Phylogeography and population genetics of Australian freshwater Andrew Timothy Mather Bachelor of Science Honours Class 1 (Genetics) Bachelor of Science (Marine Biology and Zoology)

A thesis submitted for the degree of Doctor of Philosophy at The University of in 2015 School of Biological Sciences

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Abstract Diversity in ’s freshwater fish fauna is relatively depauperate when compared to other landmasses. However the family Melanotaeniidae is one of Australia’s most widespread and speciose groups of freshwater fishes. As such, they are an ideal group in which to examine freshwater phylogeography within Australia, as they offer the opportunity to compare with different niches, but similar evolutionary history. This dissertation investigates patterns of genetic diversity in three species of Australian freshwater rainbowfishes, two co-distributed species with distinct niches in undisturbed , and one species in an urban habitat, and explores the historical and contemporary processes that have influenced them. In my first chapter I used two co-distributed species to test the hypothesis that a widespread habitat generalist will have lower levels of genetic diversity and population structure than a closely related habitat specialist. I used sequence from one mitochondrial gene and one nuclear gene to investigate patterns of genetic diversity in M. splendida and M. trifasciata and to determine how differences in habitat preference and historical changes in drainage boundaries have affected patterns of connectivity and isolation. M. splendida, a widespread species found in the vast majority of freshwater in northern Australia, showed high levels of genetic diversity, and very little population structure across its range. Conversely, M. trifasciata, having a greatly contracted distribution to the northernmost of Queensland and the Northern Territory and habitat preference for faster flowing, highly oxygenated upland streams, showed extremely high levels of population structure. While phylogeographic patterns differed, both showed a strong relationship between stream length and genetic distance. For M. trifasciata genetic distance was best explained by stream length within catchments, and an ocean distance at 100x coast length, likely reflecting infrequent dispersal between catchments at times of low sea level (r2 = 0.82). M. splendida had a much shallower relationship with geographic distance, and genetic distance was best explained by stream length and a weaker ocean distance (10x coast length), suggesting greater rates of gene exchange. These results suggest that, although these species are co-distributed they appear to have experienced different evolutionary histories, with differences in habitat preference within waterways resulting in contrasting scales of genetic patterns. In chapter two I identified hybrid zones between co-distributed M. splendida and M. trifasciata at the periphery of M. trifasciata’s distribution. I used morphological identification, mtDNA sequences and two nuclear single nucleotide polymorphism (SNP) diagnostic restriction assays to characterize incidence, levels and directionality of gene flow between these two reciprocally monophyletic taxa. Four populations were identified as having undergone extensive hybridization between M. splendida and M. trifasciata. Patterns of gene flow between the two taxa were different in different hybridizing populations with complete mitochondrial capture evident in

ii two populations, uni-directional introgression in a third population, and a complete mixture of morphological hybrids and bi-directional gene exchange in the fourth population. This diversity in patterns of hybridization between two species is unusual and could potentially be due to local environmental conditions, although further research is required to determine the processes that are driving this pattern. In chapter four I investigated how different aspects of habitat degradation affect the genetic diversity of an Australian native , Rhadinocentrus ornatus, distributed in a highly developed region (southeast Queensland) and what impacts this may have on this species’ conservation status. Based on mtDNA sequence data from 327 individuals and 20 populations, I identified three distinct genetic lineages that were allopatric at the stream level. Indicators of habitat degradation had large negative effects on measures of genetic diversity, with close proximity to urban development and alterations to waterways associated with drastically reduced measures of genetic diversity across three distinct mtDNA lineages (evolutionary significant units). Low effective population sizes and low standing genetic variation in degraded habitats may result in reduced adaptive potential in this already threatened narrow range endemic. The only surveyed populations with high genetic diversity were found in already protected national parks. Many historical populations of R. ornatus in the highly developed Greater Brisbane Region are already probably extinct, and without further study and management this may be the fate of presently genetically depauperate populations in urban areas. This thesis represents the most comprehensive study to date of rainbowfish population genetics. Researching multiple species within the same , and sampling a large proportion of each species range has provided me with substantial power to infer the evolutionary and contemporary processes that have shaped genetic diversity and connectivity in three species. Contrasting phylogeographic patterns from two co-distributed species with different environmental niches occurring in relatively pristine habitats provided insight into long-term evolutionary processes. Sampling large numbers of populations, both in pristine and developed habitats, provided information on ecologically relevant forces shaping fine scale connectivity. This work adds to growing literature on population genetics of Australian freshwater fishes and specifically how historical changes in landscape connectivity, hybridization and urban development shape patterns of genetic diversity in Australian rainbowfishes.

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Declaration by author

This thesis is composed of my original work, and contains no material previously published or written by another person except where due reference has been made in the text. I have clearly stated the contribution by others to jointly-authored works that I have included in my thesis.

I have clearly stated the contribution of others to my thesis as a whole, including statistical assistance, survey design, data analysis, significant technical procedures, professional editorial advice, and any other original research work used or reported in my thesis. The content of my thesis is the result of work I have carried out since the commencement of my research higher degree candidature and does not include a substantial part of work that has been submitted to qualify for the award of any other degree or diploma in any university or other tertiary institution. I have clearly stated which parts of my thesis, if any, have been submitted to qualify for another award.

I acknowledge that an electronic copy of my thesis must be lodged with the University Library and, subject to the policy and procedures of The University of Queensland, the thesis be made available for research and study in accordance with the Copyright Act 1968 unless a period of embargo has been approved by the Dean of the Graduate School.

I acknowledge that copyright of all material contained in my thesis resides with the copyright holder(s) of that material. Where appropriate I have obtained copyright permission from the copyright holder to reproduce material in this thesis.

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Publications during candidature Mather, A.T., Hancox, D. and Riginos C. 2015. Urban development explains reduced genetic diversity in a narrow range endemic freshwater fish. Conservation Genetics 16(3): 625-634.

Thomas Huelsken, Heike Wägele, Björn Peters, Andrew Mather & Michael Hollman. 2011. Molecular analysis of adults and egg masses reveals two independent lineages within the infaunal gastropod Naticarius onca (Röding, 1798) (Caenogastropoda: Naticidae). Molluscan Research 31(3): 141-151.

Published abstracts

Mather, A.T., Riginos, C. 2013. Contrasting phylogeographic patterns of two co-distributed Australian freshwater fish. EcoTas – Ecological Society of Australia and New Zealand, Auckland, New Zealand.

Mather, A.T., Riginos, C. 2014. Contrasting phylogeographic patterns of two co-distributed Australian freshwater fish. International Biogeography Society, Canberra, Australia.

Publications included in this thesis

Mather, A.T., Hancox, D. and Riginos C. 2015. Urban development explains reduced genetic diversity in a narrow range endemic freshwater fish. Conservation Genetics 16(3): 625-634– incorporated as Chapter 4.

Contributor Statement of contribution Andrew Mather (Candidate) Designed Project (60%) Wrote the paper (70%) Daniel Hancox Collected samples Designed Project (10%) Edited paper (10%) Cynthia Riginos Designed Project (30%) Wrote and edited paper (20%)

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Contributions by others to the thesis

Chapter one: Dr Cynthia Riginos - editing draft

Chapter two: Dr Cynthia Riginos assisted with formulating ideas for this chapter, as well as with analysis and draft editing. Jeffrey Hanson helped with spatial genetic analysis. Dr Lisa Pope helped make maps in ArcGIS, measured overwater distances for analysis and assisted with draft editing. Thomas Richards, Jodie Haig and Peter Waldie assisted with field collections. Jason Sheehan assisted with data collection. Dr Peter Unmack, Dr Timothy Page and Dr Michael Hammer for providing some samples for analysis

Chapter three: Dr Cynthia Riginos assisted with formulating ideas and draft editing. Thomas Richards assisted with formulating ideas and draft editing

Chapter four: Dr Cynthia Riginos assisted with formulating ideas and draft editing. Daniel Hancox assisted with formulating ideas and sample/data collection. Dr David Aguirre-Davies for assistance with statistical analysis.

Statement of parts of the thesis submitted to qualify for the award of another degree

None.

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Acknowledgements Firstly and most importantly I would like to thanks Dr Cynthia Riginos for allowing and funding me to conduct a research project outside of the direct scope of her current research program. The help, guidance and support she has given me over the years has been immeasurable. I am also very grateful to Dr Lyn Cook and Associate Professor Robbie Wilson for their encouragement and support.

A big thank you to the Riginos Lab for their support and friendship over the years. Thankyou to Libby Liggins, Thomas Huelsken, Lisa Pope, Jody Shields, Dean Blower, Jude Keyse, Penny Mills, Iva Popovic, Din Mathias, Josh Thia, Lachlan Gleeson, Henry North and Jason Sheehan for the never-ending coffee runs chats discussions that have helped keen me sane over the years, it has been greatly appreciated.

Without my field assistants I would never have been able to achieve this so a massive thankyou to Thomas Richards, Peter Waldie, Jodie Haig and Daniel Hancox for taking time out of your own PhDs/lives to help me when I needed it most! Without you guys this project would never have got off the ground, and we got to have some amazing adventures in the wilderness as a bonus!

To all my friends at uni outside the lab I am grateful for the extra curricular activities that have kept us all busy, so thankyou Hugh Winwood-Smith, Patrick Bunn, Tegan Streeter, David Aguirre-Davies, Jayne Oliver, Luke Ambrose, Liana, Evans, Greg Walter, James Hereward, Tyrone Lavery and Luis Darcy.

Permitting agencies and Australian ethics approvals without which this research would not have been possible are: Queensland EcoAccess Permits (WITK06370609 and WITK08665711), Queensland Fisheries (95992), University of Queensland Ethics (SIB/936/08), Northern Territory Department of Resources (S17/3197) and Kakadu National Park (771).

Finally, but definitely not least, a HUGE thank you to my wonderful parents Jane and Peter, who have put up with me couch surfing when times were tough, helped read my work when I was struggling and for never-ending chats about science that got me interested in the first place and have kept me interested all the way through.

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Keywords phylogeography, biogeography, landscape genetics, rainbowfish, freshwater fish, population genetics, hybridization, introgression, urban development, genetic diversity

Australian and New Zealand Standard Research Classifications (ANZSRC)

ANZSRC code: 060302, Biogeography and Phylogeography, 40% ANZSRC code: 060411, Population, Ecological and Evolutionary Genetics, 40% ANZSRC code: 060204, Freshwater Ecology, 20%

Fields of Research (FoR) Classification

FoR code: 0603, Evolutionary Biology, 40% FoR code: 0604, Genetics, 40% FoR code: 0602, Ecology, 20%

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Table of Contents:

List of Figures……………………………………………………………………………………..x

List of Tables………………………………………………………………………….…………..xi

Chapter One – General Introduction…………………………………………..………….………1

Chapter Two - Comparative riverscape genetics of two Australian rainbowfishes………………9 Abstract………………………………………………………………………………...…...9 Introduction………………………………………………………………………………..10 Methods……………………………………………………………………………………16 Results……………………………………………………………………………………..20 Discussion…………………………………………………………………………………30

Chapter Three – Repeated breakdown of species boundaries in peripheral habitats in two broadly sympatric Rainbowfishes (Melanotaeniidae)……………………………………………..33 Abstract………………………………………………………………………………...….33 Introduction………………………………………………………………………………..34 Methods……………………………………………………………………………………38 Results……………………………………………………………………………………..40 Discussion…………………………………………………………………………………43

Chapter Four – Urban development explains reduced genetic diversity in a narrow range endemic freshwater fish…………………………………………………………………………...46 Abstract………………………………………………………………………………...….46 Introduction………………………………………………………………………………..47 Methods……………………………………………………………………………………51 Results……………………………………………………………………………………..53 Discussion…………………………………………………………………………………58

Chapter Five – General Discussion………………………………………………………………62

Literature Cited………………………………………………………………………………..…65 Appendices………………………………………………………………………………………..76

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List of Figures:

Figure 2.1: Map of distributions and sampling locations of M. splendida and M. trifasciata in northern Australia……………………………………………………………………….………14 Figure 2.2: Median joining haplotype networks constructed for M. splendida and M. trifasciata for mtDNA ATPase and nuclear S7 intron 1…………………………………………………...….22 Figure 2.3: Divergence by distance plots for M. trifasciata and M. splendida………………...…29 Figure 3.1: Photos of M. splendida, M. trifasciata and morphological intermediates……………36 Figure 3.2: Map of distributions and sampling locations of M. splendida and M. trifasciata in northern Australia……………………………………………………………………….………37 Figure 3.3: Example of agarose gel used for genotyping RFLP restriction assays……………….39 Figure 3.4: Restriction maps for S7 intron 1 and RAG1 PCR isolates and anzyme cut sites for M. splendida and M. trifasciata……………………………………………………...…………39 Figure 3.5: Sample locations and genotypes for all putative hybrids……………………………..41 Figure 3.6: Statistical parsimony haplotype networks of mtDNA for all Mary and Morline Rockhole individuals including M. trifasciata with introgressed M. splendida haplotypes……….42 Figure 4.1: Map of southeast Queensland with sample locations for R. ornatus and statistical parsimony haplotype networks for three identified mtDNA clades……………...……..50 Figure 4.2: Unrooted Bayesian phylogenetic tree constructed in MrBayes using 499 base Pairs of R. ornatus mtDNA ATPase8………………………………………………………..…….53

Supplementary Figure 2.1: Phylogenetic tree constructed in MrBayes for Melanotaeniidae using mitochondrial ATPase…………………………………………………………..…………..77 Supplementary Figure 2.2: Phylogenetic tree constructed in MrBayes for Melanotaeniidae using nuclear S7 intron 1………………………………………………………………………….78 Supplementary Figure 2.3: Median joining haplotype network constructed for M. splendida with putative subspecies identified based on known distributions……….…………………………….79

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List of Tables:

Table 2.1: Estimates of genetic diversity for M. splendida for ATPase8 and S7 intron 1…………25

Table 2.2: Estimates of genetic diversity for M. trifasciata for ATPase 8 and S7 intron 1………..26

Table 2.3: IMa2 summary statistics for M. splendida and M. trifasciata………………………….27

Table 2.4: Summary statistics for Mantel tests and MRDM analyses of Divergence by Distance…………………………………………………………………………….……………….28

Table 4.1: Genetic diversity and measures of neutrality in populations of R. ornatus…………….55

Table 4.2: Estimates of effect of human impact predictors on genetic diversity measures using generalized linear models…………….……….……….……….……….………………...….57

Supplementary Table 2.1: Pairwise ΦST table of all population pairs of M. splendida for mtDNA ATPase8. …….……….……….………………………………...……………....……...….80

Supplementary Table 2.2: Pairwise ΦST table of all population pairs of M. trifasciata for mtDNA ATPase8. …….……….……….…………………...………………....………………...….81

Supplementary Table 2.3: Pairwise ΦST table of all population pairs of M. splendida for nuclear S7 intron 1…….……….……….……...………………………………………....……...….82

Supplementary Table 2.4: Pairwise ΦST table of all population pairs of M. trifasciata for nuclear S7 intron 1…….……….……….……………………...………………………....……...….83

Supplementary Table 4.1: Habitat information and diversity statistics used for linear models.… 84

Supplementary Table 4.2: Pairwise ΦST table of all population pairs of R. ornatus for mtDNA ATPase8…….……….……….…………...…………………………………....……...…...85

xi Chapter One - Introduction Biogeography of Freshwater Fishes Organisms that spend their whole lifecycle in freshwater habitats are limited in their ability to disperse across a landscape (BURRIDGE et al. 2008). As a consequence, populations of fishes and other freshwater organisms often are highly genetically structured spatially (WARD et al. 1994). Isolation of local populations for long periods of time will lead to populations being affected differently by genetic drift and natural selection over time. Consequently, molecular divergence between populations is often large compared with that of terrestrial organisms separated by similar geographical distances (WARD et al. 1994; AVISE et al. 1998). As such, freshwater organisms often represent island-like populations, and provide excellent study systems for investigating how historical landscape changes drives evolutionary patterns. This dissertation contributes to the field of population genetics and phylogeography in aquatic ecosystems. Specifically, several empirical case studies were conducted that focus on how historical landscape connectivity, potential for hybridization and urbanization have affected genetic diversity in Australian freshwater fishes.

Biogeography and Phylogeography of Australia’s freshwater fishes Australia’s freshwater fish fauna is relatively depauperate when compared to those in other continents, with only ~300 currently described species (ALLEN et al. 2002). An ever- increasing body of research investigating genetic diversity within taxa has revealed a large amount of cryptic genetic diversity within currently described Australian species (e.g.

HURWOOD and HUGHES 1998; MCGUIGAN et al. 2000; COOK and HUGHES 2010; UNMACK and

DOWLING 2010; COOK et al. 2011; HUEY et al. 2013; UNMACK et al. 2013; COOK et al. 2014). Australia, and northern Australia in particular, has undergone significant landscape evolution across the Holocene and Pleistocene epochs, with sea-level change due to cyclical periods of glaciation and ice cap melt altering coastline patterns and riverine landscapes greatly

(TORGERSEN et al. 1985; VORIS 2000; CHIVAS et al. 2001). Changes in landscape across northern Australia over time have potentially allowed otherwise isolated populations to re- connect with one another during periods of time when sea levels were lower. During repeated periods of exceptionally low sea-levels over the last ~200,000 years, the was isolated from the Arafura and Coral Seas and formed an inland freshwater/brackish lake, referred to as ‘Lake Carpentaria’ (TORGERSEN et al. 1985; CHIVAS et al. 2001). This inland lake likely connected a large portion of river systems in northern Australia, as well as several

1 southern flowing rivers in Papua New Guinea (TORGERSEN et al. 1985; VORIS 2000; CHIVAS et al. 2001). Studies of the giant freshwater prawn, Macrobrachium rosenbergii (recently revised to M. spinipes) (DE BRUYN et al. 2004), and several fish species in the genera Neosilurus and

Oxyeleotris (HUEY et al. 2013) suggest that periods of lowered sea-levels across the Pleistocene may have connected populations, allowing the exchange of genetic material between otherwise previously isolated populations. Similar studies however, on species with highly disjunct distributions that employed molecular clocks to determine whether divergence times among populations across this same region matched with low sea levels over the last glacial maximum had found that divergence among populations was much older (COOK and HUGHES 2010; COOK et al. 2014).

Models of connectivity in riverine landscapes Several models have been suggested to explain connectivity of populations in riverine landscapes for freshwater organisms with different dispersal potentials. These include the Stream-Hierarchy model, the Headwater model, the Death Valley model and a panmixia model of connectivity. The Stream-Hierarchy model, described originally by MEFFE and VRIJENHOEK (1988), suggests that populations that are geographically more proximate and that occur in the same branches of a riverine network will be more genetically similar to one another than to those in different branches. This is similar to an isolation-by-distance model but takes into account the complexity of a stream network where linear distance between populations does not necessarily equate with ease of connectivity. The Headwater model suggests that populations that are physically close to one another at the headwaters of streams will be more connected to one another and will be genetically more similar to one another than populations that are only distantly connected but that occur within the same stream (FINN et al. 2007; HUGHES et al. 2009). This model mostly applies to organisms that have a terrestrial dispersal phase (e.g. taxa that can walk via terrestrial habitat between catchments), and therefore is less relevant to most fish. The Death Valley model generally applies to species that have relatively low dispersal potential within a riverine network and describes populations that are isolated from one another, regardless of the geographical distance that separates them within a riverine landscape (MEFFE and VRIJENHOEK 1988). Finally, a panmictic model of genetic connectivity suggests that all populations are completely connected within a riverine landscape, and any genetic structure among populations will be negligible.

2 The Stream Hierarchy and Death Valley models have been used to describe connectivity in river drainages, where barriers to dispersal are based on each organism’s contemporary ability to disperse across a catchment. These models can potentially also be extrapolated to encompass the distribution of stream taxa across a landscape where connectivity is no longer possible, but where connectivity has been in the past.

Hybridization and Freshwater Fish Hybridization between closely related but divergent taxa in nature can occur as a result of several processes, including secondary contact between populations (reviewed in HEWITT 2000). Secondary contact can occur via exotic introductions by humans, or via processes of geographical change, such as after drainage rearrangement in aquatic systems (ALLENDORF and

LUIKART 2007). Hybridization can produce several effects on species and between divergent genetic lineages. The mixing of genomes among closely related species can lead to novel forms, and sometimes result in novel species (JIGGINS and MALLET 2000; COYNE and ORR 2004;

SEEHAUSEN 2004; JØRGENSEN and MAURICIO 2005; RIESEBERG et al. 2007). Evidence for hybridization leading to evolution of new species has been reported in mountain butterflies in the genus Lycaeidae in North America (GOMPERT et al. 2006). Hybridization between two Lycaeidae species that occur in adjacent but different habitats has led to evolution of a new species that is able to survive in an intermediate habitat where neither of the parent species can exist (GOMPERT et al. 2006). Hybrid speciation has also been implicated as an integral part of some adaptive radiations, and several examples have been suggested to have occurred in aquatic environments (DOWLING and SECOR 1997; SALZBURGER et al. 2002). Another potential outcome of hybridization can be amalgamation of previously divergent taxa via creation of hybrid swarms, where resulting species possesses a mixture of the parental species genes (e.g. CHILDS et al. 1996; FITZPATRICK et al. 2008; HUDSON et al. 2011). For example, introduction of mallard ducks in New Zealand, and subsequent hybridization with the native grey duck, has led to creation of a hybrid swarm, and this resulted in extinction of purebred grey ducks (GILLESPIE 1985). How common inter-specific hybridization is in nature varies among organisms, but it has been suggested to be common in freshwater fish (ALLENDORF and WAPLES 1996; SCRIBNER et al. 2000). Many examples of hybridization between closely related taxa have been reported after anthropogenic introductions of otherwise allopatric populations of fishes (estimated to be around 50% in cases of hybridization reported in North American freshwater fishes in a review by

SCRIBNER et al. (2000). Evidence for hybridization in natural populations of freshwater fishes

3 however, continues to increase, with many examples reported in Cyprinid carps and minnows

(ALVES et al. 2001; ABOIM et al. 2010; BROUGHTON et al. 2011), barbels (LAJBNER et al. 2009;

GANTE et al. 2015), and African cichlids (SALZBURGER et al. 2002) to name just a few. There are also several instances of putative natural hybridization reported for Australia’s endemic freshwater fishes. The western carp gudgeon, Hypseleotris klunzingeri, has been shown to hybridize with two sympatric lineages of Hypseleotris, where both taxa sexually parasitise H. klunzingerii to produce viable offspring (BERTOZZI et al. 2000; SCHMIDT et al. 2011). Several studies have also found evidence for mitochondrial introgression between co-occurring Melanotaeniid rainbowfish species across northern Australia, and have suggested that when sibling species occur in sympatry, hybridization is one potential outcome (ZHU et al. 1994;

MCGUIGAN et al. 2000; UNMACK et al. 2013). A major barrier to crossing and hybridization among Australia’s freshwater fish however, is the relatively low levels of sympatric geographical distributions among closely related species, and high levels of endemism in most biogeographic regions (UNMACK 2001). To date, no studies have directly investigated hybridization in rainbowfish taxa, and large-scale population genetic studies of species occurring in sympatry are rare.

