Identification, distribution and diet of Tasmanian predators inferred by scat DNA

Elodie Modave

Bachelor of geography, Geomatics and Geometrology and Master of Biology of

Organisms and Ecology – Université de Liège - Belgium

Institute for Applied Ecology

Faculty of Education, Science, Technology and Mathematics

University of Canberra, ACT, 2601, Australia

A thesis submitted in fulfilment of the requirements of the degree of Doctor of

Philosophy at the University of Canberra - Australia

September 2017 Elodie Modave – University of Canberra

ABSTRACT

Species interactions within an ecosystem can appear in many ways and because of this variety, changes to one species can have cascading effects on other species or the environment. The outcomes of these interactions can be direct or indirect and happen at the intra or inter-specific level and studying them in more depth can provide greater understanding of network functions in a geographic area. Organism interactions in an ecological community are often difficult to measure and study because of the multitude of processes happening simultaneously. Nonetheless, DNA-based techniques are available to explore some of the interactions occurring in nature.

In this thesis, I focus on the Tasmanian faunal intreactions. This provides a real-life example of an ecosystem that has been subjected to considerable change in its faunal assemblage in the past century owing mainly to invasive species (Chapter 1).

I investigate the identity, the distribution and the vertebrate diet of mammal predators in Tasmania: Sarcophilus harrisii (Tasmanian devil), Dasyurus viverrinus (eastern quoll),

Dasyurus maculatus (spotted-tailed quoll), Felis catus (feral cat) and Canis lupus familiaris

(dog). I aim to explore predator dynamics that evolved among them such as the effect of invasive species through direct predation, competition or niche utilisation.

The need to identify, detect and monitor species can be achieved by analysing the

DNA left by species in the environment. This provides a non-invasive technique to explore interactions among a cryptic, rare and threatened assemblage of species. By extracting eDNA

(environmental DNA) from trace samples, sequencing and correctly identifying the species it belongs to, I can obtain a wide range of information without the need to directly interfering with the multitude of species under study. The limiting factors of dealing with DNA can be overcome with new technologies and the costs are less extravagant than a few decades ago.

i - Identification, distribution and diet of Tasmanian predators inferred by scat DNA Elodie Modave – University of Canberra

Scats (i.e. faeces) provide a valuable source of eDNA and genetic information that they contain can persist for up to a few months in the environment, even when subjected to weather. This provides information about multiple predators and prey that can be discriminated in the laboratory, as well, as scats are a mixture of species DNA. Scats, combined with GPS localisations, provide single tools to evaluate interactions about predators relating to their environment, other predators and their prey. Because our target species, the predators in Tasmania, are relatively big in size, finding their faecal material in the landscape is relatively simple and DNA extraction has been made easier with the development of specialised kits.

I was able to develop a mini-barcode on the 12S rRNA mitochondrial gene region to identify the six large to medium-sized carnivores of Australia from scats (chapter 2). I used bioinformatics to develop the best primers from a range of sequences included in a reference

DNA database. The amplification success of the mini-barcode was assessed using known tissue samples and tested in the laboratory for its sensitivity using a serial dilution of tissue samples from the Tasmanian predator species. To examine its sensitivity further, I applied the mini-barcode to DNA extracted from known captive animal scats collected in 2011 and correct identification was recorded for all successfully sequenced portions of DNA.

This barcode was applied on ca. 1500 field-collected scats to model predator distribution in Tasmania (chapter 3) using several species distribution modeling methods. My data suggested that dogs did not influence the distribution of any species but were negatively influenced by devils. The devils and cats influenced negatively the distribution of each other and both influenced negatively the distribution of eastern quolls. Devils were mainly restricted to rainforests and eucalypt forests and woodlands, and cat scats were found mainly in non-eucalypt forests and woodlands. No habitat preference could be determined for quolls

ii - Identification, distribution and diet of Tasmanian predators inferred by scat DNA Elodie Modave – University of Canberra or dogs, and spotted-tailed quolls were removed from the study, owing to the small sample size.

Finally, I identified the vertebrate prey intake from more than 170 Tasmanian predator scats by applying two mitochondrial barcodes, 12SV5 and 16SMam and conclude on a vertebrate diet for Tasmanian devil, eastern and spotted-tailed quoll, cat and dog

(chapter 4). I found that cats have a wide diet which includes small to medium-sized vertebrates, compared to the other predators in this study while devils had a more specific diet, feeding mainly on five native prey taxa. The two quolls could not be discriminated with these two barcodes, but applying the mini-barcode developed in Chapter 2, amplifying a different short region from the 12S rRNA gene, on the scats identified as “quoll” (genus level only), I could distinguish them at species level. They had similar restricted diet feeding mainly on possums, pademelons and wallabies and separated their diet by size with the eastern quoll predating on smaller prey in general. Finally, dogs were also feeding on native wildlife such as half of their diet consisted of native mammals and half was livestock/dog food. Overall, I detected between nine (for quolls) and 31 (for cats) different prey items per predator with a total of 44 prey taxa identified within 176 scats and showed the value of using scat metabarcoding for future detection and/or monitoring surveys.

I determined that using scats, considerable information about identity, distribution and diet was obtained by retrieving DNA from trace samples enabling the detection of interactions among species relating to their environments. Tools are now available to detect possibly rare Tasmanian species without going into the trouble of trapping or observing them directly and enabling management solutions to be developed based on the type of information gathered with them.

iii - Identification, distribution and diet of Tasmanian predators inferred by scat DNA Elodie Modave – University of Canberra

TABLE OF CONTENTS ACKNOWLEDGMENTS ...... ix LIST OF FIGURES ...... xiii LIST OF TABLES...... xv LIST OF APPENDICES...... xvi LIST OF PUBLICATIONS LINKED WITH THIS THESIS ...... xvii

CHAPTER 1: GENERAL INTRODUCTION...... 1 BACKGROUND ...... 1 Detecting a species...... 1 Using scats as a source of eDNA...... 3 Reliability in identifying a species from DNA ...... 4 Tasmania...... 6 Analysis of diet ...... 9 AIMS AND STRUCTURE OF THE THESIS ...... 10 Description of chapters ...... 11

CHAPTER 2: A SINGLE MINI-BARCODE TEST TO SCREEN FOR AUSTRALIAN MAMMALIAN PREDATORS FROM ENVIRONMENTAL SAMPLES ...... 15 ABSTRACT...... 15 Keywords ...... 16 BACKGROUND ...... 16 DATA DESCRIPTION ...... 20 RESULTS ...... 21 Development of a new mammal mini-barcode...... 21 Bioinformatic evaluation of the mini-barcode...... 22 Evaluation of the amplification success and sensitivity of the AusPreda_12S primers ...... 24 Evaluation of amplification success from trace samples using known-origin scats...... 27 DISCUSSION...... 32 Considerations when working with scats...... 34 Conservation implications ...... 35 Future work...... 35 METHODS ...... 36 Selection of a candidate marker gene ...... 36 Development of a reference database for the 12S rRNA gene ...... 37

v - Identification, distribution and diet of Tasmanian predators inferred by scat DNA Elodie Modave – University of Canberra

Development of primers for the mini-barcode...... 38 Bioinformatic evaluation of the mini-barcode...... 41 Evaluation of the amplification success and sensitivity of the AusPreda_12S primers ...... 42 Evaluation of amplification success from trace samples using known-origin scats...... 43 Availability of supporting data and material ...... 44 List of abbreviations ...... 44

CHAPTER 3: HABITAT MODELING OF PREDATORS IN TASMANIA INFERRED BY GENETIC MONITORING OF SCATS: PRESENCE-ONLY OR PRESENCE- ABSENCE DATA? ...... 45 ABSTRACT...... 45 INTRODUCTION ...... 46 Factors affecting species distribution...... 48 Detection of species with eDNA...... 48 Models of Tasmanian predators...... 49 The presence-absence versus presence-only data conflict...... 49 Native and introduced predators in Tasmania ...... 50 Habitat suitability models for Tasmanian predators ...... 52 MATERIAL AND METHODS...... 54 Field work ...... 54 Samples selection...... 55 DNA extraction...... 55 PCR amplification...... 56 Sequencing...... 57 Predator identification...... 57 Patterns of co-occurrence of predators – First model: species interactions only...... 58 Species distribution modeling in R – Second model: scats as presence-absence data ...... 59 MaxEnt – Third model: scats as presence-only data ...... 65 MaxEnt - Third model: atlas data ...... 65 RESULTS ...... 66 Sequencing and predator identification ...... 66 Patterns of co-occurrence of predators – First model: species interactions only...... 67 Species distribution modeling in R – Second model: scats as presence-absence data ...... 69 Devil model ...... 71 Cat model...... 71

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Eastern quoll model ...... 71 Dog model...... 72 MaxEnt – Third model: scats as presence-only data ...... 74 MaxEnt - Third model: atlas data ...... 76 DISCUSSION AND CONCLUSIONS ...... 78 Comparison of models using scat data, atlas data and literature ...... 78 Limitations ...... 82 Application of SDMs built with scat data...... 86

CHAPTER 4: METABARCODING OF SCATS AS A TOOL FOR THE DETECTION AND MONITORING OF VERTEBRATE PREDATORS AND THEIR PREY ...... 87 ABSTRACT...... 87 INTRODUCTION ...... 88 METHODS ...... 95 Building a reference database ...... 97 Field work ...... 99 Samples selection...... 99 DNA extraction...... 100 qPCR to assess extraction dilutions ...... 100 qPCR with MID tagged primers ...... 101 AMPure cleaning ...... 102 Sequencing...... 103 Bioinformatic analyses...... 103 Predator identification tested against identification obtained using other primers...... 105 Selecting the closest relative when an identified taxon is non-Tasmanian ...... 105 Determining the vertebrate diet of each predator...... 106 Determining the vertebrate diet based on a predator location ...... 106 Detection of a rare prey...... 107 Confirming the identity of species using DNA and range obtained from atlas data ...... 107 RESULTS ...... 108 Samples selection...... 108 DNA extraction and qPCR to assess extraction dilutions...... 110 qPCR with MID tagged primers ...... 110 AMPure cleaning ...... 110 Bioinformatic analyses...... 110

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Predator identification tested against identification obtained using other primers...... 111 Selecting the closest relative when an identified taxon is non-Tasmanian...... 112 Determining the vertebrate diet of each predator and based on a predator location...... 113 Detection of a rare prey...... 120 Confirming the identity of species using DNA and range obtained from atlas data ...... 122 DISCUSSION AND CONCLUSION ...... 123 Predator diets obtained with this study and comparison with other published studies...... 125 Limitations of the metabarcoding technique...... 129 Detecting rare species ...... 129 Impact of invasive predators on native prey...... 130 Limits of detection using DNA...... 131 Primer sets used in this study and their limitation ...... 131 Future studies...... 132

CHAPTER 5: GENERAL CONCLUSION...... 133 Key findings...... 133 Importance of using non-invasive samples...... 137 Importance for conservation – management implications and new insights gained from this thesis ...... 137 Future studies and further work ...... 138

APPENDICES ...... 141-200 REFERENCES ...... 201-218

viii - Identification, distribution and diet of Tasmanian predators inferred by scat DNA Elodie Modave – University of Canberra

ACKNOWLEDGMENTS

This experiment was filled with personal growth, friendships, professional learning and achievements. I am proud today to have found the courage and motivation to accomplish a PhD.

First, I would like to thank my supervisor team, Stephen Sarre, Anna MacDonald and

Bernd Gruber, for all their help, useful comments, support, guidance, fun and kindness all along my candidature. I am grateful for the opportunity to have been chosen and trusted to embark on this journey with you. I would also like to thank my previous supervisor from the

Department of Primary Industries, Parks, Water and Environment Tasmania (DPIPWE),

Stephen Harris for his comments, help, care and sympathy as well. Thanks to Cat Campbell as well who shared the same project as me as a PhD student too for her advices, laughs, field work company, and everything else that was related to the project and beyond. I would like to thank Elise Dewar and Candida Barclay from DPIPWE that helps with the project, as well as other people that helped all along the way: Arthur Georges, Aaron Adamack, Peter Unmack,

Elise Furlan, Sumaiya Quasim, Llara Weaver, Jim Richley, Megan Hamilton, Iylani (Nur)

Ramlee and all the volunteers that helped us in the field.

I would like to thank the Institute for Applied Ecology for their help with all the administrative tasks and the making sure of the well-being of PhD students in general (office temperature!, barbecues, dinners …), Barbara Harriss, Ross Thompson, Liz Drummond,

Grant Collett, Susan Ward, Em Carlson, Helen Thomas and Jane Ebner. Thanks also to all the other PhD students and others from the IAE that help me not only with my work but with building new friendships in a place where I arrive not knowing anyone: Cat Campbell,

Rheyda Hinlo, Jonas Bylemans, Adrian Dusting, Suman, Foyez Shams, Shayer Alam, Alan

Couch, Sally Hatton, Teresa Gonzalez, Yasmin Cross, Alissa Monk, Rob Ubrihien, Daniela

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Cortez, Dorjee, Anthony Davidson, Andrew O’Reilly-Nugent, Rakhi Palit, Alex Henderson,

Margarita Medina, Matt Young and last but not least, Angelica Lopez.

This project was funded by Project 1.L.21 of the Invasive Animals Cooperative

Research Centre entitled “Mechanised Extraction and Next Generation Sequencing for the

Analysis of Trace DNA in Predator Scats”, and would not have been possible without their scholarship. I would especially like to thanks Tony Buckmaster, Andreas Glanznig and all the team behind the IA CRC for their support, annual camps and their financial contribution to several academic conferences and trainings. I met a lot of great friends at their annual camps such as Nadya Urakova, Michal Smielak, Aleona Swegen, Amy Iannella, Lana Harriott,

Jessica Sparkes, Frances Zewe, Papori Barua and Natalie Banks.I would also like to acknowledge the NRM North (Tasmania) for giving me a grant entiteled “Using eDNA to detect endangered New Holland mice in Tasmania” to be able to complete my metabarcoding study.

I would like to thank the staff from the Australian museum where I nearly spent two months in the Australian Centre for Wildlife Genomics under the supervision of Rebecca

Johnson and Greta Frankham. There, I worked on rhinoceroses’ horns and tiger bones, along with Greta, Anja Divljan, Kyle Ewart, Prue Armstrong, Scott Ginn, Melissa Danks and

Siobhan Dennison.

On a more personal note, I would like to thank my family at home for their support and my new family here in Australia for all the weekend parties, dinner parties, barbecues, cat sharing, Christmases, drinks, and so on, Charlie and Liz and Jon and Angelica. You made a life outside of the PhD possible and enjoyable far from our families and friends. I would like to thank Murphy (my cat! That’s the level of craziness I reached) for his company and presence, especially during the last couple of months of my thesis that I mostly spent at home

x - Identification, distribution and diet of Tasmanian predators inferred by scat DNA Elodie Modave – University of Canberra and I would also like to thank Bram for all his support (especially towards the end when I became a bundle of nerves!) and love. I am so happy of the new family we started in June of this year. To Bram and Chloé.

xi - Identification, distribution and diet of Tasmanian predators inferred by scat DNA Elodie Modave – University of Canberra

LIST OF FIGURES

FIGURE 1.1: Separation between mainland Australia and Tasmania, shoreline 8 reconstruction of the Bass Strait modified from Lambeck and Chappell, 2001 FIGURE 2.1: Gel showing the amplification success using AusPreda_12S mini- 27 barcode on 45 tissue samples FIGURE 2.2: Results of the sliding window analysis conducted using the R 40-41 package SPIDER for the 12S rRNA gene using a window size of 20 bp FIGURE 3.1: Interactions between species and preferred habitat types based on a 53 review of the literature FIGURE 3.2: predictors used to build the species distribution models 62 FIGURE 3.3: Map of the 714 predator scats identified from a systematic survey 67 FIGURE 3.4: Plot obtained for the four negative co-occurrences analysed where 69 the expected co-occurrence was bigger than 1 FIGURE 3.5: Interactions between species based on the outputs of cooccur 69 FIGURE 3.6: Confusion matrix for the devil model showing the accuracy of the 70 model obtained using a GLMM. In this example, the accuracy is 89% representing a good value FIGURE 3.7: Interactions between species and preferred habitat types based on 74 the outputs of the GLMM using scat data FIGURES 3.8: summary results of the devil model (A), cat model (B), eastern 75 quoll model (C) and dog model (D) using MaxEnt and scat data FIGURE 3.9: Interactions between species and preferred habitat types based on 75 the outputs of MaxEnt using scat data FIGURES 3.10: summary results of the devil model (A), cat model (B) and 77 eastern quoll model (C) using MaxEnt and atlas data FIGURE 3.11: Interactions between species and preferred habitat types based on 77 the outputs of MaxEnt using atlas data FIGURE 3.12: Interactions between species and preferred habitat types based on 82 the combining outputs of the literature, Cooccur, GLMM and MaxEnt using scat or atlas data FIGURE 4.1: Steps to follow to produce the libraries for metabarcoding 96 FIGURE 4.2: Trimming of low quality bases at the 3’ end from single ended 104 reads obtained from a MiSeq sequencer using Trimmomatic. Figure before (a) and after (b) treatment. The quality per base raised in the three runs. Illustrated example for run 1 FIGURE 4.3: Map of the location of the 287 scat samples selected for 109 metabarcoding based on the model FIGURES 4.4: Frequency of occurrence of prey in the scats of five Tasmanian 115- predators: Tasmanian devil, Sarcophilu harrisii (92 scats), spotted-tailed quoll, 117 Dasyurus maculatus (12 scats), eastern quoll, Dasyurus viverrinus (8 scats), feral cat, Felis catus (50 scats) and domestic dog, Canis lupus familiaris (14 scats) FIGURE 4.5: Predator diet difference using a NMDS 118

xiii - Identification, distribution and diet of Tasmanian predators inferred by scat DNA Elodie Modave – University of Canberra

FIGURE 4.6: Comparison of cat diets in three types of habitats: dry eucalypt 119 forest and woodlands, scrub, heathland and coastal complexes and agricultural, urban and exotic vegetation FIGURE 4.7: Map representing the seven instances where a long-nosed potoroo 121 (Potorous tridactylus) was present in a scat FIGURE 4.8: scat range versus the atlas data range for the long-nosed potoroo 122 (Potorous tridactylus) FIGURE 4.9: scat range versus the atlas data range for two predators and two 123 prey

xiv - Identification, distribution and diet of Tasmanian predators inferred by scat DNA Elodie Modave – University of Canberra

LIST OF TABLES

TABLE 2.1: Summary of results of genetic distance-based evaluations of the 23 AusPreda_12S mini-barcode TABLE 2.2: Results of qPCR tests: determine amplification success of the 25-26 AusPreda_12S mini-barcode from low template DNA TABLE 2.3: PCR and DNA sequencing results from 57 known-origin scat 29-31 samples screened using the AusPreda_12S mini-barcode TABLE 2.4: PCR primers used in this study 38 TABLE 3.1: Co-occurrence table obtained for the four negative co-occurrence 68 analysed where the expected co-occurrence was bigger than 1 using the package cooccur on R. The matrices represent the number of survey units in which each species was found to be absence (0) or presence (1) compared to an interacting species TABLES 3.2: summary results of the devil model (a), cat model (b), eastern quoll 73 model (c) and dog model (d), after removing non-significant predictors using a GLMMPQL TABLE 4.1: Diet of target predators found in the literature 93 TABLE 4.2: PCR primers used in this study 99 TABLE 4.3: Diet of target predators found in this study 128

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LIST OF APPENDICES

APPENDIX 2.1: Samples included in the 12S rRNA reference sequence 141-145 database used for primer design APPENDIX 2.2: R code for sliding windows analysis implemented using 146 SPIDER APPENDIX 2.3: R code for genetic distance based evaluation of the 147-150 AusPreda_12S mini-barcode implemented using SPIDER APPENDIX 2.4: Detailed results of genetic distance based evaluation of the 151-157 AusPreda_12S mini-barcode APPENDIX 2.5: Samples included in the laboratory evaluation of the 158-159 AusPreda_12S mini-barcode APPENDIX 2.6: Consensus sequences obtained from 53 known-origin scats by 160-164 amplification with the AusPreda_12S mini-barcode APPENDIX 3.1: Cooccur results with datasets from 2010 and 2014 separrately 165 APPENDIX 3.2: R code for building an SDM for the Tasmanian predators 166-169 APPENDIX 3.3: 789 scats used for building the distribution model 170-187 APPENDIX 4.1: Information about the New Holland mouse that explain which 188-189 scats were selected APPENDIX 4.2: R code for the scats selection for metabarcoding 190-191 APPENDIX 4.3: Resulting diet from metabarcoding study per predator 192-200

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LIST OF PUBLICATIONS LINKED WITH THIS THESIS

CHAPTER 2: A single mini-barcode test to screen for Australian mammalian 15 predators from environmental samples CHAPTER 3: Habitat modeling of predators in Tasmania inferred by genetic 45 monitoring of scats: presence-only or presence-absence data? CHAPTER 4: Metabarcoding of scats as a tool for the detection and monitoring of 87 vertebrate predators and their prey

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CHAPTER 1: GENERAL INTRODUCTION

BACKGROUND

An ecosystem is built on interacting individuals within the same species, between species and with the habitat they are occupying and a better understanding of species interactions would contribute to construct better networks of species communities (Glen and

Dickman 2005). What would be the consequences of these interactions on ecosystems?

Interactions of species occur through competition, predation, herbivory, habitat alteration, parasitism, disease, and genetic effects (O’Brien and Evermann, 1988; Dickman, 1996a;

Courchamp et al., 1999; Stokes et al., 2009a; Stokes et al., 2009b). To study interactions of species among them and with their environment, one must first be able to detect a species.

The primary goal of understanding species interactions is for conservation and the link between species presences in a certain type of habitat, with or without other species being present has been often used to validate the preservation planning of areas (Gu and

Swihart 2004). The detection of species is also relevant when working on managing an area.

The early detection of invasive species is critical as well as the detection of rare native species that can be threatened by non-natives (Dlugosch and Parker 2008; Wilcox et al. 2013) for multiple reasons: they can hybridize or compete with closely related native species such as they can try to replace them in the landscape by performing similar roles (Wilcox et al.

2013). Thus, being able to monitor native and the non-native taxa they influence is highly important, but obtaining precise information about these mechanisms is often costly or requires techniques not always available to managers (Gu and Swihart 2004).

Detecting a species

Multiple approaches exist and traditional methods of detection include trapping, ad hoc sightings, spotlighting, noise or scent surveys and so on. Depending on the taxa studied,

1 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA Elodie Modave – University of Canberra the method of choice will vary. For example, point count surveys are often used to count bird populations, where each occurrence (seen or heard) is recorded by observers in an area during a certain amount of time (Thompson 2002; Alldredge et al. 2007). Scent-stations were used successfully to study the interactions of two Pseudalopex fulvipes (Darwin’s fox) populations in Chile, for abundance measures of mammals in America and collection of hair samples of

Eurasian lynx populations was used for the same purpose (Conner et al. 1983; Jimenez et al.

1991; Schmidt and Kowalczyk 2006). Camera traps are often used when studying multiple species as the requirements necessary to trap one species might be different of the ones of another or if observation of interactions among species is seek. This method was used to monitor wildlife population living in a planted forest, to observe scent-marking tree use by cheetahs in South Africa and to survey small terrestrial mammals in Australia which demonstrated a more cost-effective technique than trapping them because there was no requirement to capture and tag animals to only cite a few (Ragg et al. 2000; Marnewick et al.

2006; Giman et al. 2007; De Bondi et al. 2010).

The downside of these techniques is that they are often time consuming (Cutler and

Swann 1999; Ragg et al. 2000; Marnewick et al. 2006; Giman et al. 2007; De Bondi et al.

2010), hard to apply depending on the taxa studied and may cause stress to some species

(Palme et al. 2005) or revealed inaccurate (Thompson 2002; Alldredge et al. 2007).

Limitations with morphology-based identification called the need to use a different tool that would bypass these constraints(Hebert et al. 2003). An alternative to these approaches is to use the DNA found in the environment: eDNA (environmental DNA). This technique can be particularly useful when dealing with rare or trap shy species, but a key feature is that it provides a non-invasive method that is appropriate when working with multiple native or threatened wildlife. Usually, eDNA is referred to by DNA found in water samples (Olson et al. 2012; Pilliod et al. 2013; Bohmann et al. 2014), but here, I used the term to refer to DNA

2 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA Elodie Modave – University of Canberra found in scats, as they are left in the terrestrial environment, in much the same way as DNA is left in the water environment.

DNA identification through barcodes (diagnostic portion of DNA) is increasingly used in ecological studies (Hebert et al. 2003; Armstrong and Ball 2005; Meusnier et al.

2008; Andersen et al. 2012; Kress et al. 2015). Metabarcoding is an extension of the single species case where multiple DNA fragments are identified from a single sample, usually by means of next generation sequencing approaches (Cristescu 2014)

Using scats as a source of eDNA

Predator scats provide an opportunity to gain information about the distribution of predators and the animals they prey upon. They can be found in prominent locations for some species and are often along landscape features and provide a systematic way to sample a potentially big area in one point in time to investigate the behaviour of multiple species simultaneously (Godsell 1983; Belcher 1995a; Banks et al. 2002; Pemberton et al. 2008;

Glen et al. 2011). Nevertheless, this approach comes with considerable challenges because only short DNA fragments can be extracted from trace samples owing to DNA degradation

(Deagle et al. 2006; Hajibabaei et al. 2006), the DNA recovered from scats will likely be of low concentration, hence the genes selected should be diagnostic enough among species and of good resolution at the species-genus or family level according to the study requirements.

The use of scats as a tool to monitor rare species is increasingly being investigated owing to their likely easy access and the potential to provide information such as abundance estimates, home-range sizes and diet (Piggott and Taylor 2003; Piggott et al. 2006). Primer sets used will depend on the questions being asked and the depth of the DNA analyses required for the study being undertaken.

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Using a metabarcoding method for diet analyses for terrestrial mammals is still in early stages of development and using scats as a source of DNA is a novel technique that I wanted to investigate in this thesis by combining the two approaches. Potential difficulties can arise from using scats to detect small taxa, but they were overcome in this study.

The interactions investigated here are competition (for food or habitat) and predation.

In this study, I focus on predator-environment, predator-predator and predator-prey interactions to explore which interactions influence predator distributions and diets. A diversity of tools is required to enable the detection and identification of predators and their prey and to investigate predator-prey interaction, I study the diet of predators through analyse of eDNA as well.

Reliability in identifying a species from DNA

DNA is increasingly being used to identify species in the environment (Hebert et al.

2003; Kress and Erickson 2008; Yao et al. 2010; Kress et al. 2015; chapter 2) and is considered as a reliable method yet, it requires development and evaluation for each new system or group of taxa under study. The use of barcodes to identify species from DNA has been demonstrated before and found that it has the capacity to provide useful information

(Hebert et al. 2004; Hajibabaei et al. 2006). Usually, mitochondrial DNA is explored compared to nuclear DNA owing to their multiple copies in each cell hence their increased presence in trace samples, their good resolution of identification at species level, and their circular genome helping the preservation of DNA (Palumbi and Cipriano 1998; Kocher et al.

2017). Databases are therefore needed to be able to compare an unknown sequence recovered from the eDNA sample to an already sequenced known specimen and online databases such as BOLD (Ratnasingham and Hebert 2007) or GenBank (NCBI) exist. Caution and validation is yet to apply as erroneous sequences are common on such databases. Sequences accessed from these databases then need to be trimmed at the specific gene region under study such as

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Cyt B (Bataille et al. 1999), 12S (W Shehzad et al. 2012), 16S (Moura et al. 2011) or CO1

(Clare et al. 2007) as they are mitochondrial gene regions often used in identification studies.

Here, I picked 12S and 16S as they demonstrated their efficacy in other studies dealing with the identification of mammals or other vertebrates from trace amount of DNA (e.g. ancient samples; Murray et al. 2013, or trace samples; Xiong et al. 2016).

To maximise the confidence of the identifications made throughout this study, I am using a diversity of tools in the computer or laboratory. These include building a DNA reference database for the two mitochondrial genes used in this thesis; 12S rRNA and 16S rRNA, which are known to provide good resolution at species level (Macdonald and Sarre

2016). To build the most comprehensive database possible, I collaborated with Dr Anna

MacDonald and Catriona Campbell with each of us focusing on different aspects of the mammalian fauna of Tasmania. My focus was on Tasmanian rodents although this did not preclude the collection of sequences for other taxa. To build this component of the database, I downloaded sequences from my target predators and prey (e.g. Dasyurus maculatus and

Hydromys chrysogaster), but also mainland relatives (e.g. Notomys alexis) , closely related species (e.g. Rattus fuscipes), invasive species that are known to be in Tasmania (e.g. Mus musculus) and livestock or domestic animals (e.g. Gallus gallus) from GenBank (NCBI:

National Center for Biotechnology Information, USA, http://www.ncbi.nlm.nih.gov/genbank/, Geer et al. 2009) and added my own sequences if they were under-represented on the online database (chapters 2, 3 and 4). Because I was interested in 12S and 16S, only sequences from these target mitochondrial regions were kept to acquire a widespread coverage of Australian taxa based on these regions. I also determine the species of origin of captive animal scats in a blind test and correctly identified predators

100% of the time that DNA could be recovered and was of acceptable quality (percentage quality of the consensus sequence created on Geneious; Biomatters, Auckland, New Zealand;

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Kearse et al. 2012; chapter 2). I also use atlas data to investigate the ranges of all predators and ten randomly selected prey to compare the distributions obtained with my scat data to the ones available with atlas data (chapter 4). Finally, I also use different portions of these two genes in the metabarcoding study and compared identifications between them and use the mini-barcode developed in chapter 2 as a third method to confirm the identifications (chapter

4).

Tasmania An ongoing investigation of the presence and distribution of Vulpes vulpes (red fox) in Tasmania provided an opportunity to investigate the interactions taking place in Tasmania.

Here, I wanted to observe and possibly measure the drivers of predator distribution in

Tasmania and examine predator diet in relation to invasive species as a case study. Tasmania was chosen because this area was previously surveyed by the team I entered at the University of Canberra (Sarre et al. 2013). These surveys were designed for the fox eradication program and it presented an opportunity to broaden the value of such surveys by undertaking a predator-prey survey in autumn 2014. Tasmania represents as well an area that underwent a large-scale habitat change in the past ca. 200 years.

Tasmania is an 64,300 km2 island south of the Australian mainland and was detached from the mainland around 10.000-14.000 years ago (Figure 1.1; Hocking and Driessen, 2000;

Lambeck and Chappell, 2001; Jones et al. 2004). This is the last place on earth including the top marsupial predator, Sarcophilus harrisii (Tasmanian devil; Jones 1997). This species is restricted to Tasmania since 1570 (Johnson and Wroe 2003). Its disappearance on mainland

Australia made it critically important on the view point of conservation and with the outbreak of the devil facial tumour disease (DFTD) in 1996 in Tasmania, their status is intensively monitored (Jones, 1997; Jones et al. 2004; Jones et al. 2007; Deakin et al. 2012; Brüniche-

Olsen et al. 2013; Hollings et al. 2013). Dasyurus viverrinus (eastern quolls) are also

6 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA Elodie Modave – University of Canberra restricted to Tasmania since 1964 (Nathan 1966) and Dasyurus maculatus (spotted-tailed quolls) declined in range and abundance in the mainland for the past ca. 120 years (Edgar and

Belcher 1995). Quolls collapse on the mainland is probably owed to the introduction and spread of red foxes and Felis catus (feral cats; Rolls 1969; Glen and Dickman 2005). With the introduction of feral cats in Tasmania since the European settlement (ca. 1788; Strahan,

1995; Dickman, 1996a; Smith and Quin, 1996; Short et al. 2002; Jones et al. 2007), Canis lupus familiaris (dogs) population starting to go feral, the recent introduction of foxes in the state (ca. 1998-2001; Jones et al. 2004; Saunders 2006; Berry et al. 2007; Vine et al. 2009;

Saunders et al. 2010; Sarre et al. 2013) and the disappearance of Thylacinus cynocephalus

(thylacine) in 1936 after being hunted to extinction (Paddle 2002), a whole new predators dynamic is likely to have developed. The evolution of the predator assemblage in Tasmania owing to the extinction of thylacines, the decrease in devils, with the outbreak of DFTD, and quolls and the introduction of cats, dogs and foxes since the European settlement established intricated relationships I wanted to investigate and clarify.

Figure 1.1 represents a shoreline reconstruction of the Bass Strait formation, separating Tasmania from the mainland. The isolation of Tasmania made Tasmanian species evolutionary distinct from their mainland relatives.

7 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA Elodie Modave – University of Canberra

Figure 1.1: Separation between mainland Australia and Tasmania, shoreline reconstruction of the Bass Strait modified from Lambeck and Chappell, 2001.

The negative effects on native wildlife by feral cats, red foxes and domestic dogs or

Canis lupus dingo (dingoes) are well documented in Australia, through predation (Robley et al. 2004; Denny and Dickman 2010;Wang and Fisher 2012; Peacock and Abbott 2014), competition for food (Glen and Dickman 2008; Glen and Dickman 2013) or space and dens

(Glen and Dickman 2008). A table and detailed information of diet per predator can be found in chapter 4 (table 4.1 and table 4.3) and the metabarcoding method I apply on scats will help to compare my results to the ones found usually in the literature. As foxes were not identified from DNA in my work, but were one of the reasons why Tasmania was selected as a field study, and are still potentially part of the predator guild in the state, I decided not to mention it further, when unnecessary.

Endangered species, as well as invasive species often require species-specific habitat management options that solicit understanding of their respective habitat use and interactions with other species and studying interactions among them, with the environment and with their prey can help gather valuable information for their management with potential development of areas of conservation interest, or eradication of invasive species. By the use of models

8 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA Elodie Modave – University of Canberra

(Species Distribution Models or SDM), I attempt to study some of the interactions occurring amongst Tasmanian predators and the link between a species presence and the habitat characteristics. The use of models in ecology is now accepted and a great tool for measuring ecosystems interactions (Kearney and Porter 2009; Pearce and Boyce 2006; Brotons et al.

2004).

Analysis of diet

Predicting the result of disturbances in general is a central objective of food web theory. The introduction or extinction of a species is one such disturbance that can radically change the food web stability (Kamran-Disfani and Golubski, 2013; Valenzuela et al., 2013) by altering the abundance and/or the spatial structure of a native species and lead to a radical modification of the global functioning of the ecosystem disrupted (Courchamp et al. 2003).

Average food consumption rates are correlated with the number of predators and the quantity of prey availability (Abrams 1984) and superpredators (i.e. predators at the top of their food chain) regulate and stabilise food-web through their effects on mesopredators and prey. The loss of a superpredators is likely to release mesopredators (i.e. medium-sized predator in the middle of a food chain) and consequently, harm native prey through trophic cascade effects

(Allen et al. 2013) where the effects of disruption cascade down the chain linking predator- prey interactions (Zavaleta et al., 2001; Glen and Dickman, 2005). Newly introduced predators can change the equilibrium of a trophic cascade (Kamran-Disfani and Golubski

2013) but in the presence of foragers that can adapt to newly replaced species, food webs complexity stabilizes community composition through food web dynamics balancing environmental perturbations (Kondoh 2003). Community ecology and ecosystem functions can be understood by the analysis of food webs and their dynamics but the information about a generalist predator’s diet is difficult to obtain and often assessed by morphological studies of gut or scat contents. DNA-based techniques provide an alternative approach that has the

9 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA Elodie Modave – University of Canberra potential to provide greater detail and accuracy than morphological approaches (Pompanon et al. 2012). Genetic methods have obvious benefits in studying the diet of predators which eat soft-bodied prey, prey with fragile bones or completely digested prey (Deagle et al., 2005;

Casper et al., 2007; Deagle et al., 2009). Sequencing of DNA in faecal samples for example, can discern more species than other dietary analysis techniques (Deagle et al. 2009) and

DNA-based species identification can still detect a prey with no visible remains (Jarman et al.

2004).

AIMS AND STRUCTURE OF THE THESIS

In this thesis, I address some questions that relate to the development and application of DNA-based detection. These are as follow: (1) can we confidently identify Tasmanian,

Australian medium to large mammal predators from a short (ca. 200 bp) DNA sequence? (2)

Can such sequences be applied to field samples? (3) if so, can it be used to determine the key factors that drive species distribution? (4) Can we observe any patterns of co-occurrence or avoidance between species? (5) Are there any specific habitat types restricting their distribution? (6) Can we detect prey from predator scats that usually would require trapping to observe as a tool for building understanding of faunal networks? (7) Can it help to target regions of conservation interest? (8) Can we also obtain good diet coverage?

I address three main aims in this thesis. The first is to develop a mini-barcode that could be used to reliably identify predators found in Australia from scat DNA. I wanted to evaluate the sensitivity and specificity of this mini-barcode on tissue samples and captive animal scats. For that approach, I needed to develop a reference DNA database that included

Australian quoll sequences as they were lacking from public databases such as GenBank. My second aim was to apply this barcode to field collected scats to identify Tasmanian predator presences, to use those presences to test for interactions between predators and to build

10 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA Elodie Modave – University of Canberra distribution models for them. Finally, I aimed to apply metabarcoding approaches to predator scats to detect specific diets for differences between multiple taxa and environments, and to survey for rare species, Pseudomys novaehollandiae (the New Holland mouse) and Potorous tridactylus (the long-nosed potoroo) as case studies for the monitoring and detection of elusive or rare species in the landscape. Each chapter is written as a paper for publication, except chapters 1 and 5 which act as a general introduction and a general conclusion.

Description of chapters

In chapter 2, I develop the mini-barcode used to identify Australian mammal predator using comparison between a short portion of DNA recovered in scats and a reference DNA database. Scat DNA is often degraded, meaning that longer DNA sequences, including standard DNA barcoding markers, are difficult to recover. Instead, I target a short (178 bp) mini-barcode to serve as a diagnostic marker. I develop the mini-barcode using bioinformatic tools and then test the sensitivity and specificity of the marker on tissue samples and captive animal scats. I could accurately detect and discriminate among eastern and spotted-tailed quolls, cats, dogs, foxes and Tasmanian devils from trace DNA samples.

In chapter 3, I apply the mini-barcode on scats from unknow species collected in the field in Tasmania. Once I determine the predator identification of more than 700 scats, I use mathematical models to investigate the drivers of predator distributions in Tasmania, combining biotic and abiotic effects. I then build species distribution models for four of the six target species for which there was sufficient data, and obtain patterns of co-occurrence of multiple species, directions of these interactions, and combined effects of the landscape using non-invasive scat samples. As an outcome of this work also, I described that, in this case, scats are likely to represent presence-absence rather than presence-only data. These two types of presence data in distribution modeling can lead to different models and different

11 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA Elodie Modave – University of Canberra interpretations, hence caution should be applied and to increase the confidence of my affirmation, repeated surveys in similar areas should be performed.

In chapter 4, I apply a metabarcoding technique on scats to identify multiple species in a single trace sample. I wanted to detect a rare mouse, the New Holland mouse to inform wildlife managers of where to focus the conservation programs to protect this endangered rodent species. The long-nosed potoroo detection was also investigated as part as the detection and monitoring of a rare native species. I also gather precious information about the diet of the three native and two invasive carnivores found in Tasmania. My technique demonstrates to be effective for detecting small prey in scats and obtain good diet coverage.

12 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA CHAPTER 2: A SINGLE MINI-BARCODE TEST TO SCREEN FOR AUSTRALIAN

MAMMALIAN PREDATORS FROM ENVIRONMENTAL SAMPLES

Published as: Modave, E., MacDonald, A.J., Sarre, S.D. 2017. A single mini-barcode test to screen for Australian mammalian predators from environmental samples. GigaScience, https://doi.org/10.1093/gigascience/gix052. Presented as published, with minor formatting changes.

ABSTRACT

Background: Identification of species from trace samples is now possible through the comparison of diagnostic DNA fragments against reference DNA sequence databases. DNA detection of animals from non-invasive samples, such as predator faeces (scats) that contain traces of DNA from their species of origin, has proved to be a valuable tool for management of elusive wildlife. However, application of this approach can be limited by the availability of appropriate genetic markers. Scat DNA is often degraded, meaning that longer DNA sequences, including standard DNA barcoding markers, are difficult to recover. Instead, targeted short diagnostic markers are required to serve as diagnostic mini-barcodes. The mitochondrial genome is a useful source of such trace DNA markers, because it provides good resolution at species level and occurs in high copy numbers per cell.

Results: We developed a mini-barcode, based on a short (178 bp) fragment of the conserved

12S rRNA mitochondrial gene sequence, with the goal of discriminating amongst the scats of large mammalian predators of Australia. We tested the sensitivity and specificity of our primers and can accurately detect and discriminate amongst quolls, cats, dogs, foxes and devils from trace DNA samples.

Conclusions: Our approach provides a cost effective, time efficient and non-invasive tool that enables identification of all eight medium-large mammal predators in Australia, including

15 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA native and introduced species, using a single test. With modification, this approach is likely to be of broad applicability elsewhere.

Keywords:

12S rRNA; Dasyurus; DNA barcoding; DNA detection; marsupial; monitoring

BACKGROUND

The looming biodiversity crisis, referred to by some as the Sixth Mass Extinction

(Ceballos et al. 2015), has made the conservation of wildlife a rapidly growing concern.

There is an urgent need to document the distribution of biodiversity as the foundation for identifying effective solutions to wildlife management issues. The rapid and reliable identification of species at local and regional scales can provide the first step towards determining the distribution of biodiversity in the landscape and changes that might be occurring in that distribution.

Advances in genetics and genomics have revolutionized many areas of biology and in particular, the identification of wildlife from trace and environmental samples (e.g. water, soil and faeces, or scats) is now possible through DNA barcoding (Boyer et al. 2012; Meusnier et al. 2008; Alberdi et al. 2012; Kress et al. 2015), where the identity of an unknown sample is established by comparing DNA sequences obtained from that sample to an appropriate reference sequence database. The application of DNA barcoding for the identification of species from such environmental DNA (eDNA) samples is useful, particularly when the target species is rare, elusive, difficult to trap or observe without direct interference with live animals, or where morphological identification is problematic (Berry and Sarre 2007; Wilcox et al. 2013; Rees et al. 2014; Berry et al. 2007; Korstian et al. 2015). It also makes possible the identification of diet from scats where morphological determinations are likely to be unsuitable for many elements of the diet (Wasim Shehzad et al. 2012; Koester et al. 2013;

16 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA Gupta and Kumar 2014; Grattarola et al. 2015; Shores et al. 2015). Consequently, eDNA analysis from environmental samples collected across a broad spatial and temporal distribution has great potential for enhancing biodiversity management, but is yet to be widely implemented (Takahara et al. 2013; Fujiwara et al. 2016).

The DNA associated with environmental samples tends to be of low quantity or quality and can be degraded. To ensure that markers for eDNA detection are specific and sensitive, target sequences, also known as mini-barcodes, should be short (i.e. 100-200 base pairs (bp); Deagle et al. 2006; Hajibabaei et al. 2006; Valentini et al. 2009; Boyer et al. 2012) and yet have high discriminatory power (Bahrmand et al. 1996; Meier et al. 2006; Boutros et al. 2009; Furlan et al. 2015). Marker selection therefore needs to account for the range of species likely to be encountered, as well as discriminating among potential sister taxa.

Mitochondrial DNA genes (mtDNA) are usually targeted because they occur in multiple copies in each cell and are therefore more common in trace samples than nuclear sequences, because they can give good resolution of identification at species level, and because their genome is circular, which helps preserving the DNA in some instances. In regions where little is known of the genetic characteristics of the faunal assemblage, identifying the most appropriate DNA sequences to target the fauna present to achieve acceptable levels of accuracy is a challenging exercise and requires a reference database that is sufficiently comprehensive to ensure accurate species assignment (Macdonald and Sarre 2016). In short, we need DNA barcoding markers that are appropriate to the question being addressed, the ecosystem considered and the taxonomic group studied. Most importantly, if DNA detection is going to be of practical benefit, we need to maximise its effectiveness by developing mini- barcodes that target as many taxa as possible, thus minimising the number of tests that need to be applied. Most DNA barcode studies so far implemented for detection of specific species from terrestrial systems have targeted single species (examples in Wilcox et al. 2013; Berry

17 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA et al. 2007; Wheat et al. 2016; Morin et al. 2016) to avoid the ambiguity that might arise by attempting to simultaneously identify multiple closely related taxa. Here, we tackle this problem using all extant medium-large Australian mammalian predators as a case study.

Australia has a unique assemblage of medium-large mammalian predators, including a suite of marsupials of Gondwanan heritage intermixed with relatively recently arrived eutherian mammals introduced by humans (Woinarski et al. 2015; Burbidge and McKenzie

1989). Here, we develop a DNA mini-barcode to discriminate amongst these key predators, with the goal of species identification using eDNA extracted from scats. We targeted the top native marsupial predators that are likely to produce large easily visible scats including: six species of quoll (four Australian and two New-Guinean; Dasyurus maculatus, D. viverrinus,

D. geoffroii, D. hallucatus, D. albopunctatus and D. spartacus), the Tasmanian devil

(Sarcophilus harrisii), and the extinct thylacine (Thylacinus cynocephalus), as well as key eutherian mammal predators: the native dingo (Canis lupus dingo), and the introduced domestic dog (Canis lupus familiaris), red fox, (Vulpes vulpes), and domestic cat (Felis catus) that are now feral in much of the country. Most of the native marsupial predators have been in decline since, or even before, European settlement in 1788 (Elkin 1951). Tasmanian devils and the thylacine became extinct on the Australian mainland within the last 3000 to

4000 years (Brown 2006), but still existed on the island of Tasmania at the time of European settlement. The thylacine has subsequently been hunted to extinction (King 1988; Paddle

2002) while devil populations have decreased dramatically since the 1990s following the emergence of Devil Facial Tumour Disease (Hollings et al. 2013; Brüniche-Olsen et al. 2013;

Bender et al. 2014). Several species of quoll, together with the dingo, have declined in distribution and abundance on the Australian mainland since European settlement from multiple causes that probably include habitat destruction, hunting, predation by cats and foxes, the spread of cane toads (Burnett 1997; Belcher 2003; Glen et al. 2009) and in the case

18 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA of dingos, hybridisation with domestic dogs. Although declining or extinct on the mainland, substantial populations of the Tasmanian devil, the spotted-tailed quoll (D. maculatus) and the eastern quoll (D. viverrinus) remain on the island of Tasmania where they have important ecological roles (Fancourt et al. 2013). However, recent evidence of foxes in Tasmania (Sarre et al. 2013) and potential competition with feral cats (Nogales et al. 2004; Lazenby et al.

2015) compound the issue, and have stimulated an urgent need to understand threats to native predator populations and enable effective management.

Two factors generally limit the application of a DNA barcoding approach. First, short diagnostic sequences that encompass the range of species to be targeted are difficult to find and are likely to be specific to a particular faunal assemblage. Second, the full suite of potential target organisms tends to be poorly known in most natural systems, and reference

DNA sequences are not available for many wildlife species, necessitating the development of reference libraries to guide marker selection and interpretation of results. Our goal was to develop a mini-barcode that can identify all medium to large mammal predators in Australia in a single analysis, including quolls, to species level. This has been difficult to achieve using existing genetic markers because of the high levels of sequence conservation observed between quoll species. We compiled a reference tissue collection and identified a mini- barcode based on the conserved 12S rRNA mitochondrial region that discriminated among taxa with minimal variation within species (Palumbi and Cipriano 1998; Hillis et al. 1996).

We evaluate the specificity and sensitivity of this mini-barcode using the framework outlined in Macdonald and Sarre 2015 and MacDonald and Sarre 2016. By targeting all extant medium to large carnivores in Australia, we aim to produce a mini-barcode that can be applied broadly within continental Australia as well as Tasmania. We demonstrate that despite close homology among some taxa, it is possible to design and implement eDNA markers with high discriminatory power for key continental terrestrial fauna incorporating

19 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA both marsupials and eutherian mammals. Our approach can be implemented in other parts of the world by targeting appropriate fauna assemblage in the development of the mini-barcode.

DATA DESCRIPTION

We identified the 12S rRNA gene as a target for development of a mini-barcode marker. We developed a reference DNA database for this gene, including 174 sequences from 24 genera and 41 mammal species. Sequences were obtained from GenBank, with additional targeted sequencing conducted for target species under-represented in GenBank.

Sequences were aligned, trimmed to 901 bp, and additional information on sample and sequence origins are provided in Appendix 2.1.

We used the R package SPIDER (Brown et al. 2012) to conduct a sliding window analysis

(Boyer et al. 2012) to identify a short diagnostic region of the 12S rRNA gene suitable for use as a mini-barcode marker. R code for this analysis is provided as text format (Appendix

2.2).

Following design of the AusPreda_12S primers, we conducted bioinformatic and laboratory evaluations of the sensitivity and specificity of the mini-barcode. We created two modified versions of our reference 12S rRNA database, trimmed to include only the 178 bp flanked by the mini-barcode AusPreda_12S primers. The “FULL” database included all 174 sequences from the original database, while the “UNIQUE” database included a subset of 44 sequences, where singleton species (species represented by only one haplotype) were removed, and where each remaining haplotype was represented by only a single sequence, and where singleton species (species represented by only one haplotype) were removed. We used the R package SPIDER to conduct genetic distance based evaluations of the AusPreda_12S primers, to identify the risks of incorrect or ambiguous species identifications based on this

20 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA sequence. R code for these analyses is provided as text format (Appendix 2.3) and detailed results are provided in Appendix 2.4.

