The Dispersal of Honey Possums, Tarsipes Rostratus, in Relation to Habitat Fragmentation and Fire

The dispersal of honey possums, Tarsipes rostratus, in relation to habitat fragmentation and fire

Rachel Louise Clancy Bachelor of Science (Hons)

This thesis is presented for the degree of Doctor of Philosophy of the University of Western Australia, School of Animal Biology, 2011

For Wilbur

Acknowledgements

The completion of this thesis would not have been possible without the generous support and assistance provided by the following people. From the University of Western Australia I would like to thank my coordinating supervisor, Professor Dale Roberts, who provided invaluable and continuous direction, advice and encouragement. Thank you for not giving up on me, and for sticking with me. I would also like to thank my second supervisor Dr Oliver Berry for his support and guidance with the genetic component of this thesis.

Funding of this study was provided by an Australian Postgraduate Award, the School of Animal Biology (UWA), the Linnean Society of NSW, the M.A. Ingram Trust (State Trustees Victoria), and the Australian Geographic Society.

Fieldwork carried out in Yanchep National Park, Yeal Nature Reserve and the Gnangara Pine Plantations was licensed under permits from the Western Australian Department of Environment and Conservation (DEC) and with permission from the University of Western Australia’s Animal Ethics Committee.

This work would not have been possible without the generous in-kind assistance provided by DEC, Wanneroo. Specifically, I would like to thank Dr Mark Garkaklis, Dr Barbara Wilson, Brent Johnson, Alice Reaverley, Natalia Huang, Leonie Valentine, Clayton Sanders, and importantly, Tim Gregson and all the Wanneroo crew guys who provided massive support in the trapping component of this study. Thank you to Janine Kuehs and Tracey Sonnerman for producing the polygons and original GIS files that allowed me to produce the maps presented in Chapter 5.

I would also like to thank the following people for their contributions to tissue collection used in the phylogeography study- Professor Don Bradshaw, Tub Liddelow, Dr Mike Bamford, Johnny Prefumo, Janine Liddelow, as well as the Western Australian Museum. Thanks must also go to my field volunteers- Gregor Buchanan, Christine Bowman, Jill and Peter Bowman, and Liz Clarke. I would also like to extend my gratitude to Dr Kate Bryant and Murdoch University’s undergraduate students for their help with my habitat assessments. For their much valued help in the genetics lab and my microsatellite development, Maxine Beveridge, Janine Rix, and Sharron Perks, thank you. Thanks must also go to Matt Johnson for providing me with his extensive GIS expertise.

Finally I would like to thank my family and friends for supporting me throughout this PhD. Thank you to Kerry Knott for providing me with a family away from home and most importantly to Jenny and Greg Clancy for their emotional and financial support. Thank you for picking me up every time I fell.

Abstract

The honey possum Tarsipes rostratus is a tiny (7-16g) highly specialised flower-feeding marsupial endemic to south-western Australia. This study utilised both demographic and genetic data to investigate the effects of habitat fragmentation and fire on honey possum dispersal at both broad and local scales. At a broad scale, genetic analyses revealed very little phylogeographic structuring across the complete range of the honey possum. Overall phylogeographic structuring of T. rostratus was shallow and it is probable that honey possums have maintained recent genetic connectivity.

I conducted a trapping study in and around the Gnangara Pine Plantations approximately 60 km north of Perth, Western Australia, in both fragmented and continuous habitat. This study was intended to investigate honey possum density, demography and dispersal using mark- recapture and genetic tools for assessing population structure and dispersal. Previous studies relying on mark-recapture and radio tracking have generated contradictory or inconclusive data in relation to dispersal capacity: e.g. a radio tracking study suggested mark-recapture data may underestimate dispersal, particularly by males. All previous studies have been conducted in cool climate, more continuous habitats across the south coast and may not be representative of much of the range of this species, which ranges from Shark Bay to the edge of the Nullarbor Plain.

Fragmented patches in the pine plantation recorded the highest average number of honey possums caught per trapping session. Assignment tests revealed no population structuring within the Gnangara pine plantation and indicated that all individuals were part of a single population. Further analysis using spatial autocorrelation also revealed no fine scale genetic structure over distances of up to 20 km indicating that the matrix of the pine plantation is not a barrier to honey possum dispersal. While the current landscape is not affecting the genetic connectivity of honey possums, current pine removal plans will accentuate fragmentation of natural habitats and are likely to reduce the species ability to move through the landscape.

Fire did not appear to influence the distribution of honey possums within this study area. Animals were caught in recently burnt, as well as long unburnt areas and there was no correlation between the average number of honey possums caught per trapping session and the fire age of a habitat. There was some evidence that abundance was highest 7-9 years post fire - contrasting with a peak at 20 years at Fitzgerald River approximately 450 km south-east

iii of my study. Vegetation condition was a much more important predictor of abundance than fire.

This study has shown that honey possums are able to utilise habitat of all fire ages and may not be as ‘fire-sensitive’ as previously thought. Selective foraging behaviour by T. rostratus may indirectly generate genetic connectivity during times of climatic pressure on preferred food plants. For example, if potential impacts of climate change such as decreased rainfall affect the distribution of preferred food plants, gene flow throughout the landscape may increase as a result of honey possums being forced to move greater distances to access adequate food resources. Future studies on this species should incorporate both demographic and genetic analyses to further understand how this species responds to different landscapes and landscape manipulations, as it is representative of a deep phylogenetic lineage with a long history in south-western Australia: a biodiversity hotspot with major environmental pressures due to clearing of native vegetation for agriculture and forestry and changing climates.

Contents

Acknowledgements...... i Abstract...... iii Contents...... v

Chapter 1 Introduction...... 1 1.1 Conservation issues: threats and disturbance...... 1

1.2 Habitat fragmentation in south-western Australia...... 1 1.2.1 Impacts of fragmentation: demographic studies...... 2 1.2.2 Impacts of fragmentation: genetic insights...... 4

1.3 Fire impacts in south-western Australia...... 5 1.3.1 Impacts of fire: demographic studies...... 6 1.3.2 Impacts of fire: genetic insights...... 7

1.4 Impacts of fragmentation and fire: a case study in the Gnangara and Yanchep pine plantation...... 8

1.5 Thesis context...... 11

Chapter 2: Phylogeographic structure of the honey possum, Tarsipes rostratus in south- western Australia...... 14 2.1. Introduction to Phylogeography...... 14 2.1.1. The study of phylogeography...... 14 2.1.2. Evolutionary and geographical context...... 14 2.1.3. Phylogeography- a tool for conservation biology...... 15

2.2. Methods...... 17 2.2.1. Sample collection...... 17 2.2.2 DNA extraction...... 20 2.2.3 Mitochondrial DNA amplification and sequencing...... 20 2.2.4 Data analyses...... 21

2.3 Results...... 22

2.4 Discussion...... 29

Chapter 3: Honey possum distribution within the Gnangara pine plantations...... 32 3.1 Introduction...... 32 3.1.1 Conflicting land use in south-western Australia...... 32 3.1.2 The Gnangara Mound: an example of conflicting land use in south-west 32 Western Australia......

3.2 Methods...... 34 3.2.1 Study species...... 34 3.2.2 Study area...... 34 3.2.3 Trapping...... 39

3.3 Results...... 40 3.3.1 What is the pattern of occurrence of honey possums?...... 40 3.3.2 Does habitat fragmentation affect the distribution of honey possums?...... 42 3.3.3 Is there a difference between fragmented and unfragmented sites for the number of honey possums caught?...... 42

3.4 Discussion...... 48 3.4.1 What is the pattern of occurrence of honey possums?...... 48 3.4.2 Does fragmentation affect the distribution of honey possums?...... 49 3.4.3 Is there a difference between fragmented and unfragmented sites for the number of honey possums?...... 50

Chapter 4. Habitat fragmentation and honey possum dispersal: a genetic approach...... 54 4.1 Introduction...... 54 4.1.1 Habitat fragmentation and dispersal capabilities...... 54 4.1.2 Plantations and fragmented habitat...... 55

4.2 Methods...... 57

4.2.1 Laboratory Analysis...... 57 4.2.3 Genetic Diversity of Populations...... 57 4.2.4 Assignment Tests...... 58

4.3 Results...... 60 4.3.1 Genetic Diversity of Populations...... 60 4.3.2 Assignment Tests...... 65

4.4 Discussion...... 71 4.4.1 Genetic subdivision within and between populations...... 71 4.4.2 Assignment tests and population structure...... 72

Chapter 5: The effect of fire and other habitat variables on honey possum dispersal in a fragmented landscape...... 75 5.1 Introduction...... 75 5.1.1 Fire in the landscape...... 75 5.1.2 Fauna responses to fire...... 76 5.1.3 Tarsipes rostratus: a fire sensitive species?...... 77

5.2 Methods...... 78

5.3 Results...... 83 5.3.1 Do honey possums maintain gene flow throughout the landscape?...... 83 5.3.2 Does the spatial pattern of fire affect the distribution of honey possums?...... 85 5.3.3 Do habitat variables predict honey possum densities?...... 89

5.4 Discussion...... 94 5.4.1 Maintaining gene flow...... 94 5.4.2 Fire and habitat quality...... 94

Chapter 6: The dispersal of honey possums, Tarsipes rostratus, in relation to habitat fragmentation and fire: conclusions and management implications...... 98 6.1 Movements and dispersal...... 98

vii

6.2 Fire and vegetation quality...... 99

6.3 Management...... 100

References...... 103

Appendix 1: Microsatellite development...... 121

viii

Chapter 1: Introduction

1.1 Conservation issues: threats and disturbance

Habitat loss and fragmentation from agriculture, logging, urban development and fire are threats facing species, communities and ecosystems throughout the world (Henle, Davies et al. 2004; Krauss et al. 2010; Lambeck 1997). Conservation biology was first developed as a discrete discipline in the early to mid 1980s as a response to the increasingly negative impacts of anthropogenic activity (Soulé 1985). As a new branch of science, it was considered a crisis discipline, concerned with making decisions or recommendations without having all the data or knowing all of the facts (Soulé 1985). Conservation biology draws on core disciplines such as population biology, ecology and genetics (which on their own are not comprehensive enough to fully address the threats to biological diversity) to provide advice to decision makers based on available evidence (Soulé 1985).

