Comparative Phylogeography and the Population Genetics of Three Endangered Freshwater Euastacus Spp

Comparative Phylogeography and the Population Genetics of Three Endangered Freshwater Euastacus spp. Crayfish and the Commensal Temnosewellia flatworms; from Mountaintops in Queensland, Australia

Author Hurry, Charlotte

Published 2016

Thesis Type Thesis (PhD Doctorate)

School Griffith School of Environment

DOI https://doi.org/10.25904/1912/461

Downloaded from http://hdl.handle.net/10072/367606

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

Comparative phylogeography and the population genetics of three endangered freshwater Euastacus spp. crayfish and the commensal Temnosewellia flatworms; from mountaintops in Queensland, Australia

Charlotte Hurry Bachelor of Science (Hons) Griffith School of Environment Griffith Sciences Griffith University

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

October 2015

Q: Why wouldn't the crayfish share? A: Because he was a little shellfish! Synopsis The overall aim of this project was to consider several freshwater invertebrates that are restricted both geographically and climatically to determine their population genetic structure, population distribution and population viability. The study was set in a framework of genetic analysis as genetics can be used to answer a whole suite of conservation focused questions. Specifically, the study concentrated on the population dynamics of three freshwater crayfish from the genus Euastacus: E. hystricosus, E. urospinosus and E. robertsi. Also, in this study I explored the role of two commensal flatworms as proxy species, to aid in disentangling the population structure of their crayfish hosts. These flatworms were Temnosewellia batiola, which is specific to E. hystricosus and Temnosewellia albata, which is specific to E. robertsi.

As part of this study, the known distribution of E. urospinosus was increased from 200 km2 to 1225 km2 in area with 26 locations being identified in the Brisbane and Mary River catchments of SE Queensland, Australia (Hurry et al. 2015 Chapter three). Calculations that were undertaken after publication with the ALA (2015) online tool have now calculated an area of occupancy of approximately 76 km2 or an extent of occurrence of 610 km2. Further, mitochondrial DNA sequence data were then used to investigate the population structure and the phylogeographic divergence between four uplands. There was significant population differentiation for the species which conforms to the headwater model of genetic structure. Fragmentation between these uplands was found to be historical as the first divergence between lineages was dated at ~2.1 million years ago (mya).

Six novel microsatellite loci were developed and combined with new and existing haplotype data for the mtDNA COI gene to explore the population genetic structure of the endangered Conondale spiny crayfish E. hystricosus (Chapter five). The species is mostly co-located with E. urospinosus and is also believed to be restricted to upland areas within SE Queensland. The objective was to determine the spatial genetic variation among populations of the Conondale spiny crayfish from which I could infer genetic structure, dispersal patterns and effective population size. Significant population structure was found between three uplands, as evidenced by Bayesian-based cluster analyses and AMOVA. Low genetic variability and extremely small effective population sizes were detected in these uplands.

Parasites have been used as proxy species in genetic studies to infer host genetic structure; hence, two of the chapters in this thesis explore the ectocommensal flatworm genus Temnosewellia to infer additional information on the genetic structure of the host. The comparative phylogeography of the endangered crayfish host Euastacus robertsi and its flatworm Temnosewellia albata was explored in Chapter four (Hurry et al. 2014). By constructing phylogenetic trees based on mitochondrial COI and 28S nuclear ribosomal gene

sequences, the study investigated how evolutionary history has shaped patterns of intraspecific molecular variation in the two freshwater commensals. The genetic data indicated that several vicariance events have led to the population subdivision of E. robertsi across three mountaintops. Further, the results revealed that phylogeographic patterns shared between the two species were largely congruent, as they featured a shared history of limited dispersal between the mountaintops. The focus of the study was then directed at the flatworm T. batiola which shares a close life history with its host E. hystricosus (Chapter six). In this study seven novel microsatellite markers were developed for T. batiola and population structure was determined, which was then compared to the data obtained in Chapter five. Patterns of population structure were extremely similar in the two taxa, as both datasets demonstrated distinct genetic differentiation between three upland areas. However, we also found severe heterozygote deficits at the majority of the localities studied, which suggested that the flatworms have a history of self-fertilisation or parthenogenesis.

This study illustrated that the three upland areas of Conondale National Park (NP), Bellthorpe NP and Maleny region/Kondalilla NP; and the three mountaintops of Mt Finnigan, Thornton Peak and Mt Pieter Botte should be treated as genetically distinct populations for which inter-population dispersal was negligible for their respective crayfish species. Overall, there was no reason to suggest that the conservation rating of ‘endangered’ be downgraded for the crayfish. Further, recommendations were made that the flatworms should also be considered as being at risk of extinction; these suggestions were based purely on the data of the area of occurrence of the crayfish (which is the same for the flatworms) and the fragmentation between populations. Hence, research on habitat viability is needed. Our finding was that Temnosewellia were useful as a proxy species. However, its reproductive strategy should be explored further.

In conclusion, it was suggested that future conservation plans should seek to preserve the genetic integrity of the uplands/mountains and to develop plans which seek to maintain connectivity within uplands in order to resist fine scale population structure.

Acknowledgements In August 2010 I had moved to Brisbane from Melbourne. It was six months before I started my PhD. David my French boyfriend had just accepted a job in Brisbane so that he could join me from France. It is now September 2015 and in that time I got engaged, married, broke my wrist, got pregnant and gave birth to Mathilde in Brisbane, and 10 days later moved back to Melbourne. I have survived the last 18 months working from home, surviving on little sleep and plagued by constant day care sicknesses. Finally, here I am typing the finishing touches to my labour of love. I have spent 4.5 years doing something that I have loved and that has challenged me. Yet, none of this would have been possible without the support of my family, friends and colleagues who backed me through the good and the crazy times.

A huge thank you to my supervisors Professor Jane Hughes and Dr Daniel Schmidt who let me take the lead, yet they were always there to guide me. I still remember the first time I met Jane. Although I was incredibly interested in conservation genetics I wasn’t sure I was cut out for the job. Jane assured me that working on animals was a breeze compared to plants (my honours project) and immediately asked me when I was starting; I felt I was given no choice. She has been an amazing supervisor. I am still not sure that genetics is easy but it has been an astounding four and half years of figuring it out and I am stronger for it. Dan is an incredibly patient supervisor, even when I have screwed up my face in confusion at coalescence theory he has never made me feel stupid. I feel especially lucky to have found such great supervisors who have supported me every step of the way.

Our lab group has always been another great comfort in this journey. I always enjoyed Monday morning Geek, where we would attempt to dissect journal articles. It is amazing how quickly you can transition from being the one trying to avoid eye contact to the one chattering away because you finally get it. Our group of ‘Geeks’ were amazing and I’ll miss working with them all, past and present: Gemma, Arlene, Kathryn, Sofie, Ceaira, Joel, Tim, Bob, Rash, Ashley, Kate, Ana, Haile, Yusri, Alison, Courtney, Olivier, Sharmeen, Dale, Jeremy and Leo.

Lab work and equipment Kathryn Real was the only one of us that ever really knew what was happening in the lab. She gave us all a good kick up the butt when we needed it. What I will really remember is that she was always there when I needed her. Dominic Valdez and Fran Sheldon were extremely supportive and I’d like to thank them for the loan of equipment and the technical support, Dom provided much of his time to helping ensure that my fieldwork went without a hitch. Rad Bak for providing me with his time and expertise with the water quality meters.

Analysis Rebecca Citroen provided much hand holding with R scripts after I had nearly lost my mind trying to get my assignment plots plotted. Elizabeth James, my honours supervisor was a great support throughout the analysis stage of this thesis. Thanks to Liz and the Royal Botanic Gardens, Melbourne for the use of the genetic software. After I had moved to

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Melbourne I had no access to these softwares and I couldn’t have finished this PhD without their support.

Writing To Sofie Bernays and Anne-Sophie Lotti, my writing buddies. I was working from home for 18 months and we used to log on, say hi and then work together for 30 minute sessions throughout the day. If it wasn’t for those daily writing sessions I would never have finished this PhD. They helped to keep me on track and those five minute chats kept me from going insane from loneliness. To several of my long term friends that gave up their time and expertise to grammar check sections of my thesis: Evie Franzidis who checked three of my chapters and, Emma Dresser, Nicki Holmes and Verity Bristow who shared the responsibility for the introduction and discussion.

Fieldwork To my team of field work volunteers: - Wow, those fieldtrips were fun. Kathryn and Dave, the first of many people to help me in the field and the skills I learnt from that fieldtrip were valuable, 1st lesson was to always carry salt because there are leeches everywhere. To Dave Sternberg, to whom I owe a special thanks for being my saviour after I broke my wrist in the field. Dave, fashioned me a sling from my headband, kept me upright across a slippery creek, drove me to the hospital and then spent the day packing up our belongings whilst I enjoyed morphine in the emergency room. Frik and Eugénie, locking ourselves in the car as the leeches crawled up the sides of the vehicle baying for our blood. Paul, Christine and David, I can still hear the calls of “Cray-on” and “I ‘urt my tit”. Paul telling me not to be paranoid as nobody was going to come all the way out to the research station and break in. Sofie, the fieldtrip from hell, where the research station was robbed (my laptop and our tents) and we drove for our lives to a house full of vodka drinking Russians (btw thanks to Vas’s family). Jenny, the time we got ‘The Beast’ stuck in the mud just as the rain and dark were setting in and her auntie was kind enough to host two mud covered scruffians for the night. Bob, the time we drove to Bellthorpe and immediately got ‘The Beast’ stuck in the mud off some beaten track, which was stupid because we should never have set out in a downpour to begin with. Tim and Kaye, those enjoyable fieldtrips mixing up the grit of fieldwork with cups of tea in Maleny. Andrew Mather and Peter Waldie, enjoying campfires after a hard day’s work. Peter Stevens, offering me the prime salmon in the hope that those crayfish had expensive tastes. Pete Tangney, him “do you ever see snakes?” me” yes, just there”. Jamie-Lee, whose enthusiasm in the project was greatly appreciated. Sally, the only one willing to head out at 4am to go look down crayfish holes. Alex, Simone, Maria, that was such a great fieldtrip and it felt more like a nice weekend camping with good friends. Dan, Rash, Sharmeen, helping me find the temperature loggers, especially when I was too heavily pregnant to go into the creeks myself.

Lake Baroon Catchment Care Group To everyone at LBCCG for giving me an incredible amount of support through this project. A special thanks to Mark Amos for being a great guide and ‘go to’ guy, such a nice person. Peter and Fiona Stevens for their wonderful hospitality at Arley Farm; beautiful accommodation (which I can highly recommend), and great coffee and conversation. The land owners Steven Langley, Col Cork, Mat Cork and Ed Lawley for letting me drive ‘The Beast’ through your fields and plunder your creeks for crayfish.

The Sunshine Coast Council for permission to sample at Mary Cairncross reserve, Dilkusha Nature Reserve and The Australian Wildlife Conservancy for site access.

David A special thank you to my husband and best friend, David gets a special section to himself. David put a lot of hours into to this project. He was there in the field with me all those times my search for a field assistant failed. He became skilful at catching eels, and navigating flooded creeks in the pitch black. He donned a lab coat and assisted me with DNA extractions when I was 38 weeks pregnant when I was trying desperately to get my lab work finished before I gave birth. He has looked after our little girl every Saturday for the last nine months and also every Sunday for the last three months. He has helped me to the very end, as he helped me in formatting this thesis. A special thank you for all the hugs, cups of tea and words of encouragement. David, you are awesome.

Funding This project was very generously funded which is much appreciated. $10,000 Lake Baroon Catchment Care Group – Maleny $10,000 Griffith School of Environment $1,000 Wildlife Preservation Society of Queensland

Permits Department of Environment and Resource Management for provision of Permits WITK 10469111 and TWB/40/2011. General Fisheries Permit 153894.

Contents Synopsis ...... I Acknowledgements ...... III Contents ...... VI List of Figures ...... X List of Tables ...... XI Declaration ...... XII Acknowledgement of papers included in this thesis ...... XIII Chapter One: General Introductions ...... 1 1.1 Conservation of endangered species ...... 1 1.2 Strategies in conservation biology ...... 3 1.2.1 Habitat fragmentation ...... 4 1.2.2 The distribution of species ...... 7 1.2.3 The phylogeography of east coast Australia ...... 8 1.3 Conservation genetics of populations ...... 10 1.3.1 Comparative genetic studies ...... 11 1.4 Freshwater crayfish ...... 13 1.4.1 Genetic studies of crayfish ...... 14 1.4.2 The Australian crayfish genus: Euastacus ...... 16 1.5 Temnocephalida ...... 20 1.5.1 Ecology of Temnocephalans: Temnosewellia ...... 21 1.6 Aims...... 22 1.7 Layout ...... 24 Chapter Two: General Methods ...... 27 2.1 Fieldwork ...... 29 2.1.1 Sampling (Chapter four) ...... 29 2.1.2 Sampling (Chapters three, five, and six) ...... 29 2.2 Recorded data ...... 30 2.2.1 Crayfish: Life stage and sex determination ...... 31 2.2.2 Flatworms – size and measures of intensity ...... 31 2.3 Tissue sampling for DNA analysis ...... 32 2.4 Further methods ...... 32 2.5 Research issues ...... 33

VI Chapter Three: Phylogeography and limited distribution of the endangered freshwater crayfish Euastacus urospinosus ...... 35 3.1 Abstract ...... 35 3.2 Introduction ...... 36 3.3 Material and Methods ...... 40 3.3.1 Study area ...... 40 3.3.2 DNA extraction, amplification, and sequencing ...... 41 3.3.3 Networks, phylogenetic trees and divergence estimates ...... 42 3.4 Results ...... 44 3.4.1 Distribution ...... 44 3.4.2 Genetic variation ...... 44 3.4.3 Networks, phylogenetic trees and divergence estimates ...... 45 3.5 Discussion ...... 47 3.6 Conclusion ...... 51 3.7 Acknowledgements ...... 52 3.8 Chapter 3 Supplementary Tables ...... 53 Chapter Four: Shared phylogeographic patterns between the ectocommensal flatworm Temnosewellia albata and its host, the critically endangered freshwater crayfish Euastacus robertsi 55 4.1 Abstract ...... 56 4.2 Introduction ...... 56 4.3 Materials and Method ...... 60 4.3.1 Study area ...... 60 4.3.2 DNA extraction, amplification, and sequencing ...... 60 4.3.3 Networks, phylogenetic trees and divergence estimates ...... 61 4.4 Results ...... 63 4.4.1 Temnosewellia albata ...... 63 4.4.2 Euastacus robertsi ...... 66 4.4.3 Genetic variation ...... 66 4.5 Discussion ...... 70 4.5.1 Shared phylogeographic patterning ...... 70 4.5.2 Time of divergence ...... 72 4.6 Acknowledgements ...... 74 4.7 Chapter 4 Supplementary Tables ...... 74

VII Chapter Five: Low Genetic Variability and Effective Population Size of an Upland Specialist: Novel Microsatellite Loci Quantify the Effect of Habitat Fragmentation on the Endangered Freshwater Crayfish Euastacus hystricosus ...... 79 5.1 Abstract ...... 79 5.2 Introduction ...... 80 5.3 Methods ...... 84 5.3.1 Study area and sampling technique ...... 84 5.3.2 DNA extraction, amplification, and microsatellite development ...... 86 5.3.3 Data analyses ...... 88 5.3.4 Population genetic structure ...... 89 5.3.5 Effective population size and migration rates ...... 91 5.4 Results ...... 92 5.4.1 Distribution and morphological assessment...... 92 5.4.2 Genetic differentiation and population structure ...... 93 5.4.3 Effective population size and migration rates ...... 98 5.5 Discussion ...... 99 5.6 Acknowledgements ...... 103 Chapter Six: The ectocommensal flatworm Temnosewellia batiola: a suitable proxy to detect the population genetic structure of its endangered host freshwater crayfish ...... 104 6.1 Abstract ...... 105 6.2 Introduction ...... 106 6.3 Methods ...... 109 6.3.1 Study area and sampling technique ...... 109 6.3.2 DNA extraction, amplification, and microsatellite development ...... 111 6.3.3 Data analyses ...... 112 6.4 Results ...... 115 6.4.1 Genetic differentiation and population structure (11 localities) ...... 115 6.4.2 Genetic differentiation and population structure (two localities) ...... 117 6.5 Discussion ...... 121 6.6 Conclusion ...... 125 6.7 Acknowledgements ...... 125 Chapter Seven: General Discussion ...... 127 7.1 Temnosewellia flatworms ...... 127 7.2 Population structure ...... 129 7.3 Genetic diversity and effective population size ...... 131

VIII 7.4 Conservation gains ...... 132 7.5 Conclusion ...... 136 References ...... 137 Appendices ...... 156

IX List of Figures FIGURE 1.1: MAP OF QUEENSLAND, AUSTRALIA. 10 FIGURE 1.2: DISTRIBUTIONS OF 43 OF THE 50 EUASTACUS SPECIES 18 FIGURE 1.3: TEMNOSEWELLIA SPP. 22 FIGURE 2.1: COLLECTING CRAYFISH IN BOX TRAPS AT IN CONONDALE NP; SE QUEENSLAND, AUSTRALIA 30 FIGURE 2.2: DETERMINING SEX OF CRAYFISH 30 FIGURE 2.3: MEASURING OCL 31 FIGURE 2.4: TEMNOSEWELLIA BATIOLA 31 FIGURE 2.5: EUASTACUS HYSTRICOSUS 32 FIGURE 3.1: LOCATIONS OF EUASTACUS UROSPINOSUS IN SE QUEENSLAND 40 FIGURE 3.2: A) MAP SHOWING THE STREAM NETWORKS WITHIN THE FOUR UPLAND AREAS WHERE EUASTACUS UROSPINOSUS IS LOCATED IN SE QUEENSLAND, AUSTRALIA. ALSO SHOWN ARE THE COI MITOCHONDRIAL SEQUENCE HAPLOTYPES FOUND AT EACH SAMPLE. B) THE MOST PARSIMONIOUS HAPLOTYPE NETWORK FOR THE COI MITOCHONDRIAL SEQUENCE 45 FIGURE 3.3: BAYESIAN (BA) CONSENSUS TOPOLOGY OF MITOCHONDRIAL DNA COI SEQUENCE DATA FOR EUASTACUS SPP. SAMPLED IN SE QUEENSLAND, AUSTRALIA 46 FIGURE 4.1: MAP OF NE QUEENSLAND, AUSTRALIA WHICH SHOWS THE THREE MOUNTAINTOP HABITATS (Δ) OF EUASTACUS ROBERTSI AND TEMNOSEWELLIA ALBATA 59 FIGURE 4.2: COMPARISON OF TWO BAYESIAN (BA) CONSENSUS TOPOLOGIES OF THE COI MTDNA DATASETS (A), AND A PARSIMONY NETWORK GENERATED ON TCS, OF 28S RIBOSOMAL DNA SEQUENCE DATA (B) 65 FIGURE 4.3: ISOLATION BY MIGRATION MODEL, USING COI AND 28S, SHOWING THE PAIRWISE DIFFERENCE IN DIVERGENCE BETWEEN THREE MOUNTAINS 69 FIGURE 5.1: MAP OF SAMPLE SITES FOR EUASTACUS HYSTRICOSUS IN SE QUEENSLAND, AUSTRALIA. 90 FIGURE 5.2: SPATIAL GENETIC STRUCTURE OF POPULATIONS OF EUASTACUS HYSTRICOSUS 94 FIGURE 5.3: INDIVIDUAL CLUSTER MEMBERSHIP ASSIGNMENT FOR EUASTACUS HYSTRICOSUS 96 FIGURE 6.1: MAP OF LOCALITIES FOR TEMNOSEWELLIA BATIOLA IN SE QUEENSLAND, AUSTRALIA. 110 FIGURE 6.2: INDIVIDUAL CLUSTER MEMBERSHIP ASSIGNMENT FOR TEMNOSEWELLIA 116 FIGURE 6.3: COMPARISON OF THE POPULATION GENETIC STRUCTURE OF THE FLATWORM TEMNOSEWELLIA BATIOLA (A) AND ITS CRAYFISH HOST EUASTACUS HYSTRICOSUS (B) 117 FIGURE 6.4: DISCRIMINANT ANALYSIS OF PRINCIPAL COMPONENTS SHOWING THE ORDINATION PLOT WITH MINIMUM SPANNING TREE FOR FIVE GENETIC CLUSTERS WHICH WERE DERIVED FROM 11 SAMPLE LOCALITIES FOR THE FLATWORM TEMNOSEWELLIA BATIOLA 118 FIGURE 6.5: DISCRIMINANT ANALYSIS OF PRINCIPAL COMPONENTS SHOWING THE ORDINATION PLOT OF THE GENOTYPE CLUSTERS OF THE FLATWORM TEMNOSEWELLIA BATIOLA AT KONDALILLA FALLS 119 FIGURE 6.6: DISCRIMINANT ANALYSIS OF PRINCIPAL COMPONENTS SHOWING THE ORDINATION PLOT OF THE GENOTYPE CLUSTERS OF THE FLATWORM TEMNOSEWELLIA BATIOLA AT STONY CREEK LOCALITY 120

X List of Tables TABLE 3.1: RESULTS FROM P-DISTANCE MODEL OF EVOLUTIONARY DISTANCE BETWEEN FOR THE FOUR UPLAND AREAS WHICH REPRESENT THE SUBPOPULATIONS OF EUASTACUS UROSPINOSUS IN SE QUEENSLAND, AUSTRALIA 47 TABLE 3.2: ANALYSIS OF MOLECULAR VARIANCE SHOWING THE PARTITIONING OF GENETIC VARIATION AMONG CATCHMENTS AND UPLANDS FOR EUASTACUS UROSPINOSUS FROM SE QUEENSLAND, AUSTRALIA 47 SUPPLEMENTARY TABLE S3.1: ALL GEOGRAPHIC LOCATIONS OF EUASTACUS UROSPINOSUS FROM THE BRISBANE AND MARY CATCHMENTS IN SE QUEENSLAND, AUSTRALIA 53 SUPPLEMENTARY TABLE S3.2: DETAILS OF ALL COI SEQUENCES OF THE QUEENSLAND CRAYFISH USED IN THIS STUDY 54 TABLE 4.1: GENBANK ACCESSION NUMBERS FOR TEMNOSEWELLIA SPECIES 63 TABLE 4.2: COMPARISON OF GENETIC DIVERSITY BETWEEN TEMNOSEWELLIA ALBATA AND EUASTACUS ROBERTSI 64 TABLE 4.3: GENBANK ACCESSION NUMBERS FOR EUASTACUS ROBERTSI. 64 TABLE 4.4: DIVERGENCE ESTIMATES BETWEEN CLADES. COMPARISON IS BETWEEN SEQUENCED COI HAPLOTYPES, FOR TEMNOSEWELLIA ALBATA AND EUASTACUS ROBERTSI IN QUEENSLAND, AUSTRALIA 66 TABLE 4.5: PARAMETER ESTIMATES FROM AN ISOLATION WITH MIGRATION MODEL FOR THREE PAIRWISE MOUNTAINTOP POPULATION COMPARISONS 67 SUPPLEMENTARY TABLE S4.1: SAMPLE LOCATIONS FOR TEMNOSEWELLIA ALBATA AND EUASTACUS ROBERTSI: TEMNOSEWELLIA ALBATA (T.A); EUASTACUS ROBERTSI (E.R) 74 SUPPLEMENTARY TABLE S4.2: EUASTACUS ROBERSTI-DETAILS OF COI AND 28S SEQUENCES USED IN PHYLOGENETIC ANALYSIS 74 SUPPLEMENTARY TABLE S4.3: TEMNOSEWELLIA ALBATA—DETAILS OF COI AND 28S SEQUENCES USED IN PHYLOGENETIC ANALYSIS 76 TABLE 5.1: CHARACTERISATION AND PCR CONDITIONS MICROSATELLITE REGIONS FOR THE FRESHWATER CRAYFISH EUASTACUS HYSTRICOSUS 87 TABLE 5.2: SAMPLE LOCATIONS IN SE QUEENSLAND OF THE CRAYFISH EUASTACUS HYSTRICOSUS 90 TABLE 5.3: GENETIC DIVERSITY INDICES PER SAMPLE SITE OF THE CRAYFISH EUASTACUS HYSTRICOSUS 95 TABLE 5.4: ANALYSIS OF MOLECULAR VARIANCE SHOWING THE PARTITIONING OF GENETIC VARIATION FOR EUASTACUS HYSTRICOSUS FROM SE QUEENSLAND, AUSTRALIA 97 TABLE 5.5: Ne ESTIMATES FOR THREE POPULATIONS OF EUASTACUS HYSTRICOSUS 98 TABLE 5.6: CONTEMPORARY MIGRATION ESTIMATES OF EUASTACUS HYSTRICOSUS BETWEEN THREE UPLANDS 99 TABLE 5.7: CONTEMPORARY MIGRATION ESTIMATES OF EUASTACUS HYSTRICOSUS BETWEEN STREAMS 99 TABLE 6.1: DETAILS OF SAMPLE LOCATIONS AND GENETIC DIVERSITY INDICES FOR MICROSATELLITE LOCI FOR THE FLATWORM TEMNOSEWELLIA BATIOLA 112 TABLE 6.2: CHARACTERISATION OF MICROSATELLITE REGIONS FOR THE PLATYHELMINTH FLATWORM TEMNOSEWELLIA BATIOLA 113 APPENDIX A: SITE CHARACTERISTICS OF 14 SAMPLE SITES 156 APPENDIX B: BODY SIZE OF TEMNOSEWELLIA BATIOLA FROM TWO LOCALITIES 159 APPENDIX C: MEASURES OF PARASITISM FOR THE FLATWORM TEMNOSEWELLIA BATIOLA 164

XI Declaration This work has not previously been submitted for a degree or diploma in any university. To the best of my knowledge and belief, the thesis contains no material previously published or written by another person except where due reference is made in the thesis itself.

Charlotte Hurry

XII Acknowledgement of papers included in this thesis ALL PAPERS INCLUDED ARE CO-AUTHORED Section 9.1 of the Griffith University Code for the Responsible Conduct of Research (“Criteria for Authorship”), in accordance with Section 5 of the Australian Code for the Responsible Conduct of Research, states: To be named as an author, a researcher must have made a substantial scholarly contribution to the creative or scholarly work that constitutes the research output, and be able to take public responsibility for at least that part of the work they contributed. Attribution of authorship depends to some extent on the discipline and publisher policies, but in all cases, authorship must be based on substantial contributions in a combination of one or more of:  conception and design of the research project  analysis and interpretation of research data  Drafting or making significant parts of the creative or scholarly work or critically revising it so as to contribute significantly to the final output.

Section 9.3 of the Griffith University Code (“Responsibilities of Researchers”), in accordance with Section 5 of the Australian Code, states: Researchers are expected to:  Offer authorship to all people, including research trainees, who meet the criteria for authorship listed above, but only those people.  Accept or decline offers of authorship promptly in writing.  Include in the list of authors only those who have accepted authorship  Appoint one author to be the executive author to record authorship and manage correspondence about the work with the publisher and other interested parties.  Acknowledge all those who have contributed to the research, facilities or materials but who do not qualify as authors, such as research assistants, technical staff, and advisors on cultural or community knowledge. Obtain written consent to name individuals.

Included in this thesis are papers in Chapters three, four, five and six which are co-authored with other researchers. My contribution to each co-authored paper is outlined at the front of the relevant chapter. The bibliographic details (if published or accepted for publication)/status (if prepared or submitted for publication) for these papers including all authors, are:

Chapter three Hurry CR, Schmidt DJ, M Hughes JM. (2015) Phylogeography and limited distribution of the endangered freshwater crayfish Euastacus urospinosus. Australian Journal of Zoology. Online: http://dx.doi.org/10.1071/ZO15006

XIII Chapter four Hurry CR, Schmidt DJ, Ponniah M, Carini G, Blair D, Hughes JM. (2014) Shared phylogeographic patterns between the ectocommensal flatworm Temnosewellia albata and its host, the endangered freshwater crayfish Euastacus robertsi. PeerJ 2:e552 https://dx.doi.org/10.7717/peerj.552 Chapter five Low genetic variability and effective population size of an upland specialist: novel microsatellite loci quantify the effect of habitat fragmentation on the endangered freshwater crayfish Euastacus hystricosus (written for publication, not submitted) Chapter six The ectocommensal flatworm Temnosewellia batiola: a suitable proxy to detect the population structure genetic of its endangered host freshwater crayfish (written for publication, not submitted)

(Where a paper(s) has been published or accepted for publication, you must also include a statement regarding the copyright status of the paper(s). Chapter four: This paper was published under the CC-BY 4.0 license. The authors retain copyright and the license allows for anyone to copy and redistribute the material in any medium or format for any purpose as long as it's properly attributed. Chapter three: The following wording was attached to the copyright agreement. I seek your permission to archive a post-print of this publication in a public access institutional repository after peer review. By agreeing to publish my paper you consent to this addendum. Appropriate acknowledgements of those who contributed to the research but did not qualify as authors are included in each paper.

(Signed) (Date) 11 September 2015 Charlotte Hurry

(Countersigned)____ (Date)__ 14/9/2015 Supervisor: Jane Hughes

XIV The one process now going on that will take millions of years to correct is the loss of genetic and species diversity by the destruction of natural habitats. This is the folly our descendants are least likely to forgive us. E.O. Wilson

Chapter One: General Introductions 1.1 Conservation of endangered species

Biodiversity is important to healthy and functioning ecosystems as each species plays an integral role, ranging from nutrient storage and cycling to the provision of food and the protection of water resources. It has been suggested that the effects of biodiversity loss on ecosystem function have been overemphasised (Thompson and Starzomski 2007), whereas, experimental evidence has clearly shown the connections between biodiversity changes and ecosystem functioning (Cardinale et al. 2012; Duffy 2008). Current extinction rates of species are now at two to three orders of magnitude higher than would be expected when compared to the baseline data in the fossil records (Barnosky et al. 2011). The current crisis has been described as the “sixth mass extinction”, which suggests that the current rate of species loss is far greater than the rate of replacement by new species (Ceballos et al. 2015; Vines 1999). The loss of populations is just as widespread and as frequent as the loss of species (Ceballos and Ehrlich 2002; Ceballos et al. 2015). It has never been more important to act immediately towards the conservation of biodiversity with a particular emphasis on endangered species and their populations.

Freshwater taxa have suffered massive losses of biodiversity; they are at least three times more threatened than terrestrial species (Revenga et al. 2005; Richter et al. 1997). Due to the thermal properties of water, climate change may be particularly poignant for freshwater taxa as many aquatic species are poikilothermic meaning their body temperature varies considerably with the external environment. Increases in stream temperatures are likely to raise the metabolic rate of organisms. Pounds et al. (2006; 1999) correlated the extinctions

of several aquatic species to the extended hot and dry periods associated with historical climate change.

Symbiotic relationships between species are mostly overlooked in conservation management plans, even though coextinctions of closely aligned species may occur. Symbiosis is the close and often physical interaction between two different organisms. These relationships can be parasitic (where one organism is harmed by the interaction), mutualistic (where both individuals benefit) or commensalistic. For example, in nature there are many examples of commensal relationships, i.e. where one organism benefits through its association with a second organism with no ill effect or benefit to the second organism (Dickman 1992; Forester 1979; Ross 1971). Benefits for the commensal may include access to food and other resources, protection from predation, as well as transportation in the case of low mobility organisms. In cases where symbionts are host-specific or relationships are obligate, if the host organism becomes extinct then its associated taxa will also undergo coextinction.

Coextinctions are generally not well studied, probably because they mostly affect parasites and mutualists that are often regarded as being undesirable. Due to a lack of research there is no real indication of the number of coextinctions that occur. Conversely the number of extinctions that occur each year for higher taxa, such as mammals and birds, are reasonably well documented (Butchart et al. 2006; Woinarski et al. 2015). Management plans that only consider host taxa are not necessarily guaranteed to protect the dependant species (Moir et al. 2011). For example, if numbers of the host species falls below certain threshold values, or the host species become spatially fragmented, then transmission between the hosts may be untenable for dependant species, leading to extinction of the dependant species which precedes that of the host (Dunne and Williams 2009). More research is needed on the size, structure, and the dynamics of host populations and their associated taxa in order to inform management actions that will limit coextinctions of the dependant species (Moir et al. 2010).

Australia has been described as 'mega diverse' due to the large amount of biodiversity it supports relative to the percentage of global surface area it covers (Australian Bureau of

Statistics 2015). In 2010 the collective ‘Forests of East Australia’ were recognised as the 35th biodiversity hotspot, which combines the Eastern Australian temperate forests and Queensland’s tropical rainforests. These forests have been found to contain a large number of endemic species that are under immediate threat from deforestation, as over 70 % of the forests have been cleared (Williams et al. 2011). As such, it has become necessary to focus conservation efforts on species and their populations from this part of the world.

1.2 Strategies in conservation biology

Conservation biology is the study of nature and biodiversity with the aim of protecting species, habitats and ecosystems. A key concern in the conservation of endangered species is the diagnosis of species decline and how best to manage future losses (Caughley 1994). Effective conservation measures are needed to support species persistence within communities and ecosystems, but this requires information on how species or populations are distributed and how they may respond to changes to their habitat.

The IUCN Red List of Threatened Species TM is a well utilised catalogue of plants, fungi and animals, which provides taxonomic, conservation status and distribution information. Scientifically based information is set against a list of categories and criteria in order to determine the relative risk of extinction of each species. The three most threatened categories of extant species are: Critically Endangered, Endangered and Vulnerable. There are five criteria used to evaluate if a taxon should be included in a threatened category (IUCN 2015): A . Population size reduction, where the population reduction is measured over 10 years or 3 generations, whichever is longer. B. Geographic range in the form of either B1 (extent of occurrence) AND/OR B2 (area of occupancy). C. Small population size and decline. D. Very small or restricted population. E. Quantitative Analysis. Although the animals that are classified in these threatened categories are not always officially recognised in local government protection plans, the assessments made in the Red List are of great value in attaining protected species orders.

Another strategy to manage decline of endangered species and biodiversity has been to create reserves or protected areas. This strategy can be beneficial in protecting

3 communities of species but often reserves and national parks are under resourced or not managed effectively (Rands et al. 2010). For instance, reserves are often created without full consideration of the networks required by many species to move easily between populations, such as creating and maintaining corridors (Groeneveld 2010). Also many reserve designs will favour one species and overlook many others. It is very difficult to create a ‘one size fits all’ strategy. Therefore, it is imperative that adequate life history and demographic data are compiled for the majority of organisms that inhabit any reserve.

The maintenance of patterns and levels of diversity in a system is upheld as being a conservation measure necessary to conserve species (Bonin et al. 2007; Frankham et al. 2002). Genetic diversity is the foundation of evolutionary potential/future adaptation of species to environmental changes. There will be a degree of genetic consequence for conservation actions that seek to preserve just a subset of the total populations, even if all other criteria used to select populations have been adequately assessed (Neel and Cummings 2003). The measurement of genetic diversity, within and among subpopulations, may be useful for conservation plans as it highlights those subpopulations that have a paucity of genetic diversity and identifies those that contain the majority of diversity. Further, genetic methods can determine the degree and historical timeframes of habitat fragmentation, which highlights the genetic separation between populations. Knowing whether gene flow exists between fragments of habitat is imperative when deciding how to manage endangered taxa.

1.2.1 Habitat fragmentation Losses of biodiversity and the endangerment of species have been associated with habitat fragmentation (Loreau et al. 2001; Tilman et al. 1994). A number of factors contribute to habitat fragmentation, including climate changes and anthropogenic land-use. Habitat fragmentation is a major challenge to the conservation of species because it leads to the loss of natural and continuous corridors, which confines many taxa to small patches of suitable habitat. Hence, a majority of populations of endangered species are subdivided into different breeding groups, either they are separated by natural habitat fragments or via the creation of nature reserves.

4 Fragmentation is occurring at unprecedented rates, especially in developing countries, and in parts of Australia. Extensive land clearance that started after European settlement in the 19th century has shown few signs of abatement (Mackey et al. 2008). Human induced habitat fragmentation has been linked to the overgrazing of livestock, mining, road development, changes to natural fire regimes, and logging is one of the world’s biggest causes of habitat destruction (Veblen 2000). Not only does logging remove essential forest habitat, it also contributes to the losses of indigenous species, which facilitates surface soil erosion and run off leading to loss of nutrients (Groffman et al. 2003), which then allows colonisation by invasive species.

Habitat fragmentation may also result from changes in climate and topology. Climate change (historical, current and future) has been identified as one of the key drivers of change to natural systems. All the groups of species present on earth today have experienced glacial-interglacial cycles from 20,000 – 12,000 years ago (Dawson et al. 2011). In Australia it has been predicted that temperatures in coastal regions are likely to rise by 0.3 - 2.7 °C by the year 2050 and that precipitation will drop in these regions by up to 40% (IPCC 2007). Not only do rising temperatures associated with climate change have the capacity to irreversibly damage ecosystems and instigate species extinctions, but they also induce range contraction and facilitate habitat fragmentation. Range contractions and retreating to refugia have allowed organisms to persist through past cycles of climate change (Schneider et al. 1998), but in many cases this has led to the long term fragmentation of populations.