We conducted PCRs to evaluate amplification success using the AusPreda_12S primers on tissue samples from a range of mammal species. Details of samples used are provided in

Appendix 2.5. We also tested amplification success from known-origin scats collected from six different predator species. All PCR products successfully amplified from scats were sequenced to confirm predator of origin: resulting sequences are provided here for FASTA format (Appendix 2.6).

RESULTS

Development of a new mammal mini-barcode

We selected the 12S rRNA gene as a promising candidate marker for development of a mini-barcode and developed a 12S rRNA reference sequence database for Australian mammals comprising 174 sequences. Within the 12S rRNA gene, we identified a 178 bp diagnostic mini-barcode region that displayed high levels of inter-specific variation. Within this region, the proportion of zero non-conspecific K2P distances was equal to zero for windows of 175 bp in length, and the number of diagnostic nucleotides per window was high.

We identified two potential primer sites with high proportions of zero non-conspecific K2P distances (>0.8) and low numbers of diagnostic nucleotides (0-1 nucleotides per 20bp window). We designed two conserved primers, AusPreda_12SF and AusPreda_12SR, to amplify this mini-barcode from a range of mammal species. The final PCR product was 218 bp in length, including the primers.

21 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA Bioinformatic evaluation of the mini-barcode

We used three different genetic distance based analyses to estimate the risks of species mis-identification when using our AusPreda_12S primers on samples of unknown origin (Table 2.1, Appendix 2.4). These analyses used versions of the 12S rRNA reference sequence database, trimmed to include only the 178 bp mini-barcode region. A nearNeighbour analysis of all sequences (the “FULL” database) correctly identified 155 sequences and incorrectly identified 19 sequences. All incorrectly identified sequences except one western quoll (D. geoffroii) from GenBank originated from species for which only a single reference sequence was available (i.e. singleton species), and thus the nearest neighbour was automatically another species. In most cases this nearest neighbour was a member of the same genus. For example, the nearest neighbour of the only bronze quoll (D. spartacus) sequence available was from the western quoll (D. geoffroii). This close genetic similarity has also been shown by Woolley et al. (Woolley et al. 2015). The western quoll incorrectly identified with the nearest neighbour analyses was closely related to the bronze quoll which can indicate that this particular western quoll sequence from GenBank

(KJ780027) was possibly mis-identified. Further analyses using a database including only unique haplotypes, from which singleton species were excluded (the “UNIQUE” database) identified correctly all 44 sequences.

22 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA Table 2.1: Summary of results of genetic distance-based evaluations of the AusPreda_12S mini-barcode. FULL (1% threshold) UNIQUE (3.5% threshold) Correct Incorrect No Correct Incorrect No / True / False Ambiguous ID / True / False Ambiguous ID Nearest neighbour 155 19 - - 44 0 - - Best close match 147 3 0 24 42 0 0 2 Thresh ID 142 3 5 24 42 0 0 2 FULL (3.5% threshold) Correct Incorrect No / True / False Ambiguous ID Best close match 152 6 0 16 Thresh ID 141 5 12 16

Legend: Summary of results of genetic distance-based evaluations of the AusPreda_12S mini- barcode conducted using the R package SPIDER to analyse the “FULL” (at 1% and 3.5% thresholds) and “UNIQUE” (at 3.5% threshold) reference sequences databases. The thresholds were calculated based on the minimum cumulative error (Appendix 2.3) and the 3.5% threshold for the “FULL” database allows for comparison between the two databases. The specified genetic distance thresholds were used for the bestCloseMatch and threshID analyses.

BestCloseMatch and ThreshID analyses, which both assume that sequences from a single species fall within a specified genetic distance threshold, correctly identified 147 and

142 sequences respectively in the “FULL” database using the 1% threshold given by the minimum cumulative error. Three sequences were incorrectly identified in both analyses:

Dasyurus spartacus (AF009892), Pseudantechinus macdonnellensis (EU086642) and

Pseudantechinus roryi (EU086650) each representing singleton species, and falling within the 1% genetic distance threshold of a congeneric species enabling them to be mistaken for their close relatives. Five D. geoffroii sequences were correctly identified using

BestCloseMatch but were ambiguously identified in the ThreshID analysis because of a close similarity (within the 1% genetic distance threshold) with the single D. spartacus sequence.

A further 24 sequences could not be identified in either analysis because all other sequences within the reference database were more than 1% different. The majority of these sequences

23 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA were from singletons, but a more relaxed genetic distance threshold (2%-5%) identified them correctly. BestCloseMatch and ThreshID analyses of the “UNIQUE” database identified correctly 42 of 44 sequences, but the two remaining sequences, both from Dasycercus cristicauda, could not be identified (Table 2.1; details of results: Appendix 2.4). As noted previously, these sequences would have been correctly identified if a genetic distance threshold of 5% was used. This represents a high level of divergence between two conspecific sequences, but as both of these sequences were obtained from GenBank, and the origin of one of the samples is unknown, we cannot rule out sample misidentification or sequencing error in this instance.

Using a 3.5% genetic threshold for the “FULL” database, to allow for comparison with the results obtained with the “UNIQUE” database, correctly identified more sequences with the BestCloseMatch analysis which was to be expected using a more relaxed genetic threshold allowing for more mismatches among sequences. Nevertheless, six sequences previously resulting in an “No ID” match became correctly identified and two became incorrectly identified. The western quoll (KJ780027) became incorrectly identified using a higher threshold which, once again, lead us to believe that this sequences from GenBank was incorrectly identified to start with. Comparing the ThreshID results with the more conservative approach used with the 1% threshold, five sequences that were previously correctly identified became ambiguous and from eight sequences resulting in a “No ID” match, four became correctly identified, two became incorrectly identified and two had an ambiguous identification.

Evaluation of the amplification success and sensitivity of the AusPreda_12S primers

Our mini-barcode was successfully amplified from all 45 tissue samples tested, including samples from a wide taxonomic range of Australian mammals (40 species), as well

24 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA as a reptile, an amphibian and a bird (Figure 2.1, Appendix 2.5). This demonstrates the broad applicability of the primers across the mammalian taxa and their potential applicability to other vertebrate classes. Because we aimed to target both marsupial and eutherian mammals, we were unable to identify a mini-barcode that amplified only the six target species.

We also successfully amplified our mini-barcode from a wide range of input template

DNA concentrations. We set up serial dilutions of DNA from six predator species.

Amplification was successful for all three qPCR replicates from all six species for all dilutions from 9 ng / µl to 9 pg / µl inclusive, demonstrating that the primers can amplify from low quantity DNA. Amplification success was less consistent at the highest and lowest

DNA concentrations, estimated at 90 ng / µl, 0.9 pg /µl and 0.09 pg / µl (Table 2.2) indicating that reliability of predator detection from DNA below 9 pg / µl may be poor. Failure to amplify from highly concentrated DNA, despite successful amplification from dilutions of the same DNA extracts, may reflect the presence of PCR inhibitors in these extracts, which were obtained from museum and roadkill specimens.

Table 2.2: Results of qPCR tests conducted to evaluate amplification success of the AusPreda_12S mini-barcode from low template DNA. Six DNA samples were serially diluted, with amplification success determined by comparison of CT values1 for three replicates of each dilution. Species Dilution Replicate 1 Replicate 2 Replicate 3 CT Mean2 1 in 10 (9 ng/µl) 12.444 14.281 13.373 13.366 1 in 100 (0.9 ng/µl) 16.3463 13.399 13.368 13.384 Cat 1 in 1000 (0.09 ng/µl) 19.252 23.382 23.994 22.209 N22b 1 in 10 000 (9 pg/µl) 31.252 27.486 27.604 28.781 1 in 100 000 (0.9 pg/µl) 31.483 31.476 29.386 30.782 1 in 1 000 000 (0.09 pg/µl) Undetermined Undetermined Undetermined - 1 in 10 (9 ng/µl) 14.303 13.019 15.363 14.228 1 in 100 (0.9 ng/µl) 15.879 16.791 16.623 16.431 Dingo 1 in 1000 (0.09 ng/µl) 19.719 19.237 17.424 18.793 AA15020 1 in 10 000 (9 pg/µl) 22.652 24.957 25.196 24.268 1 in 100 000 (0.9 pg/µl) Undetermined Undetermined Undetermined - 1 in 1 000 000 (0.09 pg/µl) Undetermined Undetermined Undetermined -

25 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA 1 in 10 (9 ng/µl) 14.128 13.509 13.449 13.695 1 in 100 (0.9 ng/µl) 17.267 20.8663 17.235 17.251 Eastern 1 in 1000 (0.09 ng/µl) 17.662 21.523 21.385 20.190 quoll 1 in 10 000 (9 pg/µl) 24.346 26.474 25.653 25.491 UC1214 1 in 100 000 (0.9 pg/µl) Undetermined Undetermined 34.570 34.570 1 in 1 000 000 (0.09 pg/µl) Undetermined Undetermined Undetermined - 1 in 10 (9 ng/µl) 13.460 13.928 14.048 13.812 Spotted- 1 in 100 (0.9 ng/µl) 17.517 16.447 18.653 17.539 tailed 1 in 1000 (0.09 ng/µl) 20.374 19.540 17.003 18.972 quoll 1 in 10 000 (9 pg/µl) 27.511 25.453 23.851 25.605 A3395 1 in 100 000 (0.9 pg/µl) 30.158 30.132 25.107 28.466 1 in 1 000 000 (0.09 pg/µl) Undetermined 35.172 Undetermined 35.172 1 in 10 (9 ng/µl) 15.547 15.528 14.628 15.234 1 in 100 (0.9 ng/µl) 19.566 17.524 16.860 17.983 Red fox 1 in 1000 (0.09 ng/µl) 21.915 22.827 22.360 22.367 UC0401 1 in 10 000 (9 pg/µl) 26.672 25.460 25.508 25.880 1 in 100 000 (0.9 pg/µl) 31.672 30.914 28.863 30.483 1 in 1 000 000 (0.09 pg/µl) Undetermined 31.601 Undetermined 31.601 1 in 10 (9 ng/µl) 15.502 16.8103 14.536 15.019 1 in 100 (0.9 ng/µl) 19.736 18.729 19.702 19.389 Tasmanian 1 in 1000 (0.09 ng/µl) 23.517 22.999 21.591 22.702 devil 1 in 10 000 (9 pg/µl) 27.216 28.006 24.130 26.451 A3357 1 in 100 000 (0.9 pg/µl) 30.876 30.734 28.977 30.196 1 in 1 000 000 (0.09 pg/µl) 32.534 Undetermined Undetermined 32.534

1 Numbers represent observed CT (cycle threshold) values for each replicate qPCR of a series of DNA dilutions. The CT value represents the number of cycles required for the fluorescent signal of a qPCR machine to cross the predetermined threshold, here set at 5000 ΔRn. 2 Undetermined results were excluded when calculating mean CT. 3 Where the qPCR traces were of an irregular shape (three replicates), the replicate was excluded when calculating mean CT.

26 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA Figure 2.1: Gel showing amplification success from 45 known tissue samples representing 40 species, using the AusPreda_12S mini-barcode primers developed in this study, and a PCR negative. The expected amplicon size is 218bp. Samples are grouped by species as follows: lanes 1 and 2: Felis catus, 3: Canis lupus familiaris, 4: Canis lupus dingo, 5 and 6: Dasyurus viverrinus, 7 and 8: Dasyurus maculatus, 9 and 10: Vulpes vulpes, 11 and 12: Sarcophilus harrisii, 13: Oryctolagus cuniculus, 14: Lepus capensis, 15: Bos Taurus, 16: Ornithorhyncus anatinus, 17: Trichosorus vulpecula, 18: Petaurus breviceps, 19: Tachyglossus aculeatus, 20: Potorous tridactylus, 21: Bettongia gaimardi, 22: Dactylopsila trivirgata, 23: Burramys parvus, 24: Macropus rufogriseus, 25: Thylogale billardierii, 26: Pseudomys gracilacaudatus, 27: Pseudocheirus peregrinus, 28: Antechinus minimus, 29: Tiliqua nigrolutea, 30: Vombatus ursinus, 31: Isoodon obesulus, 32: Macropus giganteus, 33: Parameles gunnii, 34: Sminthopsis leucopus, 35: Mus musculus, 36: Planigale gilesi, 37: Rattus lutreolus velutinus, 38: Phascogale tapoatafa, 39: Hydromys chrysogaster, 40: Macropus rufus, 41: Vicugna pacos, 42: Dasyurus hallucatus, 43: Lathamus discolour, 44: Geocrinia laevis, 45: Dasyurus geoffroii, 46: PCR negative.

Evaluation of amplification success from trace samples using known-origin scats

We tested the ability of the AusPreda_12S primers to correctly identify known predators by analysing scats from captive animals. 57 scats were tested and amplified product was obtained from 53 samples. We obtained good quality DNA sequences, ranging from 116 bp to 182 bp in length, from 49 (92%) of these 53 scats (Appenfdix 2.6). The species of

27 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA origin was correctly identified for all 49 samples, with scat DNA sequences matched to appropriate GenBank reference sequences with 97-100% sequence identity (Table 2.3).

28 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA Table 2.3: PCR and DNA sequencing results from 57 known-origin scat samples screened using the AusPreda_12S mini-barcode. Sample Scientific name Common name Amplified Sequenced Closest sequence % e valueβ match using IDα BLAST 100111-27 Canis lupus familiaris Dog Y Y Dog 99.4 1.55E-84 120111-02 Canis lupus familiaris Dog Y Y Dog 100 6.52E-78 121010-11 Canis lupus familiaris Dog Y Y Dog 99.4 1.22E-85 121010-16 Canis lupus familiaris Dog Y Y Dog 98.4 2.08E-83 121010-17 Canis lupus familiaris Dog Y Y Dog 99.4 1.98E-83 121010-30 Canis lupus familiaris Dog Y Y Dog 99.4 5.54E-84 121010-52 Canis lupus familiaris Dog Y N NA NA NA 121010-53 Canis lupus familiaris Dog Y Y Dog 98.9 2.60E-82 121010-54 Canis lupus familiaris Dog Y Y Dog 99.4 1.22E-85 121010-56 Canis lupus familiaris Dog Y Y Dog 98.9 7.22E-83 121110-55 Canis lupus familiaris Dog Y Y Dog 99.4 5.54E-84 170211-12 Canis lupus familiaris Dog N NA NA NA NA 041110-66 Dasyurus maculatus Spotted-tailed quoll Y Y Spotted-tailed quoll 98.4 2.08E-83 101110-9 Dasyurus maculatus Spotted-tailed quoll Y Y Spotted-tailed quoll 98.2 2.33E-72 170211-25 Dasyurus maculatus Spotted-tailed quoll Y Y Spotted-tailed quoll 99.4 1.55E-84 041110-01 Dasyurus viverrinus Eastern quoll Y Y Eastern quoll 99.4 2.25E-72 041110-04 Dasyurus viverrinus Eastern quoll Y Y Eastern quoll 100 2.05E-88 041110-07 Dasyurus viverrinus Eastern quoll Y Y Eastern quoll 100 4.80E-74 041110-15 Dasyurus viverrinus Eastern quoll Y Y Eastern quoll 100 1.01E-54 041110-74 Dasyurus viverrinus Eastern quoll Y Y Eastern quoll 100 1.19E-85 041110-80 Dasyurus viverrinus Eastern quoll Y Y Eastern quoll 100 9.34E-87 100111-05 Dasyurus viverrinus Eastern quoll Y Y Eastern quoll 100 3.34E-86 100111-31 Dasyurus viverrinus Eastern quoll Y Y Eastern quoll 100 3.34E-86

29 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA 120111-32 Dasyurus viverrinus Eastern quoll N NA NA NA NA 120111-33 Dasyurus viverrinus Eastern quoll Y Y Eastern quoll 100 2.61E-87 170211-14 Dasyurus viverrinus Eastern quoll Y Y Eastern quoll 100 2.61E-87 100111-04 Felis catus Feral cat Y Y Feral cat 100 1.54E-79 120111-10 Felis catus Feral cat Y Y Feral cat 100 1.56E-79 120111-12 Felis catus Feral cat Y Y Feral cat 100 1.58E-79 120111-31 Felis catus Feral cat Y N NA NA NA 170211-13 Felis catus Feral cat Y Y Feral cat 99.2 3.36E-60 170211-21 Felis catus Feral cat Y Y Feral cat 100 1.61E-79 170211-22 Felis catus Feral cat Y Y Feral cat 100 1.55E-79 041110-42 Sarcophilus harrisii Tasmanian devil Y Y Tasmanian devil 100 4.02E-80 041110-47 Sarcophilus harrisii Tasmanian devil Y Y Tasmanian devil 100 9.34E-87 041110-48 Sarcophilus harrisii Tasmanian devil Y Y Tasmanian devil 100 2.61E-87 041110-53 Sarcophilus harrisii Tasmanian devil Y Y Tasmanian devil 100 2.47E-82 041110-59 Sarcophilus harrisii Tasmanian devil Y Y Tasmanian devil 100 7.32E-88 121010-06 Sarcophilus harrisii Tasmanian devil Y Y Tasmanian devil 100 4.02E-80 121010-22 Sarcophilus harrisii Tasmanian devil Y Y Tasmanian devil 99.4 5.58E-84 200910-24 Sarcophilus harrisii Tasmanian devil Y Y Tasmanian devil 100 9.34E-87 200910-25 Sarcophilus harrisii Tasmanian devil Y Y Tasmanian devil 100 2.61E-87 080211-04 Vulpes vulpes Red fox Y Y Red fox 99.4 1.22E-85 080211-05 Vulpes vulpes Red fox Y Y Red fox 99.4 5.54E-84 080211-06 Vulpes vulpes Red fox Y N NA NA NA 080211-07 Vulpes vulpes Red fox Y Y Red fox 97.2 9.35E-61 080211-08 Vulpes vulpes Red fox Y Y Red fox 99.4 5.54E-84 080211-09 Vulpes vulpes Red fox N NA NA NA NA 080211-10 Vulpes vulpes Red fox Y Y Red fox 100 6.52E-78 080211-11 Vulpes vulpes Red fox Y Y Red fox 98.9 5.66E-84

30 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA 080211-12 Vulpes vulpes Red fox Y N NA NA NA 080211-13 Vulpes vulpes Red fox Y Y Red fox 98.8 3.99E-75 080211-14 Vulpes vulpes Red fox Y Y Red fox 100 6.52E-78 080211-15 Vulpes vulpes Red fox Y Y Red fox 99.1 2.63E-50 080211-16 Vulpes vulpes Red fox Y Y Red fox 100 6.52E-78 080211-17 Vulpes vulpes Red fox N NA NA NA NA 080211-18 Vulpes vulpes Red fox Y Y Red fox 97.8 1.23E-80

α % ID is the percentage pairwise identity between the query sequence and the matching sequence identified using BLAST.

β The e-value represents the number of BLAST hits expected by chance. The lower the e-value is, the better.

31 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA DISCUSSION

Non-invasive environmental DNA-based methods can provide a novel approach to the detection of cryptic animals in large-scale surveys (Schwartz et al. 2007), with applications to wildlife management. Such DNA approaches can make important contributions to the ability to detect incursions or monitor established invasive species (Darling and Blum 2007; Bastos et al. 2011; Sarre et al. 2013) or to detect very rare or declining species of conservation significance (Jerde et al. 2011; Rees et al. 2014).

Here, we report a PCR-based mini-barcode test for medium-large Australian mammalian predators. This test can amplify DNA from and discriminate among the four quoll species found in Australia, as well as the Tasmanian devil (the only other extant large marsupial predator) and introduced mammal carnivores with a high level of accuracy. We expect that these primers will also amplify DNA from both species of New Guinean quoll.

Previous studies, aimed at identifying species from scats or hairs, have applied barcoding methods to detect individual species across multiple time points (examples in Fernández et al.

2006; McKelvey et al. 2006). Here we have shown that it is also possible to identify multiple species by implementing a single DNA test, using a straightforward PCR and Sanger sequencing approach. All clear sequences obtained from 49 scats of six target predator species were correctly identified to species level. In the small number of cases where a clear sequence was not obtained from a scat, we found that the sequences obtained were mixed, probably arising from the amplification of two or more species in the same sample. This could arise from cross contamination among samples but is more likely the result of the amplification of prey DNA present in the scat (Hausknecht et al. 2007; Grattarola et al.

2015). We have previously observed this phenomenon when using a single species test to detect fox DNA, where rabbit or hare DNA were sometimes erroneously amplified (Burnett

1997). This demonstrates the need to account for the history of samples analysed (how they

32 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA were obtained, how fresh they were upon collection, and how samples and DNA extracts were stored) and the importance of a DNA sequencing step in any of these analyses to enable recognition of non-specific PCR amplification. In practice, mixed sequences cannot be used to identify the predator with confidence and therefore such samples must be excluded from analysis. In addition to successful amplification of scat DNA, we demonstrate that our mini- barcode primers can successfully amplify low-template DNA (at least as low as 0.9 pg / µl) from museum samples. This provides further evidence of the utility of this marker for application to eDNA studies.

Whilst DNA metabarcoding may more clearly determine which species are represented in mixed samples, metabarcoding methods are relatively costly and require more specialist equipment, which may not be available to many wildlife managers. In this study,

PCR and Sanger sequencing reliably identified the predator of origin for 86% of scat samples, which is likely to be sufficient for many management applications and is a higher success rate than has been reported for several other faecal DNA studies (for example Sarre et al. 2013), where 79% of sequences were amplified using a 134 bp fragment and (Cheng and Lin 2016), where <70% of sequences were amplified using regions ranging from 243 bp to 708 bp according to target taxon). Using our mini-barcode, DNA can be screened for the presence of multiple Australian predator species in a single and inexpensive test, without the need to develop and apply a set of species-specific primers for each predator of interest. We provide a non-invasive instrument with potential utility for scientists or managers working with endangered or invasive Australian predators, but a similar approach could be used to target predator assemblages in other regions.

The bioinformatic evaluation of our mini-barcode shows that this marker can reliably discriminate among the eight target predator species (eastern, western, northern and spotted- tail quolls, Tasmanian devils, cats, dogs and foxes) in Australia. The close genetic similarity

33 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA between the bronze quoll (from New Guinea) and the western quoll (from Australia), described above and supported by (Woolley et al. 2015), may pose some problems for reliable species identification from unknown samples, but the different geographic distributions of these two species will likely provide a clear identification in most cases. The most appropriate threshold to be used will depend on the management context and the relative importance of false positive identifications, but in most cases, an ambiguous or “No

ID” identification would be a better result for a sample than to result in a correct identification when this is erroneous.

Further development of our reference database, to include additional D. albopunctatus and D. spartacus sequences, will be required to better understand the utility of this test for identification of specimens to species level in New Guinea. Likewise, a better reference database would improve the relevance of this DNA test for application to historic samples.

Sequences from the extinct thylacine could be clearly identified in our initial analyses, but this species could not be included in the UNIQUE database for further bioinformatic analysis because only one 12S rRNA haplotype was available. Finally, because we are working with mitochondrial DNA which is maternally inherited, we cannot currently use this test to distinguish between dogs and dingos, in part because of the prevalence of hybrids in many wild populations (Savolainen et al. 2004; Radford et al. 2012).

Considerations when working with scats

One important consideration for future studies using the AusPreda_12S primers is the need to understand the ecological role of the species from which eDNA is detected.

Typically, predator DNA is the most abundant in scats, owing to the release of epithelial cells during defecation (Symondson 2002; Jarman et al. 2004; Deagle et al. 2005). However, because there are multiple potential sources of DNA in scat samples, it is also possible that

34 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA these primers will amplify DNA from prey species. In some cases, this will be obvious, for example where the scats of the prey species detected are clearly morphologically different from carnivore scats. However, other results may be more difficult to interpret, for example where mixed sequences, representing two different predator species which could potentially predate upon one another, are obtained from the same sample.

Conservation implications

The AusPreda_12S primers provide an opportunity to enhance monitoring of predators across Australia for conservation purposes (Galimberti et al. 2015). For example, western quolls were successfully re-established in Western Australia in 1987 after a recovery plan implemented over 13 years, in areas previously baited with 1080 to remove introduced species (Morris et al. 2003). Western quolls from Western Australia were also re-introduced to the Flinders Ranges in South Australia in 2014, and that population is now breeding in the wild, with more than 60 young born since their relocation (Department of the Environment

2015), (Katsineris 2015). Eastern quolls were re-introduced from Tasmania to Mulligans Flat

Woodland Sanctuary, in the Australian Capital Territory, in early 2016 (Hunt 2016). There are also proposals to reintroduce devils to south-eastern mainland Australia to reduce the negative impact that dingo control has on small-mammals through mesopredator release

(Glen and Dickman 2005; Johnson and VanDerWal 2009; Ritchie and Johnson 2009; Daniel

O Hunter et al. 2015). The development of this mini-barcode now provides a new tool with which to monitor these re-introduced species, and the non-native predators that threaten them, from non-invasive samples.

Future work

In the future, this predator identification tool may be used to model the distribution of predators in Tasmania or mainland Australia, supplementing more traditional data obtained

35 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA from live trapping and sightings. It is now possible to reliably detect a predator of interest from non-invasive samples. Using the AusPreda_12S primers in an initial sample screening step may provide further opportunities to study the diets of each specific predator, by identifying samples to include in targeted metabarcoding studies. This test could also be more broadly useful, with potential application to detection and monitoring of the two New

Guinean quoll species.

METHODS

Selection of a candidate marker gene

We compiled initial reference databases for three mitochondrial genes, 12S rRNA,

16S rRNA and ND2, all of which have proven useful for species detection in other studies

(Deagle et al. 2005; Di Finizio et al. 2007; Pompanon et al. 2012; Taberlet et al. 2012;

Deagle et al. 2014). These databases used sequences collected mainly from GenBank

(GenBank, NCBI; Geer et al. 2009).

We used the R package SPIDER to identify potential mini-barcodes from these initial reference databases. Our criteria were to identify regions of between 100 and 200 bp in length

(the maximum that can be reasonably amplified from many eDNA samples) that displayed high levels of inter-specific variation within the region, and that were flanked by primer sites that were well-conserved across all taxa, but particularly across our six key Tasmanian target species. For each gene, we conducted a sliding window analysis with window sizes of 100,

125, 150 and 175 bp, to identify potential mini-barcodes. For each window, we evaluated the number of diagnostic nucleotides per window and the proportion of zero non-conspecific

K2P distances, to identify regions with high inter-specific variation, that may be used to discriminate among species. Subsequently, we used further sliding window analyses to identify conserved primer sites adjacent to candidate mini-barcode regions. We used window

36 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA sizes of 20, 25 and 30 bp to identify potential sites for primer development. Of these, a window size of 20 gave the best results, so we adopted 20 bp as the standard primer length.

We were not able to identify any candidate mini-barcode markers that met all of our criteria from the 16S rRNA and ND2 genes, so all subsequent work was focused on the 12S rRNA gene.

Development of a reference database for the 12S rRNA gene

We constructed a reference database for the 12S rRNA gene. This included representatives of native and introduced Tasmanian mammal predators and their potential prey species, their mainland Australian relatives, livestock and other introduced species (i.e. goat, sheep, horse, wild boar, cow and fallow deer) and humans. Importantly, all six recognised quoll species (four Australian and two New Guinean) were represented (Appendix

2.1). The final reference database consisted of 174 sequences representing 41 species from 24 genera. We obtained the majority of sequences from GenBank, but we generated additional sequences from a selection of species that were under-represented in the public database.

DNA was extracted from tissue samples from museum specimens, road-killed animals, and western quoll tissues collected during a reintroduction program in the Flinders Ranges (South

Australia) involving quolls of Western Australian origin (Moseby et al. 2015). We used a salting out method (MacDonald et al. 2011) with minor modifications as follows: our lysis buffer included 10% SDS and tissues were digested in a thermomixer for three hours at 56 °C with mixing at 500 rpm. DNA pellets were air dried for 30-60 minutes and re-suspended in

50 µl of ddH2O. Genomic DNA extracts were quantified using a Nanodrop ND1000 spectrophotometer (Thermo Fischer Scientific) and samples were diluted with ddH2O to a final concentration of ca 40 ng/ µl. The entire 12S gene region was amplified by PCR using primers 12C and 12gg (Table 2.4). PCRs of 25 µl final volume contained 0.4 µM of each

37 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA primer, 1x MyTaqTM red mix (Bioline) and ca 3.2 ng/ µl of genomic DNA. Cycling conditions were: 95 °C for 2 mins; ten cycles of 95 °C for 20 s, a touchdown from 60 °C - 50

°C for 20 s, and 72 °C for 1 min; then 35 cycles of 95 °C for 20 s, 50 °C for 20 s, and 72 °C for 1 min; followed by a final extension at 72 °C for 4 mins. PCR products were visualised on a 1.7% TBE agarose gel (Agarose I: Amresco, Solon, OH, USA) run for 40 mins at 90 V.

Hyperladder 50 bp (Bioline, Australia) was included to serve as a size reference. Amplicons were cleaned using Diffinity rapid tips (Scientific Specialties, Inc., California, USA) and prepared for sequencing following protocols recommended by the Biomolecular Resource

Facility (Australian National University) before being sequenced in both directions on a 96 capillary 3730 DNA Analyzer (Applied Biosystems). Forward and reverse sequences for each sample were manually checked, trimmed of primer sequences and low quality bases at the 3’ ends, and aligned using Geneious 8.1.7 (Biomatters, Auckland, New Zealand) (Kearse et al.

2012). The final alignment was 901 bp in length.

Table 2.4: PCR primers used in this study.

Marker Sequence (5’ – 3’) Amplicon Reference length 12C & 12GG 12C: AAAGCAAARCACTGAAAATG 1061 bp Springer et al. 1995 12GG: TRGGTGTARGCTRRRTGCTTT AusPreda_12S AusPreda_12SF: CCAGCCACCGCGGTCATACG 218 bp This study AusPreda_12SR: GCATAGTGGGGTCTCTAATC

Development of primers for the mini-barcode

A sliding window analysis of our 12S rRNA reference database, using the R package

SPIDER (Brown et al. 2012), identified a candidate mini-barcode of 344 bp in length. The proportion of zero non-conspecific K2P distances was equal to zero for bases 66 to 410 of our alignment, using a sliding window analysis with 175 bp windows, and each window included high numbers of diagnostic nucleotides (51-69 per window). Within this candidate mini-

38 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA barcode, a sliding window analysis using 20 bp windows identified two short, highly conserved regions suitable for primer design (Figure 2.2 and Appendix 2.2). These potential primer sites had a high proportion of zero non-conspecific K2P distances (>0.8) and low numbers of diagnostic nucleotides (0-1 per window). Within these regions, we manually designed the primers AusPreda_12SF (5’-CCAGCCACCGCGGTCATACG-3’) and

AusPreda_12SR (5’-GCATAGTGGGGTCTCTAATC-3’) (Table 2.4). These primers flank a region of high inter-specific variation and amplify a product of 218 bp in length (178 bp excluding primers).

39 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA 40 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA Figure 2.2: Results of the sliding window analysis conducted using the R package SPIDER for the 12S rRNA gene using window sizes of a) 175 bp and b) 20 bp to identify candidate mini- barcode regions and conserved primer sites respectively. For all panels, the x axes represent the position of each window within the sequence alignment, with each data point marking the position of the first nucleotide of one window. The first (top) panels display the mean K2P distances (a measure of genetic differentiation among species, where a value of zero means that sequences are identical) calculated for each window, with K2P values represented on the y-axes. The second panels represent the proportion of zero cells in the K2P distance matrix. A high proportion of inter-specific genetic distances that are equal to zero indicates sequences that are highly conserved among species. The third panels display the number of nucleotides that are diagnostic among species within each window. The fourth (lowest) panels indicate the proportion of zero non-conspecific K2P distances within each window. When this value is 0, it indicates that the sequence region has high potential to discriminate among species. The area boxed within each panel denotes a) the regions containing the first bases where a mini-barcode of ca 175 bp can be developed and b) the regions containing the first bases where conserved primer sites can be developed.

Bioinformatic evaluation of the mini-barcode

We used additional functions of the R package SPIDER to estimate the risks of species mis-identification when using our AusPreda_12S primers on samples of unknown origin. These analyses were conducted using two versions of our 12S reference database, trimmed to include only the 178 bp of sequences flanked by the AusPreda_12S primers. The

“FULL” database included all 174 sequences present in the original database. The

“UNIQUE” database was a subset of the “FULL” database in which each haplotype was represented by only a single sequence, and in which singleton species (species represented by only one haplotype) were removed. This included 44 sequences representing 16 species from

12 genera.

Pairwise genetic distance was calculated for each pair of sequences using the “raw” model. We conducted bioinformatic analyses using the nearNeighbour, bestCloseMatch, and threshID functions to identify the taxa most likely to be misidentified or ambiguously identified using our primers. R code for these analyses is provided in Appendix 2.3. The nearNeighbour function determines, for each sequence in the reference database, whether the most closely related sequence originates from a conspecific, with two outcomes possible:

41 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA “true” or “false”. A genetic distance threshold must be specified for the bestCloseMatch and threshID functions to account for intra-specific variation. We estimated the most appropriate genetic thresholds to use for the “UNIQUE” and “FULL” databases to be 3.5% and 1% respectively based on the thresholds with the lowest cumulative error. The bestCloseMatch analysis identified the most closely related sequence, within the specified genetic distance threshold, and its species of origin, for each query sequence. The threshID analysis extended this, to consider species of origin for all sequences within the genetic distance threshold.

These analyses had four possible outcomes: “correct”, “incorrect”, “ambiguous” and “no identification” (Brown et al. 2012). The “FULL” database was also analysed with a 3.5% genetic threshold to allow for comparison with the results of the “UNIQUE” database.

Evaluation of the amplification success and sensitivity of the AusPreda_12S primers

We screened a panel of DNA samples from 45 specimens representing 40 species

(Appendix 2.5) to evaluate amplification success of the AusPreda_12S primers. DNA was extracted from tissue samples as described above, and amplified with the AusPreda_12S primers using the same cycling conditions as for the 12C and 12gg primers above, with PCR products visualised on a 1.7% TBE agarose gel to determine amplification success (Figure

2.1).

To test the sensitivity of our primers to detect low template DNA samples, we set up serial dilutions of six DNA extracts originating from museum samples, representing each of the six mammal predators that might be detected in Tasmania (Tasmanian devil, eastern quoll, spotted tail quoll, cat, dog and fox). The DNA concentration of each original DNA extraction was determined using a QuBit Fluorometer and the Qubit dsDNA BR Assay Kit

(Thermo Fisher) and diluted with ddH2O if necessary to obtain a starting concentration of 90 ng / µl. We then set up a series of six 10 X dilutions from each of these “undiluted” (90 ng /

42 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA µl) samples. For each dilution of each sample, we performed three qPCR replicates, each with a total volume of 25µl including 1X Gold buffer (Applied Biosystems), 2 mM MgCl2,

0.4 mg / ml BSA, 0.4 µM of each primer, 0.6 µl SYBR green (1:2000 Life Technologies nucleic acid gel stain), 0.25 mM of each dNTP, 1 unit of AmpliTaq GoldTM (Applied

Biosystems) and 2 µl of the appropriate DNA dilution. qPCRs were conducted using a Viia7

Real-Time PCR system (Thermo Fisher Scientific) with an initial step of 95 °C for 5 mins; followed by 40 cycles of 95 °C for 30 s, 57 °C for 30 s and 72 ° for 30 s. We conducted a comparative CT analysis using the ViiA7 software v1.2.4, with a threshold of 5,000 ∆Rn. For each dilution of each DNA sample we calculated the mean CT value and the standard deviation across PCR replicates.

Evaluation of amplification success from trace samples using known-origin scats

We used previously-extracted DNA from 57 scats of known-origin collected in 2010-

2011 from captive animals, including eastern quolls, spotted-tailed quolls, Tasmanian devils, foxes, cats and dogs. DNA was extracted using a combined chelex (Bio Rad Laboratories,

Hercules, California, USA) and spin column (Mega quick-spin Total Fragment DNA

Purification Kit, Intron Biotechnology) methods (Ramsey et al. 2015). We evaluated amplification success from these samples using the AusPreda_12S primers, by conducting

PCRs and visualising PCR products by gel electrophoresis as described above.

All amplified products were sequenced in both directions using the AusPreda_12S primers, following the methods described above for primers 12C and 12gg. Forward and reverse reads were aligned in Geneious 8.1.7 using a global alignment with free end gaps

(Geneious alignment) allowing 65% similarity. Primers were trimmed and a consensus sequence was generated for each sample. Consensus sequences were compared against the

GenBank database using nucleotide BLAST (NCBI BLAST, RRID:SCR_004870,

43 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA MEGABLAST with the “nr” option and a maximum hit of 20) to identify the most likely species of origin.

Availability of supporting data and material

The datasets and R code associated with this article are provided as supporting information.

All DNA sequences generated during this study have been submitted to GenBank: accession numbers KX786294 to KX786344. Methods can also be found in Protocols.io (E Modave et al. 2017)

List of abbreviations

BLAST: Basic Local Alignment Search Tool: Tool available through NCBI to compare an unknown sequence to existing sequences in a public database. bp: base pairs: pairs of nucleotides in a DNA or RNA strand

CT value: cycle threshold: the number of cycles required for the fluorescent signal of a qPCR machine to cross the predetermined threshold.

DNA: deoxyribonucleic acid mtDNA: mitochondrial DNA eDNA: environmental DNA

PCR: polymerase chain reaction, a method used to amplify a target DNA or RNA strand rRNA: ribosomal ribonucleic acid

TBE: Tris/Borate/EDTA: buffer for gel electrophoresis

44 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA CHAPTER 3: HABITAT MODELING OF PREDATORS IN TASMANIA INFERRED

BY GENETIC MONITORING OF SCATS: PRESENCE-ONLY OR PRESENCE-

ABSENCE DATA?

For submission as: Modave, E., Gruber, B., MacDonald, A.J., Sarre, S.D. 2017. Habitat modeling of predators in Tasmania inferred by genetic monitoring of scats: presence-only or presence-absence data? Target journal: Methods in Ecology and Evolution.

ABSTRACT

Introduction: The interactions among native and introduced predators is the focus of this paper. We wanted a mathematical model describing this effect, in conjunction with the abiotic effects of the landscape. Here, we developed species distribution models (SDMs) for large carnivores found in Tasmania (Sarcophilus harrisii - Tasmanian devils, Dasyurus viverrinus - eastern quolls, D. maculatus - spotted-tailed quolls, Felis catus - cats and Canis lupus familiaris - dogs) to identify patterns of co-occurrence among species, directions of those interactions, and combined effects of the landscape using non-invasive scat (faeces) samples. There are discrepancies among scientists to consider scats as presence-only or presence-absence data and we wanted to investigate that effect further.

Methods: Predator distributions in Tasmania were investigated through DNA scat analyses collected systematically over half of the state. SDMs were built to examine species interaction which each other and the landscape influence on their respective distribution.

Three modeling methods were used: cooccur (where only interactions between pairs of species are assessed), a general linear mixed model (GLMM; a presence-absence approach) and MaxEnt (a presence-only approach – using scat data or atlas data). We wanted to bring answers to the following questions: 1) which species are co-occurring in an area or avoiding

45 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA each other? 2) What is the effect of the landscape/the abiotic factors on a species’ distribution and 3) should scats be considered presence-only or presence-absence data?

Results: The three modeling methods used in this study and the information from the literature gave different results. Cooccur, the GLMM and MaxEnt using atlas data (from the

Natural Values Atlas) gave more similar results with the literature than the MaxEnt model using scat data. From a comparison among the three modeling methods that provided similar outputs, and supported by the literature, we can conclude that devils negatively influenced the distribution of cats, eastern quolls and dogs whilst cats negatively influenced eastern quoll and devil distributions. Devils were found mainly in rainforests, eucalypt forests and woodlands and cats were mainly found in non-eucalypt forests and woodlands.

Conclusions: We show that the application of three different distribution modelling approaches (cooccur, GLMM, MaxEnt) to data collected as part of a systematic scat collection in Tasmania gave similar results to each other and to those expected from the literature, suggesting that these data behave very much like presence-absence type data.

However, this statement is debatable and leads to substantial argumentations. Patterns of co- occurrence of predators could be observed between cats, eastern quolls and Tasmanian devils, and habitat type preference could be described for devils and cats due to the large amount of data available. With the current decline of devils owing to a facial tumor disease, and the increase of feral cats and the distribution of dogs, this study shows again the seriousness of controlling the spread of invasive predators in Tasmania.

INTRODUCTION

The distribution of species in the landscape is crucial to understand the functioning of ecosystems. Environmental components will often influence the distribution of species, but interactions with other species could also be a powerful force but can be difficult to detect

46 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA (Thompson 1999; Soberon and Peterson 2005). Obtaining information about species distribution can inform us about what drives and limits their range. Species distribution is a function of biotic and abiotic factors in the landscape and will to some extent, be determined by them. Species engage with each other through competition, predation, herbivory and other interactions, as well as with the environment around them such as slope or climate variables.

It is widely accepted that the interactions among species is a key determinant of their distribution extent, but usually, such studies are conducted at scales that are small relative to species ranges and rarely take into account interactions among species at a larger geographical scale (see example in Wisz et al. 2013).

Species distribution modeling (SDM) aims to establish a statistical relationship between a response variable and a set of predictors (Guisan et al. 2002) and is increasingly important for ecologists seeking to understand the factors that determine the distribution of species coinciding with the ongoing environmental changes at local and global spatial scales

(Engler et al. 2004; Rodríguez et al. 2007). For conservation planning, SDM can provide information of any appropriate habitat for a given species in the landscape studied (Wintle,

Elith, et al. 2005) thus contribute to management solutions. Monitoring a species or group of species by modeling the species distribution can be used to inform future policy decision making by providing the information needed to predict distributions or extrapolate on other species distribution (Manley et al. 2004; Jones 2011; Wisz et al. 2013). SDMs of predator communities can improve understanding of the factors that affect species distribution based on their interactions with others while also interacting with landscape features, thus will provide ecologists with tools to better understand the ecosystems functioning (Glen and

Dickman 2005).

47 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA Factors affecting species distribution

Variable predictors, such as temperature, climate, slope and aspect associated with presence-only (coordinates where the presence of a species was recorded) or presence- absence data (including the record of absence of a species) are used to build SDMs. The short-comings of building an SDM with presence-only data is a lack of actual absence data (a lot of models now are using background data or pseudo-absence points randomly generated).

Presence data can be difficult to obtain, but presence-absence data are even more challenging to access (Soberon and Peterson 2005) and often require repeated surveys of the same area to obtain a reasonable estimation of likelihood that a record is indeed an absence. Data obtained from museum or natural atlas (presence-only data; e.g. NVA: Natural value atlas; Tasmanian government) are also challenging in the way that they can generally be collected sporadically, contain spatial bias and omission errors (when a species is mistakenly considered to be absent; Rondinini et al. 2006). That can limit the amount of actual data used compared to the amount of data available and the quality of the modeling approach adopted. Systematic surveys provide an alternative approach to ad hoc sightings, but are prohibitively difficult and expensive to obtain on a larger scale.

Detection of species with eDNA

A new way of gathering data is by using eDNA (environmental DNA). That enables a systematic collection at large scale and a simultaneous collection of multiple taxa, to add species interactions in the models, without requiring sightings, which can be unreliable, or trapping, which can be difficult, time consuming, and very hard to undertake at the same time in multiple sites. In addition, the distribution of some species can be difficult to obtain when dealing with cryptic or rare animals. DNA monitoring can therefore prove useful through

DNA barcoding where a portion of DNA from a non-invasive sample can identify a certain species or group of species (Hajibabaei et al. 2007; Thomsen et al. 2012; Bohmann et al.

48 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA 2014; chapter 2). The non-invasive part of the process allows us to monitor species without directly interfering with them, thus removing the challenges of dealing with live caught animals and the stress caused to them.

Models of Tasmanian predators

We chose to model the distribution of predators in Tasmania, Australia, as a test of building such model, integrating biotic as well as abiotic factors. We used DNA sequences recovered from scats (i.e. faeces) to estimate species distribution in the Tasmanian landscape and to test for species interactions. Such data may have applications through providing information that fits somewhere between presence-only data (data usually obtained from museum or atlas) and presence-absence data (data obtained with repeated systematic surveys of an area). The problem we want to address in this study is to know if scat surveys can provide information considered as presence-absence data or presence-only data. Some examples of SDM built with non-invasive data exists nowadays, but lack of dealing with the biotic factors influencing as well the distribution of species is still present. An elusive population of Lynx pardinus (Iberian lynx) habitat was modeled in Spain by Fernández et al.

(2006) thanks to scat identification, carnivores occurrence in US was modeled using scats, cameras and hair traps (Long et al. 2011) and Vulpes macrotis (the kit fox) () distribution was measured in a scat deposition survey in the southwestern part of North America because this species is now rare (Dempsey et al. 2015).

The presence-absence versus presence-only data conflict

Presence-absence versus presence-only data is a widely discussed subject in a lot of studies building species distribution models (Václavík and Meentemeyer 2009). Presence- absence data provide more powerful models of species distribution (Brotons et al. 2004), but are much harder to gather because they require an estimation of the risk of a false absence.

Scat surveys are traditionally considered as presence-absence surveys (Lunney et al. 2000;

49 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA Sullivan et al. 2002; Rhodes et al. 2006; Gormley et al. 2011). It is argued that picking up all observed predator scats during a survey provides an unbiased assessment of both the presence and absence of a species in the survey unit (Fernández et al. 2006). Identifyin true absences in fauna surveys depends on the detectability of a species (Wintle, Kavanagh, et al. 2005).

Detecting a scat in the landscape will be different per surveyor, type of vegetation at the location, the visibility of the deposition site and a variety of other factors including species habits. It will also depend on the abundance-rarity of the species at the surveyed location, and in the area in general (MacKenzie et al. 2002; Royle and Kéry 2007; Gormley et al. 2011).

Models based on presence-absence data come with a strong assumption that a zero is a true absence. Consequently, some other models based on scat surveys prefer modeling presence- only data (Sarre et al. 2013) to lower this assumption, but in such cases, the model’s predictive power is lower as the inferred niche using background data can be biased because this approach requires generation of false absences. Because our survey was based on systematically selected survey units based on an already existing presence-only model (Sarre et al. 2013), the scats collected can represent presence-absence data such as they were subsequently collected in survey units chosen by a maxlike model (Royle et al. 2012; presence-only model) as Gormley et al. (2011) did in their study of Cervus unicolor (sambar deer) in Victoria. Their survey was based on a previous presence-only model allowing them to collect presence-absence data on the second survey.

Native and introduced predators in Tasmania

The negative effects on native wildlife by Felis catus (feral cats) and Canis lupus familiaris (domestic dogs) are well documented in Australia, through predation (Robley et al.

2004; Denny and Dickman 2010;Wang and Fisher 2012; Peacock and Abbott 2014), competition for food (Glen and Dickman 2008; Glen and Dickman 2013) or space and dens

(Glen and Dickman 2008). The island state of Tasmania in Australia constitutes an

50 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA opportunity to study the relationships that have formed between native and invasive species on an island ecosystem that stayed untouched until the past ca 200 years. Some introductions of alien species into Tasmania are well documented. For example, the introduction of the exotic Lupinus arboreus (bush lupin) and Apis mellifera (honeybee) demonstrating the spread of weed species (Stout et al. 2002), and predation by Carcinus maenas (green crab) on

Katelysia scalarina (sand cockle), a bivalve (Walton et al. 2002), provide evidence of the accidental introduction of invasive owed to the economic development. Studies involving physical trapping, camera trapping, noise and odour traps were set among the carnivores we are dealing with in Tasmania (feral cats, dogs, Sarcophilus harrisii - Tasmanian devils,

Dasyurus viverrinus - eastern quolls and Dasyurus maculatus - spotted-tailed quolls) and revealed that feral cats avoid devils in space and this is to be expected from a top-predator – mesopredators interaction (Lazenby and Dickman 2013). A second study involving physical trapping and camera traps showed no evidence of a negative relationship between devils and cats however, a separation in time was evident, with cats being more active during the day and devils more active at night (Fancourt et al. 2015). They also observed a non-negative relationship between cats and quolls even though they were sharing the same areas. A third study by Jones et al. (2004) demonstrated that cats predated on juvenile eastern quolls more than on adults, compared to foxes or native carnivores. We can also argue that the same case might be for juvenile devils and spotted-tailed quolls. Spotted-tailed quolls might even be more at risk with the cat presence in Tasmania, as their diet overlap the most with other native predator species (Jones and Barmuta 1998). They already must deal with this challenge in face of devils and male eastern quolls (which are bigger than females) but cats compete for food with them as well, so we could see a decline in spotted-tailed quolls over the years

(Molsher 1999; Glen and Dickman 2005). Cats effect on native fauna is serious and can increase owing to the DFTD (Devil facial tumour disease) that appeared in 1996 and

51 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA decimated the devil population up to 90% of its abundance in some regions (McCallum et al.

2007; Deakin et al. 2012).