Habitat fragmentation threatens many species all over the world and is seen as one of the greatest contributors to species extinctions (Henle, Lindenmayer et al. 2004). Fragmentation results in species being limited to a small number of habitat patches, which are surrounded by a matrix of agricultural or developed land that is unsuitable habitat (Saunders et al. 1991; Tallmon et al. 2002). The habitat patches may be isolated from one another by a significantly modified or degraded landscape and may be divided from the original habitat by roads, fences, or other physical barriers impeding the free movement of species (Primack 1998). The process of habitat fragmentation poses a much greater risk to some species than others (Henle, Davies et al. 2004). Fragmentation isolates populations, reducing gene flow and genetic diversity, which can ultimately lead to the extinction of small local populations.

1.2 Habitat fragmentation in south-western Australia

South-western Australia is a highly fragmented landscape and is an excellent area for studying the impacts of habitat fragmentation on fauna species (Hoehn et al. 2007). There are a number of studies on the population dynamics at a landscape scale for marsupial species in south- western Australia (e.g. Cooper 2000; Hayward et al. 2003), as well as frogs (e.g. Driscoll 1998) and reptiles (e.g. Hoehn et al. 2007; Sarre et al. 1995). While there are studies that have shown

1 some fauna to be quite sensitive to habitat fragmentation and disturbance (e.g. Wayne et al. 2006), there are other studies that have reported species (such as the gecko, Gehyra variegata) that disperse readily through fragmented landscapes (e.g. Hoehn et al. 2007; Sarre et al. 1995). Wayne et al. (2006) investigated associations between abundances of the threatened western ringtail possum (Pseudocheirus occidentalis) and anthropogenic disturbances, including fragmentation and fire, at both local and landscape scales within Jarrah forests of south-western Australia. This study showed a negative association of abundances with forest fragmentation and distance from non-remnant vegetation (agriculture and plantations). There are species in south-western Australia however, that have persisted with increased habitat fragmentation through habitat generalism and an ability to utilize remnants (Sarre1998).

To appreciate the effects of fragmentation on fauna, there is a need for explicit measures of dispersal (Gruber & Henle 2008). The honey possum is an example of a species endemic to south-western Australia (Russell & Renfree 1989) where the current knowledge of dispersal capabilities is limited to studies only likely to detect short distance dispersals (see Bradshaw & Bradshaw 2002; Bradshaw et al. 2007; Everaardt 2003; Garavanta et al. 2000). For example, mark- recapture studies, such as that by Garavanta et al. (2000) are not likely to pick up any long-distance dispersal events, and although radio-tracking can provide a more detailed insight into movement on a local scale (Bradshaw & Bradshaw 2002), it too is likely to miss long- distance movement. The honey possum is a good candidate for studying the impacts of habitat fragmentation as it is found in habitats that are fragmented at both broad and very local scales (Bradshaw & Bradshaw 1999; Hopper & Burbidge 1989). A reduction in habitat area and connectivity can greatly affect persistence of many species in fragmented landscapes (Banks et al. 2005), so it is reasonable to expect that fragmentation will be influencing the population viability of honey possums in south-western Australia.

1.2.1 Impacts of fragmentation: demographic studies

To understand the ecology of many species, and their response to disturbance, knowledge of dispersal (long-distance movement away from existing habitat) is especially relevant (Eldridge et al. 2001; Lindenmayer et al. 2005). Traditionally, mark-recapture studies have been used to investigate the impacts of disturbance, such as fragmentation and fire, to populations within the landscape. For example, Lindenmayer et al. (1999) used a combination of hairtubing (a

2 technique for detecting animals from the analysis of fur collected in a small portable bait station) and trapping to investigate the effects of landscape context and habitat fragmentation on mammals in large continuous areas of Eucalyptus forest, in areas dominated by exotic softwood Radiata Pine (Pinus radiata) trees, and fragments of eucalypt forest surrounded by an extensive P. radiata plantation. A major finding of this study (using extensive demographic data) was that no significant differences were found in mammal presence or abundance between sites located in continuous habitat and sites in fragments surrounded by plantation. The demographic data alone however, do not necessarily indicate whether this result was due to animals from potential source populations in continuous habitat moving through the plantation and colonising the remnants, or populations residing in remnants resisting local extinction (Lindenmayer et al. 1999).

As well as being limited in the ability to determine finer scale processes such as determining the difference between colonising source populations, and residents resisting extinction (Lindenmayer et al. 1999), mark-recapture studies may underestimate landscape scale processes. For example, honey possum mark-recapture studies on the south coast of Western Australia have suggested this species is relatively sedentary (e.g. Bradshaw & Bradshaw 2002; Garavanta et al. 2000). Garavanta et al. (2000) suggested in their study of movement patterns of honey possums in the Fitzgerald River National Park that there was limited movement and no evidence of dispersal, based on an intensive mark-recapture study. In Scott National Park, although reporting larger utilisation areas, Bradshaw & Bradshaw (2002), showed similar short-term movement patterns for the honey possum using radio tracking. However, conventional approaches to assessing how dispersal occurs (e.g. trapping, radio tracking) may be limited in their ability to detect long-distance dispersal events and answer questions at the landscape or whole of species range scale, and may therefore not reflect all aspects of a species’ true dispersal capabilities.

Recently, genetics has been used to complement demographic studies and mark-recapture results to gain a more in-depth understanding of species’ dispersal capabilities. For example, genetic methods, such as assignments tests and spatial genetic autocorrelation show great promise as tools for assessing landscape level patterns of dispersal (Berry et al. 2004; Peakall & Smouse 2006; Blackmore et al. 2011). These new approaches measure genetically effective dispersal at a realistic landscape scale, not just local movement. Mark-recapture and radio- tracking methods alone may miss long distance dispersal as a result of the scale being too

3 small, however when used in conjunction with genetic methods, larger scale processes can be more thoroughly investigated.

1.2.2 Impacts of fragmentation: genetic insights

There is a rapidly growing literature on the use of genetic techniques to answer ecological questions (Berry et al. 2004; Carreon-Martinez & Heath 2010; Manel et al. 2005; Pearse & Crandall 2004; Simmons et al. 2010) and improvements in the availability of DNA data and computational power mean that analytical approaches are now being used in population genetic analysis that were not available previously (Pearse & Crandall 2004). Population genetic approaches such as using mitochondrial DNA (mtDNA) in phylogeographic studies have made a significant contribution to conservation biology in recent years (Byrne 2007). By understanding the phylogeny of species as well as its history in relation to range expansion and contraction, we may be better able to predict how future gene flow will be impacted by threatening processes such as habitat fragmentation (Bermingham & Moritz 1998).

The use of nuclear genes has also resulted in the ability to answer questions of ecological, evolutionary and conservation relevance (Manel et al. 2005). An example of this is the use of assignment methods in investigating population structure. Assignment methods use genetic information such as microsatellites to ascertain population membership of individuals (Manel et al. 2005). Assignment tests have been found to be very accurate in measuring dispersal at finer spatial scales. For example, Berry et al. (2004) compared long-term mark-recapture records of natal dispersal of skinks with results of assignment tests based on microsatellite DNA data and found that they provided similar results. This was an important finding as accurate results were obtained within three months using assignment tests compared with seven years of fieldwork for the mark-recapture data (Berry et al. 2004). In a study on estimating connectivity in marine populations however, Saenz-Agudelo et al. (2009) found assignment tests were not always accurate under different gene flow scenarios, although they did identify immigrants from distant (genetically distinct) populations.

Demographic data and genetic data can be useful in investigating dispersal capability, however combining demographic data with phylogeographic and landscape genetic approaches (such as microsatellite assignment tests) may provide the best approach to answer

4 conservation questions relating to the population structure and demographic history of a species (Pearse & Crandall 2004).