Effects of Urbanisation and Land Development on Genetic Diversity Another process that has major potential to affect levels and patterns of genetic diversity in many natural populations is urban expansion and development. Urban development, associated with natural habitat destruction and fragmentation has long been implicated in reducing both relative abundance and species diversity in natural populations in a variety of organisms (PRINGLE 2001; FISCHER and LINDENMAYER 2007). Loss of habitat is one of the major threats to many and freshwater fish are no exception in this regard (e.g.

ARTHINGTON and MARSHALL 1995; HUGHES et al. 1999; SADDLIER et al. 2013). Alteration of riverine landscapes is extremely common and potentially damaging when land is cleared for urban development. Many fish species require aquatic habitat types that are affected when riparian zones around waterways are cleared. Clearing of riparian zones for development can impact waterways in a variety of ways (ALLAN 2004). The process can increase the amount of sunlight that reaches the water, which in turn can increase growth of aquatic plants. This results in clogging of waterways and alters the natural water flow regime (ALLAN 2004; CROOK et al. 2015). Changing flow regimes can increase the susceptibility of waterways to invasive fish species such as Gambusia holbrooki and Xiphophorus sp. These exotics have been shown to have large negative effects on native fish species, competing with them for resources as well as

4 predating on eggs of native species and damaging individuals by fin nipping. Several native species (red-fin blue-eye and honey blue-eye) in southeast Queensland are under threat directly as a result of introductions of G. holbrooki (HOWE et al. 1997). Also, construction of dams and weirs disrupts natural water flow and acts to isolate populations (NILSSON et al. 2005). This increases the chance of loss of genetic diversity through genetic drift as populations become smaller and isolated from one another. The effect of habitat destruction on species abundance and diversity due to human expansion has been well documented in plants and some animals. Its effect on genetic diversity has, however, not been well documented, and in a rapidly changing environment may be just as important.

Study organisms: Rainbowfish Rainbowfish (family Melanotaeniidae) comprise seven genera; Glossolepis, Chilatherina, Pelangia, Cairnsichthyes, Melanotaenia, Iriatherina and Rhadinocentrus, that are endemic to Australia, Papua New Guinea and several of the eastern Islands in Indonesia and the Arafura Sea. Four genera: Cairnsichthyes, Melanotaenia, Iriatherina and Rhadinocentrus are found in Australia. Rainbowfish are one of the most diverse families of freshwater fish in Australia, with the vast majority of this diversity present in the genus Melanotaenia. The other three only each comprise a single described species. Australia has sixteen described Melanotaenia species, that are all widely distributed across the continent, ranging from the Murray-Darling River system in the south, across most of Queensland and Northern Territory waterways, and spreading west into the northern regions of Western Australia, south to the Pilbara region. Rainbowfish are often the most abundant native fish where they are present, often forming large multi-species shoals with other Melanotaenia species, as well as blue-eyes (Pseudomugil), glassfish (Ambassis) and hardyheads (Craterocephalus). All Rainbowfishes are sexually dimorphic, with males possessing bright colour patterns and elongated fins that are used in sexual displays. Their largely northern distribution has meant that very little research has been undertaken on this group (as well as many other Australian freshwater fishes with tropical distributions) compared to fishes with temperate distributions in the southern half of Australia. This is due to difficulties in accessing many of the remote regions in which they occur.

Melanotaenia splendida – Checkered Rainbowfish Melanotaenia splendida is a widespread species of rainbowfish that occur naturally in northern Australia, southern Papua New Guinea and in several Islands in the Gulf of

Carpentaria, Torres Strait and the Arafura Sea (ALLEN et al. 2002). It currently has four

5 recognized subspecies, of which three are found in Australian waterways: M. splendida splendida inhabiting freshwater rivers on the east coast of Queensland, Australia from the Fitzroy River in the south to the tip of Cape York; M. splendida inornata inhabiting coastal rivers from the tip of Cape York, west throughout the Gulf of Carpentaria, through Arnhemland to the Adelaide River region of the Northern Territory; and M. splendida tatei that occurs in inland waterways of the Northern Territory and Western Queensland. M. splendida grow to around 14cm and are generally highly abundant in a variety of freshwater habitats, including the main stem of large waterways, small creeks, swamps and lakes (ALLEN et al. 2002). M. splendida are characterized by multiple gold longitudinal bands along their flanks that give them a checkered appearance. Their colour patterns are highly variable, even given that all individuals possess colourful mottled markings on their dorsal, anal and caudal fins.

Melanotaenia trifasciata – Banded Rainbowfish In contrast to M. splendida, M. trifasciata has a vastly limited and disjunct distribution across far-northern Australia. Melanotaenia trifasciata is only found in western flowing rivers of , Queensland from the in the south, to the in the north. There are also several isolated populations on the east coast of Cape York in the Iron Ranges and around the Cooktown region several hundred kilometers to the south. M. trifasciata is also found in two regions of the Northern Territory: in the western rivers of Kakadu National Park, and in eastern Arnhemland, from the Liverpool River in the west to the Nhalinbuoy region in the east. M. trifasciata, unlike M. splendida, are also very restricted in their preferred habitats. They are commonly found in smaller, upland creeks, preferring fast flowing, highly oxygenated water and are much less common in the main stem of waterways and lower reaches of river systems (ALLEN et al. 2002; TAPPIN 2010). However, even with these differences in habitat preference M. trifasciata and M. splendida commonly form large multi-species schools with each other, as well as several other co-distributed species. M. trifasciata also have highly variable colour patterns, although they are easily distinguishable from other co-distributed rainbowfishes as they have a single thick black horizontal stripe that runs the length of their body.

Rhadinocentrus ornatus – Ornate Rainbowfish Rhadinocentrus ornatus is a small-bodied (to ~6cm) rainbowfish that is endemic to southeast Queensland and northern New South Wales, including the sand islands of , and . It occurs in lowland, coastal sandy tannin stained creek and swamp

6 habitats of this region of Australia, often surrounded by a Melaleuca habitat commonly known as wallum. Populations are often found in heavily tannin stained waterways, caused by oils from the Melaleuca forests that often surround the streams. They consist of a complex of several genetic lineages (PAGE et al. 2004; SHARMA and HUGHES 2011). Currently, R. ornatus are the only described species in the genus, however, studies by PAGE et al. (2004) and Adams (unpublished), using mitochondrial DNA and allozyme markers respectively, suggest that multiple highly divergent genetic lineages are present in this taxon. R. ornatus lineages are split into several groups, with a northern clade identified as occurring north of the on the Sunshine Coast, Queensland, up to the Yeppoon area in central eastern Queensland. This lineage includes a number of populations on Fraser Island. There is also a southeast Queensland clade, which occurs from the Noosa River catchment in the north, to the Clarence River in northern New South Wales. South of the Clarence to the Bellinger River on the New South Wales central coast is home to a northern New South Wales clade. MtDNA sequencing analysis has suggested that these clades have been separated for around 1.5 million years, and there is some evidence to suggest that they are divergent species

(PAGE et al. 2004). Sampling techniques used in the study by PAGE et al. (2004) were not sufficiently intense to identify lineage boundaries. Recently R. ornatus have disappeared rapidly from the greater Brisbane region that is the centre of their distribution (TAPPIN 2010). Currently, only two populations are known from small waterways in Brisbane, with all other previously known populations now thought to be extirpated. Similar contractions in distribution, along with large decreases in abundance have been observed in other small-bodied freshwater fish species that were previously common. The Oxleyan pygmy perch () and the honey blue-eye (Pseudomugil mellis) have both undergone large reductions in distribution and are now isolated to several waterways around the Sunshine Coast, Queensland and the sand islands of Moreton Bay.

Thesis Structure This thesis contributes empirical studies to the growing body of literature on landscape genetics of various freshwater taxa in Australia. Chapters Two, Three and Four document patterns of cryptic diversity, landscape genetics, hybridization and effects of urbanization on freshwater fish in Australia, followed by a concluding chapter (Chapter Five). Chapters Two, Three and Four have been prepared as articles for publication, with Chapter Four published (Conservation Genetics), Chapters Two and Three are currently in preparation for external peer review. Following is a brief explanation of the content of each chapter.

7

Chapter two uses a comparative framework to investigate the role of historical landscape changes and niche specificity on genetic patterns in two co-sampled and closely related freshwater rainbowfish species in the genus Melanotaenia. I test the hypothesis that a widespread habitat generalist (M. splendida) will exhibit low levels of population structure, with evidence for genetic connectivity following relatively recent geological fluctuations in sea level and changes to riverscape connectivity. In contrast, I hypothesize that a habitat specialist (M. trifasciata) with a contracted range will have high levels of population structuring, with connectivity among populations being much older and less common than in M. splendida.

Chapter three investigated patterns of introgression within populations of two reciprocally monophyletic species of Melanotaenia in far northern Australia. We identify repeated evidence of introgression in populations at a range edge between Melanotaenia splendida and M. trifasciata. Patterns of introgression varied, with evidence of mitochondrial introgression, uni- directional introgression of nuclear and mitochondrial genes, and a complex mix of bi- directional introgression and morphological intermediates found in different populations.

Chapter four investigated the effects of an urban distribution on genetic diversity in a native fish species, R. ornatus, hypothesizing that long-term habitat exploitation will result in reduced genetic diversity in affected populations. We examine populations that have been protected in national parks for long periods of time alongside populations that are within or near developed urban or farmland locations.

Chapter five summarizes the major findings of each chapter and explains the contributions of this thesis to understanding population genetics and phylogeography of Australia’s unique endemic freshwater fauna.

8 Chapter Two – Comparative riverscape genetics of two Australian rainbowfishes

Abstract Riverine landscapes in northern Australia have changed drastically due to cyclical periods of glaciation across the Pleistocene epoch. Fluctuations in sea level altered connectivity among populations of freshwater organisms, shaping their evolution. Dispersal capabilities also affect how populations connected through periods of lowered sea levels. Here I compared patterns of genetic diversity in two co-distributed species of freshwater fish in the genus Melanotaenia to test the hypothesis that a widespread habitat specialist will possess lower levels of genetic diversity and show more population structure than a closely related habitat generalist. We used sequence data from one mitochondrial gene and one nuclear gene to investigate patterns of genetic diversity in M. splendida and M. trifasciata and to determine how differences in habitat preference and historical changes in drainage boundaries have affected patterns of connectivity and isolation. M. splendida, a widespread species found in the vast majority of freshwater habitats in northern Australia, showed high levels of genetic diversity and very little population structure across its range. Conversely, M. trifasciata, having a greatly contracted distribution to the northernmost rivers of Queensland and the Northern Territory and habitat preference for faster flowing, highly oxygenated streams at the top of watersheds, showed extremely high levels of population structure. While phylogeographic patterns differed, both showed a strong relationship between geographic distance and genetic differentiation between populations. For M. trifasciata genetic differentiation was best explained by overwater distance between catchments, and an ocean distance optimally scaled at 1.16x106 times coast length, likely reflecting only very infrequent historical dispersal between catchments at times of low sea levels. M. splendida showed a much shallower relationship with geographic distance, and genetic differentiation was best explained by stream length and a much weaker scaled ocean distance (11.98 times coast length between catchments), suggesting greater rates of historical gene exchange. The results suggest that, although these species are co-distributed, they appear to have experienced different evolutionary histories, with differences in habitat preference within waterways resulting in contrasting scales of patterns of genetic diversity.

9 Introduction Alterations in landscapes over time strongly shape genetic diversity in freshwater organisms. Watercourses change in flow patterns, salinity levels, and sometimes change their physical location, leaving molecular signatures in the organisms that inhabit them (WATERS et al.

2001; BURRIDGE et al. 2006). Glacial periods across the Pleistocene have seen sequential exposure and flooding of regions across the world, and these altered flow regimes influence range expansions and contractions depending on habitat (reviewed in HEWITT 2000). Diversification as a result of landscape processes can be investigated via examination of differential effects of genetic drift among populations. A multitude of molecular tools are now available to investigate patterns of colonization and contraction of populations through the Pleistocene and to identify how geological changes to landscape have affected genetic diversity levels (HUGHES et al. 2009). Northern Australia has experienced significant landscape evolution during the late

Pleistocene and Holocene epochs (TORGERSEN et al. 1985; VORIS 2000; CHIVAS et al. 2001). The most recent period of glaciation (ending approx. 10,000 years ago) saw large portions of the Gulf of Carpentaria in northern Australia drained of water, and northern Queensland and Arnhemland connected periodically via land bridges to southern New Guinea (Fig. 1) (TORGERSEN et al. 1985;

CHIVAS et al. 2001). The Gulf of Carpentaria was also isolated from water bodies that contemporaneously surround it and intermittently formed an inland freshwater/brackish lake

(TORGERSEN et al. 1985; CHIVAS et al. 2001). During periods of glaciation many major waterways in western Cape York Peninsula, southern New Guinea, the Gulf of Carpentaria and eastern Arnhemland would have drained into ‘Lake Carpentaria’ instead of into the ocean. During periods of low sea levels, Lake Carpentaria could potentially have facilitated connectivity among populations of freshwater organisms in this region. Previous studies on the giant freshwater prawn, Macrobrachium rosenbergii, support this hypothesis with evidence for now isolated populations across the Gulf of Carpentaria showing patterns of recent connectivity linked to

Pleistocene sea-level change (DE BRUYN et al. 2004). In the present-day, Australia’s northern landscape also experiences major change on a yearly basis. Northern Queensland has distinct wet and dry seasons and the northern regions of the Northern Territory have a summer monsoon season. Both regions consist of large areas of low altitude that undergo annual flooding, with much of western Cape York being less than 120 m above sea-level and all of north-eastern Kakadu (Northern Territory) forming a connected floodplain during the monsoon season. Yearly flooding events combined with low topography have the potential to connect otherwise distinct river-systems and link populations of isolated freshwater organisms that live in them. These locations are extremely remote and difficult for

10 humans to regularly access and traverse, with restricted access to large portions of northern Queensland and much of the Northern Territory posing impediments for research. The remoteness and other challenges (extreme climates, dangerous aquatic fauna) mean that previous studies have been limited, with only a handful of published studies available for this highly diverse region in northern Australia (e. g. DE BRUYN et al. 2004; COOK and HUGHES 2010; UNMACK and DOWLING

2010; HUEY et al. 2013; UNMACK et al. 2013; COOK et al. 2014). Population genetic comparisons of closely related and co-distributed congeners can provide insights into how biological differences between species result in contrasting genetic patterns across a landscape. In particular, differences in ecological tolerances or niche specificity that closely related species require can potentially lead to divergent evolutionary histories (AVISE et al. 1987). These differences in evolutionary history can be examined using various molecular tools, and several models of ecological connectivity have been proposed for stream organisms to explain different observed patterns (reviewed by CROOK et al. 2015). These include the ‘Stream Hierarchy’ model, where connectivity of organisms follows the geographical structure of a particular waterway in a hierarchical manner. Under this model, sites within a stream will be more closely connected than sites in adjacent streams, and sites in adjacent streams will be more closely connected than sites in distant streams (MEFFE and VRIJENHOEK 1988). In contrast, the ‘Death Valley’ model describes population structure based on a lack of contemporary connectivity between populations across a landscape due to physical barriers including ephemeral waterways, or due to individual niche specificity or lack of dispersal capability limiting movement between waterways (MEFFE and VRIJENHOEK 1988). The Stream Hierarchy and Death Valley models are used to describe patterns of population structure in contemporarily connected waterways, however here we have extrapolated them to include waterways that have historically shared connections when sea levels were lower. A third model, the Headwater model describes population connectivity based on physical distance between populations within streams at their headwaters rather than connectivity via a waterway and often applies to organisms with limited potential to disperse across terrestrial habitat as well (FINN et al. 2007; HUGHES et al. 2009). Finally, the widespread gene flow model, or panmixia, is where river networks have little influence on population connectivity between sites, population structure is low, and dispersal potential between sites is high. Such a scenario might arise for instance if monsoonal flooding creates seasonal connections between waterways, or if terrestrial dispersal potential is high. Molecular techniques can be used to improve our understanding of historical dispersal, connectivity, and relative isolation among populations of

11 freshwater organisms and can also be used to better understand the processes that have shaped the distributions of freshwater species across contemporary landscapes. Rainbowfishes (Melanotaeniidae) are one of the most species rich families of freshwater fishes in Australia. Rainbowfishes are endemic to Australia and New Guinea, and consist of seven genera, five of which occur in Australia. Melanotaenia is the most diverse genus in Australia, with sixteen species currently described. They are small-bodied schooling fish that often form large multispecies shoals, and they are frequently the most abundant freshwater fish where they are found (ALLEN et al. 2002). Melanotaenia are common to most freshwater systems in northern Australia making them ideal candidates for testing hypotheses of how niche specialization affects population structuring in closely related taxa. Melanotaenia splendida is a widespread, habitat generalist that is commonly found in most freshwater habitats of northern Australia. It is most typically found in stream habitats with low rates of water flow (PUSEY et al. 2004). It often co-occurs with several congeners, including Melanotaenia trifasciata, Melanotaenia maccullochi, Melanotaenia australis and Melanotaenia nigrans. M. splendida is found from , throughout northern and northwestern Queensland, the northern half of the Northern Territory and the northernmost rivers of Western Australia (Fig. 2.1). In contrast, M. trifasciata has a highly contracted and disjunct range across far northern Queensland and northeast Northern Territory. Unlike M. splendida, M. trifasciata is a habitat specialist that requires high flow regimes to prosper, and is most commonly found in waterways with perennial flow (TAPPIN 2010). M. trifasciata is often found in small, fast flowing streams, and it is much less common in the main stem of river systems than M. splendida. Rainbowfishes are known to interbreed quite readily in artificial aquarium environments, and recent research has shown examples of mitochondrial introgression between co-occurring species throughout northern Australia and Papua New Guinea (UNMACK et al. 2013). These instances of mtDNA introgresion include M. splendida and M. trifasciata as well as most other Australian rainbowfish species (UNMACK et al. 2013). In this study we investigated historical processes that have influenced population genetic structure in two sympatric and closely related freshwater fish species of far-northern Australia using both mitochondrial and nuclear sequence data to test several models of ecological connectivity. We hypothesized that differing habitat preferences and distribution within a waterway would affect genetic population structure within species. Specifically, we predicted that M. splendida, a widespread habitat generalist, would exhibit relatively low levels of population structure across its distribution with patterns consistent with historical panmixia due to connectivity among many currently isolated waterways, and that any evidence of genetic

12 divergence would be consistent with recent isolation. We predicted that M. trifasciata, a habitat specialist with a contracted range, would show significantly higher levels of population structuring following patterns extrapolated from the Stream Hierarchy model. This study adds to a small body of literature in a relatively understudied and remote part of the world where genetic diversity patterns within species are largely unknown but this region is under increasing threat from land and habitat degradation by large-scale mining operations and agriculture.

13

14 Figure 2.1: Map of sampling locations with insets showing sites in Northern Territory (left) and Queensland (right). Light grey shading around coastline represents historical coastline at Last Glacial Maximum when sea levels were ~120m lower. Groupings of colors correspond to regions identified as mitochondrial clades in M. trifasciata and as indicated with region names and dashed lines on Queensland inset map, with southeast Cape York Peninsula (SECY) represented with shades of brown, northern Cape York (NCY) with blue, northwest Cape York (NWCY) with purple and southwest Cape York (SWCY) with green. Circles represent populations where both M. trifasciata and M. splendida were sampled, upward facing triangles where only M. trifasciata were sampled, and downward facing triangles where only M. splendida were sampled.

15 Methods Field Collections Melanotaenia splendida and Melanotaenia trifasciata were identified in the field based on morphology, where colour patterns are distinct between species. Samples were collected from 40 locations in far and the Northern Territory (Fig. 2.1) between 2011 and 2012 using seine nets. Small pieces of the lower caudal fin were taken and fish were released alive after sampling. Tissue samples were fixed in 100% ethanol and stored in a -20oC freezer. Where possible up to 30 individuals were collected for each species, however this was not possible in all sampling locations, as both species were not always present. In instances where perfect pairing of populations of M. splendida and M. trifasciata was not possible, the nearest possible location (within the same river drainage) was used for analyses.

DNA Extraction, Amplification and Sequencing Whole genomic DNA was extracted from fin-clip samples using a salt precipitation and ethanol cleanup method modified from ALJANABI and MARTINEZ (1997). Quality and quantity of genomic DNA were visualized using gel electrophoresis on 1% agarose gels. A ~950 base pair fragment spanning the mitochondrial genes ATPase6, ATPase8 and cytochrome oxidase III was amplified using primers designed by BERMINGHAM et al. (1997) using a BioRad Thermocycler. A 750 base pair fragment of the nuclear S7 gene was amplified using a nested PCR protocol, using primers designed by CHOW and HAZAMA (1998). Samples that were successfully amplified were prepared for sequencing using a standard ExoSap cleanup protocol (reagents and protocols from New England Biolabs), and mailed to Macrogen Korea for sequencing on an ABI 3730XL genotyping machine. Sequences were aligned and edited using the sequence-editing program CodonCode Aligner version 5.1.4 (http://www.codoncode.com). Haplotypes of S7 sequences were identified via the PHASE method (STEPHENS et al. 2001; STEPHENS and DONNELLY 2003) implemented through the online software SeqPHASE (http://seqphase.mpg.de/seqphase/) (FLOT 2010). Rates of recombination of the nuclear S7 gene were estimated using the four-gamete test

(HUDSON and KAPLAN 1985) implemented in DnaSP v5.1 (LIBRADO and ROZAS 2009).

Phylogenetic and Phylogeographic Patterns In order to determine the phylogenetic relationships between sampled M. splendida and M. trifasciata and to determine if our de novo sequences were consistent with published phylogenies, mtDNA and nuclear sequences were aligned with sequences from a rainbowfish phylogeny

(UNMACK et al. 2013), and separate Bayesian searches using the GTR+I+G model for one million generations were conducted using the phylogenetic program MrBayes (HUELSENBECK and

16 RONQUIST 2001). Haplotype relationships were visualized using median joining networks constructed in Network Publisher (BANDELT et al. 1999) (http://www.fluxus-engineering.com).