Habitat suitability models for Tasmanian predators

Habitat suitability models for cats (Jones 1977; Molsher et al. 2005; Abbott 2002,

2008), spotted-tailed quoll (Fox and Jones 2004; Jones and Barmuta 2000), eastern quolls

(Hocking and Guiler 1983; Taylor 1986; Driessen et al. 1991; Jones and Barmuta 2000) and

Tasmanian devils (Daniel O Hunter et al. 2015) exist in the literature. No species distribution model for dogs was found owing to dismissal of dog as part as the predators intraguild in studies. Cats show a wide variety of habitat types with a preference for forests (Jones 1977;

Molsher et al. 2005; Abbott 2002, 2008). Spotted-tailed quoll in north eastern Tasmania prefer thick forests in wet areas (Fox and Jones 2004), and were mostly found in rainforest with devils present as well (Jones and Barmuta 2000) in another study. Eastern quolls show proportionally to the other habitats, a bigger use of open grassland (Jones and Barmuta 2000) but were recorded to be more present in wet and dry forests after fire (when a clearing was happening; Hocking and Guiler 1983; Driessen et al. 1991) and more abundant in scrub and buttongrass moorlands in the western part of Tasmania (Taylor 1986). Devils are positively influenced by eucalypt forests in Australia (Hunter et al. 2015), and so far, the models developed do not incorporate the effects of interactions with other predators. From the literature, we expect to see an influence of cats over native predator distribution in Tasmania and we do not expect dogs to influence the distribution of species as most of them are farm dogs (restricted to a certain environment). Interactions among Tasmanian native predators will be interesting to investigate as they evolved together for the past 14000-8000 years, once

Tasmania became isolated from mainland Australia (Pardoe et al. 1991; Lambeck and

Chappell 2001), and studies showed they do not compete for dens (Glen and Dickman 2008) but the introduction of invasive carnivores are likely to alter these interactions. Spotted-tailed

52 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA quolls may compete for food as they have the biggest diet overlap with the two other native predators all through the year (Jones and Barmuta 1998), but as long as they can shift their diet in areas of convergence of multiple species, if they do not face invasive predators, the availability of food items should keep them sustained. Devils are known to predate on juveniles of quolls, so we might see this impact with our results (Jones, Smith, et al. 2004).

Endangered species, as well as invasive species often require species-specific habitat management options that solicit understanding of their respective habitat use and interactions with other species (Figure 3.1). We expect to observe similar outputs when comparing results of our models.

Figure 3.1: Interactions between species and preferred habitat types based on a review of the literature

Here, we aim to develop models building SDMs with scat data collected from 189 sites around eastern Tasmania from cats, dogs, Tasmanian devils, eastern and spotted-tailed quolls and we aim to answer the following questions: 1) Can we use scat data to identify species interactions among the large Tasmanian predators? 2) Are there any specific habitat types restricting the distribution of a species? 3) Can we compare the output obtained from a model built with scats as presence-absence data and as presence-only data? 4) Can we also compare when just considering the interactions between species? 5) How is it relating to literature? For the first time, dogs are included as part of the predator guild that may influence the distribution of other predators. This survey is also the first on such a large-scale

53 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA scat of predators in Tasmania and the first time that DNA approaches targeting multiple taxa have been used.

Our key findings here are that the results found using a general linear mixed model

(with scats as presence-absence data) are consistent with what is known from the literature and by building a co-occurrence matrix with the R package (Development Core Team 2016) cooccur (Veech 2013, 2014; Griffith et al. 2016). Tasmanian devils negatively influence cats, eastern quolls and dogs. Cats negatively influence the distribution of eastern quolls and devils and devil scats tend to be found in rainforests and eucalypt forests and woodlands whilst cat scats are found in non-eucalypt forests and woodlands. Based on the number of DNA identifications reach per species, we could only be confident in the cat and devil niches.

MATERIAL AND METHODS

Field work

In order to build SDMs for Tasmanian predators, we conducted a survey of predator scats in autumn 2010 and 2014 (different units surveyed). The surveys were part of predator- prey surveys, in a collaboration between the Department of Primary Industries, Parks, Water and environment Tasmania (DPIPWE Tasmania) and the University of Canberra. Systematic surveys were established over the eastern half of Tasmania and designed to survey two survey units in a day. A survey unit was a square of 3 x 3 km, set at a location with 6% of suitability for fox habitat as the surveys where developed firstly for the fox eradication program (Sarre et al. 2013). One in three squares was surveyed randomly in 2014 (area twice as spread as in 2010) for 2.5h by a team of two. Only scats containing hairs, bones, insect parts or seeds were collected and a GPS location were recorded with a walking GPS unit. The field work took place from February to June 2014 and ca. 3000 scats were collected.

54 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA Samples selection

Due to constraints of resources only a subsample of all possible scats was analysed.

1465 were further analysed using a random selection where a random number was allocated to each scat. The scats were then sorted by ascending numbers and the first 1500 were selected. The locations were imported to ArcGIS 10.2 ESRI (Environmental Systems

Research Institute, California) to ensure to maximize the spatial coverage of the area, and some scats were manually added or removed for better extent of the data to end up with 1465 scats to sequence with the predator identification test (developed in chapter 2).

We also included predators identified from 50 scats following a metabarcoding analysis (chapter 4).

DNA extraction

DNA was extracted from scats using a Chargeswitch Forensic DNA purification kit

(Thermo Fischer Scientific) following the manufacturers recommendations in a trace DNA laboratory to prevent contamination. No interactions between the laboratory and any other building, or air conducts, handling DNA experiments were allowed and the personnel had to have a shower and could not interact with pets just before entering the premise. Scats were cut into small pieces with a scalpel cleaned with 10% bleach and 80% ethanol and put in 1ml of Chargeswitch lysis buffer with 10µl of proteinase K. Gloves were changed between each scat to avoid cross-contamination. Extraction negatives (tubes without DNA) were also created. All were put on a thermomixer overnight.

The ensuing steps followed the manufacturer protocols with minor modifications.

New tubes containing 200µl each of purification buffer from the same kit were done and the digested lysis product was added to them. 20µl of magnetic beads were added to each tube for the DNA to bind to the beads. The tubes were placed on a magnetic rack and cleaned

55 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA twice using the washing buffer provided in the kit. Finally, the DNA was eluted with 150µl of the elution buffer provided. This DNA was then diluted 50 times in nuclease-free water

(Thermo Fisher Scientific, USA) to undergo the next step of the analysis. We decided to dilute the DNA 50 times as per our previous results to maximize our chances to obtain a product following amplification by PCR. They were stored in a -20°C freezer in the waiting of PCR amplification if this step was not performed directly after.

PCR amplification

The AusPreda_12S primers (forward: 5’-CCAGCCACCGCGGTCATACG-3’ and reverse: 5’-GCATAGTGGGGTCTCTAATC-3’; chapter 2) were applied to the diluted DNA for the subsequent PCR amplification step. This primer set proved to be effective at identifying each of the Tasmanian predator even discriminating amongst the closely related quoll species (chapter 2). The PCR mix was as follow: 0.4 µM of each primer, 1x MyTaqTM red mix (Bioline), 2 µl of the diluted genomic DNA and ddH20 to make up a final volume of

25 µl. Cycling conditions were similar to the ones from chapter 2: 95 °C for 2 mins; ten cycles of 95 °C for 20 s, a touchdown from 60 °C - 50 °C for 20 s, and 72 °C for 1 min; then

35 cycles of 95 °C for 20 s, 50 °C for 20 s, and 72 °C for 1 min; followed by a final extension at 72 °C for 4 mins. PCR products were visualised on a 1.7% TBE agarose gel (Agarose I:

Amresco, Solon, OH, USA) run for 40 mins at 90 V and a Hyperladder 50 bp (Bioline,

Australia) was included to act as a size reference.

DNA fromthe 50 metabarcoded scats was extracted as above, but MID-tagged primers where attached to each scat sample to ensure the allocation of samples after the amplification and metabarcoding steps where all samples were pooled together. Metabarcoding allows to identify multiple taxa in a unique sample (Pompanon et al. 2012).

56 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA Sequencing

Amplicons were cleaned using Diffinity rapid tips (Scientific Specialties, Inc.,

California, USA) and prepared for sequencing following protocols recommended by the

Biomolecular Resource Facility (Australian National University) before being sequenced in both directions on a 96 capillary 3730 DNA Analyzer (Applied Biosystems). Forward and reverse sequences for each sample were treated as per chapter 2: they were manually checked, trimmed of primer sequences and low quality bases at the 3’ ends, and aligned using

Geneious 8.1.7 (Biomatters, Auckland, New Zealand; Kearse et al. 2012). We discarded the resulting sequences that were too short (< 90 bp) or of too bad quality (chromatograph with mixed sequences; on Geneious, the bases are coloured from light blue for good quality to dark blue for bad quality). The sequencing of the metabarcoding data is described in full in

Chapter 4.

Predator identification

Consensus sequences were created from each alignment and the BLAST tool (Basic

Local Alignment Search Tool) was used to compare each sequence against sequences from

GenBank using the nr database (all non-redundant GenBank+EMBL+DDBJ+PDB sequences) and a Megablast tool (a fast comparison of a nucleotide query sequence against a nucleotide sequence database that only finds the matches with high similarity). We recorded the consensus sequence quality percentage, the species with the highest percentage pairwise identity in the hit-table obtained with Blast and the e-value (expected value: the score for a given species that you would expect solely by chance) where the lower the e-value, the more likely that the hit is real.

57 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA The predators of each of the 50 added samples from the metabarcoding study were defined as being the representative with the highest number of reads of DNA sequences in the sample.

Overall, 789 scats produced a sequence from a good quality based on the chromatograph produced after sequencing and 75 on these were from non-target animals to reach a number of 714 scats identified as being one of the five predator species. The high failure rate can introduce false-absence data in our study, but building backgound data by using presence-only data can introduce the same error factor. This high failure rate could have been reduced by repeating samples that failed the first time, but owing to money and time constraints, we decided to test a sinlge time as many scats as we could.

Patterns of co-occurrence of predators – First model: species interactions only

We imported each data point into ArcGIS 10.2, using GPS coordinates obtained during the field work. We only included the scats that were identified as being one of our five target species (i.e. 714 scats) and used the intersect function to assign each scat to their respective survey unit before exporting those locational data from ArcGIS 10.2 into an excel file. We used the package cooccur (Veech 2013, 2014; Griffith et al. 2016) in R

(Development Core Team 2016) to obtain a pattern of co-occurrence between all large mammalian predators in Tasmania. This package associates species pairs as randomly, negatively or positively correlated by calculating observed and expected frequencies of co- occurrence. It then compares these frequencies with the possibility that this co-occurrence could have been obtained by chance alone (Waterway et al. 2016; Griffith et al. 2016). The pairwise approach used in cooccur is highly relevant in ecological research (Strona and

Veech 2015; Arita 2016) because it can lead to more exhaustive and complete analyses of ecological frameworks. Because of the elimination of spotted-tailed quoll data from our

58 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA cooccur analysis (not enough data - only 22 presences - and no interactions found with any other species), we were only able to model four (two native: devils and eastern quolls and two introduced: cats and dogs) predators in Tasmania.

This method is used here as a way to test for patterns of co-occurrence before building

SDMs combining the interactions and abiotic factor effects on species distribution (Hijmans and Elith 2016) and cooccur showed its efficacy in other scientific researches using the package (Laiolo 2017; Royan et al. 2015). Our data consisted of 714 samples collected from

189 collection sites (i.e. survey units). These comprised 58 scats collected in 2010 and 656 collected in 2014 survey. Initial analyses of the samples from these two years obtained similar results in the sense of similar directions of species interactions (results in Appendix

3.1) so we combined the two survey years. Cooccur associates each presence of a species with another one in the same site presenting us with the opportunity to build species distribution models that incorporate other taxa as well as environmental variables.

Species distribution modeling in R – Second model: scats as presence-absence data

The detection probability of species under study is critical to assess the availability of presence-only versus presence-absence data and is discussed for a cryptic carnivore at the end of an eradication program (Ramsey et al. pers. comm). In the present case, we can argue that detection probabilities for our species of interest are greater than in the latter study as

Tasmanian devils and spotted-tailed quolls are known to defecate in latrines (several scats deposited in the same location as a mark of territoriality) and their scats are relatively big

(Godsell 1983; Belcher 1995; Pemberton et al. 2008; Glen et al. 2011) while eastern quolls are believed to drop their scats in prominent locations, such as on top of a rock and dogs use scats as a way of providing information to mates or competitors (Banks et al. 2002) such as they drop them in obvious locations. Feral cats are usually thought of burying their scats

59 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA (Bradshaw et al. 1996; Edwards et al. 2000), but it is not a behavior observed at all time

(Fitzgerald and Karl 1979; Molsher 1999), plus, their increased abundance make their scats easy to locate in the landscape as they are numerous. In general, these predator scats were easy to spot and often were located along landscape features, which our survey design considered.

We built SDMs in R following the approach of Hijmans and Elith (2016).

Specifically, we obtained raster layers from the DPIPWE Tasmania for the aspect (the direction that the surface faces at the location), the vegetation, the slope and the elevation of

Tasmania at a resolution of 25m x 25m grid cell size, downloaded the annual mean temperatures and the annual precipitation from WorldClim (Hijmans et al. 2014) and created

4 raster layers for every presence-absence by our targeted predators. To create the raster layers, we exported the presence-absence of each species per survey unit obtained using cooccur and added the coordinates of the center of each survey unit to build a raster and obtain a map of presences (1) or absences (0) of species in each survey unit. We set the extent as the extent of our survey (xmin: 396000, xmax: 609000, ymin: 5191000 and ymax:

5485000), representing the eastern half of Tasmania. We also used the resample function to obtain the same extent and resolution as the other raster layers from DPIPWE, because the cell resolution of 25m x 25m was fine (in the sense of small). For the aspect raster layer, the geographic north was set to 0, so that east was 90, the south was 180 and flat areas were set at

-1. This raster layer provides information about slope direction and sun illumination at a given location. The elevation was represented in meters (from 1.5 m to 1610 m altitude), the slope in degrees and the vegetation as one of 11 groups: 1: Moorland, Sedgeland, Rushland and Peatland, 2: Non-Eucalypt Forest and Woodland, 3: Wet Eucalypt Forest and Woodland,

4: Other Natural Environments, 5: Dry Eucalypt Forest and Woodland, 6: Scrub, Heathland and Coastal Complexes, 7: Rainforest and Related Scrub, 8: Agricultural, Urban and Exotic

60 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA Vegetation, 9: Native Grassland, 10: Highland Treeless Vegetation and 11: Saltmarsh and

Wetland. The mean annual temperature is in °Cx10 (so the first decimal could be kept and the number was an integer (5.1°C to 13.2°C) and the annual precipitation is recorded in mm (520 mm to 2672 mm); Figure 3.2.

61 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA Figure 3.2: predictors used to build the species distribution models

Legend: nine predictors used to build SDM for each of the four predator species. The four first predictors represent the presences (1; in pink) of a species in the surveyed area. These predictors are used to measure the interaction between species. The five predictors left are used to determine the preferred habitat type of the response variable (the species modeled), they represent the slope, the aspect, the vegetation, the annual precipitation and the mean annual temperature.

62 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA We checked the collinearity of our predictors using the function VIF (the variance inflation factor) from the usdm package (Naimi 2015), for inter-dependence of predictors, resulting in the removal of elevation from our predictor variables because of high collinearity with mean annual temperature (VIF of 3.9 for elevation and of 4.5 for temperature). We considered that of these two variables, an animal was more sensitive to temperature than to elevation per se and so omitted elevation (the VIF value decreased to 1.2 for temperature after the removal of elevation) and a VIF below 4 is usually considered as a good value.

Altogether, we had nine predictors: the presence-absence of another carnivore (four predictors), the vegetation type, the aspect, the slope, the mean annual temperature and the annual precipitation.

We used the position of each species per scat location to build the SDMs (714 locations instead of 189 if we considered only survey units). Using scat locations instead of survey units allowed us to keep the precision of a vegetation type in a given location and allowed us to avoid having to take the average of the slope, aspect, temperature and precipitation of a 3 x 3 km area. When extracting values from our raster layers, the presence of an interacting species at one point in a survey unit was assumed to constitute the presence of the species at every location in this survey unit. In this way, we maintained the effect of interaction between species, such that multiple species could be present at the same coordinate. We considered that multiple scats collected from a 3 x 3km survey unit represent an opportunity to establish a presence for multiple species in that area, rather than the single species that might be determined if a scata was considered the unit of survey. . This is because our sites comprised an area bounded by 3 x 3 km and thus incorporating a maximum distance between two scats ofca. 4.2km. Each predator species included in this study can travel this distance in one or a few days and we could not determine the age of any particular scat was not determined so two scats in the same survey unit could be from a few days to a

63 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA few months apart. Thus, we considered any two scats from different species within the same unit to be evidence of co-occurrence. To account for the repeated measurement on the 3 x 3 km sites in our model (pseudo-replication) we included the survey unit identification as a random factor and therefore used a general linear mixed model (GLMM) to establish the relationship between a species and its predictors. The linear predictors in such incorporate random effects (the survey units in our example) in addition to the fixed-effects parameters

(Bates 2010) thus accounting for pseudo replication. Several functions for creating a GLMM exist and three were tested in this study, lmer (linear mixed effects in R), glmmML (i.e. generalized linear model with maximum likelihood) and glmmPQL (i.e generalized linear model with penalized quasi-likelihood), and they all provided similar results so the latter was chosen (Broström 2003). One scat record was removed from our database owing to an inaccurate GPS location to end up with a total of 713 positions. We kept only the predictors that had a significant (p-value < 0.05) influence on the distribution of a species. We did not possess camera traps data to validate the use of scats as presence-absence data and did not use atlas data in our study as these data were not collected through a systematic survey unit approach comparable to ours and therefore would incorporate bias associated with species presences in one point in time and on the observer roamings.

We estimated the accuracy of our models by calculating a confusion matrix using the predict function from the R? stats package (Team 2014) and the ConfusionMatrix function from the caret package (Kuhn 2008; 2016) and use the area under the receiver operating characteristic (AUROC or AUC) as a way of visualising the accuracy of our models. A model with an AUC between 0.7 and 0.9 is usually considered as acceptable, and a value above 0.9 is considered as really good (Swets 1988; Godsoe 2010). See Appendix 3.2 for the R code.

64 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA MaxEnt – Third model: scats as presence-only data

We wanted to compare the outcomes of the literature, the cooccur method and of the

GLMM using presence-absence data with the commonly used presence-only approach. The machine learning approach used by MaxEnt (Maximum Entropy, Phillips et al. 2006) was selected using scats as presence-only data. MaxEnt model is widely used in ecological studies as it provides similar or better results as other models for modeling presence-only data

(Gormley et al. 2011; Hijmans and Elith 2013). MaxEnt creates randomly selected pseudo- absences, combines these with the presence points and to the predictors (the nine predictors used to build the GLMM) to obtain an index of habitat suitability (0 – unsuitable habitat to 1

– suitable habitat). MaxEnt does not provide a p-value for the variables, but still estimates the directions of the predictors influencing the response variable. Importing the same predictors with the presences of our four predators into the MaxEnt Java software, we created 10,000 random background points and used linear features only to allow for comparison with the models build using GLMM (Phillips and Dudík 2008). We also used a prevalence number calculated by dividing the number of survey units with a presence of a species by the total number of survey units evaluated (i.e. 189).

MaxEnt - Third model: atlas data

Finally, we built a MaxEnt model using data gained from atlas (NVA – accessed on

April 7, 2017). We know that atlas data are presence-only data (Pearce and Boyce 2006) and if the outputs coincide with the outputs we obtained using scats as presence-absence data

(GLMM), cooccur and the literature, more confidence can be drawn to view this scat survey as a collection of presence-absence information. Only the data recorded between 2010 and

2016 and only some types of data were kept (camera trapping data, trapping data, capture data, carcass data, scat data and sighting data) discarding the data without valid information.

65 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA We used ArcGIS 10.2 and the function intersect to only keep the data falling within our survey units to obtain the same range that allows for comparison with the outputs obtained from the GLMM. We used the same options selected in the previous MaxEnt model for the features type, the prevalence and number of background points.

RESULTS

Sequencing and predator identification

Out of 1465 selected scats, 739 samples produced a clean sequence of good quality and were identified to the species level. The predator was identified from a further 50 scats using a metabarcoding technique (see Chapter 4) to reach a total of 789 scats (Appendix 3.3).

Overall, we identified scats from 387 Tasmanian devils, 117 eastern quolls, 22 spotted-tailed quolls, 128 cats and 60 dogs (Figure 3.3). Sequences from a further 75 scats revealed non- target animals including rats, Tasmanian pademelons and 15 other species (most likely prey items that amplified instead of the predator DNA) as well as human (most likely the result of contamination during collection or laboratory analysis) resulting in 714 target species identified.

66 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA Figure 3.3: Map of the 714 predator scats identified from a systematic survey

Patterns of co-occurrence of predators – First model: species interactions only

Cooccur analysis of predator sequences from scats collected in 2010 and 2014 showed significant negative relationships between cats and devils (p-value < 0.001), and between cats and eastern quolls (p-value < 0.001). It also showed negative relationships between dogs and devils (p-value: 0.016) and between dogs and eastern quolls (p-value: 0.049). Overall, with the 714 scats assigned to 189 survey units with the R package cooccur and with the complete dataset (2010-2014), we could determine 10 pairs of species to analyze with an expected co- occurrence greater than 1, four of which representing significant relationships including 4 species (feral cats, dogs, Tasmanian devils and eastern quolls). The number of survey units including more than one scat was 114 and 75 survey units contained only one scat. The maximum of scats in a survey unit was 34 comprising 32 devil scats and two spotted-tailed quoll scats (Table 3.1 and Figure 3.4). A diagram of the interaction observed was built

67 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA (Figure 3.5) to allow for comparison among the three modeling methods used in this study

(interactions obtained with cooccur, species distribution model using scats as presence-

absence data and species distribution model using presence-only data with scats and atlas

data) and literature to test for agreements among different approaches. We then built 4

species distribution models with the 4 species with higher interest (all without the spotted-

tailed quoll) using the R platform.

Table 3.1: Co-occurrence table obtained for the four negative co-occurrences analysed where the expected co- occurrence was bigger than 1 using the package cooccur on R. The matrices represent the number of survey units in which each species was found to be absence (0) or presence (1) compared to an interacting species. sp1_name sp2_name sp1_inc sp2_inc obs_cooccur prob_cooccur exp_cooccur p_lt p_gt Dogs Eastern quolls 32 33 2 0.03 5.6 0.049 0.999 Dogs Tasmanian devils 32 112 13 0.1 19 0.016 0.994 Eastern quolls Feral cats 33 77 5 0.071 13.4 < 0.001 0.999 Feral cats Tasmanian devils 77 112 29 0.241 45.6 0 1

cats cats dogs dogs 0 1 0 1 0 1 0 1 0 160 83 0 184 72 0 176 19 0 187 30 devils eastern quolls devils eastern quolls 1 48 29 1 28 5 1 99 13 1 31 2

Legend: Sp1_inc: number of survey units with species 1 Sp2_inc: number of survey units with species 2 Obs_cooccur: observed number of survey units that have both species Prob_cooccur: probability that both species occur at a survey unit Exp_cooccur: expected number of survey units with both species p_lt: probability that the two species would co-occur at a frequency less than the observed number of co-occurrence sites if the two species were distributed independently from one another. Can be interpreted as negative interaction. p_gt: probability of co-occurrence at a frequency greater than the observed frequency. Can be interpreted as positive interaction. p_lt and p_gt can be interpreted as p-value with a threshold of 0.05

68 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA Figure 3.4: Plot obtained for the four negative co-occurrences analysed where the expected co- occurrence was bigger than 1.

Legend: The random co-occurrences are represented in grey boxes, while negative co- occurrences are in orange.

Figure 3.5: Interactions between species based on the outputs of cooccur

Species distribution modeling in R – Second model: scats as presence-absence data We modeled the distribution of species for which we had sufficient data: Tasmanian devils, feral cats, eastern quolls and dogs using scats as presence-absence data and a GLMM.

The accuracy of each model was calculated by building a confusion matrix obtained by calculating the actual values compared with predicted values such that one diagonal (from top

69 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA left to bottom right) of the matrix shows the true positives and trues negatives and the other diagonal (from top right to bottom left) represents the false positives and negatives (Figure

3.6).

Figure 3.6: Confusion matrix for the devil model showing the accuracy of the model obtained using a GLMM. In this example, the accuracy is 89% representing a good value. FALSE TRUE FALSE 274 52 TRUE 28 359

Accuracy 0.8878 95% CI (0.8623, 0.91) No Information Rate 0.5764 p-value [Acc > NIR] < 0 Kappa 0.7726 McNemar's test p-value 0.01013 Sensitivity 0.9073 Specificity 0.8735 Pos Pred Value 0.8405 Neg Pred Value 0.9276 Prevalence 0.4236 Detection rate 0.3843 Detection prevalence 0.4572 Balanced accuracy 0.8904

The ROC allowed the calculation of the performance of the model thanks to the calculation of an AUC value. The area under the graph built with the ROC (where the x-axis represents the false-positive proportion of the prediction of a presence or an absence against the proportion of true-positive presence or absence on the y-axis) with a high value means that a site with a high predicted suitability is actually a site with a presence and a low AUC represents areas where the species is supposed to be absent but was recorded (Hijmans and

Elith 2013).

70 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA Devil model

The application of a GLMM analysis to our 387 instances of devil presences (Table

3.2a for the summary) found four of our predictors to be significant (p-value < 0.05; Table

3.2a). We also found that cats and devils are negatively correlated, such that in areas where cat scats are highly present, devils tended not to be around (p-value < 0.001). The eastern quoll presences also influenced negatively devil distributions (p-value < 0.001). The preferred habitat by devils were found to be rainforests (p-value: 0.02) followed by wet eucalypt forest and woodlands (non-significant; p-value: 0.068) and scats are more likely to be present in colder areas (p-value: 0.009). The model was accurate at 89% and the AUC value was of 0.94. Based on the AUC value, this model is considered to be good.

Cat model

After extraction of the values from the raster layers with the cat presences, we ended up with 128 presences of cats. Two of our predictors were significant following a GLMM analysis (Table 3.2b for the summary). The devil and eastern quoll presences influenced negatively the distribution of cats (p-values < 0.001). Cat scats tended to be found in native grassland habitat type (non-significant; p-value: 0.075) and not in rainforests (non- significant; negative interaction, p-value: 0.058). The model had a 92% accuracy, representing a good value with the AUC was 0.96 and therefore rates as a good model

(Lawson et al. 2014).

Eastern quoll model

After extraction of the values from the raster layers with the eastern quoll presences, we had 117 presences of eastern quolls to which we applied a GLMM (Table 3.2c for the summary) with one significant predictor (cats). Cats negatively influenced the presence of eastern quolls (p-value < 0.001), as did devils, but not as significantly as cats (p-value:

71 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA 0.0562). The model was accurate at 94%, with an AUC of 0.98. This model produces an almost prefect AUC value that can be considered as good.

Dog model

We had 62 presences recorded to which we applied a GLMM (Table 3.2d for the summary) and one of our predictors was significant. The presence of devils (p-value < 0.001) was negatively correlated with the presence of dogs. The dogs avoid the most the other natural environments (non-significant; p-value before the final model: 0.056) which were represented by lichen lithosere, sandy and muddy areas. They also avoid wet eucalypt forest and woodlands (p-value before the final model: 0.03). These vegetation types were removed with the final step of the model. The model was accurate with a value of 96% and the AUC was 0.98. Again, it denoted a good model. Then again, a diagram representing the outputs of the GLMM was created (Figure 3.7).

72 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA Tables 3.2: summary results of the devil model (a), cat model (b), eastern quoll model (c) and dog model (d), after removing non-significant predictors using a GLMMPQL. a – devil model

DEVILS Value Std.Error DF t-value p-value (Intercept) 4.778 2.04 513 2.339 0.02 cat -1.613 0.34 513 -4.744 < 0.001 eastern quoll -1.481 0.39 513 -3.760 < 0.001 vegetation 3 1.803 0.98 513 1.832 0.068 vegetation 7 2.374 1.02 513 2.337 0.02 mean annual temperature -0.043 0.02 513 -2.626 0.009 Accuracy: 0.89 AUC: 0.94 b – cat model

CAT Value Std.Error DF t-value p-value (Intercept) 0.058 0.99 514 0.059 0.953 devil -1.926 0.33 514 -5.756 < 0.001 eastern quoll -1.634 0.51 514 -3.23 0.001 vegetation 7 -2.474 1.3 514 -1.897 0.058 vegetation 9 2.227 1.25 514 1.784 0.075 Accuracy: 0.92 AUC: 0.96 c – eastern quoll model EASTERN QUOLLS Value Std.Error DF t-value p-value (Intercept) -2.183 0.364 522 -5.994 < 0.001 devil -0.751 0.392 522 -1.914 0.056 cat -1.964 0.583 522 -3.368 0.001 Accuracy: 0.94 AUC: 0.98 d – dog model

DOGS Value Std.Error DF t-value p-value (Intercept) -2.816 0.321 522 -8.764 < 0.001 devil -1.745 0.434 522 -4.020 < 0.001 avoid vegetation 3 (not kept in the final model) / / 0.03 avoid vegetation 4 (not kept in the final model) / / 0.056 Accuracy: 0.96 AUC: 0.98

Legend: the value represents the logarithmic value of a predictor on the response variable after being subject to the model. The standard error is the estimate of the strength of the relationship between the response variable and the predictor. The DF (degrees of freedom) is the number of values in the final calculation of a statistic that are free to vary. The t-value is the value of the t- statistic to test if the corresponding regression coefficient is different from 0. The p-value is the possibility to obtain such relationship by chance. The lower the value is, the more significant the predictor is to predict the distribution of the response variable.

73 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA Figure 3.7: Interactions between species and preferred habitat types based on the outputs of the GLMM using scat data.

MaxEnt – Third model: scats as presence-only data

Using a presence-only type of modeling combined with the scat data, we obtained models for Tasmanian devils, cats, eastern quolls and dogs (Figures 3.8). We only kept the three most important variables to explain the distribution of the response variable (Songer et al. 2012; Smart et al. 2012; Sobek-Swant et al. 2012). We did not model spotted-tailed quolls as the aim of using MaxEnt is mostly to allow a comparison with the results obtained with the

GLMM. The only species interactions observed using this analysis are the positive impact of devils on cat distribution and vice versa. The other species do not influence the distribution of each other and the preferred vegetation type of species could not be accessed. Finally, the temperature might influence the distribution of cats and devils such as they are more present if the area is warmer and devils are found in dryer areas as well. Again, a diagram representing the outputs of the MaxEnt analysis was created (Figure 3.9).

74 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA Figures 3.8: summary results of the devil model (A), cat model (B), eastern quoll model (C) and dog model (D) using MaxEnt and scat data.

Legend: Outputs from the four models using MaxEnt: devil (A), cat (B), eastern quoll (C) and dog (D). The permutation importance is the percentage of the importance of a variable after the MaxEnt model is reevaluated after the permutation of training presences and background data. As no p-values are available when using MaxEnt, only the three most important variables were selected. The response curves show the effect of each environmental variable on the MaxEnt prediction.

Figure 3.9: Interactions between species and preferred habitat types based on the outputs of MaxEnt using scat data.

75 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA MaxEnt - Third model: atlas data

Finally, using a presence-only type of modeling combined with atlas data, we obtained models for Tasmanian devils, cats and eastern quolls (we discarded the dog data because only one atlas data was available; Figures 3.10). We did not model spotted-tailed quolls as the aim of using MaxEnt is to allow a comparison with the results obtained with the literature, cooccur, GLMM and MaxEnt with scat data. The devil response to the cat presences gave a peculiar output and cannot be interpreted. The presence of eastern quolls seemed to influence positively the distribution of cats and the presence of devils influenced negatively the distribution of eastern quolls. Devils might be influenced by eucalypt forests and woodlands in general (encompassing three vegetation groups) and drier areas in general whilst cats might be influenced by non-eucalypt forests and woodlands, scrub, heathland and coastal complexes and native grasslands. Eastern quolls are more present in colder areas. A graph representing the outputs of the MaxEnt analysis was created (Figure 3.11).

76 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA Figures 3.10: summary results of the devil model (A), cat model (B) and eastern quoll model (C) using MaxEnt and atlas data.

Legend: Outputs from the three models using MaxEnt: devil (A), cat (B) and eastern quoll (C). The permutation importance is the percentage of the importance of a variable after the MaxEnt model is reevaluated after the permutation of training presences and background data. As no p-values are available when using MaxEnt, only the three most important variables were selected. The response curves show the effect of each environmental variable on the MaxEnt prediction.

Figure 3.11: Interactions between species and preferred habitat types based on the outputs of MaxEnt using atlas data.

77 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA DISCUSSION AND CONCLUSIONS

In this study, we aimed to measure what was driving predator distributions in

Tasmania by attributing non-invasive samples to a predator through DNA identification. We attempted to observe patterns of co-occurrence or avoidance among species and we established SDMs combining species interactions with environmental niche for four species

(Tasmanian devils, cats, eastern quolls and dogs) based on scats as presence-absence data and presence-only data (scat data and atlas data). For wildlife managers, these can provide valuable information of where to apply management decisions, and it proves useful for monitoring species at large spatial scale, or understanding their ecology (preferred habitat and altitude for example; MacKenzie 2005). Scats provide a relatively new type of data used to build models of species distribution and demonstrated their usefulness to build potentially strong models from data collected systematically over two session of field work representing a large-scale area with the acquisition of data for multiple taxa simultaneously.

Comparison of models using scat data, atlas data and literature

Three model techniques (cooccur, GLMM and MaxEnt) were tested to study species interactions with or without the environmental data. The main aim was to compare them with the literature to confirm if the scats collected in this study could be interpreted as presence- absence data or not. For that, we can compare the graphs built with the outputs obtained by our review of literature, by the cooccur analyses and by the two SDM methods (presence- absence with the GLMM and presence-only with MaxEnt using scats or atlas data).

When comparing the interactions of species only, both the outputs of cooccur and the

GLMM agree with the negative influence of devils on cats found in literature and it is to be expected because of the size of this top-predator (Lazenby and Dickman 2013). We also had the opposite influence of cats on devils that is not reported in literature with our cooccur

78 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA analyses and the GLMM. We disagree with Fancourt et al. (2015) that did not find any negative relationship between cats and devils. We found strong evidence that cats and devils influence each other distribution by not being present in the areas where the other species is.

Devils are thought to be unable to kill cats to predate on them but the latter have been found on their stomach and scats contents, probably eaten as carrion (Guiler 1970). What could explain the negative interaction between cats and devils measured in this study? It could be the different requirements seek by both species, predation by one over the juveniles of the other, or any other interaction we can only assumed with scats. More appropriate studies focused on these interactions would give us suitable answers such as camera traps for example. The positive interaction between cats and devils found with MaxEnt using scat data is in contradiction with all two other analyses (cooccur and GLMM), the literature and

MaxEnt using atlas data. The negative influence of cats on eastern quolls was observed with some of the literature, the cooccur analyses and the GLMM. A positive influence of eastern quolls on cat distribution was found with MaxEnt using atlas data and might be erroneous as well. The negative influence of devils on eastern quolls, as observed in the literature, was also found with the GLMM but was not significant (p-value: 0.056) and found with MaxEnt using atlas data. Nevertheless, MaxEnt with scat data and cooccur did not show this interaction and eastern quolls are known to be more vigilant when facing Tasmanian devils (Jones 1998), so once again, the GLMM is closer to what is reported in literature. There is a possible predation by cats and devils on juvenile quolls as observed by Jones et al. (2004) and Fancourt et al.

(2015). The negative interaction by devils towards eastern quolls found with the GLMM might be explained by a niche separation, they do not eat the same food (Jones and Barmuta

1998), they build different den sizes (Glen and Dickman 2008) and they showed a preference for different habitat types in this study. So, they might just avoid each other because their respective requirements are different. The ghost of competition past (Connell 1980) is a well-

79 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA known theory that states that co-evolving competitors will eventually adapt to different niches to survive in the presence of each other. Jones and Barmuta (2000) came up with a similar argument to explain niche differentiation among sympatric co-evolved dasyurids.

Finally, the negative interaction of devils on dogs was not found in literature, mainly because dogs were ignored in such studies. We can see here that they should be included as an intraguild predator as they might strongly influence the distribution of native predators. We only found a negative influence of devils on dogs in our GLMM analyses and we can only speculate that if the feral dog population increases, the opposite interaction will be observed.

Again, no interactions with species were found with dogs in our MaxEnt analyses. The problem there was mainly that not enough data was available to us to obtain a strong MaxEnt model. Our results obtained with cooccur and GLMM demonstrate that the distribution of a species influence the distribution of others, so we agree with Lazenby and Dickman (2013) and are in confirmation with the competition stated by Molsher (1999) and Glen and

Dickman (2005) between native and introduced. So, for determining the direction of the interaction (positive or negative) between predator species, the GLMM was stronger than the outputs obtained with MaxEnt and scats are likely to be presence-absence data. The problem seen in multiple studies and encountered here is the assumption made for an absence representing a data. Nevertheless, the results obtained with MaxEnt suggest total contradictions with the information from multiple published studies suggesting that we have, in fact, presence-absence data. This assumption needs testing by repeating surveys in the same areas multiple years in a row for example to increase our confidence in saying that our scat data are presence-absence data.

Looking at the environment itself, devils showed a preference for rainforests and eucalypt forests in the literature (Jones 2000; Daniel O Hunter et al. 2015) and that was found also with the GLMM (the preference for eucalypt forests was almost significant only; p-

80 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA value: 0.068) and MaxEnt using atlas data. The preference for dry areas found with MaxEnt are in contradiction with both the literature and the outputs of the GLMM as the habitats found with them are mainly wet areas. MaxEnt results for eastern quolls habitat preference is less consistent with the literature such as in literature, eastern quolls are mostly found in open grasslands, forests after a fire event and scrubs and buttongrass moorlands (Hocking and

Guiler 1983; Driessen et al. 1991; Jones 2000; Jones and Barmuta 2000) which are in contradiction with the colder areas preference found with MaxEnt. Finally, the cat habitat preference for native grasslands found with MaxEnt using atlas data are in contrast with the forest type they usually prefer found in literature and MaxEnt (with atlas data) but eastern quolls are believed to prefer this vegetation type (Jones and Barmuta 2000) and an habitat competition might explain the negative relationship between them. Studies states that cats preferred habitats with denser vegetation, maybe because of the protection offered against

Tasmanian devils (Wang and Fisher 2012) such as forests (Jones 1977; Molsher et al. 2005;

Abbott 2002, 2008Figure 3.12 for a summary). No habitat preference was detected for dogs, which can mean that we did not have enough data available or that this plasticity to adapt to a lot of environments shows the ability of a species to be widespread over the island. That can be interpreted as dogs being more flexible in their requirements, in the way that they are not influence by the abiotic factors as strongly as the other predators. It is also possible that dog distributions are strongly influenced by human mediated movement rather than particular habitat preferences and there is no way with our DNA technique of knowing that. A limitation of our approach is that some of the models for habitat preferences are not supported by ecological studies and might not be a good fit. According to the results combined, we are leaning toward seeing scats as presence-absence data in our study. Because the systematic survey unit selection was based on a MaxLike model (presence-only), because we spent 2.5 h per survey unit and because the detectability of scats from the predators

81 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA studied was sufficient, we can increase our confidence for dealing with presence-absence data. Nevertheless, our sampling effort showed to be insufficient for all species but devils, so the conclusions drawn from this study need to be approved or disproved with more data per predator.

Figure 3.12: Interactions between species and preferred habitat types based on the combining outputs of the literature, Cooccur, GLMM and MaxEnt using scat or atlas data.

Maybe more data are necessary to improve the p-values or interpretations of certain predictors and the better similarity between GLMM, cooccur and MaxEnt (with atlas data) models and having the presence of main prey can also influence the distribution of a predator, but we believe it is unlikely that more abiotic predictors would result in better models. We believe that the conclusions will stay the same and observed similar results than those recorded in literature.

Limitations

We could observe pattern of co-occurrence of species, as well as influence of a predator on another one. We could define habitat preference for some species and observe a potential competition between native and introduced predators for preference in habitats but improvements can be brought. We can repeat surveys in the same areas multiple times to increase the confidence that scats can be viewed as presence-absence data and build a GLMM only as it was the best fit. We can also increase the scat number per species studied by

82 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA spending more hours surveying an area for example. We can see that with the 387 devil data we have, the model following a GLMM is really consistent with both the cooccur analyses and what is found in the literature, nevertheless, we cannot give a proper threshold of the additional data needed from which we can be confident building a SDM based on a GLMM as it will be species dependent. If a species is really restricted to a specific niche, less data points will be needed to build a confident model of species distribution, but the opposite is also true. So, we recommend following what is known from the literature, building a cooccur analysis and comparing the results with a GLM or GLMM if pseudo-replications are occurring such as in our study.

Our study site comprises the eastern half of Tasmania, so the model did not consider if differences could be observed in the western part of the state. The habitats occurring in the west can also be very different from those under study, especially the higher precipitation rate, so potentially very distinct interactions can occur between predators. It is already a bigger area studied than in most of the other studies found on distribution of predators using scat data, and the results demonstrated to be effective. We took this study as a proof of concept that a model based on non-invasive data, to investigate patterns of co-occurrence of multiple species associated with the effect of the landscape, was possible to build, accurate and lead to reliable interpretations. In the future, similar processes could also be applied on the western half of Tasmania to obtain a model describing species distributions for a whole island. But even more, the same approach can lead to model species distribution anywhere in the world as long as scats are easily detectible and collectable. Non-invasive sampling also remove the stress caused to the animal, considers rare or elusive species and demonstrated to be a good method not to disrupt the native wildlife (Sarre et al. 2013; Taberlet and Fumagalli

1996). Scat surveys are between the poor-quality presence-only approaches (opportunistic sampling) and best presence-absence approaches (but very labour intensive) and depending

83 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA on the number of data available, closer to represent presence-absence data. We find inconsistencies in the models used likely to be caused by different underlying assumptions of the models about the nature of the data. In particular, the nature of the data (presence-only or presence-absence data) and singular use of species co-occurrence or abiotic factors are likely to cause inconsistencies among analyses.

The data were collected over two sessions of field work (2010 and 2014) and the field work effort was rather inexpensive and easy compared to traditional trapping methods. The

DNA extraction, amplification and identification were the most expensive part of the method, but offer an alternative to conventional approaches. The identification of predators was also more reliable than morphology alone and all potential errors emerging from atlas data were removed here (except in our last model). In our laboratory analyses, the identification of a scat with DNA could be improved as nearly half of the samples were lost at this stage. Money and time constraints did not allow for a repeat of samples that could increase our success rate in identifying the predators from more scats and reducing the probability of false-negative identification. Building the GLM model without including a mixed-effect could also allow for better interpretations. One solution would be to change the scale used in our survey design, smaller survey units could allow to keep data by survey units and remove the pseudo- replication of co-occurrence of species. An estimation of detectability of Tasmanian predator scats could also help us remove the doubt that Tasmanian predator scat surveys are actual presence-absence data. During the survey design, the interactions between the probability of detection and the habitat variables should be determined to correctly interpret outputs of species distribution models (Gu and Swihart 2004).

Spotted-tailed quolls are not often identified, and our marker proved successful previously (Chapter 2). This species might be collapsing faster than others or be harder to detect than thought. All the attention is focused on the devil facing the DFTD, but their scats

84 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA were the most abundant type of scats collected. Almost 20 years ago spotted-tailed quolls were rarely trapped in a study by Jones and Barmuta (2000) in Tasmania. In contrast, the species was commonly recorded in 1991 by Rounsevell et al. (1991). Scat detectability should not be a problem here as they are known to using latrine (Belcher 1995).

Dogs are not simply farm dogs staying on the farms. We collected scats from every environment suggesting that they can be considered as part of the predator intraguild and may play a more important role in interacting with other predators than previously thought with the possibility of human mediated movement explaining their presence. With the devil populations collapsing owing to the DFTD (Jones, 1997; Jones et al., 2004; Jones et al., 2007;

Deakin et al. 2012; Brüniche-Olsen et al., 2013; Hollings et al., 2013) , and a constant increase in cat abundance (Jones et al. 2007), the latter could worsen and accelerate the decline of devils and predation of eastern quolls by cats could intensify if cats become more nocturnal as the time niche would be now available (Fancourt et al. 2015). Cats are also known to be more carnivorous and opportunistic than quolls (Chris R Dickman 1996) and with the rarity of spotted-tailed quolls, that could give them the advantage needed to become the new top-predator in Tasmania, with devastating impacts on native fauna and the economy

(Hardman et al. 2016; Gong et al. 2009). So, unless removing cats from all Tasmania (with also the removal of rabbits and other small alien prey species that are potentially increase with the removal of cats; Dickman 1996), there nothing else that will provide an effective solution for the problem of invasive predators in the state. We cannot put all native predators in cat-free islands. First, our data suggest that they are likely to interact negatively with each other and the lack of space could drive the extinction of one or multiple species by food competition, predation or niche competition that would be aggravated in this scenario

(Zavaleta et al. 2001).

85 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA The high number of scats for which we could not obtain a species identification in this study could also introduce a bias in false-absence information but that risk could be reduced by repeating PCRs and sequencing to improve the detection rate. That would also increase our sample number and provide more accurate model of species distribution as we saw with devils.

Application of SDMs built with scat data

Modeling species distribution from scats identified using DNA provide results that are mostly consistent with what is found with literature while removing the hustle linked to dealing directly with the animals, trapping or atlas data often of unknown quality. The biggest advantage is that we conducted a systematic survey and collected data in two points in time providing us with a picture of what is happening simultaneously in our study site. We could also take into account rare or elusive species that could be found in areas previously not surveyed because they may not be considered when planning a survey owing to unawareness of this species in that target location. The same can be said for species that may not be considered because of their distribution is presumed to be known (e.g. dogs). The information obtained from this kind of study can help on-ground wildlife managers to identify areas of concern or where conservation action might be identified. This could also bring in the attention on a specific species interaction that can lead to a specific conservation action.

86 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA CHAPTER 4: METABARCODING OF SCATS AS A TOOL FOR THE DETECTION

AND MONITORING OF VERTEBRATE PREDATORS AND THEIR PREY

For submission as: Modave, E., Campbell, C.D., Dewar, E., Gruber, B., MacDonald, A.J.,

Sarre, S.D. 2017. Metabarcoding of scats as a tool for the detection and monitoring of vertebrate predators and their prey. Target journal: Molecular Ecology

ABSTRACT

Introduction: Metabarcoding (the identification of multiple DNA fragments in a unique environmental sample) of predator faeces (scats) has the potential to provide an alternative approach to the detection and diet of predators and to the detection and monitoring of potential prey without direct interference with the animal(s). Although in its infancy this approach has now been used in a number of situations, yet interpretation of the information that arises from them is in need of refinement. Here, we apply a metabarcoding approach to the identification of predators and their prey from scats collected in the eastern half of

Tasmania. Our goal was to detect rare species: Pseudomys novaehollandiae (the New

Holland mouse), and Potorous tridactylus (the long-nosed potoroo) that would normally require trapping for their observation, and to identify the diet of mammalian carnivores present in the areas of the selected scats.

Methods: 287 scats were selected based on the last observations of the New Holland mouse a rare and possibly extinct rodent species in Tasmania representing a case study for detecting rare species with DNA. Two mitochondrial genes (12S rRNA and 16S rRNA) were amplified following protocols for a next-generation sequencing technique on an Illumina MiSeq sequencer to identify the taxa contents in predator scats.

Results: 176 scats provided identification at the species level for the predator that passed the scat with one to seven prey taxa detected. These included native mammals, birds, skinks and

87 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA invasive mammals and livestock. We identified the diet of Sarcophilus harrisii (Tasmanian devil; 92 informative scats), Felis catus (cats; 50 scats), Canis lupus familiaris (dogs; 14 scats) scats, Dasyurus viverrinus (eastern quolls; 8 scats) and D. maculatus (spotted-tailed quoll; 12 scats). We identified 44 different prey taxa including long-nosed potoroo (seven scats) but did not detect New Holland mouse DNA.

Discussion: We demonstrate that our technique was effective for detecting small prey in scats. Determining the probability that the absence of New Holland mouse DNA in our samples represents a true absence of the New Holand Mouse in the areas sampled, or indeed in Tasmania, will require an empirical estimation of the probability of detection. We recommend that metabarcoding on scats continues to be used for detecting a rare species with assignments of unknown sequences to a DNA reference database built with museum samples for the target species and relatives at the genes investigated.

INTRODUCTION

Comprehend ecosystem and biodiversity functionings are critical milestones in conservation biology and understanding complex relationships between representatives of different trophic levels can provide us with answers for protection of endangered species or the management of important conservation areas. Correctly detecting and locating species in their environment is a critical first step in diagnosing interactions among wildlife and their environment. Larger species have traditionally been detected through physical trapping, sighting or people and dog surveys. These approaches provide unique and valuable data about the distribution of species and can also be used to examine predatory preferences through diet access by the analysis of gut (Baker et al. 2014) or scat (i.e. faecal samples) contents for hair, bone remains, or insect parts (Watanabe et al. 2003). Nevertheless, such approaches can be time-consuming and require high levels of field skills while also risking unintended side effects through the disturbance of captured individuals. Increasingly, camera traps are being

88 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA used to overcome these difficulties by offering the remote detection of wildlife (Marnewick et al. 2006; Giman et al. 2007; De Bondi et al. 2010). They can provide valuable information about behaviour and species interactions but are unlikely to bring comprehensive information about predator prey relationships. A new class of detection approaches is emerging in which

DNA in the environment is being used to implement a record of species presence (Foote et al.