Waterways are naturally fragmented by waterfalls, temperature variations and natural dams. However, they have been further fragmented by human interactions, such as, dams, roads, water withdrawals, species introductions and pollution (Fuller et al. 2015). The fragmentation of waterways is an underappreciated threat to biodiversity because freshwater ecosystems are largely ignored in conservation planning. This is surprising because freshwater ecosystems are reported as being the most threatened ecosystems on Earth (Bos et al. 2005; Revenga et al. 2005). When considering the effects of climate change on stream habitats the importance of forested riparian zones should be considered as the shade is integral in maintaining cool water temperatures (Moore et al. 2005). As stream

5 temperatures increase water evaporation may disrupt stream connectivity and facilitate fragmentation leading to population isolation. To date global biodiversity conservation plans have been largely directed at terrestrial ecosystems so it is now more important than ever to understand the drivers of extinctions for freshwater taxa.

Headwaters are an important ecosystem for the taxa that inhabit them. There are many taxa that can be classified as headwater specialists because they are restricted to higher elevation primary tributaries and rely on the specific conditions of headwaters throughout their entire life history (Finn et al. 2006). Conversely, many taxa are transient in headwaters as they use these sections of the stream network as spawning grounds or as nursery areas (Baumgartner et al. 2004; Pelicice and Agostinho 2008). Other taxa may move into the headwaters as downstream conditions become less favourable (Meyer et al. 2007). Headwater streams are particularly threatened by changes in land use due to their small drainage areas (Fritz et al. 2006), and fragmentation of these habitats threatens these species through further isolation.

The negative effects of habitat fragmentation on freshwater taxa are relatively poorly documented. Cumberlidge et al. (2009) determined that the majority of the world’s freshwater crabs are living in habitats that are severely fragmented and subject to increasing deforestation. Endemic species are a common feature of freshwater ecosystems most likely due to their insular nature coupled with much habitat fragmentation (Strayer and Dudgeon 2010). Howard et al. (2015) noted that endemic species are more at risk than non-endemics. For instance, in Malawi over 90 % of 927 lake fish species were found to be endemic and vulnerable to extinction (Department 2010; Howard et al. 2015). Further, many freshwater crayfish are highly endemic and range restricted (Jones et al. 2007; Richman et al. 2015; Whiting et al. 2000), which is why many of them are now classified as endangered (IUCN 2015). In highly endemic and low dispersing taxa, historical processes may be more important than current habitat variables for explaining spatial patterns, particularly patterns in species richness (Graham et al. 2006).

1.2.2 The distribution of species Biogeography is the study of the distributional patterns of living things across geographical space. It is a discipline that overlaps with the studies of evolution, ecology, taxonomy and molecular systematics. As a part of this thesis I use phylogeography, which is a sub discipline of biogeography, to explore long term habitat fragmentation and its effects on population structure for several headwater specialists: two (of three) endangered freshwater crayfish and one (of two) commensal flatworms.

Phylogeography became a popular discipline after John Avise explored the concept in 1987 (Avise et al. 1987). Phylogeography investigates the geographic distributions of genealogical lineages by combining population genetics and systematics of species or closely related species (Avise 2000). Intraspecific phylogeography compares spatial and temporal patterns of genetic differentiation within and among populations in order to determine the ecological and evolutionary processes responsible for those patterns (Avise 2000). Comparative phylogeography is the study of the effects of evolutionary history and biogeography on the distribution of genetic variation in codistributed species (Gutiérrez- García and Vázquez-Domínguez 2011). Studies of this type may be valuable in predicting the simultaneous divergence of codistributed taxa as it is expected that these species share common histories (Gutiérrez-García and Vázquez-Domínguez 2011; Nieberding and Olivieri 2007). Therefore it is possible to compare shared patterns of genetic diversification of taxa. Further, comparative phylogeographic studies are useful for those species at risk of coextinction, as it is possible to determine the extent of their shared genealogical histories.

A number of hypotheses have been proposed to explain the origin and spatial separation of taxa; including dispersal and vicariance (Ronquist 1997). Dispersal is the movement of individuals and by tracking gene flow, which is a product of dispersal, it is possible to determine whether or not dispersal has been successful, i.e. if the dispersing individuals reproduce and pass on their genetic code to the next population (Broquet and Petit 2009). Gene flow from migration prevents population subdivision and genetic differentiation resulting from genetic drift. Landscape obstructions such as mountain ranges or inadequate corridors may be effective barriers to dispersal, even for highly mobile species (Jaquiéry et al. 2011; Simmons et al. 2010). Due to dispersal barriers most organisms have a patchy

rather than continuous distribution. Vicariance occurs when a widespread ancestor diverges after its initial distributional range has subdivided (Ronquist 1997). Ultimately, both vicariance and dispersal can contribute to the spatial separation of taxa, which may lead to extreme genetic structuring and/or speciation of taxa. Thus, by combining genetic and spatial data it is possible to detect historical dispersal pathways or vicariance events (Sork et al. 1999).

1.2.3 The phylogeography of east coast Australia Temperate rainforest of Gondwanan origin was once distributed along the entire length of the east coast of Australia (Martin 2006). At the beginning of the Cenozoic era the climate in continental Australia was mainly warm and humid, with warm to cool temperate rainforest (Martin 2006). During the Pliocene epoch, forests were starting to retract and be replaced by arid grass land. The subsequent aridification across the continent continued during the Mio-Pliocene epochs and rainforest habitat reduced further (Martin 1998). After the Pliocene epoch, the climate became progressively cooler and drier and as a consequence rainforests further contracted to ‘mountaintop islands’ along the east coast of Australia (Harvey 2002; Kershaw 1981). The temperature regimes during the Pliocene would have certainly had an impact on the distribution and structure of populations of fauna and flora, especially within the north and the coastal east of Australia (Schneider et al. 1998; Schneider and Moritz 1999).

By the early Pleistocene epoch, climate conditions were similar to today but there were higher levels of precipitation (Martin 2006). During this period the cycles of rainforest contraction and expansion continued and these ‘cycles’ have been shown to correlate well with glacial-interglacial periods (Kershaw et al. 2007). As a consequence of these ‘cycles’, ranges contracted and habitats expanded (Kershaw 1994). Further, extensive drainage rearrangement, which occurred throughout eastern Australia, would have influenced the genetic structure of stream/river populations (Haworth and Ollier 1992; Hurwood and Hughes 1998; Ollier 1978). During the last 230,000 years, there have been further rainforest expansions that occurred during the wetter interglacial periods. These were replaced by drier rainforest and sclerophyll vegetation as the glacial periods became drier (Kershaw et

8 al. 2007). Rainforest expansions may have allowed periods of gene flow between the now disconnected mountaintop populations of eastern Australia.

The historical changes to Australia’s rainforests are reflected in the vicariance and dispersal patterns of rainforest taxa. A number of Australian studies have revealed significant phylogeographic structure among animal populations that have been attributed to bioclimatic and landscape changes during the Cenozoic era (65 – 0 mya) (Doak 2005; Harvey 2002; Hurwood and Hughes 1998; Nicholls and Austin 2005). Of most interest to this thesis is the geographic structure that has been observed in the crayfish genus Euastacus. The majority of species are geographically isolated on mountaintop ‘islands’ of eastern Australia.

In this thesis, I focused on two geographic areas that contain mountaintop ‘islands’ (Fig. 1.1). The first series of mountaintops is situated in the Daintree region of northeast Queensland. The second area rests within the Mary and Brisbane River catchments in SE Queensland where there are several uplands. Throughout this thesis, the terms upland and mountain will be used interchangeably. Phylogeographic breaks have been detected for many freshwater taxa between the Mary and Brisbane River catchments in the Conondale Ranges, SE Queensland, including fish (Thacker et al. 2007), frogs (Doak 2005) and shrimps (Hughes et al. 1995). Page and Hughes (2014) compared the division for 16 taxa between the two drainage catchments and determined that populations most likely separated 1.6 million years ago (mya) during the early Pleistocene epoch. The mountaintops in the Daintree, northeast Queensland are most notable for being separated from more southern mountaintops due to the historical fragmentation across the Black Mountain Corridor. The split between the north and south of this area is believed to have arisen during the Tertiary period with finer scale effects seen as a function of Pleistocene climatic variations (Zeisset and Beebee 2008). Here, significant geographic structuring has been detected in frogs (Schneider et al. 1998), lizards (Edwards and Melville 2010) and snails (Hugall et al. 2002), amongst others.

Figure 1.1: Map of Queensland, Australia. Triangles are the mountaintop locations used in this study (red: Daintree NE Queensland; green: Conondale range SE Queensland). BMC is the Black Mountain Corridor.

1.3 Conservation genetics of populations

Conservation genetics is the amalgamation of molecular and population genetics with ecology and biodiversity sciences. In the early 1980’s, Frankel and Soulé (1981) and Schonewald-Cox et al. (1983) were the proponents of the discipline. They laid out the framework that linked conservation of species and populations to evolutionary and genetic components (Ouborg 2009). Conservation genetic researchers have used genetic techniques to determine divergence dates among lineages, quantitatively measure gene flow, identify hybrid zones, detect cryptic species, determine historical biogeography, detect low Ne, identify population bottlenecks, low genetic variability and inbreeding depression (Baker et al. 2004; Fraisse et al. 2014; Hughes et al. 2012; Hughes et al. 2015; Miller et al. 2014; Walling et al. 2011).

A central concept in conservation genetics is fitness. Fitness can be defined as short-term fitness, such as viability or fecundity. Alternatively, it can be defined as “potential” fitness. The idea is that genetic variation in a lineage is necessary for future adaptations to habitat change. Potential fitness is harder to measure than short-term fitness. Most often it is measured as the loss of genetic variance as this loss can limit the possibility of future adaptations. Hence, the associated loss of genetic diversity from inbreeding depression, hybridisation or genetic drift is associated with loss of potential fitness (Soulé and Mills 1992).

As discussed earlier, population isolation through habitat fragmentation has negative impacts as it disrupts dispersal and gene flow (the movement of genetic information among populations) and may facilitate extinctions of populations or whole species. Population isolation may also result in genetic drift in populations, which is the random decrease or increase in the frequency of an allele. Remnant populations i.e. those on the periphery of suitable habitat tend to have the least genetic diversity and low effective population sizes

(Ne; a statistical parameter useful in assessing the rate of loss of genetic variation), and therefore are most affected by genetic drift (Groeneveld 2010). Barring mutation or migration, over time populations undergoing drift will become increasingly genetically dissimilar from other populations and, as drift is directional, it ultimately leads to evolutionary change. Therefore, the use of conservation genetic techniques is essential for understanding the presence/absence of gene flow between structured populations and the length of time that populations have been genetically separated. These measures may aid in conservation planning that seeks to maintain evolutionary processes, adaptive potential and to minimise the risk of future extinctions (Frankham et al. 2009).

1.3.1 Comparative genetic studies The addition of sympatric congeners or codistributed taxa into conservation genetic studies is a technique which may provide additional power and greater inferences in research of the population structure for all organisms being studied.

When choosing two species for comparative genetic studies it is important to consider the nature of their shared life histories. Preferably their life histories and genealogies should show concordance, as species with only short periods of close association may exhibit different ecological and population genetic characteristics. For instance, they may have responded differently to stochastic events or their dispersal strategies and habitat preferences may have differed through time (Gutiérrez-García and Vázquez-Domínguez 2011). There are several reasons that the genetic structure of close symbiotic species can be linked to the demographic structure of host populations. Genomes may co-evolve directly through the selective advantage of particular combinations of alleles (variants to a gene), and symbiotic species have genomes that may be correlated with their hosts due to sharing of life histories and/or patterns of dispersal (James et al. 2011). Also, correlation between genomes may be due to indirect factors, such as shared responses to environmental heterogeneity (e.g. spatial dependence); however, this process involves selection and it is beyond the scope of this thesis.

An association that is particularly useful in comparative genetic studies is the host parasite relationship (Nieberding and Olivieri 2007). This is because geographic barriers and historical processes are likely to have had similar consequences in these species that are reflected in their shared phylogeographic histories (Gutiérrez-García and Vázquez- Domínguez 2011). The spatial and genetic structure observed in parasites are often better resolved and more likely to reflect the true history of divergence than patterns seen in host taxa (Whiteman and Parker 2005). This relies on a number of factors being true, mainly that the parasite gene tree is similar to the parasite species tree. Second, parasites should have smaller Ne and smaller generation times than their hosts.

The level of host specificity is also an important feature in determining which parasites will make better proxies. If parasites have only one host, on which they spend their entire life history, they are more likely to show concordance with their hosts compared to parasites that live on several hosts throughout their lifespan (Nieberding and Olivieri 2007). Further, vertical transmission of parasites between hosts usually indicates that their common histories will be matched (Whiteman and Parker 2005). However, codivergence may still be observed when the mode of transmission is horizontal, as long as parasites are horizontally

transmitted within, but not among, diverging hosts (Nieberding and Olivieri 2007). If the above factors hold true, there will be fewer conflicts in the analysis of population structure and species delineation (Gandon and Michalakis 2002; Nieberding and Olivieri 2007).

In freshwater taxa, comparative phylogeographic and population genetic research has been mainly limited to fish, leaving a gap in the research for other vertebrates and invertebrates (Gutiérrez-García and Vázquez-Domínguez 2011). Freshwater crayfish are prime candidates for comparative genetic studies as many of them share close life histories with symbiotic species. Crayfish have a mass of microbes, viruses, fungi and bacteria that are generally too small to see. Additionally, most crayfish are host to a number of more conspicuous symbiotic species. For instance, Euastacus crayfish are associated with three types of ectocommensals/ectoparasites, the Temnocephala flatworms, mites and Oligochaete worms (McCormack 2012).

1.4 Freshwater crayfish

Freshwater crayfish are found on every continent, except for continental Africa, the Indian sub-continent and Antarctica (Crandall and Buhay, 2008). There is a breadth of research to support the hypothesis that freshwater crayfish are of monophyletic origin (Aguirre-Urreta 1992; Ahyong and O’Meally 2004; Crandall et al. 2000; Porter et al. 2005). Babcock et al. (1998) found fossil evidence that dated freshwater crayfish back to a marine ancestor during the late Paleozoic era ~285 mya, and determined that crayfish have been burrowing since 245 mya. However, most other studies are more conservative in their estimates. Given the current geographical distribution and the strong support for a monophyletic origin, Crandall and Buhay (2008) suggested that freshwater crayfish originated in Pangaea during the Triassic period (185 to 225 mya), afterwards spreading within Mesozoic freshwater ecosystems. Subsequently, the ancestral lineage was geographically divided during the separation of Laurasia and Gondwana ~185 mya, which led to the formation of the northern hemisphere super family, Astacoidea Latreille, 1802, and the southern hemisphere super family, Parastacoidea Huxley, 1878 (Crandall and Buhay 2008; Martin et al. 2008; Rode and Babcock 2003). Within Astacoidea there are two families, Astacidae and Cambaridae, which are found throughout the northern hemisphere (Crandall and Buhay 2008). Parastacidae is

the least studied of the crayfish families. It is the only family found throughout the southern hemisphere with 85% of the taxon located within Australia. Of these, eight of the ten extant genera are located along the east coast (Schultz et al. 2009). Fossil evidence suggests that the Parastacoidea have been in Australia for ~106-116 mya (Martin et al. 2008).

Currently, there are ~600-640 species of freshwater crayfish worldwide (Crandall and Buhay 2008; De Grave et al. 2009). Of these species, 149 are listed on the IUCN Red List (Furse 2014; IUCN 2015; Richman et al. 2015) as endangered, critically endangered or vulnerable and, of these 60 are found in Australia. These species are considered to be at risk due to habitat fragmentation, small population sizes and small extent of occurrence. Additionally, the population stability of these crayfish is believed to be threatened by anthropogenic input such as translocations, habitat destruction, pollution, over-fishing and introduction of disease vectors (Coughran and Furse 2010). Changing temperature is seen to be a particular threat, especially those that unable to disperse to higher/cooler altitudes (Furse 2014).

1.4.1 Genetic studies of crayfish Phylogenetics is the study of the evolutionary history and the relationships among individuals and/or groups of organisms by examining heritable traits, most often DNA. In the study of freshwater crayfish phylogenetic methods have determined that diversification of the crayfish complex Orconectes virilis, in North America, should be placed in the early Pleistocene epoch (Mathews et al. 2008). The authors speculated that diversification of the complex may correlate with biogeographic changes that occurred in North America as a result of the glacial cycles of the early Pleistocene. Also in North America, migration pathways have been detected in the species Procambarus liberorum , which appears to have originated in the headwaters of the White River in the Ozark Mountains, Arkansas followed by a steady southern migration into the north flank of the Ouachita Mountains (Arkansas River drainage) (Crandall et al. 2009). A New Zealand study of Koura crayfish used the mtDNA marker (COI) to determine that there were three major koura lineages, which was contrary to expectations of two lineages (Apte et al. 2007). Additionally, the cryptic West Coast group was found to be more closely related to P. zealandicus (South Island) than to P. planifrons (North Island and part of South Island). These findings led the authors to conclude that these crayfish had diverged earlier than the final development of Cook Strait (Late

Pleistocene). Hence, the divergence of the koura could be placed in the mid to late Pliocene when tectonic movements and mountain building led to displacement of freshwater crayfish and diversification events. In Australia vicariance has led to the separation of Euastacus spp. on mountain refugia, which most likely started in the Pliocene and persisted in these locations through the Pleistocene (Ponniah and Hughes 2004, 2006; Shull et al. 2005). In comparison, studies on the Australian species Cherax dispar within Queensland have shown that divergence between mainland clades of closely located C. dispar diverged ~5 mya (early Pliocene); whereas, a clade that was sampled from two nearby islands diverged ~13 mya during the Miocene (Bentley et al. 2010). The earlier divergence dates are interesting because this species is far more widespread than the nearby Euastacus spp.

Conservation genetic research of crayfish is less established than phylogenetic and phylogeographic studies. However, there have been several conservation studies of European freshwater crayfish that are classified in a threatened category (Gouin et al. 2001; Gouin et al. 2000, 2006; Schubart and Huber 2006). Also within Europe, invasive species have been investigated as they are often problematic in waterways and threaten the viability of local crayfish (Barbaresi et al. 2003; Yue et al. 2010). Mitochondrial DNA analysis of the European crayfish Austropotamobius pallipes pallipes found unexpected genetic similarities between British and French populations (Grandjean and Souty-Grosset 2000a, b). Conversely, populations from southern France were both genetically and demographically independent from northern France. These findings led the authors to classify the southern French populations as potential conservation management units. The study of Procambarus spp. in Arkansas, North America determined three possible new lineages of freshwater crayfish that are closely related to P. liberorum (Crandall et al. 2009). The authors suggested that P. reimeri be added to the IUCN Red List as an endangered species, as they found that it has a small geographic range and that suitable habitat is in decline. A study on the subterranean crayfish Orconectes spp. complex (Buhay and Crandall 2005) detected high levels of gene flow among populations, which was insightful because it suggested that subterranean waterways were connected and gene flow was not as limited as previously thought.

Although the amount of genetic research of freshwater crayfish is increasing there are very few studies that use microsatellite markers. Highly variable microsatellite markers are the most applicable to fine scale contemporary studies (Selkoe and Toonen 2006). It crayfish studies that have used microsatellite markers they have mostly detected extremely low genetic variability in crayfish. For instance, Matallanas et al. (2012) combined mtDNA and microsatellite markers to determine that Spanish populations of Austropotamobius italicus had low levels of genetic diversity throughout their range, which the authors attributed to bottlenecks and genetic drift. However, genetic diversity in this species was not as low as had been detected in populations from northern Spain and Italy (Diéguez‐Uribeondo et al. 2008). Later, Vorburger et al. (2014) developed eight microsatellite loci for the European species Austropotamobius torrentium. They found that genetic variability within loci was very low as there were just 1-7 alleles per locus; they also determined that the five sampled populations were significantly genetically divergent.

Within Australia microsatellite markers have been used to study just a handful of species, Euastacus bispinosus (Miller et al. 2014), Cherax quadricarinatus and Cherax bicarinatus (Baker et al. 2008), Cherax tenuimanus (Imgrund et al. 1997) and, Geocharax gracilis (Sherman et al. 2012). The research of Baker et al. (2008) was large scale, sampling 300 individuals from Australia’s north, confirming that the two species, Cherax quadricarinatus and Cherax bicarinatus, are divergent. The most recent study of Miller et al. (2014) looked at the distributional range of Euastacus bispinosus and determined that this species has extremely low genetic diversity, with no genetic variability within South Australian populations.

1.4.2 The Australian crayfish genus: Euastacus The focal genus of this thesis is the freshwater crayfish Euastacus. Of the ten Australian crayfish genera Euastacus Clark, 1936, has the most species (Ahyong 2014). Crayfish in this genus are amongst the largest in the world (Furse and Coughran 2011) with the Murray River crayfish, Euastacus armatus, being the second largest crayfish in the world. Euastacus are known as the spiny crayfish as many within the genus have short, thick spikes on their claws and carapace. Yet a small number of species barely have spikes at all; these are the dwarf varieties of Euastacus (McCormack 2012). Euastacus are polytrophic in the food chain

supporting both bottom-up and top-down ecosystem processes. Consequently, food preferences differ throughout the genus from mostly algal detritus (Turvey and Merrick 1997a) to meat (Johnston et al. 2011; Momot 1995). Euastacus often display an ontogenetic shift in diet as the active juveniles become relatively inactive adults. Such a varied interaction within the food web illustrates the interplay that Euastacus provides within the bioenergetic interactions of the macro benthic component of a stream, as well as their role in modifying competitive relationships within the aquatic insect fauna (Momot 1995). Euastacus can be divided into two broad classes: upland specialists and lowland generalists. They tend to inhabit cool permanent waters, often at high elevations. They have narrow tolerances for oxygen and temperature meaning that many Euastacus show extreme sensitivity to oxygen-consuming organic pollution, which makes Euastacus a good bioindicator species when tracking ecosystem health (Merrick 1997).

1.4.2.1 The phylogeography of Euastacus The majority of Euastacus species are cold-adapted and as such, they are believed to be restricted to mountaintop refugia (Ponniah and Hughes 2004). Within the east and northeast of Australia the climate is subtropical and tropical. Higher temperatures in the lowlands separating these mountaintops are thought to be a barrier to crayfish dispersal (Ponniah and Hughes 2004; Shull et al. 2005). Upon several of these mountaintops endemism is common; therefore, it is not unusual to observe only a single species of Euastacus. Due to the wide ranging distribution and large number of Euastacus species along the east coast (Fig. 1.2), Riek (1959) speculated that the distribution of this genus reflected a pattern of simultaneous vicariance. Ponniah and Hughes (2004) tested this theory against the alternate theory that divergence in Queensland was a factor of south to north dispersal. Those authors used genetic methods and, as mentioned earlier, found that vicariance was the most likely explanation for the observed separation of species. They also placed the divergence of the Queensland species within the Pliocene epoch: 1.5 mya between E. urospinosus and E. setosus, and 4.9 mya between E. hystricosus and E. sulcatus. Later, Shull et al. (2005) undertook an Australia-wide study of Euastacus that confirmed that vicariance was the most likely reason for diversification of the species. Those authors also determined that the two species, E. fleckeri and E. robertsi, were genetically divergent from the rest of the genus. The split between these crayfish and the rest of Euastacus was

believed to be due to the well known biogeographic barrier of the Black Mountain Corridor (Ponniah and Hughes 2004; Schneider and Moritz 1999; Shull et al. 2005). In addition, morphological differences have previously been found in these two species, which sets them apart from the rest of the genus (Morgan 1988). The vicariance and subsequent diversification of Euastacus are mostly likely linked to warming climates during the Pliocene which pushed taxa up into higher elevations with mountain refugia.

Figure 1.2: Distributions of 43 of the 50 Euastacus species, image used is from the study by Shull et al. (2005). Colours respond to phylogenetic groupings: Blue: northern clade; Red: central clade and Green: southern clade.

1.4.2.2 Euastacus in this study Very little is known about the life histories and population structure of the majority of the 50 Euastacus species. Three Queensland species were selected for analysis in this thesis, Euastacus robertsi Monroe, 1977; Euastacus hystricosus, Riek 1951 and Euastacus urospinosus, Riek, 1956. These species have received limited attention and yet they are classified as critically endangered or endangered (IUCN 2015). These three species are all upland specialists and their population size is believed to be small because their habitat is thought to be fragmented across mountaintops (Coughran and Furse 2010; Ponniah and Hughes 2004).

Euastacus hystricosus and E. urospinosus are codistributed across the Brisbane and the Mary Catchments in SE Queensland (Fig. 1.1), where they occupy cool shaded headwater streams. E. hystricosus has been recorded at elevations 400 m above sea level and E. urospinosus has been recorded at elevations above 250 m. Before this study, the distribution of E. urospinosus was believed to be limited because it was only known from three locations within the Mary catchment (Borsboom 1998; Morgan 1988; Smith et al. 1998). The third crayfish, E. robersti, is a species from NE Queensland and is restricted to three mountaintop populations, where it occurs at elevations above 750 m. This species has only been collected in small numbers and it has been assessed as critically endangered (IUCN 2015).

Euastacus hystricosus is the largest of the three crayfish and is grouped with the giant spiny crayfish, which are generally more conspicuous and aggressive than the smaller dwarf spiny crayfish and the intermediate spiny crayfish (McCormack 2012). The dwarf spiny crayfish, including E. urospinosus, spend more time in their burrows and are mostly nocturnal, whereas, the intermediate spiny crayfish, such as E. robersti, shares characteristics of giant and dwarf crayfish types. Very little is known of the dispersal ability of these three Euastacus species. It is probable that, as with other members of this genus, they are able to walk across the terrestrial landscape (Bone et al. 2014; Hughes et al. 2009). However, this is only possible as long as their gills are kept hydrated, which most likely limits the longevity of overland dispersal, especially on warm days. There is currently very little data on other population parameters of these species, which makes it difficult to determine if these crayfish can adapt to environmental change. It is presumed that these species are already

living at the limits of their environmental tolerances, as they cannot move any further up in their mountain habitats, and that temperature is a major factor limiting contemporary dispersal (Bone et al. 2014; Ponniah and Hughes 2006). If this is the case the effects of future climate change will further threaten these already endangered species.

1.5 Temnocephalida

In this thesis I also researched two free living flatworms from the family Temnocephalida, Platyhelminthes Blanchard, 1849. There are approximately 23 genera and 122 species of Temnocephalida worldwide (Tyler et al. 2006-2012). Cannon and Joffe (2001) proposed that the Temnocephalida were of Gondwanan origin (350 mya). Additionally, it has been suggested that they share a common ancestry with Dalyelliidae and Typhloplanidae despite originating later than these taxa (Littlewood et al. 1999). The alternate theory is that they are not common ancestors to these taxa, in which case a number of independent invasions may have occurred into freshwater environments (Schockaert et al. 2008). Temnocephalida are comprised of two main groups: the northern Scutarielloidea, mainly from Eurasia; and the southern group Temnocephalidae, whose distribution spans Australia, New Zealand, Madagascar, the South Pacific and Central America (Jennings 1997). It is the Temnocephalidae that are of interest to this thesis.

The phylogeographic origins of the Temnocephalidae Monticelli 1899 are somewhat uncertain, although it is believed they are not monophyletic (Littlewood et al. 1999). Research of the Australian genera has shown that they are strongly associated with their parastacid hosts (Sewell et al. 2006). The ancient association between the Temnocephalidae and their freshwater crustacean hosts can be seen in their shared geographic range. Australia is recognised as the global centre of Temnocephalidae diversity with more than 50% of the total species (Schockaert et al. 2008). In Australia there are 91 named temnocephalan species from 13 genera (Sewell 2013). The temnocephalans are endemic to the east coast of Australia, and they are distributed along the Great Dividing Range from Cooktown in North Queensland to the border between South Australia and Victoria (Morgan 1988, 1997; Sewell 2013).

1.5.1 Ecology of Temnocephalans: Temnosewellia Temnocephalans are classified as free living, as they are non-parasitic and capable of motility. Within Australia they live as commensal organisms, usually on a variety of crustacean hosts, in particular the freshwater crayfish Euastacus (Sewell et al. 2006). For the most part they are host-specific (Sewell et al. 2006). The Temnocephalans have flat squat bodies and rarely grow larger than 12 mm (Tsyrlin et al. 2002). They have a posterior organ called a ‘sucker’ with which they attach themselves to the carapace of the host. They use this sucker along with their five anterior tentacles in a looping motion to move along the carapace. This motion has been referred to as leech-like (Sewell 2013). Furthermore, whilst the host is active in the water the flatworms wave their tentacles, most likely to catch food passing in the flow (Jennings 1997). Their diet consists mainly of very small crustaceans, rotifers, insect larvae and nematodes.

Temnosewellia has 52 species in Australia, with at least one other described from Japan (Damborenea and Brusa 2009) (see Fig. 1.3). Research on Temnosewellia has been largely taxonomic (Damborenea and Brusa 2009; Damborenea and Cannon 2001; Sewell et al. 2006). In this study I research the population genetic structure of two species: Temnosewellia batiola Sewell, Cannon and Blair, 2006 and Temnosewellia albata Sewell, Cannon and Blair, 2006. Both species are believed to be host-specific, although T. batiola has been identified living on both Euastacus hystricosus and E. urospinosus (Sewell et al. 2006). Due to analysis performed in this thesis I found that the identification of T. batiola on Euastacus urospinosus is questionable (see Chapter three and six). If they are indeed found on E. urospinosus they also share this host with at least one other genus of flatworm - Temnohaswellia munifica Sewell, Cannon and Blair, 2006. T. munifica and T. batiola are easily distinguishable from each other as T. munifica lacks body pigment and has six tentacles, whilst, T. batiola has brown pigmentation and five tentacles. Also, T. munifica is not host-specific and is only found in limited numbers; these are the two reasons that it was not included in this study.

These flatworms are ectocommensal species that are generally host specific to their Euastacus hosts. They are often found in high abundance on particular hosts, and by their very association with their hosts these flatworms may be at risk of coextinction along with

them. The worth of Temnosewellia flatworms to biodiversity values is underappreciated. They are not recognised as endangered in any documentation. Even so, their value to this project is paramount as the close association that they share with the hosts allows them to be used in comparative genetic studies as proxy species for the determination and confirmation of the population structure within their hosts.

Figure 1.3: Temnosewellia spp. as viewed through the electron microscope Source: Sewell et al. (2006) and Sewell (2013)

1.6 Aims

The overriding aim of the thesis was to determine the distribution of genetic diversity and the population distribution of three threatened freshwater crayfish and their ectocommensal flatworms. The methods used included comparative phylogeography, coalescent-based molecular approaches and population genetic measures of structure and gene flow, in addition to on-site surveys of potential habitat. The research findings may act as a guide in conservation plans, when assigning land to reserves, habitat rehabilitation and for the assessment of threat towards these species. Finally, I will discuss how the findings from our assessment of genetic diversity within subpopulations relates to the historical fragmentation between them, and how this information may be used to manage genetic diversity in subdivided populations.

The mtDNA COI gene was used to examine the phylogeographic population structure of the crayfish, and one of the flatworms, across their range. Novel microsatellite loci were also developed for two taxa, E. hystricosus and T. batiola, in order to assess levels of

contemporary population structure. Previous research in Queensland, in particular the Conondale Range, has shown a high degree of genetic structuring in headwater specialists. Therefore, I expected to find population structure among uplands for these taxa. I also wanted to determine if individuals sampled from within the uplands were more genetically similar when compared to individuals from different uplands. Again, I expected there would be genetic differentiation between uplands.

In Chapter three, these concepts were explored for the species E. urospinosus, where population structure was examined across four uplands in the Conondale and Blackall Ranges. By sampling non-reserve areas within the Mary River catchment, it was believed that the known range of the species would be expanded. Another aim was to determine which model of genetic structure was applicable to this taxon. Previous research has suggested that Euastacus generally follow the headwater or Death Valley model of genetic structure (Bratby 2004; Ponniah and Hughes 2006; Hughes at al. 2013). As the species was located in a number of headwaters I expected the headwater model would be the most applicable as it allows for the occurrence of gene flow across drainage divides.

In Chapter four, the mtDNA COI gene was used to assess population structure in the north eastern species E. robertsi. It was in this chapter that the concept of using the commensal flatworm T. albata as a proxy species was introduced. By conducting a comparative phylogeographic study it was expected that I would determine: (1) if there was evidence of past and present connectivity between populations of E. robertsi and (2) if historical genetic patterns of colonisation and dispersal were congruent between T. albata and E. robersti. If the topologies and relative depth of gene trees were found to be similar between the host and the flatworm, then it may be deduced that T. albata shares a closely linked evolutionary history with its crayfish host.

In Chapter five, six novel microsatellite loci were developed for E. hystricosus. This dataset was combined with new and existing haplotype data for the mtDNA gene COI. The aims were to use these genetic markers to test for population structure and to estimate effective population size within and between three upland locations. A number of studies of co- located taxa have shown genetic structure for COI within this region, we expected to see

isolation between populations from the different uplands to support previous hypotheses that intervening warmer lowland limits connectivity in this species. Low levels of migration between uplands, coupled with low Ne and low genetic diversity, would indicate that this species may be at risk from the effects of genetic drift and inbreeding depression. However, as the species is likely to have some capacity for terrestrial dispersal it was expected that gene flow would be detected within the uplands.

In Chapter six, seven novel microsatellite loci were developed to investigate the population genetic structure of the flatworm T. batiola, in order to compare patterns of genetic structure with the host E. hystricosus. By comparing the dataset to Chapter four, it was expected that shared patterns of genetic structure would be observed between these taxa. If these taxa have similar patterns of genetic structure it would confirm that migration between uplands is negligible for the host but that gene flow within uplands is possible.

The final results of this thesis may be a guide for reassessing the conservation rating of the three crayfish. Further, the genetic information and geographic locations of the flatworms may also be sufficient to trigger further research on habitat variables which may allow these taxa to be added the IUCN Red List in a threatened category. The results of this thesis may also be useful in the development of conservation plans, particularly within the non-reserve Maleny region. Land and catchment care groups in this region have expressed an interest in tailoring their management and conservation plans according to the conservation status and distribution of E. hystricosus and E. urospinosus. Further, it is anticipated that the results will provide important information on population connectivity and levels of genetic diversity for upland and headwater specialists.

1.7 Layout

Aside from the general chapters of the introduction, methods and discussion, this thesis contains four research chapters (Chapters three to six). These chapters have been written as a series of published and unpublished journal articles. As this thesis differs from a traditional thesis, so too does the content. Each research chapter contains an abstract and acknowledgements. The general methods chapter was kept brief as each research chapter

contains the full methods. Further, as a consequence of the thesis style, there was unavoidable overlap and repetition in the chapters. For instance, all the chapters have maps/tables with location information; in some cases these tables duplicate information. Supplementary information has been appended to the end of the relevant chapter, so that they are easily accessible when reading the relevant chapter. All other tables/figures that were not relevant to the main chapters were appended to the end of the thesis. Finally, as chapters three and four have been published I will refer to them in the thesis in two ways; either by referencing the journal or by chapter number.

A forerunner in crayfish ecology

“Smallness and randomness are inseparable.” M. Soulé (1985) Chapter Two: General Methods The research chapters (three to six) in this thesis have their own methods sections. Therefore, presented here is a brief general methods section that focuses on the study area and the design of the research

This research was undertaken in two separate geographic locations within Queensland Australia ~1400 km apart (straight line) (Fig. 1.1). Within the uplands of the Conondale and Blackall Ranges in SE Queensland are the only recorded locations for the following three species: Euastacus hystricosus, Euastacus urospinosus and Temnosewellia batiola (Refer to Fig. 1 in Chapters three, five and six). This area is 90 km north of Brisbane and spans the two catchments of the Mary and the Brisbane Rivers; located between the latitudes and longitudes of -26.91, 152.75 and -26.62, 152.50. The climate here is subtropical with hot wet summers and cool dry winters (Australian Bureau of Meteorology 2015), with average daily temperatures ranging from 18 °C to 28 °C in summer and 11 °C to 20 °C in winter. Stream temperatures have been recorded at highs of 23 °C during the summer months (December/January) in northern Conondale (Borsboom 1998).The flow rates in streams differs seasonally but the headwater creeks have continuous flow throughout the year (Pusey et al. 1993). In summer and autumn, during the wet season, the streams are prone to large discharge as summer storms have been known to unleash in excess of 800 mm within a one month period (van den Honert and McAneney 2011). These discharges are a major source of disturbance that moves boulders and whole tree trunks through the creeks (Mosisch and Bunn 1997; C.R. Hurry pers. obs.). The stream conditions across the ranges are similar. Headwater streams have high slopes (2-5%) and the streambeds are composed of armoured beds of boulders, cobbles, gravel, and areas of exposed bedrock.