1.3 Fire impacts in south-western Australia

Fire is another critical disturbance affecting fauna populations within south-western Australia. The Australian environment has long been affected by fire and as a result, many flora and fauna species have developed adaptations to variable fire frequencies and regimes (Burrows & Wardell-Johnson 2003; Friend & Wayne 2003; Gill 1981; Kemp 1981; Sutherland & Dickman 1999). It is important here to note that species are not adapted to fire per se, and ‘adaptation’ should be considered in terms of species persistence in an environment with variable fire frequencies, intensities and regimes (Christensen 1980; Gill & Bradstock 2003; Pausas & Keeley 2009). By understanding the adaptive responses by flora and fauna, it has been proposed that fire can be used as a management tool to maintain a mosaic within the environment to maximize ecological sustainability and biodiversity (Attiwill 1994; Burrows & Wardell-Johnson 2003; Friend & Wayne 2003). Aspects such as frequency, size and intensity of fire must be adapted in the management regime to accommodate the attributes and life histories of the organisms within the ecosystem being managed (Attiwill 1994).

There is little published research on the cumulative effects of bushfire on fauna (Friend & Wayne 2003) as opposed to single fire events. There is a need to contribute knowledge to make improvements to prescribed burning and bushfire management that is currently over- simplified and incomplete in predicting impacts of fire regimes on fauna (Friend & Wayne 2003; Sutherland & Dickman 1999). It has been repeatedly claimed in the literature on fire ecology that no one fire regime is favourable for all organisms and that changes in fire regimes can change patterns of biodiversity (Burrows & Abbott 2003; Burrows & Wardell-Johnson 2003; Friend & Wayne 2003; Gill & Bradstock 2003). As a solution to this problem it has also been claimed that by changing aspects of fire regimes such as scale and intensity, a mosaic of patches is created within the landscape (Burrows & Abbott 2003; Gill & Bradstock 2003) and that these patches may provide diverse habitat opportunities thus promoting biodiversity (Burrows & Wardell-Johnson 2003), and protect sensitive or threatened species from the impacts of large, homogenizing bushfires. For example, large scale, high intensity fires may have a severe impact on honey possums (which reside in the elevated vegetation) as they would either be killed as a direct result of the intense fire, or be predated or starve within a

5 few days due to not being able to access suitable flowers upon which to feed (Friend & Wayne 2003). However, if fires are patchy and of a lower intensity, the animals may stand a better chance of surviving the initial blaze as well as finding sufficient vegetation to shelter in, and feed upon.

1.3.1 Impacts of fire: demographic studies

Previous investigations into the effects of fire on fauna have focussed on species’ responses to single fire events using mark-recapture studies, with much of the literature concentrating on small mammal succession patterns following fire events (e.g. Catling et al. 2001; Fox 1982; Fox & McKay 1981; Monamy & Fox 2000; Sutherland & Dickman 1999). This literature shows that species’ responses to disturbance can be quite varied, with marked contrasts in responses indicating that responses to disturbance in the landscape are quite complex. Land managers may therefore be required to tailor management actions to the needs of particular elements of the biota rather than use indicator species responses to predict those of other species present within the landscape (Lindenmayer et al. 2008). For example, where evidence suggests that a particular species is sensitive to altered habitat from fragmentation or burning regimes, management can be directed at those species in the landscape (Lindenmayer et al. 2008).

Several studies on honey possums have been undertaken as part of a large long-term research project in the Fitzgerald River National Park. Mark-recapture data collected in mature, long- unburnt vegetation, suggest honey possums were sedentary (Garavanta et al. 2000). Garavanta et al. (2000) also suggested that if this sedentary behaviour is characteristic of the species then dispersal after a disturbance might be impeded. For example, they may not be able to effectively recolonise an area after local extinction due to fire. However, another study in the Fitzgerald River region caught honey possums in recently burnt areas only a few months after fire with higher numbers of juveniles caught than adults (Everaardt, 2003). This study also showed very high turnover rates in recently burnt areas, suggesting that honey possums may be more mobile than previously thought. It was also found that while honey possums appeared to prefer foodplants that do not re-flower after fire for many years (Banksia and Dryandra species), the availability of alternatives provided adequate sustenance (Everaardt, 2003). Bradshaw & Bradshaw (2002), working in Scott River National Park, found that the home ranges and utilization areas of honey possums did not differ significantly in burnt and unburnt areas of their study, despite large differences in plant cover. Home ranges in both

6 areas were more extensive than reported by Garavanta but these data also indicate successful dispersal or persistence in burnt areas. If honey possums have successfully recolonised burnt areas and are using them in the same way, it is perplexing that they showed little or no dispersal capabilities in the earlier study (Garavanta et al. 2000).

1.3.2 Impacts of fire: genetic insights

Studies on the impacts of fire on the marsupial fauna of Australia have generally been restricted to the use of demographic data from mark-recapture results (see Sutherland & Dickman 1999). The current knowledge of honey possum behaviour based on demographic data is contradictory (Bradshaw et al. 2007; Everaardt 2003; cf. Garavanta et al. 2000; Wooller et al. 1981). Garavanta et al. (2000) and Wooller et al. (1981) found this species was sedentary suggesting they would not disperse and effectively re-colonise after disturbance events such as fire or imposed fragmentation. These inferences are contradicted by the discovery of high number of juveniles in recently burnt areas (Everaardt 2003), the fact that they do not build nests or retain their young suggesting young disperse (Russell & Renfree 1989) and the extensive use of burnt areas in the Scott River region (Bradshaw et al. 2007). As already described for fragmentation studies, the use of genetic data may provide a much more detailed insight into longer-term impacts of fire on aspects such as metapopulation structure, dispersal capabilities and potential selection pressures from fire regimes (He et al. 2010). Complemented by demographic data, genetic data may generate new insights into landscape management for an ancient marsupial lineage in an ancient landscape.

This study therefore investigated the impacts of fragmentation, matrix structure and fire history on honey possum structuring and population dynamics using both demographic and genetic data. Previous studies have focused on populations across the south coast of Western Australia. My data were collected close to Perth in the Yanchep-Gnangara area and add a new geographic dimension to data for this species.

1.4 Impacts of fragmentation and fire: a case study in the Gnangara and Yanchep pine plantation

The Gnangara/Yanchep pine plantation north of Perth in south-western Australia is an example of habitat fragmentation at a local scale. The plantation is approximately 50,000 ha in size and contains small remnant patches of honey possum habitat, some of which are completely isolated from the large, continuous areas of surrounding native vegetation in Yanchep National Park and Yeal Nature Reserve (Figure 1.2). The remnant patches within the plantation range in size from 6 to just over 100 ha and support coastal heath and Banksia woodland habitat. Most of the native vegetation was cleared some 60 –70 years ago when the pine plantations were established and the remnants were left usually because they were unsuitable topography for planting and harvesting pine trees.

The plantation is a substantial size, which not only fragments the native habitat within it, but also separates Yanchep National Park on the western side from Yeal Nature Reserve in the east. The plantation is situated on the Gnangara Mound, which includes the largest connected area of remnant native woodland on the Swan Coastal Plain (Horwitz et al. 2008). The area is also subject to a rotational prescribed burning regime of seven year intervals by DEC (Government of Western Australia 2009), and as a result, contains patches of habitat of differing fire ages.

 How does habitat fragmentation and fire impact on the dispersal of honey possums?  How can results from this study be used to manage fire-affected fragmented landscapes for honey possum conservation?

The specific intended outcomes were:

i) Broad scale phylogeography: A study on the broad scale phylogeography of honey possums. To investigate species level patterns of historical dispersal that may be affected by historical natural habitat and more modern anthropogenic fragmentation.

ii) Local, landscape level overview of dispersal patterns: This will be an overview of patterns of dispersal of honey possums in relation to distribution and abundance, as well as fire history and habitat fragmentation patterns (based on both demographic and genetic data).

iii) Management context: The last outcome is to build plans for habitat connectivity, for long term sustainable populations that are under continuing threat from habitat clearing and development, and fire.

Figure 1.1 Yanchep National Park and Yeal Nature Reserve located approximately 60 km north of Perth, Western Australia. The Gnangara and Yanchep pine plantations are located between Yanchep National Park in the west and Yeal Nature Reserve in the east, and contain scattered fragments of remnant native vegetation that forms honey possum habitat.

1.5 Thesis context

The honey possum, Tarsipes rostratus, is a small marsupial endemic to south-western Australia. It is one of the smallest Australian marsupials with adult males weighing 7-11 grams and adult females 8-16 grams. Honey possums have a number of morphological and physiological adaptations as they feed exclusively on nectar and pollen (Russell & Renfree 1989). Honey possums are dependent on coastal heath and Banksia woodland habitats of south-western Australia (Wooller et al. 2004), a region that is under continuing pressure from urban development and other land use changes. The distribution of the honey possum follows floristic patterns associated with two nodes of high species richness of the plant family Proteaceae, associated with the sandplains of the western and southern coasts of Western Australia (Figure 1.2). Honey possum distribution was probably once continuous throughout the south-west; however, most populations now exist within these two coastal zones (Garavanta 1997). This research project focuses on the honey possum and its ability to persist in a fragmented landscape where continuing development and fire are critical repeating disturbances.