2012; Mahon et al. 2013; Rees et al. 2014; Hunter et al. 2015).

High-throughput sequencing techniques allow us to sequence simultaneously a set of multiple mixed samples quickly. That capacity, makes possible the sequencing of thousands of amplicons. Coupled with metabarcoding (the identification of multiple DNA fragments in a unique environmental sample) this approach as the capacity to revealed what predators have consumed (Mardis, 2008a; Pompanon et al., 2012; Shehzad et al., 2012; Hatem et al., 2013;

Zhou et al., 2013). The main advantages of high-throughput based techniques is that they can provide accurate identification of an assembly of food item of diversified predators diet with really low effort (Pompanon et al., 2012). High-throughput sequencing is quick (Metzker,

2009) and becomes cheaper every year (van Dijk et al. 2014), although cross-contamination is common (Mitchelson 2011). This is leading to a new area in ecology where complex relationships between species can be identified with high fidelity(Taylor and Harris 2012).

Identifying a species from DNA through metabarcoding is achieved by amplifying and sequencing short target fragments of DNA from scats, and then assigning an identification through comparisons of the amplified sequences to those of a reference database (Boyer et al. 2012; Meusnier et al. 2008; Alberdi et al. 2012; Meusnier et al. 2008;

Kress et al. 2015; Tedersoo et al. 2015; chapter 2). Scats are an easy source to gather DNA fragments left by animals (chapter 2) and may constitute the best opportunity of detecting a rare or cryptic species (Bohmann et al. 2014). Target sequences in such an approach have to be short as DNA is usually degraded in environmental samples (Jurado-Rivera et al. 2009;

89 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA Kress et al. 2015) and metabarcoding of trace samples demonstrated high sensitivity in other studies (Tillmar et al. 2013; Cristescu 2014; Nichols et al. 2016).

Metabarcoding studies of environmental DNA (eDNA) have been used to monitor macroinvertebrate in rivers (Hajibabaei et al. 2011) or amphibians and bony fishes in water systems and outperform traditional detection techniques (Valentini et al. 2016).

Metabarcoding determine its usefulness also for identifying DNA accumulated in soil with accuracy and sensitivity for identifying the vertebrate diversity (Andersen et al. 2012) and in determining the distribution of earthworm communities in the ground as a reaction to land- uses in the French Alps (Pansu et al. 2015). This method has also been extensively used for diet studies on non-invasive samples. Studies focussed on identifying prey from seals or

Steller sea lion scats, when the animals have been fed a known diet. These feeding trial researches would be used to compare morphology to molecular detection (Casper et al. 2007) or detecting multiple taxa using group-specific primers (Deagle et al. 2005). Studies were also developed to investigate dietary trends in seabirds (Deagle et al. 2007), arthropods in the diet of a lizard in the Caribbean (Kartzinel and Pringle 2014), plants in sympatric lemmings species when resources are scarce (Soininen et al. 2015) or in two species of deer to compare macroscopic identification to metabarcoding success (Nichols et al. 2016). The latter study demonstrated that more taxa were described with DNA and that the taxonomy precision was better. Wider diet analyses are now possible thanks to advances in genetics and metabarcoding. For example, De Barba et al. (2014), analysed 91 brown bear scats for plants, invertebrates and vertebrate presences. They made sure of the detection depth by adding controls to their analyses. More recently, plants and vertebrates have been identified in diet of leopard cat scats in Pakistan and in China and the possibility of extending this type of research to a bigger area was mentioned (Shehzad et al. 2012; Xiong et al. 2016).

90 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA Metabarcoding of faecal material in particular, has the potentiality of a new approach that can combine both species detection and diet analyses in a single instance. This approach has the advantage of providing remote detection without direct interactions with predators, while providing additional information about their prey. If robust, metabarcoding provides the opportunity to study predator prey relationships and as a corollary, the ability to detect smaller (prey) animals in the environment without trapping. The detection and diet analysis methods could be applied to non-invasive samples using metabarcoding and high-throughput approaches that use a well-defined set of primers. This approach could provide the capacity to monitor multiple species in a wide area and identify regions of conservation interest for animals under study, their prey or both and investigate the diet of species to examine predator-prey interactions. To determine the accuracy and success of such approach, we need to assess that we have low rates of contamination, high concordance with known distribution of key taxa, high concordance with the predator detection test (which itself has been shown to work with high fidelity; chapter 2) and high concordance between common prey items known to be consumed and those identified using metabarcoding. Here, we propose to metabarcode more than 280 scats, widespread over the eastern half of Tasmania, Australia (area covering almost 31 000 km2) of eutherian and marsupial carnivores encountered in the region to identify the vertebrate diet with the ability to detect and possibly monitor both native and introduced prey and predators for possible conservation purposes.

In the island state of Tasmania, Australia, several native species are already classified as extinct mainly owing to predation by invasive. Alien species predating on native prey is the main concern in this study and can lead to extinction (Robley et al. 2004; Denny and

Dickman 2010; Wang and Fisher 2012). The diet of the two introduced (Felis catus - cats and

Canis lupus familiaris - dogs) and three native (Dasyurus viverrinus - eastern quolls, D. maculatus - spotted-tailed quolls and Sarcophilus harrisii - Tasmanian devils) predators

91 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA encountered in Tasmania was studied previously using various methods, but focus was set on

Australian mainland (except for the devil and eastern quoll) and its prey availability. In

Tasmania, the diet could presumably be comparatively distinct.

Tasmanian devils consume large prey, mainly as carrion but can also predate on quolls, wallabies, possums and are found to consume fishes, birds and invertebrates as well

(Pemberton et al. 2008). Eastern quolls probably exhibit the most omnivorous diet of all the predators found in Tasmania. They can consume fruits and insects, are known to scavenge, and also predate on birds and medium to small mammals (Strahan 1995; Jones and Barmuta

1998; Watts 2008; Peacock and Abbott 2014) with invertebrates forming the main diet item

(Belcher, 1995; Jones and Barmuta, 2000). The spotted-tailed quoll is an opportunistic predator, with a wide diet that it is known to adapt when the prey availability is fluctuating.

The high proportion of mammals in its diet classifies it as a hypercarnivore, but birds, reptiles and insects can also appear in its diet in smaller proportions (Jones and Barmuta 1998; Glen and Dickman 2006). This quoll has the biggest diet overlap with the two other native predators all through the year (Jones and Barmuta 1998). Cats predate on small to medium- sized vertebrates, are generalist, scavenger and opportunistic predators and can rapidly adapt to different prey items (Catling, 1988; Smith and Quin, 1996; Barratt, 1997; Courchamp et al., 1999a; Courchamp et al., 1999b; Pontier et al., 2002; Medina et al., 2010; Bonnaud et al.,

2011). Oryctolagus cuniculus (rabbits) form a large part of feral cat diets in Australia (Robley et al. 2004), but a study by Doherty et al. in 2015 demonstrated a quadratic relationship between cat diet and the consumption of arthropods and rabbits as a function of latitude with a decrease in these food items at latitudes below 35°. As Tasmania is situated between 40 and

44° latitude south, we expect to see only a small presence of rabbit in their diet which may have flow on effects to the prevalence of alternative prey like Dasyurids and rodents in their diet. Dogs do not receive a lot of attention as there is not a big proportion of feral dogs.

92 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA Instead, Canis lupus dingo’s (dingoes; their closest relatives in Australia) are generalist predators that have a plastic diet capable of adaptation to localised prey items (Allen et al.

2012). In north eastern Australia, macropods, possums and bandicoots were common in their diet. Dingoes prefer medium-large prey, but they can adapt to smaller prey items if the resources are present (Allen et al. 2012; Pascoe et al. 2012). As there are no dingoes in

Tasmania, dogs’ diet could be entirely different (Table 4.1).

Table 4.1: Diet of target predators found in the literature

Predator Prey items References Tasmanian devils  large prey items, mainly  Belcher, 1995; Jones, (Sarcophilus harrisii) as carrions 1997; Jones and Barmuta  juveniles eastern quolls 1998 (Dasyurus viverrinus)  Jones and Barmuta 1998;  wallabies and possums Jones and Barmuta 2000; (Macropodidae and Jones et al. 2004 Phalangeridae)  Belcher, 1995; Jones, 1997; Johnson and Wroe, 2003; Watts, 2008 Eastern quoll  Invertebrates, fruits,  Strahan 1995; Jones and (Dasyurus viverrinus) carrions, birds, medium to Barmuta 1998; Watts small mammals 2008; Peacock and Abbott 2014; Belcher, 1995; Jones and Barmuta, 2000 Spotted-tailed quoll  high proportion of  Jones and Barmuta 1998; (Dasyurus maculatus) mammals (i.e. Glen and Dickman 2006 hypercarnivore), birds, reptiles, insects Cat (Felis catus)  small to medium sized  Catling, 1988; Smith and vertebrates, carrions, Quin, 1996; Barratt, opportunistic feeder 1997; Courchamp et al.,  Rabbits (Oryctolagus 1999a; Courchamp et al., cuniculus) 1999b; Pontier et al.,  Dasyurids and rodents 2002; Medina et al., 2010; Bonnaud et al., 2011  Robley et al. 2004  Doherty et al. 2015 Dog (Canis lupus  ? familiaris)

93 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA Applying metabarcoding on scats, we are going to investigate the diet of these predators in Tasmania for their content in vertebrate prey items and compare the results from what is known from the literature. But 1) can metabarcoding be also be used as a tool to detect rare species in a wide area as well as provide information about the diet of predators?

2) Can it help target regions of conservation interest? 3) Can it show us the impact that predator-prey interactions such as an invasive predator could have on a native prey? 4) We can go deeper by wondering if the diet itself can define a predator? 5) Thus, can we guess, with the presence of prey species, if an area is suitable for a certain predator to be in?

We also apply our metabarcoding approach to the detection of rare or elusive species with particular focus on a rare rodent species, Pseudomys novaehollandiae (New Holland mouse), as a case study to show that DNA detection might be more powerful than traditional trapping methods for detecting a scarce species, and to investigate in which predator diet this species was most present for conservation aims. The New Holland mouse is one of the five native rodents in Tasmania. It has a disjoint and contracting range over four Australian states and is listed as endangered in Tasmania and Victoria (Tasmanian Threatened Species

Protection Act 1995; Flora and Fauna Guarantee Act 1988). It is threatened by habitat loss or modification, as well as predation from feral animals such as cats (Lazenby et al., 2008; Pye,

1991). There have been very few formal records of its existence in Tasmania over the past 10 years and it is believed to be possibly extinct. The New Holland mouse is likely to be prey for many carnivores in the Tasmanian landscape and as such, is an ideal candidate to test the power of DNA metabarcoding over traditional trapping methods. We also try to detect

Potorous tridactylus (long-nosed potoroo), that is in need of management, and in so doing, provide information on determining which species predates the most on it, hopefully providing information that can underpin management action.

94 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA With the introduction of alien predators in Tasmania, predator-prey interactions are altered and all the ecosystem needs to reach a new equilibrium if it can or destructive effects are to be expected, and might have already started, on the native wildlife. A great amount of diet information was obtained by using a metabarcoding technique on scats with a potential for managing areas of concern by detecting and possibly monitor a prey of interest and the accuracy of using metabarcoding was determined. Our study demonstrated a real example to apply management for long-nosed potoroo in the north of the state to protect them from cats.

METHODS

The steps detailed below outline the approach used to construct libraries that are used for next-generation sequencing and identify predators and prey in Tasmanian predator scats

(Figure 4.1)

95 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA Figure 4.1: steps to follow to produce the libraries for metabarcoding

96 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA Building a reference database

We built a reference database on Geneious 8.1.7 (Biomatters, Auckland, New

Zealand, Kearse et al. 2012) using museum specimens from a wide range of mammals from

Tasmania, their mainland counterparts and introduced mammals in Tasmania. We want to be able to compare our unknown DNA sequences contained in scats to known species described by experts. We downloaded all the available sequences on Genbank (NCBI: National Center for Biotechnology Information, USA, http://www.ncbi.nlm.nih.gov/genbank/, Geer et al.

2009) in November 2016 for mammals, vertebrates, primates and rodents to obtain the complete array of possible sequences targeted we could extract from scats (using a UNIX command line prompt with the entries: wget 'ftp://ftp.ncbi.nlm.nih.gov/genbank/gbmam*.seq.gz', wget 'ftp://ftp.ncbi.nlm.nih.gov/genbank/gbrod*.seq.gz', wget 'ftp://ftp.ncbi.nlm.nih.gov/genbank/gbpri*.seq.gz' and wget 'ftp://ftp.ncbi.nlm.nih.gov/genbank/gbvrt*.seq.gz'). We also included extracts of DNA of museum tissues of several taxa that were not well represented on GenBank (e.g. Tasmanian quolls and rodents) with a salting out method with minor modifications (MacDonald et al.

2011) following chapter 2. Our final DNA concentration was of 40 ng/ µl. The New Holland mouse DNA was extracted from two skin samples from Tasmania obtained from the Queen

Victoria Museum and Art Gallery (QVMAG) and from two liver samples from New South

Wales from the Australian museum.

We amplified two mitochondrial gene portions, 12S and 16S, for the same reasons as mentioned in chapter 2: these genes are often used in metabarcoding studies because of their good resolution for identifying animals at the species level, they occur in multiple copies in each cell, increasing the chance of amplification and their genome is circular, reducing the

97 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA early degradation of the DNA. They also are highly conserved within our mammal, and more general vertebrate, target groups. As such they are likely to amplify across multiple taxa.

They demonstrated their efficacy in other studies dealing with the identification of mammals or other vertebrates from trace samples (e.g. ancient samples; Murray et al. 2013, or trace samples; Xiong et al. 2016). A ca 1061bp of the 12S mitochondrial gene region was amplified by PCR using primers 12C-12gg (Table 4.2) following the same steps as in chapter

2. PCR products were visualised on a 1.7% TAE agarose gel (Agarose I: Amresco, Solon,

OH, USA) run for 40 min at 90 v. with Hyperladder 50 bp (Bioline, Australia) acting as a size reference. Amplicons were cleaned and sequenced using the same method as in the cited paper and the forward and reverse sequences were aligned using Geneious 8.1.7 after trimming of the primers and low-quality bases. A ca 1275bp of the 16S mitochondrial gene region was also PCR amplified with the Mammal_16S primers (Table 4.2). Again, the same methods for PCR, cleaning and sequencing of the DNA regions were used as in chapter 2.

We designed the Mammal_16S forward primer as no existing primer was fitted for our study and did not encompass all our target species. For that, we created an alignment on Geneious

8.1.7 of all the target species with the sequences that we had and found a conserved DNA region within the 16S rRNA gene that encompassed more than 1000 bases. We used the 16S reverse primer designed by Springer et al. (1997). Overall, 321 species were correctly sequenced from museum samples on Geneious for 12S and 204 sequences were obtained for the 16S gene. Our GenBank database was almost 24 Go.

98 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA Table 4.1: PCR primers used in this study

Primers Target Sequence (5’ – 3’) Amplicon Reference group length amplified 12C & 12GG Vertebrates 12C: AAAGCAAARCACTGAAAATG ca 1061 bp Springer et al. 1995 12GG: TRGGTGTARGCTRRRTGCTTT Mammal_16S Mammals Mammal_16SF: CGAGCCTGGTGATAGCTGGTT 1275bp This study 16SR: AGAGGRTTTGAACCTCTG Springer et al. 1997 AusPreda_12S Six AusPreda_12SF: CCAGCCACCGCGGTCATACG 218 bp chapter 2 Tasmanian AusPreda_12SR: GCATAGTGGGGTCTCTAATC predators 12SV5 Vertebrates 12SV5F: TAGAACAGGCTCCTCTAG 73 bp to Riaz et al. 2011 (metabarcoding) 12SV5R: TTAGATACCCCACTATGC 110 bp 16SMam Mammals 16SmamF: CGGTTGGGGTGACCTCGGA 131 bp Ficetola et al. 2010 (metabarcoding) 16SmamR: GCTGTTATCCCTAGGGTAACT

Field work

The field work was conducted as per chapter 3. Briefly, this involve the survey of 3 x

3 km2 systematically selected over the eastern half of Tasmania and collection of scats

containing hairs, bones, insect parts or seeds. A survey was conducted in autumn 2014 and a

total of ca 3000 scats was collected.

Samples selection

One of our aim was to detect the New Holland mouse, a critically rare species that

potentially can be the prey of multiple predators. We selected several scats on the highest

probability of locating this mouse in the hope of detecting it and targeting regions of interest

for management planning using a model. Some models already exist for locating this species,

but they are based on vegetation only (e.g. Lazenby et al. 2008; Wilson and Laidlaw 2003).

We built a general linear model (GLM) based on vegetation, fire events, previous

observations and elevation using field collected scat data. GLM are better fitted and relevant

to study ecosystems compared to Ecological Niche Factor Analysis (ENFA) for example

because of their better accuracy to represent the reality (Brotons et al. 2004) and the random

Forest element used build a multitude of decision trees and output the mean prediction of the

99 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA individual trees if used as a regression model, in other words, select scats with the highest likelihood score on multiple decision trees. In the model, two sets of data were created: training and testing. A model is usually built using the training set and the testing set is used to test the power of the model. Random decision forests correct for decision trees' custom of overfitting to the training data set (Liaw and Wiener 2002). For more details about the scats selection, refer to Appendix 4.1.

We used raster layers provided by the DPIPWE Tasmania (Department of Primary

Industries, Parks, Water and environment) for fire, elevation and habitat types. We also included four predator scats that were collected by volunteers, engaged through the ‘Green

Guardians’ network, in 2016 from specific sites of interest in the north east of Tasmania for incorporation into the analysis (Appendix 4.2).

DNA extraction

DNA was extracted from scats as per chapter 3. Each batch of extraction (24 samples) included an extraction negative. The DNA was diluted 10 and 100 times in nuclease-free water (Bioline, London, UK) to undergo the next step of the analysis. qPCR to assess extraction dilutions

In the laboratory, we can only account for the fact that DNA amplification could be inhibited by the presence of residual proteins and such, so we performed three dilution states to obtain the best dilution by diluting the possible inhibitors. The three dilution states of each

DNA samples (undiluted, 1 in 10 dilution and 1 in 100 dilution) were subjected to a first quantitative PCR (qPCR) run on a Viia 7 Real-Time PCR system (Thermo Fisher Scientific) to check which of the dilutions demonstrated the best DNA amplification results (plot and CT value: the number of cycles, below 33 per our requirements, required for the fluorescent signal of a qPCR machine to cross the predetermined threshold, here set at 5000 ΔRn). The

100 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA qPCR mastermix was: 1X Gold buffer, 2 mM MgCl2, 0.4 mg/ml of BSA, 0.4 µM of each

16SMam generic primer (table 1), 0.6 µl SYBR green (1:2000 Life Technologies nucleic acid gel stain), 0.25 mM of dNTPs, 1 unit of AmpliTaq GoldTM (Applied Biosystems), 2 µl of genomic DNA (unknown concentration) and ddH20 to make up a final volume of 25 µl. The runs were as followed: an initial step of 95°C for 5 mins; followed by 40 cycles of 95°C for

30 s, 57°C for 30s and 72° for 30 s. The negative extractions were discarded if they did not amplify any DNA.

We cannot account for other types of contaminations, such as field contamination

(before the scats were collected, and after the collectors gathered them) or laboratory contamination (by other technicians working in the laboratory with other taxa for example), but DNA contamination during extraction or amplification are a common source of error

(Taberlet et al. 1999) and we minimised this effect by working in a trace DNA laboratory where contamination was avoided at best. The second source of contamination could be in the field. Scats can be contaminated by insects or other species, which are not eaten by the animal defecating but are found in the results. Human contamination is the last source of error and can come from the person collecting or extracting the scats (Freeland 2007). False- negative (i.e. individual present, but not detected) or false-positive (i.e. individual not present, but wrongly detected) results occasionally occur (King et al., 2008; Boessenkool et al., 2012;

Yoccoz, 2012) and caution is to be taken regarding unexpected sequencing results (non-

Tasmanian species in our case). qPCR with MID tagged primers

The MID-tags, attached to the primers, were used to allocate each sample to a scat and a location once all scat samples are pooled together in an unique tube send for sequencing. Keeping only the best dilution per sample from the previous step, the exact same

101 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA process as the one described in the first qPCR check was applied, for the diet analyses, to the

269 scat DNA samples and the eight negative samples left using this time MID tagged primers for 12SV5 (ca 97 bp) vertebrate barcode and 16SMam (ca 92 bp) mammal barcode

(Table 4.2), this time adding 1µl more of genomic DNA and 1 µl less of ddH20 to make up a final volume of 25 µl. We added positive controls (multiple tubes with DNA from Macropus parryi (whiptail wallaby), collected in 1988 in Queensland, from the Australian National

Wildlife Collection, ACT: M14000) in each run and the qPCR runs were as previously described with a final extension step of 10 mins. at 72° to make sure complete amplified fragments were achieved. Our resulting sequences were as follow: P5 (that attached to the sequencer) – MID tagged primer (that allows association between a sequence and a sample) – the unknown sequence – P7 (an extension removed during the bioinformatic analyses).

AMPure cleaning

Four to ten samples were pooled together based on their similar CT values in sterile low-bind tubes (Axygen 1.5 ml MaxyClear Snaplock Microcentrifuge Tube) and 12S and

16S samples were kept separate until the last step with the creation of a superpool (where all pools of four to ten samples were finally pooled together). We conducted an AMPure clean- up (Agencourt AMPure XP PCR purification kit, Beckman Coulter life science) on each pool adding 1.2x ratio of beads to each tube and placing them on a magnetic rack. After two washes with 70% ethanol, EB buffer from the kit was used to elute the DNA that was transfer to a new low-bind tube. A final amplicon library was generated pooling all pools in one low- bind tube. The same process as describe directly above was applied to conduct a second

AMPure clean-up. EB buffer was again used to elute the library and concentration was checked on a Qubit Fluorometer (Invitrogen, Grand Island, NY, USA). The final library was sent to the Ramaciotti Centre (University of New South Wales, Australia) for Next-

102 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA Generation sequencing on an Illumina sequencer. In total, three runs were sent for sequencing.

Sequencing

Several next-generation sequencing platforms exist for high-throughput analyses of several million of sequences (Lerner and Fleischer 2010). We chose an Illumina MiSeq sequencer (Illumina, US) such as superior data quality and read lengths made this sequencer a sequencer of choice. The MiSeq platform can generates 1.5-2 Gb of 300 bp reads in a run

(Shokralla et al. 2012) and a base-calling algorithm allocates each sequence with quality values and a quality checking tool removes poor-quality data (Mardis, 2008b; Shendure and

Ji, 2008; Shokralla et al., 2012). We sent three single-ended runs to sequence with a 300 bp cycle kit.

Bioinformatic analyses

We received fasta.qz files from Ramaciotti and trimmed low quality bases at 3' end of

Illumina single ended reads using Trimmomatic (Bolger et al. 2014; Figure 4.2). The

Illumina adapters were trimmed as well and we removed the sequences with a quality below

16 every window size of 14 bp (quality score measured in phred score, the most common assessor of a sequencing platform accuracy. It represents the sequencer’s probability that a base is called incorrectly). The non-targeted reads, of a size below 80 bp and above 200 bp, were discarded as well. We then analysed the reads on Unix with the command line prompt using the Obitools following the tutorial about the wolves’ diet on the developers website

(Shehzad et al. 2012; Boyer et al. 2016). The ngsfilter command allowed us to assign each sequence record to their corresponding tags and primers combination. Only reads with a perfect match with their MID-tagged primers were kept, the rest was discarded owing to possible tag-jumping effects (Schnell et al. 2015). The obiuniq command was used to

103 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA dereplicate reads into unique sequences (reads can be repeats of the same DNA molecule),

then obistat and obigrep were performed to remove rare sequences and erroneous ones. We

decided to only keep sequences that occurred 10 times or more and that were longer than 80

bp for 12S rRNA and 100 bp for 16S rRNA following the recommendations by Shehzad et

al. (2012). That represents the length of the barcodes without primers. Once denoising has

been done, we assigned the barcodes to the identity of species. For that step, we used two

different databases, the one coming from all the GenBank sequences from mammals,

vertebrates, primates and rodents, trimmed at our 12S and 16S sizes, and the one designed by

us. Assignment to a species was only kept if they were 95% or more identical to a taxon in

the databases. Finally, we decided to set a threshold of 100 sequences per prey found in a

predator diet (across all scat samples), and 10 sequences per scat sample, if less, sequences

were discarded (Shehzad et al. 2012).

Figure 4.2: Trimming of low quality bases at the 3’ end from single ended reads obtained from a MiSeq sequencer using Trimmomatic. Figure before (a) and after (b) treatment. The quality per base raised in the three runs. Illustrated example for run 1.

a) Quality score across all bases, from the first base to the 259th. A quality above 20 is considered as good. b) Quality score across all bases after using Trimmomatic, from the first to the 200th base. We trimmed the Illumina adapters used, and trimmed the sequences with a quality below 16 every window size of 14 bp. We cropped the reads at a maximum of 200 bp and discarded all reads below 80 bp.

104 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA Predator identification tested against identification obtained using other primers

We first double check the predator identification of nearly each sample by comparing the results found with the 12S gene to the results found with the 16S gene. We also used another method as per chapters 2 and 3 to confirm or reject the predator identification from each of our sample. In our metabarcoding results, the highest our number of reads is, the highest presence the DNA is in the scat, which indicate the presence of a predator species as per release of epithelial cells during defecation (Albaugh et al. 1992; Wasser et al. 1997). We used the same DNA extract from the selected scats and applied the AusPreda_12S forward and reverse primers (“Predator identification test”; Table 4.2; chapter 2) that only amplifies the most abundant DNA sequence in a scat, which is most of the time (90%; chapter 3) the predator DNA, and sequenced the resulting ID with a Sanger method at the Biomolecular

Resource Facility (Australian National University) on a 96 capillary 3730 DNA Analyzer

(Applied Biosystems). Forward and reverse sequences for each sample were manually checked, trimmed of primer sequences and low quality bases at the 3’ and 5’ ends, and aligned using Geneious 8.1.7. The sequences were individually compared on GenBank using

BLAST (Basic Local Alignment Search Tool) to obtain an identification. We then compared the two methods for predator identification and it showed particularly useful for identifying the quoll species. Indeed, the short 12S and 16S regions we used for the metabarcoding could not discriminate among quolls, thus the resulting identification was Dasyurus (genus level only) without further details, whilst the AusPreda_12S site selected for the identification of the predator (chapter 2) could.

Selecting the closest relative when an identified taxon is non-Tasmanian

Each ranked taxon found in the metabarcoding results was meticulously investigated for its origin. If not present in Tasmania, the animal was checked for its closest relative in

105 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA Tasmania. We did the same process for both genes analysed. Altogether, one subspecies, 23 species, three genera and one family were investigated to eliminate sequences resulting from

PCR malfunctions (Shehzad et al. 2012), contaminations or obvious erroneous identifications. We combined both genes and the taxa identified with GenBank and our database after removing all inaccurate sequences to build a diet per predator.

Determining the vertebrate diet of each predator

We could only account for the 176 scat samples that had predator and prey information. They represented scats of 92 Tasmanian devils, 50 cats, 14 dogs, 12 spotted- tailed quolls and 8 eastern quolls. A diet per predator was built with the occurrence of prey taxa in scats. Using R (Development Core Team 2016), we constructed a non-metric multidimensional scaling matrix (NMDS; Taguchi et al. 2001). We input a matrix containing all predators, all prey taxa and the position (X, Y) from each scat to observe if the predators would cluster together according to their diet such as the items in the diet itself could help identify a predator. Such information could help build better predictive models of species interactions (chapter 3; example in Brown et al. 2014). This is to investigate if each predator feeds on a specific diet, that no other predator shares. NMDS collapse information from multiple dimensions (from multiple predator and prey species with environmental data from chapter 3 in our case) into just a few, for better visualization and interpretation. NMDS uses rank orders contrary to other ordination techniques that depend on Euclidean distances, like

Principal Coordinates Analysis, such as it provides a flexible technique that can adapt to a wide range of data.

Determining the vertebrate diet based on a predator location

Because we were looking for the presence of the New Holland mouse in diet, we were limited by the number of samples selected and the locations of each scat. Dry eucalypt forest

106 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA and woodlands, scrub, heathland and coastal complexes and agricultural, urban and exotic vegetation were the three vegetation types investigated. The two main vegetation types preferred by the New Holland mouse, areas of a recent fire event (2008-2012) compared to when the scats were collected (2014) and an elevation from 0 to 800 meters limited the comparisons of diet in different habitats we could do on our results. We did not have a lot of samples, owing to money and time constraints, but we still identified 92 devil scats and 50 cat scats which represented 50% of the total number of scats analysed. Our sampling number for the two species was sufficient to elaborate on the diet based on different habitats. We imported the scat location data on R and simply extract values from the three raster layers used for our scat selection to examine in which vegetation type and elevation each scat was located. With this information, we investigated and compared the diet of cats and devils in three types of vegetation selected by the model.

Detection of a rare prey

We gathered a lot of information about the diet of the predators in Tasmania and showed we could detect and possibly monitor native prey species with our technique. As examples, we tried to detect the New Holland mouse and the long-nosed potoroo, both species being in urgent need of management. With each of our analysed sample, we could target a prey item, learn which species was predating on it and the location of the scat this species was detected in.

Confirming the identity of species using DNA and range obtained from atlas data

To add confidence to our prey identifications, we randomly selected ten prey species from the most represented ones to the rare ones by recording the number of occurrence in scats (all predators combined) and creating three groups: common (four taxa with more than

40 occurrences), moderate (29 taxa with two to 19 occurrences) and rare (11 taxa with only

107 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA one recorded occurrence). We then assigned a random number to each prey with excel and classified them by high to low number, we selected two common, four several and four rare prey. We also selected the five predators found in Tasmania. Using atlas data collected from

NVA (Natural value atlas; Tasmanian government), we exported their observations on

ArcGIS 10.2 ESRI (Environmental Systems Research Institute, California) and edited the data with obvious errors (in water) or on King or Flinders islands. This was done to obtain a range on Tasmanian mainland only (including small islands) to allow for comparison with our collected scats. Using the minimum bound geometry function in ArcGIS, we draw polygons (convex hull) encompassing all atlas data remaining. We then imported our identifications from scats and draw a polygon around each species range obtained. We investigated if our observations fell in or out of the atlas data polygons. If inside, this would provide another confirmation of species identifications with our DNA method, if outside, that can mean three things: 1) the DNA identification is a false-positive one, which mean that we must be cautious to trust our data, 2) the species was not recorded at that location because no observation was collected, or 3) the species increased its range since last surveyed.

RESULTS

Samples selection

We collected ca 3000 scats in 4 months and based on our model (created depending on historic observations from atlas, fire events, preferred vegetation and elevation), a file of selected scats was created where the last column represents the percentage of likelihood to detect the New Holland mouse from the scat samples (Appendix 4.2). 287 scats were selected

(Figure 4.3) that showed between 85% and 30% likelihood to detect the New Holland mouse in scats deposited in these regions. The likelihood after that decreased to less than 10% in our model so we decided not to include these scats. The 2016 scats (ID: 16884, 16885, 16886 and

108 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA 16887) were respectively of 76.38%, 47.42%, 8.36% and 47.36%. We decided to keep the sample 16886 even if the percentage of detection was low as we found that the specific collection of the scat deserved a more detailed inspection than what the model predicted.

Figure 4.3: Map of the location of the 287 scat samples selected for metabarcoding based on the model

109 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA DNA extraction and qPCR to assess extraction dilutions

The DNA was extracted from the 287 selected scats and the diluted DNA that showed the best amplification plot with a CT value between 15 and 33 was kept for further analyses.

18 scats and 19 extraction negatives were discarded when no DNA amplification was observed in either of these dilutions. 269 scats and 8 extraction negatives were kept and subjected to the following steps. qPCR with MID tagged primers

The samples that failed the qPCR runs with the MID tagged primers the first time were repeated in a second run. If it failed again, the sample was discarded from further analyses. 17 samples for 12S and 24 samples for 16S were discarded following the second trial. Seven positive controls were added for both genes.

AMPure cleaning

In total, three runs were sent for Next-Generation sequencing on an Illumina platform.

Around 45 million reads were created for a total of 265 samples for 12S (252 scat DNA samples, six negatives and seven positive controls) and 258 samples for 16S (245 scat DNA samples, six negatives and seven positive controls).

Bioinformatic analyses

A library of short, high quality DNA sequences for two genes was edited in Obitools resulting in 225 samples with reads for 12S (214 scat DNA samples, four negative and seven positive controls) and 206 samples with reads for 16S (194 scat DNA samples, five negative and seven positive controls). One run had a high failure rate and several samples were lost. In all, the final database incorporating two genes contained 229 scats with information. Four scats had just human contamination DNA in them, hence were discarded and the final sample

110 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA number was 225. For 11scats (4.8%), we only had information about the predator, and no reads for the prey. Contamination of human DNA was removed from the results. It is less than probable that a predator fed on a human carcass, or that we collected a human scat. Eight of twelve negative controls showed reads of eight different taxa in them. The number of reads for each of them varied from 19 to 759 (not big number when compared to the ten of thousand reads for certain taxa in a scat). The scats analysed for prey that were in the same

DNA extraction batch as these negative controls were investigated. There is no universally accepted threshold, it ranges from removing singletons to only accepting the most abundant reads. If a similar taxon was identified as being a prey in a scat, the number of reads for this taxon was observed such as if there was less than ten times the number of reads in the scat than in the negative control, the prey item was removed from the diet for this specific scat for caution. We ended up removing 12 prey items from devils in eight scats (Thylogale billardierii - Tasmanian pademelons, Macropus rufogriseus - red-necked wallaby and

Dasyurus – quolls at the genus level), two prey in one scat for dogs (Red-necked wallaby and

Tasmanian devil), four prey in three scats for eastern quolls (Tasmanian devil and Tasmanian pademelon) and one prey item in one scat for spotted-tailed quolls (Tasmanian devil).

Predator identification tested against identification obtained using other primers

We identified 225 scats with the metabarcoding technique, and compared them to the identification found with the predator identification test (chapter 2). Eight scats were identified as eastern quolls, 14 as spotted-tailed quolls, 96 as devils, 67 as cats and 21 as dogs. We could not distinguish among seven quolls samples, so they were identified only at the genus level and one scat could only be identified at the family level (a Dasyurid: 18935).

Finally, 11 scats were from an unknown predator (where no reads of a predator species could be detected), but precious information about the prey presences was still available. Out of our

225 resulting identifications, only 11 scats (4.9%) failed to reveal a predator identification

111 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA using both methods (metabarcoding and predator test), 78 scats (34.7%) failed with the predator identification test, but worked in the metabarcoding analyses, demonstrating that the latter is more sensitive than the Sanger sequencing approach. In only a single scat (0.4%) was a predator identified using the predator identification test but not using the metabarcoding approach. Of the remaining scats for which identification was obtained from both methods, the predator identified varied in 24 (10.7%) of cases. Fourteen of those arose because the metabarcoding sequence was unable to distinguish between the two quolls but the predator identification test could resolve this issue and all 14 of them were assigned to a species using this test. Finally, ten ambiguous scat identifications were determined by the predator identification test when uncertain identification was found with the metabarcoding analyses

(between a quoll and a devil each time).

Selecting the closest relative when an identified taxon is non-Tasmanian

For the 12SV5 rRNA gene, a vertebrate marker, one subspecies, 12 species, three genera and one family were found outside of Tasmania. The review of each of these revealed two taxa right at the infra-class level (Marmosops – genus and Marmosops parvidens – species), one family right at the order level (Psittacidae), two taxa right at the super-family level (Oriolus – genus and Steatomys spp. - species), five taxa right at the family level

(Meleagris – genus and Cuon alpinus, Saproscincus mustelinus, Pseudantechinus bilarni and

Carcaral caracal – species), six species and one subspecies right at the genus level (Rattus sordidus, Thylogale stigmatica, Acanthiza apicalis, Macropus eugenii, Sericornis frontalis and Dasyurus hallucatus – species and Zoothera dauma aurea - subspecies). For the

16Smam rRNA gene, a common mammal marker, 11 species were found outside of

Tasmania. Four species were right at the family level (Pseudantechinus bilarni, Dasykaluta rosamondae, Parantechinus apicalis and Murexchinus melanurus) and seven species were

112 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA right at the genus level (Dasyurus spartacus, Felis margarita, Felis chaus, Thylogale stigmatica, Macropus dorsalis, Sminthopsis dolichura and Sminthopsis archeri).

After investigating each of these individually, combining the resulting identifications found with 12S and 16S, combining the number of reads with relatives identified from

Tasmania with the metabarcoding and combining the identifications found with GenBank and our own database, only three taxa identified in Tasmanian scats could not be identified from a

Tasmanian species. These included Sericornis frontalis (species), Orilus (genus) and

Zoothera dauma aurea (sub-species). In these three cases, the closest relative known to occur in Tasmania was selected from the literature as being the most likely taxa identified,

Sericornis humilis (species), Corvida (super-family) and Zoothera lunulata (species).

Determining the vertebrate diet of each predator and based on a predator location

A diet per species was built for the five predators identified in Tasmania (Appendix

4.3 and Figure 4.4). The devil diet was calculated based on 92 scats and the species had the widest diet of the three native predators with 24 prey items, nine of which were only found in one scat. Four devil scats had no prey detected in them (not part of the 92 scats), and up to seven prey were detected in one scat. The top three prey by devils were quolls (identified only at the genus level: found in 89% of scats), the Tasmanian pademelon (found in 59% of scats), and red-necked wallabies (found in 46% of scats). The spotted-tailed quoll diet was obtained with 12 scats. Two scats had no prey identified (not part of the 12 scats). They fed on nine different prey items, four of which were only found in one scat. A maximum of three prey taxa were found in a single scat. Trichosurus vulpecula (brushtail possums; found in

50% of scats) and Tasmanian pademelons (found in 33% of scats) constituted the top two prey items in the spotted-tailed quoll diet. The eastern quoll diet was calculated based on eight scats representing nine prey items as well, six of which were only found in one scat and

113 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA were a maximum of four prey were identified in one scat. The most represented prey itemswere the red-necked wallaby and Petaurus breviceps (sugar glider; found in 35% of scats each). The distinction between a quoll predating on a devil and a devil predating on a quoll was based on the number of read per sample. The higest number of reads was selected as representating the predator. Usually, 70% of DNA obtained from a scat is from the predator species owing to the release of epithelial cells in the environment (Albaugh et al.

1992; Wasser et al. 1997; Symondson 2002; Jarman et al. 2004; Deagle et al. 2005).

For the introduced predators, the cat diet was the widest of all predators, with 31 prey items detected based on 50 scats. 17 scats were found without any prey detected (not part of the 50 scats) and 23 prey were found in more than one scat. Up to seven different prey taxa were identified in one scat. Mus musculus (house mouse) was the most represented prey item in the diet (found in 34% of scats), followed by a Niveoscincus skink (5 possible species; found in 22% of scats), the long-nosed potoroo and the Tasmanian pademelon (found in 12% of scats each). The presence of Perameles gunnii (eastern barred bandicoot) in two cat scats can show the negative impact of cat on threatened native wildlife. Finally, the dog diet was obtained using 14 scats. Seven dog scats did not amplify prey DNA (not part of the 14 scats) and the diet comprised ten prey items where six scats were found with only one prey in them.

The top four prey items were: Chicken and similar birds (i.e. Phasianinae, found in 36% of scats), cattle of the Bos genus (found in 21% of scats), Ovis (sheep) and red-necked wallabies

(found in 14% of scats each).

114 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA Figures 4.4: Frequency of occurrence of prey in the scats of five Tasmanian predators: Tasmanian devil, Sarcophilu harrisii (92 scats), spotted-tailed quoll, Dasyurus maculatus (12 scats), eastern quoll, Dasyurus viverrinus (8 scats), feral cat, Felis catus (50 scats) and domestic dog, Canis lupus familiaris (14 scats)

115 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA 116 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA 117 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA A NMDS was used to compare the specificity of the diet per predator type (Figure

4.5). The cat and the devil seem to have the most different diet of all the predators but a big overlap among all the predator diet could not allow for more statistics to examine if a diet was specific to a predator.

Figure 4.5: Predator diet difference using a NMDS.

Legend: The diet per predator was implemented in a non-metric multidimensional scaling (NMDS) matrix and no predator had a distinctive diet based on the information retrieved from 176 scats. The Bray-Curtis test is commonly used dissimilarity function by ecologists because is unaffected by the addition or removal of predator or prey species in the ecosystem studied and a good NMDS has a stress factor below 0.2 (Rees et al. 2005; White et al. 2005). The black characters are predators and the red characters are prey.

Extracting values from the vegetation, elevation and fire raster for cats and devils only

(for which we had enough data to come to a sensible interpretation), we could identify three vegetation groups with enough representatives of cats and devils in each to compare the diet in different settings. Cats were the only species to show major difference in their diet across habitats (no differences were found with devil scats)

We decided to only show the results for cats: 27 cat scats were found in dry eucalypt forest and woodlands, 15 cat scats were in scrub, heathland and coastal complexes and 19 cat scats were in agricultural, urban and exotic vegetation. The comparison of diet in these three

118 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA vegetation types is on Figure 4.6. Cats increase their consumption of native prey in eucalypt forests such as they consumed more possums, wombats, antechinus, quolls and long-nosed potoroo in comparison with the two other vegetation types. Niveoscincus skinks are a big part of their diet independently of the cat’s location and we could see an increase of diversity of skinks in the diet of cats in agricultural, urban and exotic vegetation. There were also less birds predated on in the latter habitat where they were subject to more predation in forests or in coastal complexes.

Figure 4.6: Comparison of cat diets in three types of habitats: dry eucalypt forest and woodlands, scrub, heathland and coastal complexes and agricultural, urban and exotic vegetation

Legend: the numbers in brackets represent the number of scats in the cat diet where a prey item was present. For the representation of house mice in the cat diet in agricultural, urban and exotic vegetation, the limitation of space prevented us to draw a bar to 12 scats.

119 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA Detection of a rare prey

Seven instances where the long-nosed potoroo was detected were recorded. Six of them were presences in cat scats and one was in an unknown predator. This predator could be the same cat that the one that defecate the scat 18903 as the scats where close together

(Figure 4.7) and the scats 18955-18956-18957 represent probably the same predator individual as well. Knowing that a cat can take between several hours to several days to digest a vertebrate prey (examples with sea lions and seals; Deagle et al. 2005; Casper et al.

2007) and can travel 6.5 km a day around the centre of their home range, with only 600 meters of linear distance (Fitzgerald and Karl 1986; Edwards et al. 2001; Meek 2003; Recio et al. 2010) we can draw circles around the scats to approximate the location of the long- nosed potoroo before it was eaten.

With the distance separating four locations where long-nosed potoroos were found based on our analysed scats, we can support that four long-nosed potoroos were eaten by cats in the coastal North of Tasmania and management towards potoroos or cats can be applied there. This is just an illustration of using metabarcoding as a detection method for a prey item. We can then determine, for any species detected in our diet analyses, where it is best to focus conservation of this species, or control the species eating this prey at that location.

Similar methods can be used for detecting other rare prey such as a bird or, using different primers, any other taxa. We did not detect the New Holland mouse in any of our samples.

If we look at thelong-nosed potoroo range based on atlas data (NVA) and the range based on our finding, allowing for an extra distance owing to digestion time of the predator, this can support the accuracy of a metabarcoding of DNA based on two genes to correctly identify prey taxa (Figure 4.8) except for the two scats near Musselroe (18961 and 18963), hence the range obtained with the Atlas data might not be widespread enough.

120 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA Using metabarcoding results to identify areas to conserve rare species should also consider areas of suitable habitat, and require potential combination with other data such as camera surveys and trapping once the species is detected being present in an area.

Figure 4.7: Map representing the seven instances where a long-nosed potoroo (Potorous tridactylus) was present in a scat

Legend: The scats 18955-18956-18957 represent probably the same predator individual, same as to be said for scats 18902 and 18903.

121 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA Figure 4.8: scat range versus the atlas data range for the long-nosed potoroo (Potorous tridactylus)

Legend: the scat range is in blue and the atlas data range is in red. The fact that the scat range is outside of the range based on atlas data is owed to the digestion time of the predator consuming the potoroo.

Confirming the identity of species using DNA and range obtained from atlas data

For the 15 species analysed, we obtained a range that fell inside the range created based on atlas observations (see examples for two predator and two prey species on Figure

4.9). Because the atlas data were abundant for all our species, the range obtained from the atlas data was never narrow which mean that the species recorded are not extremely rare. To our knowledge, only the New Holland mouse is severely rare in the state.

122 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA Figure 4.9: scat range versus the atlas data range for two predators and two prey

Legend: the scat range is in blue and the atlas data range is in red. We represented an abundant predator and prey (cat and red-necked wallaby) and a rare predator and prey (eastern quoll and tawny frogmouth). It demonstrates a high correlation between the range of a species obtained with atlas data and the range of the same species based on our DNA identification.

DISCUSSION AND CONCLUSION

Our aim in this chapter was to metabarcode non-invasive samples (scats) widespread over the eastern half of Tasmania to detect vertebrate prey present in the diet of native and invasive carnivores with the ability to detect a rare species for management planning. We analysed the diet of the five large predators in Tasmania with nine to 31 different prey items detected for each. In total, 44 prey taxa were identified within 176 scats. Other studies usually

123 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA refer to a lower number of prey detected, and rarely about multiple predators, using traditional or DNA-methods (Jones and Barmuta 1998; Jones et al. 2004; Robley et al. 2004;

Glen and Dickman 2006; Pemberton et al. 2008; Allen et al. 2012; Pascoe et al. 2012;

Peacock and Abbott 2014). The information obtained for predator and prey identifications were accurate based on their distribution using atlas data and an extra confidence could be obtained for the predator identification using the predator identification test developed in chapter 2. The contaminations observed in the negative controls were managed in a cautious manner by removing the taxa where not enough difference between the number of reads for a prey item in a scat or in a negative was found. Human contamination in several samples was to be expected and cannot be avoided (Shehzad et al. 2012; Boessenkool et al. 2012).

The identification mistakes obtained in several samples occurred for multiple reasons: the first one is simply that the databases had not enough to none representatives in them. That is the case for most of the lizards and birds found. The 12S rRNA marker could identify other vertebrate than mammals, but our own database was developed primarily to identify mammals, and GenBank lack of bird and lizard sequences of representatives present in

Tasmania. For example, Zoothera lunulata had no representatives on GenBank, so only the closest relative could be match to the sequences, Zoothera dauma aurea. We were unable to obtain museum tissue samples for Antechinus swainsonii, A. vandyke and Sminthopsis leucopus, and they are poorly represented on GenBank (no sequences available for A. vandyke, a newly described species). For other taxa, including Rattus sordidus, that are not present in Tasmania, but are closely related to the Tasmanian R. lutreolus, the markers we used (ca 100 bp) were unable to discriminate at that level. A different identification could also be found using GenBank and our own database. This happened often for rodents and quolls, as they were poorly represented on GenBank but highly represented in our database.

In these instances, because of the number of representatives in our database, we decided that

124 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA the identification using our database was more powerful than using GenBank (example with

Steatomys spp. found with GenBank which identified as a Mus musculus with our database, or Dasyurus spartacus which was in fact a Dasyurus maculatus). The latter example is one of the reasons why we needed a powerful 12S mini-barcode to discriminate amongst quolls species (chapter 2). The main aim in this study was to identify a wide range of mammals to species level, if this resolution could not be achieved, we still gather a lot of precious information. DNA identification is another tool that can be used in conservation, such as even if the genus level could only be reached for some prey items, morphology studies can often only reach genus levels as well.

We found that devil have an opportunistic diet (not a lot of prey items are detected in a lot of scats and that their diet does not vary among habitats) and cats are more able to adapt their food intake in various habitats. This shows this species is well suited not only to be introduced to, but invade a new territory. We also demonstrated that the information obtained could also be used to detect rare species and determine critical areas of conservation. But then, depending on the taxa to protect, scats should be collected and analysed in a similar manner than in this chapter based on the taxa known distribution, by, for example, building a

GLM and assigning to each scat a percentage of likelihood to detect the species.

Predator diets obtained with this study and comparison with other published studies

Devils were found to have a diet that was limited to larger sized species such as

Tasmanian pademelons and red-necked wallabies. These data concord with the dietary species found by Jones and Barmuta (1998) who analysed scat contents using a microscope.

It showed to have a restricted diet with only five main prey items but are capable of a broader diet as we found 24 prey items. Almost two third of them were only found in one scat, which indicates that it is an opportunistic carnivore that will fed on what is available, probably carrions, so it does not preclude them from being more generalist given that one individual

125 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA scat contained up to seven species. Tasmanian devil diet comprised 11 native mammals, with only five highly represented taxa: quolls (could only be discriminated at the genus level),

Tasmanian pademelons, red-necked wallabies, brushtail possums and wombats, five birds, one skinks and seven invasive and livestock mammals. Except the five widely represented said taxa, the other predation seems like being opportunistic feedings (Pemberton and Renouf

1993; Jones and Barmuta 1998) because a carcass was available.