One of the IUCN Red List criteria for assessing the level of threat for the Euastacus in this study relies on determining the area, extent and/or quality of habitat. In the study area of SE Queensland, logging has been a feature of the landscape since 1870, 40 years after the first European migrants arrived in the Blackall Ranges. At the turn of the 20th century timber

cutting was reduced and many people turned to dairy farming, crops and pasture. These practices are still the mainstay of the townships in the area. Maleny is the biggest township with a population of ~3500. There are a number of national parks in the study area that are currently managed by the Department of National Parks, Recreation, Sports and Racing (NPRSR) under the Nature Conservation Act 1992. Within the Blackall Range are Mary Cairncross Scenic Reserve, Kondalilla National Park (NP), Mapleton Falls, Mapleton NP and the Maleny Township. The Mary Cairncross Scenic Reserve is located on the Maleny Plateau and is comprised of 0.55 km2 of subtropical rainforest, which is one of the last remnants of the original rainforest in this region. Kondalilla NP is an area of 3 km2 and has been a NP since 1945. Mapleton Falls (3 km2) became a national park in 1973 and Mapleton NP (65 km2) was gazetted on 3 June 2011 (The State of Queensland 2015). Curramore Sanctuary, to the northwest of Maleny is a 1.75 km2 nature reserve owned and managed by the Australian Wildlife Conservancy. The dominant forest type here is rainforest and tall eucalypt trees. Within the Conondale Range are the Conondale NP and the Bellthorpe NP. Conondale NP was established in 1977 and is ~368 km2. It is an area of mostly continuous forest and a mosaic of plantations (> 45 km2 total area) and various types of native forest (> 60 km2) (Bentley et al. 2000). The Bellthorpe NP is a tract of land that used to be state forest, which was the site of the old Brandon’s saw mill that operated between 1940 and 1963 (Kerr 1998).

The second study area (Chapter four) is the only known range of E. robertsi and T. albata (Coughran and Furse 2010; Ponniah and Hughes 2006). The known localities are in the Wet Tropics Bioregion and World heritage listed area within the Daintree NP, founded in 1981 (Refer to Fig. 1 in Chapter four). The Daintree rainforest is the largest continuous rainforest on the continent and the park has two main forested areas that border the towns of Mossman and Daintree village. The Daintree NP has two sections, Mossman Gorge and Cape Tribulation, the later being the location of the three uplands in this study (located between the latitudes and longitudes-16.18, 145.37 and -15.80, 145.30). The climate is tropical with hot wet summers and cool dry winters. Average summer temperatures in the lower latitudes reach as high as 32 °C but they don’t fall much lower than 25 °C in the winter months. All the sample sites were on mountaintops with elevations over 1000 m: Mount Pieter Botte (elevation 1009 m), Thornton Peak (137 5m) and Mount Finnigan (1083 m).

2.1 Fieldwork

2.1.1 Sampling (Chapter four) Northeast Queensland: these sites were sampled as part of an earlier study of the Euastacus genus (Ponniah and Hughes 2004); however, many of the samples had not been genetically analysed. On each mountaintop a single site was sampled, except for Mount Finnigan where two creeks were sampled. All the sample sites were above 750 m elevation. The samples were collected by Dr Mark Ponniah.

2.1.2 Sampling (Chapters three, five, and six) Southeast Queensland: at each location a 200 m transect was measured along the meander of the reach and coloured markers were placed at 25 m intervals along it. A variety of baited traps were then placed at ~5 m intervals. The trap types include: yabby ring nets, box traps, refuge traps and opera house traps (Fig. 2.1). The types of bait are as follows: tinned fish, dry cat food, fresh fish, fresh meat and bananas. Traps were placed on the stream bed for one to ten hours (overnight). If no captures were made a seine net was passed several times over the streambed in pools <1 m depth. Seine netting provided information on presence/absence of crayfish even when none were caught in the traps. This is because newly released young crayfish were sometimes found in the soft sediments. Electro-fishing was attempted in several streams and was deemed inadequate as a catch method. None of the Euastacus crayfish were affected by the electro fisher, whereas, Cherax spp. were easily captured. As explained in Chapter three E. urospinosus were mainly caught in modified Norrocky traps which were not baited and were placed directly into burrows (Bryant et al. 2012; Norrocky 1984). Across all sample sites, a total of 565 E. hystricosus and 29 E. urospinosus were captured and tissue was removed for later analysis.

Figure 2.1: Collecting crayfish in box traps at in Conondale NP; SE Queensland, Australia

Photo of Charlotte Hurry

Temnosewellia batiola were removed from the carapace of each crayfish by catching the flatworm in the thumb nail. This method was easier than using forceps. Between one and 10 flatworms were removed, flatworms less than 1 mm (visually assessed) were left on the host. In the case where multiples were removed, every effort was made to take a range of different sized flatworms. The habitat for ectocommensals may be considered in two ways: 1. the area of the host that it inhabits – its infrapopulation. 2. The stream in which it is sampled – locality (or suprapopulation).

2.2 Recorded data

The location of each crayfish capture was recorded to the nearest 25 m. These measurements were Figure 2.2: Determining taken in case later analysis found strong in-stream sex of genetic structuring. The total number of flatworms crayfish was counted on every captured crayfish. Very small flatworms were visually assessed, flatworms smaller than 1mm and eggs were ignored during data collection. This was done so that additions of new hatchlings during the four month sampling period were not counted. Several habitat qualities were recorded at each site, these included: stream substrate type, percentage canopy cover and land use (please refer to Appendix A for sampling notes).

2.2.1 Crayfish: Life stage and sex determination The sex of all crayfish was determined by looking for the external sex organs and determining their sexual maturity (whether they are gravid or ovigerous), using techniques described by Turvey and Merrick (1997b; Fig. 2.2). In males it is the amount of gonopore inflation and in the females it is the patterns in the setae around the female gonopores. The size of each crayfish was measured as the occipital carapace length (OCL; mm) (Fig. 2.3), this measure along with signs of sexual maturity was Figure 2.3: Measuring OCL used as a surrogate for age (Morgan 1988).

2.2.2 Flatworms – size and measures of intensity The following data of flatworm size and measures of intensity were not used in the final research chapters; all data can be found in the Appendices B and C.

At 14 localities a total of 565 E. hystricosus of between 11 mm and 159 mm OCL were captured and assessed for the occurrence of T. batiola flatworms (578 flatworms were removed for later genetic analysis)(Fig. 2.4). The flatworms were present on all parts of the infected hosts but tended to gather in large groups near the claws. There were 460 crayfish (OCL > 29 mm) that harboured at least one flatworm and from Figure 2.4: Temnosewellia batiola, (shown in blue circle) these a total of 16,269 grouped together on the claw of E. hystricosus. Photo: C. Hurry flatworms were counted.

Following the example of Hubbart et al. (2011) and Bush et al. (1997) general patterns of parasitism were summarised for each locality. General patterns of parasitism are as follows: prevalence which is designated as the proportion of crayfish infested by one or more flatworms, the abundance relates to the number of flatworms observed on an individual

crayfish hosts and the intensity is the number of flatworms found on an infested crayfish. The body size of the flatworms was measured (mm) from two localities (Stony Ck and Kondalilla Falls). Measurements were taken across the longest and the widest parts of the body of recently deceased specimens (not preserved).

2.3 Tissue sampling for DNA analysis

For the DNA component of the study the fourth right walking leg was removed from each crayfish by pulling firmly. This was deemed to be the most humane way as it mimics the natural process of limb loss in the wild and allows the exposed joint to immediately close and form a protective barrier (Cooper 1998; Juanes and Smith 1995). The removal of the leg by cutting with scissors was considered unsuitable as blood loss was apparent (C.R. Hurry Figure 2.5: E. hystricosus, pers. obs.). The leg was placed immediately into a plastic photo: C Hurry bag. The bag was marked with a crayfish ID and the temporary site ID; the bag ID was placed on a piece of card along with the date and the temporary site ID and placed into the bag (Fig. 2.5). If flatworms were removed from a crayfish the whole flatworm/s was placed into the same bag as the crayfish leg. The collected tissue was immediately placed on ice and moved into a -80 °C freezer as soon possible. Resampling was avoided by removing the same leg on each crayfish. The full regeneration of the leg is expected to occur after two moults (Cooper 1998); therefore, care was taken to complete all sampling at a site within a 6 month period.

2.4 Further methods

Please refer to the appropriate chapters for all laboratory methods and the DNA extraction protocols and data analyses. All lab work was performed by me except for the samples used in Chapter four (E. robertsi and T. albata) as the DNA extractions and the sequencing PCRs were carried out by Dr Giovannella Carini; however, all the subsequent work including sequence alignment and genetic analyses were done by me. For information on the development of the microsatellite markers for Euastacus hystricosus and Temnosewellia batiola, please refer to Chapters five and six respectively.

2.5 Research issues

Due to the nature of any research there will always be modification and shelving of ideas. As the chapters in this thesis were prepared for publication, there was not much opportunity to discuss all research issues. Therefore, I break from convention and present those issues here within the methods section. I hope that they may be useful in informing future research projects.

Site access was an issue for fieldwork. The issues that I faced were that the majority of sites were only accessible with off-road vehicles; many of the roads and tracks had become overgrown since they had last been accessed in previous research. Further, during the sampling years of 2012 and 2013, tropical storms were particularly persistent which meant that some sites became too dangerous to reach. In addition to the issues of site access, prior research of E. urospinosus had led me to believe that these crayfish were reasonably accessible. However, I found that spotting them in their burrows was easier than trapping. When I reviewed the literature it became evident that earlier studies had made taxonomic assessments with just one or two individuals, not the large number of individuals needed for a genetic population study. In the study by Borsboom (1998) he had observed the species in large numbers but this research was conducted at a single location over many years.

During the DNA extractions of the crayfish, I found that the DNA was of a higher quality if the exoskeleton was removed prior to grinding of tissue. This is because the crustacean exoskeleton contains astaxanthin, a colour carotenoid, which has been identified as problematic for extracting high quality DNA (Fox 1973; Li et al. 2011). Further, I found that the DNA extracted from the flatworms degraded very quickly if it was stored in the refrigerator for long periods. I did not identify the reasons for this but to guarantee accuracy I ensured that each individual was genotyped within a few weeks.

I was unable to capture large number of three of the species: E. robertsi and T. albata (Chapter four) and E. urospinosus (Chapter three); therefore, microsatellite markers were not developed for these species. Microsatellite development is very costly and time consuming which is counterproductive for small sample sizes. There were further issues in

the microsatellite development for E. hystricosus and T. batiola. For E. hystricosus, 194 potential loci were rejected as they were monomorphic, which may be linked to the overall low genetic diversity that I observed in this species and crayfish in general. For example, low variability in microsatellite markers (1-7 alleles) has also been detected in eight novel microsatellites developed by Vorburger et al. (2014) for Australian Cherax spp.

The analysis of historical migration rates were removed from this thesis. A powerful software for this type of analysis is Migrate-N (Beerli 2009), which differs from BAYESASS as the estimates of migration reflect long-term dispersal that is based on a coalescent approach (Beerli and Felsenstein 2001). Migrate-N allows prior hypotheses regarding gene flow to be tested based on marginal likelihoods, which are calculated for the different models and tested in a log Bayes factor framework (Beerli and Palczewski 2010). Migrate was used in the initial analyses in Chapters five and six. However, in Chapter five, it was impossible to obtain sufficient parameters in the E .hystricosus dataset which would allow the analysis to run to coalescence; therefore, the results were inconsistent. Migrate-N was also used for the T. batiola dataset in Chapter six with better results. However, as there were large heterozygote deficits for most loci within all the localities, it was decided to remove the results from the final manuscript.

Chapter Three: Phylogeography and limited distribution of the endangered freshwater crayfish Euastacus urospinosus

Hurry, Charlotte R.; Schmidt, Daniel J. and Hughes, Jane M. Australian Rivers Institute, Griffith University, Nathan, Qld, Australia

Published

Hurry, C. R., D. J. Schmidt, and J. M. Hughes. 2015. Phylogeography and limited distribution of the endangered freshwater crayfish Euastacus urospinosus. Australian Journal of Zoology:Published early online.

Author Contributions  Charlotte R. Hurry conceived and designed the experiments, collected the samples, performed the experiments, analysed the data, researched and wrote the paper, prepared figures and/or tables.  Daniel J. Schmidt contributed to the design of experiments, advised on genetic methods, reviewed drafts of the paper.  Jane M. Hughes contributed to the design of experiments, advised on genetic methods, reviewed drafts of the paper. Note Upon review of this thesis changes were made to the conclusion that differ from the published version

Including: Calculations that undertaken after publication with the ALA (2015) online tool that have now calculated an area of occupancy of approximately 76 km2 or an extent of occurrence of 610 km2.

3.1 Abstract

Conservation plans can benefit from understanding patterns of genetic structure because many endangered species are spatially fragmented. In particular, headwater species in high elevations are expected to exhibit a high level of population structure, as dispersal through

low land streams may be limited. Euastacus urospinosus is an endangered freshwater crayfish that, until recently, was thought to have a distribution of just 200 km2. In the current study, we identified a total of 26 locations for this species across a 1225 km2 region spanning the Brisbane and Mary River catchments of SE Queensland, Australia. We then used mitochondrial DNA sequence data to investigate the population structure and the phylogeographic divergence between four uplands. We found significant population differentiation for this species which conforms to the headwater model of genetic structure. Further, we found that fragmentation between these uplands is most likely historical, as the first divergence between lineages dated back 2.1 million years. Overall, we found no reason to remove the conservation rating of ‘endangered’ for this species. Conservation plans should seek to preserve the genetic integrity of these uplands by considering them to be genetically distinct and isolated populations.

Keywords: Headwater model, genetic structure, phylogeographic divergence, distribution, phylogenetic, Cherax leckii

3.2 Introduction

Understanding the spatial distribution and genetic diversity of endangered species is crucial for developing effective conservation plans. In particular, habitat fragmentation may influence the genetic composition of populations due to a lack of dispersal/gene flow between sites. A lack of gene flow may result in reduced levels of genetic diversity within populations, which often leads to negative effects in terms of reproductive fitness, levels of inbreeding, disease resistance and adaptive responses (Caballero et al. 2010). These factors may negatively influence the future potential of many species. For instance, generation time and/or population size combined with a lack of genetic diversity may limit the adaptive potential of species in the face of rapid climate change (Hoffmann et al. 2015). As such, conservation strategies can use research in genetic diversity and genetic structure to gauge the evolutionary potential of populations when their environment and habitat are undergoing change.

Aquatic organisms have been identified as being at particular risk due to habitat change, as many freshwater taxa are range restricted due to hydrology, stream chemistry, temperature or natural and anthropogenic barriers (Cook et al. 2008; Sala et al. 2000; Woodward et al. 2010). In particular, headwater stream taxa are confined to high elevations and are expected to exhibit a high level of population structure, as intervening low elevations and stream topography limit their dispersal. Hughes (2007) found that dispersal in upland streams was comparatively lower than in lowland areas, which translates to a greater amount of genetic structure at upland sites (specifically > 400 m elevation). The ability of taxa to disperse between sites relies heavily upon their mechanism of dispersal in addition to stream morphology and habitat connections. The following models have been proposed to explain the genetic patterns seen in riverine systems: the headwater model, the stream hierarchy model and the Death Valley model (Finn et al. 2007; Hughes et al. 2013; Hughes et al. 2009). The headwater model considers those taxa that are unable to move down the stream network and are thus isolated to the uppermost reaches. However, certain headwater specialists have some capacity for overland dispersal, which may allow dispersal and gene flow among nearby headwaters and even across catchment boundaries. The stream hierarchy model proposes that genetic connectivity reflects the dendritic nature of the streams. The third model is the Death Valley model which refers to highly differentiated populations that are completely isolated.

The freshwater crayfish is one taxon for which habitat fragmentation and range restriction is commonplace. Approximately 149 of the world’s freshwater crayfish are now classified as endangered (IUCN 2015; Richman et al. 2015). Thus it is unsurprising that there has been a significant shift in crayfish genetic research from taxonomic surveys to conservation studies (Fetzner et al. 2002). These studies demonstrate that many crayfish species are living in fragmented populations. For instance, there have been a number of discoveries of cryptic species in geographically separated populations (Apte et al. 2007; Larson et al. 2012; Mathews et al. 2008). Also, molecular work on the European white clawed crayfish Austropotamobius spp., has uncovered several species and has led to the identification of four evolutionarily significant units (ESUs) (Grandjean et al. 2002; Grandjean et al. 2000). Low levels of genetic diversity in crayfish have been found to be common for those species that have experienced bottlenecks, such as the Glenelg spiny crayfish (Euastacus bispinosus)

in southern Australia and the white clawed crayfish (Austropotamobius pallipes) in Spain (Grandjean et al. 2001; Miller et al. 2014); however, highly mobile species such as the invasive red swamp crayfish (Procambarus clarkia) are often found to have high levels of genetic diversity (Quan et al. 2014).

The crayfish assemblage of Australia is one of the most diverse in the world, second only to the USA. The Australian crayfish genus Euastacus is one such taxon that has been found to be limited to mountaintop ‘islands’ throughout the eastern coast of Australia (Ponniah and Hughes 2004, 2006), as 33 of the 50 species are only found at elevations above 250m (Coughran and Furse 2010; McCormack 2012). On the IUCN Red List (2015) many members of this genus have been classified as ‘endangered’ or ‘critically endangered’ and yet fewer than 10 genetic studies have been undertaken on them. Euastacus urospinosus, Riek 1956; Morgan 1988 is a freshwater crayfish which spends most of its time in an intricate system of burrows. It is listed as ‘endangered’ (Endangered B1ab (iii), IUCN 2015), based upon habitat fragmentation, area of occurrence and quality of habitat. It is classified as a ‘no take’ species in Queensland, Australia; however, as it is not protected under any official wildlife protection act there may be little enforcement of this rule. Currently it complies with the IUCN listing because until recently it was known to inhabit just three locations within a 200km2 area of occurrence in SE Queensland (Borsboom 1998; McCormack et al. 2010; Morgan 1988; Smith et al. 1998). A recent survey by McCormack and Van Der Werf (2013) found that the species is more widespread than previously known; however, their study was restricted to the Brisbane River catchment.

For this study, we used DNA sequence data from a single mitochondrial locus (cytochrome oxidase subunit I (COI) to identify the genetic structure of the species. We chose this genetic marker because it has been used to identify population structure in a number of other headwater stream specialists from within the study area, including, Paratya australiensis (Hurwood et al. 2003) and Euastacus hystricosus (Ponniah and Hughes 2006), which allows for comparisons of genetic structure within the region. Further, we surveyed locations within the Mary River catchment that were not previously documented. The addition of non-reserve areas and private land is particularly important as land use is a primary source of habitat fragmentation and may affect water quality. Euastacus urospinosus have been

identified as being at risk due to loss of forest habitat and changes to hydrology associated with anthropogenic changes in land use (Coughran and Furse 2010).

Drawing on previous research from headwater taxa in this region we expected to uncover a high degree of genetic structuring between upland areas. Also, we expected that warmer lowland areas would limit dispersal and that those individuals within the same upland would be more genetically similar than individuals from different uplands. In accordance with the headwater model we expected that within-stream dispersal would be limited, but, due to the capacity for overland dispersal of E. urospinosus we expected that dispersal between catchments would be possible for a small number of migrants. Before our study, there were two recorded locations for E. urospinosus in the Mary catchment: Mary Cairncross Reserve and Mapleton National Park (NP). We expected to uncover more established sites on non- reserve land within the Mary catchment. The results of our study may be a guide for reassessing the conservation rating of this species. Also, this study is of particular significance to land and catchment care groups within the Maleny region, as several of them have expressed an interest in tailoring their management and conservation plans according to the conservation status and distribution of this crayfish.

Figure 3.1: Locations of Euastacus urospinosus in SE Queensland. White circles and green diamonds indicate all known locations; the green diamonds also indicate sites where samples have been collected for sequencing of COI mtDNA. The blue ovals represent reserves and national parks. The thick orange lines are the borders between catchments

3.3 Material and Methods

3.3.1 Study area The study took place within an area of ~1225 km2 in SEQ. The upland areas sampled in this study are Conondale NP, Bellthorpe NP, Curramore sanctuary and the Maleny region which is the most populated township in the area (Fig. 3.1). To determine the full distribution of this species, an extensive literature search was conducted of all recorded sightings and collections of E. urospinosus (Borsboom 1998; Morgan 1988; Shull et al. 2005; Ponniah and Hughes 2006; McCormack and Van Der Werf 2013). In addition to spotlighting burrows, we laid traps throughout the area, where creeks were accessible. In an effort to determine the full extent of occurrence (EOO) and the area of occupancy (AOO) of the species, approximately 30 creeks (over 200 m elevation) and adjoining tributaries were surveyed as part of this study. These included sites in and around the Maleny region that were sourced through local knowledge and also included creeks where the co-located E. hystricosus is

found. We also surveyed creeks to the north of Summer Creek (Ck) in Conondale NP and to the west of Curramore and Maleny but we found no evidence of this species. In a previous study, McCormack and Van Der Werf (2013) surveyed sites adjacent (east and west) to the Conondale and Bellthorpe NPs and found no subpopulations there. Sites to the north of Mapleton or to the east of Maleny were not surveyed as part of this study. As we used new and historical records, a number of geo-locations were very close together; therefore, we classified several sites as one. Our parameters for this were that sites that were located within the stream network and less than 1 km apart should be classified as one site as there was a chance that the habitat was continuous. Furthermore, there were a number of previously recorded locations that were excluded from our study because the stated coordinates did not match the given locality. Samples were collected in baited box traps or in modified Norrocky traps (Norrocky 1984; Bryant et al. 2012), the latter of which were placed directly inside the burrows. By using these methods very few specimens were caught; different methods of placing nets in the burrows or tubes on the streambed were not successful. An alternative method of digging out the crayfish was rejected as too destructive to habitat. The EEO and the AAO were calculated using the total number of recorded locations (26) with the online tool provided by The Living Atlas of Australia (AOO: 0.02 degree grid; EOO: minimum convex hull (ALA 2015).

3.3.2 DNA extraction, amplification, and sequencing Genomic DNA was extracted from the leg tissue of 40 E. urospinosus (24 fresh collections and 16 museum specimens, Supplementary Table S3.1) using a salting out extraction method (Aljanabi and Martinez 1997). Of the 16 museum samples, two sequences were already available on GenBank; however, as we identified one of these samples as E. hystricosus (KC2838; DQ006400 (Shull et al. 2005)), we decided to eliminate error by re- extracting and re-sequencing the DNA directly from the two museum samples. A final edited 616 base pair fragment of the mtDNA COI gene was produced after polymerase chain reaction (PCR) using the COI primer set LCO-1490 and HCO-2198 of Folmer et al. (1994). PCR conditions were as follows: denaturation of DNA occurred at 95°C for 5 mins, followed by 30 cycles of 94°C denaturing for 1 min, 55°C annealing for 30 sec, and 72°C extension for 1 min, followed by a final 68°C extension step for 5 min. PCR products were then purified using exonuclease I/shrimp alkaline phosphatase (ExoSap). Samples were then sent to Macrogen

Inc. (Seoul, Republic of Korea) for sequencing on the ABI 3130 Automated Capillary DNA Sequencer (Applied Biosystems). Of the 40 E. urospinosus samples extracted and PCR’d only 33 were deemed of a quality high enough for sequencing. Ten Cherax spp. and eight E. hystricosus were also sampled from within the study area; which were extracted and sequenced as above.

The nucleotide sequences for COI were aligned and edited with SEQUENCHER v4.9 (Gene Codes Corporation). Of the 33 E. urospinosus sequences, 12 were sequenced a second time to resolve any ambiguities in the sequencing process. Any base pair anomalies identified in the contig were changed to the consensus sequence according to the following conditions: 1) the nucleotide base was not evident in at least one other sequence; 2) the base anomaly was not evident after re-sequencing of the DNA. The mtDNA sequences were then visually assessed for the occurrence of nuclear mitochondrial pseudogenes (numts) and additional stop codons using techniques described in Bensasson et al. (2001) and Song et al. (2008); none were found. DNAsp (Librado and Rozas 2009) was then used to determine haplotypes, their frequencies, and haplotype and nucleotide diversities for upland areas. In DNAsp we also performed Tajima’s D and Fu's FS tests for selective neutrality (Tajima 1989, Fu 1997). The COI region was also sequenced for a number of other crayfish from the study area to be used as out-groups in the phylogenetic trees. For both E. hystricosus (N=9) and Cherax spp. (N=8), we sequenced 600 bps. For E. hystricosus we found no new haplotypes, so the four available haplotypes from GenBank were used in the construction of the Bayesian tree (Supplementary Table S3.2). A BLAST search of the Cherax spp. identified six of the samples as Cherax dispar and two sequences which matched GenBank Accession KM039114 of Cherax leckii (99% sequence identity, R. Eprilurahman, M. B. Schultz, N.-W. V. Wei and C. M. Austin, unpubl. data). We mention this specifically as Cherax leckii is currently listed as ‘critically endangered’ as it is known from just one site in NSW. No formal identification was made as part of this study and this discovery warrants further investigation.

3.3.3 Networks, phylogenetic trees and divergence estimates Phylogenetic trees were constructed for the COI data using unique haplotypes. To place the E. urospinosus haplotypes in an evolutionary context, a selection of other crayfish haplotypes was also used to construct the phylogenetic trees. These were the E. hystricosus

sampled from within the study area, and sequences obtained from GenBank (See Supplementary Table S3.2) of E. setosus, E. jagara and E. monteithorium which have been found to be the most evolutionarily similar to E. urospinosus in addition to being located in SEQ (Shull et al. 2005). We then selected the two species of co-located C. dispar and C. leckii. to serve as an out-group to the Euastacus genus. Maximum parsimony (MP) and Bayesian tree searches were performed on the COI sequence alignment. MEGA v.6 (Tamura et al. 2013) was used to construct MP trees and to test the clock model; a clocklike evolutionary rate was rejected at a 5% significance level. The Max-mini branch-and-bound search was selected as it is guaranteed to locate all MP trees. We ran 10,000 MP bootstrap replicates. In this program standard parsimony was selected, in which all nucleotide changes are weighted equally. This analysis was run five times. Bayesian tree construction for the data was run using BEAST2 v2.3.0 (Bouckaert et al. 2014; Drummond and Rambaut 2007; Drummond et al. 2012). As we were using a Bayesian framework with a small number of sequences, there was no model selection applied to this data. Instead, in BEAST2 we auto- partitioned the data and implemented the RBS package (http://beast2.cs.auckland.ac.nz/index.php/Add-ons). RBS contains a reversible jump based substitution model for nucleotide data which jumps between models in a hierarchy of models and is most suitable for datasets of small size (Bouckaert et al. 2014). The Yule Tree prior was used alongside the lognormal relaxed-clock model, where 95% of the probability density was contained within the highest and lowest values taken from the literature. These values were 0.0083 and 0.012 substitutions/site/lineage/million years (Chirocephalus, Crustacea: Ketmaier et al. 2003). The Markov chain Monte Carlo (MCMC) was run for 200,000,000 iterations, sampling every 10,000th step and a burnin of 10,000,000 steps. The analysis was run five times. Convergence, mixing and effective sample size of model parameters (> 200) were assessed using the program Tracer v1.5 (Drummond and Rambaut 2007). The trees were then summarised and displayed in TreeAnnotator v2.2.1 (Bouckaert et al. 2014) and Figtree v1.4.0 (Drummond and Rambaut 2007).

Using just the E. urospinosus haplotypes, a haplotype network was constructed for the COI data, in TCS v1.21 (Clement et al. 2000), with 95% parsimoniously plausible branch connections between haplotypes. We used Arlequin v3.5.1.2 (Excoffier and Lischer 2010) to perform an analysis of molecular variance (AMOVA) within and among upland areas, and

within and among catchments (the Mary River catchment includes Curramore sanctuary, Maleny and Summer Ck in Conondale NP; the Brisbane River catchment includes Bellthorpe NP and East Kilcoy Ck in Conondale NP). Genetic variance explained at the catchment vs. upland level would provide evidence to either support or refute the stream hierarchy model which specifies that the majority of structure will be among catchments, or the alternative headwater model, in which we should see most of the structure among uplands. Three measures of evolutionary distance between uplands were calculated in Mega v6.06: p- distance, Jukes/Cantor model and the Log/Det paralinear.

3.4 Results

3.4.1 Distribution As part of this study, E. urospinosus has now been recorded at a total of 26 locations (Fig. 3.1, Supplementary Table S3.1). For the conservation rating this corresponds to an area of occupancy of 76 km2 or an extent of occurrence of 610 km2 (ALA 2015). Although we searched locations < 200 m elevation, the site with the lowest elevation was 277 m above sea level in the Maleny region, while the highest was 669 m in Bundaroo Ck, northern Conondale NP.

3.4.2 Genetic variation A total of 33 Euastacus urospinosus sampled from the Brisbane and Mary River catchments were sequenced for 616 bp of the COI mtDNA region. Fifteen unique haplotypes were identified (Fig. 3.2) of which 33% represented single individuals (GenBank Accession: KP751628: KP751641). The target fragment contained no gaps and was variable at 31 sites (5.0%) of which 30 were parsimony informative (4.9%). Our mitochondrial data did not deviate significantly from predictions of neutrality (Tajima’s D: 0.37 p> 0.1; Fu’s F: 1.02 p> 0.1). The global haplotype gene diversity value of 0.94 was high which reflects the large number of haplotypes detected. When partitioned by upland, the haplotype gene diversity was > 0.8 for Bellthorpe NP and Maleny, and > 0.4 for Curramore sanctuary and Conondale NP which was to be expected due to the small sample sizes in these two areas. As the sample size in this study was small, summary statistics should be viewed with caution. Haplotypes Euro06, Euro04 and Euro15 were the most common (N=5).

Figure 3.2: a) Map showing the stream networks within the four upland areas (green ovals) where Euastacus urospinosus is located in SE Queensland, Australia. Also shown are the COI mitochondrial sequence haplotypes (large coloured circles) found at each sample site (small dots). b) the most parsimonious haplotype network for the COI mitochondrial sequence. Circle size is proportional to the number of individuals with that haplotype. Haplotype and clade numbers correspond to Fig. 3.3 and Supplementary Table S3.1

3.4.3 Networks, phylogenetic trees and divergence estimates The Bayesian and MP inference produced the same optimal tree topology; therefore, only the Bayesian tree has been included (Fig. 3.3). The Bayesian tree topologies for the full dataset of crayfish show that the E. urospinosus group together with posterior probability of 1.0. There was very strong support for four distinct subclades within the E. urospinosus clade with posterior probabilities of 0.95 – 1.0; these subclades correspond to the four uplands. Divergence time of the Conondale subclade from the other three subclades was

estimated at ~2.1 mya. The Bellthorpe clade diverged from those of Curramore and Maleny ~1 mya, while divergence between Curramore and Maleny was dated at ~0.7 mya.

Figure 3.3: Bayesian (BA) consensus topology of Mitochondrial DNA COI sequence data for Euastacus spp. sampled in SE Queensland, Australia. BA posterior probabilities are shown below the node. Corrected node ages (million years) are shown above the nodes in brackets are the 95 % confidence intervals. Codes next to species name represent the voucher ID associated with each haplotype, as found on GenBank. ‘Euro’ voucher IDs are specific to this study (See Fig. 3.2 and Supplementary S3.1)

There were four to six mutational steps separating Maleny, Curramore and Bellthorpe. No haplotypes were shared between the four upland areas. Genetic structure was difficult to assess for Conondale and Curramore as they both had just two haplotypes and small sample sizes. The two locations along Little Cedar Ck in Curramore Reserve yielded two haplotypes. Conondale contained two haplotypes, which were two mutational steps from each other but were disconnected from the main network, showing 3% divergence from the other three subclades. A measure of the genetic distance between Conondale and the other subclades is shown in Table 3.1; there were no discernible differences between the distance models used, as the values were similar. There were > 5 haplotypes in both Bellthorpe and

Maleny which gives us a clearer picture of historical patterns of migration within these uplands. Maleny was the most diverse with six haplotypes from the tributaries of Obi Obi Ck in the Mary River catchment. Bellthorpe contained five haplotypes, haplotype Euro06 was the only haplotype which was found at multiple locations as it was shared across three streams but it was not found within the two intervening creeks.

Table 3.1: Results from p-distance model of evolutionary distance between the four upland areas which represent the subpopulations of Euastacus urospinosus in SE Queensland, Australia, as identified by Mega V6.06 Curramore Maleny Bellthorpe Conondale Conondale Bellthorpe 0.036 Maleny 0.016 0.030 Curramore 0.012 0.021 0.034

The large and significant ΦST value (p< 0.001) derived from the AMOVAs confirms that there is significant genetic structure at both the catchment and the upland level (Table 3.2). However, the fact that 85% of this variation is explained by upland as opposed to only 34% by catchment suggests that the genetic structure of these crayfish most closely fits the headwater model of genetic structure, rather than the stream hierarchy model, in which catchment would explain most of the variation.

Table 3.2: Analysis of molecular variance (AMOVA) showing the partitioning of genetic variation among catchments and uplands for Euastacus urospinosus from SE Queensland, Australia, as identified in software Arlequin. Level of partitioning df % of variance Fixation index p Among catchments 1 34 0.34 <0.001 Among streams within catchments 7 58 0.86 <0.001 Within streams 25 9 0.91 <0.001 Level of partitioning df % of variance Fixation index p Among uplands 3 85 0.85 <0.001 Among streams within uplands 5 6 0.42 <0.001 Within streams 25 9 0.91 <0.001

3.5 Discussion

The total geographic range of Euastacus urospinosus has now been shown to be no less than 1225 km2 but with an area of occupancy of approximately 76 km2. We have recorded an additional nine locations that were not previously recorded for this species. Specifically, this

study highlights the existence of this species within a number of non-reserve areas of Maleny, where habitat type differs to that found with the national parks. Also, the species has not previously been reported from Curramore sanctuary, which provides an intermediate location between Maleny and Conondale NP. Mitochondrial DNA sequence variation within E. urospinosus revealed strong geographic structure across the study area in a pattern reflecting the headwater model of spatial genetic structure. Fragmentation exists between these four uplands but it is most likely historical. The most closely related haplotypes were found within upland areas and there was limited haplotype sharing within two of the upland areas.

Even after extensive surveying, E. urospinosus was not found outside the study area but due to the density of forest in much of the area it is difficult to access many creeks; hence, we propose that the distribution may be even greater. It has been suggested that habitat preference is a limiting factor in the distribution of this species (McCormack and Van Der Werf 2013). Specifically, McCormack and Van Der Werf (2013) stated that Bangalow Palms (Archontophoenix cunninghamiana) must be present and that Cherax spp. must be absent. Indeed, we observed these conditions at most sites but we also noticed that a small number of our Maleny sites were different. For instance, we did not see Bangalow Palms at all sites and we were able to sample E. urospinosus alongside Cherax dispar in less than pristine habitat in a small tributary running through a dairy farm. At this small site, E. hystricosus were also present. Perhaps this sharing of habitat is due to a paucity of ideal environmental conditions in the developed and intensively farmed Maleny region.

As anticipated, E. urospinosus formed a monophyletic group, with a common ancestor dated at ~2.1 mya. The four upland areas formed four monophyletic lineages. The oldest of these was the Conondale lineage. The Conondale NP has been well studied as it forms part of an influential biogeographic barrier for a number of freshwater taxa, including the caddisfly species, Tasimia palpata,(Múrria and Hughes 2008), freshwater turtle Chelodina expansa (Hodges et al. 2014) and Fleay's Barred Frog Mixophyes fleayi (Doak 2005). We expected to have reduced power associated with the small dataset; however, our results were in agreement with a number of other studies in the region. Our estimation that the Conondale lineage dates to ~2.1 mya was in agreement with studies by Hurwood et al. (2003) on the

divergence of the co-located aytid shrimp P. australiensis between Bellthorpe NP and Conondale NP, as they found that populations have been separated between these two uplands for 2-3 million years. We did not expect to find such similar dates of divergence given that P. australiensis have been shown to be genetically structured within streams and they do not have the same capacity for terrestrial dispersal as spiny crayfish (Hughes et al. 1995). The similarity in results would suggest that there is a geographic break between Bellthorpe NP and Conondale NP which has been an impediment to dispersal for freshwater taxa. Although we found no data specific to these two uplands, Múrria and Hughes (2008) suggested that during the most recent glaciations the lowlands between the Conondale NP and the nearby uplands of Main Range and Lamington NP were dry and that fragmented rainforest patches did not rejoin. Our divergence times as calculated through Bayesian analysis suggested that our other three uplands were connected more recently 1 mya; however, there is no available phylogeographic data for us to make comparisons with other taxa in these uplands. When we compared the divergence dates between species from our study with those of Ponniah and Hughes (2004) we found that our estimate of 4.9 mya divergence between E. hystricosus and E. sulcatus matched their estimate exactly. In contrast, our estimated divergence of 3.8 mya between E. setosus and E. urospinosus was much earlier than the 1.5 million years estimated by Ponniah and Hughes (2004). Our estimates were based on a range between two possible substitution rates which may account for the discrepancy. Overall, our results are in agreement with the assertion by Ponniah and Hughes (2004) that speciation of Euastacus most likely originated during the Pliocene and that since that time taxa have persisted within the same mountaintops where differentiation has taken place.