Chapter 1 has provided an introduction to conservation issues including threats and disturbances to mammal species in south-western Australia. For honey possums, fragmentation and fire are likely critical threats to persistence in a disturbed environment. The second chapter of this thesis investigates phylogeographic pattern in honey possums potentially reflecting both deep historical and modern habitat fragmentation patterns. The honey possum is the only species in the family Tarsipedidae and represents a monophyletic clade approximately 30 million years old (Kirsch et al. 1997; Bininda-Edmonds et al. 2007). Honey possums may follow the broad regional patterns of divergence shown by other south- western Australian fauna species but because of their dependence on plants as food sources, may more closely track biogeographic patterns in the flora (patterns for both groups in Byrne et al. 2011). For example, Hopper & Gioia (2004) suggested historical biogeographical tracks that divided regions with similar patterns of speciation of flora in south-western Australia. The tracks divided the south-west into four regions: the high rainfall zone along the west coast and south-west corner, the south-west inland, the southeast coastal zone, and a north-south transitional rainfall region inland from the coast. Chapter 2 therefore poses the question: What are the historical patterns of honey possum dispersal in south-western Australia?

Chapter 3 investigates the local distribution of honey possums in a fragmented landscape using trapping results from a mark-recapture study in the Gnangara and Yanchep Pine Plantations aimed at assessing movement patterns and population structure and persistence. This chapter poses two research questions:

 Does habitat fragmentation affect the distribution and dispersal patterns of honey possums at a local scale?  Are there barriers to dispersal associated with a fragmented landscape such as the Gnangara area?

Chapter 4 reports genetic techniques used to further investigate honey possum dispersal abilities in fragmented habitats. This chapter also combines demographic and genetic analyses to obtain a more detailed understanding of honey possum movement patterns and gene flow in the landscape. Chapter 4 poses the questions:

 Is honey possum dispersal influenced by geographical isolation of habitat at a local scale?  Do habitat patches connect habitat and facilitate movement?

The impact of fire and other habitat variables on occurrence and population structure is reported in Chapter 5 critically relating the results from this study to the particular life history attributes of honey possums. This chapter poses the questions:

 Does fire history or habitat condition influence fine-scale genetic structure of honey possum populations?  Do long unburnt or unfragmented habitat areas provide source populations?

In Chapter 6 management implications are discussed in relation to the results obtained in this study and how this research can contribute to land management and ongoing conservation efforts for honey possums.

Figure 1.2 The distribution of the honey possum, Tarsipes rostratus in south-western Australia. Dots on the map represent specimens recorded by NatureMap (Department of Environment and Conservation, Western Australia).

Chapter 2: Phylogeographic structure of the honey possum, Tarsipes rostratus in south-western Australia 2.1 Introduction to Phylogeography 2.1.1 The study of phylogeography

Phylogeography is the field of study that deals with the processes of geographical distributions of genealogical lineages (Avise 1998), and is the link between the study of micro- and macroevolutionary processes (Bermingham & Moritz 1998). Phylogeography attempts to take into account the geographic distribution of a species in establishing their phylogeny and understanding the effects of evolutionary processes upon that phylogeny. Phylogeographic analyses can contribute to ecological and evolutionary studies in various ways (Bermingham & Moritz 1998). For example, phylogeography can contribute to conservation management by understanding the historical responses to landscape changes and identifying areas of evolutionary isolation (Moritz & Faith, 1998). Early phylogeographic studies used mitochondrial DNA sequence data (mtDNA) (Avise 1998; Bermingham & Moritz 1998), which allowed genealogies to be traced across the genetic boundaries between populations, species and higher taxonomic levels (Bermingham & Moritz, 1998) but they suffered from the weakness that they generated gene not species trees. More recent phylogeographic studies have continued that approach (e.g. Edwards & Roberts 2011) but have also added nuclear genes for improved phylogeographic resolution (e.g. Cooper et al. 2011). For spatial genetic analyses of populations, nuclear genes have been used to estimate allele frequencies within populations and in analysis of phylogeographic history, population structure and dispersal (Avise, 2009).

2.1.2 Evolutionary and geographical context

The flora of the south-west of Australia has developed in isolation since the mid Tertiary, resulting in a very remarkable and unique flora (Hopper 1979; Nelson 1981). South-western Australia was a centre of diversification for Australian plant genera (Nelson 1981), and is renowned for its flora, especially for its high rate of endemism (Hopper 1979; Hopper & Gioia 2004; Myers et al. 2000; Nelson 1981), which is regarded as indicative of long periods of isolation (Nelson 1981). The evolution of this floral diversity within the south-west has been fairly extensively explored and is relatively well understood (Hopper 1979; Hopper & Gioia 2004), however the processes involved in diversification for endemic animal species within

14 south-western Australia has undergone comparatively little investigation (Edwards et al. 2007; Edwards & Roberts 2011, Cooper et al. 2011). For long-term conservation, there is a need to understand the evolutionary processes that generate diversity of fauna as well as flora and to promote future diversification and preserve evolutionary potential (Moritz 2002).

Although relatively little is known about the phylogeography of the south-western fauna, a number of recent studies (e.g. Edwards et al. 2007; Gouw et al. 2006; Cooper et al 2011) have provided evidence that some faunal species may follow similar divergence patterns to those of the south-western flora. Hopper & Gioia (2004) suggested historical biogeographical tracks that divided regions with similar patterns of speciation of flora in south-western Australia. The tracks divided the south-west into several regions: the high rainfall zone along the west coast and south-west corner, the south-west inland, the southeast coastal zone, and a north-south transitional rainfall region inland from the coast.

There are several examples of south-west fauna following these broad regional patterns of divergence. For example, in a study on the phylogeographical patterns of a south-western Australian frog, Edwards et al. (2007) found that there were two major lineages of Crinia georgiana one of which occurred in the high rainfall zone of the west coast and the other along the southeast coastal zone. Gouw et al. (2006) found similar divergence patterns in their study on Cherax preisii, a freshwater crayfish. Gouw et al. (2006) established a clear separation of western and southern C. preisii populations, which also corresponded to observed morphological differences within the species. A similar phylogeographical pattern could be expected for the honey possum, Tarsipes rostratus. This endemic species feeds solely upon the nectar and pollen of flowers, favouring those belonging to the families Proteaceae and Myrtaceae, and in particular the genus Banksia (Russell & Renfree, 1989). It is therefore plausible that the honey possum has divergence patterns similar to those of the south-western flora: the food plants upon which it depends.

2.1.3 Phylogeography- a tool for conservation biology

Phylogeographical analyses can also be a useful tool in aiding conservation priority decisions in relation to habitat fragmentation. The brush-tailed phascogale (Phascogale tapoatafa) was historically found in all mainland Australian states but has experienced a massive range contraction, with south-western populations separated from south-eastern populations by

1500 km of unsuitable habitat (Spencer et al. 2001). Spencer et al. (2001) identified distinct geographical clades in their study, which for conservation management purposes represent clear Evolutionary Significant Units (ESUs). This conclusion was based on significant genetic differences between northern, south-western and south-eastern populations of P. tapoatafa, which may reflect a limited capacity to disperse in fragmented habitat (Spencer et al. 2001). The establishment of ESUs through the use of phylogeographical analyses can drive the approach and design of translocation programs, conserving historically isolated and independently evolving sets of populations (Moritz 1999).

Just as phylogeographical studies can identify potential barriers to dispersal by identifying clades representing clear ESUs; it can also illustrate where there may be a lack of geographical structuring within a particular species range. Phylogenetics can also be useful in assessing genetic relationships between multiple sub-species and morphologically similar species (Pope et al. 2001; Zenger et al. 2005), especially in relation to range contractions. For example, several phylogenetic studies of the sub-species of the southern brown bandicoot (Isoodon obesulus) showed little support for the current morphologically based taxonomy of Isoodon (Pope et al. 2001; Zenger et al. 2005). Both studies also suggested that I. auratus (golden bandicoot) and I. obesulus are representatives of a single species that was once widespread across Australia and has undergone range expansions and contractions.

By understanding the phylogeny of a species as well as its history in relation to range expansion and contraction, we may be better able to predict how future gene flow will be impacted by threatening processes such as habitat fragmentation, fire, climate change and other changing land uses. In this study, the phylogeography of the honey possum, a tiny marsupial endemic to south-western Australia, was investigated as a first step to understanding population structure at a broad level, and to gain a more thorough understanding of the evolutionary potential of this species.

2.2 Methods 2.2.1 Sample collection

Samples for this study were collected from both live T.rostratus individuals, as well as from the Western Australian Museum Tissue Collection and stored in 70% ethanol. As part of this study, ear biopsies from live individuals were obtained from pitfall trapping at Gnangara, Yanchep, Walpole, and Cooljarloo Mine. Details of live trapping procedures are outlined in Chapter 3. The other tissue samples that were obtained from live individuals were provided by researchers from additional research sites near Margaret River, Augusta and Ellenbrook. The remaining tissue samples (skin biopsies) were from the Western Australian Museum Tissue Collection. A total of 76 individuals were sampled from 22 locations across south-western Australia, with 37 samples from live trapping and 39 samples from the WA Museum tissue collection (Table 2.1, Figure 2.1). Although sampling did not include individuals from every known population within the south-west, it did represent populations from across the majority of the range.