The two quoll species separated their diet by size with the eastern quoll predating on smaller prey in general. They both did not have a wide diet with only nine prey items detected for each, but maybe the number of scats was not a big enough sample number, or they predate as well on more insects, invertebrate and plants that were not targeted in this study (Jones and Barmuta 1998; Peacock and Abbott 2014). Our findings contrast with the supposed wide diet and high presence of mammals in the spotted-tailed quoll diet found by

Glen and Dickman (2006). Both quoll species had nine prey taxa identified from their diets, seven and eight of which were native mammals for spotted-tailed quoll and eastern quoll diet respectively. They had a similar diet concerning native mammals, as well as with the

Tasmanian devil, with only one skink species found in eastern quoll diet and one bird species and an invasive mammal in spotted-tailed quoll diet that separate them. So, it is not only the spotted-tailed quoll that we found have a diet overlap with devils or eastern quoll, as Jones and Barmuta (1998) suggested, but all three of them. Maybe this distinction would have been noticed if we studied the female versus male diet, but we could not do that with the primers selected. The presence of Tasmanian devils in the eastern quoll diet can mean they predate on juveniles or that the Tasmanian devil reads was in fact a Dasyurid read that was supposed to be identified as an eastern quoll as well. The same can be said for the high representation of quolls in the devil diet, as we now know that the markers used for metabarcoding are not perfect to discriminate among Dasyurids.

126 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA Ten prey only were found in dogs’ diet representing five native and five introduced and livestock mammals. This DNA can come from two possible sources: or they are farm dogs eating dog food (that included beef, chicken and others) or they predate on cow, goat and sheep carcasses and steal chickens from farms. We did not see a plastic behaviour for dogs’ food intake with a generalist diet as Allen et al. (2012) observed for dingoes. Dogs are believed to be opportunistic predators but there is little to none information of the role of dogs as an intraguild predator (Vanak and Gompper 2009) and according to our results, they consume a large portion of native mammals unlikely to be found in dog food. This demonstrate the importance of including dogs when analysing diet for conservation purposes.

Cats had the widest diet of all predators with 31 prey detected and only eight of which were only found in one scat. Overall, they consumed 15 native mammals, eight birds, three skinks and five introduced or livestock taxa (e.g. House mouse or chicken). That shows the devastating impact of feral cats in Tasmania on native fauna. Compared to the devil diet, cats predate on smaller prey than the latter.

Tasmanian pademelons were highly identified from cat (12% of scats), Tasmanian devil (59% of scats) and spotted-tailed quoll diets (25% of scats) and the red-necked wallaby was found in big proportion in devil (46% of scats), eastern quoll (38% of scats) and dog diets (14% of scats).

The presence of rabbits in high proportion in spotted-tailed quoll scats (present in

17% of scats) compared to their presence in only 5% of devils scats and 2% of cats scats is in accordance with Doherty et al. (2015) that stated that rabbits are not a big prey item for cats in such latitudes as Tasmania. That can be devastating if the Tasmanian population of spotted-tailed quolls is declining (chapter 3) meaning that rabbit population could expand dramatically.

127 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA Table 4.3: Diet of target predators found in this study

Predator Main prey items found with a metabarcoding technique Tasmanian devils  Opportunistic feedings (Sarcophilus harrisii)  quolls (genus level - Dasyurus)  Tasmanian pademelons (Thylogale billardierii)  Red-necked wallabies (Macropus rufogriseus)  Brushtail possums (Trichosurus vulpecula)  Wombats (Vombatus ursinus) Eastern quoll (Dasyurus  Red-necked wallabies (Macropus rufogriseus) viverrinus)  Sugar gliders (Petaurus breviceps)  Brushtail possums (Trichosurus vulpecula)  Tasmanian pademelons (Thylogale billardierii)  Eastern grey kangaroos (Macropus giganteus)  Ringtail possum (Pseudocheirus peregrinus) Spotted-tailed quoll  Brushtail possums (Trichosurus vulpecula) (Dasyurus maculatus)  Tasmanian pademelons (Thylogale billardierii)  European rabbits (Oryctolagus cuniculus)  Red-necked wallabies (Macropus rufogriseus) Cat (Felis catus)  House mice (Mus musculus)  Skins (genus level - Niveoscincus)  Long-nosed potoroo (Potorous tridactylus)  Tasmanian pademelons (Thylogale billardierii) Dog (Canis lupus familiaris)  Opportunistic feedings  Chicken and similar birds (subfamily level - Phasianinae)  Cattle (genus level – Bos)  Sheep (genus level – Ovis)  Red-necked wallabies (Macropus rufogriseus)

Dry eucalypt forest and woodlands, scrub, heathland and coastal complexes and agricultural, urban and exotic vegetation were the three vegetation types investigated. We gathered enough information to investigate the diet of cats and devils in these three different habitats. The first thing to notice was that devils did not vary their diet depending on the three different habitats studied. That means that they have a narrower diet than cats that showed an increase in consumption of native mammals in eucalypt forests compared to an increase in introduced rodents in agricultural lands. If cat proliferation continues, that can mean that in the places both species are interacting, devils might have to switch their diet for other prey items, and that might not be a possibility with the destructive consequence that follows, such

128 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA as more predation on native by an invasive. Quolls were identified in 87% of devil scats and

Tasmanian pademelons were found in 59% of devil scats, with cats feeding on both prey, dogs predating on quolls as well and quolls predating on Tasmanian pademelons too, there is a red flag to raise such as native might have to change one of their main prey intake.

Tasmanian pademelons must be abundant in Tasmania (information supported by NVA) to sustain each predator in Tasmania (except dogs).

Limitations of the metabarcoding technique

The main problem with metabarcoding scats is that we cannot distinguish if it is a primary or secondary predation that we observe, we cannot detect if a species fed on a carrion or hunted for the prey, or if a species fed, stepped or urinated on a scat (King et al. 2008). For this information, direct observation is necessary, but was not the aim of this study.

Detecting rare species

Using this amount of information to detect a species is a possibility and was shown in this study with the long-nosed potoroo example. We could not detect the New Holland mouse, that can be because this species is so rare that even DNA could not be detected in scat, no DNA molecule was in the selected scats, or the species is simply extinct in Tasmania.

We suggest that our technique should continue to be used for detection of the New Holland mouse as we tested both markers that we used for metabarcoding in the laboratory with museum samples and it demonstrated its efficacy, plus we detected other small mammals in the scats, such as Antechinus spp., or house mice, so we know that this tool is working for this purpose and should continue to be used for future detection surveys. What we needed and missed here, was data in relation to vertebrate digestion times in mammal guts as suggested by King et al. (2008). Some studies with seals and sea lions determined the time a known prey was passing through the gut and until when this prey DNA was still detected in scats

(Deagle et al. 2005; Casper et al. 2007). Being able to quantify the number of prey ingested

129 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA by a predator would also provide useful information to implement effective management solutions. The treatment of low levels of contaminations in the negative controls should also be assessed and a threshold should be determined for when to include an identification in a sample extracted in the same batch as the negative control when dealing with mammals.

Finally, the application of a metabarcoding technique with scats collected in the same surveys across time periods would provide information about the evolution of predator-prey dynamics in a changing environment and can guide managers for conservation solutions to apply.

Impact of invasive predators on native prey

Native carnivores are supposed to predate on native prey (as found in this study), but with the introduction of dogs, and mainly feral cats since European settlement (Elkin 1951;

Woinarski et al. 2015), population dynamics changed with destructive effect on native wildlife. Cats do not consume a lot of rabbits, so if cats were removed from an area, there is no need, at the moment, to include a simultaneous removal of rabbits either. But then attention needs to be brought to introduced rodents which constitute a big proportion of cat’s diet, especially in agricultural and urban areas, showing that house mice for example, are widely available. But in areas where New Holland mice were supposed to be, house mice were detected in the diet in high proportion which demonstrates a positive impact of cats on invasive mice, but demonstrates as well the apparent competition of house mice and New

Holland mice as supported by multiple studies (Norton 1987b; Wilson 1991; Smith and Quin

1996; Hocking and Driessen 2000). There is also the possibility that house mice occupy the vacant niche left by extinct native rodents as suggested by Morton and Baynes (1985) and

Smith and Quin (1996). A similar metabarcoding method should target birds and lizard for the development of a site-specific database as ours for mammals asserted its utility. GenBank lack of many Tasmanian birds and lizards sequenced to the species level but with the

130 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA information gathered in the present study, we could see the big proportion of birds in cat diet as well.

Limits of detection using DNA

The limits of detection using scat DNA can come from different sources: uncertainty of taxonomy and poor representation of some taxa on online databases such as GenBank and scat DNA degradation with time as discussed by Ramsey et al. (2015). The problem of under-representation of Tasmanian skinks on GenBank for example might cause a problem for our interpretations. Even if a match with 100% confidence match is found with a

Niveoscincus pretiosus (Tasmanian tree skink), because the other Tasmanian skinks from the same genus (closely related) are not represented on GenBank, and because the 12S and 16S primers used here are mainly targeting mammals, the identification of this species in scats should be interpreted cautiously. We can increase the confidence of identifying the correct species by sequencing multiple individuals of the target taxa prior to our study, as we did by developing our own reference database for mammals.

Primer sets used in this study and their limitation

The 12S rRNA and 16S rRNA portions of genes we used in this study were designed to target a wide range of vertebrates and mammals in particular. Results with only the predator identification are owed to the fact that a high number of reads are from the predator and only a small amount are representing prey. This big difference in number means that these reads are discarded during bioinformatic analyses. It can also show the utility of using invertebrate, plant or fungus primer sets to identify other prey items that might be present in high proportion in the diets. They showed the need of another 12S rRNA barcode to identify the predator in several instances (especially among the dasyurids predators). We also did not target invertebrate, insects, plants and fungi for money and time constrains but that is a potentiality in future studies using scats when methods and sequencing will be cheaper.

131 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA Future studies

We demonstrated the efficacy of metabarcoding scats for diet and detection purposes.

We found between nine and 31 prey items in each diet with 44 different prey taxa identified, and between zero and seven prey in a single scat. In the future, this method can be applied anywhere in the world but we recommend the development of a site-specific reference database of target species and relatives first to make sure of the correct identifications of taxa detected with a next-generation sequencer.

132 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA CHAPTER 5: GENERAL CONCLUSION

This thesis was about developing and applying DNA detection approaches to predator scats to determine the distribution and diet of mammalian predators found in eastern

Tasmania. My goal was to better understand the interactions occurring in Tasmania among predators, with the environment and their prey.

Key findings

In chapter 2, I developed a mini-barcode for the identification of the predator of origin of scats. I used bioinformatics to develop the best primers from available sequences on

GenBank (NCBI: National Center for Biotechnology Information, USA, http://www.ncbi.nlm.nih.gov/genbank/, Geer et al. 2009) and developed a reference library, incorporating additional sequences, with which to assign unknown sequences observed in field samples to known taxa. The amplification success of the mini-barcode was assessed using known tissue samples and tested in the laboratory for its sensitivity using a serial dilution of tissue samples from my six target species; Tasmanian devils, eastern quolls, spotted-tailed quolls, cats, dogs and red foxes. The mini-barcode was highly sensitive amplifying concentration until ca 10 pg/ µl, demonstrating its suitability for use in trace DNA samples. To examine its sensitivity further, I applied the mini-barcode to DNA extracted from known captive animal scats collected in 2011. 49 scats out of 57 amplified good quality

DNA that could, in each instance, be identified to the correct species of origin through sequencing. The eight samples for which a result could not be obtained failed because they did not amplify any DNA or, if they did, a mixed and/or bad quality sequence was recovered and could not be sequenced. In no case was a mis-identification recorded. Overall, this suggests that the barcode has a high sensitivity and thus provides a non-invasive tool that can be used in a single test to identify Australian medium to large mammal predators from trace

133 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA samples. This, in turn, makes possible the detection and/or monitoring of carnivores

Australia-wide.

In chapter 3, this barcode was applied on 1465 field-collected scats (over two sessions of field work) providing 714 predator species identification (the others failed or gave a prey species identification) to identify predator interactions with each other and with the environment. The failure rate between applying the mini-barcode to field-collected scats or captive animal scats is most likely owed to higher rates of degradation in the field. Such degradation is to be expected from environmental DNA (eDNA) because of trace samples being exposed to environmental factors such as weather or other taxa interfering with them.

Maybe a second or third trial at PCR of these samples would provide more resulting identifications, but money and time constraints prevented me to run more tests. I could build models for four species: the Tasmanian devil, the eastern quoll, the feral cat and the dog and according to my first analysis using the cooccur package in R, invasive species (cats and dogs) were negatively correlated with the presence of native predators (Tasmanian devils and eastern quolls). I then built species distribution models (SDMs) for these four species following the recommendations by Hijmans and Elith (2013) using a GLMMPQL

(Generalized Linear Mixed Model with Penalized Quasi-Likelihood; Broström 2003; Bolker et al. 2009) and MaxEnt (Maximum Enthropy; Phillips and Dudík 2008) for comparison, especially because of the uncertainty of viewing scats as presence-absence or presence-only data. My data suggested that dogs did not influence the distribution of any species but were negatively influenced by devils. Devils and cats negatively influenced the distribution of each other and both negatively influenced the distribution of eastern quolls. This could be caused by niche requirements being different among species, predation on juveniles or simply avoidance to prevent conflicts. Devils were mainly restricted to rainforests and eucalypt forests and woodlands and cats were mostly found in non-eucalypt forests and woodlands. No

134 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA habitat preference could be determined for quolls or dogs, and spotted-tailed quolls were not included in this study probably owing to the small sample size. I determined that in this case, scats are more likely to be presence-absence data because of similar outputs observed among literature, the cooccur analysis and the GLMMPQL. The success of using scats to build strong SDMs, combining biotic and abiotic interactions, was determined with data collected in two points in time only compare to models usually constructed with atlas data over a large period of time, usually comprising non-reliable data and giving rise to presence-only models.

In chapter 4, I wanted to detect rare species in Tasmania and to investigate the diet of predators using scat DNA as well. Using metabarcoding, I analysed 287 scats from which

225 gave species composition information and 176 of them contained information for predator and prey providing the first such comprehensive DNA-based diet analysis of

Tasmanian predators. I discovered that, consistent with findings from conventional dietary studies (Catling, 1988; Smith and Quin, 1996; Barratt, 1997; Courchamp et al., 1999a;

Courchamp et al., 1999b; Pontier et al., 2002; Medina et al., 2010; Bonnaud et al., 2011), cats have a wide diet which includes small to medium-sized vertebrates, compared to the other predators in this study. They consumed 15 native mammals, eight birds, three skinks and five introduced or livestock taxa (e.g. House mouse or chicken). That shows the devastating impact of feral cats in Tasmania on native fauna. Devils had a more specific diet, feeding mainly on five native prey taxa (Dasyurus - quolls – genus level only – found in 80 scats, Thylogale billardierii - Tasmanian pademelons – found in 54 scats, Macropus rufogriseus - red-necked wallabies – found in 42 scats, Trichosurus vulpecula - common brushtail possums – found in 30 scats and Vombatus ursinus - common wombats – found in

15 scats). The other taxa found in their scats (from 1 to 6 scats over 92) probably reflect on their opportunistic behavior to eat carrions (Pemberton and Renouf 1993). The two quolls had similar restricted diet feeding mainly on possums, pademelons and wallabies. Almost no

135 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA other vertebrates (i.e. birds, fishes, reptiles or amphibians) than mammals were detected in their scats. The two quoll species separated their diet by size with the eastern quoll predating on smaller prey in general. They both did not have a wide diet, but it is maybe owed to the small sample size, or that they predate on more insects, invertebrates and plants that were not targeted in this study. Finally, dogs were also feeding on native wildlife such as half of their diet consisted of native mammals and half was livestock/dog food. Ten prey only were found in dogs’ diet representing five native and five introduced and livestock mammals. This demonstrate the importance of including dogs when analysing diet for conservation purposes.

Overall, I detected between nine (for quolls) and 31 (for cats) different prey items per predator with a total of 44 prey taxa identified within 176 scats and showed the value of using scat metabarcoding for future detection and/or monitoring surveys. Other studies usually refer to a lower number of prey detected, and rarely about multiple predators, using traditional or

DNA-methods (Jones and Barmuta 1998; Jones et al. 2004; Robley et al. 2004; Glen and

Dickman 2006; Pemberton et al. 2008; Allen et al. 2012; Pascoe et al. 2012; Peacock and

Abbott 2014). For predator identifications, I used the primer developed in chapter 2 as well, as the markers used for the metabarcoding study revealed not suitable to discriminate among

Dasyurids. These markers were developed with the aim of identifying the widest range of vertebrate possible to encounter in Tasmania such as they possibly do not have a good resolution at species level in every instance. This data can be integrated into conservation management as similar studies have been driving conservation outcomes. Multiple examples are provided in Thomsen and Willerslev (2015), such as the monitoring of rare and endangered marine mammals (Foote et al. 2012) or freshwater species communities

(Thomsen et al. 2012) or the detection of rare aquatic fishes in Northern Europe (Jerde et al.

2011). I determined that using scats, I obtained a lot of information about identity, distribution and diet of species retrieving DNA from trace samples. I observed interactions

136 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA among species related to their environments. I have now a tool to detect possibly rare

Tasmanian species without going into the trouble of targeting them directly and can implement management solutions thanks to the amount of information gathered in this thesis such as restoration of habitats or removal of species in an area.

Importance of using non-invasive samples

Using scats as a source of DNA to study predator distributions and predator-prey interactions revealed itself a reliable non-invasive method and allow the accumulation of a large amount of data. It is especially useful when dealing with rare and cryptic predators hard to observe in the landscape and provided a systematic survey design and easy field work with the possibility of studying multiple species. Compared to other detection method, it allowed a quickly gathering of a certain amount of data in one or two session of field work that would otherwise require to directly interact with the animals, with the potentiality of stress, or using other non-invasive methods such as camera traps that would require a great amount of work to sort out picture afterwards. Plus, dealing with multiple taxa that require different trapping methods reveals difficult using traditional methods. Using eDNA is then an effective compromise. The laboratory work is more substantial and technical, but using DNA identification provides another tool that, depending on the context and situation, is well suited to survey for biodiversity.

Importance for conservation – management implications and new insights gained from this thesis

Wildlife managers need to be able to detect and possibly monitor species in the landscape to understand patterns of co-occurrence and interactions between species. That is now achieved in Tasmania and could potentially be repeated elsewhere. I also wanted to better understand predator-prey interactions to be able to come up with areas where to

137 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA primarily focus conservation solutions. I achieved these aims by obtaining more information to better understand interactions happening in Tasmania involving predators. This study provided a new mini-barcode that can be applied on a trace sample (e.g. scat, hair, saliva) to be able to identify a terrestrial predator with a single test. The novelty of this approach is that

I created a group-specific primer set that provides information previously only possible through the application of several species-specific primers (each needing their own test). The modeling chapter provided a test of this mini-barcode on a big dataset from a real-life study site. This can provide information for wildlife managers on the interactions of multiple predators and their abiotic environment. For the first time, multiple species were studied at once on a large-scale area combined with niche information. This demonstrates that obtaining multi-variate models of predator communitites can be more helpful for on-gound managers as it indicates simultaneous interactions happening to a single organism and is closer to reality.

Finally, the last chapter illustrates the novelty of a metabarcoding approach by applying a next-generation sequencing to scats collected over half a state to investigate the diet of multiple predators at the same time. Plus, the information obtained is useful to detect and monitor species in the landscape. This aspect is usually targeted solely on studies without the diet information. Here, I killed two birds with one stone.

Future studies and further work

It could be interesting to apply the mini-barcode on scats collected in the western half of Tasmania to obtain a state-wide distribution of predators and understand the interactions happening in a whole large island. That could serve as a case study to then be used as comparison with other islands in the world. This method could also be applied, with the right development of a mini-barcode, anywhere in the world. The success rate of the predator identification test could increase if applied to sample replicates such as more scats can be identified to a predator and get used to improve the outputs of the SDMs. The detectability of

138 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA scats in the landscape could also be estimated using blind field tests for example. That could remove the doubt that Tasmanian predator scat surveys, based on a suitability model for a predator, are actual presence-absence data and time gained not doing a comparison with a presence-only model can be spend at analyzing more scats and describing the preferred habitat type per predator.

As for the metabarcoding study, similar approaches could be applied on other scats

(collection in western Tasmania, application of the metabarcoding method on more scats, increase of sensitivity of the metabarcoding approach by doing replicates, assessment of scat detectability in the landscape). I showed, using two short mitochondrial genes, that it was possible to gather information about 49 taxa (44 prey and five predators) using scat DNA to detect a species (would it be prey or predator) and obtain good diet coverage. The next step would then allow me to be able to quantify this data to know the number of animals eaten by a predator. This could provide information regarding the impact of a single predator on several prey. I pointed out the need of feeding trials to evaluate the range of digestion times per predator, in captive animals first and in the wild second as it would provide valuable information about the location of an important prey in the view point of conservation and management solutions. The treatment of low levels of contamination in negative controls need to be formally assess by using scats from a feeding trial for example, to be able to set a threshold number of reads to accept or discard an identification in a sample. Such trials have been carried out in seals and sea lions for example and demonstrated their benefits (Deagle et al. 2005; R M Casper et al. 2007). Finally, the application of DNA detection approaches across time to observe changes in predator-prey dynamics would provide a measurement of a possible unbalanced ecosystem and determine species or areas to treat urgently. I recommend that metabarcoding on scats continues to be used for detecting a rare species with

139 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA assignments of unknown sequences to a DNA reference database built with museum samples for the target species and relatives at the genes investigated.

140 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA APPENDICES

APPENDIX 2.1: Samples included in the 12S rRNA reference sequence database used for primer design

Genbank Source of Tissue accession Scientific name Common_name tissue sample Sample ID type AF038308 Antechinus bellus Faun antechinus GenBank AF038306 Antechinus flavipes Yellow-footed antechinus GenBank AF038309 Antechinus godmani Atherton antechinus GenBank AF038310 Antechinus leo Cinnamon antechinus GenBank KX786294 Antechinus minimus Swamp antechinus DPIPWE A874 ear AF038307 Antechinus minimus Swamp antechinus GenBank KX786295 Antechinus minimus Swamp antechinus DPIPWE UC1531 ear AF038314 Antechinus naso Long-nosed antechinus GenBank AF038311 Antechinus spp antechinus GenBank KX786296 Antechinus spp antechinus UC UC1220 ear ASU87399 Antechinus stuartii Brown antechinus GenBank AF038304 Antechinus swainsonii Dusky antechinus GenBank JN817347 Bos taurus Cow GenBank JN817349 Bos taurus Cow GenBank KC153975 Bos taurus Cow GenBank AB499818 Canis lupus Domestic dog GenBank AB499819 Canis lupus Domestic dog GenBank AB499820 Canis lupus Domestic dog GenBank AB499821 Canis lupus Domestic dog GenBank AB499822 Canis lupus Domestic dog GenBank AB499823 Canis lupus Domestic dog GenBank AB499824 Canis lupus Domestic dog GenBank AB499825 Canis lupus Domestic dog GenBank AB499816 Canis lupus familiaris Domestic dog GenBank AB499817 Canis lupus familiaris Domestic dog GenBank AY012152 Canis lupus familiaris Domestic dog GenBank AY656738 Canis lupus familiaris Domestic dog GenBank AY656745 Canis lupus familiaris Domestic dog GenBank AY656750 Canis lupus familiaris Domestic dog GenBank AY729880 Canis lupus familiaris Domestic dog GenBank CFU96639 Canis lupus familiaris Domestic dog GenBank EU408254 Canis lupus familiaris Domestic dog GenBank EU408263 Canis lupus familiaris Domestic dog GenBank EU408271 Canis lupus familiaris Domestic dog GenBank KJ940969 Capra hircus Goat GenBank KM360063 Capra hircus Goat GenBank AJ885203 Dama dama Fallow deer GenBank AF009889 Dasycercus cristicauda Crest-tailed mulgara GenBank EU086640 Dasycercus cristicauda Crest-tailed mulgara GenBank KJ868108 Dasykaluta rosamondae Little red kaluta GenBank AF009888 Dasyuroides byrnei Kowari GenBank

141 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA KJ868109 Dasyuroides byrnei Kowari GenBank AF009890 Dasyurus albopunctatus New Guinean quoll GenBank KJ780025 Dasyurus albopunctatus New Guinean quoll GenBank KJ780026 Dasyurus albopunctatus New Guinean quoll GenBank KX786297 Dasyurus geoffroii Western quoll DPAW AA68007 ear KX786298 Dasyurus geoffroii Western quoll DPAW AA68010 ear KX786299 Dasyurus geoffroii Western quoll UA AA68037 ear KX786300 Dasyurus geoffroii Western quoll UA AA68040 ear KX786301 Dasyurus geoffroii Western quoll UA AA68043 ear KX786302 Dasyurus geoffroii Western quoll UA AA68045 ear AF009891 Dasyurus geoffroii Western quoll GenBank FN666605 Dasyurus geoffroii Western quoll GenBank KJ780027 Dasyurus geoffroii Western quoll GenBank KX786303 Dasyurus hallucatus Northern quoll DPAW AA68028 ear KX786304 Dasyurus hallucatus Northern quoll DPAW AA68029 ear KX786305 Dasyurus hallucatus Northern quoll DPAW AA68030 ear KX786306 Dasyurus hallucatus Northern quoll DPAW AA68034 ear KX786307 Dasyurus hallucatus Northern quoll DPAW AA68036 ear AY795973 Dasyurus hallucatus Northern quoll GenBank DHU87400 Dasyurus hallucatus Northern quoll GenBank KJ780031 Dasyurus hallucatus Northern quoll GenBank KJ780032 Dasyurus hallucatus Northern quoll GenBank KJ780033 Dasyurus hallucatus Northern quoll GenBank KJ780034 Dasyurus hallucatus Northern quoll GenBank NC007630 Dasyurus hallucatus Northern quoll GenBank KX786308 Dasyurus maculatus Spotted-tailed quoll DPIPWE A3359 unknown KX786309 Dasyurus maculatus Spotted-tailed quoll DPIPWE A3361 unknown KX786310 Dasyurus maculatus Spotted-tailed quoll DPIPWE A3364 unknown KX786311 Dasyurus maculatus Spotted-tailed quoll DPIPWE A3365 unknown KX786312 Dasyurus maculatus Spotted-tailed quoll DPIPWE A3369 unknown KX786313 Dasyurus maculatus Spotted-tailed quoll DPIPWE A3375 unknown KX786314 Dasyurus maculatus Spotted-tailed quoll DPIPWE A3376 unknown KX786315 Dasyurus maculatus Spotted-tailed quoll DPIPWE A3395 unknown KX786316 Dasyurus maculatus Spotted-tailed quoll DPIPWE A3397 unknown KX786317 Dasyurus maculatus Spotted-tailed quoll DPIPWE A3926 unknown KX786318 Dasyurus maculatus Spotted-tailed quoll DPIPWE A4667 unknown KX786319 Dasyurus maculatus Spotted-tailed quoll DPIPWE A4699 unknown KX786320 Dasyurus maculatus Spotted-tailed quoll DPIPWE A4701 unknown KX786321 Dasyurus maculatus Spotted-tailed quoll DPIPWE AA15132 ear KX786322 Dasyurus maculatus Spotted-tailed quoll DPIPWE AA15133 ear AF009893 Dasyurus maculatus Spotted-tailed quoll GenBank KJ780028 Dasyurus maculatus Spotted-tailed quoll GenBank KJ780029 Dasyurus maculatus Spotted-tailed quoll GenBank KJ780030 Dasyurus maculatus Spotted-tailed quoll GenBank KX786323 Dasyurus maculatus Spotted-tailed quoll ANU NME211 ear KX786324 Dasyurus maculatus Spotted-tailed quoll ANU NME281 ear

142 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA KX786325 Dasyurus maculatus Spotted-tailed quoll UC UC0589 ear KX786326 Dasyurus maculatus Spotted-tailed quoll UC UC0626 ear KX786327 Dasyurus maculatus Spotted-tailed quoll UC UC1480 ear AF009892 Dasyurus spartacus Bronze quoll GenBank KX786328 Dasyurus viverrinus Eastern quoll DPIPWE A3363 unknown KX786329 Dasyurus viverrinus Eastern quoll DPIPWE A3372 unknown KX786330 Dasyurus viverrinus Eastern quoll DPIPWE A3394 unknown KX786331 Dasyurus viverrinus Eastern quoll DPIPWE A3396 unknown KX786332 Dasyurus viverrinus Eastern quoll DPIPWE A4698 unknown KX786333 Dasyurus viverrinus Eastern quoll DPIPWE AA15126 ear KX786334 Dasyurus viverrinus Eastern quoll DPIPWE AA15129 ear KX786335 Dasyurus viverrinus Eastern quoll DPIPWE AA15130 ear KX786336 Dasyurus viverrinus Eastern quoll DPIPWE AA15131 ear DVU87401 Dasyurus viverrinus Eastern quoll GenBank KX786337 Dasyurus viverrinus Eastern quoll UC UC1205 ear KX786338 Dasyurus viverrinus Eastern quoll UC UC1213 ear KX786339 Dasyurus viverrinus Eastern quoll UC UC1214 ear KX786340 Dasyurus viverrinus Eastern quoll UC UC1215 ear KX786341 Dasyurus viverrinus Eastern quoll UC UC1452 ear KX786342 Dasyurus viverrinus Eastern quoll UC UC1692 ear KX786343 Dasyurus viverrinus Eastern quoll UC UC1696 ear JQ340128 Equus caballus Horse GenBank JQ340135 Equus caballus Horse GenBank JQ340151 Equus caballus Horse GenBank FCU20753 Felis catus Cat GenBank KX786344 Felis catus Cat USYD N22b ear NC001700 Felis catus Cat GenBank Y08503 Felis catus Cat GenBank KC257328 Homo sapiens Human GenBank KC257351 Homo sapiens Human GenBank KJ801423 Homo sapiens Human GenBank KP229447 Homo sapiens Human GenBank KP877128 Homo sapiens Human GenBank KR135843 Homo sapiens Human GenBank KR135882 Homo sapiens Human GenBank KT366045 Homo sapiens Human GenBank KT749783 Homo sapiens Human GenBank KT886412 Homo sapiens Human GenBank GU937113 Lepus capensis Brown hare GenBank NC015841 Lepus capensis Brown hare GenBank AY609350 Myoictis wavicus Tate's three-striped dasyure GenBank AY609351 Myoictis wavicus Tate's three-striped dasyure GenBank KJ868129 Myoictis wavicus Tate's three-striped dasyure GenBank AF009894 Neophascogale lorentzii Speckled dasyure GenBank KJ868130 Neophascogale lorentzii Speckled dasyure GenBank AJ001588 Oryctolagus cuniculus European rabbit GenBank

143 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA NC001913 Oryctolagus cuniculus European rabbit GenBank KF302444 Ovis aries Sheep GenBank KF302451 Ovis aries Sheep GenBank KF302456 Ovis aries Sheep GenBank FN666601 Parantechinus apicalis Dibbler GenBank AF009895 Phascolosorex dorsalis Narrow-striped marsupial shrew GenBank AY536433 Phascolosorex dorsalis Narrow-striped marsupial shrew GenBank KJ868144 Phascolosorex dorsalis Narrow-striped marsupial shrew GenBank AF009884 Pseudantechinus bilarni Sandstone false antechinus GenBank EU086642 Pseudantechinus macdonnellensis Fat-tailed false antechinus GenBank EU086643 Pseudantechinus mimulus Alexandria false antechinus GenBank EU086650 Pseudantechinus roryi Rory Cooper's false antechinus GenBank EU086645 Pseudantechinus woolleyae Woolley's false antechinus GenBank JX475454 Sarcophilus harrisii Tasmanian devil GenBank JX475455 Sarcophilus harrisii Tasmanian devil GenBank JX475456 Sarcophilus harrisii Tasmanian devil GenBank JX475457 Sarcophilus harrisii Tasmanian devil GenBank JX475458 Sarcophilus harrisii Tasmanian devil GenBank JX475459 Sarcophilus harrisii Tasmanian devil GenBank JX475460 Sarcophilus harrisii Tasmanian devil GenBank JX475461 Sarcophilus harrisii Tasmanian devil GenBank JX475462 Sarcophilus harrisii Tasmanian devil GenBank JX475463 Sarcophilus harrisii Tasmanian devil GenBank JX475464 Sarcophilus harrisii Tasmanian devil GenBank JX475466 Sarcophilus harrisii Tasmanian devil GenBank JX475467 Sarcophilus harrisii Tasmanian devil GenBank NC018788 Sarcophilus harrisii Tasmanian devil GenBank SHU87402 Sarcophilus harrisii Tasmanian devil GenBank EF545573 Sus scrofa Wild boar GenBank EF545575 Sus scrofa Wild boar GenBank EF545580 Sus scrofa Wild boar GenBank FJ515780 Thylacinus cynocephalus Thylacine GenBank FJ515781 Thylacinus cynocephalus Thylacine GenBank NC011944 Thylacinus cynocephalus Thylacine GenBank GQ374180 Vulpes vulpes Red fox GenBank JN711443 Vulpes vulpes Red fox GenBank KF387633 Vulpes vulpes Red fox GenBank KP342452 Vulpes vulpes Red fox GenBank KT448287 Vulpes vulpes Red fox GenBank NC8434 Vulpes vulpes Red fox GenBank Y08508 Vulpes vulpes Red fox GenBank

ANU: the Australian National University DPAW: the Department of Parks and Wildlife, Western Australia DPIPWE: the Department of Primary Industries, Parks, Water and Environment, Tasmania

144 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA UA: the University of Adelaide UC: the University of Canberra USYD: the University of Sydney

145 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA APPENDIX 2.2: R code for sliding windows analysis implemented using SPIDER install.packages("spider") library(spider)

# set your working directory # change regarding the location of your files setwd("C:/SPIDER")

# read the alignments for 12S 12Sref <- read.dna("12S_Database_sequences.fasta", format="fasta") dimnames(12Sref)

12Sreflength <- sapply(12Sref, length) table(12Sreflength) # "12Sref" contains 174 sequences of the same length: 901bp

# Genus and species names aa <- strsplit(dimnames(12Sref)[[1]], split = "_") Genus12S <- sapply(aa, function(x) paste(x[1], sep="_")) Spp12S <- sapply (aa, function(x) paste( x[1],x[2], sep="_")) Genus12S Spp12S

# summary statistics 12Srefstat <- rep("12Sref", length(Genus12S)) dataStat(Spp12S,Genus12S) seqStat(12Sref)

####################### Sliding window #######################

12SWin <- slidingWindow(12Sref, width = 20, interval=1) # we decided to set the width at 20 nucleotides as it is the common size of a primer length(12SWin) 12SWin[[1]] 12SAna <- slideAnalyses(12Sref, Spp12S, width = 20, interval = "codons", distMeasures = TRUE, treeMeasures = TRUE) # the interval is the distance between each window, "codons" set the interval size to 3 str(12SAna) plot(12SAna)

146 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA APPENDIX 2.3: R code for genetic distance based evaluation of the AusPreda_12S mini-barcode implemented using SPIDER install.packages("spider") library(spider)

# set your directory # change regarding the location of your files setwd("C:/SPIDER")

# read the "FULL" and "UNIQUE" alignments full <- read.dna("FULL.fasta", format="fasta") dimnames(full) unique <- read.dna("UNIQUE.fasta", format="fasta") dimnames(unique) fulllength <- sapply(full, length) table(fulllength) uniquelength <- sapply(unique, length) table(uniquelength)

# "FULL": Contain 174 sequences of the same length: 172bp # "UNIQUE": Contain 44 sequences of the same length: 177bp

# Genus and species names # "FULL" head(dimnames(full)[[1]]) bb <- strsplit(dimnames(full)[[1]], split = "_") Genusfull <- sapply(bb, function(x) paste(x[1], sep="_")) Sppfull <- sapply (bb, function(x) paste( x[1],x[2], sep="_")) Genusfull Sppfull # "UNIQUE" head(dimnames(unique)[[1]]) aa <- strsplit(dimnames(unique)[[1]], split = "_") Genusunique <- sapply(aa, function(x) paste(x[1], sep="_")) Sppunique <- sapply (aa, function(x) paste( x[1],x[2], sep="_")) Genusunique Sppunique

# specify sequence names # "FULL" bb_full <-(strsplit(dimnames(full)[[1]]," ")) seqnames_full <- sapply(bb_full, function(x) paste(x[1])) # "UNIQUE" aa_unique <-(strsplit(dimnames(unique)[[1]]," ")) seqnames_unique <- sapply(aa_unique, function(x) paste(x[1]))

# summary statistics # "FULL" fullstat <- rep("full", length(Genusfull)) dataStat(Sppfull,Genusfull,2) seqStat(full,161) # "UNIQUE" uniquestat <- rep("unique", length(Genusunique)) dataStat(Sppunique,Genusunique,2) seqStat(unique,162)

147 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA ####################### M O D E L #######################

# Calculate pairwise genetic distances between sequences using the "raw" model # "FULL" fullDist <- dist.dna(full, pairwise.deletion = TRUE, model="raw") fullDistRawMat <- dist.dna(full, pairwise.deletion = TRUE, model = "raw", as.matrix=TRUE) # Rename the columns and rows in fullDistRawMat to the format "Genus_species_accession" newnames_full<-rep(NA,dim(fullDistRawMat)[1]) for(i in 1:dim(fullDistRawMat)){ newnames_full[i]<-strsplit(colnames(fullDistRawMat)[i]," ")[[1]][1] } colnames(fullDistRawMat)<-newnames_full rownames(fullDistRawMat)<-newnames_full

# "UNIQUE" uniqueDist <- dist.dna(unique, pairwise.deletion = TRUE, model="raw") uniqueDistRawMat <- dist.dna(unique, pairwise.deletion = TRUE, model = "raw", as.matrix=TRUE) # Rename the columns and rows in uniqueDistRawMat to the format "Genus_species_accession" newnames_unique<-rep(NA,dim(uniqueDistRawMat)[1]) for(i in 1:dim(uniqueDistRawMat)){ newnames_unique[i]<-strsplit(colnames(uniqueDistRawMat)[i]," ")[[1]][1] } colnames(uniqueDistRawMat)<-newnames_unique rownames(uniqueDistRawMat)<-newnames_unique

# Identify the optimal genetic distance threshold for the raw model for "FULL" threshValfull <- seq(0,0.1, by = 0.005) threshfull <- lapply(threshValfull, function(x) threshOpt(fullDist, Sppfull, thresh = x)) threshfullMat <- do.call(rbind, threshfull) threshfullMat barplot(t(threshfullMat) [4:5,], names.arg=paste((threshfullMat[,1]*100), "%")) # best threshold for "FULL" is: 1%

# Identify the optimal genetic distance threshold for the raw model for "UNIQUE" threshValunique <- seq(0,0.1, by = 0.005) threshunique <- lapply(threshValunique, function(x) threshOpt(uniqueDist, Sppunique, thresh = x)) threshuniqueMat <- do.call(rbind, threshunique) threshuniqueMat barplot(t(threshuniqueMat) [4:5,], names.arg=paste((threshuniqueMat[,1]*100), "%")) # best threshold for "UNIQUE" is: 4%

####################### Nearest Neighbour ####################### # nearest neighbour analyses - nearest neighbour is the same spp? # "FULL" table(nearNeighbour(fullDist, Sppfull)) # "UNIQUE" table(nearNeighbour(uniqueDist, Sppunique))

# to work out which sequences have FALSE results in nearNeighbour NN_full_Raw<-(nearNeighbour(fullDist, Sppfull)) nearN_full_Raw<- data.frame(name=seqnames_full, result=NN_full_Raw) nearN_full_Raw[nearN_full_Raw$result=="FALSE",]

# to explore the False identifications # change the threshold to the value of interest # change the number in [[square brackets]] to the query sequence # see which other sequences are with the threshold specified for the query sequence withinF<-list() for (i in 1:dim(fullDistRawMat)){withinF[[i]]<-colnames(fullDistRawMat)[fullDistRawMat[i,]<0.02]} withinF[[141]]

148 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA ####################### best close match ####################### # "FULL" # best close match analyses using a threshold of 1% table(bestCloseMatch(fullDist, Sppfull, thresh = 0.01))

# to work out which sequences have no ID results in bestCloseMatch analyses bcmfullRaw<-(bestCloseMatch(fullDist, Sppfull, thresh = 0.01)) bestCloseMatch_full_Raw <- data.frame(name=seqnames_full, result=bcmfullRaw) bestCloseMatch_full_Raw[bestCloseMatch_full_Raw$result=="no id",]

# to work out which sequences have incorrect results in bestCloseMatch analyses bestCloseMatch_full_Raw[bestCloseMatch_full_Raw$result=="incorrect",]

# to explore the noID and incorrect results # change the threshold to the value of interest # change the number in [[square brackets]] to the query sequence # see which other sequences are with the threshold specified for the query sequence withinF<-list() for (i in 1:dim(fullDistRawMat)){withinF[[i]]<-colnames(fullDistRawMat)[fullDistRawMat[i,]<0.02]} withinF[[141]]

# "UNIQUE" # best close match analyses using a threshold of 4% table(bestCloseMatch(uniqueDist, Sppunique, thresh = 0.04))

# to work out which sequences have no ID results in bestCloseMatch analyses bcmuniqueRaw<-(bestCloseMatch(uniqueDist, Sppunique, thresh = 0.04)) bestCloseMatch_unique_Raw <- data.frame(name=seqnames_unique, result=bcmuniqueRaw) bestCloseMatch_unique_Raw[bestCloseMatch_unique_Raw$result=="no id",]

# to explore the noID results # change the threshold to the value of interest # change the number in [[square brackets]] to the query sequence # see which other sequences are with the threshold specified for the query sequence withinU<-list() for (i in 1:dim(uniqueDistRawMat)){withinU[[i]]<- colnames(uniqueDistRawMat)[uniqueDistRawMat[i,]<0.02]} withinU[[1]]

####################### threshID ####################### # "FULL" # threshID analyses using a threshold of 1% table(threshID(fullDist, Sppfull, thresh = 0.01))

# to work out which sequences have no ID results in threshID analyses tifullRaw<-(threshID(fullDist, Sppfull, thresh = 0.01)) threshID_full_Raw <- data.frame(name=seqnames_full, result=tifullRaw) threshID_full_Raw[threshID_full_Raw$result=="no id",]

# to work out which sequences have no ambiguous results in threshID analyses threshID_full_Raw[threshID_full_Raw$result=="ambiguous",]

# to work out which sequences have no incorrect results in threshID analyses threshID_full_Raw[threshID_full_Raw$result=="incorrect",]

# to explore the noID, ambiguous and incorrect results # change the threshold to the value of interest # change the number in [[square brackets]] to the query sequence # see which other sequences are with the threshold specified for the query sequence withinF<-list()

149 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA for (i in 1:dim(fullDistRawMat)){withinF[[i]]<-colnames(fullDistRawMat)[fullDistRawMat[i,]<0.02]} withinF[[141]]

# "UNIQUE" # threshID analyses using a threshold of 4% table(threshID(uniqueDist, Sppunique, thresh = 0.04))

# to work out which sequences have no ID results in threshID analyses tiuniqueRaw<-(threshID(uniqueDist, Sppunique, thresh = 0.04)) threshID_unique_Raw <- data.frame(name=seqnames_unique, result=tiuniqueRaw) threshID_unique_Raw[threshID_unique_Raw$result=="no id",]

# to explore the noID results # change the threshold to the value of interest # change the number in [[square brackets]] to the query sequence # see which other sequences are with the threshold specified for the query sequence withinU<-list() for (i in 1:dim(uniqueDistRawMat)){withinU[[i]]<- colnames(uniqueDistRawMat)[uniqueDistRawMat[i,]<0.02]} withinU[[1]]

150 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA APPENDIX 2.4: Detailed results of genetic distance based evaluation of the AusPreda_12S mini-barcode

Unique Unique Unique Genbank Full NN Full BM Full TI NN BM TI accession Scientific name Common name result result result result result result Comments Singleton species, nearest neighbour = AF038308 Antechinus bellus Faun antechinus FALSE No_ID No_ID NA NA NA Antechinus flavipes Yellow-footed Singleton species, nearest neighbour = AF038306 Antechinus flavipes antechinus FALSE No_ID No_ID NA NA NA Antechinus bellus Singleton species, nearest neighbour = AF038309 Antechinus godmani Atherton antechinus FALSE No_ID No_ID NA NA NA Antechinus stuartii Cinnamon Singleton species, nearest neighbour = AF038310 Antechinus leo antechinus FALSE No_ID No_ID NA NA NA Antechinus stuartii KX786294* Antechinus minimus Swamp antechinus TRUE Correct Correct TRUE Correct Correct Would be correctly identified with 2% AF038307 Antechinus minimus Swamp antechinus TRUE No_ID No_ID TRUE Correct Correct threshold KX786295* Antechinus minimus Swamp antechinus TRUE Correct Correct NA NA NA Singleton species, nearest neighbour = Long-nosed Antechinus swainsonii / Dasycercus AF038314 Antechinus naso antechinus FALSE No_ID No_ID NA NA NA cristicauda Singleton species, nearest neighbour = AF038311 Antechinus spp antechinus FALSE No_ID No_ID NA NA NA Antechinus stuartii Singleton species, nearest neighbour = KX786296* Antechinus spp antechinus FALSE No_ID No_ID NA NA NA Antechinus minimus / swainsonii Singleton species, nearest neighbour = ASU87399 Antechinus stuartii Brown antechinus FALSE No_ID No_ID NA NA NA Antechinus godmani Antechinus Singleton species, nearest neighbour = AF038304 swainsonii Dusky antechinus FALSE No_ID No_ID NA NA NA Antechinus minimus JN817347 Bos taurus Cow TRUE Correct Correct NA NA NA JN817349 Bos taurus Cow TRUE Correct Correct NA NA NA KC153975 Bos taurus Cow TRUE Correct Correct NA NA NA AB499818 Canis lupus Domestic dog TRUE Correct Correct TRUE Correct Correct AB499819 Canis lupus Domestic dog TRUE Correct Correct TRUE Correct Correct AB499820 Canis lupus Domestic dog TRUE Correct Correct NA NA NA AB499821 Canis lupus Domestic dog TRUE Correct Correct NA NA NA

151 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA AB499822 Canis lupus Domestic dog TRUE Correct Correct NA NA NA AB499823 Canis lupus Domestic dog TRUE Correct Correct NA NA NA AB499824 Canis lupus Domestic dog TRUE Correct Correct TRUE Correct Correct AB499825 Canis lupus Domestic dog TRUE Correct Correct NA NA NA Canis lupus AB499816 familiaris Domestic dog TRUE Correct Correct NA NA NA Canis lupus AB499817 familiaris Domestic dog TRUE Correct Correct NA NA NA Canis lupus AY012152 familiaris Domestic dog TRUE Correct Correct NA NA NA Canis lupus AY656738 familiaris Domestic dog TRUE Correct Correct NA NA NA Canis lupus AY656745 familiaris Domestic dog TRUE Correct Correct TRUE Correct Correct Canis lupus AY656750 familiaris Domestic dog TRUE Correct Correct TRUE Correct Correct Canis lupus AY729880 familiaris Domestic dog TRUE Correct Correct NA NA NA Canis lupus CFU96639 familiaris Domestic dog TRUE Correct Correct NA NA NA Canis lupus EU408254 familiaris Domestic dog TRUE Correct Correct NA NA NA Canis lupus EU408263 familiaris Domestic dog TRUE Correct Correct NA NA NA Canis lupus EU408271 familiaris Domestic dog TRUE Correct Correct NA NA NA KJ940969 Capra hircus Goat TRUE Correct Correct NA NA NA KM360063 Capra hircus Goat TRUE Correct Correct NA NA NA Singleton species, nearest neighbour = Capra AJ885203 Dama dama Fallow deer FALSE No_ID No_ID NA NA NA hircus Dasycercus Would be correctly identified with 5% AF009889 cristicauda Crest-tailed mulgara TRUE No_ID No_ID TRUE No_ID No_ID threshold Dasycercus Would be correctly identified with 5% EU086640 cristicauda Crest-tailed mulgara TRUE No_ID No_ID TRUE No_ID No_ID threshold Dasykaluta Singleton species, nearest neighbour = KJ868108 rosamondae Little red kaluta FALSE No_ID No_ID NA NA NA Pseudantechinus roryi