We found that the majority of streams were fixed for one haplotype and there was little evidence of haplotypes being shared between streams. These results indicate that historical migration has been limited or non-existent even between closely located streams. This is surprising as most freshwater crayfish are able to spend hours on land giving them the capacity for overland dispersal. Although there are no data on this for E. urospinosus, we know that crayfish such as the redclaw (Cherax quadricarinatus) can survive up to 48 hours out of water (Mills et al. 1994) and Euastacus sulcatus is often seen walking through its rainforest habitat (Furse et al. 2004). However, there was some evidence that haplotypes

are shared between streams within Maleny and Bellthorpe NP. The haplotypes Euro07 was found at two sites in the Maleny region, the shortest distance between these sites is overland. Hence, there is evidence that migration may have occurred at distances over 1 km. In Bellthorpe NP, haplotype Euro06 was shared among three widely spaced streams and we expect that more extensive sampling would find Euro06 across all creeks in this upland.

The result of haplotype sharing observed in Bellthorpe NP contrasts with the research of Ponniah and Hughes (2006), of the co-located spiny crayfish E. hystricosus, as they found no haplotype sharing within the upland. However, not as many sites were sampled for E. hystricosus and haplotype diversity is much lower for this species, for instance, Bellthorpe NP contains two haplotypes both fixed at stream level, then there is one haplotype throughout Conondale NP and one throughout the Maleny region (Ponniah and Hughes 2006; Hurry et al. Chapter five). It is important to remember that as mtDNA sequence data is maternally inherited (for most organisms); male mediated gene flow was not detected. Thus, future studies should consider the use of diploid microsatellite markers in order to detect gene flow in both sexes. Comparatively, new data from ongoing studies of E. hystricosus which has used novel microsatellite markers, suggests that there is a higher level of connectivity within the uplands of Conondale NP, Bellthorpe NP and Maleny (Hurry et al. Chapter five). It is possible that E. hystricosus has a better capacity for dispersal than its smaller congener. From 33 individuals of E. urospinosus we uncovered 15 distinct haplotypes, which indicates that further sampling is needed to determine the true nature of haplotype genetic diversity for this species.

Across the full geographic range, the geographic structure of E. urospinosus seemed to reflect the headwater model of spatial genetic structure (Finn et al. 2007; Hughes et al. 2009). Our dataset was small which may reduce the power of some analyses, however, our results were in agreement with other studies that have demonstrated that headwater species have high intraspecific genetic diversity between uplands (Bernays et al. 2014; Finn et al. 2011). As shown by the AMOVA, the majority of the genetic variation occurred between the uplands rather than between catchments. For mountaintop populations of crayfish genetic structure often closely resembles the Death Valley model, which predicts that isolated populations which are hydrologically unconnected will also be highly

differentiated genetically; examples of this are E. robertsi and C. dispar (Ponniah and Hughes 2006; Bentley et al. 2010; Hurry et al. 2014). In contrast, many of the larger Euastacus species, such as E. sulcatus, E. spinifer, E. fleckeri and the co-located E. hystricosus show genetic patterning that conforms to the headwater model (Bratby 2004; Ponniah and Hughes 2006). As the Death Valley model and the headwater model are representative of fragmentation between populations and potential habitats, it would seem that habitat fragmentation exists as a natural state for many northern Australian crayfish.

3.6 Conclusion

Through this study, we were able to identify a number of new locations for E. urospinosus; in particular within the non-reserve areas within the Maleny region. The genetic diversity that we found was highly structured meaning that there may be limited or no dispersal between habitats within a stream. The results of this study can be used to make informed conservation management decisions. For instance, the current endangered listing of the IUCN Red List is based on population decline and limited extent of occurrence. We have now shown that these crayfish are indeed restricted geographically to an area of occupancy of 76 km2. We propose that the four upland areas (for which we have genetic sequences) be treated as distinct fragmented populations, and that these uplands are actively conserved in order to preserve genetic integrity. As this species is habitat restricted and is severely fragmented between populations, area of occupancy is the better indicator of threatened status. Therefore, we can conclude that at least one set of the parameters for the endangered listing are valid and suggest that this species is classified as Endangered B2a, as the area of occupancy of E. urospinosus is less than 100 km2 across four extremely fragmented upland locations. Further assessment should be undertaken with regards to parameter b iii, as the threat of continuing decline is considerable in the Maleny subpopulation. Although habitat fragmentation of the upland areas is most likely historical, consideration should still be given to maintaining or creating links within upland areas. Perhaps of most importance is the maintenance of terrestrial and stream connectivity within the Maleny region where the fragmented subpopulations of this species are competing with urbanisation.

3.7 Acknowledgements

Funding for this project was provided by Lake Baroon Catchment Care Group (LBCCG), Griffith School of Environment and the Wildlife Protection Preservation Society of Queensland. We especially thank LBCCG in their support for providing accommodation and much needed local contacts, including volunteers and landowners: Sally Watter, Mark Amos, Mat Cork, Ed Lawley, Col Cork, Peter and Fiona Stevens, and Steven Lang. Also thank you to Mary Cairncross Reserve, The Sunshine Coast Council, Dilkusha Nature Reserve and The Australian Wildlife Conservancy for site access. Permits: Department of Environment and Heritage Protection WITK1046911; TWB/40A/2011; General Fisheries Permit 153894. We would also like to thank two reviewers and the editor for their comments and contribution towards this manuscript.

3.8 Chapter 3 Supplementary Tables

S3.1: All geographic locations of Euastacus urospinosus from the Brisbane and Mary Catchments in SE Queensland, Australia. A greyed area shows where sites have been classed as one site in this study due to stream proximity. The superscripts in the ‘Creek’ Column represent the number of individuals sequenced at that location (see Fig. 3.2). EURO refers to haplotype ID. Latitude Longitude Creek Locality Catchment Recorded by Year EURO

-26.817740 152.677830 Trib. Flagstone Ck Bellthorpe Brisbane McCormack and Van Der 2012 Werf -26.822670 152.677181 Goodla Ck1 Bellthorpe Brisbane McCormack and Van Der 2012 06 Werf -26.763070 152.554590 Trib. West Kilcoy Ck Bellthorpe Brisbane McCormack and Van Der 2012 Werf -26.863170 152.689700 Trib. Branch Ck Bellthorpe Brisbane McCormack and Van Der 2012 Werf -26.857883 152.720400 Stony Ck2 Bellthorpe Brisbane Hurry, C 2013 03,12 -26.857900 152.720300 Stony Ck1 Bellthorpe Brisbane McCormack and Van Der 2012 06 Werf -26.856680 152.696350 Branch Ck2 Bellthorpe Brisbane McCormack and Van Der 2012 06 Werf; Hurry, C -26.853419 152.672989 Mary Smokes Ck1 Bellthorpe Brisbane McCormack and Van Der 2012 10 Werf -26.857730 152.677630 Branch Ck1 Bellthorpe Brisbane McCormack and Van Der 2012 13 Werf -26.860870 152.707720 Branch Ck1 Bellthorpe Brisbane McCormack and Van Der 2012 13 Werf -26.756370 152.546370 West Kilcoy Ck Conondale Brisbane McCormack and Van Der 2012 Werf -26.741030 152.570920 East Kilcoy Ck Conondale Brisbane McCormack and Van Der 2012 Werf -26.745930 152.571650 East Kilcoy Ck Conondale Brisbane McCormack and Van Der 2012 Werf -26.743519 152.568520 East Kilcoy Ck1 Conondale Brisbane McCormack and Van Der 2012 11 Werf -26.691667 152.608333 Bundaroo Ck Conondale Mary Borsboom, A 1982 -1994 -26.688000 152.634000 Booloumba Ck Conondale Mary Borsboom, A 1982 -1994 -26.700000 152.600000 N. Booloumba Ck Conondale Mary McCormack and Van Der 2012 Werf -26.648333 152.621472 Summer Ck3 Conondale Mary Hurry, C 2013 01 -26.763228 152.880636 Trib. of Obi Obi Maleny Mary Hurry, C 2013 -26.752190 152.851353 Trib. Lawley Ck Maleny Mary Hurry, C 2013 -26.735400 152.846560 Trib. Of Bridge Ck Maleny Mary Hurry, C 2013 -26.777741 152.880254 Mary Cairncross Maleny Mary Sewell et al. (geo-points updated) 1991 -26.764611 152.885710 Fryers Ck1 Maleny Mary Borsboom, A; Hurry, C 1999; 05 2013 -26.777741 152.880254 Fryers Ck3 Maleny Mary Borsboom, A; Hurry, C 1999; 07 2013 -26.766667 152.858333 not stated1 Maleny Mary Shull et al.; 1995; 07 Ponniah and Hughes 1999 -26.772779 152.795749 Dairy, Trib. Obi Obi2 Maleny Mary Hurry, C 2013 02 -26.736357 152.891470 Dilkusha, Trib. Obi Maleny Mary Hurry, C 2013 14,15 Obi6

-26.759981 152.869821 Maleny Precinct, Trib. Maleny Mary Hurry, C 2013 08 of Obi Obi2 -26.633333 152.866667 not stated Mapleton Mary Morgan, G 1982 -26.683333 152.866667 Small Ck near falls Kondalilla Mary Sewell et al. 1991 -26.701000 152.733889 Little Cedar Ck4 Curramore Mary Hurry, C 2012 04 -26.692039 152.741389 Little Cedar Ck1 Curramore Mary Hurry, C 2012 09

S3.2: Details of all COI sequences of the Queensland crayfish used in this study. *indicates sequences used in the construction of the Bayesian phylogenetic tree. ^ indicates new haplotypes found as part of this study.

Haplotype name Species GenBank Locality accession # CloneKO_kc2838_HA Euastacus hystricosus* AY324350 Maleny DQ006400 Curramore AY800368 Kondalilla NP CloneBO_HC Euastacus hystricosus* AY324351 Bellthorpe AY800370 kc2691_HB Euastacus hystricosus* DQ006348 Conondale NP AY800369 KC2672_KC2673_HD Euastacus hystricosus* DQ006346 Bellthorpe NP DQ006347 AY800371 KC2763 Euastacus jagara* AY324359 Shady Ck QLD KC2765 Euastacus monteithorum* DQ006357 Kroombit Ck QLD Kc2693 Euastacus setosus* DQ006379 Greenes Falls QLD KC2660 Euastacus sulcatus* DQ006393 Tallebudgera Ck QLD BEL2 Cherax leckii KM039114 Robertson SE QLD Cleck01 Cherax leckii^ KP751627 Maleny Cdis06 Cherax dispar^ KP751626 Maleny

Chapter Four: Shared phylogeographic patterns between the ectocommensal flatworm Temnosewellia albata and its host, the critically endangered freshwater crayfish Euastacus robertsi

Charlotte R. Hurry1, Daniel J. Schmidt1, Mark Ponniah1, Giovannella Carini1, David Blair2 and Jane M. Hughes1

1Australian Rivers Institute, Griffith University, Nathan, Qld, Australia 2School of Marine and Tropical Biology, James Cook University, Townsville, Qld, Australia

Published Hurry, C. R., D. J. Schmidt, M. Ponniah, G. Carini, D. Blair, and J. M. Hughes. 2014. Shared phylogeographic patterns between the ectocommensal flatworm Temnosewellia albata and its host, the endangered freshwater crayfish Euastacus robertsi. PeerJ 2:e552.

Author Contributions

 Charlotte R. Hurry analysed the data, researched and wrote the paper, prepared figures and/or tables.  Daniel J. Schmidt advised on genetic methods, reviewed drafts of the paper.  Jane M. Hughes contributed to the design of experiments, advised on genetic methods, reviewed drafts of the paper.  Mark Ponniah conceived and designed the experiments.  Giovannella Carini performed the experiments.  David Blair reviewed drafts of the paper, provided additional genetic sequences which he had obtained, provided advice on DNA extraction methods for the flatworms, read and corrected/commented on earlier drafts.

4.1 Abstract

Comparative phylogeography of commensal species may show congruent patterns where the species involved share a common history. Temnosewellia is a genus of flatworm, members of which live in commensal relationships with freshwater crustaceans hosts. By constructing phylogenetic trees based on mitochondrial COI and 28S nuclear ribosomal gene sequences, this study investigated how evolutionary history has shaped patterns of intraspecific molecular variation in two such freshwater commensals. This study concentrates on the flatworm Temnosewellia albata and its critically endangered crayfish host Euastacus robertsi, which have a narrow climatically-restricted distribution on three mountaintops. The genetic data expands upon previous studies of Euastacus that suggested several vicariance events have led to the population subdivision of Euastacus robertsi. Further, our study compared historical phylogeographic patterning of these species. Our results showed that phylogeographic patterns shared among these commensals were largely congruent, featuring a shared history of limited dispersal between the mountaintops. Several hypotheses were proposed to explain the phylogeographic points of differences between the species. This study contributes significantly to understanding evolutionary relationships of commensal freshwater taxa.

Keywords Dispersal, Fragmented habitat, Haplotype sharing, Crustaceans, Comparative phylogeography, Headwater, Invertebrates

4.2 Introduction

There are many examples of commensal relationships between aquatic organisms, perhaps none more prevalent than in the relationship between crustacean hosts and Platyhelminthes. Both marine and freshwater crustaceans worldwide have been shown to have persistent infestations of Platyhelminthes flatworms (McDermott et al. 2010; Ohtaka et al. 2012). However, not all of these associations are parasitic, many are commensal or mutualistic. An example of a commensal association is the one between the eastern Australian freshwater crayfish genus Euastacus and their ectocommensal temnocephalan flatworms. These flatworms are mostly host-specific and the most prevalent of just three, known, external symbionts on Euastacus (McCormack 2012). Many temnocephalans are

classified as free living, i.e., nonparasitic and capable of motility. They use the host purely as a mechanism to facilitate transport and/or feeding. The close association between the host and its ectocommensal may be exploited to develop an understanding of the phylogeographic history of both species.

Host-commensal associations can be examined using molecular data, which may demonstrate congruent patterns between host and commensal (James et al. 2011; Whiteman et al. 2007). The correlation of genetic variation between interacting species may be linked to indirect factors such as shared responses to environmental heterogeneity (e.g., spatial dependence) or due to species sharing similar life histories and/or movement patterns (James et al. 2011). Hence, we can use genetic data to explore potential habitat boundaries, identify dispersal patterns, identify divergence events, discover cryptic gene flow or determine points of origin (Barbosa et al. 2012; Harris et al. 2013; Nieberding et al. 2004). For instance, Nieberding et al. (2004) explain how inferences can be made on host phylogeographic history by using the species which has the higher rate of molecular evolution (usually the symbiont) as a “biological magnifying glass”; i.e., the detection of previously unknown historical events of the host as derived from the phylogeographic history of the symbiont. Vertical transmission in particular allows “parasites” to be used to infer genealogical history of the host (Rannala and Michalakis 2003; Whiteman and Parker 2005). An improved understanding of evolutionary relationships between taxa with closely dependent life-histories can lead to increased insight into phylogeographic patterns which may be an important factor when considering conservation management plans for endangered species (Toon and Hughes 2008; Whiteman et al. 2007). Further, phylogeographic histories are likely to track one another if the host exists in highly subdivided populations, as is the case for many headwater species (Hughes et al. 2009; McLean et al. 2008).

In this study we present the first comparative phylogeographic analysis of the ectocommensal flatworm Temnosewellia albata Sewell, Cannon and Blair, 2006 (Platyhelminthes, Temnocephalida, Temnocephalidae) and its critically endangered crayfish hosts Euastacus robertsi Monroe, 1977 (Arthropoda, Decapoda, Parastacidae); freshwater invertebrates of headwater streams. Our study seeks to understand the association between these

commensals and is one of just a handful of phylogeographic studies of an ectocommensal flatworm. Our comparisons of phylogeographic histories were attained by sequencing the mitochondrial cytochrome oxidase subunit 1 (COI) and the nuclear 28S ribosomal DNA. Previous studies have suggested the diverse array of Euastacus species in eastern Australia evolved through vicariance of formerly widespread ancestral taxa that became isolated in upland refuges of the eastern highlands during the Pliocene drying of the Australian continent (Ponniah and Hughes 2004, 2006; Shull et al. 2005). In these studies two species, E. robertsi and E. fleckeri were found to comprise a highly divergent monophyletic group within the genus. This phylogenetic separation of the two most northern Euastacus and the rest of the genus is coincident with a significant biogeographical barrier, the ‘Black Mountain Corridor’. Further, Ponniah and Hughes (2006) suggested that intervening lowland has been an effective barrier to dispersal in these species. We present a fine scale study which investigates historical patterning of E. robertsi across three mountaintops. These mountaintops in northern Queensland are located in an area < 100 km2 and are the only known locations of E. robertsi. In a previous phylogeographic study, just twenty E. robertsi individuals were sampled. Small samples are likely to miss rare alleles and potentially under-represent the full phylogeographic history of a given locus. Limited distribution combined with anthropogenic disturbances (Coughran and Furse 2010), have led to this species being categorised as critically endangered.

Temnosewellia albata is the only known ectosymbiont associated with E. robertsi. Very little is known of its ecology, highlighting the need for further studies on T. albata to better understand the level of the association between this species and its host. In depth studies on another crayfish ectosymbiont, Branchiobdellida, have shown that associations previously regarded as mutualistic may actually be weakly parasitic in times of overabundance (Brown et al. 2002; Brown et al. 2012). Although T. albata are not known to be parasitic we still expect that their life histories are closely aligned with their hosts. Currently, they are thought to be strictly host-specific and to undergo their entire life cycle on a single host (Jones and Lester 1992; Sewell et al. 2006). Infestations of Temnosewellia follow an over-dispersion pattern, where a few hosts carry many individuals (Wild and Furse 2004; C.R. Hurry pers. obs.). For several species of Temnosewellia it has been shown that abundance, prevalence and flatworm body size are all positively correlated with crayfish size

(Euastacus sulcatus/Temnosewellia spp. Wild and Furse 2004; Euastacus hystricosus/Temnosewellia batiola C.R. Hurry unpubl. data (see Appendices B and C). These observations, offer a strong indication that newly hatched Temnosewellia may colonise small or young crayfish. As Euastacus carry their offspring for several months after hatching (McCormack 2012), it is most likely that transmission of Temnosewellia albata is vertical. Even though T. albata are considered host-specific to E. robertsi, a single worm has been reported from a crayfish of the Cherax depressus complex sensu Riek, 1951, sampled at a site > 500 km from the current habitat of E. robertsi (Sewell et al. 2006). However, this identification has not been confirmed by further collection or molecular analyses.

Our study extends on previous phylogeographic research into Euastacus robertsi by using larger sample sizes and incorporating data from an additional locus. We then consider the phylogeography of an ectocommensal flatworm and seek to explore the longevity and history of the relationship between host and commensal. By conducting a comparative phylogeographic study we were able determine: (1) if there is evidence of past and present connectivity between populations of Euastacus robertsi and (2) if historical genetic patterns of colonisation and dispersal were congruent between T. albata and E. robersti. If T. albata shares a closely linked evolutionary history with its crayfish host, the topologies and relative depth of gene trees should be similar between the host and the flatworm.

Figure 4.1: Map of NE Queensland, Australia which shows the three mountaintop habitats (Δ) of Euastacus robertsi and Temnosewellia albata

4.3 Materials and Method

4.3.1 Study area The study area was located in the Daintree rainforest, which is the largest continuous rainforest on the continent. Situated within the wet tropics in northern Queensland, Australia, the tropical climate has hot wet summers and cool dry winters. The three mountaintops inhabited by T. albata and E. robersti are Mount Pieter Botte (elevation 1,009 m), Thornton Peak (1,375 m) and Mount Finnigan (1,083 m) (Fig. 4.1, Supplementary Table S4.1). The area which is <100 km2, is believed to contain the entire range of both species (Coughran and Furse 2010; Morgan 1988, 1997; Ponniah and Hughes 2006). Samples were collected, as per (Ponniah and Hughes 2004), in stream reaches at elevation > 750 m. On each mountaintop one site was sampled except for Mount Finnigan where two stream reaches were sampled.

4.3.2 DNA extraction, amplification, and sequencing Total genomic DNA was extracted from the leg tissue of E. robertsi and from whole samples of T albata as per methods outlined in Carini and Hughes (2006). A final edited 610 base pair fragment (E. robertsi) and 603 base pair fragment (T. albata) of the mtDNA COI gene was produced after polymerase chain reaction (PCR) using the COI primer set LCO-1490 and HCO-2198 of Folmer et al. (1994). PCR conditions were: denaturation of DNA occurred at 95 °C for 5 min, followed by 30 cycles of 94 °C denaturing for 1 min, 55 °C annealing for 30 s, and 72 °C extension for 1 min, followed by a final 68 °C extension step for 5 min. Dye terminator cycle sequencing reactions were used for sequencing (Perkin Elmer, Foster City, CA) as per manufacturer's instructions. Sequencing was carried out on an Applied Biosystems (Foster City, CA) 3130xl automated sequencing machine.

We also wanted to include nuclear gene data in the analysis to compare phylogenetic patterns for the two species. Therefore, we included a 734 base pair edited fragment (E. robertsi) and a 692 base pair edited fragment (T albata) of 28S ribosomal DNA. We used the primers Rd1a and Rd4b and used the PCR conditions of Crandall et al. (2000).

The nucleotide sequences for COI and 28S were aligned and edited with SE- QUENCHER v4.9 (Gene Codes Corporation). The mtDNA sequences were visually assessed for the occurrence of nuclear mitochondrial pseudogenes (numts) using techniques described in Bensasson et al. (2001) and none were found.

4.3.3 Networks, phylogenetic trees and divergence estimates For the 28S data we constructed haplotype networks using TCS v1.21 (Clement et al. 2000) for E. robertsi and T. albata. Phylogenetic trees were constructed for the COI data using unique haplotypes. For the E. robertsi tree, E. fleckeri was selected as an out-group, as it has previously been shown to be the sister species of E. robertsi (Ponniah and Hughes 2004). Temnosewellia aphyodes was chosen as an out-group for the T. albata tree, as it is the resident flatworm of E. fleckeri (Sewell et al. 2006). We used jModeltest v0.1 (Posada 2008) to choose the best-fit substitution model for each COI dataset. Using the Akaike information criterion the model selected for T. albata was TPM2uf+I+G and for E. robertsi was TIM3+I. Tree construction for each data set was run using Bayesian analyses. MrBayes v3.1.2 (Huelsenbeck and Ronquist 2001) was used for tree topology comparison, and BEAST v1.7.5 (Drummond and Rambaut 2007; Drummond et al. 2012) was used to construct rooted ultrametric trees for comparison of node divergence times.

In MrBayes, a MCMC chain of 2,000,000 iterations was used with a sample frequency of 100. The first 25% of iterations were discarded as burnin. MEGA v5.10 (Tamura et al. 2011) was used to calculate uncorrected percentage divergence between clades. In BEAST a lognormal relaxed clock model was first used to estimate divergence times of clades. However, the data could not reject a strict clock (ucld.stdev included zero); therefore, a strict clock model was used along with a coalescent constant size tree prior. Owing to lack of fossil calibration points and uncertainties in transferring molecular clock rates across taxa, we chose to incorporate a range of rates from the literature to place an approximate timeframe on COI divergences in the E. robertsi and T. albata datasets. Clock rates were used to describe a lognormal prior for the estimated clock rate, where 95% of the probability density was contained in highest and lowest values taken from the literature. For the temnocephalans these values were 0.0027 and 0.015 substitutions/site/lineage/million years (Schmidtea mediterranea, Platyhelminthes: Lázaro et al. (2011); Dugesia,

Platyhelminthes: Solà et al. (2013)). For the crayfish the values were 0.0083 and 0.012 substitutions/site/lineage/million years (Chirocephalus, Crustacea: Ketmaier et al. (2003)). Convergence, mixing and effective sample size of model parameters (> 200) was assessed using the program Tracer v1.5 (Drummond and Rambaut 2007) after running the analysis for 108 generations.

To investigate the magnitude of genetic divergence in each taxon, without reliance on transformation using molecular clock rates, we fitted the COI and 28S datasets to a two population isolation-with-migration model (IM), implemented in the software IM (v.12/17/2009; Hey and Nielsen (2004). Three pair-wise population comparisons (among the three mountaintop populations) were made for E. robertsi and T. albata. To ensure that results were consistent, each pair-wise comparison was run a minimum of three times (18 h/run) with different random number seeds. Model parameters of interest were taken from the peaks of the estimated distributions. These were population splitting time (t) scaled by the (unknown) geometric mean of the mutation rates for COI and 28S, and between- population migration rates (m1, m2).

4.4 Results

4.4.1 Temnosewellia albata A total of 63 T. albata individuals were taken from 20 crayfish hosts sampled across five locations on three mountaintops (> 700 m above sea level) (see Supplemental Information). Sixty one T. albata were sequenced for 603 bp of the COI mtDNA region. Seventeen unique haplotypes were identified (GenBank Accession: Table 4.1, see Supplemental Information).

Table 4.1: GenBank accession numbers for Temnosewellia species. All sequences were generated as part of this study. Species and molecular marker Haplotype ID and GenBank Mountaintop location accession number Temnosewellia albata (COI) TEM_FI1; KJ930397 Mt Finnigan/Thornton Peak Temnosewellia albata (COI) TEM_FI2; KJ930398 Mt Finnigan Temnosewellia albata (COI) TEM_FI3; KJ930399 Mt Finnigan Temnosewellia albata (COI) TEM_FI4; KJ930396 Mt Finnigan/Thornton Peak Temnosewellia albata (COI) TEM_FI5; KJ930400 Mt Finnigan Temnosewellia albata (COI) TEM_PB1; KJ930401 Mt Pieter Botte Temnosewellia albata (COI) TEM_PB2; KJ930402 Mt Pieter Botte Temnosewellia albata (COI) TEM_TP10 (D. Blair); KJ930412 Mt Finnigan Temnosewellia albata (COI) TEM_TP7; KJ930409 Thornton Peak Temnosewellia albata (COI) TEM_TP2; KJ930404 Thornton Peak Temnosewellia albata (COI) TEM_TP3; KJ930405 Thornton Peak Temnosewellia albata (COI) TEM_TP8; KJ930410 Thornton Peak Temnosewellia albata (COI) TEM_TP9; KJ930411 Thornton Peak Temnosewellia albata (COI) TEM_TP4; KJ930406 Thornton Peak Temnosewellia albata (COI) TEM_TP5; KJ930407 Thornton Peak Temnosewellia albata (COI) TEM_TP6; KJ930408 Thornton Peak Temnosewellia albata (COI) TEM_TP1; KJ930403 Thornton Peak Temnosewellia aphyodes (COI) 530FR (D. Blair); KJ958928 Mt Lewis Temnosewellia albata (28S) TEM_1; KJ941013 Mt Finnigan/Thornton Peak Temnosewellia albata (28S) TEM_2; KJ941014 Mt Pieter Botte/Thornton Peak

The target fragment contained no gaps and was variable at 91 sites (15%) of which 87 were parsimony informative (14%, Table 4.2). Between one and ten T. albata were sequenced for COI per crayfish (mean = 3.2). Average heterozygosity of T. albata sampled from one individual crayfish was not consistently lower compared to the average heterozygosity of T. albata sampled from a number of different crayfish. Due to sequencing issues, a small subset of the T. albata was used in the sequencing of the 28S ribosomal DNA region. For the

28S region, eight samples were sequenced for 692 base pairs and two unique haplotypes were identified (Table 4.1). The target fragment contained one gap at site 20 and had nine (1.3%) variable sites, all of which were parsimony informative (Table 4.2).

Table 4.2: Comparison of genetic diversity between Temnosewellia albata and Euastacus robertsi Species name and molecular marker N Haplotypes Hd π S Temnosewellia albata COI 61 17 0.89 0.071 91 Temnosewellia albata 28S 8 2 0.57 0.0096 9 Euastacus robertsi COI 64 6 0.65 0.023 31 Euastacus robertsi 28S 24 4 0.67 0.0076 12

Table 4.3: GenBank accession numbers for Euastacus robertsi. The new sequences that were generated as part of this study are highlighted in boldface type. Molecular marker Haplotype ID and GenBank accession number Mountaintop location COI FI-A; DQ006368, DQ006372, DQ006377, Mt Finnigan/Thornton Peak DQ006378, AY800362, DQ006369, AY324346 COI FI-P; KJ939254 Mt Finnigan COI TP1; DQ006370, DQ006376 Thornton Peak COI PB1; DQ006373, AY800364, AY324347 Mt Pieter Botte COI TP3; AY800363 Thornton Peak COI TP2; KJ939253 Thornton Peak 28S F1; EU920988 Mt Finnigan/Thornton Peak 28S P1; KJ941016 Mt Pieter Botte 28S T1; KJ941015 Thornton Peak 28S T2; KJ941017 Thornton Peak

Figure 4.2: Comparison of two Bayesian (BA) consensus topologies of the COI mtDNA datasets (A), and a parsimony network generated in TCS, of 28S ribosomal DNA sequence data (B). (A) BA posterior probabilities are shown above the node. The colours represent the location where the haplotype was sampled. Numbers represent the number of individuals sampled with that haplotype. Dashed lines represent a specific linkage where flatworms were sampled from hosts with that haplotype. (B) Haplotype frequency is indicated by the circle size (smallest 1, largest 8). The circle fill colour indicates sample site. Circles on connecting lines indicate the number of base pair mutations between haplotypes.

4.4.2 Euastacus robertsi For E. robertsi, 610 base pairs of the COI region were available for 64 individuals (including 16 from GenBank) (see Supplemental Information). Six unique haplotypes were discovered (GenBank accession: Table 4.3, see Supplemental Information). The target fragment contained no gaps and was variable at 31 sites (5%) of which 30 sites (97%) were parsimony informative (Table 4.2). The out-group consisted of three sequences from E. fleckeri (including two from GenBank). Twenty five of the E. robertsi individuals were sequenced for 733 base pairs of 28S ribosomal DNA and, four unique haplotypes were identified (Table 4.3). The target fragment contained no gaps and had 12 variable sites (1.6%; Table 4.2) 11 sites were parsimony informative (92%).

Table 4.4: Divergence estimates between clades (see Fig. 4.2A). Comparison is between sequenced COI haplotypes, for Temnosewellia albata and Euastacus robertsi in Queensland, Australia. Pairwise Comparison Substitutions/site/lineage/million years Diverged ±95% HPD Lower Upper (mya) (mya) Temnosewellia albata Clade A-B 0.0027 0.015 11 3.4 – 25

Clade A-B-C 0.0027 0.015 15 4.4 – 32

Euastacus robertsi Clade D-E 0.0083 0.012 2.6 1.5 – 4.3

4.4.3 Genetic variation 4.4.3.1 Comparison of COI tree topology between species Tree topologies for both taxa featured two or three deeply divided in-group clades (Fig. 4.2A). The main difference between taxa was, for T. albata there was division into three well-supported clades (clades A, B, C), whereas for their crayfish hosts E. robertsi, two well- supported clades (D, E) were identified. The three T. albata clades corresponded strongly with the three separate mountaintop populations, although one clade (B) included a mixture of samples from Thornton Peak and Mt Finnigan. The spatial pattern of the two clades in the E. robertsi tree grouped together samples from Mt Pieter Botte and Thornton Peak together (clade D). Clade E was comprised mostly of samples from Mt Finnigan.

Table 4.5: Parameter estimates from an isolation with migration model for three pairwise mountaintop population comparisons Temnosewellia albata Euastacus robertsi Comparison Parameters HiPt HPD90Lo HPD90Hi Parameters HiPt HPD90Lo HPD90Hi

qF 1.702 0.361 6.448 qF 0.1700 0.0154 1.126 1.Mt Finnigan qP 0.147 0.049 1.522 qP 0.0163 0.0163 0.992 - qA 44.82 19.75 103.1 qA 30.85 6.423 32.50 2.Mt Pieter Botte t 0.308 0.053? 14.99? t 0.173 0.022? 12.69? mF 0.005 0.005 0.515 mF 0.005 0.005 0.995 mP 0.005 0.005 1.285 mP 0.005 0.005 1.355

qF 0.965 0.170 4.596 qF 0.1 0.02 0.86 1.Mt Finnigan qT 3.440 0.313 41.802 qT 1.661 0.2518 7.100 - qA 38.46 13.03? 103.1 qA 27.545 10.52? 91.49? 2.Thornton Peak t 0.878 0.068? 7.238? t 0.6225 0.0375? 14.99? mF 0.005 0.005? 8.325? mF 0.005 0.0050? 5.755? mT 1.225 0.005 7.27 mT 0.275 0.015 3.855

qT 3.563 0.6600 16.75 qT 3.020 0.3583 16.63 1. Thornton Peak qP 0.087 0.087 1.478 qP 0.0217 0.0217 0.5852 - qA 49.21 13.03? 171.6? qA 23.18 8.446 89.94 2.Mt Pieter Botte t 0.292 0.068? 7.328? t 0.5175 0.0225? 14.99? mT 0.055 0.005 1.685 mT 0.265 0.005 3.565 mP 0.005 0.0050? 5.845 mP 0.005 0.0050? 8.795? Notes. HiPt, the value of the bin with the highest count; HPD90Lo, lower bound of the estimated 90% highest posterior density (HPD) interval. A question mark ‘?’ indicates unreliable or limit due to flat or incomplete posterior probability distribution sampled; HPD90Hi, upper bound of the estimated 90% highest posterior density (HPD) interval; q, the effective population size, population indicated by the letter (F, T, P); qA, ancestral population; m, the migration rate per gene copy per generation, letters indicate the population (F, T, P); t, a divergence estimate (not transformed to years).

4.4.3.2 Comparison of 28S tree topology between species The 28S haplotype networks were similar in structure for both species, as both feature two groups of haplotypes separated by quite a large mutational distance (10-11 bases; Fig. 4.2B). In both networks, there were samples collected at Thornton Peak (blue: Fig. 4.2B) which shared the same haplotype, or a very similar haplotype (one or two bases different), to a number of the samples collected at Mt Pieter Botte. Also, for both species, haplotype sharing was evident for samples collected at Thornton Peak and Mount Finnigan. Another point of congruence between the species is that there was a large mutational distance separating Mt Pieter Botte and some samples from Mt Finnigan. Finally, the 28S haplotype networks showed strong similarities to the COI phylogenetic trees; the main difference being that the T. albata 28S data exhibited two clades compared to three for the COI data.

4.4.3.3 Divergence estimates and isolation-with-migration model Percent divergence of the T. albata COI clades (shown in Fig. 4.2) was 12% for both clades A- C and B-C, and 10% for clades A-B. The median divergence time calculated by BEAST for clades A-B was ~11 mya (Table 4.4). Percent divergence for E. robertsi for clades D-E was 5%. The median divergence time calculated by BEAST for clades D-E was ~2.6 mya. Evaluation of the population divergence time parameter (t), incorporating both COI and 28S data in an IM model revealed no difference between T. albata and E. robertsi for the three among-mountaintop comparisons (Fig. 4.3; Table 4.5). Mean point estimates of t were 0.303 for crayfish and 0.338 for temnocephalans with broadly overlapping 95% credibility intervals. Note that these values are unscaled parameter estimates. Conversion into units of real time would require scaling these values by the (unknown) substitution rates for each locus and by the (unknown) generation time of each species.

Assessment of among-mountaintop migration using the IM model indicated no migration was compatible with the data for most pairwise comparisons (Table 4.5). However, the data did support non-zero migration from the Mt Finnigan population into the Thornton Peak population for both taxa: crayfish (mFtoT = 0.2; i.e., parameter estimate converted to demographic units representing effective number of migrants per generation), and temnocephalans (mFtoT = 1.9). Non-zero migration was also detected for crayfish from the Mt Pieter Botte population moving into the Thornton Peak population (Table 4.5). However, the temnocephalan data did not mirror this pattern.

0.008 TvF a) Euastacus robertsi 0.007

0.006 TvP 0.005

0.004 p 0.003

0.002 FvP

0.001

0 0 2 4 6 8 10 12 14 16

0.01 b) Temnosewellia albata

0.008 TvP

0.006 p

0.004 TvF

0.002 FvP

0 0 2 4 6 8 10 12 14 16

Figure 4.3: Isolation with migration model, using COI and 28S, showing the pairwise difference in divergence between three mountains. Black line, Thornton peak and Mt Finnigan; grey line, Thornton peak and Mt Pieter Botte; dashed line, Mt Finnigan and Mt Pieter Botte.

4.5 Discussion

By studying molecular data of a critically endangered freshwater crayfish and its ectocommensal flatworm, we found evidence that the phylogeographic patterning in T. albata is consistent with that of the host, E. robertsi. We suggest that populations on the mountain peaks separated sometime during the Pliocene. Contrary to earlier research on E. robersti (Ponniah and Hughes 2006) we found some haplotype sharing between these mountains. We suggest that haplotype sharing among mountaintops for both species is a product of post-divergence gene flow, although dispersal events between these mountain peaks have been infrequent.