Table 2.1 Latitude and longitude of Tarsipes rostratus populations sampled across south- western Australia. Abbreviations in parentheses represent sample abbreviations used in Table 2.2 for haplotypes found.

Latitude Longitude Live Trapping Locations: Gnangara Pine Plantation (31-57) 31°30`18"S 115°44`33"E Walpole (WL#) 34°53`32"S 116°54`10"E Ellenbrook (EL#) 31°44`32"S 115°59`20"E Cooljarloo Mine (CM#) 30°39`47"S 115°21`12"E JL (West Coast) (JL#) 33°52`41"S 115°11`10"E Augusta (AU#) 34°15`33"S 115°14`32"E

WA Museum Tissue Collection Localities: Albany (AL#) 35°00`00"S 117°52`00"E MIS (Eneabba Misc.) (MIS) 29°51`25"S 115°16`05"E Fitzgerald River National Park (FR#) 34°59`00"S 118°10`00"E Wanneroo (WN#) 31°18`00"S 115° 48`00"E Boddington (BD#) 32°58`00"S 116°27`00"E Eneabba (EA#) 29°54`00"S 115°16`00"E Zuytdorp (ZP#) 27°15`42"S 114°01`09"E Hopetoun (HT#) 33°40`55"S 120°11`56"E Carnamah (CH#) 29°40`58"S 115°52`58"E Yanchep (YC#) 31°33`00"S 115°41`00"E Margaret River (MR#) 33°54`50"S 115°00`57"E Waggrakine (WG#) 28°46`00"S 114°37`00"E Bella Vista Nature Reserve (BV#) 28°32`00"S 114°38`00"E Wicherina Dam Reserve (WH#) 28°40`00"S 115°00`00"E Muchea (MC#) 31°38`32"S 115°55`03"E Ravensthorpe (RV#) 33°34`30"S 120°36`21"E

 Zuytdorp

 Bella Vista Nature Reserve  Wicherina Dam  Waggrakine

 Carnamah  Eneabba

 Cooljarloo Mine  Muchea  Yanchep/Gnangara  Ellenbrook

 Boddington

 Ravensthorpe  West Coast  Hopetoun  Margaret River   Augusta Fitzgerald River  Walpole National  Albany Park

Figure 2.1 Phylogeographic sampling locations representing populations from across the range of Tarsipes rostratus in south-western Australia. A total of 76 individuals were sampled from 22 locations across south-western Australia, with 37 samples from live trapping and 39 samples from the WA Museum tissue collection. While sampling did not include individuals from every known population within the south-west, it did represent populations from across the majority of Tarsipes’ range.

2.2.2 DNA extraction

DNA was extracted following the exact protocol of the DNeasy Blood and Tissue Kit (Qiagen). DNA samples were then stored at 4°C until required.

2.2.3 Mitochondrial DNA amplification and sequencing

The 5´ end of the mitochondrial DNA (mtDNA) control region was amplified (845 base pairs) in a polymerase chain reaction (PCR) using primers 47-F (5’-AAT ACT GGC CTT GTA AGC CAA-3’) and 1461-R (5’-TAT GAA TAA TTG GGG TTG TAG-3’), which were developed from a Tarsipes control region sequence. DNA was amplified in a 25µL reaction containing 2.5µL 10x Buffer,

1.25µL 50mM MgCl2, 2.0µL 10mM dNTPs, 1.0µL of 10µM primer, 1.25µL 1U BIOTAQ™ Red DNA polymerase (Bioline), 15.0µL water and 1.0µL of DNA. The PCR cycle consisted of 1 min at 94°C, 1 min at 57°C, and 1 min at 72°C for 27 cycles, followed by a 10 min final extension at 72°C. PCR products were run out on a 1.5% agarose gel and cleaned with a Mo Bio UltraClean DNA Purification kit (Mo Bio Laboratories) for sequencing. Sequences were generated with BigDye™ Terminator automatic sequencing (Macrogen) and sequence editing and alignment were done manually.

In addition to the mtDNA control region, 584bp of the mtDNA gene cytochrome c oxidase subunit I (COI) were amplified, due to initial difficulties in amplifying some individual samples from the control region. Mitrovski et al. (2007) also amplified the COI gene in their study on the mountain pygmy possum (Burramys parvus) after experiencing difficulties in amplifying the cytochrome b region. As Mitrovski et al. (2007) had success in using the universal primers described in Folmer et al. (1994) to amplify the COI gene in B. parvus, these primers were adapted slightly (using a Tarsipes sequence) to amplify the COI gene in Tarsipes mtDNA. DNA was amplified in a 25µL reaction containing 2.5µL 10x Buffer, 1.25µL 50mM MgCl2, 2.0µL 10mM dNTPs, 1.0µL of 10µM primer (LCO1490-Tros: 5´-GAT GAC CAG ATT TAT AAC GTT GTA G-3´; HCO2198-Tros: 5´-TAG ACT TCT CCA TGT CCA AAA AAT CA-3´), 1.25µL 1U BIOTAQ™ Red DNA polymerase (Bioline), 13.0µL water and 3.0µL of DNA. The PCR cycle consisted of 1 min at 94°C, 1 min at 57°C, and 1 min at 72°C for 40 cycles, followed by a 10 min final extension at 72°C. PCR products were run out on a 1.5% agarose gel and cleaned with a Mo Bio UltraClean DNA Purification kit (Mo Bio Laboratories) for sequencing. Sequences were generated with BigDye™ Terminator automatic sequencing (Macrogen) and sequence editing and alignment

20 were done manually. Although a total of 76 individuals were sampled, DNA could only be successfully amplified from 70 individuals.

2.2.4 Data analyses

Phylogenetic analysis was performed using MEGA 4 (Tamura et al. 2007). COI sequences for 70 animals were trimmed, aligned, and a neighbour-joining tree (Saitou & Nei 1987) was formed using Kimura 2-parameter model (Kimura 1980) as the distance measure. Nodal support was determined using 1000 bootstrap replicates (Felsenstein 1985). To further examine the robustness of the conclusions drawn based on the neighbour-joining tree, maximum- parsimony analysis was also conducted, with the most parsimonious trees established using the close-neighbour-interchange method (Nei & Kumar 2000). Burramys parvus (mountain pygmy possum), Cercartetus concinnus (Western pygmy possum), and Trichosurus vulpecula (brushtail possum) were used as outgroups in both the neighbour-joining tree and maximum parsimony analysis. Trees were condensed into a majority rule consensus tree. Phylogenetic analysis was further investigated by constructing a haplotype network using the statistical parsimony approach as implemented in the program TCS Version 1.2.1 (Clement et al. 2000).

Population differentiation and comparisons were examined using Arlequin Version 3.5 (Excoffier et al. 2005). Genetic diversity and sequence divergence were calculated, and differentiation among populations was examined by means of exact tests of differentiation.

Divergence among sites was examined using pairwise FST values, where statistically significant divergence was determined using 10,000 permutations of haplotypes between populations (Excoffier et al. 2005). To test if the data conformed to neutral expectations and demographic equilibrium, Tajima’s (1989) D and Fu’s (1997) Fs tests were calculated using Arlequin 3.5 (Excoffier et al. 2005). The significance of the statistics was tested by generating random samples under the hypothesis of selective neutrality and population equilibrium using a coalescent simulation with 10,000 permutations. Fu’s Fs test is especially sensitive to population demographic expansion, which generally leads to large negative Fs values.

2.3 Results

Approximately 584 bp of COI sequence were analysed from70 Tarsipes individuals. Excluding the outgroups (Burramys, Cercartetus, Trichosurus), 26 variable sites were found within the sequence (4.5%), which produced 26 haplotypes. Eight haplotypes were found to be shared among localities (Table 2.2), however only three haplotypes were common to both north western populations and south coast populations. Haplotype 2 was the most abundant (present in 30% of all sampled individuals) and most widely shared haplotype, found in Gnangara, Albany, Augusta, Cooljarloo Mine, Bella Vista Nature Reserve, Ellenbrook, Hopetoun, Muchea, Wicherina Dam Reserve, Margaret River, Walpole, and Ravensthorpe populations. Haplotype 6 was also abundant, representing 18.6% of all sampled individuals from eight different populations. Among the populations Waggrakine, Carnamah, and Boddington, possessed unique haplotypes not found in any of the other populations sampled (Table 2.2).

Levels of sequence divergence (variation) within populations were very low and ranged between 0 (for populations containing multiple samples with the same haplotypes) and 0.27% nucleotide diversity. The mean sequence divergence among all populations was 0.08%. The Yanchep population had the highest sequence divergence with 0.27%, followed by the Ellenbrook population with 0.18%. The Ravensthorpe, Margaret River, and Boddington populations had multiple individuals sampled: all with the same haplotype. There were several significant differences in pairwise FST comparisons between populations (Table 2.3). Cooljarloo

Mine populations were significantly divergent from Albany (FST =0.29, p=0.01346 ± 0.0011),

Augusta (FST =0.20, p=0.00861 ± 0.0008), Hopetoun (FST =0.17, p=0.02752 ± 0.0016), Walpole

(FST =0.28, p=0.02198 ± 0.0015) and Boddington (FST =0.66, p=0.01495 ± 0.0013) populations.