152 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA AF009888 Dasyuroides byrnei Kowari TRUE Correct Correct TRUE Correct Correct KJ868109 Dasyuroides byrnei Kowari TRUE Correct Correct TRUE Correct Correct Dasyurus AF009890 albopunctatus New Guinean quoll TRUE Correct Correct TRUE Correct Correct Dasyurus KJ780025 albopunctatus New Guinean quoll TRUE Correct Correct TRUE Correct Correct Dasyurus KJ780026 albopunctatus New Guinean quoll TRUE Correct Correct NA NA NA Within 2% of other D. geoffroii sequences, KX786297* Dasyurus geoffroii Western quoll TRUE No_ID No_ID TRUE Correct Correct potential confusion with D. spartacus KX786298* Dasyurus geoffroii Western quoll TRUE Correct Ambiguous TRUE Correct Correct KX786299* Dasyurus geoffroii Western quoll TRUE Correct Correct NA NA NA KX786300* Dasyurus geoffroii Western quoll TRUE Correct Ambiguous NA NA NA KX786301* Dasyurus geoffroii Western quoll TRUE Correct Ambiguous NA NA NA KX786302* Dasyurus geoffroii Western quoll TRUE Correct Ambiguous NA NA NA AF009891 Dasyurus geoffroii Western quoll TRUE Correct Correct NA NA NA FN666605 Dasyurus geoffroii Western quoll TRUE Correct Ambiguous TRUE Correct Correct KJ780027 Dasyurus geoffroii Western quoll TRUE Correct Correct TRUE Correct Correct KX786303* Dasyurus hallucatus Northern quoll TRUE Correct Correct TRUE Correct Correct KX786304* Dasyurus hallucatus Northern quoll TRUE Correct Correct NA NA NA KX786305* Dasyurus hallucatus Northern quoll TRUE Correct Correct NA NA NA KX786306* Dasyurus hallucatus Northern quoll TRUE Correct Correct NA NA NA KX786307* Dasyurus hallucatus Northern quoll TRUE Correct Correct NA NA NA AY795973 Dasyurus hallucatus Northern quoll TRUE Correct Correct TRUE Correct Correct DHU87400 Dasyurus hallucatus Northern quoll TRUE Correct Correct TRUE Correct Correct KJ780031 Dasyurus hallucatus Northern quoll TRUE Correct Correct TRUE Correct Correct KJ780032 Dasyurus hallucatus Northern quoll TRUE Correct Correct TRUE Correct Correct KJ780033 Dasyurus hallucatus Northern quoll TRUE Correct Correct NA NA NA KJ780034 Dasyurus hallucatus Northern quoll TRUE Correct Correct NA NA NA NC007630 Dasyurus hallucatus Northern quoll TRUE Correct Correct NA NA NA KX786308* Dasyurus maculatus Spotted-tailed quoll TRUE Correct Correct TRUE Correct Correct KX786309* Dasyurus maculatus Spotted-tailed quoll TRUE Correct Correct NA NA NA KX786310* Dasyurus maculatus Spotted-tailed quoll TRUE Correct Correct NA NA NA KX786311* Dasyurus maculatus Spotted-tailed quoll TRUE Correct Correct NA NA NA KX786312* Dasyurus maculatus Spotted-tailed quoll TRUE Correct Correct NA NA NA KX786313* Dasyurus maculatus Spotted-tailed quoll TRUE Correct Correct NA NA NA KX786314* Dasyurus maculatus Spotted-tailed quoll TRUE Correct Correct NA NA NA

153 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA KX786315* Dasyurus maculatus Spotted-tailed quoll TRUE Correct Correct NA NA NA KX786316* Dasyurus maculatus Spotted-tailed quoll TRUE Correct Correct NA NA NA KX786317* Dasyurus maculatus Spotted-tailed quoll TRUE Correct Correct NA NA NA KX786318* Dasyurus maculatus Spotted-tailed quoll TRUE Correct Correct NA NA NA KX786319* Dasyurus maculatus Spotted-tailed quoll TRUE Correct Correct NA NA NA KX786320* Dasyurus maculatus Spotted-tailed quoll TRUE Correct Correct NA NA NA KX786321* Dasyurus maculatus Spotted-tailed quoll TRUE Correct Correct NA NA NA KX786322* Dasyurus maculatus Spotted-tailed quoll TRUE Correct Correct NA NA NA Would be correctly identified with 4% AF009893 Dasyurus maculatus Spotted-tailed quoll TRUE No_ID No_ID TRUE Correct Correct threshold KJ780028 Dasyurus maculatus Spotted-tailed quoll TRUE Correct Correct TRUE Correct Correct KJ780029 Dasyurus maculatus Spotted-tailed quoll TRUE Correct Correct TRUE Correct Correct KJ780030 Dasyurus maculatus Spotted-tailed quoll TRUE Correct Correct TRUE Correct Correct KX786323* Dasyurus maculatus Spotted-tailed quoll TRUE Correct Correct TRUE Correct Correct KX786324* Dasyurus maculatus Spotted-tailed quoll TRUE Correct Correct NA NA NA KX786325* Dasyurus maculatus Spotted-tailed quoll TRUE Correct Correct NA NA NA KX786326* Dasyurus maculatus Spotted-tailed quoll TRUE Correct Correct NA NA NA KX786327* Dasyurus maculatus Spotted-tailed quoll TRUE Correct Correct NA NA NA Singleton species, nearest neighbour = AF009892 Dasyurus spartacus Bronze quoll FALSE Incorrect Incorrect NA NA NA Dasyurus geoffroii KX786328* Dasyurus viverrinus Eastern quoll TRUE Correct Correct TRUE Correct Correct KX786329* Dasyurus viverrinus Eastern quoll TRUE Correct Correct TRUE Correct Correct KX786330* Dasyurus viverrinus Eastern quoll TRUE Correct Correct NA NA NA KX786331* Dasyurus viverrinus Eastern quoll TRUE Correct Correct NA NA NA KX786332* Dasyurus viverrinus Eastern quoll TRUE Correct Correct NA NA NA KX786333* Dasyurus viverrinus Eastern quoll TRUE Correct Correct NA NA NA KX786334* Dasyurus viverrinus Eastern quoll TRUE Correct Correct NA NA NA KX786335* Dasyurus viverrinus Eastern quoll TRUE Correct Correct NA NA NA KX786336* Dasyurus viverrinus Eastern quoll TRUE Correct Correct NA NA NA DVU87401 Dasyurus viverrinus Eastern quoll TRUE Correct Correct NA NA NA KX786337* Dasyurus viverrinus Eastern quoll TRUE Correct Correct NA NA NA KX786338* Dasyurus viverrinus Eastern quoll TRUE Correct Correct NA NA NA KX786339* Dasyurus viverrinus Eastern quoll TRUE Correct Correct NA NA NA KX786340* Dasyurus viverrinus Eastern quoll TRUE Correct Correct NA NA NA KX786341* Dasyurus viverrinus Eastern quoll TRUE Correct Correct NA NA NA KX786342* Dasyurus viverrinus Eastern quoll TRUE Correct Correct NA NA NA KX786343* Dasyurus viverrinus Eastern quoll TRUE Correct Correct NA NA NA

154 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA JQ340128 Equus caballus Horse TRUE Correct Correct TRUE Correct Correct JQ340135 Equus caballus Horse TRUE Correct Correct TRUE Correct Correct JQ340151 Equus caballus Horse TRUE Correct Correct NA NA NA FCU20753 Felis catus Cat TRUE Correct Correct TRUE Correct Correct KX786344* Felis catus Cat TRUE Correct Correct NA NA NA NC001700 Felis catus Cat TRUE Correct Correct NA NA NA Y08503 Felis catus Cat TRUE Correct Correct TRUE Correct Correct KC257328 Homo sapiens Human TRUE Correct Correct TRUE Correct Correct KC257351 Homo sapiens Human TRUE Correct Correct NA NA NA KJ801423 Homo sapiens Human TRUE Correct Correct NA NA NA KP229447 Homo sapiens Human TRUE Correct Correct NA NA NA KP877128 Homo sapiens Human TRUE Correct Correct NA NA NA KR135843 Homo sapiens Human TRUE Correct Correct TRUE Correct Correct KR135882 Homo sapiens Human TRUE Correct Correct NA NA NA KT366045 Homo sapiens Human TRUE Correct Correct NA NA NA KT749783 Homo sapiens Human TRUE Correct Correct NA NA NA KT886412 Homo sapiens Human TRUE Correct Correct NA NA NA GU937113 Lepus capensis Brown hare TRUE Correct Correct NA NA NA NC015841 Lepus capensis Brown hare TRUE Correct Correct NA NA NA Tate's three-striped AY609350 Myoictis wavicus dasyure TRUE Correct Correct TRUE Correct Correct Tate's three-striped Would be correctly identified with 3% AY609351 Myoictis wavicus dasyure TRUE No_ID No_ID TRUE Correct Correct threshold Tate's three-striped KJ868129 Myoictis wavicus dasyure TRUE Correct Correct NA NA NA Neophascogale Would be correctly identified with 2% AF009894 lorentzii Speckled dasyure TRUE No_ID No_ID TRUE Correct Correct threshold Neophascogale Would be correctly identified with 2% KJ868130 lorentzii Speckled dasyure TRUE No_ID No_ID TRUE Correct Correct threshold Oryctolagus AJ001588 cuniculus European rabbit TRUE Correct Correct NA NA NA

155 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA Oryctolagus NC001913 cuniculus European rabbit TRUE Correct Correct NA NA NA KF302444 Ovis aries Sheep TRUE Correct Correct NA NA NA KF302451 Ovis aries Sheep TRUE Correct Correct NA NA NA KF302456 Ovis aries Sheep TRUE Correct Correct NA NA NA Parantechinus Singleton species, nearest neighbour = FN666601 apicalis Dibbler FALSE No_ID No_ID NA NA NA Pseudantechinus macdonnellensis Phascolosorex Narrow-striped AF009895 dorsalis marsupial shrew TRUE Correct Correct TRUE Correct Correct Phascolosorex Narrow-striped AY536433 dorsalis marsupial shrew TRUE Correct Correct NA NA NA Phascolosorex Narrow-striped Would be correctly identified with 2% KJ868144 dorsalis marsupial shrew TRUE No_ID No_ID TRUE Correct Correct threshold Pseudantechinus Sandstone false Singleton species, nearest neighbour = AF009884 bilarni antechinus FALSE No_ID No_ID NA NA NA Pseudantechinus macdonnellensis Pseudantechinus Fat-tailed false Singleton species, nearest neighbour = EU086642 macdonnellensis antechinus FALSE Incorrect Incorrect NA NA NA Pseudantechinus roryi Pseudantechinus Alexandria false Singleton species, nearest neighbour = EU086643 mimulus antechinus FALSE No_ID No_ID NA NA NA Pseudantechinus roryi Pseudantechinus Rory Cooper's false Singleton species, nearest neighbour = EU086650 roryi antechinus FALSE Incorrect Incorrect NA NA NA Pseudantechinus macdonnellensis Pseudantechinus Woolley's false Singleton species, nearest neighbour = EU086645 woolleyae antechinus FALSE No_ID No_ID NA NA NA Pseudantechinus macdonnellensis JX475454 Sarcophilus harrisii Tasmanian devil TRUE Correct Correct NA NA NA JX475455 Sarcophilus harrisii Tasmanian devil TRUE Correct Correct NA NA NA JX475456 Sarcophilus harrisii Tasmanian devil TRUE Correct Correct NA NA NA JX475457 Sarcophilus harrisii Tasmanian devil TRUE Correct Correct NA NA NA JX475458 Sarcophilus harrisii Tasmanian devil TRUE Correct Correct NA NA NA JX475459 Sarcophilus harrisii Tasmanian devil TRUE Correct Correct NA NA NA JX475460 Sarcophilus harrisii Tasmanian devil TRUE Correct Correct NA NA NA JX475461 Sarcophilus harrisii Tasmanian devil TRUE Correct Correct NA NA NA JX475462 Sarcophilus harrisii Tasmanian devil TRUE Correct Correct NA NA NA JX475463 Sarcophilus harrisii Tasmanian devil TRUE Correct Correct NA NA NA JX475464 Sarcophilus harrisii Tasmanian devil TRUE Correct Correct NA NA NA

156 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA JX475466 Sarcophilus harrisii Tasmanian devil TRUE Correct Correct NA NA NA JX475467 Sarcophilus harrisii Tasmanian devil TRUE Correct Correct NA NA NA NC018788 Sarcophilus harrisii Tasmanian devil TRUE Correct Correct NA NA NA SHU87402 Sarcophilus harrisii Tasmanian devil TRUE Correct Correct NA NA NA EF545573 Sus scrofa Wild boar TRUE Correct Correct NA NA NA EF545575 Sus scrofa Wild boar TRUE Correct Correct NA NA NA EF545580 Sus scrofa Wild boar TRUE Correct Correct NA NA NA Thylacinus FJ515780 cynocephalus Thylacine TRUE Correct Correct NA NA NA Thylacinus FJ515781 cynocephalus Thylacine TRUE Correct Correct NA NA NA Thylacinus NC011944 cynocephalus Thylacine TRUE Correct Correct NA NA NA GQ374180 Vulpes vulpes Red fox TRUE Correct Correct TRUE Correct Correct JN711443 Vulpes vulpes Red fox TRUE Correct Correct NA NA NA KF387633 Vulpes vulpes Red fox TRUE Correct Correct TRUE Correct Correct KP342452 Vulpes vulpes Red fox TRUE Correct Correct NA NA NA KT448287 Vulpes vulpes Red fox TRUE Correct Correct NA NA NA NC008434 Vulpes vulpes Red fox TRUE Correct Correct NA NA NA Y08508 Vulpes vulpes Red fox TRUE Correct Correct NA NA NA

Results of nearNeighbour (NN), bestCloseMatch (BM) and threshID (TI) analyses conducted on two different 12S rRNA reference databases for Australian mammals, each 178 bp in length. The FULL database includes all sequences represented in the 901 bp reference database and the UNIQUE database includes only those sequences that represent unique haplotypes, with singleton species excluded. A 1% genetic distance threshold was used for the FULL databse and a 4% genetic distance threshold was used for the UNIQUE database. NA denotes samples that were not included in the UNIQUE database. *these sequences were generated in this study.

157 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA APPENDIX 2.5: Samples included in the laboratory evaluation of the AusPreda_12S mini-barcode

Source of tissue Tissue Class Scientific name Common name sample Sample ID type Amplified Mammalia Antechinus minimus Swamp antechinus DPIPWE UC1531 Ear Y Mammalia Bettongia gaimardi Tasmanian bettong DPIPWE AA15141 Ear Y Mammalia Bos taurus Cow ANU Cow01 Unknown Y Mammalia Burramys parvus Mountain pygmy possum ANWC M25000 Liver Y Mammalia Canis lupus dingo Dingo UC AA15020 Ear Y Mammalia Canis lupus familiaris Dog UC Uterus Uterus Y Mammalia Dactylopsila trivirgata Striped possum ANWC M24957 Liver Y Mammalia Dasyurus geoffroii Western quoll UA AA68037 Ear Y Mammalia Dasyurus hallucatus Nothern quoll DPAW AA68034 Ear Y Mammalia Dasyurus maculatus Spotted-tailed quoll DPIPWE A3395 Unknown Y Mammalia Dasyurus maculatus Spotted-tailed quoll DPIPWE A3397 Unknown Y Mammalia Dasyurus viverrinus Eastern quoll UC UC1692 Ear Y Mammalia Dasyurus viverrinus Eastern quoll UC UC1696 Ear Y Mammalia Felis catus Feral cat USYD A09 Ear Y Mammalia Felis catus Feral cat UTAS C97 Unknown Y Mammalia Hydromys chrysogaster Water rat DPIPWE A3385 Unknown Y Mammalia Isoodon obesulus Southern brown bandicoot DPIPWE AA15119 Ear Y Mammalia Lepus capensis Brown hare UC UC1085 Ear Y Mammalia Macropus giganteus Eastern grey kangaroo ANWC M29510 Liver Y Mammalia Macropus rufogriseus Bennett's wallaby DPIPWE AA15169 Ear Y Mammalia Macropus rufus Red kangaroo ANWC M37017 Liver Y Mammalia Mus musculus House mouse ANWC M30793 Liver Y Mammalia Ornithorhyncus anatinus Platypus ANU Platypus Unknown Y Mammalia Oryctolagus cuniculus Rabbit DPIPWE UC1491 Skull/skin Y Mammalia Parameles gunnii Eastern barred bandicoot DPIPWE AA15122 Ear Y Mammalia Petaurus breviceps Sugar glider ANWC M28613 Liver Y Mammalia Phascogale tapoatafa Brush-tailed phascogale ANWC M16331 Liver Y Mammalia Planigale gilesi Giles' planigale ANWC M16496 Liver Y Mammalia Potorous tridactylus Long-nosed potoroo DPIPWE AA15138 Ear Y Mammalia Pseudocheirus peregrinus Ringtail possum DPIPWE AA15158 Ear Y Mammalia Pseudomys gracilacaudatus Eastern chestnut mouse ANWC M16419 Liver Y Mammalia Rattus lutreolus velutinus Swamp rat DPIPWE SRR12 Unknown Y Mammalia Sarcophilus harrisii Tasmanian devil DPIPWE A3366 Unknown Y Mammalia Sarcophilus harrisii Tasmanian devil DPIPWE A3370 Unknown Y Mammalia Sminthopsis leucopus White-footed dunnart ANWC M24767 Liver Y Mammalia Tachyglossus aculeatus Echidna ANU Echidna Unknown Y Mammalia Thylogale billardierii Tasmanian pademelon DPIPWE AA15146 Ear Y Mammalia Trichosorus vulpecula Brushtail possum DPIPWE AA15156 Ear Y Mammalia Vicugna pacos Alpaca UC AA15048 Testis Y Mammalia Vombatus ursinus Common wombat DPIPWE UC0590 Ear Y Mammalia Vulpes vulpes Red fox UC AA15002 Ear Y Mammalia Vulpes vulpes Red fox UC UC0401 Ear Y Amphibia Geocrinia laevis Tasmanian smooth frog DPIPWE C1258 Unknown Y

158 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA Aves Lathamus discolor Swift parrot TMAG B7985 Unknown Y Reptilia Tiliqua nigrolutea Blotched blue-tongue lizard UC UC1083 Tail Y

ANU: the Australian National University DPAW: the Department of Parks and Wildlife, Western Australia DPIPWE: the Department of Primary Industries, Parks, Water and Environment, Tasmania UA: the University of Adelaide UC: the University of Canberra USYD: the University of Sydney

159 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA APPENDIX 2.6: Consensus sequences obtained from 53 known-origin scats by amplification with the AusPreda_12S mini-barcode

>041110-01_consensus Alignment of 2 sequences: 041110-01_12SF.ab1, 041110-01_12SR.ab1 (reversed) CGGCGTAAGGGTGTTTAAGCATAAGCTTCACAAATAAGGTTAAAACTCAACTACGCTGTAATACG CCCCAGTTGACATTAAAATACGCAACTTACGTGACTTTACTATTGCTGAAGACACTAAAGCTAAGG TACAAACTGGGATTAGAGACCCCAT

>041110-04_consensus Alignment of 2 sequences: 041110-04_12SF.ab1, 041110-04_12SR.ab1 (reversed) ATTAACCCAAATTAACAGAAAAACGGCGTAAAGGGTGTTTAAGCATAAGCTTCACAAATAAGGTT AAAACTCAACTACGCTGTAATACGCCCCAGTTGACATTAAAATACGCAACTTACGTGACTTTACTA TTGCTGAAGACACTAAAGCTAAGGTACAAACTGGGATTAGAGACCCCACT

>041110-07_consensus Alignment of 2 sequences: 041110-07_12SF.ab1, 041110-07_12SR.ab1 (reversed) GCGTAAAGGGTGTTTAAGCATAAGCTTCACAAATAAGGTTAAAACTCAACTACGCTGTAATACGC CCCAGTTGACATTAAAATACGCAACTTACGTGACTTTACTATTGCTGAAGACACTAAAGCTAAGGT ACAAACTGGGATTAGAGACCCCAC

>041110-15_consensus Alignment of 2 sequences: 041110-15_12SF.ab1, 041110-15_12SR.ab1 (reversed) GTAAGGGTGTTTAAGCATAAGCTTCACAAATAAGGTTAAAACTCAACTACGCTGTAATACGCCCCA GTTGACATTAAAATACGCAACTTACGTGACTTTACTATTGCTGAAGACACTAAAGC

>041110-42_consensus Alignment of 2 sequences: 041110-42_12SF.ab1, 041110-42_12SR.ab1 (reversed) ATTAACCCAAATTAACAGAAACACGGCGTAAAGGGTGTTTAAGCACGCAACCAAACAAATAAAGC TAAAATTCAATTATGCTGTAATACGCCCTAGTTGATACTAAAATACACAACTTACGTGACTTTACT AAAGCTGAAGACACTAAAGCTAAGGTACAAACTGG

>041110-47_consensus Alignment of 2 sequences: 041110-47_12SF.ab1, 041110-47_12SR.ab1 (reversed) ATTAACCCAAATTAACAGAAACACGGCGTAAAGGGTGTTTAAGCACGCAACCAAACAAATAAAGC TAAAATTCAATTATGCTGTAATACGCCCTAGTTGATACTAAAATACACAACTTACGTGACTTTACT AAAGCTGAAGACACTAAAGCTAAGGTACAAACTGGGATTAGAGACCC

>041110-48_consensus Alignment of 2 sequences: 041110-48_12SF.ab1, 041110-48_12SR.ab1 (reversed) ATTAACCCAAATTAACAGAAACACGGCGTAAAGGGTGTTTAAGCACGCAACCAAACAAATAAAGC TAAAATTCAATTATGCTGTAATACGCCCTAGTTGATACTAAAATACACAACTTACGTGACTTTACT AAAGCTGAAGACACTAAAGCTAAGGTACAAACTGGGATTAGAGACCCC

>041110-53_consensus Alignment of 2 sequences: 041110-53_12SF.ab1, 041110-53_12SR.ab1 (reversed) ATTAACCCAAATTAACAGAAACACGGCGTAAAGGGTGTTTAAGCACGCAACCAAACAAATAAAGC TAAAATTCAATTATGCTGTAATACGCCCTAGTTGATACTAAAATACACAACTTACGTGACTTTACT AAAGCTGAAGACACTAAAGCTAAGGTACAAACTGGGATT

>041110-59_consensus Alignment of 2 sequences: 041110-59_12SF.ab1, 041110-59_12SR.ab1 (reversed) ATTAACCCAAATTAACAGAAACACGGCGTAAAGGGTGTTTAAGCACGCAACCAAACAAATAAAGC TAAAATTCAATTATGCTGTAATACGCCCTAGTTGATACTAAAATACACAACTTACGTGACTTTACT AAAGCTGAAGACACTAAAGCTAAGGTACAAACTGGGATTAGAGACCCCA

>041110-66_consensus Alignment of 2 sequences: 041110-66_12SF.ab1, 041110-66_12SR.ab1 (reversed) GCGGTCATACGATTAACCCTAAATTAACAGAAAAACGGCGTAAAGGGTGTTTAAGCTACATACCT CACAATAAAGTTAAAAGCTAACTACGCTGTAATACGCCCTAGTTGACATTAAAATACGCAACTTAC GTGACTTTACTATTGCTGAAGACACTAAAGCTAAGGTACAAACTGGGATTA

>041110-74_consensus Alignment of 2 sequences: 041110-74_12SF.ab1, 041110-74_12SR.ab1 (reversed) ATTAACCCAAATTAACAGAAAAACGGCGTAAAGGGTGTTTAAGCATAAGCTTCACAAATAAGGTT AAAACTCAACTACGCTGTAATACGCCCCAGTTGACATTAAAATACGCAACTTACGTGACTTTACTA TTGCTGAAGACACTAAAGCTAAGGTACAAACTGGGATTAGAGACC

>041110-80_consensus Alignment of 2 sequences: 041110-80_12SF.ab1, 041110-80_12SR.ab1 (reversed)

160 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA ATTAACCCAAATTAACAGAAAAACGGCGTAAAGGGTGTTTAAGCATAAGCTTCACAAATAAGGTT AAAACTCAACTACGCTGTAATACGCCCCAGTTGACATTAAAATACGCAACTTACGTGACTTTACTA TTGCTGAAGACACTAAAGCTAAGGTACAAACTGGGATTAGAGACCCC

>080211-04_consensus Alignment of 2 sequences: 080211-04_12SF.ab1, 080211-04_12SR.ab1 (reversed) ATTAACCCGAACTAATAGGCCCACGGCGTAAAGCGTGTTTAAGATAACATATTACTAAAGTTAAA ACTTAACTAAGCCGTAAAAAGCTACAGTTACCATAAAATATACTACGAAAGTGACTTTAAAATTTT CTGATTACACGATAGCTAAGACCCAAACTGGGATTAGAGACCCCACTA

>080211-05_consensus Alignment of 2 sequences: 080211-05_12SF.ab1, 080211-05_12SR.ab1 (reversed) ATTAACCCGAACTAATAGGCCCACGGCGTAAAGCGTGTTTAAGATAACATATTACTAAAGTTAAA ACTTAACTAAGCCGTAAAAAGCTACAGTTACCATAAAATATACTACGAAAGTGACTTTAAAATTTT CTGATTACACGATAGCTAAGACCCAAACTGGGATTAGAGACCCCA

>080211-07_consensus Alignment of 2 sequences: 080211-07_12SF.ab1, 080211-07_12SR.ab1 (reversed) TACGATTAACCCGAACTAATAGGCCCACGGCGTAAAGCGTGTTTAAGATAACATATTACTAAAGTT AAAACTTAACTAAGCCGTAAAAAGCTACAGTTACCATAAAATATACTACGAAAGTGACTTAAAAT TCTGATACAC

>080211-08_consensus Alignment of 2 sequences: 080211-08_12SF.ab1, 080211-08_12SR.ab1 (reversed) ATTAACCCGAACTAATAGGCCCACGGCGTAAAGCGTGTTTAAGATAACATATTACTAAAGTTAAA ACTTAACTAAGCCGTAAAAAGCTACAGTTACCATAAAATATACTACGAAAGTGACTTTAAAATTTT CTGATTACACGATAGCTAAGACCCAAACTGGGATTAGAGACCCCA

>080211-10_consensus Alignment of 2 sequences: 080211-10_12SF.ab1, 080211-10_12SR.ab1 (reversed) ATTAACCCGAACTAATAGGCCCACGGCGTAAAGCGTGTTTAAGATAACATATTACTAAAGTTAAA ACTTAACTAAGCCGTAAAAAGCTACAGTTACCATAAAATATACTACGAAAGTGACTTTAAAATTTT CTGATTACACGATAGCTAAGACCCAAACTGG

>080211-11_consensus Alignment of 2 sequences: 080211-11_12SF.ab1, 080211-11_12SR.ab1 (reversed) ATTAACCCGAACTAATAGGCCCACGGCGTAAAGCGTGTTTAAGATAACATATTACTAAAGTTAAA ACTTAACTAAGCCGTAAAAAGCTACAGTTACCATAAAATATACTACGAAAGTGACTTTAAAATTTT CTGATTACACGATAGCTAAAACCCAAACTGGGATTAGAGACCCCACTA

>080211-13_consensus Alignment of 2 sequences: 1-080211-13_12SF.ab1, 6-080211-13_12SR.ab1 (reversed) ATTAACCCGAACTAATAGGCCCACGGCGTAAAGCGTGTTTAAGATAACAATATTACTAAAGTTAA AACTTAACTAAGCCGTAAAAAGCTACNAGTTACCATAAAATATACTACGAAAGTGACTTTAAAAT TTTCTGATTACACGATAGCTAAGACCCAAACTGG

>080211-14_consensus Alignment of 2 sequences: 2-080211-14_12SF.ab1, 7-080211-14_12SR.ab1 (reversed) ATTAACCCGAACTAATAGGCCCACGGCGTAAAGCGTGTTTAAGATAACATATTACTAAAGTTAAA ACTTAACTAAGCCGTAAAAAGCTACAGTTACCATAAAATATACTACGAAAGTGACTTTAAAATTTT CTGATTACACGATAGCTAAGACCCAAACTGG

>080211-15_consensus Alignment of 2 sequences: 3-080211-15_12SF.ab1, 8-080211-15_12SR.ab1 (reversed) CATATGTACTAAAGTTAAAACTTAACTAAGCCGTAAAAAGCTACAGTTACCATAAAATATACTACG AAAGTGACTTTAAAATTTTCTGATTACACGATAGCTAAGACCCAAACTGG

>080211-16_consensus Alignment of 2 sequences: 4-080211-16_12SF.ab1, 9-080211-16_12SR.ab1 (reversed) ATTAACCCGAACTAATAGGCCCACGGCGTAAAGCGTGTTTAAGATAACATATTACTAAAGTTAAA ACTTAACTAAGCCGTAAAAAGCTACAGTTACCATAAAATATACTACGAAAGTGACTTTAAAATTTT CTGATTACACGATAGCTAAGACCCAAACTGG

>080211-18_consensus Alignment of 2 sequences: 5-080211-18_12SF.ab1, 10-080211-18_12SR.ab1 (reversed) ATTAACCCGAACTAATAGGCCCACGGCGTAAAGCGTGGTTTAAGATAACATATTACTAAAGTTAA AACTTAACTAAGCCGTAAAAAGCTACAGTTACCATAAAATATACTACGAAAGTGACTTTAAAATTT TCTGATTACACGATAGCTAAGACCCAAACCGGGATTAGAGCCCCACTA

>100111-04_consensus Alignment of 2 sequences: 100111-04_12SF.ab1, 100111-04_12SR.ab1 (reversed)

161 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA ATTAACCCAAACTAATAGACCCACGGCGTAAAGCGTGTTACAGAGAAAAAAATATACTAAAGTTA AATTTTAACTAGGCCGTAGAAAGCTACAGTTAACATAAAAATACAGCACGAAAGTAACTTTAACA CCTCCGACTACACGACAGCTAAGACCCAAACTGGGATTAGAGACC

>100111-05_consensus Alignment of 2 sequences: 100111-05_12SF.ab1, 100111-05_12SR.ab1 (reversed) ATTAACCCAAATTAACAGAAAAACGGCGTAAAGGGTGTTTAAGCATAAGCTTCACAAATAAGGTT AAAACTCAACTACGCTGTAATACGCCCCAGTTGACATTAAAATACGCAACTTACGTGACTTTACTA TTGCTGAAGACACTAAAGCTAAGGTACAAACTGGGATTAGAGACCC

>100111-27_consensus Alignment of 2 sequences: 100111-27_12SF.ab1, 100111-27_12SR.ab1 (reversed) ATTAACCCAAACTAATAGGCCTACGGCGTAAAGCGTGTTCAAGATACTTTAACACTAAAGTTAAA ACTTAACTAAGCCGTAAAAAGCTACAGTTATCATAAAATAAACCACGAAGGTGACTTTATAATAA TCTGACTACACGATAGCTAAGACCCAAACTGGGATTAGAGACCCCAC

>100111-31_consensus Alignment of 2 sequences: 100111-31_12SF.ab1, 100111-31_12SR.ab1 (reversed) ATTAACCCAAATTAACAGAAAAACGGCGTAAAGGGTGTTTAAGCATAAGCTTCACAAATAAGGTT AAAACTCAACTACGCTGTAATACGCCCCAGTTGACATTAAAATACGCAACTTACGTGACTTTACTA TTGCTGAAGACACTAAAGCTAAGGTACAAACTGGGATTAGAGACCC

>101110-09_consensus Alignment of 2 sequences: 101110-9_12SF.ab1, 101110-9_12SR.ab1 CTTTAGTGTCTTCAGCATAGTAAGTCACGTAAGTTGCGTATTTTAATGTCAACTAGGGCGTATTACA GCGTAGTTAGCTTTTAACTTTATTGTGAGGTATGTGCTTAAACACCCTTTACGCCGTTTTTCTGTTA ATTTGGGTTAATCGTATGACCGCGGT

>120111-02_consensus Alignment of 2 sequences: 120111-02_12SF.ab1, 120111-02_12SR.ab1 (reversed) ATTAACCCAAACTAATAGGCCTACGGCGTAAAGCGTGTTCAAGATACTTTAACACTAAAGTTAAA ACTTAACTAAGCCGTAAAAAGCTACAGTTATCATAAAATAAACCACGAAGGTGACTTTATAATAA TCTGACTACACGATAGCTAAGACCCAAACTGG

>120111-10_consensus Alignment of 2 sequences: 120111-10_12SF.ab1, 120111-10_12SR.ab1 (reversed) ATTAACCCAAACTAATAGACCCACGGCGTAAAGCGTGTTACAGAGAAAAAAATATACTAAAGTTA AATTTTAACTAGGCCGTAGAAAGCTACAGTTAACATAAAAATACAGCACGAAAGTAACTTTAACA CCTCCGACTACACGACAGCTAAGACCCAAACTGGGATTAGAGACCCC

>120111-12_consensus Alignment of 2 sequences: 120111-12_12SF.ab1, 120111-12_12SR.ab1 (reversed) ATTAACCCAAACTAATAGACCCACGGCGTAAAGCGTGTTACAGAGAAAAAAATATACTAAAGTTA AATTTTAACTAGGCCGTAGAAAGCTACAGTTAACATAAAAATACAGCACGAAAGTAACTTTAACA CCTCCGACTACACGACAGCTAAGACCCAAACTGGGATTAGAGACCCCAT

>120111-33_consensus Alignment of 2 sequences: 120111-33_12SF.ab1, 120111-33_12SR.ab1 (reversed) ATTAACCCAAATTAACAGAAAAACGGCGTAAAGGGTGTTTAAGCATAAGCTTCACAAATAAGGTT AAAACTCAACTACGCTGTAATACGCCCCAGTTGACATTAAAATACGCAACTTACGTGACTTTACTA TTGCTGAAGACACTAAAGCTAAGGTACAAACTGGGATTAGAGACCCCA

>121010-06_consensus Alignment of 2 sequences: 121010-06_12SF.ab1, 121010-06_12SR.ab1 (reversed) ATTAACCCAAATTAACAGAAACACGGCGTAAAGGGTGTTTAAGCACGCAACCAAACAAATAAAGC TAAAATTCAATTATGCTGTAATACGCCCTAGTTGATACTAAAATACACAACTTACGTGACTTTACT AAAGCTGAAGACACTAAAGCTAAGGTACAAACTGG

>121010-11_consensus Alignment of 2 sequences: 121010-11_12SF.ab1, 121010-11_12SR.ab1 (reversed) ATTAACCCAAACTAATAGGCCTACGGCGTAAAGCGTGTTCAAGATACTTTAACACTAAAGTTAAA ACTTAACTAAGCCGTAAAAAGCTACAGTTATCATAAAATAAACCACGAAAGTGACTTTATAATAA TCTGACTACACGATAGCTAAGACCCAAACTGGGATTAGAGACCCCACTA

>121010-16_consensus Alignment of 2 sequences: 121010-16_12SF.ab1, 121010-16_12SR.ab1 (reversed) TACGATTAACCCAAACTAATAGGCCTACGGCGTAAAGCGTGTTCAAGATACTTTAACACTAAAGTT AAAACTTAACTAAGCCGTAAAAAGCTACCAGTTATCATAAAATAAACCCACGAAAGTGACTTTAT AATAATCTGACTACACGATAGCTAAGACCCAAACTGGGATTAGAGACCCCA

>121010-17_consensus Alignment of 2 sequences: 121010-17_12SF.ab1, 121010-17_12SR.ab1 (reversed)

162 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA ATTAACCCAAACTAATAGGCCTACGGCGTAAAGCGTGTTCAAGATACTTTAACACTAAAGTTAAA ACTTAACTAAGCCGTAAAAAGCTACAGTTATCATAAAATAAACCACGAAAGTGACTTTATAATAA TCTGACTACACGATAGCTAAGACCCAAACTGGGATTAGAGACCCC

>121010-22_consensus Alignment of 2 sequences: 121010-22_12SF.ab1, 121010-22_12SR.ab1 (reversed) ATTAACCCAAATTAACAGAAACACGGCGTAAAGGGTGTTTAAGCACGCAACCAAACAAATAAAGC TAAAATTCAATTANTGCTGTAATACGCCCTAGTTGATACTAAAATACACAACTTACGTGACTTTAC TAAAGCTGAAGACACTAAAGCTAAGGTACAAACTGGGATTAGAGAC

>121010-30_consensus Alignment of 2 sequences: 121010-30_12SF.ab1, 121010-30_12SR.ab1 (reversed) ATTAACCCAAACTAATAGGCCTACGGCGTAAAGCGTGTTCAAGATACTTTAACACTAAAGTTAAA ACTTAACTAAGCCGTAAAAAGCTACAGTTATCATAAAATAAACCACGAAAGTGACTTTATAATAA TCTGACTACACGATAGCTAAGACCCAAACTGGGATTAGAGACCCCA

>121010-53_consensus Alignment of 2 sequences: 121010-53_12SF.ab1, 121010-53_12SR.ab1 (reversed) ATTAACCCAAACTAATAGGCCTACGGGCGTAAAGCGTGTTCAAGATACTTTTACACTAAAGTTAAA ACTTAACTAAGCCGTAAAAAGCTACAGTTATCATAAAATAAACCACGAAAGTGACTTTATAATAA TCTGACTACACGATAGCTAAGACCCAAACTGGGATTAGAGACCCCA

>121010-54_consensus Alignment of 2 sequences: 121010-54_12SF.ab1, 121010-54_12SR.ab1 (reversed) ATTAACCCAAACTAATAGGCCTACGGCGTAAAGCGTGTTCAAGATACTTTTACACTAAAGTTAAAA CTTAACTAAGCCGTAAAAAGCTACAGTTATCATAAAATAAACCACGAAAGTGACTTTATAATAATC TGACTACACGATAGCTAAGACCCAAACTGGGATTAGAGACCCCACTA

>121010-56_consensus Alignment of 2 sequences: 121010-56_12SF.ab1, 121010-56_12SR.ab1 (reversed) ATTAACCCAAACTAATAGGCCTACGGCGTAAAGCGTGTTCAAGATACTTTTACACTAAAGTTAAAA CTTAACTAAGCCGTAAAAAGCTACAGTTATCATAAAATAAACCACGAAAGTGACTTTATAATAATC TGACTACACGATAGCTAAGACCCAAACTGGGAGTAGAGACCCCAC

>121110-55_consensus Alignment of 2 sequences: 121110-55_12SF.ab1, 121110-55_12SR.ab1 (reversed) ATTAACCCAAACTAATAGGCCTACGGCGTAAAGCGTGTTCAAGATACTTTAACACTAAAGTTAAA ACTTAACTAAGCCGTAAAAAGCTACAGTTATCATAAAATAAACCACGAAAGTGACTTTATAATAA TCTGACTACACGATAGCTAAGACCCAAACTGGGATTAGAGACCCCA

>170211-13_consensus Alignment of 2 sequences: 170211-13_12SF.ab1, 170211-13_12SR.ab1 (reversed) CAGGGGGTAAGGGTGTTACAGAGAAAAAATATACTAAAGTTAAATTTTAACTAGGCCGTAGAAAG CTACAGTTAACATAAAAATACAGCACGAAAGTAACTTTAACACCTCCGACTACACGACAGCTAAG ACCCAAACTGG

>170211-14_consensus Alignment of 2 sequences: 170211-14_12SF.ab1, 170211-14_12SR.ab1 (reversed) ATTAACCCAAATTAACAGAAAAACGGCGTAAAGGGTGTTTAAGCATAAGCTTCACAAATAAGGTT AAAACTCAACTACGCTGTAATACGCCCCAGTTGACATTAAAATACGCAACTTACGTGACTTTACTA TTGCTGAAGACACTAAAGCTAAGGTACAAACTGGGATTAGAGACCCCA

>170211-21_consensus Alignment of 2 sequences: 170211-21_12SF.ab1, 170211-21_12SR.ab1 (reversed) ATTAACCCAAACTAATAGACCCACGGCGTAAAGCGTGTTACAGAGAAAAAAATATACTAAAGTTA AATTTTAACTAGGCCGTAGAAAGCTACAGTTAACATAAAAATACAGCACGAAAGTAACTTTAACA CCTCCGACTACACGACAGCTAAGACCCAAACTGGGATTAGAGACCCCACTA

>170211-22_consensus Alignment of 2 sequences: 170211-22_12SF.ab1, 170211-22_12SR.ab1 (reversed) ATTAACCCAAACTAATAGACCCACGGCGTAAAGCGTGTTACAGAGAAAAAAATATACTAAAGTTA AATTTTAACTAGGCCGTAGAAAGCTACAGTTAACATAAAAATACAGCACGAAAGTAACTTTAACA CCTCCGACTACACGACAGCTAAGACCCAAACTGGGATTAGAGACCC

>170211-25_consensus Alignment of 2 sequences: 170211-25_12SF.ab1, 170211-25_12SR.ab1 (reversed) CCGCGGTCATACGATTAACCCAAATTAACAGAAAAACGGCGTAAAGGGTGTTTAAGCACATACCT CACAATAAAGTTAAAAGCTAACTACGCTGTAATACGCCCTAGTTGACATTAAAATACGCAACTTAC GTGACTTTACTATTGCTGAAGACACTAAAGCTAAGGTACAAACTGG

>200910-24_consensus Alignment of 2 sequences: 200910-24_12SF.ab1, 200910-24_12SR.ab1 (reversed)

163 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA ATTAACCCAAATTAACAGAAACACGGCGTAAAGGGTGTTTAAGCACGCAACCAAACAAATAAAGC TAAAATTCAATTATGCTGTAATACGCCCTAGTTGATACTAAAATACACAACTTACGTGACTTTACT AAAGCTGAAGACACTAAAGCTAAGGTACAAACTGGGATTAGAGACCC

>200910-25_consensus Alignment of 2 sequences: 200910-25_12SF.ab1, 200910-25_12SR.ab1 (reversed) ATTAACCCAAATTAACAGAAACACGGCGTAAAGGGTGTTTAAGCACGCAACCAAACAAATAAAGC TAAAATTCAATTATGCTGTAATACGCCCTAGTTGATACTAAAATACACAACTTACGTGACTTTACT AAAGCTGAAGACACTAAAGCTAAGGTACAAACTGGGATTAGAGACCCC

>discarded_080211-06_consensus Alignment of 2 sequences: 080211-06_12SF.ab1, 080211-06_12SR.ab1 (reversed) TCCAGCCACCGCGGGCCCCGAGCGGCCCTAATCTTTCCCGCCCGGATTAANNGASMGCGGCGTAA AGYGWGTTTCASATAAYATWTKTACTRAMGTTAAYACTTAAGTAAKCCGKWWGAAGCTACMGTT ACCMTAAAATATACTAYGAAAGTGWCTTTAAAAKCTTCTGATTACACGATAGCTAAGACCCAGCT TGG

>discarded_080211-12_consensus Alignment of 2 sequences: 080211-12_12SF.ab1, 080211-12_12SR.ab1 (reversed) TCCAGCCACCGTGGGGTGCCCGCGGTCCTACGCTTTCCCGCCCGAATTGAAGCSNCGGCGTAAGCG TGTTTCAGATAACATATGTACTAATAGTTAARACTTAAGTAAGCCGTWAGAAGCTACAGTTACCAT AAAATATACTACGAAAGTGACTTTAAAATTTTCTGATTACACGATAGCNNTAAGACCCAAACTGG GATTAGAGACCCCACTATGCACCCACTATGCA

>discarded_120111-31_consensus Alignment of 2 sequences: 120111-31_12SF.ab1, 120111-31_12SR.ab1 (reversed) GATTAACCCAAATTAACAGAAAACGGCGTAAAGGGTGTTTAAGCATAAACCTCNNATAAAATAAA GTTAAAACTCAACTATGCTGTAATACGCCCTAGTTGGCATTAAAATATACAACTTACGTGACTTTA CTACTGCTGAAGACACTAAAGCTAAGGTACAAACTGGGAT

>discarded_121010-52_consensus Alignment of 2 sequences: 121010-52_12SF.ab1, 121010-52_12SR.ab1 (reversed) GGGGATGCTAGCGTTATTCAGAATGATTGGGCGTAAAGCGTTTGTAGAAGGCTTT

164 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA APPENDIX 3.1: Cooccur results with datasets from 2010 and 2014 separrately

Legend: The random co-occurrences are represented in grey boxes, while the negative co- occurrences are in orange.

165 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA APPENDIX 3.2: R code for building species distribution models for the Tasmanian predators

### Set directory ### #setwd("C:/...") list.files() getwd() library(rJava) library(sp) library(raster) library(dismo) library(rgdal) library(vcd) library(usdm) library(pROC)

### Read presences ### ### presence-absence of a species per scat location ### pa_scat = read.table("species.csv", header = TRUE, sep=",", dec=".") ### here build the species files by changing the predator file in appendix 3.3 by adding 1 for a species presence and 0 if another species was detected class(pa_scat) head(pa_scat) coordinates(pa_scat) = ~X+Y projection(pa_scat) = CRS("+proj=utm +zone=55 +south +ellps=GRS80 +units=m +no_defs")

### presence of a species per scat location ### p_scat = read.table("p_scats_devils.csv", header = TRUE, sep=";", dec=",") ### here, use the same file as the previous one with only the 1 values and discarding the 0 values class(p_scat) head(p_scat) coordinates(p_scat) = ~X+Y projection(p_scat) = CRS("+proj=utm +zone=55 +south +ellps=GRS80 +units=m +no_defs")

#import raster layers ### from DPIPWE and from BioClim new_cat = raster("cat_map.tif") new_devil = raster("devil_map.tif") new_eastern = raster("eastern_map.tif") new_dog = raster("dogs_map.tif") new_temp = raster("temperature.tif") new_prec = raster("precipitation.tif") new_slope = raster("new_slope.tif") new_aspect = raster("aspect.tif") new_elevation = raster("elevation.tif") new_vegetation = raster("vegetation.tif")

### Collinearity # calculating variance inflation factor # create two rasterStacks otherwise the vector size is too big predictors1 = stack(new_cat,new_devil, new_eastern, new_dog) predictors2 = stack(new_slope,new_aspect, new_elevation, new_vegetation, new_temp, new_prec) vif(predictors1) # Variables VIF # 1 cat_map 1.175349 # 2 devil_map 1.198845 # 3 eastern_map 1.088021 # 4 dogs_map 1.094980 vif(predictors2) # Variables VIF # 1 new_slope 1.164308

166 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA # 2 aspect 1.030633 # 3 elevation 3.948374 # 4 vegetation 1.184076 # 5 temperature 4.495354 # 6 precipitation 1.533692 # If VIF > 4 then it is commonly assumed that there is multicollinearity # 1 variable from the 6 input variables have collinearity problem: # temperature # it is collinear with elevation # From an ecological point of view, we will remove the elevation predictor instead of the temperature one predictors3 = stack(new_slope, new_aspect, new_vegetation, new_temp, new_prec) vif(predictors3) # Variables VIF # 1 new_slope 1.161380 # 2 aspect 1.031164 # 3 vegetation 1.211829 # 4 temperature 1.245270 # 5 precipitation 1.457927 # no more collinearity problem

# create a new predictor stack without the elevation predictor # example for devils predictors = stack(new_cat, new_eastern,new_dog, new_slope, new_aspect, new_vegetation, new_prec, new_temp) extent(predictors) res(predictors) names(predictors) = c("cats","eastern_quolls","dogs", "slope","aspect", "vegetation", "annual_precipitations", "mean_annual_temperature") names(predictors) predictors

### Plot the raster maps with the data points ### par(mfrow=c(2,4)) cuts=c(0,0.25,0.5,0.75,1) pal <- colorRampPalette(c("grey88","violetred3")) plot(predictors,1, breaks=cuts, col=pal(5), xlab="X",ylab="Y") plot(p_scat, add=T, pch=3) plot(predictors,2, breaks=cuts, col=pal(5), xlab="X",ylab="Y") plot(p_scat, add=T, pch=3) plot(predictors,3, breaks=cuts, col=pal(5), xlab="X",ylab="Y") plot(p_scat, add=T, pch=3) plot(predictors,4, xlab="X",ylab="Y") plot(p_scat, add=T, pch=3) plot(predictors,5, xlab="X",ylab="Y") plot(p_scat, add=T, pch=3) plot(predictors,6, xlab="X",ylab="Y") plot(p_scat, add=T, pch=3) plot(predictors,7, xlab="X",ylab="Y") plot(p_scat, add=T, pch=3) plot(predictors,8, xlab="X",ylab="Y") plot(p_scat, add=T, pch=3)

### extract values from raster layers presabs = extract(predictors, pa_scat) pairs (presabs[,1:8], cex=0.1, fig=TRUE) dim(presabs) class(presabs) sdmdata= data.frame(presabs) pa_scat = read.table("species.csv", header = TRUE, sep=",", dec=".")