The Greater Daintree NP, a vast area of land which includes the mountains in this study, is considered to be more than 135 million years old and is a hotspot of biodiversity (Hopkins et al. 1996). Throughout the Tertiary period rainfall remained at levels high enough to sustain extensive rainforest (Frakes 1999), with a shift during the late Tertiary to drier fire-prone sclerophyll forest (Truswell 1993). These conditions during the Pliocene are believed to have had a significant impact on population distribution and structure of fauna and flora in the north and the coastal east of Australia (Schneider et al. 1998; Schneider and Moritz 1999). Rainforest contractions occurred in the wet tropics during Pleistocene glacial periods (Kershaw 1994). Over the last 230,000 years rainforest expansions have occurred during wetter interglacial periods before being replaced by drier rainforest and sclerophyll vegetation in drier glacial periods (Kershaw and Van Der Kaars 2007). These more recent periods of rainforest expansion may have facilitated movement among mountaintop populations and produced the observed pattern of unidirectional migration inferred in the genetic data of both species.

4.5.1 Shared phylogeographic patterning At two independent loci we identified haplotype sharing between mountains for the flatworm and its host. We cannot say with absolute certainty if these shared haplotypes are the result of gene flow or retention of ancestral haplotypes, but analysis using the IM model was compatible with low levels of unidirectional gene flow after population isolation.

The pattern shared by crayfish and flatworms was for migration from Mt Finnigan into Thornton Peak. We established that, in both datasets, Mt Finnigan and Mt Pieter Botte were isolated from each other due to a lack of haplotype sharing with no evidence of migration. It has long been postulated that intervening lowlands are effective barriers to dispersal for Queensland Euastacus (Ponniah and Hughes 2004, 2006).

It is also likely that the lack of migration for this species could be attributed to these two mountains being on completely separate ridges. Furthermore, we consider it possible that Mt Finnigan and Thornton Peak may have once shared a ridge making historical connections between them more likely. These connections may have been present either overland or through historical stream connections. Historical connections between these two mountains have been found for the beetle Philipis (Baehr 1995). As Euastacus are known to be able to survive for long periods out of water they have the ability to traverse over land (Furse and Wild 2002), although overland dispersal may be rarer in some species. Intervening high points along ridge lines may have allowed for historical migration pathways. Current elevations between these mountains are no lower than 350 m at some places. Therefore, although it has been shown to be a rare occurrence, it is possible that migration between sites is possible, at least between two of the mountain ridges.

The congruence that we observed in the phylogeographic pattern of T. albata and its crayfish host suggests that their evolutionary histories are spatially linked; therefore, if hosts are capable of overland dispersal, so are the flatworms. The exact mechanism of dispersal for Temnosewellia is unknown. As the genus is generally considered to be host-specific it is expected that, like other temnocephalans, they undergo their entire life cycle and subsequent generations on a single crayfish hosts (Sewell et al. 2006). The mechanisms that allow them to survive the moult phase of their hosts are not known; however, observations by Haswell (1893) and Nichols (1975), on closely related temnocephalans, noted that they may be able to survive for some time in the absence of a host. Even though the flatworm’s ectoderm is somewhat prone to desiccation (Haswell 1909; C.R. Hurry, pers. obs.), flatworms may still be able to disperse overland with their host due to the durability of their unhatched eggs. Temnosewellia will lay tens to hundreds of eggs which stick firmly to the exoskeleton of the crayfish (Wild and Furse 2004; C.R. Hurry, pers. obs.). Eggs are enclosed in a tough outer coating and have a large fluid filled cavity (Haswell 1909) which may

prevent desiccation of the unhatched young allowing long distance movement in the absence of water. As so little is known of Temnosewellia this hypothesis has not been tested and further work is needed to determine dispersal mechanisms in these flatworms.

4.5.2 Time of divergence As low levels of migration were detected it should be easier to detect founding events. Our results show that, for both these species, isolation and divergence among refugial mountaintop populations was old enough to have resulted in accumulation of mutational differences. Comparison of COI divergence times for a node marking the split between Mt Finnigan and Thornton Peak for both taxa suggested the temnocephalan divergence may be older than the crayfish (~11 mya compared to ~2.6 mya). However this result is contingent on calibration using molecular clock rates from the literature. Numerous studies have highlighted variability in substitution rates between taxa (Lanfear et al. 2010; Wilke et al. 2009), so caution is required in interpreting the divergence times presented here. By taking a different population-based approach—using the multilocus, isolation-with-migration model—we showed that the population splitting parameter t was indistinguishable between the two species. This comparison incorporates data from another locus in addition to COI, and does not depend on application of molecular clock rates (which may not be appropriate for our study species). However it does have the drawback of not being expressed in units of absolute time. Weighing-up both of these results leads us to conclude that either (1) the mountaintop divergences of both species did occur contemporaneously, but that a greater number of substitutions have become fixed in the mitochondrial genome of the flatworm compared to the crayfish (i.e., their divergence rates are different) or (2) T. albata did diverge earlier than their hosts. The second assumption is entirely possible if in the past T. albata had a different host which has now become extinct. A different host could either be an ancestral Euastacus which was not sampled as part of this study or another crustacean host altogether. If we were able to confirm the authenticity of the single T. albata sampled upon the crayfish Cherax depressus ~515 km south of Mount Pieter Botte (Sewell et al. 2006); we may find that in the past the distribution of this species was much wider. However, as this hypothesis is based upon just one sample we are cautious in offering this interpretation. Our calculations support a separation sometime during the Pliocene. As previously stated, conditions during the Pliocene are believed to have had a significant

impact on population distribution and structure of fauna and flora in the north and the coastal east of Australia due to vicariance events.

Due to the close association that Temnosewellia share with their host they may be, in future studies, considered to be a suitable proxy in resolving phylogeographic patterning in their hosts. Nieberding and Olivieri (2007) tell us that ‘parasites’ that act as suitable proxy species are without intermediate hosts and have no phase of living independently of their host.

Equally they are individuals which display smaller Ne at the population level and exhibit lower gene flow than their hosts among populations. These factors combined allow them to display a stronger population structure than their host, making them especially useful in cases where hosts are rare or hard to sample in large numbers. As Temnosewellia satisfies many of these criteria their role in future studies may be to help resolve host phylogenies or phylogeographic history. The existence in this study of three deeply divided COI lineages in the temnocephalan compared with two in the crayfish may indeed indicate that the ectocommensal genealogy records part of the crayfish history that is lost due to stochastic sorting of lineages and extinction/recolonisation events. One possibility is that a third crayfish mtDNA lineage did exist on Mt Pieter Botte, but was replaced by the Thornton Peak mtDNA lineage following a colonisation event. In this scenario the ectocommensal history acts as proxy for the crayfish history. However the extra temnocephalan mtDNA lineage might also be explained by lineage retention or by failure to detect a corresponding third lineage in our crayfish sample.

We were able to demonstrate that the association between E. robertsi and T. albata has likely persisted over several million years. The results from our study are applicable to host- commensal relationships worldwide, as they show that shared histories between such close commensal species may span millions of years. A growing number of examples in the literature are demonstrating that symbionts can be used to infer host history for conservation gains (Colwell et al. 2012), which highlight the importance of studying symbiotic species alongside their hosts. We suggest that future phylogeographic studies exploit host-commensal interactions to provide objective measures of biodiversity, population subdivisions and phylogeographic information in the host. These interactions should be considered in management plans for crayfish species, especially as this technique may prove useful when host numbers are small, due to rarity or low catch rates.

4.6 Acknowledgements

Assistance in the field was provided by C Marshall, L Roberts, S Schmidt, J Ravenscroft, and M De Zilva. Two reviewers made many insightful suggestions and provided extra reading material which vastly improved this article.

4.7 Chapter 4 Supplementary Tables

S4.1: Sample locations for Temnosewellia albata and Euastacus robertsi: Temnosewellia albata (T.a); Euastacus robertsi (E.r). Stream name Location Latitude Longitude Total E.r Total T.a Annan Ck Mt Finnigan -15°49’27” 145°16’30” 20 19 from 6 E.r Parrots Ck Mt Finnigan -15°49’00” 145°16’00” 14 8 from 3 E.r Horan’s Ck Mt Finnigan -15°49’12” 145°16’11” 1 1 from 1 E.r Roaring Meg River Mt Pieter Botte -16°03’59” 145°25’10” 14 14 from 3 E.r Hilda Ck Thornton Peak -16°09’43” 145°22’00” 23 21 from 7 E.r

S4.2: Euastacus robersti-details of COI and 28S sequences used in phylogenetic analysis. 1Ponniah and Hughes 2006; 2Shull et al. 2005; 3Hurry et al. 2014; 4Toon et al. 2009

Identification Mountain Stream Haplotype ID and GenBank accession # CO1 28S clone robTH Thornton Peak Hilda Ck AY3243471 Euastacus fleckeri-clone fleckML OUTGROUP- Mt Lewis Leichhardt Ck AY3243481 Euastacus fleckeri-clone fleckMS OUTGROUP Unknown AY3243491 Euastacus fleckeri-KC2668 OUTGROUP- Mt Lewis Leichhardt Ck DQ0063361 FI1 Mt Finnigan Annan Ck FI-A; DQ0063682 FI10 Mt Finnigan Annan Ck FI-A; DQ0063682 F1; EU9209884 FI10J Mt Finnigan Annan Ck FI-A; DQ0063682 FI11 Mt Finnigan Annan Ck FI-A; DQ0063682 F1; EU9209884 FI11j Mt Finnigan Annan Ck FI-A; DQ0063682 FI12 Mt Finnigan Annan Ck FI-A; DQ0063682 FI13 Mt Finnigan Annan Ck FI-A; DQ0063682 FI13J Mt Finnigan Parrot Ck FI-P; KJ9392543 FI14 Mt Finnigan Annan Ck FI-A; DQ0063682 F1; EU9209884 FI14J Mt Finnigan Parrot Ck FI-A; DQ0063682 FI15 Mt Finnigan Parrot Ck FI-A; DQ0063682 FI16 Mt Finnigan Parrot Ck FI-A; DQ0063682 F1; EU9209884 FI17 Mt Finnigan Parrot Ck FI-A; DQ0063682 F1; EU9209884 FI18 Mt Finnigan Parrot Ck FI-A; DQ0063682 F1; EU9209884 FI19 Mt Finnigan Annan Ck FI-A; DQ0063682 F1; EU9209884 FI1j Mt Finnigan Parrot Ck FI-A; DQ0063682 FI2 Mt Finnigan Annan Ck FI-A; DQ0063682 FI2J Mt Finnigan Annan Ck FI-A; DQ0063682 FI3 Mt Finnigan Annan Ck FI-A; DQ0063682 FI3J Mt Finnigan Parrot Ck FI-P; KJ9392543 FI4 Mt Finnigan Annan Ck FI-A; DQ0063682 F1; EU9209884 FI5 Mt Finnigan Annan Ck FI-A; DQ0063682 FI5J Mt Finnigan Parrot Ck FI-A; DQ0063682

FI6 Mt Finnigan Annan Ck FI-A; DQ0063682 F1; EU9209884 FI7 Mt Finnigan Annan Ck FI-A; DQ0063682 FI7J Mt Finnigan Parrot Ck FI-A; DQ0063682 FI8 Mt Finnigan Annan Ck FI-A; DQ0063682 F1; EU9209884 KC2669 Mt Finnigan Parrot Ck FI-A; DQ0063682 KC2670 Mt Finnigan Horan’s Ck FI-A; DQ0063692 KC2674 Thornton Peak Hilda Ck TP1; DQ0063702 KC2737 Mt Finnigan Parrot Ck FI-A; DQ0063712 KC2738 Mt Finnigan Parrot Ck FI-A; DQ0063722 KC2776 Mt Pieter Botte R.Meg River PB1; DQ0063732 KC2777 Mt Pieter Botte R.Meg River PB1; DQ0063742 KC2778 Thornton Peak Hilda Ck TP1; DQ0063752 KC2779 Thornton Peak Hilda Ck TP1; DQ0063762 KC2780 Mt Finnigan Annan Ck FI-A; DQ0063772 KC2781 Mt Finnigan Annan Ck FI-A; DQ0063782 PB2 Mt Pieter Botte R.Meg River PB1; DQ0063732 PB1 Mt Pieter Botte R.Meg River PB1; DQ0063732 PB3 Mt Pieter Botte R.Meg River PB1; DQ0063733 PB37 Mt Pieter Botte R.Meg River P1; KJ9410163 PB38 Mt Pieter Botte R.Meg River P1; KJ9410163 PB39 Mt Pieter Botte R.Meg River P1; KJ9410163 PB4 Mt Pieter Botte R.Meg River PB1; DQ0063732 PB40 Mt Pieter Botte R.Meg River P1; KJ9410163 PB41 Mt Pieter Botte R.Meg River P1; KJ9410163 PB5 Mt Pieter Botte R.Meg River PB1; DQ0063732 PB6 Mt Pieter Botte R.Meg River PB1; DQ0063732 PB7 Mt Pieter Botte R.Meg River PB1; DQ0063732 RA cytochrome Mt Finnigan Annan Ck FI-A; AY8003621 RB cytochrome Thornton Peak Hilda Ck TP3; AY8003631 RC cytochrome Mt Pieter Botte R.Meg River PB1; AY8003641 robFIN Mt Finnigan Annan Ck FI-A; AY3243461 TP1 Thornton Peak Hilda Ck TP1; DQ0063702 TP2 Thornton Peak Hilda Ck TP1; DQ0063702 TP25 Thornton Peak Hilda Ck T1; KJ9410153 TP28 Thornton Peak Hilda Ck TP1; DQ0063702 T1; KJ9410153 TP29 Thornton Peak Hilda Ck T1; KJ9410153 TP3 Thornton Peak Hilda Ck TP1; DQ0063702 TP31 Thornton Peak Hilda Ck T1; KJ9410153 TP30 Thornton Peak Hilda Ck TP2; KJ9392533 T1; KJ9410153 TP32 Thornton Peak Hilda Ck FI-A; DQ0063682 T2; KJ9410173 TP33 Thornton Peak Hilda Ck FI-A; DQ0063682 F1; EU9209884 TP34 Thornton Peak Hilda Ck FI-A; DQ0063682 F1; EU9209884 TP36 Thornton Peak Hilda Ck T1; KJ9410153 TP4 Thornton Peak Hilda Ck PB1; DQ0063732 TP5 Thornton Peak Hilda Ck TP3; AY8003631 TP6 Thornton Peak Hilda Ck TP3; AY8003631 TP7 Thornton Peak Hilda Ck TP3; AY8003631 TP8 Thornton Peak Hilda Ck TP3; AY8003631 TP9 Thornton Peak Hilda Ck TP3; AY8003631

S4.3: Temnosewellia albata—Details of COI and 28S sequences used in phylogenetic analysis

Identification Mountain Stream Haplotype ID and GenBank accession #1 Euastacus robersti CO1 28s FI12.1 Mt Finnigan Annan Ck TEM_1; KJ941013 FI12 FI12.2 Mt Finnigan Annan Ck TEM_FI1; KJ930397 FI12 FI12.4 Mt Finnigan Annan Ck TEM_FI1; KJ930397 FI12 FI12.5 Mt Finnigan Annan Ck TEM_FI1; KJ930397 FI12 FI14.1 Mt Finnigan Annan Ck TEM_FI1; KJ930397 FI14 FI14.2 Mt Finnigan Annan Ck TEM_FI2; KJ930398 FI14 FI14.3 Mt Finnigan Annan Ck TEM_FI3; KJ930399 FI14 FI16.1 Mt Finnigan Parrots Ck TEM_FI4; KJ930396 FI16 FI16.2 Mt Finnigan Parrots Ck TEM_FI1; KJ930397 FI16 FI16.3 Mt Finnigan Parrots Ck TEM_FI4; KJ930396 FI16 FI17.1 Mt Finnigan Parrots Ck TEM_FI1; KJ930397 FI17 FI17.2 Mt Finnigan Parrots Ck TEM_FI3; KJ930399 FI17 FI17.3 Mt Finnigan Parrots Ck TEM_FI1; KJ930397 FI17 FI17.4 Mt Finnigan Parrots Ck TEM_FI3; KJ930399 FI17 FI18.1 Mt Finnigan Parrots Ck TEM_FI4; KJ930396 FI18 FI19.1 Mt Finnigan Annan Ck TEM_FI5; KJ930400 FI19 FI19.2 Mt Finnigan Annan Ck TEM_FI3; KJ930399 FI19 FI19.3 Mt Finnigan Annan Ck TEM_FI3; KJ930399 FI19 FI19.4 Mt Finnigan Annan Ck TEM_FI3; KJ930399 FI19 FI20.1 Mt Finnigan Annan Ck TEM_FI1; KJ930397 F20* FI20.2 Mt Finnigan Annan Ck TEM_FI1; KJ930397 F20* FI4.1 Mt Finnigan Annan Ck TEM_FI3; KJ930399 FI4 FI4.2 Mt Finnigan Annan Ck TEM_FI3; KJ930399 FI4 FI4.3 Mt Finnigan Annan Ck TEM_FI3; KJ930399 FI4 FI7.1 Mt Finnigan Annan Ck TEM_FI3; KJ930399 TEM_1; KJ941013 FI7 FI7.2 Mt Finnigan Annan Ck TEM_FI3; KJ930399 FI7 FI7.3 Mt Finnigan Annan Ck TEM_FI3; KJ930399 FI7 PB36.1 Mt Pieter Botte R.Meg River TEM_PB1; KJ930401 PB36 PB36.12 Mt Pieter Botte R.Meg River TEM_PB1; KJ930401 PB36 PB36.13 Mt Pieter Botte R.Meg River TEM_PB1; KJ930401 PB36 PB36.2 Mt Pieter Botte R.Meg River TEM_PB1; KJ930401 TEM_2; KJ941014 PB36 PB36.3 Mt Pieter Botte R.Meg River TEM_PB1; KJ930401 PB36 PB36.5 Mt Pieter Botte R.Meg River TEM_PB1; KJ930401 PB36 PB36.6 Mt Pieter Botte R.Meg River TEM_PB1; KJ930401 PB36 PB36.7 Mt Pieter Botte R.Meg River TEM_PB1; KJ930401 PB36 PB36.8 Mt Pieter Botte R.Meg River TEM_PB1; KJ930401 PB36 PB36.9. Mt Pieter Botte R.Meg River TEM_PB1; KJ930401 PB36 PB38.1 Mt Pieter Botte R.Meg River TEM_PB1; KJ930401 TEM_2; KJ941014 PB38 PB38.11 Mt Pieter Botte R.Meg River TEM_PB1; KJ930401 PB38 PB38.2 Mt Pieter Botte R.Meg River TEM_PB1; KJ930401 TEM_2; KJ941014 PB38 PB40.1 Mt Pieter Botte R.Meg River TEM_PB2; KJ930402 PB40 509R _D Blair Mount Finnigan Horan’s Ck TEM_TP10; KJ930412 Unknown Temnosewellia Mt Lewis Leichhardt Ck 530FR; KJ958928 aphyodes - E. fleckeri OUTGROUP TP24.1 Thornton Peak Hilda Ck TEM_TP7; KJ930409 TP24* TP24.2 Thornton Peak Hilda Ck TEM_TP2; KJ930404 TP24*

TP24.3 Thornton Peak Hilda Ck TEM_TP2; KJ930404 TP24* TP24.4 Thornton Peak Hilda Ck TEM_TP3; KJ930405 TP24* TP24.5 Thornton Peak Hilda Ck TEM_TP2; KJ930404 TP24* TP24.6 Thornton Peak Hilda Ck TEM_TP8; KJ930410 TP24* TP24.7 Thornton Peak Hilda Ck TEM_TP2; KJ930404 TP24* TP26.1 Thornton Peak Hilda Ck TEM_TP2; KJ930404 TP26* TP28.1 Thornton Peak Hilda Ck TEM_TP9; KJ930411 TP28 TP28.2 Thornton Peak Hilda Ck TEM_TP2; KJ930404 TP28 TP28.6 Thornton Peak Hilda Ck TEM_TP7; KJ930409 TP28 TP31.1 Thornton Peak Hilda Ck TEM_TP3; KJ930405 TEM_2; KJ941014 TP31 TP31.3 Thornton Peak Hilda Ck TEM_TP4; KJ930406 TP31 TP31.4 Thornton Peak Hilda Ck TEM_TP3; KJ930405 TP31 TP31.5 Thornton Peak Hilda Ck TEM_TP4; KJ930406 TP31 TP32.1 Thornton Peak Hilda Ck TEM_FI4; KJ930396 TP32 TP32.2 Thornton Peak Hilda Ck TEM_1; KJ941013 TP32 TP33.1 Thornton Peak Hilda Ck TEM_TP5; KJ930407 TP33 TP33.2 Thornton Peak Hilda Ck TEM_TP6; KJ930408 TP33 TP34.1 Thornton Peak Hilda Ck TEM_TP1; KJ930403 TEM_1; KJ941013 TP34 TP34.2 Thornton Peak Hilda Ck TEM_FI1; KJ930397 TP34

Claw of Euastacus urospinosus

Photo Hurry C

Chapter Five: Low Genetic Variability and Effective Population Size of an Upland Specialist: Novel Microsatellite Loci Quantify the Effect of Habitat Fragmentation on the Endangered Freshwater Crayfish Euastacus hystricosus

Hurry, Charlotte R., Schmidt, Daniel J., and Hughes, Jane M. Australian Rivers Institute, Griffith University, Nathan, Qld, Australia

Author Contributions

 Charlotte R. Hurry conceived and designed the experiments, collected the samples, performed the experiments, analysed the data, researched and wrote the paper, prepared figures and/or tables.  Daniel J. Schmidt contributed to the design of experiments, advised on genetic methods, reviewed drafts of the paper.  Jane M. Hughes contributed to the design of experiments, advised on genetic methods, reviewed drafts of the paper.

5.1 Abstract

For upland specialists, population fragmentation across mountain terrain is likely to be a natural consequence of historical range contractions due to past climatic events. As climate change is now considered to be a major threat to the global conservation of biodiversity it is necessary to understand the current extent and fragmentation of these taxa. The endangered Conondale spiny crayfish is believed to be restricted to three upland areas in a small area of SE Queensland. Although the peaks of these mountains are less than 20 km apart, it is possible that intervening low elevations are barriers to dispersal. Our objective was to determine the spatial genetic variation among populations of the Conondale spiny crayfish, from which we could infer genetic structure, dispersal patterns and effective population size. We developed six novel microsatellite loci, and combined data from these

with new and existing haplotype data for the mtDNA COI gene. We found significant population structure between the upland areas, as evidenced by Bayesian-based cluster analyses and AMOVA. We detected low genetic variability and extremely small effective population sizes in these uplands. If this variation is reflective of the level of variation in coding genes then the evolutionary potential of the Conondale spiny crayfish is very limited. The species should continue to be classified as endangered, based on levels of fragmentation and area of occurrence. This study illustrated that the three upland areas of Conondale National Park (NP), Bellthorpe NP and Maleny region/Kondalilla NP should be treated as genetically distinct populations for which inter-population dispersal is impossible. As such, conservation plans should treat these uplands as distinct.

Keywords: headwater model, genetic structure, uplands, spiny crayfish, conservation

5.2 Introduction

Upland areas have a unique set of environmental and habitat variables that affect the population dynamics of the taxa that inhabit them. Soil type, vegetation, water quality and air and water temperature all change with increases in altitude. Many taxa are range restricted due to specific habitat preferences and as a result even small habitat differences may lead to fragmentation among subpopulations. Currently, there seems to be little consensus as to what constitutes an upland specialist. It could be that upland specialists exist within specific temperature ranges or are found only at certain elevations. Webb and Tracey (1981) suggested that in Australia, climatic zones and forest structures are significantly different at three levels: ‘lowlands’, which are less than 400 m; ‘uplands’, which are 400–800 m; and ‘highlands’, which are 800–1,600 m These levels may differ significantly in climate, which may account for the difficulty in comparing studies worldwide (Lomolino 2001). After a review of the current literature, we found that the majority of genetic studies of upland or mountaintop taxa had small sample sizes or were limited to the use of haploid and/or allozyme genetic markers. This was surprising as mountainous areas are often hotspots of biological diversity (Lomolino 2001).

Contemporary climate induced range contraction may be pushing a growing number of taxa to higher elevations where temperatures are cooler (Kerr and Kharuba 2007; Moritz et al. 2008). Increased temperature due to climate change is now considered to be one of the major threats to the global conservation of biodiversity (Thomas et al. 2004), and mountain ranges have become the last refuge for a large number of threatened and endangered taxa (Lomolino 2001). The ability of a taxon to tolerate change relies on whether it can withstand range contraction or whether populations can disperse to different altitudes (Beever et al. 2015; Buckley et al. 2013; Hill et al. 2002). However, many upland specialists may already be restricted to the tops of mountains and may lack the ability to disperse to other suitable habitats. Hence, these species are considered to be at a high risk of extinction (Hilbert et al. 2001; Williams et al. 2003). Research in this area is minimal, and researchers need to measure the population processes specific to upland species. Thus, it is important to examine dispersal ability in conjunction with population genetic processes so that we may understand if species/populations have the adaptive potential to withstand future changes to their habitat.

For many upland populations, fragmentation may be a consequence of range contractions from historical climatic events. Given time, complete isolation of subpopulations may lead to genetic speciation, as individuals become so genetically distinct that they are unable to hybridise. The risks from fragmentation are most pertinent to small isolated populations, as a lack of migrants and limited opportunities for outbreeding lead to the negative effects of inbreeding depression (Frankham 2005). Inbreeding depression is the most immediate risk to populations as it reduces genetic variability and increases the frequency of deleterious alleles, which in turn may reduce a population’s ability to withstand stochastic events, such as disease outbreaks or changes to habitat variables (Keller and Waller 2002). Arguably, naturally small and isolated populations may be at less risk from inbreeding depression as deleterious variation should have been purged through time. Yet, in some populations purging is not effective and populations continue to suffer declines in fitness (Lacy 2000).

Genetic drift is also expected to affect small isolated populations but is a longer term threat than inbreeding depression. It causes increased population structuring and a potential loss of advantageous genetic diversity in populations (Lacy 1987), which may lead to mutation

accumulation, negative population growth and extinctions (Frankham 2005; Lynch et al. 1995). Genetic drift and inbreeding depression are both dependent upon the effective population size (Ne). Ne is the size of an idealised population that experiences drift or inbreeding at the same rate as the population under consideration (Harmon and Braude

2010). Therefore, it is more informative than measuring census population size, as Ne can be used to calculate how quickly a population will lose genetic diversity or become inbred. On average, Ne is an order of magnitude smaller than the census population size (Frankham

1995). The interaction of low Ne with demographic stochasticity, genetic drift and environmental variation can lead to extinction events (Gilpin and Soulé 1986). Therefore, low levels of genetic diversity coupled with low Ne and no gene flow impacts negatively upon the adaptive potential of species/populations.

Freshwater crayfish are one taxon often found in upland habitats and many species are also headwater stream specialists (Coughran and Furse 2010; Fratini et al. 2005; Pârvulescu and Petrescu 2010; Ponniah and Hughes 2006). Haggerty et al. (2002) established that crayfish constituted ~92 % of the biomass in the headwater streams of the coastal mountain range of Washington, USA. There is also a high diversity of crayfish taxa found within mountain ranges including the south eastern Appalachian Mountains in the northern hemisphere and the Great Dividing Range in eastern Australia (Crandall and Buhay 2008). Nevertheless, there are relatively few population genetic studies on freshwater crayfish, especially in the USA and Australia. In Europe, most attention has been given to the species complex Austropotamobius which are found across a range of elevations. Most recently, Vorburger et al. (2014) used microsatellites to show that the stone crayfish (Austropotamobius torrentium) have low genetic variability across their range. This expanded on earlier work that demonstrated that low genetic diversity in Austropotamobius pallipes can be linked to historical bottlenecks (Grandjean et al. 2001) and that populations of Austropotamobius from the Iberian Peninsula are most genetically similar to Italian populations (Diéguez‐Uribeondo et al. 2008).

Of the Australian genus, Euastacus, 33 of 50 species are only found at elevations above 250 m (Coughran and Furse 2010; McCormack 2012). For the majority, their habitat is fragmented, either at the stream level, upland level or both. Consequently, it is unsurprising

that all 33 species are classified as endangered/critically endangered (IUCN 2015). A number of studies have recommended that a thorough genetic analysis of this genus be undertaken (Furse and Coughran 2011; McCormack et al. 2010). Despite this, just 10 genetic studies have been completed for Euastacus (from all elevations) (Baker et al. 2004; Hurry et al. 2014; Lawler and Crandall 1998; Miller et al. 2014; Miller et al. 2012; Ponniah and Hughes 1998; Ponniah and Hughes 2004, 2006; Shull et al. 2005; Versteegen 1996).

The current study concentrates on the Conondale spiny crayfish Euastacus hystricosus Riek, 1951. It is listed as ‘endangered’ (Endangered B1ab (iii), IUCN 2015), based on habitat fragmentation, extent of occurrence and quality of habitat. Euastacus hystricosus is an upland specialist confined mostly to the headwaters of the Brisbane and the Mary River catchments, SE Queensland, Australia. This species only occurs on three uplands in an area no larger than 875 km2. Additionally, it has a very narrow upland range and has previously only been found at elevations between 400 m and 800 m. Even though this species has the capacity for terrestrial dispersal, previous work has speculated that warmer lowland areas may limit migration events between the upland habitats (Ponniah and Hughes 2006). However, this research was small scale as the researchers used allozyme and mitochondrial sequence COI data for no more than 40 individuals of E. hystricosus (Ponniah and Hughes 2006). Furthermore, these studies did not sample creeks from the eastern distribution of the region which means they could not describe the population structure across all uplands. In comparison, studies of the co-located taxa Euastacus urospinosus (Hurry et al. 2015) and Paratya australiensis (Hurwood et al. 2003) have found significant population structure in the same region, which indicates that E. hystricosus may also be experiencing fragmentation across its geographic range. The study of E. urospinosus also showed population structure that conformed to the headwater model of genetic structure, which has been described to explain population structure for headwater species that have some capacity for terrestrial dispersal but are still habitat restricted (Finn et al. 2007; Hughes et al. 2013; Hughes et al. 2009). Hence, dispersal overland between catchments may be possible but the species may still be range limited within the drainage network.

In this paper, we show that an endangered freshwater crayfish contains low genetic diversity and is extremely range restricted between three upland regions. For this study we

developed six novel microsatellite loci for E. hystricosus. We then incorporated this data with new and existing haplotype data for a single mitochondrial locus: cytochrome oxidase subunit I (COI). Our aims were to use these genetic markers to estimate population structure and effective population size within and between three upland locations. As a number of studies of co-located taxa have shown genetic structure in the COI gene in this region, we expected to see isolation between populations from the different uplands to support the previously suggested hypothesis that intervening lowland limits connectivity in this species (Ponniah and Hughes 2006). Low levels of migration between uplands, coupled with low Ne and low genetic diversity, would indicate that that the species may be at risk from the effects of genetic drift and inbreeding depression. However, since we assume that they have the same capacity for terrestrial dispersal as is found with the giant spiny crayfish Euastacus sulcatus (Bone et al. 2014) we expected to see gene flow among streams in each upland area. Our study will provide information beneficial to conservation management plans, specifically conservation groups situated in the Mary River catchment that have expressed an interest in tailoring restoration plans to the protection of the species. Further, we anticipate that our results will provide important information on population connectivity and levels of genetic diversity for upland specialists and headwater specialists.

5.3 Methods

5.3.1 Study area and sampling technique The study took place in an area of ~875 km2 in SE Queensland that contains the only known geographic range of this species (Morgan 1988; Ponniah and Hughes 2006; Shull et al. 2005). The upland areas sampled in this study are the high elevation areas: Conondale National Park (NP), Bellthorpe NP, and the combined Kondalilla NP and Maleny region (Fig. 5.1). As we only sampled one site at Kondalilla NP we combined the data collected here with the Maleny data for subsequent analysis. This decision was taken as there are a number of high elevation streams connecting these sites and the only two low elevation sites (277-328 m) were at Bridge Creek (Ck) and Skene Ck, Maleny. These creeks have connections to Kondalilla NP. The three uplands were comparable with ‘population’ groupings found for co- located headwater species, including E. urospinosus (Hurry et al. 2015).

Figure 5.1: Map of sample sites for Euastacus hystricosus in SE Queensland, Australia. Higher elevation areas are shaded in black. Red open circles indicate sites where the collections were made for the microsatellite analysis. Small red diamonds are sites where collections were made for mtDNA analysis. The thin blue lines are the stream connections and the thick blue lines are catchment boundaries.

Thirty creeks were surveyed throughout the study area, at elevations between 200 m and 750 m. Sampling was restricted to areas of less than 400 m2 in each creek. With support from the landholders in Maleny we were able to extend the known habitat of the species to include non-reserve areas, allowing a more comprehensive analysis of population structure. Sites that were surveyed, but where no E. hystricosus were caught, included sites to the east of Conondale NP and to the west of the Maleny region/Kondalilla NP. Due to time constraints, we were not able to surveys creeks to the east or north of Maleny region/Kondalilla NP, or the west and south of Conondale. However, to our knowledge, the species has not been recorded from within these areas. Samples were collected in baited box and opera traps, or with dip nets. After capture, a single right hind leg was removed and immediately placed on ice for later DNA analysis. By removing the same leg on each animal

we were able to eliminate recaptures. The sex of the crayfish was determined by observing visible differences in male and female external anatomy and we measured the occipital carapace length (OCL; mm). The maturity of the crayfish was also determined from OCL (> 45 mm males, > 60 mm females) and signs of maturity in the gonopores (Morgan 1988). The crayfish were released immediately. Each site was sampled for a period of three to 10 days. The EEO and the AAO were calculated using the 20 locations from this study and a further 22 locations listed on The Living Atlas of Australia (ALA 2015), the calculations were made using the ALA online tool (AOO: 0.02 degree grid; EOO: minimum convex hull).

5.3.2 DNA extraction, amplification, and microsatellite development 5.3.2.1 Microsatellites Genomic DNA was extracted from the leg tissue of 244 E. hystricosus using a salting out extraction method (Aljanabi and Martinez 1997). For the development of microsatellite genetic primers, a single individual (collected at Bellthorpe NP) was processed using Ion Torrent second generation sequencing technology (ION PGMTMsequencer with 318 chip, LifeTechnologies Corporation) following the manufacturers protocols. A total of 422,624 sequence reads of a length of 5 to 392 bp were obtained with a mean read length of 142 bp. After further analysis with the QDD bioinformatics pipeline v 2.1 (Meglécz et al. 2010), a total of 24,584 sequence microsatellite motifs were identified, of which 3328 were unique. Using software Primer3 (Rozen and Skaletsky 2000), we identified 1229 potential optimal priming sets and category ‘A’ primers, as per the QDD manual. We then ordered 290 primers to test if they were suitable for analysis of genetic structure. This was based on consistent amplification, good reproducibility and the presence of multiple alleles.

These loci were screened for polymorphism using template DNA from 16 individuals; eight from Bundaroo Ck in Conondale NP and eight from Kondalilla NP. The samples were amplified in 10 μL PCR reactions containing 0.5 μL of template DNA, 0.28 mM reverse primer, 0.07 mM forward primer, 0.28 mM tagged forward primer with the addition of one of four fluorophores tags (FAM, NED, VIC, PET) (Real et al. 2009), 0.2 mM dNTPs (Astral

Scientific), 0.6 μL of 25 mM MgCl2 (Astral Scientific), 1 μL of 10× buffer (Sigma-Aldrich) and 0.5 units of RED taq polymerase (Sigma-Aldrich). The PCR thermocycler conditions were as follows; 94 °C for 5 min, followed by 35 cycles of 94 °C for 30 s, annealing temperature for

30 s (see Table 1) and 72 °C for 30 s; a further 20 min at 72 °C and a final hold at 4 °C. Fluorescently labelled tagged PCR products were run on an ABI 3130 Genetic Analyser (Applied Biosystems) following the manufacturer’s recommendations. Product lengths were scored manually and assessed for polymorphisms using GENEMAPPER version 3.1 (Applied Biosystems). After initial tests, the annealing temperatures were varied for some loci in order to improve amplification (Table 5.1). From a total of 290 potential priming sites, 90 were rejected as they failed to amplify or showed complex amplification; 194 were rejected as monomorphic loci. The number of alleles in the final six microsatellite loci ranged from two to five (Table 5.1). The six loci were examined for null alleles and scoring errors using MICROCHECKER (Van Oosterhout et al. 2004).