Eneabba populations were significantly divergent from Augusta (FST =0.17, p=0.04465 ±

0.0021) and Hopetoun (FST =0.20, p=0.03653 ± 0.0018); and Muchea was significantly divergent from Boddington (FST =0.82, p=0.04940 ± 0.0018). Exact tests of differentiation also revealed a significant difference between Cooljarloo Mine and Hopetoun populations (p=0.01968 ± 0.0012), and between Muchea and Eneabba populations (p=0.03955 ± 0.0022).

Tajima’s (1989) D and Fu’s (1997) Fs tests for demographic equilibrium suggested an overall neutrality of the data (D= 0.00038, p= 0.85; Fs= -0.22076, p> 0.05) indicating little overall evidence of demographic expansion or contraction of populations. The Fu’s (1997) Fs statistic

22 did however give a significantly negative Fs value (Fs= -3.51388, p=0.0056) for the Augusta population, and a significantly negative Fs value (Fs= -3.18164, p= 0.003) for the Cooljarloo Mine population, suggesting that these sites may have had recent population growth. It should be noted here however that significant differences could simply be a property of small sample sizes.

Neighbour-joining and maximum parsimony analyses produced trees with similar overall tree topology showing very little phylogeographic structure (Figure 2.2 and Figure 2.3) with Cercartetus concinnus (Western pygmy possum), Burramys parvus (mountain pygmy possum), and Trichosurus vulpecula (brushtail possum) used as the outgroups. Both neighbour-joining and maximum parsimony trees did not reveal any associations between haplotypes and geographic position. For example, the only clades with any real bootstrap support (69% for neighbour-joining and 67% for maximum parsimony) were haplotypes 11 and 13, both of which were from Augusta populations. The remaining clades had very weak bootstrap support reflecting no evident phylogeographic structuring.

A haplotype network constructed using the statistical parsimony approach (Clement et al. 2000), within the 95% confidence limits of a parsimonious connection, also revealed very little geographic structuring (Figure 2.4). Haplotype 6 had the biggest outgroup weight, represented by Gnangara, Cooljarloo Mine, Hopetoun, Augusta, Muchea, Wicherina Dam Reserve, Eneabba (Misc), and Yanchep populations. The network showed some minor grouping of the haplotypes 9, 1, 17, 16, 19, 21, 24, and 18, with all but haplotype 9 representing only north western populations. Overall however, the network showed no evidence of strong phylogeographic structure in this species.

4 6 1 1 1 1 1 1 2 2 2 2 2 2 2 2 3 3 3 3 3 4 4 4 4 4 5 1 5 5 7 7 8 9 0 1 2 3 7 8 8 9 0 4 7 7 8 0 1 5 6 7 3 6 2 4 6 2 1 9 8 7 9 1 2 1 6 8 2 8 1 5 1 3 5 4 31 (1) 1 C C C G G A T A T G C C A T A T C C A T A A C C T C 34,AL2,AU4,CM8,CM10, EL1,HT1,HT5,HT6,HT7,MC1,MC5,MR1,MR2,RT1,RT2,RT3,W H1,WL4, BV1,CM2 (2) 2 ...... G . . . . G ...... 39,FR2 (3) 3 . . . . . G . G . . . . G ...... 40 (4) 4 . . . . . G A G . . . . G ...... 41,58,EL2,YC1 (5) 5 . T A . . G A . . A . . G ...... 44,45,57,CM9,HT4,JL1,JL3,MC2,MC3,MC4,MIS,WH2,YC2 (6) 6 ...... G ...... L1 (7) 7 ...... G . . T T G ...... AL3 (8) 8 . T . . . G A G . . . . G ...... AU1 (9) 9 ...... G ...... C ...... AU2 (10) 10 ...... G . . . . G ...... C . AU3 (11) 11 . . . . . G . G . . . . G . . . T ...... AU5 (12) 12 ...... G . A . . G ...... AU6 (13) 13 . . . . . G . G . . . . G . . . T ...... T BD1,BD2 (14) 14 . . . A . G . G . . . . G ...... CH1 (15) 15 ...... G . . . . G ...... G T . . . . CM1,CM4,CM5 (16) 16 ...... G ...... CM3 (17) 17 A ...... CM6,EA2, EL4, WN1 (18) 18 . T . . . . . G ...... CM7 (19) 19 T ...... G ...... EA1 (20) 20 . . . A . . G . . . . G G ...... EA3,EA4,ZP1 (21) 21 ...... G C ...... HT2 (22) 22 . . . . . G ...... G ...... HT3 (23) 23 . . . . . G . G . A . . G . T . . A ...... WG1 (24) 24 ...... G ...... T . . WL2 (25) 25 ...... G . . . . G ...... T . . . WL3 (26) 26 ...... G . . . . . G A ...... Table 2.2 Twenty-six haplotypes found in 70 Tarsipes individuals sampled from 22 locations across south-western Australia (see Table 2.1 for details on abbreviated sampling locations). Eight haplotypes were shared among localities, and only three haplotypes were common to both north western populations and south coast populations. Haplotype 2 was the most abundant and most widely shared haplotype. Haplotype 6 was also abundant, and Waggrakine, Carnamah, and Boddington, possessed unique haplotypes not found in any of the other populations sampled.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 1 Gnangara 0.00 2 Albany 0.02 0.00 3 Augusta 0.06 0.01 0.00 4 Cooljarloo Mine 0.13 0.29 0.20 0.00 5 Fitzgerald River NP 0.40 0.67 0.49 0.41 0.00 6 Ellenbrook 0.18 0.16 0.06 0.24 0.56 0.00 7 Hopetoun 0.02 0.00 0.04 0.17 0.33 0.07 0.00 8 West Coast 0.00 0.22 0.11 0.21 1.00 0.05 0.19 0.00 9 Muchea 0.08 0.25 0.10 0.10 0.63 0.22 0.09 0.02 0.00 10 Mis 0.40 0.25 0.26 0.78 1.00 0.56 0.11 0.00 0.50 0.00 11 Wicherina Dam 0.13 0.11 0.18 0.29 0.33 0.13 0.19 0.00 0.50 1.00 0.00 12 Yanchep 0.30 0.10 0.03 0.30 1.00 0.42 0.06 0.00 0.29 1.00 0.17 0.00 13 Boddington 0.25 0.30 0.29 0.66 1.00 0.27 0.40 1.00 0.82 1.00 0.80 0.25 0.00 14 Bella Vista NR 0.48 1.00 0.82 0.13 1.00 0.75 0.90 1.00 0.00 1.00 1.00 1.00 1.00 0.00 15 Carnamah 0.22 0.09 0.22 0.60 1.00 0.00 0.37 1.00 0.77 1.00 0.60 0.20 1.00 1.00 0.00 16 Eneabba 0.12 0.15 0.17 0.09 0.11 0.07 0.20 0.14 0.08 0.78 0.18 0.11 0.53 0.33 0.33 0.00 17 Zuytdorp 0.02 0.09 0.14 0.27 1.00 0.17 0.30 1.00 0.57 1.00 0.33 0.50 1.00 1.00 1.00 0.78 0.00 18 Margaret River 0.04 0.20 0.21 0.15 1.00 0.06 0.26 1.00 0.29 1.00 0.00 0.00 1.00 0.00 1.00 0.11 1.00 0.00 19 Ravensthorpe 0.07 0.00 0.07 0.24 1.00 0.13 0.12 1.00 0.39 1.00 0.25 0.25 1.00 0.00 1.00 0.25 1.00 0.00 0.00 20 Walpole 0.08 0.00 0.04 0.28 0.14 0.03 0.02 0.28 0.26 0.14 0.10 0.01 0.49 1.00 0.20 0.20 0.20 0.20 0.00 0.00 21 Waggrakine 0.02 0.09 0.14 0.27 1.00 0.17 0.30 1.00 0.57 1.00 0.33 0.50 1.00 1.00 1.00 0.07 1.00 1.00 1.00 0.20 0.00 22 Wanneroo 0.16 0.17 0.34 0.50 1.00 0.27 0.49 1.00 0.75 1.00 0.60 0.20 1.00 1.00 1.00 0.33 1.00 1.00 1.00 0.38 1.00 0.00

Table 2.3 Significant differences in pairwise FST comparisons between populations, numbers in bold indicate significant differences. Cooljarloo Mine populations were significantly divergent from Albany, Augusta, Hopetoun, Walpole and Boddington populations. Eneabba populations were significantly divergent from Augusta and Hopetoun; and Muchea was significantly divergent from Boddington.

9 21 6 24 16 24 19 18 15 25 2 7 23 10 20

23 12 23

32 4 8

43 14 3

22 11 69 13 1 17 22 26 5 Outgroup-CercartetusOutgroup- Cercartetus concinnus

99 Outgroup-BurramysOutgroup- Burramys parvus 82 Outgroup-VulpeculaOutgroup- Trichosurus vulpecula

Figure 2.2 Neighbour-Joining tree with topology showing very little phylogeographic structure, run with 1000 bootstrap replications. Numbers at nodes indicate bootstrap estimates. Numbers 1-26 represent haplotypes. Neighbour-joining tree did not reveal any associations between haplotypes and geographic position.