167 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA sdmdata = cbind(presabs,pa_scat) head(sdmdata) tail(sdmdata) write.csv2(sdmdata, file = "species_predictors.csv") ### I lose one data for which XY coordinates were too close of water to have a value for vegetation, slope and aspect - this point contained the presence of a dog sdmdata2 = read.csv2("species_predictors.csv", header = TRUE, sep=",", dec=".") sdmdata2 devil=factor(sdmdata2$devil) cat=factor(sdmdata2$cats) eastern=factor(sdmdata2$eastern_quolls) dog=factor(sdmdata2$dogs) veg=factor(sdmdata2$vegetation) library(MASS) # example with devils model_species = glmmPQL(devil ~ cat+eastern+dog+slope+aspect+veg+annual_precipitations+mean_annual_temperature, random=~1|SU_ID ,family=binomial(link="logit"), data = sdmdata2) summary(model_species)

# keep only the significant predictors with a p-value < 0.05 # you can see that in the summary (command above) model_species_clean = glmmPQL(devil ~ cat+eastern+veg+mean_annual_temperature, random=~1|SU_ID ,family=binomial(link="logit"), data = sdmdata2) summary(model_species_clean) predict <- predict(model_species_clean, type = 'response') predict = matrix(predict)

# confusion matrix table=table(sdmdata2$devil, predict > 0.5) library(caret) install.packages('e1071', dependencies=TRUE) dimnames(table)[[1]] = c("FALSE","TRUE") table confusionMatrix(table) fitted.results <- ifelse(predict > 0.5,1,0) misClasificError <- mean(fitted.results != sdmdata2$devil) print(paste('Accuracy',1-misClasificError)) # Accuracy: 0.89 #ROCR Curve and AUC value library(ROCR) ROCRpred <- prediction(predict, sdmdata2$devil) ROCRperf <- performance(ROCRpred, 'tpr','fpr') plot(ROCRperf, colorize = TRUE, text.adj = c(-0.2,1.7)) auc <- performance(ROCRpred, measure = "auc") auc <- [email protected][[1]] auc # [1] 0.9435963 model_no_cats <- update(model_species_clean , .~ . -factor(cats)) summary(model_no_cats)

168 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA # if we remove cats from the model, it is significatively worse # so cats have a really powerful impact on the devil distribution # you can try with other predictors AIC(model_tt_devils_clean, model_no_cats) # df AIC # model_species_clean 15 855.2999 # model_no_cats 14 907.3094

169 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA APPENDIX 3.3: 789 scats used for building the distribution model

% Sample Consensus identity identification X Y Method sequence Predator identification e-value on number HQ % GenBank 5029 496284 5200850 Predator ID 87,2 Sarcophilus harrisii 97 2,27E-77 5030 496289 5200830 Predator ID 75 Sarcophilus harrisii 100 4,27E-90 5031 496338 5200870 Predator ID 98,8 Sarcophilus harrisii 99,4 6,35E-78 5032 496546 5200930 Predator ID 77,6 Sarcophilus harrisii 94,5 4,1E-70 5033 496545 5200930 Predator ID 100 Sarcophilus harrisii 99,4 6,33E-78 5034 496535 5200915 Predator ID 90,8 Sarcophilus harrisii 99,3 3,43E-65 5035 496539 5200920 Predator ID 95,6 Sarcophilus harrisii 99,5 9,19E-87 5037 496541 5200930 Predator ID 95,2 Sarcophilus harrisii 100 3,82E-80 5038 496539 5200918 Predator ID 84,6 Sarcophilus harrisii 99,5 2,56E-87 5039 496828 5216280 Predator ID 96,1 Felis catus 99,4 2,56E-77 5040 496873 5216260 Predator ID 73,7 Felis catus 98,5 1,38E-58 5041 496990 5216300 Predator ID 59,3 Felis catus 96 1,1E-54 5042 496954 5216390 Predator ID 98,2 Dasyurus viverrinus 98,8 1,05E-75 5043 532338 5302910 Predator ID 95,1 Sarcophilus harrisii 99,5 7,77E-88 5602 576854 5296450 Predator ID 98,3 Felis catus 98,8 1,29E-75 5616 511077 5298740 Predator ID 92,7 Felis catus 98,7 5,71E-68 5693 447640 5432515 Predator ID 94,2 Sarcophilus harrisii 98,9 3,41E-81 5960 517883 5444690 Predator ID 79,7 Felis catus 99,3 5,69E-63 5961 517687 5444500 Predator ID 98,2 Sarcophilus harrisii 98,8 3,22E-76 5968 517745 5438730 Predator ID 75,3 Dasyurus maculatus 98,2 1,92E-73 7999 488206 5286700 Predator ID 86,6 Felis catus 99,3 9,14E-61 8000 487971 5286700 Predator ID 71,9 Felis catus 100 2,85E-55 8497 464895 5404424 Predator ID 81,1 Felis catus 100 5,28E-79 8861 509459 5372340 Predator ID 87,8 Felis catus 100 5,21E-79 8862 519682 5387360 Predator ID 80 Felis catus 98,5 4,68E-75 8869 511865 5391630 Predator ID 73,8 Felis catus 98,8 1,89E-73 8871 511659 5391670 Predator ID 77,4 Rattus rattus 97,3 8,23E-67 8885 540770 5402057 Predator ID 85,3 Sarcophilus harrisii 99,4 1,17E-80 8889 506255 5394060 Predator ID 80,5 Felis catus 98,2 1,46E-74 8890 506256 5394060 Predator ID 73,8 Felis catus 100 5,21E-79 8898 511995 5408257 Predator ID 83,2 Felis catus 98,8 1,29E-75 8900 512006 5408250 Predator ID 74,1 Canis lupus familiaris 100 6,63E-78 8983 513098 5419110 Predator ID 86,1 Dasyurus viverrinus 98,9 4,64E-75 8988 513113 5419050 Predator ID 78,2 Dasyurus viverrinus 99,1 2,44E-77 9207 458489 5443981 Predator ID 100 Sarcophilus harrisii 100 1,66E-89 10720 416619 5424585 Predator ID 91,7 Felis catus 99,4 2,81E-77 10742 423466 5414801 Predator ID 83 Sarcophilus harrisii 100 3,1E-76 10751 505482 5217760 Predator ID 79,6 Dasyurus viverrinus 99,5 9,69E-87 10756 505773 5217940 Predator ID 90,9 Sarcophilus harrisii 100 4,33E-85 10757 505774 5217940 Predator ID 90,5 Sarcophilus harrisii 100 2,66E-87 10758 505769 5217940 Predator ID 88,4 Sarcophilus harrisii 99,4 8,15E-77

170 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA 10759 523869 5229200 Predator ID 95,9 Felis catus 99,6 3,38E-81 10762 523465 5229090 Predator ID 94,4 Isoodon obesulus 98,2 6,81E-73 10763 523443 5229090 Predator ID 92,9 Felis catus 98,9 8,47E-72 10768 538140 5272970 Predator ID 91,7 Felis catus 100 1,63E-79 10771 516390 5256790 Predator ID 96,7 Felis catus 100 1,63E-79 10776 502720 5222780 Predator ID 82,5 Dasyurus viverrinus 100 4.00E-64 10777 502709 5222820 Predator ID 88 Dasyurus viverrinus 99,4 6,46E-78 10779 502704 5222820 Predator ID 85,7 Dasyurus viverrinus 100 5,52E-89 10788 501723 5224170 Predator ID 93,4 Perameles gunnii 98,9 4,26E-85 10789 501768 5224200 Predator ID 84,3 Dasyurus viverrinus 100 9,97E-87 10793 502001 5224140 Predator ID 99,4 Perameles gunnii 98,9 4,24E-85 10796 501902 5224170 Predator ID 95,1 Dasyurus viverrinus 100 5,45E-89 10798 501810 5224280 Predator ID 96,1 Perameles gunnii 98,9 4,26E-85 11075 494366 5382250 Predator ID 97,1 Canis lupus familiaris 99,4 2,01E-83 11076 494467 5381220 Predator ID 95 Dasyurus maculatus 99,5 9,69E-87 11085 495765 5382100 Predator ID 99,4 Trichosurus vulpecula 98,8 4,27E-80 11086 495763 5382100 Predator ID 76,7 Trichosurus vulpecula 97,9 4,59E-64 11087 495553 5381710 Predator ID 95 Homo sapiens 100 7,61E-77 11092 517692 5444510 Predator ID 91,8 Sarcophilus harrisii 99,5 2,79E-87 11349 465103 5404586 Predator ID 59,2 Mus musculus 100 1,03E-54 11711 398908 5456165 Predator ID 98,8 Rattus rattus 100 1,48E-79 11857 598794 5363670 Predator ID 96 Sarcophilus harrisii 100 2,32E-98 11860 598764 5363660 Predator ID 95,6 Sarcophilus harrisii 99,4 3,46E-86 11866 598486 5363950 Predator ID 98,3 Sarcophilus harrisii 99,4 3,56E-86 11867 598486 5363950 Predator ID 94,2 Sarcophilus harrisii 99,4 9,42E-82 11872 598095 5352630 Predator ID 94,3 Sarcophilus harrisii 99,4 5,64E-84 11876 608070 5377330 Predator ID 95 Sarcophilus harrisii 100 2,08E-88 11878 603819 5350150 Predator ID 95,1 Felis catus 99,4 8,85E-77 11881 604232 5350250 Predator ID 95,1 Sarcophilus harrisii 99,5 7,8E-88 11884 604468 5350870 Predator ID 98,2 Sarcophilus harrisii 98,8 4,39E-80 12110 576797 5291320 Predator ID 99,4 Sarcophilus harrisii 100 7,23E-83 12113 576792 5291330 Predator ID 91,1 Sarcophilus harrisii 99,5 1,43E-74 12114 576793 5291330 Predator ID 98,3 Sarcophilus harrisii 99,4 3,56E-86 12116 576865 5291180 Predator ID 76,7 Felis catus 98,5 1,49E-74 12126 592207 5397910 Predator ID 82,2 Sarcophilus harrisii 100 1,02E-59 12128 592213 5397910 Predator ID 97,6 Sarcophilus harrisii 98,8 2,9E-76 12129 592213 5397980 Predator ID 92 Felis catus 98,8 3,73E-75 12134 579297 5399690 Predator ID 62,1 Sarcophilus harrisii 98 9,29E-77 12135 579300 5399690 Predator ID 91 Sarcophilus harrisii 100 8,88E-87 12136 579464 5399780 Predator ID 77,9 Sarcophilus harrisii 98,5 4,86E-58 12138 579582 5399900 Predator ID 87,4 Sarcophilus harrisii 99,5 2,79E-87 12139 580046 5399880 Predator ID 100 Sarcophilus harrisii 99,4 6,35E-78 12140 584052 5406910 Predator ID 90,2 Dasyurus maculatus 99,4 2,27E-77 12142 583491 5406320 Predator ID 98,3 Sarcophilus harrisii 100 3,82E-80 12145 583382 5406230 Predator ID 86,4 Canis lupus familiaris 100 6,27E-78 12146 598381 5400470 Predator ID 94,9 Sarcophilus harrisii 99,4 1,79E-73

171 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA 12146 598381 5400472 Predator ID 100 Sarcophilus harrisii 99,4 6,97E-78 12147 598095 5401290 Predator ID 98,3 Felis catus 99,4 4,42E-80 12148 599044 5400574 Predator ID 100 Canis lupus familiaris 100 6,63E-78 12227 497350 5201670 Predator ID 65 Homo sapiens 99,4 4,85E-74 12230 497284 5201650 Predator ID 95 Sarcophilus harrisii 97,5 9,42E-50 12245 532028 5459470 Predator ID 92,4 Niveoscincus pretiosus 98,7 2,37E-72 12246 532028 5459470 Predator ID 91,8 Sarcophilus harrisii 99,5 2,79E-87 12247 532027 5459460 Predator ID 65,6 Trichosurus vulpecula 99,4 8,88E-77 12248 532028 5459460 Predator ID 78,5 Dasyurus maculatus 98,3 1,26E-80 12250 532155 5459870 Predator ID 98,3 Dasyurus maculatus 98,4 7,65E-83 Uncultured bacterium 12457 412968 5443378 Predator ID 100 87,2 1,36E-50 clone 12473 438977 5423555 Predator ID 95,7 Rattus rattus 99,5 2,15E-88 12479 435471 5421871 Predator ID 99,4 Sarcophilus harrisii 98,9 1,62E-84 12489 405135 5432893 Predator ID 99,4 Sarcophilus harrisii 99,4 6,87E-78 12534 492902 5225150 Predator ID 81,1 Dasyurus viverrinus 100 7,03E-88 12535 492890 5225140 Predator ID 87 Dasyurus viverrinus 100 3,21E-86 12536 492893 5225140 Predator ID 68 Dasyurus viverrinus 100 1,64E-57 12610 504559 5203460 Predator ID 95,1 Sarcophilus harrisii 98,2 4,87E-74 12612 504778 5203210 Predator ID 87,2 Sarcophilus harrisii 99,4 3,24E-86 12619 600157 5413410 Predator ID 94,8 Sarcophilus harrisii 98,9 1,22E-80 12620 600161 5413410 Predator ID 97 Sarcophilus harrisii 98,8 5,59E-79 12658 426110 5433790 Predator ID 100 Sarcophilus harrisii 99,5 8,44E-98 12679 439468 5421342 Predator ID 100 Sarcophilus harrisii 98,8 3,22E-76 12706 441851 5420442 Predator ID 76,5 Canis lupus familiaris 98,9 5,91E-84 12720 449729 5432536 Predator ID 100 Sarcophilus harrisii 99,5 2,79E-87 12721 449935 5432950 Predator ID 98,4 Sarcophilus harrisii 99,5 2,79E-87 12722 450012 5433010 Predator ID 97,8 Isoodon obesulus 97,2 1,57E-58 12860 535166 5405920 Predator ID 98,8 Felis catus 99,4 8,88E-77 12867 535813 5404960 Predator ID 89 Felis catus 99,3 2,18E-67 12873 496034 5437050 Predator ID 99,4 Felis catus 98,8 1,3E-75 12881 487807 5237660 Predator ID 67,5 Sarcophilus harrisii 100 6,27E-73 12885 487769 5238020 Predator ID 95,6 Sarcophilus harrisii 100 1,42E-100 12886 487763 5238020 Predator ID 97,8 Sarcophilus harrisii 100 3,19E-38 12893 488036 5238520 Predator ID 91,8 Sarcophilus harrisii 99,6 2,14E-88 12894 488037 5238530 Predator ID 97,2 Sarcophilus harrisii 98,9 1,29E-85 12896 488064 5238550 Predator ID 85,1 Sarcophilus harrisii 100 2,11E-88 12914 466816 5444769 Predator ID 58,8 Sarcophilus harrisii 99,3 7,2E-62 12915 466951 5444837 Predator ID 98,9 Sarcophilus harrisii 98,9 1,29E-85 12982 462041 5436491 Predator ID 96,1 Felis catus 98,3 2,73E-82 12986 460630 5436368 Predator ID 95,4 Felis catus 99,3 1,44E-64 13122 425343 5425215 Predator ID 97,2 Sarcophilus harrisii 98,9 1,29E-85 13176 402642 5450236 Predator ID 89,4 Felis catus 99,4 2,79E-77 13200 445770 5439790 Predator ID 60,2 Canis lupus familiaris 97,9 8,87E-45 13220 412338 5431347 Predator ID 82,3 Sarcophilus harrisii 100 4,46E-85 13222 412149 5430743 Predator ID 98,9 Sarcophilus harrisii 98,9 1,29E-85 13224 412955 5430833 Predator ID 98,8 Sarcophilus harrisii 99,4 2,6E-82

172 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA 13225 412960 5430830 Predator ID 58,9 Sarcophilus harrisii 99,4 5,16E-74 13226 413120 5430815 Predator ID 97,8 Sarcophilus harrisii 98,9 1,29E-85 13227 412949 5430815 Predator ID 97,8 Sarcophilus harrisii 98,9 1,29E-85 13231 412963 5430472 Predator ID 98,8 Sarcophilus harrisii 99,4 6,97E-78 13235 465431 5419486 Predator ID 79,4 Sarcophilus harrisii 98,4 2,15E-83 13242 408194 5447031 Predator ID 92,9 Sarcophilus harrisii 99 5,06E-95 13243 407984 5446640 Predator ID 70,7 Sarcophilus harrisii 99 3,78E-91 13245 402551 5450192 Predator ID 89,9 Sarcophilus harrisii 99,5 1,1E-96 13247 402804 5450055 Predator ID 100 Felis catus 99,4 2,79E-77 13534 435357 5397944 Predator ID 95,6 Felis catus 98,6 5,95E-84 13554 460717 5436548 Predator ID 95,7 Felis catus 100 5,35E-79 13628 463516 5443832 Predator ID 96,2 Sarcophilus harrisii 99,5 2,75E-87 13636 466511 5393686 Predator ID 85,7 Dasyurus maculatus 99,2 4,21E-59 13659 469067 5417342 Predator ID 93,9 Felis catus 99,4 8,88E-77 13711 463496 5444694 Predator ID 53,6 Thylogale billardierii 100 8,26E-72 13762 432164 5418968 Predator ID 67,9 Sarcophilus harrisii 98,2 4,94E-90 13763 432165 5418968 Predator ID 97,1 Sarcophilus harrisii 99,4 7,29E-83 13764 432140 5419013 Predator ID 100 Sarcophilus harrisii 99,4 6,97E-78 13765 432101 5419033 Predator ID 95,6 Sarcophilus harrisii 98,4 6,01E-84 13784 489182 5408637 Predator ID 93,6 Sarcophilus harrisii 100 1,8E-57 13785 469191 5408650 Predator ID 88,1 Sarcophilus harrisii 98,9 5,22E-74 13805 456667 5391386 Predator ID 82,9 Trichosurus vulpecula 97,3 1,26E-59 13823 440521 5427042 Predator ID 83,1 Oryctolagus cuniculus 98,5 1,07E-91 13832 439141 5429259 Predator ID 100 Sarcophilus harrisii 100 5,91E-89 13851 441052 5437557 Predator ID 95,1 Sarcophilus harrisii 100 1,68E-89 14025 442363 5443682 Predator ID 93,3 Felis catus 99,4 2,75E-77 14113 452256 5400087 Predator ID 54,9 Oryctolagus cuniculus 99,5 6,92E-78 14234 532503 5302740 Predator ID 80,6 Sarcophilus harrisii 98,1 2,59E-77 14237 532290 5302810 Predator ID 80,7 Ovis aries 100 3,64E-54 14521 398562 5449724 Predator ID 88,9 Sarcophilus harrisii 98,4 2,14E-83 14522 398367 5449213 Predator ID 94,5 Sarcophilus harrisii 98,6 1,01E-65 14702 447708 5425813 Predator ID 85,4 Sarcophilus harrisii 98,8 3,22E-76 15008 466225 5444585 Predator ID 98,8 Sarcophilus harrisii 98,8 3,22E-76 15009 466065 5444407 Predator ID 100 Sarcophilus harrisii 98,8 5,57E-79 15010 465767 5444283 Predator ID 86,6 Sarcophilus harrisii 99,3 3,53E-65 15011 465536 5444261 Predator ID 96,7 Sarcophilus harrisii 98,9 1,29E-85 15019 466180 5393913 Predator ID 100 Dasyurus maculatus 98,2 1,91E-73 15024 470019 5406771 Predator ID 99,4 Felis catus 99,4 8,88E-77 15040 473834 5404755 Predator ID 95,1 Homo sapiens 99,4 8,88E-77 15358 429993 5419523 Predator ID 95 Sarcophilus harrisii 98,9 1,29E-85 15394 465860 5385785 Predator ID 100 Felis catus 99,4 8,88E-77 15395 465819 5385793 Predator ID 98,2 Dasyurus viverrinus 99,4 2,49E-77 15420 562941 5225890 Predator ID 70,2 Sarcophilus harrisii 97 2,32E-51 15421 569534 5228880 Predator ID 93,3 Homo sapiens 99,5 6,89E-78 15422 569534 5228890 Predator ID 100 Sarcophilus harrisii 98,9 1,28E-85 15426 569430 5229420 Predator ID 99,4 Sarcophilus harrisii 99,4 9,39E-82

173 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA 15435 575382 5236390 Predator ID 87,1 Canis lupus familiaris 98 3,52E-81 15436 575377 5236380 Predator ID 95,5 Canis lupus familiaris 99,4 1,27E-85 15437 575370 5236380 Predator ID 87 Canis lupus familiaris 99,5 8,78E-77 15438 575371 5236380 Predator ID 85,9 Canis lupus familiaris 99,2 1,85E-57 15439 567793 5242710 Predator ID 81,6 Canis lupus familiaris 98,2 2,74E-82 15471 522766 5345610 Predator ID 91,9 Corvus macrorhynchos 98,9 1,22E-80 16398 456733 5395421 Predator ID 97,8 Sarcophilus harrisii 98,9 1,64E-84 17266 600639 5450130 Predator ID 100 Dasyurus viverrinus 98,8 1,15E-75 17268 599842 5450140 Predator ID 93,3 Sarcophilus harrisii 98,8 3,23E-76 17269 599833 5450140 Predator ID 93,4 Sarcophilus harrisii 99,5 2,79E-87 17270 599836 5450110 Predator ID 98,3 Dasyurus viverrinus 98,9 4,63E-85 17271 599738 5450040 Predator ID 75,6 Sarcophilus harrisii 98,9 4,6E-85 17272 599734 5450040 Predator ID 55,6 Sarcophilus harrisii 97,6 1,27E-80 17275 600476 5450250 Predator ID 92,9 Sarcophilus harrisii 99,5 2,79E-87 17282 564804 5443910 Predator ID 97,8 Felis catus 98,8 1,29E-75 17283 601208 5470670 Predator ID 83,7 Sarcophilus harrisii 99,3 2,56E-61 17284 600952 5470670 Predator ID 96,1 Sarcophilus harrisii 98,9 1,3E-85 17285 600846 5470690 Predator ID 98,2 Sarcophilus harrisii 98,2 5,39E-74 17286 600847 5470700 Predator ID 59 Sarcophilus harrisii 97,9 3,74E-86 17289 600680 5470710 Predator ID 88,3 Sarcophilus harrisii 98,9 4,59E-85 17290 600446 5470690 Predator ID 98,9 Trichosurus vulpecula 99,4 3,55E-86 17291 600440 5470690 Predator ID 86,8 Sarcophilus harrisii 100 2,27E-72 17292 600067 5470800 Predator ID 98,3 Sarcophilus harrisii 99,4 1,27E-85 17293 600065 5470800 Predator ID 92,7 Sarcophilus harrisii 99,4 6,95E-78 17294 600071 5470800 Predator ID 98,3 Sarcophilus harrisii 98,9 1,28E-85 17296 601684 5470640 Predator ID 89 Dasyurus maculatus 99,4 2,48E-77 17297 601500 5470340 Predator ID 95,1 Sarcophilus harrisii 98,8 3,24E-76 17298 601490 5470330 Predator ID 88,6 Sarcophilus harrisii 100 4,18E-80 17299 601433 5470230 Predator ID 95,6 Sarcophilus harrisii 99,5 2,78E-87 17300 601525 5470380 Predator ID 97,6 Sarcophilus harrisii 99,4 6,95E-78 17501 557506 5326630 Predator ID 87,2 Felis catus 100 5,33E-79 17508 557518 5326850 Predator ID 95,5 Felis catus 98,9 5,89E-84 17822 600443 5413270 Predator ID 98,9 Dasyurus viverrinus 98,4 2,14E-83 17825 601099 5412590 Predator ID 91,7 Dasyurus viverrinus 98,9 4,6E-85 17837 568989 5367967 Predator ID 54,8 Sarcophilus harrisii 99,6 3,6E-96 17838 568988 5367969 Predator ID 75,7 Sarcophilus harrisii 100 1,97E-88 17841 568989 5367965 Predator ID 77,2 Sarcophilus harrisii 99,4 3,28E-86 17842 569004 5367980 Predator ID 100 Trichosurus vulpecula 99,4 1,39E-79 17843 569009 5368946 Predator ID 75,8 Felis catus 98,7 2,55E-72 17849 542921 5452480 Predator ID 96,1 Dasyurus maculatus 98,5 1,45E-95 17879 532308 5319470 Predator ID 95,2 Rattus rattus 100 1,49E-79 17885 521740 5331862 Predator ID 88,3 Sarcophilus harrisii 98,9 4,6E-85 17889 521911 5331800 Predator ID 67,5 Trichosurus vulpecula 97,7 1,42E-53 17893 507690 5325800 Predator ID 100 Sarcophilus harrisii 98,8 3,22E-76 17896 507448 5325941 Predator ID 87,6 Sarcophilus harrisii 98 3,51E-81 17898 507417 5326220 Predator ID 96,2 Sarcophilus harrisii 98,9 1,01E-86

174 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA 17899 507413 5326222 Predator ID 91,9 Sarcophilus harrisii 97,9 2,45E-72 17900 507416 5326226 Predator ID 98,9 Sarcophilus harrisii 98,9 1,29E-85 18103 530509 5223480 Predator ID 100 Dasyurus viverrinus 98,8 1,05E-75 18105 530409 5223190 Predator ID 59,4 Dasyurus viverrinus 100 1,5E-79 18106 530398 5223130 Predator ID 87,3 Dasyurus viverrinus 98,3 1,45E-79 18107 530399 5223080 Predator ID 71,5 Homo sapiens 99,4 2,28E-77 18108 530471 5222900 Predator ID 59 Dasyurus viverrinus 97,2 2,33E-56 18109 530472 5222910 Predator ID 87,4 Dasyurus viverrinus 99,2 9,61E-92 18111 530545 5223600 Predator ID 41,1 Dasyurus viverrinus 99,2 4,78E-58 18117 505408 5204360 Predator ID 72,1 Sarcophilus harrisii 100 1,34E-58 18118 505446 5204350 Predator ID 93,3 Sarcophilus harrisii 98,2 4,87E-74 18119 505449 5204350 Predator ID 88,3 Sarcophilus harrisii 99,4 1,17E-85 18120 505469 5204350 Predator ID 87,3 Dasyurus viverrinus 100 1,36E-79 Uncultured bacterium 18125 505242 5204240 Predator ID 58,9 100 2,48E-66 clone 18126 505170 5204200 Predator ID 92,2 Dasyurus viverrinus 100 4,71E-58 18127 505144 5204190 Predator ID 98,8 Dasyurus viverrinus 98,2 1,74E-73 18128 505092 5204290 Predator ID 86,7 Dasyurus viverrinus 100 9,35E-55 18129 505083 5210710 Predator ID 60,8 Dasyurus viverrinus 100 5,87E-73 18130 505123 5210730 Predator ID 72,5 Dasyurus viverrinus 100 8,91E-66 18131 505171 5210750 Predator ID 88,4 Thylogale billardierii 100 2,63E-65 18135 505047 5210850 Predator ID 96,2 Dasyurus viverrinus 100 5,52E-89 18136 505047 5210850 Predator ID 94 Dasyurus viverrinus 100 6,09E-99 18137 504996 5210910 Predator ID 98,8 Dasyurus viverrinus 98,8 1,05E-75 18138 504900 5210890 Predator ID 69,4 Dasyurus viverrinus 97,8 6,48E-57 18139 504899 5210890 Predator ID 98,8 Dasyurus viverrinus 100 1,36E-79 18141 504833 5211050 Predator ID 98,4 Dasyurus viverrinus 100 5,48E-89 18144 504833 5211050 Predator ID 93,8 Thylogale billardierii 99,1 3,12E-49 18146 504835 5211060 Predator ID 80,1 Dasyurus viverrinus 98,1 1,67E-68 18147 492839 5225910 Predator ID 63,8 Dasyurus viverrinus 99,2 2,46E-39 18149 492739 5226000 Predator ID 68,3 Dasyurus viverrinus 98,9 5,24E-63 18652 512704 5222070 Predator ID 76,4 Dasyurus viverrinus 99,5 1,78E-78 18659 510506 5258860 Predator ID 80,9 Dasyurus viverrinus 98,9 5,92E-84 18660 510324 5258660 Predator ID 61,9 Trichosurus vulpecula 98 9,58E-61 18666 510358 5258670 Predator ID 98,9 Dasyurus viverrinus 99,5 9,96E-87 18667 510342 5258660 Predator ID 100 Dasyurus viverrinus 99,4 2,49E-77 18671 506443 5232100 Predator ID 97,7 Sarcophilus harrisii 100 7,04E-83 18673 506339 5232080 Predator ID 98,4 Sarcophilus harrisii 98,9 3,61E-86 18674 506333 5232070 Predator ID 100 Sarcophilus harrisii 99,4 3,46E-86 18675 506336 5232080 Predator ID 88,7 Sarcophilus harrisii 98,6 1,62E-63 18676 506335 5232080 Predator ID 75 Sarcophilus harrisii 98,5 3,01E-92 18678 506302 5232090 Predator ID 100 Sarcophilus harrisii 99,4 6,78E-78 18679 506297 5232080 Predator ID 69,7 Sarcophilus harrisii 98,1 6,38E-68 18680 506303 5232080 Predator ID 95,8 Sarcophilus harrisii 100 4,2E-80 18681 506303 5232080 Predator ID 85 Dasyurus viverrinus 98,9 4,6E-85 18682 506297 5232070 Predator ID 74,7 Sarcophilus harrisii 99,4 1,21E-80 18683 506278 5232080 Predator ID 88,4 Sarcophilus harrisii 98,9 1,29E-85

175 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA 18685 506268 5232080 Predator ID 97,8 Sarcophilus harrisii 99,3 2,57E-61 18686 506263 5232080 Predator ID 97,3 Sarcophilus harrisii 98,9 1,01E-86 18689 506489 5232120 Predator ID 80,6 Sarcophilus harrisii 98,9 2,21E-88 18690 506488 5232120 Predator ID 100 Dasyurus viverrinus 98,9 4,64E-85 18691 506490 5232120 Predator ID 97,3 Sarcophilus harrisii 98,9 1,01E-86 18696 524131 5208790 Predator ID 88,3 Dasyurus viverrinus 98,8 1,05E-75 18697 524127 5208810 Predator ID 91,8 Dasyurus viverrinus 100 5,46E-89 18699 524162 5209400 Predator ID 99,4 Dasyurus viverrinus 99,4 2,27E-77 18709 513622 5262260 Predator ID 88,8 Canis lupus familiaris 98,2 2,68E-82 18714 510499 5258590 Predator ID 90,5 Sarcophilus harrisii 98,9 6,65E-73 18720 506951 5232390 Predator ID 95,6 Sarcophilus harrisii 99,5 2,79E-87 18721 506959 5232380 Predator ID 98,3 Sarcophilus harrisii 100 1,55E-84 18722 506963 5232370 Predator ID 96,1 Sarcophilus harrisii 98,9 1,29E-85 18723 506959 5232370 Predator ID 95,4 Sarcophilus harrisii 100 3,74E-96 18724 506932 5232350 Predator ID 86,9 Sarcophilus harrisii 98,6 1,01E-65 18725 506938 5232360 Predator ID 75,1 Sarcophilus harrisii 98,9 1,29E-85 18726 506928 5232360 Predator ID 92,9 Sarcophilus harrisii 98,8 5,55E-79 18727 506899 5232350 Predator ID 95,3 Sarcophilus harrisii 98,8 5,55E-79 18732 506835 5232320 Predator ID 97,7 Sarcophilus harrisii 100 7,04E-83 18733 506837 5232320 Predator ID 94,8 Sarcophilus harrisii 99,4 7,19E-83 18734 506834 5232310 Predator ID 92,5 Sarcophilus harrisii 99,4 2,57E-82 18735 506835 5232320 Predator ID 100 Sarcophilus harrisii 100 4,08E-80 18736 506798 5232270 Predator ID 99,5 Sarcophilus harrisii 99,5 1,02E-91 18737 506771 5232270 Predator ID 98,9 Sarcophilus harrisii 99,4 1,24E-85 18739 506665 5232120 Predator ID 94,3 Sarcophilus harrisii 100 8,83E-66 18740 506741 5232170 Predator ID 98,4 Sarcophilus harrisii 100 5,74E-94 18741 506740 5232170 Predator ID 97 Sarcophilus harrisii 99,4 5,32E-79 18742 506743 5232180 Predator ID 96 Sarcophilus harrisii 100 4,33E-85 18743 528058 5196030 Predator ID 86 Felis catus 98,6 4,18E-64 18745 524084 5209100 Predator ID 50,5 Macropus rufogriseus 99,5 2,54E-87 18746 524101 5209220 Predator ID 60,1 Homo sapiens 98 3,38E-65 18748 523769 5209540 Predator ID 87,7 Dasyurus viverrinus 98,1 1,28E-69 18905 492059 5455860 Predator ID 71,3 Sarcophilus harrisii 96,9 6,8E-68 18906 492075 5455800 Predator ID 87,9 Sarcophilus harrisii 99,5 2,79E-87 18907 492074 5455790 Predator ID 96,2 Sarcophilus harrisii 99,5 2,79E-87 18908 492042 5455820 Predator ID 87,8 Sarcophilus harrisii 98,9 1,29E-85 18909 492060 5455890 Predator ID 97,6 Sarcophilus harrisii 98,8 3,23E-76 18910 492062 5455900 Predator ID 68,9 Sarcophilus harrisii 94,7 1,17E-65 18911 492065 5455890 Predator ID 88,5 Sarcophilus harrisii 99,4 6,97E-78 18912 492061 5455900 Predator ID 90,9 Sarcophilus harrisii 94,7 4,27E-65 18913 492077 5455970 Predator ID 63,4 Canis lupus familiaris 98,5 1,9E-73 18914 511391 5451390 Predator ID 98,2 Dasyurus maculatus 98,8 1,15E-75 18916 521260 5455120 Predator ID 89,4 Sarcophilus harrisii 98,9 1,02E-86 18918 521495 5454980 Predator ID 100 Felis catus 99,4 8,91E-77 18920 519938 5455630 Predator ID 95,7 Sarcophilus harrisii 98,8 3,22E-76 18922 519929 5455620 Predator ID 96,7 Sarcophilus harrisii 99,5 2,79E-87

176 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA 18925 519799 5454950 Predator ID 85,6 Homo sapiens 98,7 1,5E-58 18926 519802 5454940 Predator ID 64,4 Antechinus minimus 98,9 4,62E-85 18927 519759 5454610 Predator ID 97 Sarcophilus harrisii 99,4 6,99E-78 18928 519904 5454480 Predator ID 86,3 Sarcophilus harrisii 98,3 2,8E-50 18929 520733 5453711 Predator ID 94,4 Sarcophilus harrisii 98,9 1,64E-84 18930 519939 5455502 Predator ID 100 Sarcophilus harrisii 98,8 3,23E-76 18932 585592 5467480 Predator ID 97,7 Felis catus 98,8 5,72E-79 18933 584477 5467520 Predator ID 85 Felis catus 98,6 5,83E-63 18934 583801 5468200 Predator ID 97 Thylogale billardierii 98,8 2.00E-78 18935 583339 5468510 Predator ID 96,3 Antechinus minimus 98,2 1,51E-74 18937 582775 5468830 Predator ID 88,2 Sarcophilus harrisii 98,5 7,62E-83 18939 583842 5468150 Predator ID 93,2 Sarcophilus harrisii 98,9 2,1E-83 18956 511615 5450520 Predator ID 93,3 Felis catus 99,4 8,91E-77 18959 589025 5467100 Predator ID 72,8 Sarcophilus harrisii 98,6 9,35E-61 18960 589029 5467100 Predator ID 74,7 Sarcophilus harrisii 98,1 9,86E-82 18963 598138 5477860 Predator ID 97,5 Dasyurus viverrinus 98,2 1,92E-73 18965 598902 5477980 Predator ID 87,2 Felis catus 98,8 1,3E-75 18966 602065 5474870 Predator ID 97,8 Sarcophilus harrisii 99,4 3,55E-86 18968 602054 5474570 Predator ID 60,1 Felis catus 100 1,9E-78 18971 602198 5474390 Predator ID 90,7 Sarcophilus harrisii 100 1,67E-89 18974 562424 5458520 Predator ID 79,8 Dasyurus maculatus 98,8 1,16E-75 18975 562338 5458510 Predator ID 54,3 Dasyurus viverrinus 96,4 1,93E-68 18976 562339 5458510 Predator ID 96,6 Dasyurus maculatus 98,4 7,68E-83 19036 532489 5460430 Predator ID 93,3 Homo sapiens 100 5,37E-79 19037 532493 5460430 Predator ID 76 Canis lupus familiaris 99,3 1,09E-70 19038 532487 5460420 Predator ID 96 Canis lupus familiaris 98,3 4,48E-80 19041 532482 5460430 Predator ID 71,7 Canis lupus familiaris 98,4 8,64E-56 19042 532476 5460430 Predator ID 95,3 Canis lupus familiaris 98,8 4,4E-80 19043 532375 5460330 Predator ID 82,2 Felis catus 99,2 5,27E-58 19044 532362 5460290 Predator ID 99,4 Canis lupus familiaris 98,9 5,96E-84 19117 589513 5483460 Predator ID 92,4 Sarcophilus harrisii 98,8 6,7E-73 19128 576686 5476510 Predator ID 80,4 Felis catus 98,9 1,15E-75 19132 576505 5476470 Predator ID 96,8 Niveoscincus pretiosus 88,9 5,21E-33 19135 579730 5475200 Predator ID 76,8 Sarcophilus harrisii 98,1 7,73E-83 19145 579694 5474980 Predator ID 97,2 Sarcophilus harrisii 98,9 1,65E-84 19150 580609 5475140 Predator ID 50 Sarcophilus harrisii 94,4 3,15E-61 19151 524432 5449540 Predator ID 88,9 Canis lupus familiaris 100 6,87E-78 19162 569358 5230720 Predator ID 94 Sarcophilus harrisii 99,5 2,78E-87 19163 569300 5230720 Predator ID 98,9 Felis catus 98,9 5,89E-84 19170 577576 5250500 Predator ID 71,5 Felis catus 100 2,22E-56 19173 572254 5248930 Predator ID 92,8 Felis catus 99,4 2,72E-77 19176 570521 5248800 Predator ID 84,4 Gallus gallus 97,2 1,94E-57 19180 552564 5252470 Predator ID 89,8 Canis lupus familiaris 97,7 1,27E-80 19181 552560 5252470 Predator ID 61,9 Canis lupus familiaris 97,6 5,85E-79 19182 552564 5252470 Predator ID 94,8 Canis lupus familiaris 98,9 1,23E-80 19187 553210 5256850 Predator ID 80,4 Felis catus 99,4 8,95E-77

177 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA 19189 553170 5257290 Predator ID 92,5 Sarcophilus harrisii 98,9 6,21E-89 19190 553174 5257290 Predator ID 76,4 Sarcophilus harrisii 98,9 5,89E-84 19191 553173 5257290 Predator ID 79,1 Sarcophilus harrisii 98,8 1,15E-75 19194 553313 5257730 Predator ID 79,6 Felis catus 99,5 2,18E-78 19199 604077 5369960 Predator ID 99,4 Canis lupus familiaris 99,4 1,24E-85 19201 601530 5470380 Predator ID 95,5 Sarcophilus harrisii 100 2,72E-87 19202 601521 5470380 Predator ID 65,6 Sarcophilus harrisii 100 1,67E-89 19204 601760 5470630 Predator ID 91,2 Sarcophilus harrisii 98,9 1,3E-85 19251 555376 5239850 Predator ID 99,4 Canis lupus familiaris 98,9 2,1E-83 19254 555305 5239740 Predator ID 78,4 Canis lupus familiaris 92,3 4,4E-49 19259 558302 5230730 Predator ID 73,2 Felis catus 100 2,27E-56 19263 558066 5230360 Predator ID 91,7 Canis lupus familiaris 100 2,42E-61 19297 598723 5363690 Predator ID 93,9 Sarcophilus harrisii 98,9 1,65E-84 19298 598683 5363700 Predator ID 90,9 Sarcophilus harrisii 100 2,81E-55 19299 598657 5363800 Predator ID 50 Sarcophilus harrisii 97,3 5,19E-69 19300 598640 5363830 Predator ID 86,1 Sarcophilus harrisii 98,9 6,21E-89 19302 488622 5279310 Predator ID 71,7 Thylogale billardierii 100 1,02E-54 19303 488621 5279310 Predator ID 78,2 Ovis aries 100 7,05E-62 19307 488610 5279420 Predator ID 92,1 Homo sapiens 100 5,33E-79 19308 488552 5279520 Predator ID 96,6 Felis catus 98,8 1,3E-75 19310 488554 5279520 Predator ID 85,9 Felis catus 99,4 8,95E-77 19311 488548 5279530 Predator ID 89,4 Felis catus 98,8 1,3E-75 19409 568640 5256370 Predator ID 100 Felis catus 98,8 1,47E-74 19411 568627 5256360 Predator ID 100 Felis catus 99,4 8,91E-77 19412 568628 5256360 Predator ID 98,1 Felis catus 98,8 4,1E-75 19422 569633 5256810 Predator ID 96 Bos taurus 98,9 7,54E-83 19423 569649 5256780 Predator ID 100 Felis catus 98,8 4,11E-75 19424 569678 5256470 Predator ID 81,4 Felis catus 97,7 8,81E-72 19432 570150 5259630 Predator ID 100 Trichosurus vulpecula 98,2 6,81E-73 19436 570305 5259180 Predator ID 90,9 Felis catus 100 5,35E-79 19452 555493 5450790 Predator ID 100 Sarcophilus harrisii 99,4 6,99E-78 19454 555512 5450390 Predator ID 95 Dasyurus maculatus 98,4 7,68E-83 19457 555608 5450300 Predator ID 98,8 Dasyurus maculatus 98,2 1,91E-73 19461 555834 5450010 Predator ID 100 Sarcophilus harrisii 98,8 3,24E-76 19462 555837 5450020 Predator ID 98,9 Sarcophilus harrisii 98,9 3,63E-86 19463 555838 5450020 Predator ID 89 Sarcophilus harrisii 98,8 3,24E-76 19464 555840 5450020 Predator ID 94,5 Sarcophilus harrisii 98,9 1,3E-85 19465 555846 5450030 Predator ID 93,9 Sarcophilus harrisii 98,8 3,24E-76 19469 555978 5449840 Predator ID 98,8 Sarcophilus harrisii 98,9 1,22E-80 19470 555976 5449830 Predator ID 84,2 Sarcophilus harrisii 98,9 9,05E-77 19471 555965 5449820 Predator ID 94,5 Sarcophilus harrisii 99,4 6,99E-78 19477 556321 5449970 Predator ID 96,7 Sarcophilus harrisii 98,9 1,3E-85 19478 556321 5449960 Predator ID 96,2 Sarcophilus harrisii 98,9 3,63E-86 19479 556321 5449960 Predator ID 95 Sarcophilus harrisii 98,9 1,3E-85 19488 549897 5476130 Predator ID 94,4 Canis lupus familiaris 99,4 1,14E-75 19489 549891 5476120 Predator ID 94,7 Canis lupus familiaris 98,3 2,04E-78

178 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA 19523 512023 5408190 Predator ID 91,4 Sarcophilus harrisii 100 4,48E-85 19524 512030 5408180 Predator ID 85,5 Canis lupus familiaris 99,4 1,28E-85 19525 512033 5408170 Predator ID 83,6 Canis lupus familiaris 99,4 1,63E-84 19526 512030 5408170 Predator ID 84,1 Canis lupus familiaris 98,4 3,07E-55 19536 505357 5233510 Predator ID 83,3 Oryctolagus cuniculus 99,3 1,31E-69 19537 505361 5233500 Predator ID 89 Sarcophilus harrisii 99,3 1,05E-70 19539 505431 5233430 Predator ID 100 Sarcophilus harrisii 99,4 5,64E-84 19540 505405 5233440 Predator ID 97,3 Sarcophilus harrisii 99,5 2,71E-87 19541 505402 5233430 Predator ID 97 Sarcophilus harrisii 100 1,91E-62 19542 505440 5233430 Predator ID 100 Sarcophilus harrisii 99,4 8,65E-77 19544 540804 5236632 Predator ID 92,8 Canis lupus familiaris 100 1,76E-57 19546 540886 5236655 Predator ID 49,1 Canis lupus familiaris 99,1 7,43E-51 19550 507738 5265310 Predator ID 98,3 Sarcophilus harrisii 100 2,81E-55 19561 549303 5470890 Predator ID 100 Sarcophilus harrisii 98,8 3,23E-76 19585 495736 5282820 Predator ID 100 Sarcophilus harrisii 99,4 1,19E-80 19598 495831 5277300 Predator ID 78,3 Trichosurus vulpecula 98,7 7,77E-72 19604 526080 5200980 Predator ID 87,8 Canis lupus familiaris 100 3,35E-70 19606 525249 5205350 Predator ID 62,4 Dasyurus viverrinus 98,7 5,11E-79 19608 525218 5205430 Predator ID 95,6 Dasyurus viverrinus 100 5,45E-89 19609 525209 5205440 Predator ID 77,9 Dasyurus viverrinus 98,8 1,05E-75 19610 525536 5205230 Predator ID 99,4 Dasyurus viverrinus 98,2 6,92E-73 19613 530076 5225440 Predator ID 74,8 Dasyurus viverrinus 98,8 1,01E-75 19614 530053 5225570 Predator ID 83,6 Dasyurus viverrinus 100 1,36E-79 19615 530074 5225620 Predator ID 93,7 Dasyurus viverrinus 98,6 4,14E-64 19621 530418 5225950 Predator ID 87,8 Dasyurus viverrinus 99,4 2,26E-77 19622 530741 5225468 Predator ID 92,7 Dasyurus viverrinus 100 5,9E-57 19624 530712 5225360 Predator ID 36,5 Dasyurus viverrinus 100 1,71E-29 19635 495101 5193340 Predator ID 81,6 Sarcophilus harrisii 100 2,46E-66 19636 495042 5193350 Predator ID 83,6 Sarcophilus harrisii 99,3 1,47E-63 19637 495039 5193350 Predator ID 91,7 Sarcophilus harrisii 100 2,64E-55 19638 495042 5193350 Predator ID 63,6 Dasycercus cristicauda 94 6,43E-63 19640 495041 5193350 Predator ID 96,5 Sarcophilus harrisii 98,9 1,11E-80 19641 495042 5193350 Predator ID 95,6 Sarcophilus harrisii 100 6,99E-88 19642 495042 5193350 Predator ID 76,1 Sarcophilus harrisii 100 6,72E-51 19643 495042 5193350 Predator ID 97,3 Sarcophilus harrisii 100 1,53E-89 19644 495047 5193350 Predator ID 92,8 Sarcophilus harrisii 100 7.00E-88 19645 494997 5193360 Predator ID 97,3 Sarcophilus harrisii 100 1,53E-89 19647 497471 5201720 Predator ID 83,1 Dasyurus viverrinus 99,6 4,15E-85 19650 497355 5201690 Predator ID 82,6 Sarcophilus harrisii 99,2 7,18E-88 19651 598630 5363830 Predator ID 76,5 Sarcophilus harrisii 100 4,14E-80 19652 598637 5363840 Predator ID 98,2 Sarcophilus harrisii 100 4,14E-80 19654 598630 5363840 Predator ID 92,4 Sarcophilus harrisii 99,4 1,17E-80 19655 598563 5363900 Predator ID 95,8 Sarcophilus harrisii 100 4,09E-80 19656 598556 5363900 Predator ID 97,8 Sarcophilus harrisii 98,9 1,65E-84 19663 607214 5385840 Predator ID 86 Sarcophilus harrisii 100 7,04E-83 19667 607248 5377640 Predator ID 78,3 Sarcophilus harrisii 99,4 1,1E-75

179 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA 19669 607240 5377640 Predator ID 85,5 Sarcophilus harrisii 99,4 1,9E-78 19676 603547 5350170 Predator ID 96,3 Felis catus 98,8 4,13E-75 19678 604009 5350110 Predator ID 99,4 Felis catus 99,4 8,95E-77 19679 604013 5350110 Predator ID 86,8 Felis catus 99,3 8,38E-62 19680 604221 5350210 Predator ID 87,4 Dasyurus maculatus 98,9 3,63E-86 19681 604220 5350220 Predator ID 100 Trichosurus vulpecula 98,2 6,86E-73 19683 604220 5350210 Predator ID 87,4 Sarcophilus harrisii 99 3,63E-86 19684 604218 5350210 Predator ID 67,6 Sarcophilus harrisii 95,5 4,77E-75 19685 604304 5350620 Predator ID 89,5 Oryctolagus cuniculus 100 7,05E-62 19686 604315 5350670 Predator ID 77 Felis catus 100 1,84E-57 20076 495940 5436880 Predator ID 82,7 Felis catus 99,4 2,79E-77 20098 505441 5217800 Predator ID 82,2 Trichosurus vulpecula 99,4 7,79E-77 20121 519305 5235330 Predator ID 98,9 Sarcophilus harrisii 99,4 7,19E-83 20123 519311 5235330 Predator ID 98,3 Sarcophilus harrisii 99,4 2,57E-82 20127 519463 5235530 Predator ID 96 Sarcophilus harrisii 99,4 7,19E-83 20128 519422 5234860 Predator ID 100 Sarcophilus harrisii 99,4 1,1E-75 20130 520101 5191420 Predator ID 84,3 Canis lupus familiaris 99,6 1,11E-80 20132 525134 5202441 Predator ID 87,1 Trichosurus vulpecula 99,4 8,18E-77 20135 526059 5201140 Predator ID 95,5 Canis lupus familiaris 100 2,26E-61 20141 530389 5225010 Predator ID 96,1 Homo sapiens 99,2 1,69E-57 20142 530410 5225020 Predator ID 98,3 Dasyurus viverrinus 100 2,49E-87 20143 530424 5225030 Predator ID 98,7 Ovis aries 96,8 1,3E-64 20144 530458 5225060 Predator ID 88,3 Dasyurus viverrinus 98,8 1,06E-75 20146 530459 5225060 Predator ID 50 Dasyurus viverrinus 98,1 2,19E-72 20149 530554 5225015 Predator ID 91,7 Thylogale billardierii 99,4 3,27E-86 20150 530590 5224940 Predator ID 67 Dasyurus viverrinus 100 5,48E-89 20156 508739 5265170 Predator ID 89,5 Dasyurus viverrinus 100 6,25E-57 20170 513961 5266725 Predator ID 99,4 Felis catus 98,8 1,33E-74 20172 513383 5234230 Predator ID 95 Sarcophilus harrisii 100 2,5E-87 20174 513284 5234380 Predator ID 96 Sarcophilus harrisii 99,4 7,19E-83 20175 513188 5234330 Predator ID 94,9 Sarcophilus harrisii 99,4 6,37E-73 20181 513432 5234840 Predator ID 89,9 Canis lupus familiaris 99,2 1,65E-52 20184 515203 5237050 Predator ID 96,2 Sarcophilus harrisii 99,5 2,71E-87 20193 515080 5237310 Predator ID 74 Sarcophilus harrisii 100 2,22E-56 20197 527392 5282930 Predator ID 98,8 Oryctolagus cuniculus 100 7,14E-83 20200 527334 5282600 Predator ID 81,6 Sarcophilus harrisii 100 1,34E-42 20201 524171 5209440 Predator ID 92,8 Dasyurus viverrinus 98,6 6,67E-62 20203 524173 5209460 Predator ID 71,3 Dasyurus viverrinus 100 3,59E-75 20206 524177 5209470 Predator ID 37 Dasyurus viverrinus 97,9 8,22E-45 20207 527243 5220389 Predator ID 61,6 Homo sapiens 99,4 1,49E-84 20211 526862 5220370 Predator ID 78,2 Dasyurus viverrinus 100 1,48E-79 20212 526779 5220380 Predator ID 87,1 Dasyurus viverrinus 100 5,86E-57 20214 526774 5220370 Predator ID 96,2 Dasyurus viverrinus 100 5,47E-89 20217 526613 5220263 Predator ID 95,1 Dasyurus viverrinus 100 5,51E-89 20219 526618 5220240 Predator ID 97 Dasyurus viverrinus 100 1,37E-79 20221 526623 5220240 Predator ID 86,7 Dasyurus viverrinus 98,5 2,19E-56