Estimating Divergence and Historical Migration

Population statistics were estimated using Arlequin v3.5 (EXCOFFIER and LISCHER 2010). Arlequin was used to gain estimates of genetic diversity within populations and to estimate pairwise

Nei’s distance between haplotypes (NEI and LI 1979). Nei’s genetic distance (NEI and LI 1979) was used as a response variable for Divergence by Distance (DBD) estimates instead of more commonly used FST or ΦST, as populations fixed for specific haplotypes can yield anomalous F statistics. Nei’s distance captures the amount of nucleotide differences between haplotypes, and allowed us to compare populations that are different from one another but are fixed for a single haplotype within populations. The neutrality statistics Tajima’s D (TAJIMA 1989) and Fu’s Fs (FU 1997) were used to test whether populations conformed to neutral equilibrium expectations. We also used a coalescent approach to estimate relative divergence of populations and historical migration rates with IMa2 (HEY and NIELSEN 2004; HEY et al. 2004; HEY and NIELSEN 2007). Population sizes (θ), migration rates (m) and time since populations diverged (t) were estimated between population pairs for both M. splendida and M. trifasciata, using both mtDNA and S7 nuclear sequence data. Because we wanted to compare similar population pairs between the two species while controlling for geography, we targeted populations from the same location where possible. Because not all sampling locations contained both species, in some instances we approximated by using the most proximate populations (e.g. no M. splendida samples were available from Jacky Jacky Creek where we collected M. trifasciata, therefore samples from the nearby Jardine River were used instead). We elected to focus on pairwise comparisons rather than multi-population analyses in IMa2 as there was no a priori hypothesis regarding population relationships to create a population level tree and also to reduce the number of parameters being estimated per coalescent search. Pilot runs were used to select upper bounds on Bayesian priors for θ, m and t such that the mode of posterior probabilities was captured and flanked by a flat or diminishing probability tail. After initial pilot runs to determine appropriate priors, we used Bayesian priors of θ = [1, 30], migration between populations = [0, 5], divergence time = [0, 10] for all population pairs, with a burn-in of 1,000,000 steps and a total of 10,000,000 steps using The University of Queensland’s High Performance Computing Unit. All population pair comparisons were repeated five times and results were found to be consistent between runs. For most population pairs the highest posterior probability of migration was zero. Where this was not the case, the log- likelihood of marginal distributions of migration at the highest point and the zero point were

17 compared in a log-likelihood test with a chi-square distribution (see NIELSEN and WAKELEY 2001;

HEY and NIELSEN 2004). Using measures of genetic diversity within populations, as well as pairwise Nei’s genetic distance (as described above) we tested for correlations between M. splendida and M. trifasciata among co-sampled sites. Similarly, to test whether divergence times between population pairs of M. splendida and M. trifasciata were consistently different from each other, a paired Wilcoxon sign- rank test was performed on estimates of divergence times calculated in IMa2. As pairing of populations for mtDNA and nuclear sequences did not match perfectly for M. splendida and M. trifasciata, representative populations for each region of Cape York Peninsula identified as having divergent ATPase haplotypes (not shared between regions) in M. trifasciata were used for this analyses.

Evaluating Predictors of Connectivity across Aquatic Landscapes of Northern Australia

Maximum Likelihood Population-Effects models (MLPE) (CLARKE et al. 2002) and Mantel tests (MANTEL 1967) were used to discern patterns of divergence by distance (DBD). MLPE models are a modification of mixed effects models that test relationships between distance matrices and are an alternative to Mantel tests, allowing multiple predictive matrices to be evaluated in a linear model framework. Although recent theoretical evaluations suggest that Mantel tests are not appropriate for spatial data (see GUILLOT 2009; LEGENDRE 2015) we retain them as a point of contrast to the MLPE method and so our results can be compared to Mantel based results from other landscape genetic studies. Only mtDNA data were used for these analyses as only six populations were analysed that were comparable between the two species for the nuclear S7 gene, and Nei’s genetic distance between population pairs was used as the response variable. A variety of different measures of landscape distance were evaluated between population pairs: 1) great circle distance, 2) overwater distance, and 3) scaled overwater distance, with ocean distance between catchments optimized to represent the most appropriate scaling factor that best describes genetic distance between population pairs. Scaled overwater distance was used here to estimate the relative strength of ocean distance between rivers as an historical barrier to dispersal in freshwater fish. Ideally bathymetric data identifying where rivers historically flowed, as well as whether they connected to one another prior to flowing into the ocean, would be used instead. However these bathymetric data were not available in fine enough detail to be used for this analysis as the region (the Gulf of Carpentaria) is composed of soft sediments that change rapidly in the strong currents that flow through it, making it extremely difficult to determine where waterways flowed during periods of lowered sea levels.

18 The three measures of geographic distance (great circle distance, overwater distance, and overwater distance with optimally scaled coastline distance) were chosen to model specific scenarios. If the Stream Hierarchy model were the best predictor of historical isolation among populations, we predicted that a measure of overwater distance would be a good estimator of DBD, and that optimal scaling of ocean distance as a barrier would improve this model. Secondly, if the Death-Valley model were the best predictor, and connectivity between populations had been both historically and contemporaneously low, we predicted that genetic distance between populations would be high and all geographic distance measures would be poor estimators of DBD. Thirdly, the headwater model is more applicable to organisms with an ability to disperse over land during some part of their life cycle; therefore we predicted that great circle distance would be the best distance model to estimate DBD. And finally, if populations were panmictic across their range we predicted that genetic distance between populations would be low and there would be no significant relationship between genetic distance and geographic distance for any of the distance measurements that were tested. Overwater distance was measured as distance along waterway to the ocean combined with distance along the coastline between river mouths using ArcGIS v10. In order to measure this, river drainages and coastline were treated as non-dynamic roadways based on Australia’s political coastline (obtained using National Catchment and Stream Environmental database v1.1.1, part of GeoFabric 2011 v2.01, Bureau of Meteorology). River distance was measured from sample site to the mid point of river mouths. As M. splendida and M. trifasciata are obligate freshwater fish, and freshwater fish cannot normally move through ocean waters, great circle distance and un-weighted overwater distance were compared with measures where ocean distance was given an optimized scaling factor. All geographic distance variables were scaled to a mean of zero and standard deviation of one. These analyses were also done with the removal of Northern Territory M. spendida populations, as there was no corresponding data for M. trifasciata for mtDNA.

Scaling of ocean distance was optimized using the equation Ci,j = Ri,j+(s∗Oi,j), where Ci,j is the total cost of traversing from one population to another, Ri,j is the cost associated with traversing the rivers needing to get from population i to population j, Oi,j is the cost associated with traversing the ocean needed to get from population i to population j, and s is the scaling factor. Scaling of ocean distance was used as a model of resistance to dispersal through saltwater environments as we predicted that dispersal through saltwater is much less likely than through freshwater, and thus would act as a stronger historical and contemporary barrier to gene flow. For a given value of s, values for Ci,j were calculated, z-scored, and then correlated with pairwise Nei's genetic distance using Mantel tests and MLPE models. To assess uncertainty in these estimates, a jack-knife analysis was conducted.

19

Results Sequence Alignment and haplotype resolution For M. splendida, 276 individuals were sequenced for mitochondrial gene region ATPase from 21 populations, and 40 individuals for S7 from 8 populations. For M. trifasciata, 286 individuals were sequenced for ATPase from 23 populations, and from 62 individuals for nuclear S7 from 11 populations. Of the 950 base pair mtDNA fragment, 608 base pairs were used for analysis in M. splendida and 639 for M. trifasciata, as these could be consistently resolved for all sequences. M. splendida and M. trifasciata mtDNA haplotypes were a minimum of 47 mutational steps different from one another. Phased haplotypes from S7 heterozygous individuals were resolved with posterior probabilities of 95% or higher, yielding 30 unique haplotypes from 102 individuals. The four-gamete test for recombination found that the maximum number of recombination events within the S7 intron for both species was zero, and further analyses were carried out with the assumption that recombination events within this 584 base-pair gene region were negligible.

Possible Hybridization In the process of initial data analysis, we found evidence consistent with introgression and hybridization between M. splendida and M. trifasciata individuals in three river systems. We identified mismatches between morphological identification, mtDNA haplotypes and nDNA genotypes in the , Northern Territory, and the Coen and McIvor Rivers in southern Cape York Peninsula, Queensland. The quantitative analyses that follow concentrate on individuals where hybridization is not suspected.

Phylogenetic and Phylogeographic Patterns Phylogenetic trees (Supplementary Fig. 2.1 & 2.2) constructed in MrBayes combining sequences collected in this study and sequences from UNMACK et al. (2013) showed that M. splendida and M. trifasciata formed two monophyletic groups that include samples from Papua New Guinea. These two reciprocally monophyletic groups were concordant with those identified for M. splendida spp. and M. trifasciata spp. by UNMACK et al. (2013). Median joining haplotype networks constructed in Network Publisher showed that Queensland populations of M. splendida split into two mtDNA clusters, with each cluster consisting of a common haplotype and many rare haplotypes (Fig. 2.2). These groups are purported to represent two subspecies (M. splendida splendida and M. splendida inornata) but we found that in several instances these putative subspecies shared the two most common mitochondrial haplotypes

20 (Supplementary Fig. 2.3). Northern Territory populations did not share any haplotypes with Queensland populations but were as few as a single mutational step different in ATPase. Northern Territory populations were all highly diverse and did not share haplotypes between sites, even between sites within the same river system. This pattern of increased diversity in the Northern Territory was also present in the nuclear S7 data, with high levels of diversity in several of the populations sampled compared to the majority of M. splendida populations sampled in Queensland. M. trifasciata, by contrast, showed phylogeographic structuring between populations for both ATPase and S7 (Fig. 2.2). Four geographical clusters of populations were identified in mtDNA across northern Queensland (SECY, SWCY, NWCY and NCY) with no shared haplotypes between clusters. These groups were, however, often only a single mutation different from one another. Networks constructed using nuclear S7 sequence data were roughly concordant with mtDNA, however individuals containing divergent northern and western Cape York mtDNA haplotypes shared several S7 nuclear haplotypes. Southeastern Cape York populations (SECY) were more divergent from northern populations, forming three mitochondrial clades that matched considerable geographical breaks in their distribution along the Queensland coastline. These clades were separated by up to ten mutational steps for ATPase. Nuclear data showed that the most southern populations were divergent from northern populations. A northeastern Queensland population (), however, shared S7 haplotypes with north and northwest Queensland populations. Northern Territory populations contained distinct S7 haplotypes from all Queensland populations, however only one Northern Territory individual (Liverpool River) is represented in the ATPase network so comparisons between ATPase and S7 for Northern Territory populations were not possible. Populations of M. splendida were less structured than M. trifasciata as estimated by consistently lower pairwise Φst among populations (Supplementary Tables 2.1 – 2.4).

21 ATPase - Melanotaenia splendida S7 - Melanotaenia splendida

ATPase - Melantotaenia trifasciata S7 - Melantotaenia trifasciata

60 45 20 25 ATPase : S7 : 1 1

Figure 2.2: Median joining haplotype networks constructed for M. splendida and M. trifasciata for both mitochondrial (ATPase) and nuclear (S7) gene regions. Colors correspond to those shown on map in Fig. 2.1. Size of haplotype shown is indicative of number of samples with that haplotype (consistent within locus).

22 Estimating Divergence and Historical Migration Diversity statistics showed that M. splendida had consistently higher measures of genetic diversity (π and θ) than M. trifasciata (Tables 2.1 and 2.2). Neutrality statistics were not significantly different from zero in most cases, and where values were significant, Tajima’s D and Fu’s Fs were not both significantly out of equilibrium. Coalescent analyses suggested that most populations of both species are isolated and not connected by gene flow. Only one population pair (migration from mid to Myalla Creek) of M. splendida had posterior probabilities of migration that were significantly non-zero (P < 0.05) (Table 2.3). Coalescent estimates of genetic diversity (θ) for M. splendida were consistently higher than for M. trifasciata (Table 2.3), which was concordant with Watterson’s θ (Tables 2.1 and

2.2) (WATTERSON 1975). A Wilcoxon signed-rank test for populations pairs representing each clade (listed in Table 2.3) identified in M. trifasciata showed that M. splendida had consistently lower divergence times (t) than M. trifasciata between population pairs estimated in IMa2 (P=0.011). If the two species had similar spatial diversity patterns we would expect measures of population diversity to be spatially correlated, but correlation tests between species using diversity measures π and θ per population location formed no significant correlation between M. trifasciata and M. splendida, with an r-value of 0.21 for π (P=0.51, n=12), and 0.39 (P=0.21, n=12) for θ. However, there was spatial concordance based on pairwise Nei’s genetic distance for population pairs that were comparable between the two species, with an r-value of 0.49 (P=0.0007, n=45).

Evaluating Predictors of Connectivity across Aquatic Landscapes of Northern Australia MLPE models and Mantel tests evaluating geographic distance measures and genetic distance had consistently lower R2 for M. splendida (0.59 - 0.67 for MLPE models and 0.32 - 0.35 for Mantel) than M. trifasciata (R2 ≥ 0.84 for MLPE models and R2 ≥ 0.74 for Mantel) (Fig. 2.3, Table 2.4). For M. splendida, overwater distance with and without optimally scaled coastline distance were both good predictors of genetic distance, with large AICc weights given to both of these models (Table 2.4). Optimal scaling of Ocean distance was substantially lower for M. splendida (11.98) than M. trifasciata (1.16x106). For M. trifasciata, overwater distance with coastline distance optimally scaled was by far the best predictor of genetic distance for both MLPE models and Mantel tests. Distance measured through watercourses was a consistently better predictor of genetic distance than great circle distance or a null model. Optimal weighting of coastline distances between river mouths consistently improved the fit of models in M. trifasciata, however for M. splendida scaling of coastline distance did not substantially improve model fits as compared to un-scaled overwater distance. Estimates of uncertainty from jack-knife analyses showed that the optimal scaling factors derived from all populations are stable, however, certain

23 combinations of populations can result in wildly different scaling factors. These outlier scaling factors tend to be large. This suggests that that the relative difficulty of traversing ocean between certain populations can be much higher than that experienced by most of the populations. Code compiled in R is available for this optimization and analyses in the supplementary materials.

24

! Region SWCY SWCY SWCY SWCY SECY SECY SECY SECY SECY WCY WCY WCY WCY NCY NCY NCY NCY

NT NT NT NT NT θ are Table # # # # # # # # # # # # # # # # # # # # # # ' measured nucleotide. measured as % per Douglas'Creek Creb'Crossing'1' Creb'Crossing'2' McIvor'River Laura'River O Burster'Creek C J Ducie'River M Lower'Wenlock Tentpole'Creek Weipa'Swamp Myalla'Creek Archer'River Coen'River East'Alligator'River Magela'Creek Ji Mo Mary'River ATPase 2. ardine'River owal'Creek m'Jim'Creek live'River id'Wenlock 1: 1: rline'Rockhole Measures per Measures of population diversity for Site or sampleswere notsuccessfully sequenced for mtDNA. for multiple tests. ‘Not Sequenced’indicates eithersamples notwere genotyped for asS7 other representatives *

' ' ' ' ' denotesP ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' '

- values< Not#Sequenced #'Individuals

11 15 43 15 12 12 16 16 12 16 15 11 11 11 5 2 6 8 6 3 4 # # # # # # #

# # # # # # # # # # # # # # 0.05.No Tajima’sD hadsignificant P '

#

Hap.'# 13 40 14 10 / 4 5 5 4 5 2 6 6 2 4 5 3 4 3 3 1 3 # # # # # # # # # # # # # # # # # # # # # #

' 0.26 0.00 0.08 0.08 0.08 0.64 0.61 0.55 0.16 0.17 0.19 0.18 0.38 0.23 0.43 0.48 0.33 0.55 0.49 0.00 0.61 π'% / # ' # # # # # # # # # # # # # # # # # # # # #

M. splendidaM. 0.17 0.00 0.10 0.15 0.15 0.60 0.54 0.54 0.16 0.25 0.27 0.14 0.25 0.25 0.32 0.34 0.43 0.55 0.56 0.00 0.50 θ'% / # ' # # # # # # # # # # # # # # # # # # # # #

Tajima's' - / / / / / / / / valuesand only JimJim Creek (mtDNA) wassignificant afterBonferroni correction 1.92 0.00 0.22 0.44 0.04 0.00 1.03 1.79 1.51 1.59 0.00 0.00 0.82 0.59 1.14 1.32 1.02 1.12 0.28 1.37 0.54 D / for A for #

' # # # # # # # # # # # # # # # # # # # # # TPase and TPase Genetic diversity S7. Fu's'Fs F F F F F / / / 2.28 1.80 2.55 2.90 4.68 0.27 0.00 1.42 2.58 1.77 0.00 1.72 3.33 2.15 1.66 1.02 0.00 4.04 0.52 0.67 0.08

/ # * * * * * # # # # # # # # # # # # # # # # ' ' ' ' ' ' Not#Sequenced Not#Sequenced Not#Sequenced Not#Sequenced Not#Sequenced Not#Sequenced Not#Sequenced Not#Sequenced Not#Sequenced Not#Sequenced Not#Sequenced Not#Sequenced Not#Sequenced Not#Sequenced #'Individuals

12 10 10 14 4 8 8 6 # # # # # # # # S7 '

# # # # # # # # # # # # # # Hap.'# # # # # # # # # # # # # # # / / / / / / / / / / / / / / 4 2 1 2 7 6 3 6 # # # # # # # # # # # # # # # # # # # # # # ' estima 0.15 0.11 0.00 0.10 0.37 0.40 0.28 0.31 π'% / / / / / / / / / / / / / / from # # # # # # # # # # # # # # ' # # # # # # # # tes and tes π

the same 0.11 0.09 0.00 0.18 0.36 0.39 0.22 0.16 θ'% / / / / / / / / / / / / / / # # # # # # # # # # # # # # ' # # # # # # # # Tajima's' region / 0.89 1.63 0.00 0.11 0.09 1.39 2.97 1.56 D / / / / / / / / / / / / / / # # # # # # # # # # # # # # ' # # # # # # # #

w ere selected, selected, ere Fu's'Fs F / / / 2.20 0.54 0.00 1.22 0.69 0.93 3.00 1.17 / / / / / / / / / / / / / / # # # # # # # # # # # # # # * # # # # # # # ' '

25

! Region SWCY SWCY SWCY SWCY SECY SECY WCY WCY WCY WCY WCY WCY WCY WCY NCY NCY NCY NCY NCY NCY NCY NCY NCY NT NT NT ATPase Table 2.2: # # # # # # # # # # # # # # # # # # # # # # # # # # ' for for S7 other as representatives from a popula * denotes P Gap'Creek McIvor'River Claudie'River Pascoe'River Olive'River Jacky'Jacky'Creek Burster'Creek Crystal'Creek Mistake'Creek Twin'Falls Fruitbat'Falls Cockatoo'Creek Gunshot'Creek Cholmendelay'Creek Dalhunty'River Ducie'River Tentpole'Creek Lower'Wenlo Mid'Wenlock Peppan'Creek Marmoss'Creek Archer'River Coen'River Mary'River Morline'Rockhole Liverpool'River

Measures of diversity per populationfor Site - values< 0.05. ' ' '

' ' ' tion containedintrogressed haplotypes, andtherefore werenot included in analyses. ' ' ' ' ' ' ' ' ck ' ' ' ' ' ' ' ' ' ' ' '

' #'Individuals

No No Tajima’sD Fu’s or Fshad significantP from NA## NA## 10 14 14 13 15 14 11 11 10 15 16 12 12 11 12 12 11 15 16 10 13

9 8 1 # # # # # # # # # # # # # # # # # # # # # # # # # #

the same '

Hap.'# 4 1 4 3 6 4 3 4 3 2 3 4 8 3 2 7 4 1 3 1 2 4 2 1 ) ) # # region # # # # # # # # # # # # # # # # # # # # # # # # M. trifasciata '

0.07 0.00 0.04 0.10 0.14 0.05 0.11 0.00 0.17 0.08 0.10 0.10 0.13 0.13 0.03 0.26 0.00 0.00 0.06 0.00 0.00 0.05 0.04 0.00

π'%' were selected, ) ) # # # # # # # # # # # # # # # # # # # # # # # # # # '

0.06 0.00 0.05 0.10 0.24 0.10 0.11 0.00 0.17 0.05 0.12 0.14 0.15 0.10 0.05 0.21 0.00 0.00 0.10 0.00 0.00 0.10 0.06 0.00 θ'% ) ) for for ATPase S7.and Genetic diversity estimates and π are θ measured as % per nucleotide. # # ' # # # # # # # # # # # # # # # # # # # # # # # #

Tajima's' or sampleswere notsuccessfully sequence ) ) ) ) ) ) ) ) ) ) ) 0.82 0.00 0.04 0.00 0.02 1.38 0.75 0.87 0.00 0.00 0.00 0.00 0 0.34 0.13 1.41 1.48 0.58 0.81 0.48 1.13 1.00 1.4 1.05 - .00 D valuesafter Bonferroni correction for multiple tests. ) ) # # ' 7 # # # # # # # # # # # # # # # # # # # # # # # #

Fu's'Fs G G ) ) ) ) ) ) ) ) ) 3.17 1.40* 0.82 0.00 0.19 0.30 0.00 2.34 1.25 0.32 0.00 0.00 0.00 0.00 0.00 0.17 0.11 0.53 1.21 0.94 0.41 0.67 0.92 0.18 ) ) # # * # # # # # # # # # # # # # # # # # # # # # # ' ' '

## #'Individuals Not#sequenced Not#sequenced Not#sequenced Not#sequenced Not#sequenced Not#sequenced Not#sequenced Not#sequenced Not#sequenced Not#sequenced Not#sequenced Not#sequenced Not#sequenced Not#sequenced Not#sequenced

12 16 12 14 10 16 12 4 8 8 2 # # # # S7 # # # # # # # . ‘NotSequenced’indicates either samples were not genotyped

' # # # # # # # # # # # # # # # ###### Hap.'# d for mtDNA. ) 1 2 1 3 3 4 2 4 2 1 1 # ) ) ) ) ) ) ) ) ) ) ) ) ) ) # # # # # # # # # # # # # # # # # # # # # # # # # ' 0.00 0.08 0.00 0.15 0.08 0.27 0.06 0.28 0.03 0.00 0.00 π'% ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) # # # # # # # # # # # # # # # ' # # # # # # # # # # # ‘N/A’ ‘N/A’ indicates that all individuals

0.00 0.09 0.00 0.15 0.11 0.21 0.06 0.20 0.06 0.00 0.00 θ'% # ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) # # # # # # # # # # # # # # # ' # # # # # # # # # # # Tajima's' ) ) ) ) ### 0.00 0.00 0.85 0.01 1.13 0.00 0.00 0.61 0.12 0.85 1.14 D ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) # # # # # # # # # # # # # # # ' # # # # # # # # # # # Fu's'Fs ) ) 0.00 0.17 0.00 0.69 0.61 0.42 0.90 0.00 0.00 0.72 0.48 ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) # # # # # # # # # # # # # # # # # # # # # # # # # # '

26

* indicates p < 0.05 a Table ! Nuclear S7 only SWCY SWCY SWCY WCY WCY NCY NT NT NT NT ! SWCY SWCY SWCY SWCY WCY WCY WCY NCY NT ! M.#splendida M.#trifasciata $ $ $ $ $ SWCY WCY SE NCY SWCY $ $ $ $ $ $ $ SECY SECY SECY NCY SECY SECY NCY $ $ $ $ $ $ $ CY 2. WCY SECY NCY WCY SECY SECY NCY ! 3: ! ! ! ! ! ! ! ! ! ! !