3 14 23

29 11 67 13 22 4

26 5 36 8 26 12 15 25 2 7 10 20 18 19 24 6 9 16 21 1 17 OutgroupOutgroup-Cercartetus- Cercartetus concinnus 29 Outgroup- Burramys parvus 100 Outgroup-Burramys 53 OutgroupOutgroup-Vulpecula- Trichosurus vulpecula

Figure 2.3 Maximum-Parsimony tree run with 1000 bootstrap replications, Numbers at nodes indicate bootstrap estimates. Numbers 1-26 represent haplotypes. Maximum-Parsimony tree did not reveal any associations between haplotypes and geographic position.

Figure 2.4 A haplotype network constructed using the statistical parsimony approach, within the 95% confidence limits of a parsimonious connection, reveals very little geographic structuring. Haplotype 6 had the biggest outgroup weight, represented by Gnangara, Cooljarloo Mine, Hopetoun, Augusta, Muchea, Wicherina Dam Reserve, Eneabba (Misc), and Yanchep populations. The network showed some minor grouping of the haplotypes 9, 1, 17, 16, 19, 21, 24, and 18, with all but haplotype 9 representing only north western populations. Overall, the network showed no evidence of strong phylogeographic structure in this species.

2.4 Discussion

The genetic analyses presented in this study reveal very little phylogeographic structure across the geographic range of T. rostratus, and the phylogeny did not appear to reflect the historical, biogeographical tracks of the flora of the south-west. Overall, sequence divergence among the populations sampled was very low with an average of 0.08% sequence divergence (variation) between all 22 populations sampled. Cercartetus concinnus, the Western pygmy possum, is a similar sized possum to the honey possum with a similar range and habitat requirements in south-western Australia but extending onto Eyre Peninsula and south-eastern South Australia. Cercartetus concinnus also exhibited low sequence divergence (0-0.75%) between disjunct Western Australian and South Australian populations separated by a maximum distance of approximately 2300 km (Pestell et al. 2008). An overall lack of phylogeographic structure was also found for the Western pygmy possum, which suggested recent genetic connectivity between populations (Pestell et al. 2008). Similarly, the results obtained in this study indicate there has likely been recent genetic connectivity among honey possum populations. The evidence of connectivity is also reflected in the topologies of bootstrapped neighbour-joining and maximum parsimony analysis, as well as the haplotype network constructed using the statistical parsimony approach – none show any structure obviously correlated with geographic origin of samples.

Contrary to these results, there was some minor indication of phylogeographical structuring reflected in the pairwise FST comparisons between populations and exact tests of differentiation. These results indicated that two of the north-western populations (Cooljarloo Mine and Eneabba) were significantly divergent from southern populations such as Albany, Augusta, Hopetoun and Walpole. The significant negative Fs statistic calculated by Fu’s (1997) Fs test for demographic equilibrium suggest that the Augusta and Cooljarloo Mine populations fit a model for population expansion –which may contribute to their divergence from more southern sites. These contrary results may also reflect small sample sizes and population expansion at a very local scale.

Although Tajima’s (1989) D and Fu’s (1997) Fs tests provided little overall evidence of demographic expansion or contraction of populations, the Fu’s (1997) Fs statistic did indicate that several populations have had recent population growth at a local scale.

The significant Fs statistics, low nucleotide divergence, lack of overall phylogeographic structuring and common haplotypes shared among geographically distinct regions supports the hypothesis that honey possums are likely to have experienced recent range expansions at the landscape scale. Sinclair (2001) suggested that a Pleistocene range expansion was likely for quokkas (Setonix brachyurus), which reflected an overall shallow genetic history but with recent restricted gene flow. Zenger et al. (2005) identified a likely large-scale population contraction and expansion associated with historical climatic fluctuations for the southern brown bandicoot (Isoodon obesulus) that was supported by small nucleotide divergence among regions and a common haplotype shared between geographically distinct regions.

If geographic structure has developed recently (e.g. imposed by anthropogenic fragmentation) then haplotypes consisting of those that are widespread throughout the species range, as well as haplotypes that are restricted in distribution could be expected (Sinclair 2001). The haplotypes found for honey possums in this study consist of several haplotypes that were found in north-western populations and south coastal populations, as well as haplotypes that were exclusive to individual populations. It is therefore plausible that honey possums underwent population expansions relating to past climatic fluctuations and may be experiencing the first genetic structuring effects of human induced fragmentation. It is important to note however, that generally the results from this study show that overall phylogeographic structuring was shallow and it is equally probable that honey possums have maintained genetic connectivity.

Continued genetic connectivity among honey possum populations throughout the south-west is supported by evidence of long-distance movements driven by selective foraging (Bradshaw & Bradshaw 1999; Bradshaw et al. 2007). Radio-tracking data and pollen grain identification has indicated that honey possums can travel substantial distances to locate restricted food sources (Bradshaw et al. 2007). During a late-summer study period Bradshaw et al. (2007) found that rather than exploiting non-favoured food sources (due to summer low abundances of known preferred food plants), individuals moved extensively to neighbouring habitats containing their preferred food plants. It is therefore quite possible that if during past climatic fluctuations favoured food plant availability was reduced or varied spatially, honey possums may have maintained gene flow across their range as a result of selective foraging.

Patterns of species’ phylogeography are an important future focus of study. Understanding a species’ response to past fragmentation events and climatic pressures, may provide valuable

30 insight into how a species will respond to current and future pressures of climate change and habitat fragmentation. For example, the frog species Crinia georgiana has an evolutionary history that is very closely related to climate, therefore raising concerns for its ability to cope with future climate change (Edwards et al. 2007). Although from the results of this study it appears as though T. rostratus’ selective foraging behaviour may aid in their genetic connectivity during times of climatic pressure on preferred food plants, the additional pressure of human induced fragmentation may result in a reduced capacity of the species to maintain current gene flow.

Chapter 3: Honey possum distribution within the Gnangara pine plantations 3.1 Introduction 3.1.1 Conflicting land use in south-western Australia

Land managers are dealing increasingly with the issue of conflicting land use all over the world (Parolo et al. 2009; Walter & Stützel, 2009). Anthropogenic activities such as agriculture, forestry and urban development all compete with natural ecosystems for valuable resources and place great pressure on the persistence of many species within those ecosystems (Saunders et al. 1991). This is the case for many ecosystems within south-western Australia. South-western Australia is a region of global significance as it is a biodiversity hotspot (Myers et al. 2000). It coincides with the most developed region of the state of Western Australia, resulting in ongoing, conflicting land use issues. Since European colonisation, most of the region’s vegetation has been altered and generated major conservation issues including massive habitat loss and fragmentation, secondary salinity caused by rising water tables (Caccetta and Dunne 2010), root-rot disease (Phytophthora), invasive weeds, and changing groundwater levels. All have a significant impact on many endemic species (e.g. Groom et al. 2000; Hopper & Gioia, 2004; Horwitz et al. 2008). The resulting conservation issues all interact to further magnify the effects of development. For example, as development continues in the south-west, there is added pressure on natural groundwater supply systems within these ecosystems to sustain growing human populations. As groundwater is extracted to meet the growing anthropogenic demand, existing habitat loss, fragmentation, and Phytophthora further contribute to the degradation of the groundwater supply by reducing the ability of these natural ecosystems to recharge water back into the catchments, and in turn, back into the groundwater supply (Government of Western Australia 2009).

3.1.2 The Gnangara Mound: an example of conflicting land use in south-west Western Australia

The Gnangara Mound is an example of an ecosystem in the south-west where land managers are dealing with conflicting land uses and trying to balance the extraction of water for the continually developing Perth region against biodiversity conservation concerns. The Gnangara groundwater system, the Gnangara Mound, supports extensive pine plantations, a group of around 600 wetlands, and the largest connected area of remnant native woodland on the

Swan Coastal Plain. Shifts from the native woodlands and heath to agriculture and horticulture require intensive localised groundwater extraction and continue to place on-going pressure on conservation values (Horwitz et al. 2008). This area is under considerable threat from further water extraction and changes to land use following removal of major pine plantations (Government of Western Australia 2009). The remnant native vegetation in this area is already highly fragmented due to agriculture, forestry, and development. It is therefore vital that no more remnant habitat is lost due to further changes in land use patterns, as destruction of habitats is a major cause of decline of many species (Lada et al. 2008).

When continuous native vegetation becomes fragmented by land clearing, small isolated populations are likely to result for many species (Hoehn et al. 2007), including the nectar and pollen-dependant honey possum (Tarsipes rostratus). Feeding exclusively on the nectar and pollen of flowers (Russell & Renfree 1989), the honey possum is particularly susceptible to the interaction of present threatening processes such as habitat fragmentation, with further land use changes. In an already fragmented environment, a decline in groundwater and rainfall patterns, and inappropriate fire regimes, may together intensify drying patterns from groundwater extraction and climate change, and therefore the distribution and availability of plants within the landscape (Horwitz et al. 2008). For the honey possum that has a secondary reliance on water supplies to their preferred foodplants, land use change may seriously exacerbate existing threats such as habitat fragmentation.