180 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA 20224 526575 5220233 Predator ID 100 Dasyurus viverrinus 100 1,38E-79 20225 526579 5220230 Predator ID 97,2 Dasyurus viverrinus 100 1,15E-85 20228 526575 5220230 Predator ID 97,1 Dasyurus viverrinus 100 1,45E-84 20229 526687 5220452 Predator ID 77,6 Dasyurus viverrinus 100 1,57E-84 20230 526767 5220500 Predator ID 88 Dasyurus viverrinus 100 6,91E-67 20234 526774 5220500 Predator ID 89,9 Dasyurus viverrinus 99,4 1,31E-74 20236 528494 5221590 Predator ID 92,2 Dasyurus viverrinus 100 1,07E-80 20237 528529 5221710 Predator ID 95 Dasyurus viverrinus 100 2,5E-87 20238 528528 5221708 Predator ID 85,7 Dasyurus viverrinus 100 5,51E-89 20243 495421 5193380 Predator ID 89,4 Sarcophilus harrisii 100 2,36E-82 20244 495617 5193620 Predator ID 74,9 Sarcophilus harrisii 100 4,05E-85 20245 495616 5193620 Predator ID 74,4 Sarcophilus harrisii 99,6 2,39E-82 20246 495477 5193680 Predator ID 75,6 Felis catus 100 4,89E-79 20247 495367 5193370 Predator ID 92,6 Sarcophilus harrisii 100 4,06E-85 20248 495367 5193370 Predator ID 95,6 Sarcophilus harrisii 100 1,53E-89 20249 495369 5193370 Predator ID 92,8 Sarcophilus harrisii 100 3,82E-80 20250 496091 5200710 Predator ID 98,2 Sarcophilus harrisii 100 3,83E-80 20280 503222 5298750 Predator ID 90,7 Sarcophilus harrisii 100 1,68E-89 20287 488646 5279280 Predator ID 98,9 Felis catus 98,3 2,75E-82 20292 488648 5279270 Predator ID 81,2 Felis catus 98,6 7,4E-62 20294 488644 5279270 Predator ID 92,7 Felis catus 98,6 2,64E-61 20298 488529 5279580 Predator ID 84 Felis catus 98,8 4,13E-75 20346 576859 5291190 Predator ID 96,7 Sarcophilus harrisii 98,9 1,29E-85 20350 576821 5291270 Predator ID 95,8 Sarcophilus harrisii 99 1,35E-90 20356 536512 5305200 Predator ID 96,7 Sarcophilus harrisii 98,9 1,29E-85 20365 554363 5313670 Predator ID 50 Sarcophilus harrisii 98,6 1,28E-64 20369 554330 5313440 Predator ID 83,1 Sarcophilus harrisii 98,9 5,85E-84 20370 554330 5313440 Predator ID 96,7 Sarcophilus harrisii 99,5 2,78E-87 20372 554314 5313110 Predator ID 67,5 Sarcophilus harrisii 99,1 1,8E-94 20378 553637 5312110 Predator ID 95,8 Sarcophilus harrisii 99,4 6,95E-78 20382 494899 5283260 Predator ID 55,2 Sarcophilus harrisii 99,4 7,29E-83 20386 501125 5315790 Predator ID 93,2 Dasyurus maculatus 98,2 1,88E-73 20390 508166 5312820 Predator ID 89,9 Sarcophilus harrisii 98,8 1,87E-73 20425 599076 5477510 Predator ID 100 Sarcophilus harrisii 100 4,18E-80 20426 601473 5475640 Predator ID 94,7 Sarcophilus harrisii 99,4 1,2E-80 20432 561669 5459480 Predator ID 52,6 Sarcophilus harrisii 98,2 3,88E-91 20442 600487 5450240 Predator ID 97,5 Thylogale billardierii 99,4 3,19E-76 20443 600489 5450240 Predator ID 92,2 Thylogale billardierii 98,9 5,96E-84 20445 599813 5450090 Predator ID 93,9 Sarcophilus harrisii 98,9 1,66E-84 20446 599730 5450030 Predator ID 82,3 Sarcophilus harrisii 98,8 3,24E-76 20447 599730 5450040 Predator ID 97,2 Sarcophilus harrisii 98,9 1,3E-85 20450 600318 5450440 Predator ID 96,8 Dasyurus viverrinus 98,1 1,43E-69 20464 564236 5410320 Predator ID 98,9 Dasyurus viverrinus 100 5,52E-89 20466 564361 5410190 Predator ID 100 Sarcophilus harrisii 100 6,61E-83 20467 564460 5410150 Predator ID 98,6 Dasyurus viverrinus 100 1,03E-101 20468 564566 5410090 Predator ID 78,6 Sarcophilus harrisii 100 1,64E-73

181 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA 20469 564563 5410090 Predator ID 97,6 Dasyurus viverrinus 98,8 8,32E-77 20470 564807 5410030 Predator ID 67,4 Niveoscincus pretiosus 97,7 1,04E-54 20472 564822 5410040 Predator ID 52,4 Homo sapiens 100 1,66E-57 20474 564822 5410040 Predator ID 100 Sarcophilus harrisii 98,7 1,59E-68 20475 564821 5410050 Predator ID 54 Homo sapiens 97,4 8,07E-72 20478 564854 5410090 Predator ID 100 Sarcophilus harrisii 99,4 6,33E-78 20479 564426 5410310 Predator ID 82,4 Dasyurus viverrinus 98,7 1,26E-69 20481 565007 5410210 Predator ID 62,4 Sarcophilus harrisii 100 9,5E-71 20482 565192 5410150 Predator ID 100 Sarcophilus harrisii 98,8 2,92E-76 20483 565210 5410160 Predator ID 91,2 Sarcophilus harrisii 99,3 5,56E-68 20484 600228 5424040 Predator ID 83,3 Homo sapiens 99,3 2,53E-66 20492 598873 5402230 Predator ID 92,2 Sarcophilus harrisii 98,9 1,66E-84 20493 598825 5403220 Predator ID 95 Sarcophilus harrisii 98,9 1,3E-85 20494 598733 5402510 Predator ID 91 Bos taurus 97,8 4,58E-80 20497 598775 5402270 Predator ID 92,7 Sarcophilus harrisii 98,8 3,22E-76 20498 600295 5413300 Predator ID 100 Dasyurus viverrinus 98,2 1,92E-73 20499 600829 5412206 Predator ID 76,4 Dasyurus viverrinus 98,1 1,11E-70 20500 600160 5413410 Predator ID 75,9 Sarcophilus harrisii 99,1 7,69E-51 20531 531584 5320358 Predator ID 92,7 Felis catus 98,9 5,91E-84 20534 521376 5331802 Predator ID 93,5 Sarcophilus harrisii 98,8 5,55E-79 20536 521377 5331803 Predator ID 95,6 Sarcophilus harrisii 98,9 1,29E-85 20537 521208 5331710 Predator ID 74,6 Sarcophilus harrisii 98,9 1,29E-85 20540 521191 5331700 Predator ID 96,2 Sarcophilus harrisii 98,9 1,01E-86 20551 580485 5399490 Predator ID 91,8 Sarcophilus harrisii 100 1,2E-69 20554 580476 5399480 Predator ID 98 Sarcophilus harrisii 99,4 2,7E-71 20555 580473 5399480 Predator ID 86,2 Sarcophilus harrisii 100 1,95E-88 20556 580474 5399480 Predator ID 86,1 Sarcophilus harrisii 100 1,69E-99 20560 580331 5399416 Predator ID 93,9 Felis catus 99,4 2,57E-77 20561 580295 5399400 Predator ID 85,5 Sarcophilus harrisii 99,1 1,4E-47 20562 580604 5399220 Predator ID 100 Sarcophilus harrisii 100 3,81E-80 20564 580631 5399470 Predator ID 100 Sarcophilus harrisii 100 3,81E-80 20567 583225 5405100 Predator ID 99,3 Sarcophilus harrisii 99,3 2,33E-61 20572 583860 5404570 Predator ID 84,6 Felis catus 98,3 5,35E-74 20573 584445 5405160 Predator ID 68,8 Sarcophilus harrisii 100 2,36E-82 20574 573348 5387110 Predator ID 80,7 Felis catus 98,8 1,48E-74 20576 573352 5387120 Predator ID 97,8 Sarcophilus harrisii 100 1,96E-88 20577 573353 5387110 Predator ID 98,9 Sarcophilus harrisii 100 1,53E-89 20579 573571 5386940 Predator ID 96,7 Sarcophilus harrisii 100 1,96E-88 20581 573579 5386940 Predator ID 98,2 Sarcophilus harrisii 100 3,83E-80 20590 580588 5384792 Predator ID 97,8 Sarcophilus harrisii 99,4 4,42E-85 20595 600249 5431140 Predator ID 100 Dasyurus viverrinus 98,2 9,29E-77 20596 600141 5431250 Predator ID 88,8 Sarcophilus harrisii 98,9 1,66E-84 20597 599960 5431430 Predator ID 98,9 Dasyurus viverrinus 98,4 2,16E-83 20600 600265 5431140 Predator ID 73 Dasyurus viverrinus 100 5,11E-58 20630 538648 5446440 Predator ID 85,6 Felis catus 98 6,09E-74 20634 538642 5447020 Predator ID 93,4 Sarcophilus harrisii 98,9 1,29E-85

182 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA 20651 507417 5326227 Predator ID 97,8 Sarcophilus harrisii 99,5 2,8E-87 20656 523263 5336654 Predator ID 100 Sarcophilus harrisii 98,8 3,23E-76 20657 523265 5336650 Predator ID 97,7 Sarcophilus harrisii 98,9 1,22E-80 20658 523257 5336640 Predator ID 93,6 Dasyurus maculatus 98,3 2,03E-78 20660 523242 5336792 Predator ID 98,3 Sarcophilus harrisii 98,9 1,29E-85 20661 523211 5336846 Predator ID 99,4 Sarcophilus harrisii 98,8 1,29E-85 20663 523195 5336886 Predator ID 67,4 Sarcophilus harrisii 98,9 5,87E-84 20701 535813 5397840 Predator ID 93,5 Sarcophilus harrisii 98,8 5,59E-79 20714 542174 5368880 Predator ID 98,9 Felis catus 99,4 2,77E-77 20714 542174 5368875 Predator ID 98,9 Felis catus 99,4 2,77E-77 20718 548028 5384550 Predator ID 88,6 Trichosurus vulpecula 99,1 9,38E-50 21064 506562 5228030 Predator ID 95,8 Sarcophilus harrisii 99,4 7,03E-78 21065 505582 5226440 Predator ID 96,7 Dasyurus viverrinus 100 1,96E-88 21066 505598 5226430 Predator ID 93,5 Dasyurus viverrinus 100 6,05E-99 21068 505588 5226430 Predator ID 89,1 Dasyurus viverrinus 100 2,72E-44 21071 505588 5226470 Predator ID 87,6 Sarcophilus harrisii 100 1,2E-90 21073 505644 5226420 Predator ID 94,9 Dasyurus viverrinus 97,2 4,02E-59 21076 505690 5226450 Predator ID 98,8 Dasyurus viverrinus 100 1,36E-79 21079 511311 5221530 Predator ID 90,8 Sarcophilus harrisii 100 2,46E-66 21097 511514 5221550 Predator ID 99,4 Sarcophilus harrisii 98,3 1,44E-79 21102 517760 5438600 Predator ID 100 Felis catus 100 5,21E-79 21104 517481 5438470 Predator ID 91,5 Felis catus 100 5,21E-79 21117 525557 5405930 Predator ID 71,5 Sarcophilus harrisii 95,9 2,19E-57 21140 487627 5237390 Predator ID 92,6 Felis catus 99,6 1,03E-81 21142 487590 5237450 Predator ID 96,9 Felis catus 99,4 8,95E-77 21143 487525 5237820 Predator ID 87 Sarcophilus harrisii 99,5 1,54E-63 21153 527336 5282600 Predator ID 97 Sarcophilus harrisii 99,4 6,79E-78 21158 508095 5275530 Predator ID 89,9 Sarcophilus harrisii 99,1 5,04E-74 21159 508085 5275450 Predator ID 96,1 Sarcophilus harrisii 100 9,5E-87 21162 507999 5275290 Predator ID 91,4 Sarcophilus harrisii 99,3 4,35E-64 21163 508010 5275320 Predator ID 95,4 Sarcophilus harrisii 99,2 4,02E-59 21165 508094 5275450 Predator ID 98,1 Sarcophilus harrisii 99,4 3,09E-76 21170 506823 5239270 Predator ID 98,8 Dasyurus viverrinus 100 1,36E-79 21174 506885 5239270 Predator ID 99,4 Sarcophilus harrisii 100 1,96E-88 21175 507064 5239360 Predator ID 73,3 Sarcophilus harrisii 100 1,14E-85 21176 507076 5239360 Predator ID 93,4 Sarcophilus harrisii 98,7 4,44E-69 21177 507205 5239443 Predator ID 87,9 Dasyurus viverrinus 100 1,38E-79 21179 507216 5239461 Predator ID 96,2 Dasyurus viverrinus 100 5,51E-86 21184 507303 5239480 Predator ID 100 Dasyurus viverrinus 100 5,51E-89 21185 506692 5239270 Predator ID 79,7 Homo sapiens 99,2 1,7E-57 21186 506695 5239270 Predator ID 61,7 Dasyurus viverrinus 96,7 5,66E-47 21187 506624 5239208 Predator ID 96 Dasyurus viverrinus 98,7 4,44E-69 21188 506667 5239200 Predator ID 97,3 Dasyurus viverrinus 100 5,51E-89 21190 506654 5239200 Predator ID 96,4 Dasyurus viverrinus 100 1,37E-79 Uncultured 21192 503384 5237220 Predator ID 79,4 100 1,62E-125 stenotrophomonas 21193 502949 5237090 Predator ID 71,7 Felis catus 100 2,39E-39

183 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA 21194 502963 5237090 Predator ID 98,3 Canis lupus familiaris 99,4 1,17E-85 21195 502892 5237100 Predator ID 95,2 Canis lupus familiaris 98,6 2,56E-66 21196 503024 5236960 Predator ID 59,6 Sarcophilus harrisii 100 1,54E-89 21198 503454 5236866 Predator ID 97,2 Canis lupus familiaris 99,4 1,17E-85 21206 513609 5233720 Predator ID 95,7 Sarcophilus harrisii 99,5 1,67E-89 21210 513405 5234090 Predator ID 97,1 Sarcophilus harrisii 99,4 1,17E-80 21215 514695 5236900 Predator ID 89 Canis lupus familiaris 98,9 4,19E-37 21224 514736 5236800 Predator ID 88,2 Canis lupus familiaris 100 7,2E-40 21225 514735 5236810 Predator ID 96,6 Sarcophilus harrisii 100 1,55E-84 21227 538687 5273720 Predator ID 93,3 Felis catus 99,4 8,65E-77 21232 540115 5266820 Predator ID 96,3 Felis catus 100 5,21E-79 21251 507977 5275260 Predator ID 100 Sarcophilus harrisii 99,4 1,17E-80 21252 508011 5275310 Predator ID 95,8 Sarcophilus harrisii 99,3 3,43E-65 21255 508104 5275490 Predator ID 83,7 Sarcophilus harrisii 100 1,28E-69 21257 508109 5275590 Predator ID 88,8 Sarcophilus harrisii 98,9 1,66E-84 21258 508085 5275610 Predator ID 92,6 Sarcophilus harrisii 98,7 2,16E-67 21262 522619 5240020 Predator ID 95,7 Felis catus 98,6 1,47E-63 21264 525030 5233920 Predator ID 95,5 Canis lupus familiaris 99,4 1,17E-85 21265 525030 5233910 Predator ID 93 Canis lupus familiaris 98,3 3,19E-49 21284 505641 5226390 Predator ID 93,4 Sarcophilus harrisii 98,4 5,45E-84 21285 505624 5226370 Predator ID 87,9 Dasyurus viverrinus 100 1,38E-79 21286 505585 5226340 Predator ID 52,9 Sarcophilus harrisii 96,5 3,42E-49 21287 505585 5226340 Predator ID 100 Sarcophilus harrisii 98,2 4,85E-74 21289 505583 5226340 Predator ID 72,4 Sarcophilus harrisii 99,6 1,55E-89 21293 505516 5226370 Predator ID 46,7 Homo sapiens 98,4 1,6E-52 21294 505470 5226320 Predator ID 98,9 Dasyurus viverrinus 100 5,52E-89 21295 505576 5226340 Predator ID 93,6 Sarcophilus harrisii 100 6,59E-83 21296 505580 5226350 Predator ID 59,2 Dasyurus viverrinus 98,2 2,36E-77 21299 505621 5226350 Predator ID 78,3 Sarcophilus harrisii 100 3,86E-80 21310 505293 5218220 Predator ID 100 Dasyurus viverrinus 99,4 2,42E-77 21312 505262 5217960 Predator ID 96,1 Sarcophilus harrisii 100 3,9E-59 21313 505385 5217960 Predator ID 76,4 Sarcophilus harrisii 98,8 3,72E-54 21314 505390 5217950 Predator ID 99,4 Sarcophilus harrisii 100 1,21E-85 21315 505388 5217960 Predator ID 93,3 Sarcophilus harrisii 98,8 3,13E-76 21316 523144 5229650 Predator ID 92,2 Canis lupus familiaris 99,4 1,24E-85 21319 523084 5229740 Predator ID 83,5 Dasyurus viverrinus 99,3 1,61E-63 21320 523072 5229750 Predator ID 83,7 Canis lupus familiaris 100 2,72E-66 21322 523113 5229880 Predator ID 98,5 Sarcophilus harrisii 100 2,93E-97 21326 523241 5230020 Predator ID 97,6 Sarcophilus harrisii 99,4 7,03E-78 21327 523253 5230040 Predator ID 75,6 Sarcophilus harrisii 99,6 9,86E-87 21328 523277 5230110 Predator ID 90,7 Canis lupus familiaris 98,9 1,42E-74 21331 527073 5283270 Predator ID 88,9 Dasyurus maculatus 98,4 2,16E-83 21332 526916 5283290 Predator ID 96,5 Sarcophilus harrisii 100 7,04E-83 21336 526911 5283310 Predator ID 92,1 Sarcophilus harrisii 98,9 8,78E-77 21352 590643 5443100 Predator ID 82,2 Sarcophilus harrisii 98,2 5,39E-74 21353 590642 5443110 Predator ID 95 Sarcophilus harrisii 98,9 1,3E-85

184 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA 21354 590633 5443110 Predator ID 93,7 Sarcophilus harrisii 98,9 2,7E-82 21355 590635 5443110 Predator ID 100 Dasyurus viverrinus 98,2 1,92E-73 21356 590570 5443120 Predator ID 79,9 Sarcophilus harrisii 99,3 9,21E-61 21453 515937 5255340 Predator ID 86,4 Sarcophilus harrisii 99,4 5,84E-84 21455 515934 5255350 Predator ID 90,2 Sarcophilus harrisii 99,4 2,57E-82 21456 515940 5255370 Predator ID 76,5 Sarcophilus harrisii 99,4 1,17E-80 21457 515995 5255470 Predator ID 84 Felis catus 99,4 8,64E-77 21460 516029 5255730 Predator ID 86 Sarcophilus harrisii 100 2,81E-55 21461 516112 5255870 Predator ID 97,2 Canis lupus familiaris 98,9 2,06E-83 21463 515732 5255260 Predator ID 94,5 Felis catus 100 1,63E-79 21465 523435 5239270 Predator ID 85,7 Homo sapiens 99,3 4,06E-64 21467 523540 5239230 Predator ID 77 Perameles gunnii 98,5 3,92E-59 21468 524909 5235739 Predator ID 78,5 Felis catus 99,2 1,34E-58 21470 524533 5235260 Predator ID 95,8 Felis catus 98,6 4,14E-64 21471 524321 5235120 Predator ID 74,8 Canis lupus familiaris 98,7 9,49E-50 21472 524621 5235340 Predator ID 92,6 Felis catus 99,3 6,58E-62 21473 502825 5222740 Predator ID 43,7 Thylogale billardierii 100 9,67E-71 21474 502764 5222760 Predator ID 92,7 Dasyurus viverrinus 99,4 2,26E-77 21475 502954 5222700 Predator ID 85 Homo sapiens 99,2 4,34E-53 21476 502958 5222730 Predator ID 80,9 Homo sapiens 99,3 6,85E-51 21482 500817 5225130 Predator ID 95,8 Dasyurus viverrinus 100 1,38E-79 21487 500869 5225130 Predator ID 63,9 Sarcophilus harrisii 99,3 2,97E-60 21490 501036 5225150 Predator ID 90,9 Dasyurus viverrinus 100 1,38E-79 21496 501162 5225080 Predator ID 100 Dasyurus viverrinus 100 4,32E-90 21553 600965 5357720 Predator ID 76 Felis catus 100 2,85E-55 21554 600955 5357870 Predator ID 78,8 Sarcophilus harrisii 97,8 6,04E-84 21555 601479 5359370 Predator ID 81,6 Canis lupus familiaris 99,4 1,25E-85 21557 601754 5359090 Predator ID 99 Sarcophilus harrisii 100 1,82E-99 21558 601754 5359090 Predator ID 97,8 Sarcophilus harrisii 99,5 2,75E-87 21560 601944 5358930 Predator ID 94,4 Sarcophilus harrisii 99,5 1,38E-95 21562 601883 5358650 Predator ID 90 Sarcophilus harrisii 99,4 3,51E-86 21563 601882 5358640 Predator ID 99,4 Sarcophilus harrisii 100 4,39E-85 21564 601879 5358650 Predator ID 91,6 Sarcophilus harrisii 99,4 2,3E-72 21565 601878 5358650 Predator ID 94,6 Sarcophilus harrisii 100 4,14E-80 21567 601523 5358560 Predator ID 84,2 Sarcophilus harrisii 100 3,38E-65 21570 589568 5350350 Predator ID 89 Felis catus 99,5 2,11E-78 21573 589696 5350330 Predator ID 66,9 Felis catus 98,1 4,55E-64 21578 589721 5350330 Predator ID 75,6 Felis catus 99,3 2,5E-61 21586 587187 5355910 Predator ID 99,4 Sarcophilus harrisii 100 5,52E-84 21590 586614 5356380 Predator ID 95,4 Sarcophilus harrisii 99,5 4,86E-95 21593 596614 5341950 Predator ID 86,6 Canis lupus familiaris 98,4 2,9E-87 21594 584322 5346210 Predator ID 96,7 Felis catus 100 1,63E-79 21596 584331 5346740 Predator ID 97,2 Felis catus 99,3 2,7E-66 21598 605001 5370120 Predator ID 87,5 Canis lupus familiaris 100 9,35E-55 21801 604887 5370330 Predator ID 84,7 Canis lupus familiaris 97,3 2,72E-61 21804 518429 5278140 Predator ID 55,8 Felis catus 95,3 8,89E-67

185 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA 21806 527258 5277900 Predator ID 98,3 Sarcophilus harrisii 98,9 1,62E-84 22003 519380 5212520 Predator ID 99,4 Sarcophilus harrisii 98,9 7,4E-83 22008 527106 5276580 Predator ID 77 Sarcophilus harrisii 98,2 9,7E-82 22017 527077 5276590 Predator ID 89,2 Sarcophilus harrisii 99,4 1,81E-73 22020 527088 5276630 Predator ID 87,2 Sarcophilus harrisii 100 1,78E-57 22027 517469 5240900 Predator ID 96,7 Sarcophilus harrisii 99,4 3,51E-86 22030 517400 5241230 Predator ID 100 Sarcophilus harrisii 100 1,66E-89 22052 503666 5245670 Predator ID 93,3 Sarcophilus harrisii 99,3 2,53E-61 8897 511990 5408257 Metabarcoding Canis lupus familiaris 8899 511994 5408257 Metabarcoding Canis lupus familiaris 10760 523871 5229200 Metabarcoding Felis catus 10761 523587 5229069 Metabarcoding Felis catus 11877 603603 5350174 Metabarcoding Felis catus 11880 604210 5350167 Metabarcoding Felis catus 12137 579542 5399850 Metabarcoding Sarcophilus harrisii 12243 532390 5460354 Metabarcoding Felis catus 12244 532032 5459468 Metabarcoding Felis catus 12871 496010 5436908 Metabarcoding Dasyurus maculatus 12874 496057 5436960 Metabarcoding Felis catus 15464 522389 5336592 Metabarcoding Felis catus 17264 600784 5449934 Metabarcoding Canis lupus familiaris 17274 600319 5450439 Metabarcoding Sarcophilus harrisii 17288 600805 5470696 Metabarcoding Felis catus 17295 601684 5470636 Metabarcoding Sarcophilus harrisii 17581 529204 5347910 Metabarcoding Sarcophilus harrisii 17582 529083 5348200 Metabarcoding Sarcophilus harrisii 17807 572438 5387686 Metabarcoding Sarcophilus harrisii 17845 542242 5451937 Metabarcoding Felis catus 17888 521907 5331800 Metabarcoding Sarcophilus harrisii 17892 521933 5331700 Metabarcoding Sarcophilus harrisii 18901 532082 5459916 Metabarcoding Dasyurus maculatus 18903 532185 5460193 Metabarcoding Felis catus 18923 519935 5455618 Metabarcoding Sarcophilus harrisii 18924 519802 5454957 Metabarcoding Felis catus 18936 583120 5468617 Metabarcoding Felis catus 18938 582778 5468825 Metabarcoding Dasyurus maculatus 18955 511563 5450568 Metabarcoding Felis catus 18957 511669 5450393 Metabarcoding Felis catus 18961 588987 5467012 Metabarcoding Felis catus 18964 597661 5478024 Metabarcoding Sarcophilus harrisii 18967 602047 5474818 Metabarcoding Felis catus 18969 602204 5474303 Metabarcoding Sarcophilus harrisii 18970 602199 5474107 Metabarcoding Sarcophilus harrisii 18977 562338 5458509 Metabarcoding Dasyurus maculatus 19040 532488 5460424 Metabarcoding Canis lupus familiaris 19046 532264 5460329 Metabarcoding Felis catus

186 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA 19049 508488 5456374 Metabarcoding Felis catus 19301 488633 5279292 Metabarcoding Felis catus 19305 488613 5279324 Metabarcoding Sarcophilus harrisii 19309 488553 5279515 Metabarcoding Felis catus 19677 603693 5350179 Metabarcoding Felis catus 19687 604013 5337696 Metabarcoding Canis lupus familiaris 20296 488541 5279541 Metabarcoding Felis catus 20559 580393 5399450 Metabarcoding Canis lupus familiaris 20575 573351 5387110 Metabarcoding Sarcophilus harrisii 21324 523161 5229949 Metabarcoding Sarcophilus harrisii 21493 501086 5225138 Metabarcoding Felis catus 21592 596681 5342134 Metabarcoding Canis lupus familiaris

% ID is the percentage pairwise identity between the query sequence and the matching sequence identified using BLAST.

The e-value represents the number of BLAST hits expected by chance. The lower the e-value is, the better.

187 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA APPENDIX 4.1: Information about the New Holland mouse that explain which scats were selected.

New Holland mice are usually considered as habitat specialist (Norton 1987b). In Tasmania, as well as in

Victoria and New South Wales, three vegetation types are often cited: open heathland, open woodlands with a heathland understory and vegetated sand dunes (Keith and Calaby 1968; Seebeck and Beste 1970; Posamentier and Recher 1974; Kemper 1977; Braithwaite and Gullan 1978; Fox and Fox 1978; Hocking 1980; Cockburn

1980; Norton 1987a; Fox and Fox 1984; Pye 1991; Lock and Wilson 1999; Wilson 1991; Hocking and Driessen

2000; Wilson and Laidlaw 2003; Lazenby et al. 2008). They all agree that New Holland mice are found when there are post-fire changes in the vegetation but some discrepancies exist among scientists about the colonization time of the New Holland mouse. Some agree that a peak in abundance is observed after two to three or six years after the disturbance (Kemper 1977; Fox and Fox 1978; Fox and McKay 1981; Fox 1982; Fox and Fox 1984; Twigg et al. 1989; Wilson et al. 1990; Wilson 1991; Friend 1993; Wilson 1996; Lock and Wilson

1999; Seebeck and Menkhorst 2000; Hocking and Driessen 2000; Wilson and Laidlaw 2003) while others agree it is seen after five to sixteen years (Lock and Wilson 1999; Pye et al. 2008). Newsome and Corbett (1975) and

Smith and Quin (1996) recognized that the elimination of recurrent small-scale burnings can reduce post-fire survival by decreasing the chance of locating appropriate seral-stage areas in space and time by small dwelling mammals like the New Holland mouse. The fire strategies have changed a lot since European settlement

(Cockburn 1978; Smith and Quin 1996; Dickman et al. 1993) and could potentially cause threatened species to become extinct by altering the organization of mammalian communities and disturbing species richness.

Frequent fires can create refuges as well as new habitats equitably shared by all species in the community, thus species are constantly replacing each other in the different post-fire stages (Fox 1982). Previous observations of this species in Tasmania occurred from Flinders Island (where fossils were found) to Coles Bay (Norton 1987;

Pye 1991; Lazenby et al. 2008). Fossils were also recorded at Flowery Gully, near Launceston (Hope 1973;

Hocking and Driessen 2000). The records of the species in a study done in 1980 suggest that the New Holland mouse was widespread in Tasmania (Hocking 1980). Then, in the 90’s, the species was thought to have a patchy distribution (Wilson 1991) and eventually, the populations density kept on declining since then to be seen for the last time in a trap in 2010 by Billie Lazenby in the Waterhouse Conservation area near Tomahawk (Natural

Values Atlas accessed on the 01/09/16; DPIPWE 2015). Pye (1991) reckoned that Mount William National Park supported the largest surviving population of New Holland mice in Tasmania. 32 scats were collected in the area and are checked with the metabarcoding analyses. Not a lot of information exist on their elevation range in

Tasmania. They are usually found around 200m altitude and 15 km from the coast (Hocking and Driessen

188 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA 2000). In Queensland and New South Wales, they can be found from 0 to 900 m (Read 1988; Van Dyck and

Lawrie 1997; Wilson and Laidlaw 2003) and in Victoria, their range decrease as they are only found form 40 to

100 m (Lock and Wilson 1999; Wilson and Laidlaw 2003).

189 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA APPENDIX 4.2: R code for metabarcoding library(plyr) library(dismo)

#set working directory setwd("C:/scats")

#### read raster layers with recent fire events (2008-2012), preferred vegetation by the New Holland mouse and the elevation were this species was usually found #### theses rasters are property of the Department of Primary Industries, Water and Environment r.fire <- raster("fire.tif") values(r.fire) <- ifelse(!is.na(values(r.fire)),1,values(r.fire)) r.veg <- raster("vegetation.tif") values(r.veg) <- ifelse(!is.na(values(r.veg)),1,values(r.veg)) r.dem <- raster("elevation.tif") predictors <- stack(r.fire, r.veg, r.dem)

#### read historic presences of this species (downloaded from the Natural Values Atlas - July 2016) nhm <-read.csv("mousehisto.csv", sep=";") head(nhm) coordinates(nhm) <- c("X", "Y")

#### exctract values from the predictors where you have presences of the mouse presvals <- extract(predictors, nhm)

#### generate background data mask <- raster("eastern_tasmania.tif") backgr <- randomPoints(mask, 500) absvals <- extract(predictors, backgr) pb <- c(rep(1, nrow(presvals)), rep(0, nrow(absvals))) sdmdata <- data.frame(cbind(pb, rbind(presvals, absvals))) sdmdata$fire[is.na(sdmdata$fire)]<- 0 sdmdata$fire <- factor(sdmdata$fire) sdmdata$vegetation[is.na(sdmdata$vegetation)]<- 0 sdmdata$vegetation <- factor(sdmdata$vegetation) sdmdata$elevation[is.na(sdmdata$elevation)]<- 0

#### GLM mglm <-glm(pb ~ fire + vegetation + elevation, data=sdmdata)

#### random Forest element to build multiple decision trees library(randomForest) model <- factor(sdmdata$pb) ~ fire + vegetation + elevation rf1 <- randomForest(model, data=sdmdata) samp <- sample(nrow(sdmdata), round(0.75 * nrow(sdmdata))) sdmdata <- apply(sdmdata, 2, as.numeric)

#### create a set of train and test data to test the models traindata <- sdmdata[samp,] traindata <- traindata[traindata[,1] == 1, 2:4] testdata <- sdmdata[-samp,]

190 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA e <- evaluate(testdata[testdata==1,], testdata[testdata==0,], mglm) summary(mglm) anova(mglm) scats <- read.csv("2014_scats.csv", sep=";") #2938 scats were collected in 2014 – file not provided head(scats) plot(r.veg) points(scats$x, scats$y) scatsxy <- scats[,c("x","y")] coordinates(scatsxy) <- c("x","y") pred.scats <-extract(predictors, scatsxy) pred.scats <- as.data.frame(pred.scats) pred.scats$fire[is.na(pred.scats$fire)] <- 0 pred.scats$fire <- factor(pred.scats$fire) pred.scats$vegetation[is.na(pred.scats$vegetation)] <- 0 pred.scats$vegetation <- factor(pred.scats$vegetation) preds <- predict(mglm,pred.scats) plot(r.dem) points(scatsxy, cex = (preds+0.2)*2, pch=16) points(nhm, pch=15, col="red") fh <-rank(preds) > max(rank(preds)) -500 plot(r.dem) points(scatsxy, cex = (preds+0.2)*2, pch=16) points(scatsxy[fh,] , cex = (preds[fh]+0.2)*2, pch=16, col="green" ) points(nhm, pch=15, col="red") res <- cbind(scats, preds) write.csv(res, "selected_scats.csv", row.names=F)

191 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA APPENDIX 4.3: Resulting diet from metabarcoding study per predator

PART 1: CAT

location (world means almost everywhere in the world except number occurrence Scientific name Antartica) Closest species in Tasmania Common name rank Status in Tasmania of reads in scats Mus musculus World (incl. Tasmania) Mus musculus house mouse species Pest 79038 17

Niveoscincus pretiosus BUT 4 other species in Tasmania not represented on Niveoscincus GenBank: N. orocryptum, N. greeni, N. Tasmanian tree skink or pretiosus Tasmania microlepidotus and N. palfreymani 4 other species genus Endemic 24718 11 extinct on the Thylogale mainland, common billardierii Tasmania Thylogale billardierii Tasmanian pademelon species in Tasmania 8486 6 Potorous tridactylus Australia (incl. Tasmania) Potorous tridactylus long-nosed potoroo species Common 3940 6 173 Extinct on the mainland, Endangered but common in Sarcophilus harrisii Tasmania Sarcophilus harrisii Tasmanian devil species Tasmania 918 5 Southern brown Isoodon Australia (incl. Tasmania) Isoodon obesulus bandicoot genus Common 73848 4 Rattus rattus World (incl. Tasmania) Rattus rattus black rat species Pest 5083 4 Bos World (incl. Tasmania) Bos taurus cattle genus Livestock 62466 3 New Guinea - Australia (incl. Petaurus breviceps Tasmania) Petaurus breviceps sugar glider species Common 36533 3 most tropical and subtropical regi 3 species: Cacatua galerita, Cacatua Psittaciformes ons (incl. Tasmania) roseicapilla, Calyptorhynchus funereus parrots order Common 29595 3 10 species: Acanthorhynchus tenuirostris, 4 are endemic to Anthochaera chrysoptera, Anthochaera Tasmania paradoxa, Lichenostomus flavicollis, (Anthochaera Manorina melancephala, Melithreptus paradoxa, New Guinea - New Zealand - affinus, Melithreptus validirostris, Lichenostomus Pacific Islands - Australia (incl. Phylidonyris melanops, Phylidonyris flavicollis, Meliphagidae Tasmania) novaehollandiae, Phylidonyris pyrrhopter honeyeaters family Melithreptus 20554 3

192 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA affinus, Melithreptus validirostris) - common New Guinea - Australia (incl. 3 species: Antechinus minimus, swainsonii Antechinus Tasmania) and vandyke Antechinus genus Common 18850 3 Rattus lutreolus Australia (incl. Tasmania) Rattus lutreolus Australian swamp rat species Common 4738 3 GENUS LEVEL: Sericornis. 1 species: Sericornis frontalis Australia but not Tasmania Sericornis humilis Tasmanian scrubwren species Common 1697 3 Trichosurus common brushtail vulpecula Australia (incl. Tasmania) Trichosurus vulpecula possum species Common 182 3 Pseudocheirus common ringtail peregrinus Australia (incl. Tasmania) Pseudocheirus peregrinus possum species Common 11770 2 Perameles Australia (incl. Tasmania) Eastern barred bandicoot bandicoots genus Common 8507 2 Macropus

174 rufogriseus Australia (incl. Tasmania) Macropus rufogriseus red-necked wallaby species Common 6649 2 Sminthopsis leucopus Australia (incl. Tasmania) Sminthopsis leucopus white-footed dunnart species Common 3540 2 New Guinea - Australia (incl. Malurus Tasmania) Malurus cyaneus superb fairywren genus Common 3031 2 Cercartetus nanus Australia (incl. Tasmania) Cercartetus nanus eastern pygmy possum species Common 2934 2 Passeriformes World (incl. Tasmania) 44 species passerines order 159 2 Vombatus ursinus Australia (incl. Tasmania) Vombatus ursinus common wombat species Common 134 2 Pseudemoia entrecasteauxii BUT 2 other species in Tasmania not represented on Pseudemoia GenBank: P. pagenstecheri and P. southern grass skink or entrecasteauxii Australia (incl. Tasmania) rawlinsoni 2 other species genus Common 1181 1 Dasyurus maculatus: Protected, common in Tasmania and Dasyurus viverrinus: extinct on the New Guinea - Australia (incl. 2 species: Dasyurus maculatus and mainland, common Dasyurus Tasmania) Dasyurus viverrinus quolls genus in Tasmania 924 1

193 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA Oryctolagus Europe - South America - cuniculus Australia (incl. Tasmania) Oryctolagus cuniculus european rabbit species Pest 386 1 2 pests and 1 3 species: Rattus lutreolus velutinus, common (Rattus Rattus World (incl. Tasmania) Rattus rattus and Rattus norvegicus rats genus lutreolus velutinus) 263 1 Turnix varia Australia (incl. Tasmania) Turnix varius (other name) painted buttonquail species Common 148 1 Acanthiza pusilla Australia (incl. Tasmania) Acanthiza pusilla brown thornbill species Common 139 1 Mostly common, 1 14 species: Anas castanea , Anas gracilis, introduced (Cygnus Anas rhynchotis, Anas superciliosa, olor) and 3 rare Biziura lobate, Cereopsis novaehollandiae, species Chenonetta jubata, Cygnus atratus, Oxyura (Dendrocygna australis, Tadoma tadornoides, Cygnus eytoni, olor, Dendrocygna eytoni, Malacorhynchus Malacorhynchus membranaceus, membranaceus, Anatidae World (incl. Tasmania) Stictonetta naevosa ducks, geese and swans family Stictonetta naevosa) 102 1

175 Liopholis whitii Australia (incl. Tasmania) Egernia whitii (genus changed) white's skink species Secure 100 1

194 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA PART 2: TASMANIAN DEVIL

location (world means almost everywhere in the world except number occurrence Scientific name Antartica) Closest in Tasmania Common name rank Status in Tasmania of reads in scats

Dasyurus maculatus: Protected, common in Tasmania and Dasyurus viverrinus: extinct on the New Guinea - Australia (incl. 2 species: Dasyurus maculatus and mainland, common in Dasyurus Tasmania) Dasyurus viverrinus quolls genus Tasmania 976168 80 Thylogale extinct on the mainland, billardierii Tasmania Thylogale billardierii Tasmanian pademelon species common in Tasmania 1280901 54 Macropus rufogriseus Australia (incl. Tasmania) Macropus rufogriseus red-necked wallaby species Common 2357291 42 Trichosurus common brushtail vulpecula Australia (incl. Tasmania) Trichosurus vulpecula possum species Common 891057 30 Vombatus ursinus Australia (incl. Tasmania) Vombatus ursinus common wombat species Common 300642 15 Indonesia - New Guinea - 176 Petaurus breviceps Australia (incl. Tasmania) Petaurus breviceps sugar glider species Common 1039 6 Oryctolagus Europe - South America - cuniculus Australia (incl. Tasmania) Oryctolagus cuniculus european rabbit species Pest 136908 5 Bos World (incl. Tasmania) Bos taurus cattle genus Livestock 4313 5 Macropus giganteus Australia (incl. Tasmania) Macropus giganteus eastern grey kangaroo species Common 1218 5 Pseudocheirus common ringtail peregrinus Australia (incl. Tasmania) Pseudocheirus peregrinus possum species Common 1181 5 Tachyglossus New Guinea - Australia (incl. aculeatus Tasmania) Tachyglossus aculeatus short-beaked echidna species Common 7252 4 Felis catus World (incl. Tasmania) Felis catus cat species Pest 1707 3 Bettongia gaimardi Tasmania Bettongia gaimardi eastern bettong species Common 9108 2 Capra hircus World (incl. Tasmania) Capra hircus goat species Livestock 7638 2

195 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA 14 species: Anas castanea , Anas gracilis, Anas rhynchotis, Anas Mostly common, 1 superciliosa, Biziura lobate, Cereopsis introduced (Cygnus olor) novaehollandiae, Chenonetta jubata, and 3 rare species Cygnus atratus, Oxyura australis, (Dendrocygna eytoni, Tadoma tadornoides, Cygnus olor, Malacorhynchus Dendrocygna eytoni, Malacorhynchus membranaceus, Stictonetta Anatidae World (incl. Tasmania) membranaceus, Stictonetta naevosa ducks, geese and swans family naevosa) 3476 2 Cercartetus nanus Australia (incl. Tasmania) Cercartetus nanus eastern pygmy possum species Common 18142 1 SUPER-FAMILY LEVEL: Corvida. 24 species: Acanthiza chrysorrhoa, Acanthiza ewingii, Acanthiza pusilla, Sericornis humilis, Calamanthus fuliginosus, Acanthornis magnus, Artamus cyanopterus, Coracina novaehollandiae, Corvus mellori, 177 Corvus tasmanicus, Acanthorhynchus tenuirostris, Anthochaera chrysoptera, Anthochaera paradoxa, Lichenostomus flavicollis, Manorina melancephala, Melithreptus affinus, Melithreptus validirostris, Phylidonyris melanops, Phylidonyris novaehollandiae, Phylidonyris pyrrhoptera, Cinclosoma punctatum, Pardalotus punctatus, tropics and temperate regions but Pardalotus quadragintus, Pardalotus Oriolus not Tasmania striatus corvida genus Commons 7564 1 Ovis World (incl. Tasmania) Ovis aries sheep genus Livestock 5804 1 4 species: Alectoris chukar, Gallus gallus, Pavo cristatus, Phasianus chickens and similar Phasianinae World (incl. Tasmania) colchicus birds subfamily Common - Livestock 4076 1 Niveoscincus pretiosus BUT 4 other species in Tasmania not represented on Niveoscincus GenBank: N. orocryptum, N. greeni, N. Tasmanian tree skink pretiosus Tasmania microlepidotus and N. palfreymani or 4 other species genus Endemic 2368 1 Lepus World (incl. Tasmania) Lepus europaeus european hare genus Pest 2071 1 Podargus Australia (incl. Tasmania) Podargus strigoides tawny frogmouth species Common 840 1

196 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA strigoides 9 species: Fulica atra, Gallinula mortierii, Gallinula tenebrosa, Porphyrio porphyrio, Porzana fluminea, Porzana pusilla, Porzana tabuensis, Rallus pectoralis, Rallus Rallidae World (incl. Tasmania) philippensis ground-birds family Common 236 1 Passeriformes World (incl. Tasmania) 44 species passerines order 165 1 178

197 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA PART 3: DOG

location (world means almost everywhere in the world number occurrence Scientific name except Antartica) Closest in Tasmania Common name rank Status in Tasmania of reads in scats 4 species: Alectoris chukar, Gallus gallus, chickens and similar Phasianinae World (incl. Tasmania) Pavo cristatus, Phasianus colchicus birds subfamily Common - Livestock 4646 5 Bos World (incl. Tasmania) Bos taurus cattle genus Livestock 20249 3 Ovis World (incl. Tasmania) Ovis aries sheep genus Livestock 10560 2 Macropus rufogriseus Australia (incl. Tasmania) Macropus rufogriseus red-necked wallaby species Common 1713 2 Felis catus World (incl. Tasmania) Felis catus cat species Pest 17723 1 Sus scrofa World (incl. Tasmania) Sus scrofa wild boar species Pest 10626 1 Extinct on the mainland, Endangered but common in Sarcophilus harrisii Tasmania Sarcophilus harrisii Tasmanian devil species Tasmania 7019 1 Dasyurus maculatus: 179 Protected, common in Tasmania and Dasyurus viverrinus: extinct on the New Guinea - Australia (incl. 2 species: Dasyurus maculatus and mainland, common in Dasyurus Tasmania) Dasyurus viverrinus quolls genus Tasmania 928 1 Trichosurus common brushtail vulpecula Australia (incl. Tasmania) Trichosurus vulpecula possum species Common 524 1 Macropus giganteus Australia (incl. Tasmania) Macropus giganteus eastern grey kangaroo species Common 416 1

198 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA PART 4: EASTERN QUOLL

location (world means almost everywhere in the world except Status in number occurrence Scientific name Antartica) Closest in Tasmania Common name rank Tasmania of reads in scats Macropus rufogriseus Australia (incl. Tasmania) Macropus rufogriseus red-necked wallaby species Common 96286 3 Indonesia - New Guinea - Petaurus breviceps Australia (incl. Tasmania) Petaurus breviceps sugar glider species Common 14362 3 Pseudocheirus common ringtail peregrinus Australia (incl. Tasmania) Pseudocheirus peregrinus possum species Common 146240 2 Trichosurus common brushtail vulpecula Australia (incl. Tasmania) Trichosurus vulpecula possum species Common 81752 2 Macropus giganteus Australia (incl. Tasmania) Macropus giganteus eastern grey kangaroo species Common 17860 2 extinct on the mainland, common in Thylogale billardierii Tasmania Thylogale billardierii Tasmanian pademelon species Tasmania 11393 2

Niveoscincus pretiosus BUT 4 other species in Tasmania not represented on GenBank: N. 180 Niveoscincus orocryptum, N. greeni, N. microlepidotus and Tasmanian tree skink pretiosus Tasmania N. palfreymani or 4 other species genus Endemic 378 1 Vombatus ursinus Australia (incl. Tasmania) Vombatus ursinus common wombat species Common 102 1 Extinct on the mainland, Endangered but common Sarcophilus harrisii Tasmania Sarcophilus harrisii Tasmanian devil species in Tasmania 71 1

199 - Identification, distribution and diet of Tasmanian predators inferred by scat DNA PART 5: SPOTTED-TAILED QUOLL

location (world means almost everywhere in the world except number occurrence Scientific name Antartica) Closest in Tasmania Common name rank Status in Tasmania of reads in scats Trichosurus common brushtail vulpecula Australia (incl. Tasmania) Trichosurus vulpecula possum species Common 99181 6 extinct on the mainland, common Thylogale billardierii Tasmania Thylogale billardierii Tasmanian pademelon species in Tasmania 44255 3 Oryctolagus Europe - South America - cuniculus Australia (incl. Tasmania) Oryctolagus cuniculus european rabbit species Pest 278907 2 Macropus rufogriseus Australia (incl. Tasmania) Macropus rufogriseus red-necked wallaby species Common 39206 2 Vombatus ursinus Australia (incl. Tasmania) Vombatus ursinus common wombat species Common 1031 1 Indonesia - New Guinea - Petaurus breviceps Australia (incl. Tasmania) Petaurus breviceps sugar glider species Common 959 1 Pseudocheirus common ringtail peregrinus Australia (incl. Tasmania) Pseudocheirus peregrinus possum species Common 959 1 Zoothera dauma GENUS LEVEL: Zoothera. 1 species: aurea Asia - Europe Zoothera lunulata Bassian thrush subspecies 564 1 181 southern brown Isoodon Australia (incl. Tasmania) Isoodon obesulus bandicoot genus Common 371 1

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