! ! θestimated MeasuresaspairforsummaryS7ATPase % of IMa2areper combinedand base statistics. sequences. ! ! ! ! ! ! ! Myall Myall Myall MidWenlock MidWenlock J Mary Mary Mary Mary * Coen Coen Coen Coen MidWenlock MidWenlock MidWenlock Jacky Mary Population*pairs ardine –

this population contains

$ $ $ $ $ $ $ $ $ $ $ $ $ MidWenlock Gap McIvor Jacky Gap Myall MidWenlock McIvor Jardine Coen MidWenlock McIvor Jardine $ McIvor ! ! ! a ! ! $ $ $ $ $ ! ! ! ! McIvor Jardine McIvor Gap Jacky ! ! * ! ! ! ! ! ! ! ! θ 0.2754 0.3961 0.3055 0.5647 0.3785 0.2127 0.1924 0.1798 0.1697 0.2477 0.0822 0.1165 0.1018 0.0871 0.1214 0.1288 0.1018 0.0969 0.0385 M. splendida * pop # # . 1 ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! * mitotype for all θ 0.9539 0.1370 0.1949 0.1270 0.2024 0.1421 0.3106 0.3558 0.1421 0.1899 0.0797 0.0135 0.0331 0.0601 0.0331 0.0307 0.0724 0.0135 0.1516 * pop # # . 2 ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! * Genetic* 0.0925!(0.295 0.695!(0.305 0.835!(0.385 0.685!(0.225 0.885!(0.485 2.685!(1.585 3.105!(1.565 5.945!(1.465 9.205!(0.935 7.245!(0.195 4.445!(0.265 7.035!(1.595 9.205!(1.085 4.295!(1.685 7.705!(0.195 9.695!(0.855 4.725!(0.145 6.295!(1.435 0.145!(0 M. trifasciata divergence ! ! $ 10) $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ 10) 10) 10) 10) 10) 10) 10) 10) 10) 10) 10) 10) 10) 10) 10) 10) 10) 10) ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! * identified fish. 1.177 0.15 0.16!(0.03 0.60 0.42 0.94 Migration*1>2 0 0.09!(0 0!( .013 0!(0 0!(0 0!(0 0!(0 0!(0 0!(0 0!(0 0!(0 0!(0 0!(0 0.02 ! ! ! ! (0 ( ( ( ! ( 0.08 0.08 0.13 0.12 $ ! $ $ $ $ $ .03 $ $ $ $ 2.783 (0 3.04 3.72 3.00 3.56 2.79 0.91 4.25 0.67 2.74 ! ! $ $ $ 4.82) 2.67 2.90 $ $ $ $ $ $ 2.51 2.28) 3.10 3.52 4.75 4.77 ) ) ) ) ) ) ) ) ) ) ! ! ! ! ! ! ! ! ! ! ) ) ! ! ! * ) ) ) )

) ! ! ! ! ! ! 0.923 0.74 Migration*2>1 0.02 0.10!(0 0.08!(0 0!( 0!( 5**(0 0!(0 0!(0 0!(0 0!(0 0 0 0!(0 0!(0 0!(0 0 0!(0 0.01 0.02 ! ! ! ! ( (0 (0 (0 ! ( 0 ! 0.13 (0 $ $ $ $ $ $ $ $ $ .10 $ $ .54 3.11 4.40 3.28 2.51 4.78 1.23 1.47 1.03 1.65 1.35 4.16 ! ! $ $ $ $ $ 4.43 1.64) 1.24) 2.99 3.62 $ $ . 4.69 4.60 5)

) ) ) ) ) ) ) ) ) ) ) ! ! ! ! ! ! ! ! ! ! ! * ) ) ) ! ! ! ! ! * ) ) ! !

27 Table 2.4: Summary statistics from models of Divergence by Distance for different measures of geographic distance. Akaike Information Criterion (AIC) used to evaluate and compare quality of model fit between models.

2 Species Model Distance Model R AICc ∆AICc AICc Type Weight M. splendida MLPE Null 0.37 210.28 68.25 0 Great-circle distance 0.59 159.87 17.84 0 Un-scaled Overwater distance 0.67 142.55 0.52 0.44 Optimally scaled ocean distance 0.67 142.03 0 0.56 Mantel Null 0 225.39 49.02 0 Great-circle distance 0.32 181.11 4.74 0.06 Un-scaled Overwater distance 0.34 177.71 1.34 0.32 Optimally scaled ocean distance 0.35 176.37 0 0.62 M. trifasciata MLPE Null 0.61 1297.55 460.32 0 Great-circle distance 0.84 1058.69 221.45 0 Un-scaled Overwater distance 0.91 928.44 91.21 0 Optimally scaled ocean distance 0.94 837.24 0 1 Mantel Null 0 1454.83 437.67 0 Great-circle distance 0.75 1105.99 88.84 0 Un-scaled Overwater distance 0.74 1118.78 101.63 0 Optimally scaled ocean distance 0.82 1017.15 0 1 AICc represent AIC with correction for finite sample sizes ∆AICc represents the change in model fit from the optimum AICc weight assigns a proportional rank for the quality of fit for each model within a set of models

28 A B 15 15

10 10

5 5

Nei's pairwise genetic distance Nei's pairwise genetic distance 0 0 −1 0 1 −1 0 1 Estimated dispersal cost Estimated dispersal cost C D

15 15 10 10

5 5 Nei's pairwise genetic distance Nei's pairwise genetic distance 0 0 −1 0 1 2 3 −1 0 1 2 3 Estimated dispersal cost Estimated dispersal cost Figure 2.3: Divergence-by-distance plots for A) M. splendida Mantel (R2 = 0.35), B) M. splendida MLPE (R2 = 0.67), C) M. trifasciata Mantel (R2 = 0.82), and D) M. trifasciata MLPE (R2 = 0.94) comparing geographic overwater distance with ocean distance optimally scaled against Nei’s genetic distance. Geographic distance scaled using z-scores.

29

Discussion Our study resolved general spatial concordance between Melanotaenia splendida and Melanotaenia trifasciata in large-scale genetic patterns, with each species exhibiting a positive and significant divergence by distance (DBD) pattern. Overwater distances were strong predictors of genetic divergence matching expectations that historical connections are well predicted by modern riverscapes, especially for the habitat specialist M. trifasciata. Genetic patterns within the species, however, were on different temporal scales, with contrasts in levels of population structure and within population genetic diversity evident between co-distributed M. splendida and M. trifasciata. M. splendida, a widespread habitat generalist exhibited lower levels of population divergence for both classical and coalescent estimators; in contrast, M. trifasciata exhibited greater levels of population divergence with strong geographic concordance. These patterns were consistent with our hypotheses that these closely related congeners shared a similar evolutionary history, albeit with differences in detail aligning well with expectations arising from habitat specialization.

Divergence by Overwater Distance As obligate freshwater fishes, genetic differentiation among populations throughout the distributions of M. splendida and M. trifasciata was expected to be correlated with their distributions in the contemporary landscape, with populations in different river-systems (i.e. separated by ocean) being divergent from one another. This pattern was predicted to be stronger in M. trifasciata than M. splendida as the latter are more able to tolerate a broader range of habitats within a river system (ALLEN et al. 2002; TAPPIN 2010), including higher salinity and lower dissolved oxygen that are found in the lower reaches of river-systems. DBD estimates calculated using MLPE models and Mantel tests supported the hypothesis that most historical connections have been within waterways, where geographic distance had a significant positive relationship with Nei’s genetic distance among populations for both species. As the marine environment is a strong contemporary barrier to gene flow for both species, we explored models where distance between river basins was given both equal and greater distance weighting than distance along freshwater stretches of rivers. Upweighted ocean distances improved DBD estimates for both species, which is expected if saltwater historically (and contemporaneously) was a strong barrier to dispersal in these obligate freshwater fish. The DBD analyses for M. splendida best matched our predictions under the Stream

Hierarchy model of connectivity (MEFFE and VRIJENHOEK 1988). However, the differences between models using overwater distance and scaled ocean distances were quite small, and proportional contributions of these models estimated using AICc weightings were similar. Under scenarios of

30 lowered sea levels during the Pleistocene these patterns would be consistent with expectations under the Stream Hierarchy model, where hierarchical genetic relatedness between populations corresponds to major contemporary rivers becoming connected to one another before flowing into the Gulf/Lake Carpentaria. Populations that become connected to one another during lowered sea levels, as was likely during the Pleistocene, would be less genetically divergent from one another than populations that were physically connected less frequently. These results also support our hypotheses that M. splendida was historically capable of dispersal among catchments (exemplified by historical migration between the Mid Wenlock River and Myalla Creek). Similar scenarios have been suggested in other freshwater organisms that are widespread throughout northern Australia, such as the giant freshwater prawn (Macrobrachium rosenbergii) (DE BRUYN et al. 2004), where northern Australian and Papua New Guinean populations appear to have been recently connected through Lake Carpentaria, and several sleepy cod (Oxyeleotris) and eel-tailed catfish (Neosilurus) species (HUEY et al. 2013), where populations of five species showed limited divergence among Gulf of Carpentaria catchments. DBD analyses for M. trifasciata were also best aligned to expectations under the Stream Hierarchy model, but at vastly different scales to those observed in M. splendida. Scaling ocean distance to have a greater impact on geographic distance among populations was the most accurate predictor of genetic distance among M. trifasciata populations. These analyses provide evidence that the historical strength of ocean as a barrier to dispersal in M. splendida was lower than for M. trifasciata. M. trifasciata had consistently older divergence times between populations and these results were consistent with predictions that a widespread, habitat generalist (M. splendida) would have more opportunities to disperse during fluctuations in sea-levels and increased connectivity between rivers, than a habitat specialist with a contracted range (M. trifasciata). Studies on similarly distributed species to M. trifasciata (spotted blue-eye, MacCulloch’s rainbowfish, and pennyfish) shared similar patterns, with large levels of mtDNA divergence between Cape York

Peninsula populations of the spotted blue-eye and McCulloch’s rainbowfish in particular (COOK and

HUGHES 2010; COOK et al. 2014). Thus there are parallel spatial genetic patterns between the two species but with outcomes predictably modified by species biology.

Biogeographical Patterns across Tropical Northern Australia In addition to the clear influence of waterways, a number of regional patterns are notable for M. splendida and M. trifasciata. For instance, M. splendida showed significant structure between populations, albeit with many instances of shared mitochondrial haplotypes among populations, whereas M. trifasciata showed significant structuring among all population pairs (significant pairwise Nei’s genetic distance – Supplementary Tables 2.1 and 2.2) except those that were within

31 the same river drainage. Divergence among populations of M. splendida appears to be consistent with that described in previous studies which have been attributed to connectivity through Lake Carpentaria during Pleistocene sea-level changes, with shared mitochondrial haplotypes among populations throughout the Gulf of Carpentaria (DE BRUYN et al. 2004; HUEY et al. 2013). M. trifasciata, on the other hand, showed significant divergence among populations throughout Cape York Peninsula indicating that no genetic material has been recently exchanged, consistent with studies on other freshwater fish with disjunct distributions from this region (COOK and HUGHES

2010; COOK et al. 2014). Populations of M. trifasciata did not share mitochondrial haplotypes between regions, and divergence between regions was often considerable, with Nei’s distance between populations up to 5-10 times greater than observed in equivalent comparisons for M. splendida. Results based on summary statistics (Tables 2.1 and 2.2) were supported by coalescent analyses using both mtDNA and S7, where divergence times were greater for M. trifasciata (Wilcoxon sign-rank test P = 0.011). Additionally the only strong indication of migration among any pair of populations was within M. splendida whereas population pairs of M. trifasciata showed no evidence of post-divergence migration among populations. These data are consistent with species that are more widespread throughout river-systems, as M. splendida are, being more readily connected to one another during periods of lower sea level than the more isolated populations of M. trifasciata.

Conclusions Our results are consistent with the hypothesis that closely related and co-distributed species will often have experienced similar evolutionary histories, but that differences in individual ecological requirements and/or behavior can result in differences in scale of genetic patterns. Previous studies on freshwater organisms in the region have suggested that Lake Carpentaria was a key driver of population structure and connectivity among populations of freshwater organisms in far northern Australia (CHIVAS et al. 2001; DE BRUYN et al. 2004; BOWMAN et al. 2010; HUEY et al. 2013), however these connections appear to be greatly affected by ecological differences between species epitomized here by the contrast between co-distributed congeners. Freshwater fish with widespread distributions (M. splendida) also share more recent divergence times and more recent common ancestors among populations than species with more disjunct distributions across the landscape (M. trifasciata), even at quite small spatial scales across Cape York Peninsula. Because rainbowfish appear unable to disperse across the north Australian landscape on contemporary timescales, increasing threats of land exploitation and impacts of climate change are likely to cause irreversible losses of endemic genetic diversity.

32 Chapter Three – Repeated breakdown of species boundaries in peripheral habitats in two broadly sympatric Rainbowfishes (Melanotaeniidae)

Abstract Some rainbowfishes are known to hybridize readily when closely related species are placed into aquariums together. In nature however, only a limited number of rainbowfish species are found frequently in sympatry and morphological hybrids have not often been identified in the field. Recent molecular studies have however found evidence for mitochondrial introgression between wild populations of certain Australian rainbowfishes. Here I identified hybrid zones between co- distributed M. splendida and M. trifasciata at the periphery of M. trifasciata’s natural distribution. External morphology, mtDNA sequence data and two nuclear single nucleotide polymorphism (SNP) diagnostic restriction assays were used to characterize incidence, levels and directionality of gene flow between these two reciprocally monophyletic taxa. Four populations were identified as having undergone extensive hybridization between M. splendida and M. trifasciata. Patterns of gene flow between the two taxa were different for different hybridizing populations with complete mitochondrial capture evident in two populations, uni-directional introgression in a third population, and a complete mixture of morphological hybrids and bi-directional gene exchange evident at the fourth site. This level of diversity in patterns of hybridization between two species is unusual and may potentially result from variation in local environmental conditions. Future studies will be required to determine the processes that are driving this pattern of specific introgression in the wild.

33 Introduction Recognition of natural hybridization and introgression of wild populations has been reported more frequently in recent decades than had previously been thought likely (DOWLING and SECOR

1997; SEEHAUSEN 2004; MALLET 2005). Studies of a vast array of taxa have revealed examples of hybridization events in plants, invertebrates and vertebrates (ARNOLD 1992; DOWLING and SECOR

1997; RIESEBERG 1997; SCRIBNER et al. 2000; MALLET 2005). Gene exchange between otherwise discrete taxa can arise from secondary contact in areas of range overlap (HEWITT 2000), or from primary differentiation between taxa (ENDLER 1977). Hybridization is most easily detected when divergent taxa are reciprocally monophyletic for most gene loci, and genetic assignment tests are efficient for detecting recent admixture and hybridization events (MALLET 2005; ALLENDORF and

LUIKART 2007). Hybridization by gene introgression among closely related taxa can occur either uni- or bi- directionally. Uni-directional gene flow is the introgression of genetic material from one species into another and is commonly observed for mtDNA where individuals will possess the mtDNA of one species while possessing the nuclear background of the second species (i.e. asymmetric introgression) (ALLENDORF and LUIKART 2007). Bi-directional gene flow occurs when gene flow between congeners has resulted in hybrid offspring, where a mixture of both parental genomes is represented in the offspring (ALLENDORF and LUIKART 2007). Many examples of hybridization in nature among closely related taxa have been linked to breakdown of species boundaries at range edges (WOODRUFF 1973; BRIDLE and VINES 2007). Theory suggests that parapatric species that have small areas of overlap at their distribution edges will be present in lower abundance, and have less ability to recognize suitable mates leading to extra-species mating (AVISE and SAUNDERS 1984). As a result, gene exchange between closely related taxa might be more likely to occur in such regions as individuals may find more individuals of a different taxon than of their own. Potentially this could result in a decreased ability for individuals to recognize conspecifics to mate with and lead to interbreeding between taxa (AVISE and SAUNDERS 1984). Where this occurs genetic diversity may increase and change adaptive potential in marginal habitats in hybrids, leading to adaptation to new habitat conditions

(RIESEBERG et al. 2007; HUDSON et al. 2011). Examples of this have been reported in sunflowers in North America, where hybridization between parapatric species has resulted in hybrids colonizing a variety of new habitats (reviewed by RIESEBERG et al. 2007). Another example of hybrid lineages adapting to new habitats was reported in two species of alpine whitefish in northwestern Europe that adapt rapidly to new habitats as glaciers retreated at the end of the Pleistocene epoch (HUDSON et al. 2010).

34 Examples of hybridization among vertebrates are becoming more common and better documented (DOWLING and SECOR 1997; SEEHAUSEN 2004; MALLET 2005). Of these, freshwater fish are relatively well represented (ALLENDORF and WAPLES 1996; SCRIBNER et al. 2000) with examples evident in Cyprinids (carps and minnows)(ABOIM et al. 2010; BROUGHTON et al. 2011;

SOUSA-SANTOS et al. 2014), cichlids (SALZBURGER et al. 2002) and Catostomid catfish

(MCDONALD et al. 2008). Many of the published examples however, represent hybridization events following direct or indirect anthropogenic introduction of one taxon into a new habitat (SCRIBNER et al. 2000). In contrast, many examples in natural populations have been linked to secondary contact events, where two species have diverged in allopatry for a period of time, and then in certain locations become secondarily sympatric with one another (SCRIBNER et al. 2000). This scenario may have the potential to allow gene exchange between the two divergent taxa if complete reproductive isolation has not been established. There are even fewer examples of broadly sympatric fish species interbreeding in parts of their range and not others (but see ABOIM et al.

2010; SCHMIDT et al. 2011). Melanotaeniid rainbowfish represent one of the most speciose groups of freshwater fishes in Australia, with sixteen currently described species, several of which naturally occur in sympatry. Rainbowfish are common in the aquarium trade, with varieties of synthetic hybrids available. Many rainbowfish that are not sympatric in nature readily breed in a synthetic aquatic environment and produce viable offspring, even between highly divergent species. For example, a cross breed called the “red devil” or “red Boesemani” rainbowfish is reported to be a cross between two species from different genera; Melanotaenia boesemani and Glossolepis incisus. Examples of hybridization in natural populations of rainbowfish are less common, but few studies to date have been undertaken on this group due to the remote regions in which they occur. Rainbowfishes are brightly coloured, sexually dimorphic and use colour displays in mate selection, all of which should promote strong selection for an individual’s own phenotype and limit potential for hybridization (LANDE 1980). While a few earlier studies of rainbowfishes have identified some evidence of mitochondrial introgression between natural populations, this has not been the primary focus of any of these studies and this pattern has only been mentioned in passing (e.g. ZHU et al. 1994; MCGUIGAN et al.

2000; UNMACK et al. 2013). A recent phylogenetic investigation of Melanotaeniid species from Australia and New Guinea suggested that mitochondrial introgression in natural populations might be quite widespread and could occur among at least ten of the sixteen native Australian species

(UNMACK et al. 2013). Here we undertake the first systematic examination of the geography and extent of hybridization between two Melanotaeniid rainbowfishes. In this study we characterize levels of gene exchange between Melanotaenia trifasciata and M. splendida, two broadly sympatric

35 Melanotaeniid rainbowfish from northern Australia. M. trifasciata and M. splendida are reciprocally monophyletic taxa (Supplementary Fig. 2.1). They are morphologically distinguishable from one another based on several traits, including general colour patterns, body shape and fin shape (Fig. 3.1). Both species are sexually dimorphic, with males of both species having elongated dorsal and anal fins and vibrant colour patterns, which are used to attract mates in sexual displays. M. trifasciata is found in far northern Cape York Peninsula, Queensland, and Kakadu and Arnhemland, Northern Territory (Fig. 3.2). M. splendida is much more widespread across northern Australia, occurring in most waterways across the northern third of the country, from Rockhampton in the east to the Kimberly region in the west. These two species co-occur throughout most of M. trifasciata’s distribution and often congregate together in large shoals. Here, we use mitochondrial and two nuclear diagnostic SNP markers to characterize introgression/hybridization in populations of M. trifasciata and M. splendida, throughout far northern Queensland and Kakadu National Park, Northern Territory. Specifically we aim 1) to identify levels of gene exchange between these taxa, and 2) whether hybridization between these two taxa is occurring more frequently at the margin of M. trifasciata’s distribution.

A B

C

D E

Figure 3.1: Photographs from Tappin (2010) (photos by Gunther Schmida and Neil Armstrong) of Melanotaeniid rainbowfishes. A) M. splendida from Mary River, Northern Territory; B) M. splendida from eastern Cape York, Queensland; C) morphological intermediate from McIvor River, Queensland; D) M. trifasciata from Mary River, Northern Territory; and E) M. trifasciata from McIvor River, Queensland.

36

both species weresampled sites arerepresented as circles.Red shading on showsmap 3.2: Figure Mapof northernAustralia showing sampling locations for M. splendida (downwards facing triangles) M. splendida’s dis tribution, Blue shading shows

and and M. trifasciata (upwards facing triangles M. trifasciata’s distribution. ).Where

37 Methods Samples were collected between September 2011 and October 2012 from northern Queensland and the Northern Territory, Australia. Small pieces of the lower caudal fin were removed and fixed in 100% ethanol, and fish were released. Fish were provisionally identified in the field based on distinguishing colour patterns. Obtaining permits to euthanize many individuals was not feasible, so only a few voucher specimens were retained whole. M. splendida can be consistently distinguished morphologically from M. trifasciata by their distinct spotted markings on fins, whereas M. trifasciata have no such markings. M. trifasciata also have a distinct black band running the length of their body, where M. splendida have a series of thin gold to black bands running the length of their body forming a chequered pattern. Juvenile fish were not sampled, as these distinguishing features were not developed sufficiently to use for identification. Whole genomic DNA was extracted using a modified salt precipitation method (ALJANABI and MARTINEZ 1997). PCR amplification and Sanger sequencing of the mitochondrial ATPase6, 8 and cytochrome oxidase III genes (BERMINGHAM et al. 1997), as well as nuclear S7 intron and RAG1 using PCR protocols adapted from Unmack (2013) were genotyped through Macrogen Korea on an ABI 3730XL. Fixed diagnostic single nucleotide polymorphisms (SNP) restriction assays were designed to distinguish between M. trifasciata and M. splendida for S7 intron and RAG1 nuclear markers. Restriction enzymes were acquired from New England BioLabs (www.neb.com). These markers distinguished between species and identified genotypes that were heterozygous for the particular locus. Amplified PCR product was digested for 16 hours, and visualized on 1.5% agarose gels (e.g. Fig 3.3). For S7 intron 1, EcoRV (cuts in M. splendida at two restriction sites, does not cut in M. trifasciata) and BsaI (cuts M. trifasciata at one restriction site, does not cut in M. splendida) were used, and for RAG1 AlwI (cuts in M. splendida at one restriction site, does not cut in M. trifasciata) was used (Figure 3.4). All M. trifasciata individuals from Mary River populations (Mary River and Morline Rickhole) had introgressed mtDNA haplotypes from M. splendida and so a statistical parsimony haplotype network between all mtDNA sequences was constructed using TCS version

1.21 (CLEMENT et al. 2000) to establish levels of divergence between haplotypes in both species in order to determine whether mtDNA introgression had occurred recently or in the distant past. If recent mtDNA introgression has occurred between M. splendida and M. trifasciata I would expect to find shared ATPase haplotypes in these populations. If introgression was historical then I would expect to populations of M. splendida and M. trifasciata to have fixed differences in mtDNA and sharing of haplotypes to be limited or entirely absent.

38 S7 RFLP digest RAG1 RFLP digest M. trifasciata M. trifasciata M. trifasciata M. trifasciata M. trifasciata M. splendida M. splendida M. splendida M. splendida M. trifasciata M. trifasciata M. splendida M. splendida Heterozygote Heterozygote

Figure 3.3: Example of agarose gel used for genotyping RFLP restriction assays. Left gel shows four M. trifasciata and four M. splendida individuals genotyped at S7. No heterozygote examples are shown here as no individuals in this dataset were found that contained heterozygote genotypes. Right gel shows two M. trifasciata, two M. splendida and two heterozygote genotypes.

Figure 3.4: Stylized restriction map of S7 intron 1 and RAG1 isolates used for restriction assay genotyping. All cut sites for each species and restriction enzymes that cut them for each species represented as black blocks.

