179 FL8 153 153 94 94 134 134 185 187 FL9 151 147 94 94 128 128 185 185 FL10 151 145 94 94 134 128 185 187 FL11 153 147 94 94 134 134 185 189 FL12 151 151 94 94 134 130 185 183 FL13 151 147 94 94 134 128 187 183 FL14 151 147 94 94 128 128 189 179 FL15 151 147 94 94 128 126 185 185 FL16 147 147 94 94 128 134 187 187 FL17 147 145 94 94 128 128 185 185 FL18 153 153 94 94 134 134 187 191 FL19 151 151 94 96 128 126 185 183 FL20 153 147 94 94 134 134 185 179 FL21 151 145 94 94 134 134 185 183 FL22 151 147 94 94 134 128 185 185 FL23 151 151 94 94 134 134 185 185 FL24 153 145 94 96 128 128 185 187 FL25 151 145 94 94 128 128 185 185 FL26 147 145 94 94 128 130 189 189 FL27 151 151 94 94 128 126 185 187 FL28 151 151 94 94 134 128 183 183 FL29 147 145 94 94 128 128 185 187 FL30 147 153 94 94 128 128 183 179 MO1 151 145 94 94 128 128 189 185 MO2 151 151 94 94 128 128 187 185 MO3 151 145 94 94 128 128 185 185 MO4 145 145 94 94 128 128 185 185 MO5 151 145 94 94 128 128 189 185 MO6 151 145 94 94 128 128 185 185 MO7 145 145 94 94 128 126 189 185 MO8 151 145 94 94 128 128 189 185 MO9 151 145 94 94 128 128 185 189 MO10 145 145 94 94 128 128 189 189 MO11 151 145 94 94 128 128 185 185 MO12 151 151 94 94 128 128 189 189 GD11 147 147 94 94 130 142 179 179 GD12 147 147 94 94 128 136 189 217 GD13 147 147 94 94 126 128 185 183 GD14 147 147 94 94 134 130 181 187 GD15 147 147 94 94 134 148 183 179 GD16 147 147 94 94 124 142 185 181 GD17 147 147 94 94 126 128 179 171 GD18 147 147 94 94 134 144 181 185 GD19 147 147 94 94 126 134 171 211 GD110 147 145 94 94 126 126 185 179 GD111 147 147 94 94 124 142 185 179 GD112 145 145 94 94 130 126 185 179 GD113 147 147 94 94 130 126 179 179 GD114 151 147 94 94 130 126 185 185 GD115 145 145 94 94 130 134 183 171 GD116 147 145 94 92 130 146 179 211 GD117 147 147 94 94 134 124 179 185

176

GD118 147 145 94 94 130 128 171 179 GD119 147 147 94 94 128 134 179 187 GD120 147 145 94 94 128 134 183 179 GD121 147 147 94 92 134 142 179 185 GD122 147 145 94 94 138 136 183 179 GD123 147 147 94 94 134 134 185 189 GD124 147 147 94 94 128 142 179 183 GD125 147 153 94 92 130 130 179 183 GD126 147 147 94 94 134 126 179 185 GD127 147 147 94 94 126 126 185 183 GD128 147 145 94 94 134 134 185 179 GD129 147 147 94 94 128 134 185 179 GD130 147 147 94 94 130 128 185 219 KFC1 151 151 94 94 134 130 183 171 KFC2 151 155 94 94 134 140 183 181 KFC3 151 151 94 94 134 134 185 171 KFC4 151 151 94 94 134 140 189 189 KFC5 151 151 94 94 134 140 171 171 KFC6 151 151 94 94 140 140 189 171 KFC7 151 151 94 94 134 140 171 185 KFC8 151 151 94 94 134 134 171 189 KFC9 151 153 94 94 130 144 171 183 KFC10 151 151 94 94 130 134 185 171 KFC11 151 153 94 94 130 130 171 171 KFC12 151 153 94 94 134 134 185 189 KFC13 151 151 94 94 140 130 189 181 KFC14 151 151 94 94 140 140 185 185 KFC15 151 151 94 94 140 130 171 171 KFC16 151 151 94 94 140 130 171 171 KFC17 151 151 94 94 134 134 171 171 KFC18 151 151 94 94 140 130 189 183 KFC19 151 153 94 94 134 130 171 171 KFC20 151 155 94 94 140 130 185 183 KFC21 151 147 94 94 134 140 185 185 KFC22 151 151 94 94 134 140 171 171 KFC23 151 151 94 94 130 140 183 183 KFC24 151 151 94 94 140 126 183 183 KFC25 151 151 94 94 140 134 171 171 KFC26 151 153 94 94 130 134 185 185 KFC27 151 147 94 94 134 142 171 171 KFC28 151 151 94 94 140 130 171 171 KFC29 151 151 94 94 134 140 185 171 KFC30 151 151 94 94 140 130 183 189 AG1 151 145 94 94 126 126 183 183 AG2 151 145 94 94 134 122 179 171 AG3 147 147 94 94 126 126 179 189 AG4 151 151 94 94 134 134 183 179 AG5 151 153 94 94 138 144 183 179 AG6 151 147 94 94 134 134 191 207 AG7 151 147 94 94 136 136 183 187 AG8 147 147 94 94 134 126 179 189 AG9 147 145 94 94 134 134 187 171 AG10 145 145 94 94 134 126 183 179 AG11 147 145 94 94 134 138 179 179 AG12 143 147 94 94 126 126 183 187

177

IF1 145 145 94 94 126 130 177 185 IF2 145 141 94 92 126 130 181 207 IF3 145 145 94 94 126 130 177 185 IF4 145 145 94 94 130 134 177 185 IF5 141 141 110 112 184 144 183 183 IF6 145 145 94 92 126 134 189 219 IF7 145 145 94 94 134 126 177 179 IF8 145 145 94 94 130 126 177 185 IF9 145 145 94 94 134 130 189 185 IF10 145 145 94 94 128 126 185 179

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