Table 5.1: Characterisation and PCR conditions microsatellite regions for the freshwater crayfish Euastacus hystricosus Locus Primer sequences (5’–3’) Repeat # Allele size Annealing Number of ID Forward motif alleles temp °C cycles Reverse EH113 GCTTAGCGCTACGGAGGTCA 134, 136, 61 30 TG 3 AATTGCACGGTGGAAACCAG 138 EH134 CACGGGTTTAGCGCATGTTT 63 40 ATT 2 195, 201 CCATTCTCCAGGCGCAGTAT EH140 CTGGTCCCAGGATCAACTGC 170, 176, 61 30 GAC 3 GGTGTGGTTCCTCGCTTCTC 179 EH144 CTCGCTGGAAATCCCACACT 63 30 CA 2 121, 123 CTTCGACAACCTTGCCACCT EH156 TGAAGCTGGTCGTTGACGTG 130, 133, 63 40 CCCAGGCGCTATTATGACCC TTA 5 136, 139, 142 EH165 GCATCAAGGCACCACTACCA 61 30 CT 2 139, 141 CCACCAAACGTCCCAGTAGG

5.3.2.2 Mitochondrial DNA sequencing For the mtDNA analysis of the COI region, we extracted DNA (as described above) for two individuals from Summer Ck, Conondale NP, one individual from Kondalilla NP and eight individuals from the Maleny region (See Hurry et al. (2015) for PCR conditions and sequencing information). The mtDNA sequences were visually assessed for the occurrence of nuclear mitochondrial pseudo genes (numts) and additional stop codons using techniques described in Bensasson et al. (2001) and Song et al. (2008), and none were found. We then

combined these data with sequence data obtained by Ponniah and Hughes (2006) and Shull et al. (2005) (GenBank Accession numbers: AY324351, AY324350, DQ006346, DQ006347, DQ006348, DQ006400; see Table 5.2). The total dataset represented 54 individuals of E. hystricosus. The nucleotide sequences for COI were aligned and edited with SEQUENCHER v4.9 (Gene Codes Corporation).

5.3.3 Data analyses 5.3.3.1 Microsatellites As samples of closely related individuals can bias allele frequency estimates (Allendorf and Phelps 1981) potential full-sibs were identified in software COLONY (Jones and Wang 2010) and five individuals were removed from the final analyses, leaving 239 individuals. We then used GENEPOP v4.3 (Rousset 2008) to calculate the linkage disequilibrium estimates between all pairs of loci of the 13 sample sites. For each site, the inbreeding coefficient (FIS), which describes how heterozygote frequencies deviate from expected proportions under Hardy–Weinberg (HWE), was also determined in GENEPOP; the significance of these values was calculated with a simulated Fisher’s Exact Test (Upton 1992). A Bonferroni correction was then applied to adjust the critical value for multiple tests. To estimate genetic diversity, we used Arlequin v3.5.1.2 (Excoffier and Lischer 2010) to calculate the mean number of alleles per locus (A) and, observed and expected heterozygosity (HO and HE).

5.3.3.2 Mitochondrial DNA DNAsp (Librado and Rozas 2009) was used to assign sequences into haplotypes. In DNAsp we also determined the haplotype frequencies, and haplotype and nucleotide diversities for each creek. We then performed Tajima’s D and Fu's FS tests for selective neutrality (Fu 1997; Tajima 1989), as significant values can indicate recent demographic change and non- neutrality.

5.3.4 Population genetic structure In order to estimate the number of population groups represented by our microsatellite data, we used two Bayesian clustering analysis programs: STRUCTURE V2.3.4 (Pritchard et al. 2000) and TESS (Chen et al. 2007; Durand et al. 2009). TESS is different to STRUCTURE as it incorporates geographic data into the analysis which should make it more sensitive to subtle geographic structure (Chen et al. 2007; François et al. 2006). In order to identify which parameters to use in the final analysis of STRUCTURE, we first ran the analysis seven times with differing values of MCMC and burnin. Once the time series plots approached equilibrium we selected final parameters with iterations of 75,000 burnin and 250,000

MCMC. The analysis was then run with these parameters a further five times with Kmax 2–13 (13 being the number of sample sites used). The algorithm was run 30 times for each K. In each case, the LOCPRIOR MODEL with admixture was selected. The LOCPRIOR MODEL was selected as the loci/allele number was low and also because the data were collected at a set of discrete locations, it assumes that samples from the same sample site come from the same population (Hubisz et al. 2009). To calculate the value ΔK, we then used STRUCTURE Harvester (Earl and vonHoldt 2012) which employs the Evanno method (Evanno et al. 2005). In TESS: two admixture models were used to calculate the likelihood under different values for Kmax 2–13; these were the conditional auto-regressive (CAR) and Bayesian spatial model (BYM). The algorithm was run 50 times for each K. Each admixture model was run five times with the final parameters selected as being burnin of 50,000 iterations followed by 200,000 iterations of sampling. The results of the CAR and the BYM model did not differ; therefore, only the results for the BYM model are given. The lowest 10% of Deviance Information Criterion (DIC) values were selected. The value(s) of K that best explained the structure in the data was determined by plotting the deviance information criterion against Kmax to find the point where the deviance information criterion stabilised. For both STRUCTURE and

TESS, the multiple runs for the selected Kmax values were combined using CLUMPP version 1.1.2 (Jakobsson and Rosenberg 2007) and plotted graphically using software R (Team 2015).

Table 5.2: Sample locations in SE Queensland of the crayfish Euastacus hystricosus

Creek name Catchment Upland Longitude Latitude Elevation (m) Total mtDNA Microsatellites Collected by: Year crayfish (COI) (individuals) collected (individuals) Branch Creek Brisbane Bellthorpe NP 152.69333 -26.85825 505 114 0 36 Hurry et al. 2012 Branch Creek Brisbane Bellthorpe NP 152.69310 -26.86110 522 6 6 0 Ponniah and Hughes 2004 1999 Branch Creek east Brisbane Bellthorpe NP 152.69944 -26.86111 498 10 0 5 Hurry et al. 2012 East Kilcoy Creek Brisbane Conondale NP 152.57240 -26.75010 584 5 5 0 Ponniah and Hughes 2004 1999 Kilcoy Creek Brisbane Conondale NP 152.54648 -26.74837 688 67 0 25 Hurry et al. 2012 Rum crossing Brisbane Conondale NP 152.54060 -26.75940 551 4 4 0 Ponniah and Hughes 2004 1999 Stony Creek Brisbane Bellthorpe NP 152.73390 -26.86110 352 8 8 0 Ponniah and Hughes 2004 1999 Stony Creek Brisbane Bellthorpe NP 152.72040 -26.85788 511 98 0 28 Hurry et al. 2012 Arley Farm, ObiObi Creek Mary Maleny 152.82913 -26.76983 431 9 2 14 Hurry et al. 2013 Booloumba Ck Mary Maleny 152.62060 -26.71220 571 6 6 0 Ponniah and Hughes 2004 1999 Booloumba Ck east Mary Maleny 152.63400 -26.68880 546 14 1 3 Hurry et al. 2012 Bridge Creek Mary Maleny 152.84656 -26.73540 277 23 1 14 Hurry et al. 2013 Bundoora Ck Mary Maleny 152.61444 -26.68889 597 77 6 35 Hurry et al.; 2012; Ponniah and Hughes 2004 1999 Dairy, Trib. ObiObi Creek Mary Maleny 152.79575 -26.77278 475 27 2 8 Hurry et al. 2013 Kondalilla Falls Mary Kondalilla NP 152.87038 -26.67070 403 61 8 26 Hurry et al.; 2013; Ponniah and Hughes 2004; 1999; 1995 Shull et al. 2005 Langley Creek, Trib. Mary Maleny 152.88064 -26.76323 396 5 1 7 Hurry et al. 2013 ObiObi Creek Lawley Creek Mary Maleny 152.85254 -26.75001 409 54 1 29 Hurry et al. 2013 Skene Ck, Maleny Mary Maleny 152.87960 -26.73430 328 1 1 0 Shull et al. 2005 1995 Summer Creek Mary Conondale NP 152.62147 -26.64833 559 4 2 0 Hurry et al. 2012 Sunday Creek Mary Conondale NP 152.53050 -26.73367 613 20 0 14 Hurry et al. 2012

To examine genetic diversity within a hierarchical structure for both datasets, hierarchical AMOVAs were performed in Arlequin v3.5.1.2 (Excoffier and Lischer 2010). As the microsatellite dataset contained missing data we followed the Arlequin documentation and ran a locus-by-locus AMOVA. The locus by locus AMOVA is able to estimate F-statistics more accurately by adjusting sample sizes at each locus. The statistical significance of the F- statistics was determined with 5000 permutations. AMOVA analysis was performed in two ways. For the first, the highest level was upland: Conondale NP, Bellthorpe NP or Maleny region/Kondalilla NP. For the second AMOVA, catchment was the highest level of the hierarchy (Mary River or Brisbane River catchments). The AMOVAs will provide evidence on how genetic variation is partitioned this species, strong structure among uplands will confirm that the uplands harbour distinct populations.

5.3.5 Effective population size and migration rates

Estimates of effective population size (Ne) were determined from the microsatellite data.

Again, the three upland areas were used for this analysis. Estimates of Ne were determined and compared using three analysis methods. First, in Software COLONY (Jones and Wang 2010), the full dataset of 239 individuals was used. Second, for the Bayesian approach in software ONeSAMP (Tallmon et al. 2008), we first discarded individuals with multiple missing data as required by the software. We then we ran two analyses, one that included all cohorts and one that contained only adults. Finally, after initial runs with priors between zero and 1000, final priors of Ne of between one and 100 were selected. The strength of ONeSAMP is that it uses a number of summary statistics and approximate Bayesian computation to estimate Ne from a single sample of microsatellite data (Beebee 2009), this approach also produces narrower confidence limits than the other two methods mentioned here. The third method used the software package NeEstimator V2, which uses a bias- corrected version of the method based on the linkage disequilibrium method (Do et al. 2014). In this program, the random mating model was selected. The genetic data for juveniles was omitted as the analysis estimates Ne from a single cohort. NeEstimator employs the use of the harmonic mean sample size which is able to capture the equivalent overall sample size, which may be effective in correcting for missing data (Peel et al. 2013). For the harmonic mean the sample size is taken as the weighted mean sample size across loci whose weights are based on the number of alleles (Do et al. 2014). 91

To test for recent migration, BAYESASS 1.3 (Wilson and Rannala 2003) was used to estimate the direction of migration that may have occurred in the last two to three generations between all pairs of populations, in addition to calculating the rates of dispersal. We initially ran BAYESASS for the three assigned uplands. The analysis was run again to determine migration between creeks within uplands. The analysis was run ten times using different random number seeds. To ensure that convergence was attained the MCMC was run for 10,000,000 iterations, with 2,000,000 runs as burnin, the MCMC chain was sampled every 2000 steps. Varying the prior rate did not affect the results; therefore, for the final five runs all other settings were kept at default.

5.4 Results

5.4.1 Distribution and morphological assessment In this study a total of 565 crayfish were trapped at 13 discrete locations, from a total of 30 surveyed locations. When we included these locations with data from previous studies by Shull et al. (2005b) and Ponniah and Hughes (2006), the crayfish have now been caught at 16 locations above 400 m (Fig. 5.1, Table 5.2), three above 328 m and one at 277 m (Bridge Ck, Maleny), which is the lowest ever elevation recorded for this species (Coughran 2008; McCormack 2012). A further 22 locations are listed on the ALA website but elevation data were not available for these sites (ALA 2015). For the conservation rating this corresponds to an area of occupancy of 104 km2 or an extent of occurrence of 986 km2 (ALA 2015). In total, we collected 225 females, 261 males, 78 of unknown sex and one hermaphrodite. Of the total collected, 253 were recorded as being mature. No mature individuals were caught in Branch Ck east, Bellthorpe NP. The mean maximum OCL was 96.7 mm but at seven sample sites the maximum OCL was less than 85 mm. The longest OCL was 156 mm, which was 17 mm larger than previously recorded (Coughran 2008). Three individuals of this approximate size were caught in the same 5 m section of creek. However, due to the conservation status of this crayfish and the threat of poaching to large crayfish we have decided not to include exact geographic locations for these three individuals.

5.4.2 Genetic differentiation and population structure The 54 E. hystricosus COI mtDNA sequences were aligned for 600 bp, including the 11 individuals sequenced as part of this study. Just four unique haplotypes were identified from 15 separate locations. Haplotypes did not differ from those previously recorded (Ponniah and Hughes 2006; Shull et al. 2005). The target fragment contained no gaps and was variable at four sites (0.6%) of which four were parsimony informative. Our mitochondrial data did not deviate significantly from predictions of neutrality (Tajima’s D: 1.68 p> 0.1; Fu’s F: 1.4 p> 0.1). Within both Maleny region/Kondalilla NP and Conondale NP, haplotype gene and nucleotide diversity were 0.0 and 0.0 as each site was fixed for one haplotype. Within Bellthorpe NP, the haplotype gene and nucleotide diversity were 0.5 and 0.0008 as there were just two haplotypes each fixed at the creek level (Fig. 5.2; also shown in Ponniah and Hughes (2006).

Figure 5.2: Spatial genetic structure of populations of Euastacus hystricosus. Closed coloured circles are sites where collections were made for mtDNA analysis; these colours represent the four haplotypes (blue HB; yellow HC; green HA; pink HD), the numbers within circles indicate the number of individuals sequenced at each site. Note: where coloured circles have a red border these sites were also used in the microsatellite analyses. Red open circles indicate sites where the collections were made for the microsatellite analysis. The yellow open rectangles are K=3 cluster assignment of TESS and the red open rectangles are K=3 cluster assignment by the program STRUCTURE (see also Fig. 5.3). The haplotype tree in the lower left of the figure shows the parsimonious network for the COI mitochondrial sequence and is similar to that found in Ponniah and Hughes (2006).

A total of six novel polymorphic microsatellite loci were discovered. These loci showed low genetic variation, with an average of 2.8 alleles per locus, ranging from 2 alleles at EH134,

EH144 and EH165 and 5 alleles at EH156 (Table 5.1). The average Ho of these loci at the 13 sample sites was between 0.26 and 0.55 (mean = 0.33) (Table 5.3). There was no significant deviation from HWE for any of the sample sites with estimates of FIS favouring heterozygote

deficit (Table 5.3). All the microsatellite loci were in linkage equilibrium (data not shown). Also, there was no evidence of null alleles at any locus. Table 5.3: Genetic diversity indices per sample site of the crayfish Euastacus hystricosus

Population Upland area Catchment N A H E H O F IS p HW Booloumba Ck Conondale NP Mary 3 1.5 0.16 0.30 0.3333 - Bundaroo Ck Conondale NP Mary 35 2.0 0.25 0.23 0.0811 n.s. Sunday Ck Conondale NP Mary 14 2.3 0.38 0.27 0.2699 n.s. East Kilcoy Ck Conondale NP Brisbane 26 2.0 0.31 0.27 0.1213 n.s. Branch Ck Bellthorpe NP Brisbane 36 3.2 0.39 0.37 0.0523 n.s. Branch Ck east Bellthorpe NP Brisbane 1 1 - - - - Stony Ck Bellthorpe NP Brisbane 28 2.2 0.42 0.45 0.0613 n.s. Dairy; trib. ObiObi Ck Maleny Mary 8 2.0 0.53 0.55 0.0813 n.s. Arley farm; ObiObi Ck Maleny Mary 14 1.8 0.45 0.41 0.1078 n.s. Bridge Ck Maleny Mary 14 2.0 0.38 0.34 0.1278 n.s. Lawley Ck Maleny Mary 29 1.8 0.41 0.33 0.2090 n.s. Langley Ck Maleny Mary 7 2.0 0.50 0.26 0.5030 n.s. Kondalilla Falls Kondalilla Mary 25 2.5 0.31 0.26 0.1770 n.s. All individuals 240 2.8 0.40 0.27 0.2840 n.s. N sample size, A average number of alleles/locus, HE expected and HO observed heterozygosity, FIS inbreeding coefficient and pHW probability of deviations from expected HWE after sequential Bonferroni adjustments (10 simultaneous tests per sample site). n.s=not significant

The STRUCTURE analysis inferred three genetic clusters (K = 3), which represented the three upland areas: Conondale NP, Bellthorpe NP and, Maleny region/Kondalilla NP as the third group (Fig. 5.2 and 5.3). The DIC values derived from TESS were plotted against different values of K and indicated that K was 5; however, after viewing the assignment plots and considering the distance between sample sites, K = 3 was deemed to be the most appropriate (Fig. 5.2 and 5.3). For TESS K=3 the genetic clusters were the same as for STRUCTURE.

Figure 5.3: Individual cluster membership assignment for Euastacus hystricosus by a) STRUCTURE (K=3) and b) TESS (K=3 and K=5). The labels on the top correspond to 12 of the

original sample sites going from East to West. Sunday Ck, Kilcoy Ck, Bundaroo Ck, Booloumba Ck, Branch Ck, Stony Ck, Dairy, Arley farm, Bridge Ck, Lawley Ck, Langley Ck, Kondalilla Falls.

When catchment was selected as the highest hierarchical level for the AMOVA there was a low but significant FCT value of 0.07 for the microsatellite data. The value of ΦCT in the mtDNA was not significant which indicates that the grouping by catchment does not explain mtDNA genetic variation for this species (Table 5.4). The hierarchal AMOVAs which assigned upland at the highest level showed clear differentiation between the three uplands with significant FCT values of 0.12 for the microsatellites and 0.67 for mtDNA (Table 5.4). In all data sets the partition of variation was highest at the upland level indicating that upland most clearly explains the partitioning of genetic variation in this species.

Table 5.4: Analysis of molecular variance (AMOVA) showing the partitioning of genetic variation for Euastacus hystricosus from SE Queensland, Australia, as identified by Arlequin v3.5.1.2 (Upland areas: Conondale NP, Bellthorpe NP, Maleny region/Kondalilla NP; Catchments: Mary and Brisbane River). a) microsatellite data set; b) mitochondrial sequence DNA (COI) Variation-MICROSATELLITES Sum of Variance Percentage *significant Squares components variation p<0.05

Among catchments 15.51 0.05 6.63 *FCT : 0.07

Among creeks 42.06 0.11 14.88 *FSC : 0.16 within catchments Within creeks 234.50 0.57 78.50 *FST : 0.22

Among uplands 32.14 0.09 12.17 *FCT : 0.12 Among creeks 25.43 0.07 9.39 *FSC : 0.11 within uplands

Within creeks 234.50 0.57 78.46 *FST : 0.22 Variation-mtDNA df Sum of Variance Percentage *significant Squares components variation p<0.05

Among catchments 1 6.03 0.05 5.52 ΦCT : 0.06

Among creeks 13 36.81 0.84 94.48 *ΦSC : 1.00 within catchments

Within creeks 40 0.00 0.00 0.00 *ΦST : 1.00

Among uplands 2 29.50 0.70 67.14 *ΦCT : 0.67

Among creeks 12 13.33 0.34 32.86 *ΦSC : 1.00 within uplands

Within creeks 40 0.00 0.00 0.00 *ΦST : 1.00

Table 5.5: Ne estimates for three populations (uplands) of Euastacus hystricosus, using three analysis methods Analysis method Upland Cohort N Mean Lower Upper 95%CL 95%CL Combined Uplands All 198 16.42 11.52 26.73 Conondale All 65 1.34 1.09 1.94 Adults 49 10.91 8.58 18.54 ONeSAMP Bellthorpe All 58 22.95 16.76 38.09

Adults 47 8.54 5.58 16.63 Maleny/Kondalilla All 75 12.42 8.93 21.52 Adults 71 9.60 7.07 14.40 Combined Uplands Adults 207 22 13 39 COLONY Conondale Adults 58 10 5 23 Full likelihood method Bellthorpe Adults 53 16 9 34 Maleny/Kondalilla Adults 96 17 9 34 Across the harmonic means NeEstimator Combined Uplands Adults 239 19.70 – 28.10 -Linkage Conondale All 78 10.40 – 12.30 disequilibrium Bellthorpe All 65 176.70 – 221.80 Maleny/Kondalilla All 96 11.1 – 77

5.4.3 Effective population size and migration rates

The upper limits of Ne for the three upland groups, as determined by three independent analyses, were consistently low (< 39), except for the NeEstimator analysis for Bellthorpe NP which was 221.8 (Table 5.5). Contemporary migration rates as assessed by BAYESASS were similar between the pairs of upland areas. Migration rates were very low with mean estimated proportion of immigrants from 0.006 to 0.024 across all pair wise comparisons (Table 5.6). The total proportion of immigrants, expressed as a percentage, was between one and four percent between all upland areas. When we determined contemporary migration between streams within each upland region we found a slightly higher proportion of immigrants of between 0.007 and 0.099 (Table 5.7). The majority of the migration was in the Conondale upland, from Bundoora Ck into Kilcoy Ck (10 %) and from Kilcoy Ck into Sunday Ck (9 %). The proportions of migrants between creeks within the Maleny region/Kondalilla NP were consistently low, all below 2 %, except for Lawley Ck into Kondalilla (7 %). Unfortunately, migration rates between several sites were not estimated as the final values fell below the suggested simulated parameter values for uninformative data in BAYESASS; these were Stony Ck, Branch Ck, Booloumba Ck, Langley Ck and The Dairy (ObiObi Ck). 98

Table 5.6: Contemporary migration estimates (last two to three generations) of Euastacus hystricosus, between three upland areas in SE Queensland Australia. Shown as the expected proportions of individuals in population i that was derived from population j. As estimated in BAYESASS. Population j Population i Conondale NP Bellthorpe NP Maleny/Kondalilla NP Prop. misassigned Conondale NP 0.959 0.018 0.023 0.041 Bellthorpe NP 0.007 0.987 0.006 0.013 Maleny/Kondalilla NP 0.013 0.011 0.976 0.023

Table 5.7: Contemporary migration estimates (last two to three generations) of Euastacus hystricosus, between streams within two upland areas in SE Queensland Australia. Shown as the expected proportions of individuals in population i that were derived from population j. As estimated in BAYESASS. Conondale NP Population j Population i Bundoora Ck Kilcoy Ck Sunday Ck Prop. misassigned Bundoora Ck 0.960 0.023 0.007 0.030 Kilcoy Ck 0.099 0.870 0.013 0.112 Sunday Ck 0.043 0.093 0.840 0.136

Maleny/Kondalilla NP Population j Population i Lawley Ck Kondalilla Falls Bridge Ck Arley Farm Prop. misassigned Lawley Ck 0.950 0.012 0.011 0.013 0.036 Kondalilla Falls 0.067 0.920 0.009 0.008 0.084 Bridge Ck 0.009 0.009 0.950 0.016 0.034 Arley Farm 0.020 0.012 0.035 0.920 0.067

5.5 Discussion

As part of this study we developed six novel microsatellite loci which we combined with mitochondrial sequence data in order to define population structure within and among the three upland areas of Conondale NP, Bellthorpe NP and Maleny region/Kondalilla NP.

Further, we demonstrated that the levels of genetic variation and Ne in these three populations of the Conondale spiny crayfish were at extremely low levels. There was no evidence of contemporary migration between uplands, which supports the assumption that fragmentation between these areas is a significant barrier to dispersal, despite the potential for terrestrial movement. There was also limited gene flow within the uplands, with some fine scale genetic structure between the creeks within the Conondale NP and Maleny region/Kondalilla NP.

Very few studies have specifically compared population genetic structure between uplands at this level of detail. Our results clearly show that genetic variation was partitioned by upland rather than by catchment, and that there are three genetically distinct populations of E. hystricosus. The three uplands broadly match population groupings found for the co- located Euastacus urospinosus (Hurry et al. 2015) indicating that Kondalilla NP and Maleny may be joined at several high elevation points. The values of contemporary migration that were calculated between the three uplands were negligible, which indicates significant fragmentation between these uplands. Further, this fragmentation may be historical, as there was no evidence of genetic diversity in the mitochondrial fragment in two of the uplands and extremely low diversity for third. Such low haplotype diversity is unusual for taxa that have the potential for dispersal and supports our theory that there are barriers to dispersal between the uplands.

When we compare our results to that of co-located freshwater taxa we see that there have been similar findings of long term fragmentation in the region. For instance, mtDNA analysis of the common aytid shrimp Paratya australiensis suggested that populations of the Conondale and Bellthorpe uplands have been separated for 2-3 million years (Hurwood et al. 2003). However, a subsequent study found that P. australiensis have likely diverged outside the Conondale region (Cook et al. 2006). A population genetic study of the dwarf spiny crayfish E. urospinosus, which is only found in our study region, also determined that Conondale NP populations had been separated from Bellthorpe NP and Maleny for ~2.1 mya (Hurry et al. 2015), and with a further separation between Bellthorpe NP and Maleny dated at ~700,000 years ago. The two taxa we mentioned above are found at lower elevations than E. hystricosus, but they have less capacity for overland dispersal and show population structure at the creek level (Cook et al. 2007; Hughes et al. 1995; Hurry et al. 2015). As E. hystricosus are mostly found above 400 m, it is likely that their population structure is a result of their reliance on environmental variables specific to upland elevations, which may be event related rather than them lacking the ability to disperse.

The lack of migration between uplands based on the microsatellite data, coupled with shallow but fixed differences for mtDNA, may indicate that the capacity of this species to

100 shift its range in response to a warming climate is seriously limited. In eastern Australia, long-term fragmentation between mountaintops accounts for the large number of different Euastacus species (Ponniah and Hughes 2004). It is not unusual for taxa that have been restricted to mountaintops for shorter periods, than those found for E. hystricosus, of time to exhibit low genetic diversity due to bottlenecks and inbreeding. For instance, the mountain gorillas of central Africa, which have most likely been separated for 20,000 years, exhibit low Ne (273) and show a high occurrence of inbreeding (Xue et al. 2015). Also, less than a century ago the chipmunk Tamias alpines from Yosemite NP, USA became range restricted on mountaintops due to increased temperatures at lower altitudes and its genetic diversity has significantly reduced during that timeframe (Moritz et al. 2008; Rubidge et al. 2012).

It has been suggested previously that Euastacus from Queensland lack the ability to move to lower elevations due to thermal intolerance. For instance, the spiny crayfish E. robertsi has been shown to have extremely low genetic diversity and to be highly fragmented between three mountaintops in north eastern Australia (Hurry et al. 2014). Bone et al. (2014) have shown that E. sulcatus from SE Queensland exhibit a breakdown of physiological function after water temperatures reach 21 °C, with the majority of individuals dying within 48 hours after being exposed to 27 °C. For the most part, our populations of E. hystricosus were found at elevations above 400 m. The exceptions were sites in Maleny, the lowest being Skene Ck at 328 m and Bridge Ck at 277 m. At Skene Ck only one individual was captured as part of an earlier study (Shull et al. 2005). At Bridge Ck, we were able to catch 16 individuals over a seven-day period. Many recaptures at this site point to a small but viable population; therefore, this species may have some capacity to survive at lower elevations if stream conditions are right. Conversely, due to the extreme genetic structuring that we observed between the uplands, we can deduce that migration through lower elevations may be rare or absent altogether.

The results indicated that historical dispersal has occurred between streams within uplands. It is likely that low levels of contemporary migration may still be occurring between streams. As haplotypes are shared across catchment boundaries, E. hystricosus shows a pattern of

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genetic structuring that conforms to the headwater model of genetic structure, just as is the case for the co-located E. urospinosus (Hurry et al. 2015). Ponniah and Hughes (2006) found there were fixed differences in the COI mtDNA in E. hystricosus between the two streams in Bellthorpe NP. There was no differentiation between these creeks based upon the assignment analysis of microsatellite data in our study. However, as we were unable to calculate contemporary migration rates between these sites it is possible there was not enough resolution in the microsatellite data to detect structure between creeks in Bellthorpe NP. Further, the observed variation between the two genetic data sets may stem from male mediated gene flow rather than a lack of gene flow, as mitochondrial genes are maternally inherited and only reflect the female lineage (Hurst and Jiggins 2005). If this is the case, given the extremely low Ne that was detected within uplands, only a small amount of gene flow would be required to overcome differentiation (Slatkin 1987). Mitochondrial studies of the smaller congener E. urospinosus found evidence of historical gene flow between several of the streams in Bellthorpe (Hurry et al. 2015). However, E. urospinosus are also found at lower elevations and may have the ability to traverse between creeks in Bellthorpe at places not currently accessible to E. hystricosus. Contemporary dispersal of E. hystricosus within the upland creeks was evident in the Conondale NP, with ~9 % of the individuals in Kilcoy Ck being derived from Bundoora Ck, and 9 % in Sunday Ck being from Kilcoy Ck. Nevertheless, there was also very little evidence of contemporary migration in the Maleny region/Kondalilla NP. In general, these findings suggest that there is some genetic structuring within uplands. The lack of dispersal between creeks was surprising as observations of E. hystricosus indicate they are highly visible and extremely active in the creeks.

Through this study, we have shown that the three uplands, Conondale NP, Bellthorpe NP and Maleny region/Kondalilla NP, should be considered as highly fragmented populations.

The three uplands contain genetically distinct populations that have extremely low Ne and low genetic diversity. Miller et al. (2014) studied the south Australian Euastacus bispinosus which has a wider distribution than our species. They suggested that, as the South Australian populations had no genetic diversity, individuals should be translocated from Victorian populations to boost genetic diversity. Conversely, for E. hystricosus, we suggest

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that the uplands be treated as separate populations and managed accordingly to avoid potential outbreeding depression of these populations have been shown to be historically isolated.

Proper management of currently inhabited creeks may help to maintain the genetic integrity within these uplands. As the majority of Maleny sites do not fall within reserve land, it is essential that conservation management for this region seeks to protect headwaters and riparian vegetation in order to retain viable habitat and maintain connectivity between subpopulations. The national parks may also benefit from applied conservation measures. Although Conondale and Bellthorpe have been classified as national parks since 1977, there is evidence that endangered aquatic taxa have disappeared since the creation of these reserves; for example, the southern gastric brooding frog Rheobatrachus silus (Ingram and McDonald 1993) and Fleay’s barred frog Mixophyes fleayi (Doak 2005). Further, there is anecdotal evidence that large crayfish are poached in these national parks (Morgan 1988; C. R. Hurry pers. obs.) and the lack of very large individuals may support this. Due to the specific habitat requirements of the species and because the three upland populations are extremely fragmented, we suggest that the AOO is considered as the primary measure for categorising the level of threat in the IUCN Red List. Therefore, this species should be considered on the IUCN Red List for the category of Endangered B2a, with further investigation into the level of habitat decline for parameter b (iii).

5.6 Acknowledgements

We would like to thank the Lake Baroon Catchment Care Group (LBCCG) in Maleny for the very generous funding of this project; and to all your staff, volunteers and land owners for help in every aspect of this project. Thank you also for the additional funding provided by Griffith School of Environment and the Wildlife Protection Preservation Society of Queensland. We would also like to thank Elizabeth James and the Royal Botanic Gardens, Melbourne for providing access to GENEMAPPER and SEQUENCHER. Permits: Department of Environment and Heritage Protection WITK1046911; TWB/40A/2011; General Fisheries Permit 153894.

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Euastacus hystricosus collected at Sunday Creek, Conondales

Photo Hurry C

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Chapter Six: The ectocommensal flatworm Temnosewellia batiola: a suitable proxy to detect the population genetic structure of its endangered host freshwater crayfish

Hurry, Charlotte R.1; Schmidt, Daniel J.1; and Hughes, Jane M.1 1Australian Rivers Institute, Griffith University, Nathan, Qld, Australia

Author Contributions

6.1 Abstract

Parasites have been identified as useful proxy species to infer host genetic structure in cases where it is difficult to obtain enough data from the host. Many ectocommensal species also have similar life history characteristics to parasites; thus, they may also be considered useful in comparative genetic studies. The platyhelminth flatworm Temnosewellia batiola is an ectocommensal of the endangered freshwater crayfish Euastacus hystricosus. In this study we developed seven novel microsatellite markers for T. batiola and determined their population genetic structure, which was then compared to an earlier genetic study of the crayfish hosts. We determined that patterns of population genetic structure were extremely similar, with both datasets showing distinct genetic differentiation among three upland areas. However, a pattern of multilocus homozygosity was observed within and across localities in the flatworm. Further investigation of two localities indicated that there was no pattern of fine scale structure in the infrapopulation. We suggested that a thorough

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investigation of the reproductive strategies in the flatworms is needed if they are to be considered as a proxy species in future studies.

Keywords: Parasite, proxy species, mountaintops, coextinction, parthenogenesis, self- fertilisation

6.2 Introduction

The parasites of endangered species face the same extinction risks as their hosts, yet there is not the same level of care or attention for these taxa. For example, only a single louse species is listed as threatened on the IUCN Red List: Haematopinus oliveri (pygmy hog sucking louse) (IUCN 2015), and there are no fleas, parasitic Platyhelminthes, mites or ticks listed, even though their hosts may be classified as such (Whiteman and Parker 2005). Although these taxa often occur in large numbers upon their hosts their continued existence is largely affected by a drop in host numbers (Dunne and Williams 2009). It would seem that the desire to conserve biodiversity is not enough motivation to protect these taxa and more reasons are needed to research and protect them. Comparative genetics is one area of science that is gaining attention and may give value to the study of parasites and other commensal taxa. It uses known host parasite interactions and shared genetic patterns to infer ‘missing’ genetic structure of the host, by unravelling host parasite dynamics at both the local (population genetic structure) and broad (co-evolutionary patterns) scales (Criscione 2008).

Parasites may be considered as proxy species to infer host genetic structure when it is difficult to obtain enough data from the host (Nieberding and Olivieri 2007), which is especially useful when hosts are rare, endangered or difficult to capture as parasites are often found in much larger numbers than their hosts. Therefore, although it may only be possible to collect a small amount of genetic information for the host, there is the potential to obtain a more accurate and comprehensive genetic signature from the parasites. In order to be considered as good proxies for the inference of host population history and genetic structure, parasites must fit certain criteria. Ideally, they should be host-specific, spend their

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entire life histories on a single host, and be transmitted vertically between hosts and their offspring (Nieberding and Olivieri 2007).

There are a number of studies that have successfully used parasites as proxy species. In particular, birds and their parasites have been well studied. For example, Kempf et al. (2009) found, when using microsatellite markers that population structure in the seabird tick Ixodes uriae was clearly associated with host-associated divergence in all the surveyed bird colonies. However, they also found that the genetic structure was not as clear when they used mitochondrial DNA (COIII), which implied that the detected structure was contemporary rather than historical. Conversely, genetic congruence is not always evident even when parasites are host-specific. For instance, Gómez‐Díaz et al. (2007) found that genetic structure was weak for three host-specific lice (Halipeurus abnormis, Austromenopon echinatumand and Saemundssonia peusi) even though strong genetic structure was observed in their hosts (Shearwater birds; Calonectris spp.). In this case, the authors suggested that the disparity was due to horizontal transmission of lice that may have occurred when different populations of shearwater birds were nesting together in wintering areas, or during sea feeding. These results indicate the importance of understanding the level and nature of the shared history between hosts and their parasites when deciding on suitable proxy parasites.

The use of commensal species as proxies is much rarer. Commensal species are those that benefit from the interaction with another organism without causing that organism harm. As such, the life history of the commensal is often closely aligned with that organism (Torres- Pratts et al. 2009). In particular, ecto and endo commensals may be ideal candidates as proxies as they often exhibit a set of traits similar to those of parasites. These two types of commensals live on or within their hosts and have a pattern of over-dispersion just like many parasite species (Bush et al. 1997). Over-dispersion is where some hosts have either no parasites, or very few, and other hosts have many. Further, many of these commensal taxa spend their entire lives on a single host. When the criteria for proxy species are satisfied (see Nieberding and Olivieri 2007) then it is possible to use commensal species in genetic studies that require proxies to infer population structure in the hosts.

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Temnosewellia (Platyhelminthes Temnocephalida: Temnocephalidae) flatworms are free living, which means that they are non parasitic and are capable of motility. As ectocommensals these flatworms gain their advantage from the host, in that they use it for mobility and protection but they cause the host no perceivable harm. However, the close life history that they share with their crustacean hosts often resembles parasitic behaviours. Temnosewellia inhabit a variety of crustacean hosts, including Holthuisana crabs and Macrobrachium shrimp, but they tend to display the strongest host specificity with their Euastacus crayfish hosts (Sewell et al. 2006). The flatworm in this study, Temnosewellia batiola Sewell, Cannon and Blair, 2006, is the ectocommensal of the endangered freshwater crayfish Euastacus hystricosus. Previously, two individual T. batiola were reported from two E. urospinosus collected in Kondalilla National Park (NP). Euastacus urospinosus is a species that is co-located with E. hystricosus (Sewell et al. 2006). However, the evidence that they also occur on this species is questionable as recent DNA sequencing on the museum collection of one of these E. urospinosus (See Fig. 3.3 KC2691) crayfish confirmed it as being E. hystricosus (Hurry et al. 2015). The second E. urospinosus specimen was not available for analysis. The flatworms can be observed in their hundreds on a single host; often in an over- dispersion pattern where they are often concentrated in groups around the claws (C.R. Hurry unpubl. data see Appendix B). Hurry et al. (2014) suggested that the genus Temnosewellia may be suitable as a proxy genus, as they identified congruence in the phylogeographic structure of the crayfish Euastacus robertsi and its host-specific flatworm T. albata in NE QLD. By comparing phylogeographic histories for these species, they determined that there was a deep divergence among three mountaintop populations which occurred sometime during the Pliocene epoch.