39 Results Five hundred and twenty-one individuals were genotyped for mtDNA ATPase, S7 intron and RAG1, including 249 M. trifasciata, 226 M. splendida, and 46 individuals that were identified as morphological intermediates in the field (McIvor River only, as all individuals at all other sites were identified as either M. trifasciata or M. splendida). Morphology and genotypes were consistent for most populations, although several mismatches were identified. McIvor River, Myalla Creek, mid Wenlock River and Coen River, Queensland, as well as two sites on the Mary River, Northern Territory, had individuals whose genotypes did not match their morphological identifications (Fig. 3.5). For fish from the McIvor River, morphological identification was not an accurate predictor of genotype, with mismatches found between phenotype and genotype for all loci. One individual identified in the field as M. trifasciata contained genotypes for all loci consistent with M. splendida genotypes. A single individual identified as M. splendida in the field contained genotypes matching M. trifasciata for all loci. Finally, 46 individuals were identified as having intermediate morphological characteristics between M. splendida and M. trifasciata in the field, and these individuals separated into two groups of genotypes, with one having all loci matching M. splendida and the other group having all loci matching M. trifasciata genotypes. No individuals were identified as having heterozygote genotypes for S7 or RAG1 in this location, suggesting that individuals where mismatches between morphology and genotypes were identified may represent backcrossed individuals. In Myalla Creek all individuals were identified morphologically as M. splendida. However two of fifteen individuals genotyped had heterozygote genotypes at the RAG1 locus. In the nearby mid Wenlock River, one individual identified as M. splendida was heterozygous at RAG1. Both species were sampled in the Coen River, and all individuals identified as M. trifasciata had genotypes matching morphological identification for all loci. However, five of 20 fish identified as M. splendida had genotypes for all loci matching M. trifasciata. In the two locations sampled on the Mary River, Northern Territory, both species were collected and all M. splendida individuals had genotypes matching M. splendida for all loci. M. trifasciata, in contrast had introgressed mtDNA matching that from M. splendida in all fifteen individuals from the two locations. M. trifasciata and M. splendida shared mtDNA haplotypes in both of these locations (Fig. 3.6), and all haplotypes were a minimum of 47 mutations from the closest M. trifasciata haplotype.

40

41 Figure 3.5: Sample locations for all Northern Territory and Queensland populations. Genotypes and number of samples for all individuals in locations where putative hybrids were found on right. Shading on map illustrates natural distributions for M. splendida (red), and M. trifasciata (blue). Site locations on map where hybrid individuals were found are outlined in bold for ease of viewing.

Figure 3.6: Statistical parsimony haplotype network of Mary River and Morline Rockhole individuals for both M. trifasciata and M. splendida. Monophyletic group including all other M. trifasciata samples is a minimum of 47 mutations divergent from any haplotype in this network.

42 Discussion Here we identified six out of 37 sampled populations in which individuals morphologically identified as either M. trifasciata or M. splendida contained genotypes consistent with hybridization or introgression between the two species. Exact patterns of introgression, however, differed between sites, with some populations characterized by mitochondrial introgression, and one population (McIvor River) containing a large number of individuals exhibiting mismatches between morphological identification and species genotypes. Levels of interspecific gene exchange varied among sites, with Myalla Creek and Mid Wenlock River sites only containing two and one individuals respectively that were heterozygous at a single locus. McIvor River, in contrast, contained many individuals whose genotypes did not match with their morphological identification. Data presented here suggested that interspecific gene flow has occurred within some sampled sites where M. trifasciata and M. splendida co-occur, although hybridization patterns that we observed varied in different locations. It is unusual to find such different patterns of gene exchange between different populations of the same species, and also to observe a patchy distribution of hybridizing populations between two broadly sympatric species, although a previous example in Australian freshwater fish exists (SCHMIDT et al. 2011). This patchiness in presence of hybridization, as well as the absence of evidence of introgression at sites of allopatry, suggest that these patterns are not due to incomplete lineage sorting between populations of M. splendida and M. trifasciata. We identified two populations (McIvor and Coen Rivers) where both nuclear and mitochondrial introgression has occurred between M. trifasciata and M. splendida. However no heterozygotes were identified for either nuclear genes sampled in this location, and no mismatches between genes was observed. This suggests that all individuals had complex backcross genotypes. As a result we cannot say categorically that hybridization between M. trifasciata and M. splendida is currently occurring. However, there is no sequence divergence between introgressed mtDNA or nuclear S7 (haplotypes shared between individuals with and without introgressed haplotypes for both genes) to suggest that these taxa have been genetically isolated for any substantial period of time, and gene flow could still be occurring. Patterns of gene flow in these two populations differed markedly, with bi-directional gene flow present in McIvor River but we only found evidence of uni- directional gene flow in Coen River samples. With data for only three loci and morphology it is difficult to interpret what has occurred in these populations. Similarly, the small numbers of heterozygote individuals identified at the RAG1 locus in Myalla Creek and Mid Wenlock River populations may provide more evidence of contemporary hybridization between these species. However a more accurate picture of what is occurring would require data from more loci. Mitochondrial introgression is a relatively common phenomenon, and has previously been identified between several pairs of sympatric Melanotaenia species (ZHU et al. 1994; MCGUIGAN et

43 al. 2000; UNMACK et al. 2013). UNMACK et al. (2013) also found evidence of historical mitochondrial introgression between species, where one of the two species had subsequently gone extinct, leaving a single species with completely introgressed genomes from a second rainbowfish species that was no longer present. We found complete mitochondrial capture of M. splendida haplotypes in all M. trifasciata sampled in Mary River, Northern Territory populations (Mary River main stem and Moline Rockhole). This mitochondrial introgression from M. splendida into M. trifasciata appeared to have been historical, as no mismatches in nuclear genotypes were identified. However, the two species shared identical mtDNA haplotypes, suggesting that this historical introgression has happened recently enough that divergence between the two species’ mtDNA has not occurred. This pattern of mtDNA capture is similar to that identified in previous research on

Melanotaeniid rainbowfishes (ZHU et al. 1994; MCGUIGAN et al. 2000; UNMACK et al. 2013), although it has not previously been identified between M. trifasciata and M. splendida. It appears from these data that hybridization has historically been quite common between populations of M. trifasciata and M. splendida, although the locations in which is has occurred are quite patchy, and the mode in which it eventuates have varied. Theory suggests that individuals are often found in lower abundance at range edges and as a result have reduced probability of finding suitable mates of the same species (AVISE and SAUNDERS

1984; BRIDLE and VINES 2007). Four of the six sites sampled here represent the southern and westernmost limits of M. trifasciata’s distribution, with McIvor River being the furthest southeastern population in which M. trifasciata and M. splendida co-occur (Gap Creek is further south but M. splendida have not been collected there), and Coen River the furthest southwestern Cape York Peninsula population in which both species occur. The Mary River and Moline Rockhole populations in the Mary River, Northern Territory are the furthest western populations in which the two species co-occur. The consistent evidence of gene exchange between M. trifasciata and M. splendida observed in peripheral populations may be a result of a reduced ability to maintain species boundaries where M. trifasciata is found in marginal habitat at its range edge. Future studies on these regions should be set up to specifically test why the majority of incidents of gene exchange are occurring in peripheral populations and not throughout all of M. trifasciata’s distribution. It is also surprising that species with sexual dimorphism and mate choice based on colour displays in males are not maintaining species boundaries. This is because theory would suggest that selection for one’s own colour morph would be strong (LANDE 1980). Breeding experiments, particularly looking at McIvor River populations where morphological intermediates are present, should be undertaken to investigate if the effects of sexual selection of mate choice is being displaced by density dependent selection in peripheral populations.

44 This is the first study to identify widespread gene exchange between natural populations of M. trifasciata and M. splendida, and adds to the growing literature identifying hybridization among reciprocally monophyletic taxa. The data presented here indicate that throughout the majority of locations that M. trifasciata and M. splendida are found in sympatry, species boundaries are maintained. However both historical and contemporary hybridization events have occurred in populations at the edge of M. trifasciata’s distribution, and reproductive isolation does not appear to hold up sufficiently in these locations. If it is suboptimal habitat at range edges that has led to the breakdown of species boundaries then it has conservation implications for these and other populations, as destruction of habitat could lead to the conditions leading to hybridization between these taxa becoming more widespread. A long history of gene exchange could also help to explain why rainbowfish in Australia and New Guinea are so diverse, as hybridization has been proposed as a means of increasing novelty in marginal habitats and has the potential to lead to hybrid speciation

(SEEHAUSEN 2004; HUDSON et al. 2010). Other instances of mtDNA introgression have been identified in Melanotaeniid rainbowfishes, and in depth studies of other species pairs should be undertaken to identify the extent to which they may have or might still be hybridizing and if this is indeed a key aspect of why we observe comparatively high genetic diversity in this group of freshwater fishes. Also, experimental work investigating mate choice and hybrid breeding patterns should be investigated to determine the mechanisms that have resulted in such diverse patterns of hybridization between M. trifasciata and M. splendida throughout their distributions.

45 Chapter Four – Urban development explains reduced genetic diversity in a narrow range endemic freshwater fish

CITATION: MATHER, A., D. HANCOX and C. RIGINOS, 2015 Urban development explains reduced genetic diversity in a narrow range endemic freshwater fish. Conservation Genetics 16: 625- 634.

Abstract Whereas the negative impact of anthropogenic habitat degradation on community species richness is well established, less is known about its effect on within species genetic diversity. Here we investigate how different aspects of habitat degradation affects genetic diversity of an Australian native freshwater fish, Rhadinocentrus ornatus, and what impacts this may have on this species’ conservation status. We perform this investigation for R. ornatus across southeast Queensland. Based on mtDNA sequence data from 327 individuals and 20 populations, we identified three distinct genetic lineages that were allopatric at the stream level. Indicators of habitat degradation have large negative effects on measures of genetic diversity, with close proximity to urban development and alterations to waterways associated with drastically reduced measures of genetic diversity across three distinct mtDNA lineages (evolutionary significant units). Low effective population sizes and low standing genetic variation in degraded habitats may result in reduced adaptive potential in this already threatened narrow range endemic. The only surveyed populations with high genetic diversity are found in protected National Parks. Many historical populations of R. ornatus in the highly developed Greater Brisbane Region are already probably extinct, and without further study and management this may be the fate of presently genetically depauperate populations in urban areas.

46 Introduction Geological alterations to landscapes play a significant role in population connectivity and isolation of freshwater organisms, often leading to highly divergent genetic lineages within species

(AVISE et al. 1987). Multiple periods of glaciation throughout the Pleistocene have caused dramatic changes in sea levels, consequently reconfiguring low lying rivers, streams, and (HEWITT

2000; VORIS 2000). In eastern Australia, there were several historical periods where the coastline extended several kilometers into the now submerged continental shelf (TORGERSEN et al. 1985;

VORIS 2000). Coastal islands around southeast Queensland, such as Fraser, Moreton and North

Stradbroke Islands, were connected to the mainland as recently as ~4000 years ago (WILLMOTT 2004). These major geographical fluctuations may have imposed significant structure on past and present within-species genetic diversity of freshwater organisms in southeastern Queensland. The specific configuration of coastal waterways at any exact time is not certain, but several contemporary river drainages may have connected to one another at lower sea levels before eventually flowing into the ocean (as suggested for Northern Australia in VORIS 2000). Specifically the major drainages of the Brisbane and Logan Rivers may have converged in Moreton Bay, and the Mary River, along with Tin Can Bay and Fraser Island drainages region within Hervey Bay, at times of lower sea-level (HUGHES et al. 1999). These historical connections are likely to have provided periods of connectivity between otherwise isolated populations of freshwater organisms, and have left an imprint on contemporary phylogeographic patterns (see PAGE and HUGHES (2014). Investigating depth and patterns of population structure in freshwater organisms can shed light on how landscape processes have affected historical populations (AVISE et al. 1987). Patterns of population structure can also help to identify regions of historical refugia, which can be important regions for conservation as they are often areas of high endemism and diversity (CARNAVAL et al. 2009). In addition to historical changes in the landscape, the growth of urban centers and the alteration of native habitats can have massive and rapid effects on genetic patterns (ALLENDORF and

LUIKART 2007). The influence of landscape degradation on phylogenetic diversity and assembly

(KNAPP et al. 2008; DINNAGE 2009; HELMUS et al. 2010) and genetic diversity (VELLEND 2004;

HELM et al. 2009; WEI and JIANG 2012) have been shown in a variety of plant communities, but the genetic effects on other communities and habitats and affects on genetic diversity within a species are less studied (for exceptions see EVANNO et al. 2009; STRUEBIG et al. 2011). Yet, such contemporary changes can cause rapid reductions in populations sizes, leading to reductions in genetic diversity over short timescales (ALLENDORF and LUIKART 2007). Reductions in genetic diversity can also reduce a species’ ability to adapt to changing environments (ALLENDORF and

LUIKART 2007; KELLERMANN et al. 2009). In the extreme, the outcome of such habitat degradation

47 can be the extirpation of species in certain areas, resulting in patchy distributions within waterways and, ultimately, to the extinction of populations (ARTHINGTON et al. 1983; ALLENDORF and

LUIKART 2007). Invasive species are also known to detrimentally affect freshwater fish communities around the world (reviewed by CUCHEROUSSET and OLDEN (2011). Examples include the introduction of the common carp, Cyprinus carpio (e.g. ATTON 1959; ZAMBRANO et al. 2006; MABUCHI et al.

2008), and various introductions of Salmonids around the globe (e.g. KRUEGER and MAY 1991;

SIMON and TOWNSEND 2003; VIGLIANO et al. 2007). In Australia, several widespread introductions of highly invasive freshwater fish have been shown to negatively impact populations of native fish through competition, predation and habitat destruction (ARTHINGTON and MARSHALL 1999). These include C. carpio (e.g. KOEHN 2004; PINTO et al. 2005) and the mosquitofish, Gambusia holbrooki

(e.g. HOWE et al. 1997; IVANTSOFF 1999), considered two of the world’s 100 most invasive species

(LOWE et al. 2000). In Queensland, negative effects of habitat degradation due to urbanization and invasive species on freshwater community assemblages have been well documented (e.g. ARTHINGTON et al.

1983; ARTHINGTON and MARSHALL 1995; ARTHINGTON and MARSHALL 1996; HUEY et al. 2013). Habitat degradation and the impact of the invasive species G. holbrooki has adversely affected vulnerable species in southeast Queensland through competition for resources, fin nipping and predation of eggs and fry (ARTHINGTON and MARSHALL 1999). For example, the honey blue-eye, Pseudomugil mellis and oxleyan pygmy perch, Nannoperca oxleyana which previously were distributed throughout southeast Queensland are now endangered due to habitat disturbance and predation by invasive species (ARTHINGTON and MARSHALL 1995). Rhadinocentrus ornatus is a species of obligate freshwater fish that is native to eastern Australia’s coastal streams. It is most commonly found in tannin-stained, acidic creeks surrounded by sandy Melaleuca swamps, a habitat known as wallum, and occasionally in rainforest streams

(ARTHINGTON et al. 1983). R. ornatus are found between the Mary River, Queensland and Coffs Harbour, New South Wales, including several of the sand islands located off the southeast Queensland coast. R. ornatus are locally abundant, however due to restricted range and local extinctions caused by human development and the fish, G. holbrooki (ARTHINGTON and

MARSHALL 1999), R. ornatus has previously been red-listed on the IUCN threatened species list

(ARTHINGTON et al. 1994). A population found in the late 1990s near Byfield, central Queensland

(MARSHALL 1988) resulted in an extension of R. ornatus’s distribution of over 350km and lead to their removal from that list.

Previous research (PAGE et al. 2004; SHARMA and HUGHES 2011), uncovered four distinct genetic lineages in R. ornatus that were estimated to be 5-7% divergent in mitochondrial DNA

48 sequence from one another, with the distribution of the lineages being closely aligned to their geography. These lineages consisted of a northern NSW clade (NWC), a southeast Queensland clade (SEQ), including Moreton and Stradbroke Islands, a central eastern Queensland clade, including Fraser Island (CEQ), and a clade that was limited to a single waterway (Seary’s Creek) in Tin Can Bay Queensland (SER) (see Fig. 4.1). More recently, Sharma and Hughes (2011) expanded on Page et al’s study, with the addition of allozyme markers and an increased sampling regime concentrating on Queensland’s Sunshine Coast and its adjacent islands, including Fraser, Moreton and North Stradbroke Islands. They uncovered two of the same clades; CEQ and SEQ, but surprisingly found only CEQ haplotypes in Seary’s Creek (SHARMA and HUGHES 2011). In the present study we examined the effect of ecosystem degradation associated with urban development and invasive species on the genetic diversity of an Australian native freshwater fish, the ornate rainbowfish, R. ornatus. These contemporary influences are described against the backdrop of historical landscape changes associated with Pleistocene climate fluctuations to provide a holistic understanding of the recent evolution and population dynamics of this species. We undertook the most extensive sampling of R. ornatus to date, including 20 populations from southeast Queensland, with a particular focus around the Tin Can Bay region that had been sparsely sampled in previous studies. We tested specifically for the effect of habitat degradation and urbanization with the prediction that populations in close proximity to urban development and with altered habitats would have reduced genetic diversity compared to those in relatively pristine habitats further from urban centers. We also assessed fine scale phylogeographic patterns in R. ornatus throughout southeast Queensland to determine patterns of historical gene flow and population connectivity.

49

Figure 4.1: Map of southeast Queensland with inset maps of Australia and Tin Can Bay, including sample sites and Statistical Parsimony networks for each major genetic clade: CEQ, SER and SEQ. Colours on map represent populations as described in the key, and haplotype size represents the number of individuals represented for that particular haplotype. Areas shaded in green represent land under the protection of Great Sandy National Park. Dashed lines indicate geographic delineations among the three major clades. 50 Methods Field Collections Rhadinocentrus ornatus were collected from 20 populations between 2008 and 2012 using handheld nets and baited fish traps. Populations were selected to include a number of urban impacted and non-impacted populations for each genetic lineage throughout southeast Queensland (Fig. 4.1). Live fish were anaesthetized using a cold knockout method in ice baths. Small sections of the lower lobe of the caudal fin were cut off while fish were unconscious. Fish were then revived in creek water for 30 minutes and released. Fin-clip samples were fixed in 100% ethanol and stored in a -20oC freezer.

DNA Extraction, Amplification and Sequencing Whole genomic DNA was extracted using a salt precipitation and ethanol cleanup method modified from ALJANABI and MARTINEZ (1997). DNA was then tested for quality and quantity by gel electrophoresis. A 950 base pair fragment of the mitochondrial gene ATPase6, ATPase8 and COIII were amplified using primers designed by BERMINGHAM et al. (1997) using a BioRad Thermocycler. The specific mtDNA marker was selected so that results were directly comparable to previous studies on this species group. This mtDNA fragment was amplified using one forward primer (AAAGCRTYRGCCTTTTAAGC) and one reverse primer (GTTAGTGGTCAKGGGCTTGGRTC). Polymerase chain reaction protocol consisted of an initial 2 minute denaturing period at 95 oC , followed by 30 cycles of 95 oC for 30s, 55 oC annealing for 30s, 72 oC extension for 45s, followed by a final 7 minute extension period at 72 oC . Successful amplification was visualized using gel electrophoresis, and then samples were prepared for sequencing using a standard ExoSap cleanup protocol using reagents sources from New England BioLabs. Samples were then sent to Macrogen Korea and were sequenced on an ABI 3730XL genotyping machine. Sequences were checked, aligned and edited for genetic analysis using the program CodonCode Aligner (http://www.codoncode.com/aligner/). Of the 950 bases sequenced, a total fragment of 499 bases was reliably sequenced across 327 individuals. This 499 base pair fragment was included in genetic analyses. All unique sequences used in this study were deposited in GenBank, accession numbers KP341014 to KP341334.

Phylogeographic Analysis

Phylogenetic trees were constructed using Bayesian analysis with MrBayes (HUELSENBECK and RONQUIST 2001) to reconstruct evolutionary relationships within R. ornatus. A MrBayes search was conducted using the model GTR+I+G with five million iterations, two chains and sampling

51 every 1000 trees, with a burn-in period of 50,000 trees. Bayesian trees presented here were edited using FigTree v1.4 (RAMBAUT and DRUMMOND 2012). Haplotype relationships within major clades identified by Bayesian genealogical analyses were visualized using statistical parsimony networks constructed in TCS v1.21 (CLEMENT et al.

2000), and population statistics were obtained using Arlequin v3.5 (EXCOFFIER and LISCHER 2010), including genetic and haplotype diversity within populations, and global and pairwise ΦST based on

Tamura and Nei’s genetic distance (TAMURA and NEI 1993). A Bonferroni correction was applied to adjust for multiple pairwise comparisons, with a Bonferroni adjusted P of 0.0002. Population structure was estimated using analysis of molecular variance (AMOVA) (EXCOFFIER et al. 1992). Heirarchical AMOVA was also conducted measuring genetic structure between regions (mitochondrial clades), among and within sites. Conformity to neutral equilibrium expectation was evaluated using the statistics Fu’s Fs (FU 1997), and Tajima’s D (TAJIMA 1989), and relative time since population expansions have occurred was estimated using the statistic Tau (ROGERS and

HARPENDING 1992).

Human Impact In order to evaluate potential causes of variance in genetic diversity among populations, we compared several different measures of diversity against measures of human impact describing each site (Supplementary Table 4.1). The measures of human impact used were: i) proximity to development, categorized as within 10km of human development or not, ii) presence or absence of invasive G. holbrooki, and iii) waterway disturbance, whether developed/disturbed or undisturbed (overgrown with noxious weeds, riparian zone cleared, concrete banks etc.). These variables allowed reliable and unambiguous categorization at each site. Undisturbed sites within Great Sandy National Park are considered as control populations: we predict that they have genetic representative of natural populations, that is prior to urban development. The statistical package R studio version 2.15 (R DEVELOPMENT CORE TEAM 2010) was used to measure the effect of these indicators of human impact on four estimates of genetic diversity (number of haplotypes, haplotype diversity, theta S and theta pi). For number of haplotypes, theta S and theta pi, generalized linear models (GLMs) for each variable were compared to null models using analysis of variance and F- tests. As haplotype diversity is measured as a proportion, we used a logit transformation (FOX et al. 2012), and linear models were compared to null models using chi-square analyses. Because clade identity (i.e. SEQ, CEQ, SER) might also affect genetic diversity, we tested for an effect of clade identity in GLMs as described above but using clade identity as a predictive variable.

52 Results Phylogeographic Analyses The MrBayes search identified three major clades within R. ornatus (Fig. 4.2). These well supported clades broadly correspond to those identified in PAGE et al. (2004), with one lineage (CEQ) primarily found on Fraser Island and included only a single population on the mainland: Snapper Creek in the north of Tin Can Bay. The ‘SEQ’ clade identified in Page et al’s study was found in all Queensland drainages from the Noosa River to our southern-most site, Currumbin Creek. However the SER lineage, which was represented by a single population from Seary’s Creek in PAGE et al. (2004) and was not identified at all in SHARMA and HUGHES (2011), was identified here in five populations within Tin Can Bay (including Seary’s Creek), as well as a distant population, Obi Obi Creek (OBI) located on the upper reaches of the Mary River.

CEQ

SEQ 100

100

0.4 100 SER

Figure 4.2: Unrooted Bayesian phylogenetic tree constructed in MrBayes from 499 bases of the mitochondrial gene ATPase8. Major clades are identified as SER, CEQ and SEQ.

Each major clade was treated separately for statistical measures of structure and genetic diversity (Table 4.1). No clades were sympatric within sites, but all occur in the Tin Can Bay region. Clade CEQ, collected in seven locations on Fraser Island and a single location, Snapper

53 Creek, on the mainland near Tin Can Bay, showed significant levels of genetic differentiation between all populations (after Bonferroni corrections) (Supplementary Table 4.1). Clade SER, collected in five locations around Tin Can Bay as well as a single population in the upper reaches of the Mary River, also had significant levels of genetic differentiation. Clade SEQ, collected in six locations between the Noosa River in the north and Currumbin Creek in the south, showed much lower levels of genetic structure than the other clades identified here. It also exhibited lower measures of genetic diversity, with only a single common haplotype found in several populations. The most genetically diverse populations in clade SEQ came from two populations located at the headwaters of the Noosa River catchment (‘Teewah Creek South’ and ‘Noosa River’), which are separated from one another by only ~8km, yet still have significant pairwise ΦST of 0.13 (P = 0.018) (Supplementary Table 4.2). This was in contrast to Currumbin and Coonowrin Creek populations, which are separated by ~130km, and share no contemporary connection, yet also exhibited no genetic differentiation, with both populations sharing a single mtDNA haplotype.

54

! SEQ Currumbin Tingalpa Coonowrin South Mooloola Noosa Teewah South SER Obi Seary's Cooloola Carland Pipeclay Teewah North CEQ Snapper Ungowa Alligator Rocky Gerroweea Coomboo Woralie Bowarrady Location Table

4.

1:

Genetic diversity

Sample Size Φ Φ Φ ST ST ST

= 0.321 = 0.623 = 0.808 15 16 14 17 27 27 28 13 14 15 16 14 15 20 12 15 15 15 16 3

and neutrality measures of in populations of # Haplotype 13 12 25 1 1 1 2 7 7 1 6 4 1 2 1 2 1 8 4 3 4 3 3

Diversity Haplotype 0.07 0.33 0.06 0.14 0.41 0.26 0.04 0.21 0.31 0.07 0.13 0.06 0.14 0.07 0.40 0.33 0.20 0.27 0.20 0.19

Theta(S) % Theta(S) 0.55 0.00 0.00 0.00 0.06 0.30 0.52 0.38 0.00 0.31 0.19 0.00 0.06 0. 1.01 0.06 0.00 0.45 0.33 0.12 0.25 0.12 0.12 00

R. ornatus. Theta(pi) % 0.29 0.00 0.00 0.00 0.03 0.37 0.46 0.19 0.00 0.18 0.18 0.00 0.11 0.00 0.64 0.07 0.00 0.39 0.17 0.13 0.13 0.08 0.05

Tajima's D ------0.000 0.000 0.000 0.848 0.000 0.000 1.503 0.000 0.324 0.000 0.221 1.289 0.155 0.380 1.262 1.210 0.227 1.058 0.483 1.831 1.518 1.002 1.498

p Taj - value ima 0.099 1.000 1.000 1.000 0.168 0.810 0.375 0.092 1.000 0 0.417 1.000 0.977 1.000 0.142 0.799 1.000 0.342 0.007 0.665 0.050 0.205 0.059 .108 ’s D D ’s

Fu's F ------0.000 0.000 0.000 0.000 0.000 1.318 0.000 0.643 0.000 0.106 4.022 0.595 0.75 0.208 6.528 2.067 0.747 9.309 2.322 1.008 1.287 0.918 1.615 7

p Fu's F - value 0.049 1.000 1.000 1.000 0.117 0.297 0.500 0.006 1.000 0.063 0.184 1.000 0.709 1.000 0.005 0.435 1.000 0.064 0.125 0.387 0.064 0.072 0.020

Tau 2.975 0.000 0.000 0.000 0.207 2.846 4.322 1.115 0.000 2.670 1.139 0.000 0.828 0.000 4.299 0.498 0.000 2.031 0.021 0.846 0.744 0.465 3.000

55 Human Impact There was a highly significant effect of proximity to development on all estimates of genetic diversity, with a drastically lower levels of genetic diversity at sites that were located within 10km of urban development or cleared rural areas (Table 4.2). Disturbed sites also had significantly reduced measures of genetic diversity than undisturbed sites, however all effect sizes except haplotype diversity were less than those seen with proximity to development (P ≤ 0.008 in the chi- square and F-tests against a null model with intercept only). The presence of invasive G. holbrooki, showed a non-significant interaction with all measures of genetic diversity. These patterns were consistent across the three genetic clades, and there was no effect of clade identity in univariate models that used clade identity as the sole predictor of any of the four estimators of genetic diversity (P ≥ 0.28).

56

Table ! holbrooki (Absence vs speciesInvasive (Native wallum vs Habitat type urban>10km development) from urban(<10km development from vs Proximity to development Predictor 4. 2:

)

Estimates of effect of human genetpredictors of Estimates effect impacton of

.

presence of

.

altered waterway) G.

.

Theta pi Theta S Haplotype diversity haplotypesNumber of Theta pi Theta S Haplotype diversity haplotypesNumber of Theta pi Theta S Haplotype diversity haplotypesNumber of Response

ic diversity usingmodels ic measures linear generalized Estimate ------10.227 0.72 1.110 7.435 0.480 3.295 2.7 1.135 1.92 2.267 7.948 1.129 10 6 4

(Df 1, 18) (Df F 10.9 19.19 14.30 - 1.07 2.85 4.28 2.58 9.80 9.56 8.65 9.43 6.03 value 8

. 0.3157 0.1087 0.0532 0.1082 0.0058 0.0063 0.0087 0.0021 0.0039 0.0004 0.0244 0.0002

P

57 Discussion Populations of Rhadinocentrus ornatus near to urban development and with degraded habitats had significantly reduced measures of genetic diversity compared to populations from more natural, undisturbed habitats. This pattern was consistent both within and across the three highly divergent lineages of R. ornatus, which are allopatric at the stream level in southeast Queensland. These results highlight the vulnerability of populations near urban development to extirpation or extinction as they have: (i) low genetic diversity probably caused by contemporary contraction of population sizes, and (ii) low levels of standing genetic variation and thus reduced capacity for adaptive change. Unmanaged alteration to their habitat could easily lead to loss of remaining populations as has been seen in Honey blue-eye and Oxleyan pygmy-perch populations around the

Greater Brisbane area (see ARTHINGTON and MARSHALL 1995; ARTHINGTON and MARSHALL 1996).

Phylogeography and Evolutionary Significant Units

The clear delineation of R. ornatus into three major clades builds on earlier work (PAGE et al. 2004; SHARMA and HUGHES 2011) and increases the precision of phylogeographic patterns with sampling from new locations. The observation of strong phylogeographic concordance for these three mtDNA clades suggests that R. ornatus should be treated as three distinct evolutionary significant units (ESUs) (sensu MORITZ 1994), in both a descriptive and conservation sense as it is important to manage these unique lineages independently from one another. High levels of differentiation between populations from different streams match general expectations for freshwater fish, and for Australian species in particular due to Australia’s old landscape (VORIS 2000; UNMACK 2001). Notable for R. ornatus are the exceptionally high levels of differentiation between several sites that are adjacent to one another (<10km). This is observed mainly among Tin Can Bay populations, where several sites are proximate and share a lowland floodplain, which would facilitate gene exchange during large flooding events. However, several of these populations do not share mtDNA haplotypes and do not appear to have had any recent contact with one another. At the broad landscape scale, the Tin Can Bay region appears to be a location of intermittent disjunction and secondary contact between the three genetic clades identified, although none of the clades are sympatric within individual streams. SEQ and SER lineages are separated here, with the south-flowing Noosa River being the furthest north population of SEQ genotypes. Noosa River populations (NOO and TWS), although physically separated from the nearest SER populations by less than 10km, are actually separated by the whole Noosa catchment and ~100km of coastline. The division between SER and CEQ populations appears to be more complicated. With historically lowered sea levels it is likely that SER populations, many of which currently share a lowland saltmarsh swamp, may have been physically connected before eventually flowing into what

58 is now Tin Can Bay. The only CEQ population on the mainland (Snapper Creek) flows into a separate section of Tin Can Bay, which is isolated from all SER populations by a deep saltwater channel. With lowered sea levels, it is possible that this formed a separate drainage channel from the rest of Tin Can Bay, with freshwater fish populations having to travel significantly further into what is now Hervey Bay in order to connect with populations that make up the SER clade. It is also possible that the Snapper Creek population, due to this separated channel, might have physically connected to Fraser Island populations through a ‘super’ drainage before flowing into the ocean. One population in the SER clade, Obi Obi Creek on the upper Mary River, complicates this result further, having a single haplotype that is shared with several SER populations. Based on its geography we predicted that this population would most likely contain CEQ haplotypes. However this was not the case. One explanation for this anomaly is present day tributaries of the Mary River may have historically flowed into Tin Can Bay (Page, personal communication). Such flow patterns may have allowed relatively recent gene flow between these populations, which may have resulted in mtDNA capture. Unpublished allozyme data has suggested that this Obi Obi Creek population is quite distinct from all other R. ornatus populations, and though they share mtDNA haplotypes with SER populations, they might represent a distinct taxon (Adams, unpublished data). Another interesting pattern is seen in the upper Noosa River. Two populations sampled in this study, Teewah Creek South and Noosa River, were significantly different from one another (Supplementary Table 4.2). However these populations are part of the same river drainage, and are separated by only ~8km river distance. This may be an indication that R. ornatus, although potentially capable of dispersal over large stretches of river, may not actually move around much at all. This result is similar to that found by SHARMA and HUGHES (2011) and if this hypothesis were accurate, it may explain why adjacent populations that share floodplains remain isolated from one another, as although the potential to disperse and interbreed during large flooding events is possible, fish may not migrate downstream to achieve this.

Human Impacts In R. ornatus, mitochondrial DNA diversity showed a striking inverse relationship with indicators of human impact. The environmental factors explored here – proximity to development, habitat disturbance and invasive species – are highly correlated and we predicted to see lower levels of genetic diversity when sites were changed from their natural state by any of these metrics. Proximity to development was the strongest predictor, with all populations that were in developed locations or close to urban development having exceedingly low measures of genetic diversity regardless of which estimator of genetic diversity was examined. Habitat disturbance was also a strong predictor of reduced genetic diversity, with presence of invasive species being the weakest

59 predictor, only having a significant negative relationship with haplotype diversity. Clade membership was not a significant predictor of population diversity, indicating that the effect of human proximity does not arise as a spurious correlation due to geographic distributions of distinct lineages. The differences in effect sizes for proximity to human development, habitat type, and invasive species may be a result of the time it takes for these predictors to affect a site and also reflects the inherently correlated nature of these aspects of human impact. For instance, a site may suffer negative effects from being in close proximity to urban areas (i.e. runoff, water pollution) before the waterway and its surrounding habitat is obviously disturbed. Furthermore, invasive G. holbrooki tend to occur in highly altered waterways, often found alongside invasive plant species that clog the waterway and reduce flow. It may be that we have captured relatively early stages of how change to a natural freshwater ecosystem affects genetic diversity in sensitive populations through successive stages of habitat degradation. This hypothesized sequence of events is worthy of future study. Other studies have investigated the effects of urbanization on plant communities, suggesting that urban land use causes reductions in species and genetic diversity (VELLEND 2004;

KNAPP et al. 2008; DINNAGE 2009; HELMUS et al. 2010). However to our knowledge this is the first study on freshwater fish that shows predictors of urban development and habitat degradation having significant effects on genetic diversity.

Discrete threats and impacts to the three distinct ESUs Rhadinocentrus ornatus is under some threat of exploitation by collectors (it is a popular aquarium species) as well as from urban development, invasive species, and habitat degradation. In the remainder of this section we discuss the conservation implications by lineage and region. CEQ and SER lineages are already somewhat protected, being located within the Great Sandy National Park. However three populations (Snapper, Teewah North and Pipeclay Creeks) are situated outside of the national park and are in relatively close proximity to urban areas of Tin Can Bay. CEQ and SER lineages appear to represent genetically healthy populations, with relatively high mtDNA haplotype diversity within each lineage, although populations outside the national park exhibit significantly lower levels of genetic diversity than those within the protection of the park. Contrasts such as those seen here have been suggested as a good indicator of a populations’ viability, and may suggest that the conservation status of R. ornatus has to be reconsidered in light of these data (FINN et al. 2009). Protected sites of CEQ and SER populations are also surrounded by relatively unaltered habitat, unlike many of the populations found further south, around the highly developed Sunshine Coast and Brisbane regions (SEQ lineage). Interestingly, North Teewah Creek populations are one of two varieties commonly found in the aquarium trade in Australia, and wild

60 caught populations include mitotypes that are only found in this location. This gives us some evidence that the patterns we are seeing in R. ornatus are not being facilitated by the release of aquarium trade fish throughout their distribution. The SEQ lineage, being widespread across southeast Queensland but with a very patchy distribution, is under the greatest threat from human development, habitat degradation and invasive species. Populations of this lineage showed the lowest levels of genetic diversity, with all but one population in proximity to human development exhibiting only a single fixed haplotype. It is also believed that several populations within this distribution have already been extirpated, with no known populations now remaining in whole river catchments between the southern Sunshine Coast and the Redlands-Logan region south of Brisbane. R. ornatus are previously known from several locations in the catchment, including Bulimba and Enoggera Creeks, but have not been recorded for several decades (TAPPIN 2010).

Conclusions To our knowledge this is the first study to show a relationship between urbanization/habitat degradation and genetic diversity in freshwater vertebrates. Although we found a strong relationship between urbanization and genetic diversity, our study represents a relatively coarse investigation of how urbanization affects genetic diversity. Further research using more precise measures of urbanization (i.e. changes to flow regime, runoff, land clearing, dissolved oxygen and turbidity) and monitoring both genetic diversity and population abundance may provide clues as to which specific aspects of urbanization and habitat degradation drive the observed phenomena. Also, using more sensitive molecular markers would increase the precision of measures of genetic diversity among sites and how they are affected by habitat alteration. The drastically lower levels of genetic diversity we see here suggest that R. ornatus is highly sensitive to disturbance to its habitat; measures to continue to conserve extant populations especially those in the vicinity of altered landscapes are important. The Tin Can Bay region is home to many aquatic species (fish, amphibians and invertebrates), some of which are narrow range endemics and may be imperiled by human impacts. It is further possible that, due to reduced genetic diversity R. ornatus may have lost the potential to adapt to future changes that are predicted under climate change scenarios. Although large areas of this study are already protected within Great Sandy National Park, many populations are not and if suitable measures are not taken to conserve these populations we may lose distinct endemic biodiversity through further degradation of unprotected sites.

61 Chapter Five – General Discussion This dissertation applied a molecular approach to investigate how genetic diversity in Australian rainbowfishes has been influenced by historical and contemporary changes to the landscapes that fish inhabit. In general, population genetic structure was extensive among local populations, particularly for Melanotaenia trifasciata and Rhadinocentrus ornatus, with most genetic diversity observed being endemic to particular river catchments. This diversity has largely been shaped by historical changes to riverine landscapes and relative connectivity between river systems. It was also evident that endemic diversity may be under threat from human demography changes and urban development, and that loss of diversity is a realized threat already at least for R. ornatus, and potentially an imminent one in some Melanotaenia species. In summary, in the second chapter a comparative framework was used to investigate how genetic diversity in closely related and co-distributed Melanotaenia taxa can be shaped differently as a result of small differences in each species’ ecological requirements. M. splendida and M. trifasciata have largely overlapping niche requirements, and are often found in large multi-species shoals. M. splendida’s more generalist ecological needs and broader distribution however, both within catchments, and more widely across northern Australia have resulted in relatively low levels of population differentiation (compared with many species of Australian freshwater fishes with comparable ranges). Results here suggest that M. splendida has dispersed more extensively between river systems in the recent past, as compared with M. trifasciata, that has a more restricted natural range and apparent narrower niche requirements. In contrast to M. splendida, populations of M. trifasciata are highly structured, and any historical connectivity among them appears to have been much older. In chapter three I have identified and characterized instances of natural hybridization events between M. splendida and M. trifasciata in multiple locations where they co-occur. All sites where hybridization was detected were at the edges of M. trifasciata’s natural distribution, including in the most western populations found in the Northern Territory, and the most southern populations in Queensland. Patterns of hybrid introgression differed markedly between sites, a phenomenon that has rarely been observed before in other freshwater fish taxa. Different patterns of hybrid introgression may suggest that breakdown of reproductive isolation in marginal habitat sites has occurred in a number of different ways, and if marginal habitat is causing reproductive isolation to breakdown and is related to climatic factors then predicted changes due to climate change factors have the potential to increase the frequency that intra-specific hybridization may occur in the future

(CHUNCO 2014; TAYLOR et al. 2014; TAYLOR et al. 2015). In chapter four I investigated fine-scale phylogeographic patterns in Rhadinocentrus ornatus, identifying three divergent allopatric lineages in southeast Queensland that represent three

62 distinct Evolutionary Significant Units (Moritz 1994; MORITZ and FAITH 1998). R. ornatus has a largely urban distribution, and all populations that occur in close proximity to urban development that were sampled showed greatly reduced levels of genetic diversity compared with comparable populations in pristine habitats. All pristine habitats were located inside already protected national parks. Several R. ornatus populations have already been extirpated from urban creeks around the Greater Brisbane Region, and because low genetic diversity levels often equates to reduced adaptive potential, perhaps the long term prognosis for urban populations is compromised (LANDE and

BARROWCLOUGH 1987; KELLERMANN et al. 2009). While overall species diversity of native freshwater fish in Australia is relatively low compared with other parts of the world (ALLEN et al. 2002), this dissertation helps to illustrate that endemic genetic diversity is quite high within currently described taxa. The taxa represented here, particularly M. splendida and M. trifasciata, are currently largely confined to pristine habitats, where there is little or no human development or land degradation currently affecting populations. Many of these populations however, are becoming increasingly under threat from expanding mining operations and proposed future development of northern Australia for agriculture and related development. In a number of instances plans have already been developed for major dams on pristine rivers in the north to promote expansion of local agriculture. Much of northwestern Queensland is leased for bauxite mining, and there are two Uranium mines established in eastern Kakadu National Park, Northern Territory. The potential damage that these operations could do to populations of freshwater fish is massive, and as results demonstrate here, we currently know very little about the real levels of diversity that is present in freshwater fish stocks in the north of Australia. The apparent effects of urban development on genetic diversity in R. ornatus should act as a warning to protect pristine aquatic habitats and the diverse fauna that they hold. Recently the Queensland ‘Wild Rivers’ act, legislation that was enacted to prevent development in buffer zones around rivers on Cape York Peninsula that were deemed pristine or close to pristine was repealed. Development of riparian zones around these ‘Wild Rivers’ has been limited to uses that had no effect on rivers. Large areas of Cape York Peninsula are currently used for cattle grazing, and the repealed act prevented grazing within one kilometer of any ‘Wild River’, among other things. The repeal of this act has re-opened riparian areas for development and grazing and this is likely to have detrimental effects on aquatic organisms in these rivers where riparian zones are impacted. The unique genetic diversity identified in many populations of M. trifasciata in chapter two, and of M. splendida to a lesser extent, would likely become increasingly under threat from removing restrictions on land use, and the loss of such diversity would be unrecoverable, and recolonization from other populations would be very unlikely or if it does occur may even be maladaptive.

63 We live in a changing world, not only from human demographic expansion and increasing development, but also from the effects of anthropogenic climate change. Rapid changes in climate that are predicted over the next century are expected to impact a variety of organisms, and freshwater organisms are predicted to be some of the most heavily affected (MORRONGIELLO et al. 2011). Obligate freshwater fish have only an extremely limited or even no ability to disperse among catchments, and increasing ambient mean temperatures and changes in rainfall due to climate change could greatly impact the habitats that they currently inhabit (PARMESAN and YOHE 2003;

MORRONGIELLO et al. 2011). Temperatures are likely to increase, as are the frequency of drought conditions (DAI 2013). Northern Australia’s landscapes undergo large fluctuations, with seasonal wet and dry periods. If droughts become more regular occurrences they will place greater stress on river systems of northern Australia. Rainfall is the only source of water for most of these rivers, and if seasonal rainfall is reduced, fragmentation of rivers will increase. In turn fragmentation of populations of freshwater organisms will increase. It is largely unknown how such species will cope with these changes. However, with no ability to move to new areas as climate and habitat changes, as is sometimes suggested as a possible outcome for organisms with good dispersal capabilities (i.e. many terrestrial and marine organisms) (ROSENZWEIG et al. 2008), the consequences for increasingly fragmented and isolated populations and species as a whole could be immense. Several areas of the research presented here could be expanded to expand current insight into what drives patterns of genetic diversity in Australia’s freshwater fish. Technological advancements in molecular biology over the last few years have been significant, with new gene sequencing technologies allowing the rapid collection of large quantities of data from a very large number of genetic loci. In the current study there were limitations on the conclusions that could be made due to the relatively small numbers of loci that were screened, in particular in determining patterns of hybridization in section three and in investigating the effects that urbanization had on genetic diversity levels in R. ornatus that employed only a single maternally inherited locus in chapter four. The cost of next generation sequencing technologies (NGST) is reducing rapidly, and their use for population genetic studies is becoming better understood and more widespread. This dissertation adds to a growing literature base that investigates population genetics and phylogeography of Australian freshwater fishes. In particular, work conducted to date in far northern Australia is quite limited, with only a handful of studies having investigated population genetics of freshwater organisms (DE BRUYN et al. 2004; COOK and HUGHES 2010; HUEY et al.

2013; COOK et al. 2014). In comparison, studies in areas of greater human populations with easier access including central and southern Australia have been much more common. In reality, general aquatic diversity in northern Australia is much more extensive than in southern regions and areas close to large human population centres, so much remains to be explored.

64 Literature Cited

ABOIM, M. A., J. MAVAREZ, L. BERNATCHEZ and M. M. COELHO, 2010 Introgressive hybridization between two Iberian endemic cyprinid fish: a comparison between two independent hybrid zones. Journal of Evolutionary Biology 23: 817-828.

ALJANABI, S. M., and I. MARTINEZ, 1997 Universal and rapid salt-extraction of high quality genomic DNA for PCR- based techniques. Nucleic Acids Research 25: 4692-4693.

ALLAN, J. D., 2004 Landscapes and riverscapes: the influence of land use on stream ecosystems. Annual Review of Ecology, Evolution, and Systematics 35: 257-284.

ALLEN, G. R., S. H. MIDGLEY and M. ALLEN, 2002 Field guide to freshwater fishes of Australia. Western Australian Museum, Perth, W.A.

ALLENDORF, F. W., and G. LUIKART, 2007 Conservation and the genetics of populations. Blackwell Pub., Malden, MA.

ALLENDORF, F. W., and R. S. WAPLES, 1996 Conservation and genetics of salmonid fishes. Conservation genetics: case histories from nature: 238-280.

ALVES, M. J., M. M. COELHO and M. J. COLLARES-PEREIRA, 2001 Evolution in action through hybridisation and polyploidy in an Iberian freshwater fish: a genetic review. Genetica 111: 375-385.

ARNOLD, M. L., 1992 Natural Hybridization as an Evolutionary Process. Annual Review of Ecology and Systematics 23: 237-261.

ARTHINGTON, A. H., A. L. CHANDICA and C. J. MARSHALL, 1994 Geographical information systems and distribution maps for the Honey blue-eye, Pseudomugil mellis and other freshwater fishes in South-eastern Queensland, pp. in Final Report to the Australian Nature Conservation Agency Endangered Species Program Centre for Catchment and In-stream Research, Griffith University: Nathan, Australia.