For this study we developed seven novel microsatellite loci that were used to investigate the population genetic structure of the flatworm T. batiola, in order to compare patterns of genetic structure with those previously detected for its host freshwater crayfish E. hystricosus in SE Queensland (Chapter five). Due to the shared patterns of genetic structure observed between T. albata and E. robertsi in Chapter four, we expected to see a similar

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shared pattern of genetic differentiation between the three upland populations of T. batiola and E. hystricosus in SE Queensland.

6.3 Methods

6.3.1 Study area and sampling technique The study covers an 875 km2 area of SE Queensland, Australia that contains the three upland populations of the spiny crayfish E. hystricosus, host to T. batiola flatworms. These uplands are the Conondale NP, Bellthorpe NP, and the combined Kondalilla NP/Maleny region (Fig. 6.1). The crayfish hosts live in cool headwater streams in rainforest habitat. The sample localities were between 277 m and 688 m elevation. In this study we use locality to refer to the creek where the crayfish hosts were captured, as the actual sample site for the flatworms is the crayfish hosts (as per methods described by Kuris et al. 1980).The habitat for ectocommensals can be considered in two ways: 1. The area of the host that it inhabits – its infrapopulation or microhabitat (a small specialized habitat within a larger habitat); and 2. The stream in which it is sampled – its suprapopulation or locality.

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Figure 6.1: Map of localities (crosses) for Temnosewellia batiola in SE Queensland, Australia. Higher elevation areas are shaded darker. The thin blue lines are the stream connections and the thick blue lines are catchment boundaries

Eleven localities were used in the flatworm population genetic analysis (Table 6.1), although there were 14 localities in which the crayfish were caught, not many flatworms were collected at three localities. For further details on the sampling design and methods used to capture the crayfish hosts, please refer to Chapter five. Temnosewellia batiola were removed from the carapace of each crayfish by catching the flatworm in the thumb nail, which was the simplest method. Between one and 10 flatworms were removed per crayfish. Flatworms less than 1 mm (visually assessed) were not removed from the host. In cases where multiple flatworms were removed, every effort was made to take a range of different sized flatworms.

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6.3.2 DNA extraction, amplification, and microsatellite development Genomic DNA was extracted using whole specimens of T. batiola by means of a salting-out extraction method (Aljanabi and Martinez 1997). For the development of microsatellite genetic primers, a single individual of T. batiola (collected from Stony Ck in Bellthorpe NP) was processed using the Ion Torrent second generation sequencing technology (ION PGMTMsequencer with 318 chip, LifeTechnologies Corporation) following the manufacturers protocols. A total of 334,985 sequence reads of a length of 13 to 390 bp were obtained with a mean read length of 145 bp. After further analysis with the QDD bioinformatics pipeline v 2.1 (Meglécz et al. 2010), a total of 5,207 sequence microsatellite motifs were identified, of which 837 were unique. Using software Primer3 (Rozen and Skaletsky 2000), we identified 252 potential optimal priming sets and category ‘A’ primers, as per the QDD manual. In order to identify suitable polymorphic microsatellite loci we trialled 42 potential primers. The loci were screened for polymorphism using template DNA from 16 individuals; eight from Bundaroo Ck in Conondale NP, and eight from Branch Ck, Bellthorpe NP. The samples were amplified in 10 μL PCR reactions containing 0.5 μL of template DNA, 0.28 mM reverse primer, 0.07 mM forward primer, 0.28 mM tagged forward primer with the addition of one of four fluorophores tags (FAM, NED, VIC, PET) (Real et al. 2009), 0.2 mM dNTPs (Astral

Scientific), 0.6 μL of 25 mM MgCl2 (Astral Scientific), 1 μL of 10× buffer (Sigma-Aldrich) and 0.5 units of RED taq polymerase (Sigma-Aldrich). The PCR thermocycler conditions are as follows: 94 °C for 5 min, followed by 35 cycles of 94 °C for 30 s, 59 °C for 30 s and 72 °C for 30 s, a further 20 min at 72 °C, and a final hold at 4 °C. Fluorescently labelled tagged PCR products were run on an ABI 3130 Genetic Analyser (Applied Biosystems) following the manufacturer’s recommendations. Product lengths were scored manually and assessed for polymorphisms using GENEMAPPER version 3.1 (Applied Biosystems). After initial tests, the annealing temperatures were varied per locus in order to improve amplification. From a total of 42 potential loci, 33 were rejected as they failed to amplify or showed complex amplification and a further three were monomorphic. We trialled the final nine polymorphic loci with DNA from 15 to 130 individuals from across 11 localities. The final microsatellite dataset was scored more than three times to identify scoring errors. The loci were examined for null alleles and scoring errors using MICROCHECKER (Van Oosterhout et al. 2004). For the subsequent population structure analyses, it was necessary to avoid pseudo-replication from using the genotypes from multiple flatworms per crayfish; therefore, we randomly 111

discarded flatworm genetic data to ensure that just a single flatworm remained from each crayfish hosts. For two localities (Stony Creek (Ck) and Kondalilla Falls; see Fig. 6.1) we had genotyped between one and eight flatworms per crayfish. All of these genotypes were later used in a separate analysis as outlined below.

Table 6.1: Details of sample locations and genetic diversity indices for microsatellite loci for the flatworm Temnosewellia batiola Population Longitude Latitude Upland area Catchment N Ar A HE HO FIS *pHW All individuals 190 1.71 5.14 - - - Sig Bundaroo Ck 152.61444 -26.68889 Conondale NP Mary 16 1.72 4.71 0.70 0.60 0.20 Sig Sunday Ck 152.53050 -26.73367 Conondale NP Mary 3 1.73 3.43 0.63 0.71 0.09 N.S Kilcoy Ck 152.54648 -26.74837 Conondale NP Brisbane 20 1.72 6.29 0.71 0.70 0.04 Sig Branch Ck 152.69333 -26.85825 Bellthorpe NP Brisbane 21 1.72 7.00 0.70 0.60 0.18 Sig Stony Ck 152.72040 -26.85788 Bellthorpe NP Brisbane 31 1.70 5.57 0.66 0.00 1.00 Sig Dairy; trib. 152.79575 -26.77278 Maleny Mary 15 1.75 5.71 0.70 0.56 0.25 Sig ObiObi Ck Arley farm; 152.82913 -26.76983 Maleny Mary 7 1.61 3.86 0.58 0.53 0.17 N.S ObiObi Ck Bridge Ck 152.84656 -26.73540 Maleny Mary 15 1.72 6.00 0.67 0.56 0.20 Sig Lawley Ck 152.85254 -26.75001 Maleny Mary 32 1.65 5.43 0.64 0.46 0.13 Sig Langley Ck 152.88064 -26.76323 Maleny Mary 5 1.75 3.29 0.61 0.57 0.52 Sig Kondalilla Falls 152.87038 -26.67070 Kondalilla NP Mary 25 1.71 5.29 0.70 0.00 1.00 Sig Booloumba Ck 152.63400 -26.68880 Conondale NP Brisbane ------east Branch Ck east 152.69944 -26.86111 Bellthorpe NP Mary ------Summer Ck 152.62147 -26.64833 Conondale NP Mary ------N sample size, A average number of alleles/locus, Ar allelic richness HE expected and HO observed heterozygosity, FIS inbreeding coefficient and pHW probability of deviation from expected HWE after sequential Bonferroni adjustments (10 simultaneous tests per sample site). *Significant at p< 0.05

6.3.3 Data analyses 6.3.3.1 Microsatellites As samples of closely related individuals can bias allele frequency estimates (Allendorf and Phelps 1981), potential clones were identified in software COLONY (Jones and Wang 2010). The analysis was run for the 11 locality dataset and for the two larger datasets of Kondalilla Falls and Stony Ck. COLONY was then also used to detect the level of similarity between flatworm genotypes in Kondalilla Falls and Stony Ck. We used the software GENETIX (Belkhir et al. 2004) to calculate a number of molecular diversity indices at each of the 11 localities. These were the mean number of alleles per locus (A) and observed and expected

heterozygosity (HO and HE). Further, we used GENEPOP v4.3 (Rousset 2008) to determine linkage disequilibrium, which was estimated between all pairs of loci, deviations from genotypic proportions expected under Hardy-Weinberg equilibrium (HWE) and the 112

inbreeding coefficient (FIS), which were tested for significance with a simulated Fisher’s Exact Test (Upton 1992). A Bonferroni correction was then applied to adjust the critical value for multiple tests. Linkage disequilibrium, deviations from HWE and FIS values were also determined for the two larger datasets of Kondalilla Falls and Stony Ck, using the methods mentioned. Due to the high efficiency of microsatellite markers in detecting contemporary structure and the high costs associated with the use of mtDNA, mtDNA was not considered for this study.

Table 6.2: Characterisation of microsatellite regions for the platyhelminth flatworm Temnosewellia batiola; *indicates loci dropped from final analyses due to high error rates. Locus Primer sequences (5’–3’) Repeat # Allele size ID Forward motif alleles Reverse TB109 TCTTTATCAGCTTGCAGATGTCTT TG 10 106, 108, 110, 112, 114, 116,118, 120, GCTCACACCCTTACAGCACA 122, 126 TB112* GCTGAGCACTTTGCACTTCA CA 6 100, 102, 104, 106, 108, 110 TGCTTGTTTATAGCAAGTCACGA TB117 ATCTCATTCCCTCACACCCA CA 7 104, 106, 108, 110, 112, 114, 116 GCCAAACTCAGTCGAAAGGA TB129 GATATCCTGCAATGGCACCT CT 17 118, 120, 122, 126, 128, 130, 132, 134, 136, 138, TCGCTACAACACTGCACAATC 141, 143, 145, 147, 149, 151, 153 TB130 CCGAAGAAATTTATCCGCTC GT 15 107, 111, 113, 115, 117, 119, 121, 123, GGTCAGGCATAGCTACGGTC 125, 127, 129, 131, 133, 135, 137 TB133* CCAATTGCGATAGCGATCAT CTC 8 114, 117, 120, 123, 125, 127, 129, 133 GCAAGAGGCATTGGAGTAGC TB136 TGTGTCAACGAGTTGGGTGT TG 13 116, 120, 122, 124, 126, 128, 130, 132, 134, 136, AACGAGATTGGTTGATTCCG 138, 140, 142 TB187 TCTCATCCTAGGAGATGCGG AC 10 155, 162, 166, 168, 170, 174, 176, 180, 182, TCGATGGTGGAATAGGACAA 186 TB188 CGCAAGATAACGGCAAGATT TTG 16 149, 153, 156, 159, 162, 165, 168, 171, 174, 177, ACAACGCTAGCTGCTGGACT 180, 186, 190, 193, 196

6.3.3.2 Population genetic structure As there were significant deviations from HWE at most of the creeks, the methods used to assign individuals into population groups were later interpreted with caution. In the earlier study of the crayfish hosts, we had used two Bayesian clustering methods (STRUCTURE V2.3.4. (Pritchard et al. 2000) and TESS (Chen et al. 2007; Durand et al. 2009), to identify population clusters in the crayfish hosts (Chapter five). Therefore, we also used these approaches in the analyses of flatworm population structure, as the associated documentation does not, in either case, specify that deviations from HWE are problematic. However, because STRUCTURE is known to potentially be affected by deviations from panmixia, such as clonality (Halkett et al. 2005), we also ran a discriminant analysis of

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principal components (DAPC) to infer the number of clusters of genetically related individuals. In the multivariate statistical approach, the variance in the sample is partitioned into a between-group and a within-group component in order to maximise discrimination between groups. This method is based purely on genetic distance, which means it is not affected by lack of random mating, or clonality. DAPC was run in R using the package Adegenet (Jombart 2008; Jombart et al. 2010).

In software STRUCTURE, we first ran the analysis seven times with differing values of MCMC and burnin, with final parameters selected of iterations of 75,000 burnin and 250,000

MCMC. The analysis was then run with these parameters a further five times, with Kmax 2– 11, with 11 being the total number of localities sampled. The algorithm was run 50 times for each K. In each case, the LOCPRIOR MODEL with admixture was selected. The LOCPRIOR MODEL was selected as the number of loci was no more than seven and the data were collected at discrete sample localities, as per recommendations (Hubisz et al. 2009). Additionally, LOCPRIOR was also used in the earlier analysis of the crayfish hosts (Chapter five). The value ΔK, was determined in STRUCTURE Harvester (Earl and vonHoldt 2012), which employs the Evanno method (Evanno et al. 2005). In the software TESS, both the CAR and BYM admixture models were used to calculate the likelihood under different values for

Kmax 2–11. For each admixture model, the algorithm was run 50 times for each K, with final parameters of burnin of 75,000 iterations followed by 250,000 iterations of sampling. As there was no discernible difference between the results of the CAR and the BYM, we present only the BYM model here. To determine the value for K, the values of deviance information criterion (DIC) were plotted against Kmax to locate the point where the DIC stabilised. In TESS, the multiple runs for the selected Kmax values were combined using CLUMPP version 1.1.2 (Jakobsson and Rosenberg 2007); in STRUCTURE, these were combined using the web-based CLUMPPAK (Kopelman et al. 2015). In both cases, the final plots were completed graphically using R software (Team 2015).

Finally, DAPC was used to identify genetic clusters by running successive K-means clustering in the find.clusters function and using the Bayesian Information Criterion (BIC). Ideally, it is the K with the lowest BIC value that determines the optimal number of clusters. However, it is possible for the BIC values to continue decreasing after the true K, especially where genetic clines and hierarchical structure exist (Jombart et al. 2010). The rate of decrease in 114

BIC values was visually examined to identify values of K. The DAPC function was then executed using K=4, 5, 6 and 7 to determine which was the most appropriate K to fit the data. Later, the localities were subdivided into the number of clusters identified by the K values; this was done by taking into account the distances between each pair of localities.

6.3.3.3 Infrapopulation genetic structure (Kondalilla Falls and Stony Ck) As mentioned above we had genotyped numerous individuals from Kondalilla Falls and Stony Ck that had been omitted from the population level analyses. We used these datasets in DAPC to determine the level of similarity between individuals, in order to determine if individuals upon a crayfish clustered together. First flatworms were removed that were singletons and the final datasets were as follows: Kondalilla Falls (N=2-8 from 17 crayfish) and Stony Ck (N=2-8 from 25 crayfish). For purposes of comparison we also ran the same analysis using the original population datasets from these localities. Based on the BIC curves produced, the DAPC function was then executed using K=3, 4 and 5 to determine which were the most appropriate Ks to fit the datasets.

6.4 Results

6.4.1 Genetic differentiation and population structure (11 localities) A total of 190 flatworms from 11 localities was used in the final population genetic analyses (Fig. 6.1, Table 6.1). No clones were detected in this dataset. In total, we discovered nine novel microsatellite loci, but two loci (TB112 and TB133) were later discarded from the final analyses due to scoring difficulties (Table 6.2). The number of alleles in the final seven microsatellite loci ranged from nine to 17 (Table 6.2). The values of Ho averaged over seven loci for the 11 sample localities was between 0.00 and 0.71 (mean = 0.48). Although no deviations from HWE were found in the initial tests (32 individuals from two localities), this was not the case in the final data set, with the majority of loci at most localities showing a deviation from HWE (Table 6.1). MICROCHECKER detected possible null alleles at several localities, which is most likely because positive FIS values suggested heterozygote deficit. All of the microsatellite loci were in linkage equilibrium (data not shown).

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Figure 6.2: Individual cluster membership assignment for Temnosewellia by STRUCTURE (a) and TESS (b). The labels correspond to 11 of the original sample localities going from East to West: Sunday Ck, Kilcoy Ck, Bundaroo Ck, Branch Ck, Stony Ck, Dairy, Arley farm, Bridge Ck, Lawley Ck, Langley Ck, and Kondalilla Falls. *K is the most likely number of populations, as identified by DIC/BIC plots

The separation of genetic clusters in the flatworm population dataset was similar to those found in the host dataset (Fig. 6.2 and 6.3; see also Chapter five), as we also observed the same pattern of genetic differentiation between Conondale NP, Bellthorpe NP and the Maleny region. However, in STRUCTURE, four genetic clusters rather than three were identified in the flatworm dataset. There was ambiguity for the Dairy locality in the STRUCTURE plot for K=4 (Fig. 6.1 and 6.2a). As consideration should always be given to meaningful biological and ecological processes when identifying the correct number of clusters (Meirmans 2015), we decided, based on distance, that Dairy was most appropriately grouped with Maleny. Hence, it was determined that the four clusters represented four upland areas: Conondale NP, Bellthorpe NP, Maleny region, and Kondalilla

NP (Fig. 6.2a and 6.3a). In TESS, by aligning the values of DIC against Kmax on a graph, the best value of K was five (Fig. 6.2b and 6.3a). These five clusters were Kondalilla NP, Conondale NP, Bellthorpe NP, Maleny, and the Dairy locality. However, when we examined plots with K=3, it appeared that this value of K might also be appropriate. Again, K=3 would reflect the three populations that we observed for the host. The final analysis using DAPC identified five clusters as the best fit. The resulting five clusters were different from the other assignment analyses but there was still a separation among the four upland areas. The

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DAPC clusters were as follows: Maleny:- 1: Dairy farm, Arley farm, Bridge Ck, Lawley Ck and Langley Ck; Bellthorpe NP:-2: Stony Ck; 3: Branch Ck; Conondale NP:-4: Bundaroo Ck, Sunday Ck and Kilcoy Ck; Kondalilla NP:-5: Kondalilla Falls (Fig. 6.3a and 6.4).

a) Temnosewellia batiola

b) Euastacus hystricosus

Figure 6.3: Comparison of the population genetic structure of the flatworm Temnosewellia batiola (a) and its crayfish host Euastacus hystricosus (b) Red rectangles are clusters identified in STRUCTURE. Yellow rectangles are clusters identified with TESS. Black rectangles are clusters identified with DAPC. This method was not used in the analysis of the crayfish.

6.4.2 Genetic differentiation and population structure (two localities) The total number of flatworms genotyped from Kondalilla Falls was 81 from 26 crayfish. The total number of flatworms genotyped from Stony Ck was 130 from 32 crayfish. Once again, there were significant deviations from HWE in both populations (p< 0.005), FIS of 1.00 as all loci were homozygous. All the loci were in linkage equilibrium (not shown). In software COLONY, two pairs of potential clones were detected at Stony Ck but these were sampled

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from different crayfish. Further, one pair of potential clones was detected at Kondalilla Falls but again these were sampled from different crayfish. When the genotypes of all the flatworms in a locality were compared in COLONY, there was no obvious grouping of genotypes by host (not shown).

When the DAPC analysis was run for the two additional genetic datasets of Kondalilla Falls and Stony Ck, the following number of clusters were identified: Kondalilla Falls K=3, in which the majority of genotypes were assigned to the third cluster (N=73 from 17 crayfish)(Fig. 6.5a); Kondalilla Falls K=3 and K=4 (N=26 from 26 crayfish) (Fig. 6.5b); Stony Ck K=5, in which the majority of genotypes were assigned to the fourth cluster (N=124 from 25 crayfish) (Fig. 6.6a); Stony Ck K=3 and K=4 (N=31) (Fig. 6.6b).These groupings reflected what was found in the COLONY analysis; genotypes did not cluster according to host but flatworms from different hosts were genetically similar to each other, indicating that there was not a great deal of genetic structure within localities or within infrapopulations.

Figure 6.4: Discriminant analysis of principal components (DAPC) showing the ordination plot with minimum spanning tree for five genetic clusters which were derived from 11 sample localities for the flatworm Temnosewellia batiola. Genetic clusters are shown by different colours, with dots representing the individuals and the groups as inertia ellipses. The bottom left insert shows eigenvalues of the four principal components in relative magnitude.

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Figure 6.5: Discriminant analysis of principal components (DAPC) showing the ordination plot of the genotype clusters of the flatworm Temnosewellia batiola at Kondalilla Falls. Genetic clusters are shown by different colours, with dots representing the individuals and the groups as inertia ellipses. The insert shows eigenvalues of the two principal components in relative magnitude. a) K=3 (N=2-8 from 17 crayfish hosts). The plot to the right side shows the partitioning of flatworm genotypes into clusters; inf=clusters 1 to 3 and ori=crayfish 1 to 17. b) Comparison of K=3 and K=4, (N=26 from 26 crayfish hosts).

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Figure 6.6: Discriminant analysis of principal components (DAPC) showing the ordination plot of the genotype clusters of the flatworm Temnosewellia batiola at the Stony Creek locality. Genetic clusters are shown by different colours, with dots representing the individuals and the groups as inertia ellipses. The bottom right insert shows eigenvalues of the two principal components in relative magnitude. a) K=5 (N=2-8 from 25 crayfish hosts). The plot to the right side shows the partitioning of flatworm genotypes into clusters; inf=clusters 1 to 5 and ori=crayfish 1 to 25. b) Comparison of K=3 and K=4, (N=31 from 31 crayfish hosts).

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6.5 Discussion

Using seven novel microsatellite markers, population genetic structure was detected in the flatworm Temnosewellia batiola that broadly reflected the same structure previously found for its crayfish host Euastacus hystricosus. In both species, populations were distinct in three uplands in SE Queensland, with some evidence of further population structure in the flatworm. Two of the localities were extremely divergent from HWE as there were no heterozygotes at any of the seven loci; these were Stony Creek (Ck) and Kondalilla Falls. At these two localities there was neither evidence of clonality nor of strong genetic structure on hosts.

When we compared the population assignment analyses between the flatworm and the host, we found strong similarities. For the crayfish hosts, three distinct and highly fragmented populations had been detected (Chapter five). The population genetic structure of the flatworms also showed a clear separation between the same three uplands. However, there was also a point of difference. In two of the three assignment analyses, Kondalilla Falls, Kondalilla NP did not cluster with the Maleny localities as it had done for the crayfish. As there was no genetic differentiation between Kondalilla NP and Maleny in the crayfish hosts, we had assumed that migration pathways must exist between these uplands at intervening high elevation points (Chapter five). However, Kondalilla is 7 km further north than the Maleny localities, which may be enough to create the additional layer of genetic structure observed in the flatworm.

Small differences in genetic structure may be attributed to species-specific characteristics, such as different life histories in the host and their symbiont (Gutiérrez-García and Vázquez- Domínguez 2011). The correct assignment of individuals to their source populations may be affected by sample size, number of loci, number of alleles, genetic diversity, and the total amount of genetic structure among populations (Kalinowski 2004). To account for the small differences between studies, we can speculate that either there was not enough power to detect the true population structure in the microsatellite data of the crayfish hosts as they had low genetic variability, or that the dispersal ability of the flatworms differs from its host. Both hypotheses are valid. Symbionts have been used a number of times to uncover population structure that was not apparent in the host. For example, Criscione et al. (2006)

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could not adequately detect population differentiation in the Steelhead Trout (Oncorhynchus mykiss) due to there being little or no neutral variation between populations. Instead they were able to detect structure when using its trematode parasite (Plagioporus shawi). Further, Whiteman et al. (2007) demonstrated that parasites were useful in discovering the cryptic evolutionary history of the endangered Galápagos hawk (Buteo galapagoensis). However, in order to be confident in our genetic assessment of shared structure between two species, it is also necessary to understand how, and if, dispersal mechanisms may differ even if the host and symbiont have a close association.

Temnosewellia are able to survive on their hosts for some time in the absence of water (Sewell 2013); therefore, we expect that even though flatworms are soft bodied aquatic organisms, that some level of terrestrial dispersal on their hosts is possible. For instance, when weather conditions are cool and wet, the flatworms may stay hydrated for reasonably long periods that would make terrestrial migration plausible, especially under the canopies of the fully forested national parks. In this situation, one would expect to see a close association in both historical and contemporary structure between the host and ectocommensal in the Conondale and Bellthorpe NPs. However, Maleny has been undergoing extensive deforestation since the 1870’s (Seto 2001), and the lack of canopy cover in this upland would have disrupted contemporary dispersal pathways and changed dispersal conditions for the soft bodied flatworms. In this case, ‘dispersal’ would be limited to the unhatched eggs on the host carapace, which may be hardy enough to resist desiccation or may withstand digestion by predators (Haswell 1893), meaning that, contemporary dispersal patterns may differ between the species. However, as the crayfish also require cool wet conditions to facilitate lengthy periods out of water (Bone et al. 2014; Little 1990) we would not expect the differences in dispersal to be large at least for short distances.

For most localities, genetic diversity in the flatworms was higher than that previously found in the crayfish hosts. Yet, at Stony Ck and Kondalilla Falls, there were no individuals with heterozygous genotypes at any of the seven polymorphic loci. Increasing the sample size at these localities to include multiple flatworms per crayfish also did not yield any heterozygote flatworms. Using the software COLONY, we were able to determine that clonality was not the cause of this discrepancy. As some crayfish may have hundreds of 122

flatworms on their carapace and flatworm size is broadly correlated with host size (C.R. Hurry unpubl. data –please refer to Appendices B and C) we expected to detect genetic similarity between flatworms on a crayfish; however, this was not the case. Flatworms in an infrapopulation (on a crayfish) were not the most genetically similar. In the cluster analysis there were no clearly identifiable clusters that were related to infrapopulation, in fact, in both localities there were very few clusters (three and five). We would have expected many more clusters if the genotypes were strongly driven by fine scale structure of crayfish hosts. A closer look at the DAPC analysis showed that the majority of genotypes were clustered together which indicates that flatworms sampled from different crayfish were genetically similar.

It is necessary to determine if some genetic process such as biparental inbreeding, parthenogenesis or extreme founder events on hosts are driving the genetic structure in the flatworms. The first step would be to exclude mechanical error and perform further checks for null alleles and scoring issues in a larger dataset by including sites that had more heterozygotes. However due to the strong patterns of homozygosity seen throughout, we do not believe error to be the principal issue for our dataset. Another possibility is that these localities contained cryptic species. If this were the case we would have expected to find linkage disequilibrium in the alleles across loci, which would have been detected in the clustering analyses. As we did not observe these patterns we ruled out both sampling of multiple species and analytical errors as the primary reasons for the extreme homozygosity in the dataset.

According to current knowledge, all Temnosewellia flatworms are sexually reproducing, diploid and hermaphroditic. Alternative reproduction strategies, such as asexual reproduction or parthenogenesis have not been reported in this genus but have been shown to be a function of reproduction in other Turbellaria, such as Dugesia and the planarian Schmidtea (Beukeboom et al. 1996; Pongratz et al. 2003). Further, self-fertilisation is common among hermaphroditic invertebrates and may be promoted by ecological factors (Casu et al. 2012; Jarne and Auld 2006). For instance, if an obligate sexually reproducing hermaphroditic symbiont occurs singly on a host, it may have the capacity to undergo self- fertilisation or asexual reproduction (Criscione et al. 2006). Self-fertilisation seems the more likely of these two alternatives based on the observed pattern of multilocus homozygosity 123

and the lack of evidence for identical multilocus genotypes (i.e. clones) within large population samples. There are a number of density-dependant processes that may drive alternate reproductive strategies and impact the dynamics of infrapopulations in helminth worms (Poulin 2007). For instance, the way that parasites are distributed among hosts may affect both Ne and mating dynamics (Criscione 2008; Criscione et al. 2005). Brown et al. (2003) found that a high parasite density on hosts inhibited fecundity in the trematode Microphallus papillorobustus, as females were unable to reach full size and full maturity. Further in 1988, Zervos documented the females in the nematode Protrellus dixoni were able to secrete hormones that inhibited the presence of more than one male within an infrapopulation. In both of these scenarios, it is possible that non-random or alternative strategies of reproduction occur that affect the genetic signature in populations. Given that hundreds of flatworms may be found on a single host (Wild and Furse 2004; C. R. Hurry unpubl. data: see Appendix C), it is feasible that density driven processes have affected the reproductive strategies of these flatworms but the mode of reproduction is still unknown.

The overall lack of clonal individuals rules out apomictic parthenogenesis (mitotic asexual reproduction) as the mode of reproduction. An alternative explanation may be automictic parthenogenesis in which offspring are created after meiosis of the egg, leading to non- clonal offspring (Lampert et al. 2007). This mode of parthenogenesis is recognised in taxa that have haploid offspring, including honey bees (Tucker 1958) and wasps (Beukeboom and Pijnacker 2000). However, as ploidy may be restored by various means it has also been observed in diploid form (Chapman et al. 2007; Watts et al. 2006). For example, offspring produced by a reproductively isolated Komodo dragon were found to be homozygous at all loci with genotypes that could be fully reconstructed from the mother’s genotype, yet they were not identical clones (Watts et al. 2006). However, given the hermaphroditic nature of the flatworms, another explanation is that these populations are highly homozygous because they are comprised of multiple isogenic lines that are a product of extensive self- fertilisation by the hermaphroditic flatworms. Repeated self-fertilisations can lead to the rapid reduction of heterozygosity and patterns similar to those observed in this study have been detected in the fish species Kryptolebias marmoratus (Tatarenkov et al. 2012). This species has an androdioecious reproductive system which means that alongside the mainly self-fertilising hermaphrodites there are a very small number of males. Therefore, extensive

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self-fertilisation has produced the pattern of multilocus homozygosity observed within and across localities; nonetheless not all the individuals are identical clones. This reproductive strategy is rare and has not previously been identified in Temnocephalans but it has been found in nematodes (Anderson et al. 2010). Further research is required to evaluate these possibilities, including what ecological factors might be acting to drive the reproductive strategies in the two highly homozygous sample locations detected in this study.

6.6 Conclusion

We could adequately describe shared patterns of population genetic structure between the flatworms and the crayfish. Nevertheless, due to the nature of the genetic diversity at the microsatellite markers we were unable to do a full-scale analysis of migration rates and effective population sizes in these flatworms. As a result, we advise that future studies using Temnosewellia incorporate the use of other genetic markers, such as mitochondrial sequences, that would work equally as well even when reproductive strategies differ. It would also be advisable to unearth the processes that are contributing to the high levels of homozygosity in the microsatellite markers. Unfortunately, this would require genetic scoring of multiple individuals from infrapopulations and from a number of localities. It may also be advisable to do full scale experimental analysis of mating strategy which was beyond the scope of the current study.

6.7 Acknowledgements

We would like to thank the School of Environment, Griffith University for funding this research. Thank you to the field assistants for their time and efforts. David Valade, Christine Gillen, Paul Duplancic, Eugénie Schwartz, Frik Bouwer, Sofie Bernays, Kathryn Real, Dave Bates, Jenny Wadsworth, Andrew Bentley, Tim Page and Kaye Stuart, Pete Tangney, Jamie- lee Young, Alex and Maria Garzon, Simone Langhans, Rashedul Mondol, and Sharmeen Rahman. Rebecca Citroen for R scripts. Elizabeth James and the Royal Botanic Gardens, Melbourne for use of software. Department of Environment and Resource Management for provision of Permits WITK 10469111 and TWB/40/2011. General Fisheries Permit 153894.

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Waking up in picturesque Maleny on the final day of fieldwork

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Never measure the height of a mountain until you have reached the top. Then you will see how low it was. Dag Hammarskjold

Chapter Seven: General Discussion The objective of this thesis was to use genetic approaches to determine the population structure of three endangered/critically endangered crayfish and their flatworms. The genetic approaches included comparative phylogeography, coalescent-based molecular approaches, and population genetics measures of gene flow. The findings were that these Queensland Euastacus were range restricted and that the uplands contained distinct populations. The separation of upland populations was most likely historical, as there was little evidence of contemporary gene flow between them. Population structure in the Temnosewellia flatworms corresponded to that of the crayfish hosts. Hence, Temnosewellia may be considered as useful proxies in studies of crayfish population structure. In the general discussion which follows I will be comparing the patterns of genetic structure and the measures of genetic diversity found for all species, in addition to providing views of the conservation values of this thesis.

7.1 Temnosewellia flatworms

A direct comparison of the two Temnosewellia species is somewhat difficult, as the population structure was measured for two different timeframes: phylogeographic comparison for T. albata and analyses of contemporary genetic structure for T. batiola. Overall it was determined that both species showed patterns concordant with their crayfish hosts. These findings indicate that they are ideal proxy species for delineating population structure in their hosts. Ours is the first study to use the ectocommensal Temnosewellia flatworms as potential proxy species. Hence these species now join the large number of parasite taxa that have been used in comparative genetic studies (Koop et al. 2014; Schaik et al. 2015; Toon and Hughes 2008; Whiteman et al. 2007). As the future research output for Euastacus and other crustacean hosts increases, perhaps Temnosewellia will be more

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widely used in these types of study, which will be especially useful when hosts are found in low numbers and the flatworms can be used to derive a larger amount of genetic information. For instance, it has now been reported that the species Temnosewellia minor has been identified, with the use of DNA barcoding, on Italian populations of Cherax destructor (Chiesa et al. 2015). Cherax destructor is an invasive species in Italy, hence T. minor could be used to help determine points of origin or describe the spread of the crayfish hosts through Europe.

The close correlation between the population structure in the crayfish hosts and the flatworms shows that these flatworms may be considered to be at risk of coextinction. It is interesting, but perhaps not surprising, that Nichols and Gómez (2011) researched 77 conservation textbooks printed between 1970 and 2010 and found that only 11 of them mentioned parasites in a positive light. Although Temnosewellia flatworms in this study are commensals and not parasites, they do exhibit a number of parasite-like behaviours. Further, from a conservation stand point the flatworms are treated with the same general disregard as parasites. Although the crayfish hosts have been assessed by the IUCN as endangered/critically endangered, there are no current conservation action plans in place for either the crayfish or the flatworms. In this thesis it was my suggestion to maintain the current IUCN threatened rating of the crayfish hosts (E. robertsi: critically endangered; E. hystricosus: endangered). Hence, it would be my recommendation to also categorise the flatworms under the same criterion, given that their area of occurrence directly coincides with that of the crayfish hosts. I must stress that in this study that I did not undertake habitat viability assessments, which need to be included for a full threat rating on the IUCN Red List.

Given the differing requirements of the flatworms to their hosts it may be beneficial if conservation plans manage them as separate entities, as, management plans for host taxa rarely consider the specific requirements of symbionts. For instance, if host abundance falls below levels that are required to sustain transmissions, it is likely that parasites and similar taxa will become extinct before their hosts (Dunne and Williams 2009), even if they occur in abundant numbers on individual hosts. It is my suggestion that the work in this thesis should

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be considered a first step in the assessment of extinction risk for this species and future work is needed to assess viability of current habitat, including the microhabitat of the host.

7.2 Population structure

The overarching result in this thesis was the distinct genetic structure detected among upland populations (mountain and uplands are used interchangeably in this discussion). In the south-eastern species (including the crayfish and the flatworms) it was observed that all upland populations were highly genetically structured. The mountain populations of the north eastern E. robersti and T. albata showed strong genetic differentiation between Mt Pieter Botte and Mt Finnegan. For instance, in T. albata, the mtDNA COI gene showed fixed differences, with one unique haplotype on each mountain. The differentiation was less obvious in the 28S gene, as shared haplotypes were observed among the uplands. It was impossible to rule out the possibility that ancestral haplotypes had been retained in these taxa. However, an ‘isolation with migration model’ indicated that low level post-divergence gene flow had occurred, which was most likely due to the existence of historical higher elevation ridges between these mountains.

Divergence dates were calculated between the upland populations of the three crayfish. In previous studies it was determined that among species divergence of Queensland Euastacus among mountaintops, was due to vicariance during the Pliocene epoch (Ponniah and Hughes 2004, 2006; Shull et al. 2005). In the uplands in this study, it was shown that divergence of upland populations occurred sometime during the early to mid-Pleistocene (2.58-0.126 million years ago (mya). Divergence dates were similar for the populations of E. robertsi (NE Queensland) and E. urospinosus (SE Queensland). For instance, the Thornton Peak population of E. robersti most likely diverged from the other two mountaintops ~2.6 mya. Likewise, the estimated divergence date of the Conondale population of E. urospinosus was ~2.1 mya, with divergence of the other populations dated between ~0.7 and 1 mya. Euastacus robertsi is believed to be an active forager, more so than the ‘shy’ E. urospinosus which is rarely encountered outside of its system of burrows (McCormack 2012). In particular, E. urospinosus adults are rarely seen in the creek (Borsboom 1998). The main difference between these species (apart from location) is that E. robertsi is found at 129

elevations above 750 m, whereas E. urospinosus is found above 250 m, and yet, gene flow between uplands was only detected, albeit in low proportions, in E. robertsi. Perhaps if we had been able to sample more E. urospinosus gene flow may have been detected between sites. However, I suspect that due to their territorial and burrowing nature, these crayfish have a lower dispersal potential. In comparison, the divergence date for the upland populations of the giant spiny crayfish E. hystricosus was ~0.4 mya in the mid to late Pleistocene (Chapter three, Fig. 3.3). Again, the differences seen between the co-located congeners are likely due to the higher dispersal ability of E. Hystricosus that may have permitted gene flow to persist for longer both between and within uplands.