ARTHINGTON, A. H., and C. J. MARSHALL, 1995 Threatened fishes of the world Pseudomugil mellis Allen & Ivantsoff, 1982 (Pseudomugilidae). Environmental Biology of Fishes 43: 268-268.

ARTHINGTON, A. H., and C. J. MARSHALL, 1996 Threatened fishes of the world Nannoperca oxleyana Whitley, 1940 (Nannopercidae). Environmental Biology of Fishes 46: 150-150.

ARTHINGTON, A. H., and C. J. MARSHALL, 1999 Diet of the exotic mosquitofish, Gambusia holbrooki, in an Australian lake and potential for competition with indigenous fish species. Asian Fisheries Science: 1-16.

ARTHINGTON, A. H., D. A. MILTON and R. J. MCKAY, 1983 Effects of urban development and habitat alterations on the distribution and abundance of native and exotic freshwater fish in the Brisbane region, Queensland. Australian Journal of Ecology 8: 87-101.

65 ATTON, F. M., 1959 The Invasion of Manitoba and Saskatchewan by Carp. Transactions of the American Fisheries Society 88: 203-205.

AVISE, J. C., J. ARNOLD, R. M. BALL, E. BERMINGHAM, T. LAMB et al., 1987 Intraspecific Phylogeography: The Mitochondrial DNA Bridge Between Population Genetics and Systematics. Annual Review of Ecology and Systematics 18: 489-522.

AVISE, J. C., and N. C. SAUNDERS, 1984 Hybridization and introgression among species of sunfish (Lepomis): analysis by mitochondrial DNA and allozyme markers. Genetics 108: 237.

AVISE, J. C., D. WALKER and G. C. JOHNS, 1998 Speciation durations and Pleistocene effects on vertebrate phylogeography. Proceedings of the Royal Society of London Series B-Biological Sciences 265: 1707-1712.

BANDELT, H.-J., P. FORSTER and A. RÖHL, 1999 Median-joining networks for inferring intraspecific phylogenies. Molecular Biology and Evolution 16: 37-48.

BERMINGHAM, E., S. S. MCCAFFERTY and A. P. MARTIN, 1997 Fish biogeography and molecular clocks: perspectives from the Panamanian Isthmus. Molecular Systematics of Fishes: 113- 128.

BERTOZZI, T., M. ADAMS and K. F. WALKER, 2000 Species boundaries in carp gudgeons (Eleotrididae : Hypseleotris) from the River Murray, South Australia: evidence for multiple species and extensive hybridization. Marine and Freshwater Research 51: 805-815.

BOWMAN, D. M. J. S., G. K. BROWN, M. F. BRABY, J. R. BROWN, L. G. COOK, M. D. CRISP, F. FORD, S.

HABERLE, J. HUGHES, Y. ISAGI, L. JOSEPH, J. MCBRIDE, G. NELSON and P. Y. LADIGES 2010 Biogeography of the Australian monsoon tropics. Journal of Biogeography 37: 201- 216.

BRIDLE, J. R., and T. H. VINES, 2007 Limits to evolution at range margins: when and why does adaptation fail? Trends in Ecology & Evolution 22: 140-147.

BROUGHTON, R. E., K. C. VEDALA, T. M. CROWL and L. L. RITTERHOUSE, 2011 Current and historical hybridization with differential introgression among three species of cyprinid fishes (genus Cyprinella). Genetica 139: 699-707.

BURRIDGE, C. P., D. CRAW, D. C. JACK, T. M. KING and J. M. WATERS, 2008 Does fish ecology predict dispersal across a river drainage divide? Evolution 62: 1484-1499.

BURRIDGE, C. P., D. CRAW and J. M. WATERS, 2006 River capture, range expansion, and cladogenesis: the genetic signature of freshwater vicariance. Evolution 60: 1038-1049.

CARNAVAL, A. C., M. J. HICKERSON, C. F. B. HADDAD, M. T. RODRIGUES and C. MORITZ, 2009 Stability Predicts Genetic Diversity in the Brazilian Atlantic Forest Hotspot. Science 323: 785-789.

66 CHILDS, M. R., A. A. ECHELLE and T. E. DOWLING, 1996 Development of the Hybrid Swarm Between Pecos Pupfish (Cyprinodontidae: Cyprinodon pecosensis) and Sheepshead Minnow (Cyprinodon variegatus): A Perspective from Allozymes and mtDNA. Evolution 50: 2014- 2022.

CHIVAS, A. R., A. GARCÌA, S. VAN DER KAARS, M. J. J. COUAPEL, S. HOLT et al., 2001 Sea-level and environmental changes since the last interglacial in the Gulf of Carpentaria, Australia: an overview. Quaternary International 83-85: 19-46.

CHOW, S., and K. HAZAMA, 1998 Universal PCR primers for S7 ribosomal protein gene introns in fish. Molecular Ecology 7: 1255-1256.

CHUNCO, A. J., 2014 Hybridization in a warmer world. Ecology and evolution 4: 2019-2031.

CLARKE, R. T., P. ROTHERY and A. F. RAYBOULD, 2002 Confidence limits for regression relationships between distance matrices: Estimating gene flow with distance. Journal of Agricultural, Biological, and Environmental Statistics 7: 361-372.

CLEMENT, M., D. C. K. A. POSADA and K. A. CRANDALL, 2000 TCS: a computer program to estimate gene genealogies. Molecular Ecology 9: 1657-1659.

COOK, B. D., and J. M. HUGHES, 2010 Historical population connectivity and fragmentation in a tropical freshwater fish with a disjunct distribution (pennyfish, Denariusa bandata). Journal of the North American Benthological Society 29: 1119-1131.

COOK, B. D., M. J. KENNARD, K. REAL, B. J. PUSEY and J. M. HUGHES, 2011 Landscape genetic analysis of the tropical freshwater fish Mogurnda mogurnda (Eleotridae) in a monsoonal river basin: importance of hydrographic factors and population history. Freshwater Biology 56: 812-827.

COOK, B. D., P. J. UNMACK, J. A. HUEY and J. M. HUGHES, 2014 Did common disjunct populations of freshwater fishes in northern Australia form from the same biogeographic events? Freshwater Science 33: 263-272.

COYNE, J. A., and H. A. ORR, 2004 Speciation. Sinauer Associates, Sunderland, Mass.

CROOK, D. A., W. H. LOWE, F. W. ALLENDORF, T. ERŐS, D. S. FINN et al., 2015 Human effects on ecological connectivity in aquatic ecosystems: Integrating scientific approaches to support management and mitigation. Science of The Total Environment 534: 52-64.

CUCHEROUSSET, J., and J. D. OLDEN, 2011 Ecological Impacts of Nonnative Freshwater Fishes. Fisheries 36: 215-230.

DAI, A., 2013 Increasing drought under global warming in observations and models. Nature Climate Change 3: 52-58.

67 DE BRUYN, M., J. C. WILSON and P. B. MATHER, 2004 Reconciling geography and genealogy: phylogeography of giant freshwater prawns from the Lake Carpentaria region. Molecular Ecology 13: 3515-3526.

DINNAGE, R., 2009 Disturbance alters the phylogenetic composition and structure of plant communities in an old field system. PloS one 4: e7071.

DOWLING, T. E., and C. L. SECOR, 1997 The Role of Hybridization and Introgression in the Diversification of Animals. Annual Review of Ecology and Systematics 28: 593-619.

ENDLER, J. A., 1977 Geographic variation, speciation, and clines. Princeton University Press.

EVANNO, G., E. CASTELLA, C. ANTOINE, G. PAILLAT and J. GOUDET, 2009 Parallel changes in genetic diversity and species diversity following a natural disturbance. Molecular Ecology 18: 1137-1144.

EXCOFFIER, L., and H. E. LISCHER, 2010 Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Molecular Ecology Resources 10: 564-567.

EXCOFFIER, L., P. E. SMOUSE and J. M. QUATTRO, 1992 Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction data. Genetics 131: 479-491.

FINN, D. S., M. S. BLOUIN and D. A. LYTLE, 2007 Population genetic structure reveals terrestrial affinities for a headwater stream . Freshwater Biology 52: 1881-1897.

FISCHER, J., and D. B. LINDENMAYER, 2007 Landscape modification and habitat fragmentation: a synthesis. Global Ecology and Biogeography 16: 265-280.

FITZPATRICK, B. M., J. S. PLACYK, M. L. NIEMILLER, G. S. CASPER and G. M. BURGHARDT, 2008 Distinctiveness in the face of gene flow: hybridization between specialist and generalist gartersnakes. Molecular Ecology 17: 4107-4117.

FLOT, J. F., 2010 SeqPHASE: a web tool for interconverting PHASE input/output files and FASTA sequence alignments. Molecular Ecology Resources 10: 162-166.

FOX, J., S. WEISBERG, J. HONG, R. ANDERSEN, D. FIRTH et al., 2012 Package for the R software: effect displays for linear, generalized linear, multinomial-logit, proportional-odds logit models and mixed-effects models. www. r-project. org. URL http://www. r-project. org, http://socserv. socsci. mcmaster. ca/jfox.

FU, Y., 1997 Statistical tests of neutrality of mutations against population growth, hitchhiking and background selection. Genetics 147: 915-925.

GANTE, H. F., I. DOADRIO, M. J. ALVES and T. E. DOWLING, 2015 Semi-permeable species boundaries in Iberian barbels (Barbus and Luciobarbus, Cyprinidae). BMC Evolutionary Biology 15: 1-18.

68 GILLESPIE, G. D., 1985 Hybridization, Introgression, and Morphometric Differentiation between Mallard (Anas platyrhynchos) and Grey Duck (Anas superciliosa) in Otago, New-Zealand. Auk 102: 459-469.

GOMPERT, Z., J. A. FORDYCE, M. L. FORISTER, A. M. SHAPIRO and C. C. NICE, 2006 Homoploid hybrid speciation in an extreme habitat. Science 314: 1923-1925.

GUILLOT, G., R. LEBLOIS, A. COULON and A. C. FRANTZ, 2009 Statistical methods in spatial genetics. Molecular Ecology 18: 4734-4756.

HELM, A., T. OJA, L. SAAR, K. TAKKIS, T. TALVE et al., 2009 Human influence lowers plant genetic diversity in communities with extinction debt. Journal of Ecology 97: 1329-1336.

HELMUS, M. R., W. B. KELLER, M. J. PATERSON, N. D. YAN, C. H. CANNON et al., 2010 Communities contain closely related species during ecosystem disturbance. Ecology Letters 13: 162-174.

HEWITT, G., 2000 The genetic legacy of the Quaternary ice ages. Nature 405: 907-913.

HEY, J., and R. NIELSEN, 2004 Multilocus Methods for Estimating Population Sizes, Migration Rates and Divergence Time, With Applications to the Divergence of Drosophila pseudoobscura and D. persimilis. Genetics 167: 747-760.

HEY, J., and R. NIELSEN, 2007 Integration within the Felsenstein equation for improved Markov chain Monte Carlo methods in population genetics. Proceedings of the National Academy of Sciences 104: 2785-2790.

HEY, J., Y. J. WON, A. SIVASUNDAR, R. NIELSEN and J. A. MARKERT, 2004 Using nuclear haplotypes with microsatellites to study gene flow between recently separated Cichlid species. Molecular Ecology 13: 909-919.

HOWE, E., C. HOWE, R. LIM and M. BURCHETT, 1997 Impact of the introduced poeciliid Gambusia holbrooki (Girard, 1859) on the growth and reproduction of Pseudomugil signifer (Kner, 1865) in Australia. Marine and Freshwater Research 48: 425-434.

HUDSON, A. G., P. VONLANTHEN and O. SEEHAUSEN, 2011 Rapid parallel adaptive radiations from a single hybridogenic ancestral population. Proceedings of the Royal Society of London B: Biological Sciences 278: 58-66.

HUDSON, R. R., and N. L. KAPLAN, 1985 Statistical properties of the number of recombination events in the history of a sample of DNA sequences. Genetics 111: 147-164.

HUELSENBECK, J. P., and F. RONQUIST, 2001 MRBAYES: Bayesian inference of phylogenetic trees. Bioinformatics 17: 754-755.

HUEY, J. A., B. D. COOK, P. J. UNMACK and J. M. HUGHES, 2013a Broadscale phylogeographic structure of five freshwater fishes across the Australian Monsoonal Tropics. Freshwater Science 33: 273-287.

69 HUEY, J. A., T. ESPINOZA and J. M. HUGHES, 2013b Natural and anthropogenic drivers of genetic structure and low genetic variation in the endangered freshwater cod, Maccullochella mariensis. Conservation Genetics: 1-12.

HUGHES, J., M. PONNIAH, D. A. HURWOOD, S. F. CHENOWETH and A. H. ARTHINGTON, 1999 Strong genetic structuring in a habitat specialist, the Oxleyan Pygmy Perch Nannoperca oxleyana. Heredity 83: 5-14.

HUGHES, J. M., D. J. SCHMIDT and D. S. FINN, 2009 Genes in Streams: Using DNA to understand the movement of freshwater fauna and their riverine habitat. BioScience 59: 573-583.

HURWOOD, D. A., and J. M. HUGHES, 1998 Phylogeography of the freshwater fish, Mogurnda adspersa, in streams of northeastern Queensland, Australia: evidence for altered drainage patterns. Molecular Ecology 7: 1507-1517.

IVANTSOFF, W., 1999 Detection of predation on Australian native fishes by Gambusia holbrooki. Marine and Freshwater Research 50: 467-468.

JIGGINS, C. D., and J. MALLET, 2000 Bimodal hybrid zones and speciation. Trends in Ecology & Evolution 15: 250-255.

JØRGENSEN, S., and R. MAURICIO, 2005 Hybridization as a source of evolutionary novelty: leaf shape in a Hawaiian composite. Genetica 123: 171-179.

KELLERMANN, V., B. VAN HEERWAARDEN, C. M. SGRO and A. A. HOFFMANN, 2009 Fundamental Evolutionary Limits in Ecological Traits Drive Drosophila Species Distributions. Science 325: 1244-1246.

KNAPP, S., I. KÜHN, O. SCHWEIGER and S. KLOTZ, 2008 Challenging urban species diversity: contrasting phylogenetic patterns across plant functional groups in Germany. Ecology Letters 11: 1054-1064.

KOEHN, J. D., 2004 Carp (Cyprinus carpio) as a powerful invader in Australian waterways. Freshwater Biology 49: 882-894.

KRUEGER, C. C., and B. MAY, 1991 Ecological and Genetic Effects of Salmonid Introductions in North America. Canadian Journal of Fisheries and Aquatic Sciences 48: 66-77.

LAJBNER, Z., V. SLECHTOVA, V. SLECHTA, M. SVATORA, P. BERREBI et al., 2009 Rare and asymmetrical hybridization of the endemic Barbus carpathicus with its widespread congener Barbus barbus. Journal of Fish Biology 74: 418-436.

LANDE, R., 1980 Sexual dimorphism, sexual selection, and adaptation in polygenic characters. Evolution 292-305.

LANDE, R., and G. F. BARROWCLOUGH, 1987 Effective population size, genetic variation, and their use in population management. Viable Populations for Conservation: 87-123.

70 LEGENDRE, P., M-J. FORTIN and D. BORCARD, 2015 Should the Mantel test be used in spatial analysis? Methods in Ecology and Evolution 6(11): 1239-1247.

LIBRADO, P., and J. ROZAS, 2009 DnaSP v5: a software for comprehensive analysis of DNA polymorphism data. Bioinformatics 25: 1451-1452.

LOWE, S., M. BROWNE, S. BOUDJELAS and M. DE POORTER, 2000 100 of the world's worst invasive alien species: a selection from the global invasive species database. Invasive Species Specialist Group Auckland,, New Zealand.

MABUCHI, K., H. SENOU and M. NISHIDA, 2008 Mitochondrial DNA analysis reveals cryptic large- scale invasion of non-native genotypes of common carp (Cyprinus carpio) in Japan. Molecular Ecology 17: 796-809.

MALLET, J., 2005 Hybridization as an invasion of the genome. Trends in Ecology & Evolution 20: 229-237.

MANTEL, N., 1967 The detection of disease clustering and a generalized regression approach. Cancer Research 27: 209-220.

MARSHALL, J. C., 1988 An extension of the known range of Rhadinocentrus ornatus. Fishes of Sahul 5: 196-197.

MCDONALD, D. B., T. L. PARCHMAN, M. R. BOWER, W. A. HUBERT and F. J. RAHEL, 2008 An introduced and a native vertebrate hybridize to form a genetic bridge to a second native species. Proceedings of the National Academy of Sciences of the United States of America 105: 10837-10842.

MCGUIGAN, K., D. ZHU, G. R. ALLEN and C. MORITZ, 2000 Phylogenetic relationships and historical biogeography of Melanotaeniid fishes in Australia and New Guinea. Marine and Freshwater Research 51: 713-723.

MEFFE, G. K., and R. C. VRIJENHOEK, 1988 Conservation Genetics in the Management of Desert Fishes. Conservation Biology 2: 157-169.

MORITZ, C., 1994 Defining ‘evolutionarily significant units’ for conservation. Trends in Ecology & Evolution 9: 373-375.

MORITZ, C., and D. P. FAITH, 1998 Comparative phylogeography and the identification of genetically divergent areas for conservation. Molecular Ecology 7: 419-429.

MORRONGIELLO, J. R., S. J. BEATTY, J. C. BENNETT, D. A. CROOK, D. N. E. N. IKEDIFE et al., 2011 Climate change and its implications for Australia's freshwater fish. Marine and Freshwater Research 62: 1082-1098.

NEI, M., and W. H. LI, 1979 Mathematical model for studying genetic variation in terms of restriction endonucleases. Proceedings of the National Academy of Sciences 76: 5269-5273.

71 NIELSEN, R., and J. WAKELEY, 2001 Distinguishing migration from isolation: a Markov chain Monte Carlo approach. Genetics 158: 885-896.

NILSSON, C., C. A. REIDY, M. DYNESIUS and C. REVENGA, 2005 Fragmentation and flow regulation of the world's large river systems. Science 308: 405-408.

PAGE, T. J., and J. M. HUGHES, 2014 Contrasting insights provided by single and multi-species data in a regional comparative phylogeographic study. Biological Journal of the Linnean Society 111: 554-569.

PAGE, T. J., S. SHARMA and J. M. HUGHES, 2004 Deep phylogenetic structure has conservation implications for ornate rainbowfish (Melanotaeniidae : Rhadinocentrus ornatus) in Queensland, eastern Australia. Marine and Freshwater Research 55: 165-172.

PARMESAN, C., and G. YOHE, 2003 A globally coherent fingerprint of climate change impacts across natural systems. Nature 421: 37-42.

PINTO, L., N. CHANDRASENA, J. PERA, P. HAWKINS, D. ECCLES et al., 2005 Managing invasive carp (Cyprinus carpio) for habitat enhancement at Botany Wetlands, Australia. Aquatic Conservation: Marine and Freshwater Ecosystems 15: 447-462.

PRINGLE, C. M., 2001 Hydrologic connectivity and the management of biological reserves: a global perspective. Ecological Applications 11: 981-998.

PUSEY, B. J., M. J. KENNARD and A. H. ARTHINGTON, 2004 Freshwater Fishes of North-Eastern Australia. CSIRO Publishing.

R DEVELOPMENT CORE TEAM, 2010 R: A language and environment for statistical computing, pp. R Foundation for Statistical Computing.

RAMBAUT, A., and A. J. DRUMMOND, 2012 FigTree version 1.4, available at: tree.bio.ed.ac.uk/software/figtree.

RIESEBERG, L. H., 1997 Hybrid origins of plant species. Annual Review of Ecology and Systematics: 359-389.

RIESEBERG, L. H., S. C. KIM, R. A. RANDELL, K. D. WHITNEY, B. L. GROSS et al., 2007 Hybridization and the colonization of novel habitats by annual sunflowers. Genetica 129: 149-165.

ROGERS, A. R., and H. HARPENDING, 1992 Population growth makes waves in the distribution of pairwise genetic differences. Molecular Biology and Evolution 9: 552-569.

ROSENZWEIG, C., D. KAROLY, M. VICARELLI, P. NEOFOTIS, Q. WU et al., 2008 Attributing physical and biological impacts to anthropogenic climate change. Nature 453: 353-357.

SADDLIER, S., J. D. KOEHN and M. P. HAMMER, 2013 Lets not forget the small fishes, conservation of two threatened species of pygmy perch in south-eastern Australia. Marine and Freshwater Research 64: 874-886.

72 SALZBURGER, W., S. BARIC and C. STURMBAUER, 2002 Speciation via introgressive hybridization in East African cichlids? Molecular Ecology 11: 619-625.

SCHMIDT, D. J., N. R. BOND, M. ADAMS and J. M. HUGHES, 2011 Cytonuclear evidence for hybridogenetic reproduction in natural populations of the Australian carp gudgeon (Hypseleotris: Eleotridae). Molecular Ecology 20: 3367-3380.

SCRIBNER, K. T., K. S. PAGE and M. L. BARTRON, 2000 Hybridization in freshwater fishes: a review of case studies and cytonuclear methods of biological inference. Reviews in Fish Biology and Fisheries 10: 293-323.

SEEHAUSEN, O., 2004 Hybridization and adaptive radiation. Trends in Ecology & Evolution 19: 198-207.

SHARMA, S., and J. M. HUGHES, 2011 Genetic structure and phylogeography of two freshwater fishes, Rhadinocentrus ornatus and Hypseleotris compressa, in southern Queensland, Australia, inferred from allozymes and mitochondrial DNA. Journal of Fish Biology 78: 57- 77.

SIMON, K. S., and C. R. TOWNSEND, 2003 Impacts of freshwater invaders at different levels of ecological organisation, with emphasis on salmonids and ecosystem consequences. Freshwater Biology 48: 982-994.

SOUSA-SANTOS, C., H. F. GANTE, J. ROBALO, P. P. CUNHA, A. MARTINS et al., 2014 Evolutionary history and population genetics of a cyprinid fish (Iberochondrostoma olisiponensis) endangered by introgression from a more abundant relative. Conservation Genetics 15: 665- 677.

STEPHENS, M., and P. DONNELLY, 2003 A comparison of bayesian methods for haplotype reconstruction from population genotype data. The American Journal of Human Genetics 73: 1162-1169.

STEPHENS, M., N. J. SMITH and P. DONNELLY, 2001 A new statistical method for haplotype reconstruction from population data. The American Journal of Human Genetics 68: 978- 989.

STRUEBIG, M. J., T. KINGSTON, E. J. PETIT, S. C. LE COMBER, A. ZUBAID et al., 2011 Parallel declines in species and genetic diversity in tropical forest fragments. Ecology Letters 14: 582-590.

TAJIMA, F., 1989 Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics 123: 585-595.

TAMURA, K., and M. NEI, 1993 Estimation of the number of nucleotide substitutions in the control region of mitochondrial DNA in humans and chimpanzees. Molecular Biology and Evolution 10: 512-526.

73 TAPPIN, A. R., 2010 Rainbowfishes: Their care & keeping in captivity. Art Publications, Australia.

TAYLOR, S. A., E. L. LARSON and R. G. HARRISON, 2015 Hybrid zones: windows on climate change. Trends in Ecology & Evolution.

TAYLOR, S. A., T. A. WHITE, W. M. HOCHACHKA, V. FERRETTI, R. L. CURRY et al., 2014 Climate- mediated movement of an avian hybrid zone. Current Biology 24: 671-676.

TORGERSEN, T., M. R. JONES, A. W. STEPHENS, D. E. SEARLE and W. J. ULLMAN, 1985 Late Quaternary hydrological changes in the Gulf of Carpentaria. Nature 313: 785-787.

UNMACK, P. J., 2001 Biogeography of Australian freshwater fishes. Journal of Biogeography 28: 1053-1089.

UNMACK, P. J., G. R. ALLEN and J. B. JOHNSON, 2013 Phylogeny and biogeography of rainbowfishes (Melanotaeniidae) from Australia and New Guinea. Molecular Phylogenetics and Evolution 67: 15-27.

UNMACK, P. J., and T. E. DOWLING, 2010 Biogeography of the genus Craterocephalus (Teleostei: Atherinidae) in Australia. Molecular Phylogenetics and Evolution 55: 968-984.

VELLEND, M., 2004 Parallel effects of land-use history on species diversity and genetic diversity of forest herbs. Ecology 85: 3043-3055.

VIGLIANO, P. H., M. F. ALONSO and M. AQUACULTURE, 2007 Salmonid Introductions in Patagonia: A Mixed Blessing, pp. 315-331 in Ecological and Genetic Implications of Aquaculture

Activities, edited by T. BERT. Springer Netherlands.

VORIS, H. K., 2000 Maps of Pleistocene sea levels in Southeast Asia: shorelines, river systems and time durations. Journal of Biogeography 27: 1153-1167.

WARD, R. D., M. WOODWARK and D. O. F. SKIBINSKI, 1994 A comparison of genetic diversity levels in marine, freshwater, and anadromous fishes. Journal of Fish Biology 44: 213-232.

WATERS, J. M., D. CRAW, J. H. YOUNGSON and G. P. WALLIS, 2001 Genes meet geology: Fish phylogeographic pattern reflects ancient, rather than modern, drainiage connections. Evolution 55: 1844-1851.

WATTERSON, G. A., 1975 On the number of segregating sites in genetical models without recombination. Theoretical Population Biology 7: 256-276.

WEI, X., and M. JIANG, 2012 Contrasting relationships between species diversity and genetic diversity in natural and disturbed forest tree communities. New Phytologist 193: 779-786.

WILLMOTT, W. F., 2004 Giant Sandcastles: Parks of the coastal sand masses, pp. 139-148. Geological Society of Australia Inc., Queensland Division.

WOODRUFF, D. S., 1973 Natural hybridization and hybrid zones. Systematic Biology 22: 213-218.

74 ZAMBRANO, L., E. MARTÍNEZ-MEYER, N. MENEZES and A. T. PETERSON, 2006 Invasive potential of common carp (Cyprinus carpio) and Nile tilapia (Oreochromis niloticus) in American freshwater systems. Canadian Journal of Fisheries and Aquatic Sciences 63: 1903-1910.

ZHU, D., B. G. JAMIESON, A. HUGALL and C. MORITZ, 1994 Sequence evolution and phylogenetic signal in control-region and cytochrome b sequences of rainbow fishes (Melanotaeniidae). Molecular Biology and Evolution 11: 672-683.

75 Appendices

76 Supplementary Figure 2.1: Bayesian phylogenetic tree constructed in MrBayes using 638 bases of ATPase mitochondrial sequence data. Samples in red correspond to samples identified in the field based on morphology as M. splendida and samples in blue correspond to samples identified as M. trifasciata. The purple sample was identified as M. australis. All other samples were downloaded from Dryad and correspond to those from the most complete rainbowfish phylogeny to date (UNMACK et al. 2013). 77 Cairnsichthyes rhombosomoides Rhadinocentrus ornatus Iriatherina werneri Melanotaenia ammeri Melanotaenia angfa 1 0.81 Melanotaenia parva 0.56 Melanotaenia kokasensis Melanotaenia sp. Rawarra Melanotaenia irianjaya 1 1 Melanotaenia arfakensis 1 Melanotaenia batanta 1 Melanotaenia misoolensis Melanotaenia fredericki 1 0.94 Melanotaenia boesemani Melanotaenia salawati Melanotaenia sp. Suswa 1 Melanotaenia catherinae Melanotaenia synergos 1 0.98 Chilatherina lorentzii Melanotaenia japenensis Melanotaenia sp. Pianfon 1 Melanotaenia vanheurni Glossolepis dorityi 0.68 Glossolepis incisus 0.61 1 Glossolepis pseudoincisus 1 Glossolepis kabia I 1 0.98 0.68 Glossolepis wanamensis 1 Glossolepis sp. Gidomen 1 Glossolepis leggetti Glossolepis multisquamata Melanotaenia rubripinnus 1 Glossolepis maculosus 0.93 Glossolepis ramuensis Melanotaenia affinis 1 1 Melanotaenia affinis 2 1 1 Melanotaenia iris Chilatherina alleni Chilatherina bleheri 1 Chilatherina fasciata Chilatherina pricei 0.98 Chilatherina sentaniensis Chilatherina axelrodi 0.93 Chilatherina campsi 0.97 1 Chilatherina sp. Sepik Melanotaenia praecox 1 Chilatherina bulolo 1 Chilatherina crassispinosa Chialtherina sp. Gidomen Melanotaenia caerulea 0.95 Melanotaenia maccullochi 1 Melanotaenia sexlineata 0.93 Melanotaenia sp. NT 1 1 Melanotaenia ogilbyi Melanotaenia sp. Dekai Melanotaenia papuae 0.98 Melanotaenia sylvatica 1 Melanotaenia sp. Bin?? 1 0.98 Melanotaenia gracilis 1 Melanotaenia pygmaea 0.56 1 Rainbowfsh phylogeny (Melanotaeniidae) - nuclear S7 Melanotaenia exquisita Melanotaenia nigrans Melanotaenia duboulayi 1 1 Melanotaenia rubrostriata Melanotaenia parkinsoni Msplen_AM1066_JARDa 1 Msplen_AM1638_MWENa Ambig_AM2059_MCIVa Msplen_AM2336_MARYa Msplen_AM2477_JIMCa Msplen_AM661_OLIVa Mtrif_AM749_MCIVa Msplen_AM782_MCIVa Msplen_AM958_BURSa 0.84 Melanotaenia australis Melanotaenia eachamensis Melanotaenia utcheensis Melanotaenia splendida inornata Melanotaenia splendida splendida Melanotaenia splendida tatei 0.99 0.75 Msplen_AM1211_MYALa 0.94 Msplen_AM1639_MWENa Msplen_AM1638_MWENb Mtrif_AM1296_CRYSa Mtrif_AM1579_PEPPa Mtrif_AM1697_MWENa 0.98 Msplen_AM2002_COENa Mtrif_AM629_OLIVa Mtrif_AM701_JACKa Melanotaenia trifasciata 3 Mtrif_AM1867_GAPCa 0.61 Mtrif_AM2045_MCIVa Melanotaenia trifasciata 2 0.57 Mtrif_AM2011_COENa Melanotaenia oktediensis Melanotaenia goldiei Mtrif_AM2194_MORLa 1 1 Mtrif_AM2650_LIVEa Melanotaenia trifasciata 4 Melanotaenia trifasciata 1 Melanotaenia herbertaxelrodi Melanotaenia kamaka 1 1 Melanotaenia lakamora Melanotaenia pierucciae 1 Melanotaenia lacustris Melanotaenia monticolor Melanotaenia mubiensis

0.5 Supplementary Figure 2.2: Bayesian phylogenetic tree constructed in MrBayes using 594 bases of nuclear S7 intron sequence. Blue (M. trifasciata) and (M. splendida) correspond to samples collected in this study. All other samples were downloaded from Dryad and were from the most complete rainbowfish phylogeny available (UNMACK et al. 2013).

78

ATPase - Melanotaenia splendida

Supplementary Figure 2.3: Median joining haplotype network constructed for M. splendida with putative subspecies identified based on known distributions. M. splendida inornata are represented in green and M. splendida splendida are represented in yellow. Central haplotypes for both subspecies are shared on rare occasions with the other subspecies.

79

Douglas Creek Douglas 1 Crossing Creb 2 Crossing Creb River McIvor River Laura Burster Creek Creek Cowall River Jardine River MidWenlock River LowWenlock Creek Tentpole Swamp Weipa Myalla Creek River Coen River Alligator East Magella Creek Creek JimJim Rockhole Morline River Mary splendida Supplementary 2.1: Table for mtDNA for ATPase. MARY 1.32 1.63 1.66 1.45 1.64 1.69 1.69 1.61 0.54 0.95 0.99 1.37 1.44 1.20 1.38 1.76 3.69 5.70 6.24 0.44 0.00 MORL 1.57 2.01 2.04 1.79 2.02 2.01 2.01 2.08 1.09 1.05 1.10 1.47 1.54 1.30 1.48 1.85 6.77 9.46 9.21 0.00 Pairwise 8.23 8.19 8.22 8.10 8.21 7.47 7.47 7.56 6.73 8.22 8.28 8.67 8.07 8.50 7.95 8.17 2.15 0.58 0.00 JIMC

MAGE 8.44 8.17 8.28 8.09 8.27 7.78 7.87 7.55 6.15 8.39 8.25 8.50 8.59 8.51 8.04 8.22 0.61 0.00 Nei’s genetic distancegeneticNei’s EALL 5.79 5.75 5.79 5.66 5.77 5.11 5.11 5.15 3.96 5.81 5.86 6.23 5.69 6.06 5.62 5.87 0.00 -0.25 COEN 1.41 1.85 1.88 1.64 1.87 0.40 0.47 1.35 0.15 0.79 0.87 1.31 0.34 1.14 0.00 -0.06 ARCH 1.04 1.48 1.51 1.26 1.49 0.28 0.34 1.08 0.45 0.52 0.94 0.15 0.77 0.00 -0.20 -0.01 -0.04 MYAL 0.86 1.30 1.33 1.09 1.31 1.30 1.30 1.38 0.12 0.63 0.00

table ofpairsall table population 1.10 1.54 1.57 1.33 1.55 0.40 0.40 1.19 0.06 0.50 0.50 0.75 0.00 WEIP -0.17 TENT 1.03 1.47 1.50 1.26 1.48 1.47 1.47 1.55 0.26 0.06 0.00 LWEN -0.30 -0.02 0.66 1.10 1.13 0.88 1.11 1.09 1.09 1.17 0.00 MWEN -0.25 0.61 1.05 1.08 0.83 1.06 1.04 1.04 1.12 0.00

of of -0.09 0.09 0.00 0.05 0.03 0.26 0.25 0.36 0.00 DUCI M. JARD 1.26 1.39 1.42 1.26 1.40 0.19 0.20 0.00 COWA -0.31 1.34 1.59 1.62 1.42 1.60 0.00 BURS 1.34 1.59 1.62 1.42 1.60 0.00 LAUR 0.49 0.01 0.04 0.03 0.00 MCIV 0.39 0.02 0.05 0.00 CRX2 0.32 0.03 0.00 CRX1 0.47 0.00 DOUG 0.00

80

Gap;Creek McIvor;River Claudie;River Pascoe;River Olive;River Jacky;Creek Burster;Creek Crystal;Creek Mistake;Creek Twin;Falls Fruitabat;Falls Cockatoo;Creek Gunshot;Creek Cholmendelay;Creek Dalhunty;River Ducie;River Tentpole;Creek LowWenlock;River MidWenlock;River Peppan;Creek Marmoss;Creek Archer;River Coen;River Supplementary 2.2: Table

COEN 13.36 14.32 C0.02 5.07 5.53 3.02 3.03 3.15 4.09 2.69 3.17 3.06 1.94 1.60 1.18 2.01 2.45 2.02 2.01 2.00 0.00 0.00 0.00 ARCH 13.21 14.17 5.07 5.52 3.02 3.03 3.15 4.09 2.66 3.17 3.06 1.94 1.60 1.18 2.01 2.45 2.02 2.01 1.99 0.00 0.00 0.00 MARM 13.36 14.33 5.07 5.53 3.04 3.03 3.15 4.09 2.69 3.17 3.06 1.94 1.60 1.18 2.01 2.45 2.02 2.01 2.02 0.00 0.00 Pairwise Nei’s Pairwise distance genetic 13.35 14.32 PEPP 5.07 5.52 3.03 3.03 3.14 4.09 2.69 3.17 3.06 1.94 1.60 1.17 2.01 2.44 2.01 2.01 2.02 0.00 MWEN 13.07 14.04 5.06 5.50 3.02 3.03 3.14 4.08 2.64 2.77 3.06 0.06 0.06 0.34 0.01 0.44 0.01 0.01 0.00 LWEN 13.33 14.30 5.05 5.51 3.03 3.02 3.13 4.07 2.68 2.76 3.05 0.05 0.05 0.33 0.00 0.43 0.00 0.00 13.38 14.35 TENT 5.07 5.52 3.04 3.03 3.14 4.08 2.69 2.77 3.06 0.05 0.05 0.34 0.00 0.43 0.00 13.81 14.78 DUCI 5.50 5.96 3.47 3.37 3.49 4.41 3.05 3.11 3.40 0.48 0.48 0.62 0.43 0.00 table ofpairsall table population 13.34 14.31 DALH 5.05 5.51 3.03 3.02 3.13 4.07 2.68 2.76 3.05 0.05 0.05 0.33 0.00 12.49 13.46 CHOL 4.21 4.67 2.20 2.18 2.29 3.23 1.84 2.16 2.21 0.31 0.12 0.00 GUNS 12.94 13.91 4.65 5.10 2.62 2.61 2.72 3.65 2.27 2.44 2.64 0.00 0.00 13.27 14.24 CKTC 4.98 5.44 2.96 2.95 3.06 4.00 2.61 2.71 2.97 0.00 12.37 13.33 FRUI 4.09 4.55 2.06 0.03 0.14 0.82 0.10 0.17 0.00

of of M. trifasciataM. 12.49 13.46 TWIN 4.20 4.66 2.18 0.14 0.25 1.16 0.21 0.00 11.59 12.56 MIST 3.72 4.15 1.67 0.07 0.18 1.09 0.00 13.26 14.26 CRYS 5.14 5.53 3.08 1.02 1.09 0.00 for mtDNA for ATPase. 12.46 13.42 BURS 4.18 4.63 2.14 0.11 0.00 12.34 13.31 JACK 4.06 4.52 2.03 0.00 12.24 13.22 OLIV 4.07 4.51 0.00

10.57 11.53 PASC 0.47 0.00 10.27 11.23 CLAU 0.00 MCIV 1.07 0.00 GAPC 0.00

81 Supplementary Table 2.3: Pairwise ΦST (Tamura & Nei, 1993) table of all population pairs of M. splendida for nuclear S7 intron 1.

MARY JIMC MYAL MWEN JARD BURS OLIV MCIV Mary River (MARY) 0.00 Jim Jim Creek (JIMC) 0.27 0.00 Myalla Creek (MYAL) 0.46 0.35 0.00 Mid Wenlock River (MWEN) 0.45 0.32 -0.05 0.00 Jardine River (JARD) 0.50 0.44 0.22 0.10 0.00 Burster Creek (BURS) 0.59 0.60 0.27 0.13 -0.02 0.00 Olive River (OLIV) 0.53 0.47 0.21 0.12 0.22 0.52 0.00 McIvor River (MCIV) 0.26 0.34 0.38 0.33 0.45 0.63 0.53 0.00

82 Supplementary Table 2.4: Pairwise ΦST (Tamura & Nei, 1993) table of all population pairs of M. trifasciata for nuclear S7 intron 1.

LIVE MORL MARY COEN PEPP MWEN CRYS JACK OLIV GAPC MCIV Liverpool River 0.00 MolineRockhole 0.00 0.00 Mary River 0.33 -0.04 0.00 Coen River 0.69 0.77 0.79 0.00 Peppan Creek 0.93 0.96 0.95 0.23 0.00 Mid Wenlock 0.72 0.80 0.81 0.01 0.06 0.00 Crystal Creek 0.92 0.94 0.94 0.26 0.01 0.07 0.00 Jacky Creek 0.84 0.88 0.88 0.15 -0.03 -0.01 0.05 0.00 Olive River 1.00 1.00 0.98 0.33 0.08 0.14 0.02 0.06 0.00 Gap Creek 1.00 1.00 0.98 0.56 0.91 0.63 0.89 0.76 1.00 0.00 McIvor River 0.94 0.97 0.96 0.53 0.85 0.58 0.83 0.72 0.94 0.84 0.00

83 Supplementary Table 4.1: Habitat information and diversity statistics used for linear models.

location Clade proximity habitat gambusia h5diversity #5haplotypes Hapolotype5diverity Theta Pi Obi5Obi5Creek SER yes degraded yes 0.00 1 1.038 0.00 0.00 Alligator5Creek CEQ no natural no 0.87 8 1.667 2.25 1.93 Snapper5Creek CEQ yes natural no 0.36 2 1.167 0.31 0.36 Woralie5Creek CEQ no natural no 0.36 3 1.250 0.62 0.38 Lake5Coomboo CEQ no natural no 0.47 4 1.364 1.23 0.65 Bowarrady5Creek CEQ no natural no 0.24 3 1.231 0.60 0.25 Gerowweea5Creek CEQ no natural no 0.59 3 1.250 0.62 0.67 Rocky5Creek CEQ no natural no 0.45 4 1.500 1.66 0.83 Ungowa5Creek CEQ no natural no 0.00 1 1.071 0.00 0.00 Seary's5Creek SER no natural no 0.49 6 1.273 1.54 0.90 Pipeclay5Creek SER yes natural no 0.53 2 1.154 0.31 0.53 Cooloola5Creek SER no natural no 0.72 4 1.444 0.97 0.90 Teewah5Creek5North SER yes natural no 0.00 1 1.067 0.00 0.00 Carland5Creek SER no natural no 0.00 1 1.077 0.00 0.00 Noosa5River SEQ no natural yes 0.82 7 1.700 1.48 1.87 Tingalpa5Creek SEQ yes degraded yes 0.00 1 1.500 0.00 0.00 Currumbin5Creek SEQ yes degraded yes 0.00 1 1.071 0.00 0.00 Coonowrin5Creek SEQ yes degraded yes 0.00 1 1.067 0.00 0.00 South5Mooloola5River SEQ yes degraded yes 0.14 2 1.167 0.31 0.14 Teewah5Creek5South SEQ no natural no 0.68 7 1.350 2.59 2.29

84

value of0.05. value below Supplementary Table ! Ungowa$Creek$(UNG) Rocky$Creek$(ROC) Gerowweea$Creek$(GER) Bowarrady$Creek$(BOW) Coomboo$Lake$(CMB) Woralie$Creek$(WOR) Snapper$Creek$(SNA) Alligator$Creek$(ALL) Teewah$Creek$South$(TWS) South$Mooloolah$River$(SMR) Coonowrin$Creek$(COO) Currumbin$Creek$(CUR) Tingalpa$Creek$(TIN) Noosa$River$(NOO) Carland$Creek$(CAR) Teewah$Creek$North$(TWN) Cooloola$Creek$(CLL) Pipeclay$Creek$(PIP) Seary's$Creek$(SER) ObiObi$Creek$(OBI) ! ! diagonal, with p diagonal, with

Significant values indicatedvalues bold. Significant are in $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ - values above (262), values diagonal. P above correction multiple After Bonferroni for tests 4.2 :

ST Φ& Pairwise(Tamura ST 1.00 0.99 0.99 1.00 0.99 1.00 1.00 0.97 0.97 1.00 1.00 1.00 1.00 0.98 0.00 1.00 0.28 0.53 0.58 0.00 OBI $ ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! 0.00 0.98 0.97 0.98 0.98 0.98 0.98 0.98 0.96 0.96 0.98 0.98 0.98 0.98 0.97 0.50 0.74 0.44 0.50 0.00 SEA $ ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! $ 0.00 0.00 0.99 0.98 0.98 0.99 0.98 0.99 0.99 0.96 0.96 0.99 0.99 0.99 0.99 0.97 0.42 0.82 0.29 0.00 PIP $ ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! $ $ 0.00 0.00 0.00 0.99 0.97 0.98 0.98 0.98 0.98 0.98 0.95 0.95 0.99 0.99 0.99 0.98 0.96 0.18 0.73 0.00 CLL $ ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! $ $ $

TWN Nei, 1993) table population Nei, of pairs 1993) all table 0.00 0.00 0.00 0.00 1.00 0.99 0.99 1.00 0.99 0.99 1.00 0.97 0.96 1.00 1.00 1.00 1.00 0.98 1.00 0.00 ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! $ $ $ $ $ 0.00 0.00 1.00 0.99 0.99 1.00 0.99 0.99 0.99 0.97 0.96 1.00 1.00 1.00 1.00 0.97 0.00 0.00 0.01 1.00 CAR $ ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! $ $ NOO 0.00 0.00 0.00 0.00 0.00 0.00 0.97 0.96 0.96 0.97 0.96 0.96 0.96 0.94 0.13 0.32 0.36 0.35 0.58 0.00 ! ! ! ! ! ! ! ! ! ! ! ! ! ! $ $ $ $ $ $ $ 1.00 0.98 0.98 0.99 0.98 0.99 0.99 0.95 0.50 0.94 1.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 TIN $ ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.99 0.99 1.00 0.99 0.99 0.99 0.97 0.25 0.01 0.00 0.00 0.00 0.00 CUR $ ! ! ! ! ! ! ! ! ! ! ! ! ! ! $ $ $ $ $ $ COO 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.99 0.99 1.00 0.99 0.99 0.99 0.97 0.25 0.01 0.00 1.00 0.00 0.00 ! ! ! ! ! ! ! ! ! ! ! ! ! ! $ $ $ $ $ $ $ SMR 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.99 0.99 0.99 0.99 0.99 0.99 0.96 0.23 0.00 0.46 0.49 0.00 0.00 ! ! ! ! ! ! ! ! ! ! ! ! ! ! $ $ $ $ $ $ $ TWS

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.96 0.94 0.95 0.95 0.95 0.95 0.95 0.94 0.00 0.00 0.00 0.00 0.01 for ! ! ! ! ! ! ! ! ! ! ! ! ! $ $ $ $ $ $ $ $ R. ornatus R. 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.79 0.66 0.72 0.73 0.70 0.70 0.75 0.00 0.00 ALL - $ ! ! ! ! ! ! ! ! ! $ $ $ $ $ $ $ $ $ $ $ value ofuncorrected 0.0002equates value to 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.97 0.81 0.82 0.90 0.84 0.86 0.00 0.00 SNA $ ! ! ! ! ! ! ! ! $ $ $ $ $ $ $ $ $ $ $ $ . Pair WOR 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.96 0.73 0.79 0.03 0.04 0.00 0.00 ! ! ! ! ! ! ! $ $ $ $ $ $ $ $ $ $ $ $ $ $ ST values STpresentedwise Φ CMB 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.94 0.70 0.77 0.02 0.00 0.24 0.00 ! ! ! ! ! ! ! $ $ $ $ $ $ $ $ $ $ $ $ $ $ BOW 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.97 0.79 0.83 0.00 0.10 0.19 0.00 ! ! ! ! ! ! ! $ $ $ $ $ $ $ $ $ $ $ $ $ $ 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.94 0.27 0.00 0.00 GER $ ! ! ! ! $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.93 0.00 0.00 0.00 ROC $ ! ! ! ! $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ UNG 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

! !

$ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $

85