As discussed in Chapter three, several models of genetic structure have been proposed for freshwater taxa. With the use of AMOVA it was determined that the genetic variation within the subpopulations of both E. urospinosus and E. hystricosus followed the headwater model of genetic structure. This shows that population structure was most influenced by the separation of uplands, rather than by catchment. The headwater model of genetic structure has also been observed in the Euastacus species E. fleckeri (Ponniah and Hughes 2006) and E. spinifer (Bratby 2004). These species of giant spiny crayfish are very active in the stream. The connectedness of populations is likely affected by their position in the stream network, in addition to physical distance and dispersal ability (Hughes et al. 2009). For many headwater species, populations become isolated in the uppermost reaches as movement through the stream network is restricted. There is evidence that historical connectivity between headwater subpopulations has been extremely limited for E. urospinosus. Although the haplotypes from closely located streams were just one or two base pairs divergent there was very little evidence of haplotype sharing between the streams. However, these findings were based on a small sample size and caution must be applied when interpreting the patterns of genetic structure detected in this species. As only four creeks were sampled in the three mountaintop populations of E. robersti, we were unable to determine with the use of AMOVA which models of genetic structure would be most appropriate for the species. It was previously believed that the species was restricted to creeks within the mountaintops; as such the most appropriate model was suggested to be the Death Valley model (Hughes et al. 2013; Ponniah and Hughes 2006). The detection of

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low levels of post divergence gene flow between two of the mountains refutes this theory, as the Death Valley model is a function of systems where there is zero gene flow (Finn et al. 2007). In future studies of E. robertsi it would be beneficial to use microsatellite markers to determine if there is any evidence of contemporary gene flow between mountaintops.

7.3 Genetic diversity and effective population size

In this thesis it was determined that E. hystricosus populations have extremely low Ne. Further there was low genetic diversity in the microsatellite marker dataset and extremely low diversity in the mtDNA sequences. Extremely low levels of genetic diversity were also detected in the DNA sequence analyses of E. robersti and T. albata. Conversely, the highest genetic diversity in the crayfish was found in the COI mtDNA region of E. urospinosus (SE Queensland). As mentioned above for E. urospinosus, the haplotypes were fixed within creeks providing evidence of fine scale genetic structure which has likely persisted for some time. These findings indicate that this species may not disperse easily within the stream network.

As the number of population genetic studies of crayfish slowly increases, a pattern is emerging of low genetic diversity and population subdivision (Buhay and Crandall 2005; Diéguez‐Uribeondo et al. 2008; Gouin et al. 2001). Recently, Miller et al. (2014) studied the Glenelg spiny crayfish (Euastacus bispinosus) in the south of Australia where they detected just seven COI haplotypes from 17 waterways. Of these, a single haplotype was fixed in the 12 sample sites in South Australia. Although this crayfish is not restricted by elevation, populations were extremely spatially structured and there was evidence for bottlenecks in a number of populations. Comparatively, in Cherax dispar from SE Queensland, Bentley et al. (2010) detected that the species had a high intraspecific genetic diversity. However, although the species was widely distributed across elevations it was still strongly genetically structured by latitude. In fact, three of the Cherax dispar lineages showed genetic patterns which most closely matched the Death Valley model of genetic structure as there was no evidence of gene flow across latitudes. Therefore, the low genetic diversity and population subdivision found in the three freshwater crayfish in this thesis may be characteristic of the taxon as a whole, as a function of long term habitat specialisation.

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Temnosewellia batiola also had moderate levels of microsatellite genetic diversity within localities, but were characterised by a significant lack of heterozygotes in the majority of creeks. By increasing the sample size in the two fully homozygote suprapopulations, it became clear that these patterns were driven by the reproductive strategy of T. batiola. As all the individuals were homozygote at all loci but there was very limited evidence of clonality at any locality, we speculated that these hermaphrodites were using self- fertilisation or automictic parthenogenesis as a reproductive strategy both of which have the potential for offspring to be produced that are not identical clones. However, these assumptions have not been tested and more work is needed to determine how the species reproduces.

7.4 Conservation gains

The ongoing persistence of these taxa may rely on good conservation planning. In this thesis the division of populations was clearly determined. It is less clear how the results of this thesis should be applied to conservation management plans. The locality surveys of the three Euastacus spp. included in this study provide additional information for the assessment of threat for the IUCN Red List. I found that the current threat statuses listed were correct, although the study did not address all the components needed to make a full assessment of extinction risk.

For the genetic component of the study, for all taxa, gene flow was limited or nonexistent between the isolated populations. Further, these populations were genetically distinct and in most cases genetic variation was small. Where Ne was measured, those values were extremely small. Genetic theory predicts that small isolated populations are most at risk of inbreeding depression; also that inbreeding depression is exacerbated with low Ne. The subsequent section explores in more detail conservation strategies that could be used based on the results of the thesis. Low genetic diversity coupled with low Ne is usually a sign of a population at risk as the combination of these factors can lead to inbreeding depression (Frankham et al. 2002). Inbreeding depression has been shown to have deleterious effects on the fitness of wild outbreeding populations (Crnokrak and Roff 1999). Furthermore, high

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genetic diversity is deemed important in order to mitigate the impacts of climate change and range contraction (Hoffmann and Sgro 2011). Although there is not a great deal of field based evidence relating extinction events to inbreeding depression in wild populations (Brook et al. 2002; Frankham 2005), it should still be considered a risk. As shown in this thesis, the divergence between the uplands was historical, which suggests populations may have endured low genetic diversity and low Ne for many thousands of years. Also, a lack of genetic diversity at neutral markers does not always reflect the true nature of diversity at adaptive genes (Hartmann et al. 2014). Further, if populations are historically divergent then genetic processes such as purging may have already removed the potential of inbreeding depression. Purging is most effective for populations that have become inbred gradually through time, as selection has had more time to be effective (Keller and Waller 2002).

One strategy that has been used (Arrendal et al. 2004; Wisely et al. 2008) and suggested (Miller et al. 2014) to bolster genetic variability within populations is to place translocated individuals into genetically depauperate populations. The idea is that matings between residents and migrants will lead to increased genetic variability in resident populations. The addition of new genetic variation into isolated populations will inevitably allow some taxa to adapt genetically to future changes to their environment (Kellermann et al. 2009; Umina et al. 2005). If translocations are to be considered, it is important to develop concise strategies before they happen. For instance, it would be better to determine which populations are most likely to respond positively. This can be done by undertaking experimental evaluation. Further, translocations should be implemented so that they ‘keep pace with’ climate change. Simply put, this requires translocation from populations that may already show some tolerance to warmer climates; i.e. edge populations or those occurring at warmer elevations (Hannah 2014). As two of the E. hystricosus subpopulations were found at elevations below 300 m (lower than previously recorded) it may be beneficial to survey if these creeks have comparatively warmer year round stream temperatures for which these subpopulations may be adapted.

There are several concerns associated with ‘translocation matings’ mainly that outbreeding depression may be introduced into the population. Outbreeding depression occurs when

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newly introduced genes displace beneficial genes that are already present and selected for in the resident population (Lynch 1997). Also associated with outbreeding depression is a decline in fitness that has been attributed to the breakup of coadapted gene complexes or due to the taxa having different adaptations (Lynch 1991; Templeton 1986). For instance, if genetic drift is working alongside natural selection it may lead to the evolution of different coadapted gene complexes in populations, even if environmental differences between populations are negligible (Whitlock et al. 1995). However, recent studies have shown that the risks of outbreeding depression may be exaggerated (Frankham et al. 2011), as the benefits of introducing new genetic material into populations outweighs the risks. Frankham et al. (2011) have suggested a number of strategies for predicting and monitoring outbreeding depression. It is worth remembering that the strategies of Frankam et al. (2011) refer to populations that have been separated less than 500 years. In historically isolated populations translocations should be approached with caution. Selection rather than genetic drift has been identified as the main driver of genetic differentiation in historically separated populations (Gavrilets 2004; Templeton 2008). Hence, in these populations, there will have been more opportunity for selection and purging of deleterious alleles, which means that these populations are already habitat adapted (Cocca et al. 2015; Keller and Waller 2002). Therefore, the addition of new genetic material into the population may swamp local adaptation or prevent future local adaptation (Storfer 1999; Templeton 1986).

A secondary risk associated with translocations is the spread of diseases, which may be present in one population but not another (Horwitz 1990). Although these risks are most associated with the introduction of invasive species it cannot be ruled out for historically separated populations, especially as crayfish are known to harbour a mass of microbes (Holdich 2003; McCormack 2012). Additionally, the risks listed above are not just a problem for the crayfish but may also be a problem for their associated taxa, including the flatworms and microbes. Therefore, translocations should only be attempted when all the risks have been considered for all species and the associated issues addressed.

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The finding in this thesis that population subdivision is historical is relevant for the creation of conservation plans that focus on current and future risks to habitat, mainly those risks associated with anthropogenic changes and a warming climate. Many conservation plans merely focus on the present which clearly is inappropriate when there is a need to understand the adaptive capacity of species through genetic diversity and genetic differentiation. Genetic changes take time to be reflected within the population as a whole, especially given that generation times of higher organisms are often between 20 and 100 years (Willis et al. 2007). Hence, the historical ecological and genetic perspective that I have presented in this thesis shows that the upland populations are currently isolated and gene flow is non-existent and that this has been the case for hundreds of thousands of years. In this case, restoration of genetic diversity among all populations is unlikely and perhaps detrimental as the low genetic diversity found per mountaintop may limit future potential to adapt to future changes to habitat. Also, as these species occur at the highest of elevations on each mountaintop they are already at the limit of their thermal tolerance, which means in a warming climate they will have nowhere to migrate.

Due to the long term isolation of populations of crayfish and flatworms studied here it has been our recommendation to preserve genetic integrity within these populations rather than translocate individuals. Conservation plans for these taxa should consider that there is a small but potential risk of inbreeding depression in the isolated populations. However, rather than taking drastic action, these populations should be monitored for associated negative impacts such as reduced fecundity or reductions to population size (D.E.W.H.A. 2008). Further, as there was some evidence of fine scale genetic structuring within uplands, the better use of conservation resources would be to maintain connectivity among creeks within the uplands. Therefore, conservation efforts within uplands should ensure that streams remain connected and that riparian zones are protected in order to keep temperatures at a minimum. Continued assessment of the creeks and riparian areas should also be carried out to determine at what rate they are dissipating, which may affect the persistence of the species at current locations. Further, planning for new roads, farms or creek diversions should consider the locations and existing connections between populations. Such measures would allow contemporary migration (where possible) which may reduce further population structuring.

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7.5 Conclusion

The results of this thesis highlight the value of using mitochondrial and nuclear genetic markers to assess levels of genetic variability among populations, in addition to defining current and contemporary structure. The usefulness of comparative studies of closely associated taxa in delineating patterns of genetic structure was also illustrated. Future studies on these taxa would benefit from increased sample sizes and the development of more microsatellite markers.

The Temnosewellia spp. were extremely useful as proxy species as the overlapping population structure between them and the hosts could easily be identified. As part of this study we found that T. batiola may have a complex reproductive strategy which should be explored further. Future studies may include more experimental work to determine whether it is parthenogenesis or self-fertilisation that is driving the patterns of genetic diversity observed in the flatworms. A more thorough analysis of Temnosewellia ecology relating levels of abundance to its host or environment are also highly recommended. There is no doubt that the IUCN Red List criterion, ‘area of occurrence’, which is applicable to the hosts is also applicable to these host-specific Temnosewellia spp.

On the basis of the population level genetic information that was found in this thesis, conservation plans may now be tailored for all five species. As we found population isolation and low genetic variability, I suggest that all five species should be classified as threatened on the IUCN Red List. In this regard, I suggest a reassessment of the conservation status of these species which includes the findings of this thesis as well as including a thorough environmental evaluation to determine the immediate and future threats to the freshwater habitats of these species.

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Appendices

Appendix A: Site characteristics of 14 samples sites of Euastacus hystricosus (Chapter five)

Site: Branch Ck; Vegetation types: Trees, shrubs, herbaceous, native; Riparian zone: Stable, highly vegetated; Land use: national park; Human alterations: dirt road and culvert at point 0m, otherwise no obvious impact; Sediment and water odours: none; Aquatic Veg: 0% cover ; Canopy: 50-75% shade; Bank Steepness: L: 45-90%, R: 45-90%; Channel gradient: steep; Channel type: meander; Stream bottom substrate: 75% boulder, 10% cobble, 10% gravel, 5% sand; Organic material: Sparse LWD, moderate COM

Site: Branch Ck east; Vegetation types: Trees, shrubs, herbaceous, native; Riparian zone: Stable, highly vegetated; Land use: national park; Human alterations: dirt road and culvert at point 0m, otherwise no obvious impact; Sediment and water odours: none; Aquatic Veg: 0% cover ; Canopy: 75-100% shade; Bank Steepness: L: 45%, R: 45%; Channel gradient: low; Channel type: meander; Stream bottom substrate: 75% boulder, 20% cobble, 5% sand; Organic material: Sparse LWD, abundant COM

Site: Kondalilla Falls; Vegetation types: Trees, shrubs, herbaceous, native moving to grasses below 0m; Riparian zone: Stable, highly vegetated north of 0m in sampling area and more sandy and less vegetated below; Land use: national park; Human alterations: This creek has several footbridges for the large number of visitors to cross over but there are no roads crossing here. At 0m, the creek passes through farm land that has access to the creek for the cows.; Sediment and water odours: no odours, substrate changes below 0m to sandy where the cows have access; Aquatic Veg: 0% cover ; Canopy: 75-100% shade north of 0m but 10 % below; Bank Steepness: L: 25-75%, R: 25-75%; Channel gradient: steep; Channel type: meander; Stream bottom substrate: 75% boulder, 10% cobble, 10% gravel, 5% sand; Organic material: moderate LWD, abundant COM Site: Kilcoy Ck; Vegetation types: Trees, shrubs, herbaceous, native; Riparian zone: Stable, highly vegetated; Land use: national park/ skirting farm land; Human alterations: dirt road and culvert at point 0m, otherwise no obvious impact; Sediment and water odours: none; Aquatic Veg: 0% cover ; Canopy: 0-25% shade; Bank Steepness: L: 10-45%, R: 10-45%; Channel gradient: low; Channel type: meander; Stream bottom substrate: 80% boulder, 10% gravel, 10% sand; Organic material: Sparse LWD, abundant COM

Site: Stony Ck; Vegetation types: Trees, shrubs, herbaceous, native; Riparian zone: Stable, highly vegetated; Land use: national park; Human alterations: dirt road and culvert at point 0m(channelization), otherwise no obvious impact; Sediment and water odours: none; Aquatic Veg: 0% cover ; Canopy: 75-100% shade; Bank Steepness: L: 10-80%, R: 10-50%; Channel gradient: moderate; Channel type: meander; Stream bottom substrate: 50% boulder, 25% gravel, 25% sand; Organic material: Sparse LWD, moderate COM

Site: Sunday Ck; Vegetation types: Trees, shrubs, herbaceous, native; Riparian zone: Stable, highly vegetated; Land use: national park; Human alterations: 4wd track straight through the creek at point 20m and obviously cows have been using the creek; Sediment and water odours: no odours but sandy at point 20m where cow impact has been seen; Aquatic Veg: 5% cover, rooted submerged and green filaments; Canopy: 0-25% shade; Bank Steepness: L: <45%, R: <45%; Channel gradient: low; Channel type: meander; Stream bottom substrate: 10% boulder, 20% gravel, 60% sand; Organic material: abundant LWD, abundant COM

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Site: Bundoora Ck; Vegetation types: Trees, shrubs, herbaceous, native; Riparian zone: Stable, Appendix A: continued highly vegetated; Land use: national park; Human alterations: big wooden bridge over creek 20m from sample Site; Sediment and water odours: none; Aquatic Veg: 0% cover , some brown filaments; Canopy: 0-25% shade; Bank Steepness: L: <45%, R: <45%; Channel gradient: low; Channel type: meander; Stream bottom substrate: 5% boulder, 35% Cobble, 50% gravel, 20% sand; Organic material: abundant LWD, moderate COM Site: Booloumba; Vegetation types: Trees, shrubs, herbaceous, native; Riparian zone: Stable, highly vegetated; Land use: national park; Human alterations: big concrete bridge over creek 20m from sample Site; Sediment and water odours: none; Aquatic Veg: 50% cover , rooted green vegetation; Canopy: 0-25% shade; Bank Steepness: L: 0-75%, R: 0-75%; Channel gradient: low; Channel type: meander; Stream bottom substrate: 15% boulder, 35% Cobble, 30% gravel, 20% sand; Organic material: abundant LWD, moderate COM

Site: Arley Farm; Vegetation types: Trees, shrubs, herbaceous, native and introduced; Riparian zone: Stable, highly vegetated for 10m either side; Land use: Farm land; Human alterations: beef farming on adjacent land but at sample Site the cows cannot get near the creek; Sediment and water odours: none; Aquatic Veg: 25% cover , rooted grasses, green brown filaments; Canopy: 0- 50% shade; Bank Steepness: L: <45%, R: <45%; Channel gradient: low; Channel type: meander; Stream bottom substrate: 25% boulder, 35% Cobble, 10% gravel; Organic material: sparse LWD, moderate COM

Site: Dairy; Vegetation types: Trees, shrubs, herbaceous, native and introduced; Riparian zone: highly vegetated in parts but very loose soil in others; Land use: Farm land; Human alterations: Obvious signs of cows crossing 5m from start of the sample Site; Sediment and water odours: none; Aquatic Veg: brown and green filaments and rooted grasses; Canopy: 0-75% shade; Bank Steepness: L: <25%, R: <25%; Channel gradient: moderate; Channel type: meander; Stream bottom substrate: 50% boulder, 25% gravel, 25% sand; Organic material: abundant LWD, abundant COM

Site: Langley Ck; Vegetation types: Trees, shrubs, herbaceous, native and introduced; Riparian zone: Stable, highly vegetated for 5-10m either side, lots of grasses in riparian veg; Land use: Farm land; Human alterations: Surrounded by cattle farms but no roads run near this section of creek; Sediment and water odours: none; Aquatic Veg: brown and green filaments and rooted grasses; Canopy: 0-25% shade; Bank Steepness: L: 25-90%, R: 25-75%; Channel gradient: steep; Channel type: meander; Stream bottom substrate: 5% boulder, 35% Cobble, 50% gravel, 20% sand; Organic material: abundant LWD, moderate COM

Site: Lawley Ck; Vegetation types: Trees, shrubs, herbaceous, native and introduced; Riparian zone: Stable, highly vegetated; Land use: Urban; Human alterations: walking tracks cut through, the creek has houses bordering it 50 m from sample Site; Sediment and water odours: none; Aquatic Veg: 0% cover , some brown filaments; Canopy: 0-75% shade; Bank Steepness: L: 25-75%, R: 25-75%; Channel gradient: steep; Channel type: meander; Stream bottom substrate: 30% boulder, 25% Cobble, 25% gravel, 20% sand; Organic material: sparse LWD, moderate COM

Site: Bridge Ck; Vegetation types: Trees, shrubs, herbaceous, native and introduced; Riparian zone: Stable, highly vegetated; Land use: farm land/urban; Human alterations: big road runs through at 25 m from sample Site; Sediment and water odours: none; Aquatic Veg: 15% cover , some rooted plants; Canopy: 0-50% shade; Bank Steepness: L: 25-75%, R: 25-75%; Channel gradient: moderate; Channel type: meander; Stream bottom substrate: 50% boulder, 15% Cobble, 35% gravel; Organic

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material: moderate LWD, moderate COM

Appendix A: continued

Site: Summer Ck; Vegetation types: Trees, shrubs, herbaceous, native, lots of weeding climbing plants making creek difficult to access; Riparian zone: Stable, highly vegetated; Land use: national park; Human alterations: none seen; Sediment and water odours: none; Aquatic Veg: 0% cover ; Canopy: 50-75% shade; Bank Steepness: L: 25-50%, R: 25-50%; Channel gradient: low; Channel type: meander; Stream bottom substrate: 75% boulder, 10% cobble, 5% gravel, 10% sand; Organic material: Sparse LWD, moderate COM

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Appendix B: Body size of Temnosewellia batiola from two localities Crayfish Crayfish Length Width OCL Crayfish Length Width OCL Crayfish ID Site mm mm mm ID Site mm mm mm JW37 CH2 4 3.5 78 ch204 KON 5.5 3.9 70 JW37 CH2 2 1 78 ch204 KON 6.2 4 70 SJ24 CH2 2.5 1.5 64 ch204 KON 6.3 4.7 70 JW10 CH2 2.5 1.5 89 ch204 KON 4.8 4 70 JW10 CH2 3.5 2.5 89 ch204 KON 5 4 70 SJ50 CH2 3 2.5 78 ch204 KON 5.9 4.8 70 DB01 CH2 1.5 1 59 ch204 KON 6 4.8 70 SJ26 CH2 5 4.3 33 ch204 KON 5.9 4.2 70 CH37 KON 7 6.5 47 ch204 KON 5 4.4 70 CH37 KON 5.5 4.5 47 ch204 KON 2 1.5 70 CH37 KON 3.5 2.5 47 ch204 KON 5.1 3.4 70 CH37 KON 5.5 4 47 ch204 KON 5.1 3.8 70 CH37 KON 6.5 5 47 ch26 KON 6 4.4 67 CH37 KON 2 1.5 47 ch26 KON 6.5 5 67 CH37 KON 4.5 4 47 ch26 KON 5 3.7 67 CH37 KON 5 4.5 47 ch26 KON 5.5 3.9 67 CH37 KON 7.5 6.5 47 ch26 KON 3 2 67 CH37 KON 4.5 4 47 ch26 KON 5 4 67 CH210 KON 1 1 58 ch26 KON 5 4.4 67 CH210 KON 1 1 58 ch26 KON 7 6 67 CH210 KON 5 3.5 58 ch26 KON 4.9 5.4 67 CH210 KON 6.5 4.5 58 ch26 KON 2 1 67 CH210 KON 1 1 58 ch26 KON 5.2 4.7 67 CH210 KON 1.5 1 58 jw37 ch2 7 5 78 CH210 KON 1.5 1 58 jw37 ch2 4.8 3.2 78 CH210 KON 4.5 3.5 58 jw37 ch2 6.8 4.4 78 CH210 KON 5.5 4.5 58 sj35 ch2 3 3 50 CH210 KON 7 5 58 sj35 ch2 4 2 50 CH210 KON 7 5 58 sj35 ch2 3 2 50 CH210 KON 6 5 58 sj15 ch2 1.8 1.5 34 CH210 KON 6 5 58 sj15 ch2 2 1.8 34 CH30 KON 1 1 58 sj15 ch2 1.7 1.2 34 CH30 KON 4.5 3.5 58 sj15 ch2 1.6 1.4 34 CH30 KON 5 4.5 58 sj15 ch2 2 1 34 CH30 KON 1 1 58 sj15 ch2 2 1 34 CH30 KON 1.5 1 58 sj28 ch2 2.5 1.8 55 CH30 KON 1.5 1 58 sj28 ch2 4 3.8 55 CH30 KON 5.5 4.5 58 sj28 ch2 7 4 55 CH30 KON 4.5 3 58 jw24 ch2 3 2.2 47 CH30 KON 1 1 58 jw11 ch2 4 2 47 CH30 KON 4.5 3 58 jw11 ch2 4 2 47 CH30 KON 3 2.5 58 sj42 ch2 4.2 2.5 38

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Appendix B: continued CH34 KON 7 6 44 sj42 ch2 3 2 38 CH34 KON 5.5 4.5 44 sj42 ch2 4 2 38 CH34 KON 5 4 44 jw28 ch2 3 2 49 CH34 KON 1 1 44 sj50 ch2 5 4 78 CH34 KON 1 1 44 sj50 ch2 5 3 78 CH34 KON 5 4 44 sj50 ch2 4 3 78 CH34 KON 3.5 3 44 jw41 ch2 5 3 74 CH34 KON 3.5 3 44 jw39 ch2 5 3 52 CH34 KON 5 4 44 jw01 ch2 2 2 37 CH34 KON 5.5 4.5 44 jw02 ch2 3 3 38 CH34 KON 5 4 44 jw02 ch2 2.8 2 38 CH34 KON 3.5 3 44 jw02 ch2 2.2 1.8 38 CH34 KON 2.5 2 44 jw02 ch2 2 1.6 38 CH41 KON 1 1 41 jw02 ch2 2 1.6 38 CH41 KON 3 2.5 41 jw02 ch2 1.7 1.2 38 CH41 KON 4 4 41 jw17 ch2 3.8 2 17 CH41 KON 1 1 41 jw17 ch2 2 1.5 17 CH41 KON 7.5 5.5 41 jw17 ch2 1.5 1 17 CH41 KON 5 4 41 jw08 ch2 3 2 29 CH41 KON 3 2 41 jw08 ch2 2.8 2 29 CH41 KON 6 4.5 41 jw08 ch2 2.2 1.2 29 CH41 KON 4 3 41 jw07 ch2 3 2 38 CH41 KON 2 1.5 41 jw07 ch2 4 4 38 CH41 KON 3.5 2.5 41 jw07 ch2 1.7 1.4 38 CH206 KON 5 3 55 jw07 ch2 3.2 3 38 CH206 KON 5 4 55 jw07 ch2 3.2 3 38 CH206 KON 2 1 55 jw07 ch2 3 2.8 38 CH206 KON 2 1 55 jw03 ch2 3 2 37 CH206 KON 2 1 55 jw03 ch2 3 2.4 37 CH206 KON 6 4.7 55 jw03 ch2 1.8 1.4 37 CH206 KON 6 4.7 55 jw03 ch2 2 1.4 37 CH206 KON 5.2 3.8 55 jw03 ch2 1.5 1 37 CH206 KON 6.3 4.8 55 jw03 ch2 2 1.4 37 CH206 KON 3.1 2.5 55 jw03 ch2 1.8 1.6 37 CH206 KON 5.2 5 55 jw38 ch2 3 2 33 CH206 KON 5 4.8 55 jw38 ch2 3 2.1 33 CH206 KON 6 4.8 55 jw38 ch2 2.9 2 33 CH23 KON 5 4 68 jw31 ch2 2 1 34 CH23 KON 6.2 5.2 68 jw31 ch2 1.8 1 34 CH23 KON 6.2 4 68 jw31 ch2 3 2.5 34 CH23 KON 6 5.2 68 jw31 ch2 3 2.8 34 CH23 KON 6.1 5 68 jw31 ch2 4 3 34 CH23 KON 6 4.8 68 jw21 ch2 4 3.2 34 CH23 KON 2 1 68 jw21 ch2 3 2.4 34 CH23 KON 7.8 4.1 68 jw21 ch2 3 2.5 34 160

Appendix B: continued CH23 KON 7.2 5.4 68 jw21 ch2 2.5 2 34 CH23 KON 6.1 4.9 68 jw21 ch2 2.6 2.1 34 CH23 KON 6 5 68 jw21 ch2 2.5 2.2 34 CH23 KON 5 3.8 68 jw19 ch2 1.8 1.6 34 CH23 KON 6.1 4 68 jw19 ch2 2 1.8 34 CH207 KON 3.9 3.2 72 jw19 ch2 1.5 1 34 CH207 KON 1 1 72 jw32 ch2 1.5 1.2 34 CH207 KON 6.7 3.6 72 jw32 ch2 1.6 1 34 CH207 KON 3.6 3.6 72 jw32 ch2 2 1.6 34 CH207 KON 5.1 5.1 72 jw32 ch2 3 1.5 34 CH207 KON 2.7 1.6 72 jw32 ch2 1.6 1 34 CH207 KON 4.8 3 72 jw30 ch2 2 1.8 32 CH207 KON 4.7 2.7 72 jw30 ch2 3 2 32 CH207 KON 5.5 4.5 72 jw30 ch2 2.5 2 32 CH207 KON 4.7 3.6 72 jw27 ch2 2 1.6 35 CH207 KON 3.4 2.8 72 jw27 ch2 2 1.4 35 CH207 KON 4.1 3.3 72 jw27 ch2 1.8 1 35 CH207 KON 3.9 3.1 72 sj41 ch2 4 2 37 CH207 KON 6 3.5 72 sj41 ch2 3 2.7 37 CH207 KON 3.1 2.4 72 sj41 ch2 2 2 37 CH207 KON 2.9 2.2 72 sj41 ch2 1.8 1.5 37 CH207 KON 2.4 1.9 72 sj41 ch2 3.4 2 37 CH207 KON 5.3 3.4 72 sj39 ch2 2 2 35 CH207 KON 4.4 3.3 72 sj39 ch2 5 3.2 35 CH207 KON 4.6 3.6 72 sj39 ch2 2.8 2 35 ch19 KON 4.15 3.19 65 sj39 ch2 2.2 2 35 ch19 KON 5.18 4.15 65 sj39 ch2 1.8 1.2 35 ch19 KON 4.12 3.25 65 sj39 ch2 1.8 1.2 35 ch19 KON 5.08 3.82 65 sj39 ch2 2.2 2 35 ch19 KON 4.18 3.55 65 sj33 ch2 4 2.8 34 ch19 KON 4.24 3.07 65 sj33 ch2 2 2 34 ch19 KON 3.39 2.86 65 sj33 ch2 4.1 3.5 34 ch19 KON 4.44 2.49 65 sj33 ch2 2 2 34 ch19 KON 6.06 3.49 65 jw29 ch2 4 3.2 36 ch19 KON 6.56 4.04 65 jw29 ch2 2.2 2 36 ch19 KON 5.16 3.25 65 jw29 ch2 2 1 36 ch19 KON 5.3 4.56 65 jw29 ch2 4 3 36 ch36 KON 6 4.8 42 sj47 ch2 6 4 73 ch36 KON 5.7 3.2 42 sj47 ch2 4 3 73 ch36 KON 7.2 5 42 sj47 ch2 3 2.8 73 ch36 KON 3.8 2.8 42 sj47 ch2 2 1.5 73 ch36 KON 6 4 42 sj47 ch2 2 1.5 73 ch36 KON 5.5 3.3 42 sj54 ch2 4 2.2 54 ch36 KON 4.2 3.1 42 sj24 ch2 4.8 3 64 ch36 KON 2 1 42 sj24 ch2 3 2 64 161

Appendix B: continued ch36 KON 5.5 4 42 sj24 ch2 2.5 2 64 ch208 KON 7.1 3.5 66 sj05 ch2 4 3.5 49 ch208 KON 6.7 6 66 sj05 ch2 3 2 49 ch208 KON 6 4.5 66 sj55 ch2 3 2 36 ch208 KON 7.2 4.5 66 sj55 ch2 2.5 2 36 ch208 KON 5.9 5 66 sj55 ch2 1.8 1 36 ch208 KON 4.1 2.8 66 sj55 ch2 1.8 1 36 ch208 KON 6 4.2 66 sj57 ch2 3 2.2 32 ch208 KON 5 4.5 66 sj57 ch2 3 2.3 32 ch208 KON 5 4.2 66 sj57 ch2 3 2.2 32 ch208 KON 5.8 3.9 66 sj20 ch2 2 1.8 52 ch208 KON 5 4.7 66 sj03 ch2 3 3 34 ch208 KON 5.9 4.4 66 sj03 ch2 4 3.1 34 ch208 KON 3 2 66 sj03 ch2 4 3 34 ch28 KON 5.2 3.8 78 sj03 ch2 4 2 34 ch28 KON 3 2 78 sj03 ch2 4 2 34 ch28 KON 4.8 4 78 sj02 ch2 2.8 2 34 ch28 KON 5.7 4.8 78 sj02 ch2 1.8 1.2 34 ch28 KON 5 4.2 78 sj02 ch2 3 2 34 ch28 KON 2.5 2 78 sj02 ch2 1.8 1 34 ch28 KON 3.9 3 78 sj39 ch2 5 4 35 ch28 KON 3 2.4 78 sj39 ch2 2.2 2 35 ch28 KON 4 3.5 78 sj39 ch2 2.3 1.9 35 ch28 KON 5 4 78 sj07 ch2 2 2 34 ch38 KON 6 4 49 sj07 ch2 4 3 34 ch38 KON 6 5.1 49 sj07 ch2 2 1.6 34 ch38 KON 5.1 3.2 49 sj07 ch2 2 1.2 34 ch38 KON 5 3.4 49 sj09 ch2 3 2.7 34 ch38 KON 4.9 4.1 49 sj09 ch2 3 2 34 ch38 KON 4.7 4 49 sj09 ch2 2 1.5 34 ch38 KON 5 3.7 49 sj25 ch2 1.9 1.3 35 ch38 KON 4.7 4 49 sj25 ch2 4 3.2 35 ch205 KON 6.1 4.1 57 sj40 ch2 2 1.8 60 ch205 KON 3.7 2.8 57 sj40 ch2 4 3 60 ch205 KON 4.1 3.8 57 sj40 ch2 3 2 60 ch205 KON 4.8 3.1 57 sj40 ch2 1.2 1 60 ch205 KON 3.2 3 57 sj40 ch2 4.2 4 60 ch205 KON 2.7 2.4 57 sj16 ch2 4.8 4 31 ch205 KON 3.9 3 57 sj16 ch2 4 3.5 31 ch205 KON 2.1 1.5 57 sj16 ch2 3 2.5 31 ch42 KON 4.2 3 41 sj16 ch2 3 2.5 31 ch42 KON 6.2 5 41 sj16 ch2 3 2.3 31 ch42 KON 2 1 41 sj16 ch2 3 2.3 31 ch42 KON 3 2.1 41 sj42 ch2 5.1 3 38 ch42 KON 4.7 4 41 sj42 ch2 6 4 38 162

Appendix B: continued ch42 KON 7 5.4 41 sj42 ch2 4.1 3 38 ch42 KON 4 3 41 sj42 ch2 3 2.8 38 ch42 KON 3.8 3.2 41 sj26 ch2 2 1.5 33 ch42 KON 4.8 2.8 41 sj26 ch2 3 2 33 ch42 KON 3 2 41 sj26 ch2 3 2 33 ch42 KON 2.8 2 41 sj26 ch2 2.8 2.2 33 ch42 KON 3 2.4 41 sj29 ch2 2 1.5 34 ch31 KON 6 4.9 44 sj36 ch2 6.1 3.9 46 ch31 KON 6.2 5 44 sj36 ch2 4.8 3 46 ch31 KON 5.5 3.5 44 sj36 ch2 2 1.5 46 ch31 KON 4 3.2 44 sj36 ch2 2 1.6 46 ch31 KON 4.9 4 44 sj36 ch2 2.8 2 46 ch31 KON 5 4.1 44 jw06 ch2 3 2 46 ch31 KON 3.4 1 44 sj30 ch2 3 3 44 ch31 KON 4.8 3.9 44 sj30 ch2 2.7 2 44 ch31 KON 6.5 5 44 sj43 ch2 7 4.5 89 ch31 KON 3.4 3 44 sj43 ch2 5 4 89 ch31 KON 3.2 2.6 44 sj43 ch2 4 3.2 89 ch31 KON 3 2.4 44 sj43 ch2 2 2 89 ch31 KON 2 1 44 jw04 ch2 3 2 39 sj49 ch2 3 2.8 34 jw04 ch2 2 1.6 39 sj45 ch2 2 2 35 sj45 ch2 4 2 35 sj45 ch2 3 2 35 sj45 ch2 3 2.8 35 sj45 ch2 2 1.8 35

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Appendix C: Measures of parasitism for the flatworm Temnosewellia batiola upon the crayfish host Euastacus hystricosus The total number of crayfish does not include individuals with occipital carapace length < 29 mm. Mean abundance includes infected and non-infected hosts. Mean Intensity of flatworms (95% CI) includes infected hosts only. Locality, creek Sunday East Bundoora Summer Booloumba Branch Branch Stony Dairy, Arley Bridge Lawley Langley Kondalilla Total name Kilcoy Creek ObiObi farm, Falls east ObiObi

Upland Conondale NP Bellthorpe NP Maleny Regions Kondalilla NP Total crayfish 17 57 60 2 10 89 1 76 22 8 17 54 4 43 460

Measures of parasitism Total 212 434 981 10 51 1350 0 2511 1137 263 636 4852 310 3522 16269 flatworms (1.3) (2.7) (6.0) (0.06) (0.3) (8.3) (0) (15.4) (7.0) (1.6) (3.9) (2.9) (1.91) (21.6) (% of total) Prevalence 7 41 40 2 2 50 0 75 20 8 16 54 3 41 359 (% of hosts (41.2) (71.9) (66.7) (100.0) (10.0) (56.2) (0) (98.7) (90.91 (100.0) (94.1) (100.0) (75.0) (95.35) (78.0) infected)

Mean 12 7 16 4 5 15 - 33 52 33 37 90 78 86 36 flatworm (-2,27) (5,10) (3-30) (1,6) (-4,14) (9-21) (25,41) (27,76) (17,49) (20,54) (73,107) (45,110) (69,103) (31,40) abundance (95% CI) Mean Intensity 30 63 50 4 26 41 - 40 60 33 4 90 79 90 56 of flatworms (-2,62) (57,68) (10,91) (1,6) (-17, 68) (28,55) (28,51) (32,87) (16,49) 2(24,60) (72,108) (33,125) (71,109) (7,105) (95% CI)

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