My goal was to evaluate honey possum densities and population structure in vegetation fragments and adjacent continuous habitat fragments in pine plantations on the Gnangara Mound, north of Perth. I had three main questions:

 What is the pattern of occurrence of honey possums?  Does habitat fragmentation affect the distribution of honey possums?  Is there a difference in estimated population size or structure between fragmented and unfragmented sites?

3.2 Methods 3.2.1 Study species

The honey possum is a small marsupial, with adult females weighing 8-16 grams, and adult males 7-11 grams. Honey possums feed solely on nectar and pollen of flowers, favouring those belonging to the families Proteaceae and Mrytaceae, and in particular the genus Banksia (Bradshaw et al. 2007; Russell & Renfree 1989). Tarsipes rostratus is the only species in the Family Tarsipedidae and it is endemic to south-western Western Australia. Honey possums are restricted to coastal sandplain heaths and open low woodlands with heath understorey (Wooller et al. 2004), and primarily occur in habitat areas of high plant species richness (Russell & Renfree 1989; Wooller et al. 2004). They have an estimated lifespan of around 12 months (Russell & Renfree 1989; Wooller et al. 2000) with no animals in the field or in captivity recorded reaching two years old (Wooller et al. 2004). They can breed continuously throughout the year (Wooller et al. 2000).

Honey possums are not considered to be a threatened species and are relatively common within their range, but they are dependent on coastal heath and Banksia woodland habitat types (Bradshaw et al. 2007). Their dependence on these specific habitat types makes the honey possum a prime candidate for studying the effects of threatening processes within the Gnangara Mound where continuing or expanding water extraction will threaten coastal heaths and Banksia woodlands particularly.

3.2.2 Study area

This study was conducted in the Gnangara region, approximately 60 km north of Perth, Western Australia (Figure 3.1). The Gnangara pine plantation consists of over 18,000 hectares of Maritime pine (Pinus pinaster), with patches of remnant bushland scattered throughout. The remnants supporting coastal heathland and Banksia woodland habitat, range in size from several hectares to several hundred hectares, and vary in their shape and degree of isolation from surrounding, unfragmented areas. The native vegetation was cleared some 50-60 years ago when the pine plantations were established and the remnants were left, usually because they were unsuitable topography for planting and harvesting pine trees (Government of Western Australia 2009). The Gnangara pine plantation separates Yanchep National Park on the western side from Yeal Nature Reserve in the east.

Trapping sites were located in eight remnant bushland patches within the pine plantations on the Gnangara Mound, and four other sites in unfragmented areas in Yanchep National Park and Yeal Nature Reserve (Figure 3.2). Sites were chosen to encompass the available range of levels of isolation and remnant size with replication of sites defined by size and degree of isolation (Table 3.1). All trapping sites were located in vegetation systems dominated by a Banksia overstorey and an understorey formed mainly by low shrubs from the families Myrtaceae, Fabaceae and Proteaceae.

Table 3.1 Trapping sites: size and degree of isolation of remnant vegetation habitat patches in the study area. Categories are used to describe size and degree of isolation of habitat patches, e.g. size category 1 = <100 ha and degree of patch isolation category 1 = unfragmented habitat. Full category descriptions are given below.

Degree of Site Patch size isolation

F1 2 3

F2 3 3

F3 2 2

F4 1 4

F5 1 4

UF6 4 1

UF7 4 1

F8 2 2

UF9 4 1

UF10 4 1

F11 4 4 F12 4 4

Patch size categories: 1=<100ha, 2=100-150ha, 3=150-200ha, 4=>200ha (includes sites with continuous habitat – see Fig. 3.2) Degree of isolation: 1=unfragmented 2=corridor present connecting “fragment” and continuous habitat 3=completely isolated habitat, <150m from continuous habitat 4=completely isolated, >500m from continuous habitat

 Gnangara/Yanchep study area  Perth

Figure 3.1 This study was conducted in the Gnangara region, approximately 60 km north of Perth, Western Australia (31°30`18"S, 115°44`33"E). The Gnangara pine plantation consists of over 18,000 hectares of Maritime pine (Pinus pinaster), with patches of remnant bushland scattered throughout.

 Yeal Nature Reserve

 F12  F11

 UF6  F8

 UF9   F4 F5  UF7  F3  Yanchep National  UF10 Park  F2  F1

Figure 3.2 Trapping sites located within the Gnangara/Yanchep study area. Trapping sites were located in eight bushland patches within the pine plantations on the Gnangara Mound (fragmented sites - F1, F2, F3, F4, F5, F8, F11, F12) and fourother sites in unfragmented areas in Yanchep National Park and Yeal Nature Reserve (unfragmented sites - UF6, UF7, UF9, UF10). Sites were chosen to encompass the available range of levels of isolation and remnant size with replication of sites defined by size and degree of isolation.

3.2.3 Trapping

At each trapping site I installed a grid of 15 pitfall traps (20L buckets), consisting of three trap lines with five pitfall traps on each line. Trap lines were separated by 10 metres and pitfalls were spaced every 10 metres along each trap line in a 3 X 5 array. Each pitfall trap also had a 5 metre drift fence (with 2.5 metres extending either side of the bucket). The drift fence design was a compromise between other studies that use long fences (see Friend et al. 1989) and trapping efforts nearby (Moore River) where high/good capture rates were achieved without drift fences (M. Bamford, Bamford Consulting: pers. comm.). The decision to not use continuous fences was also justified by a personal observation, during trapping, of a honey possum that ran towards the pitfall and climbed up and over the drift fence, away from the trap. Pestell & Petit (2007) also found that there was no significant difference in capture rates for pygmy possums in traps with or without drift fences. The trap lines on each trapping grid (where possible) ran in a north-south direction to maintain continuity throughout the study area. Sampling within the unfragmented areas was structured the same as for the fragmented areas. The sampling regime for this study was structured to compare honey possum capture rates in fragmented and unfragmented habitat, and therefore investigate the pattern of occurrence of honey possums in the study area.

Eight of the 12 sites (F2, F3, F4, F5, UF6, UF7, F8, and UF10) were trapped consistently over the period from spring 2006 to summer 2008, excluding the winter months. Honey possums were caught at all of the eight sites. A further four sites F1, UF9, F11 and F12 were trapped for shorter periods. Site F1 was trapped with the other eight sites for the first four trapping sessions, however when no captures had occurred at the site (during the same period that all other sites recorded honey possums) the site was abandoned the following year in order to concentrate trapping efforts on sites that were known to contain honey possums. Site UF9 was only trapped for one session before it was accidently burnt in a prescribed burn by the Department of Environment and Conservation. The site was abandoned as any nearby sites that were similar in habitat condition that could have been used subsequently, were also burnt in the prescribed burn. Site F11 generated a single capture and Site F12 no captures. Sites F11 and F12 were additional sites not part of the original ten replicate site plan and were only trapped in one session of the study. This was done solely to ascertain whether honey possums were present in these two fragments. There were a further 12 trapping sites in fragmented habitat that were part of the Gnangara Sustainability Strategy (Government of Western Australia 2009. Data from these sites were used in presence/absence tests but honey possums

39 caught in those sites were excluded from demographic analyses due to differences in trapping effort and design).

In any single trapping session, all sites were trapped for four consecutive nights, except on one occasion, where trapping was undertaken for seven consecutive nights as a trial to determine if increasing trap nights would increase capture rates. No honey possums were caught at any sites during the extra three nights and so trapping effort of four consecutive nights was used for the remainder of the study. For the honey possums trapped, I determined sex, weight, head length and basal tail diameter. The capture location was recorded (trap location), the animals were individually marked with ear notches, tissue samples taken and stored in ethanol, and animals were then released at their capture point. The initial trapping component generated a pattern of basic distribution of honey possums within the study area and the tissue samples obtained were used in the genetic analysis component of this project to investigate patterns of dispersal in relation to habitat fragmentation.

3.3 Results 3.3.1 What is the pattern of occurrence of honey possums?

A total of 4725 trap nights resulted in 75 captures of honey possums: an overall trap success rate of 1.6% with only 5/75 being re-captures, giving an extremely low re-capture rate of 6.7% for the entire trapping period. Capture rates varied between the sites with the highest number of captures in the fragmented Sites F4 and F5. Mean honey possum mass was 7.9g for females (n=29) or 9.7g when only females 6.0g or above were considered because they were likely to be adults (n=20). For males, mean mass was 6.0g (n=43) or 7.6g when only males 6.0g or above were considered (n=19). To compare captures, an average number of honey possums caught per trapping session was calculated for each site.

Overall, adult honey possum captures were highest in autumn (Figure 3.3). Juvenile captures followed a similar trend to adult captures, although juveniles peaked slightly earlier. Although captures of males were slightly higher, male and female capture patterns were similar throughout the study period.

Figure 3.3 Honey possum captures throughout the study area from September 2006 to March 2008. Data are amalgamated over all sites. Adult honey possum captures were highest in autumn. Juvenile captures followed a similar trend to adult captures, although juveniles peaked slightly earlier. Although captures of males were slightly higher, male and female capture patterns were similar throughout the study period.

3.3.2 Does habitat fragmentation affect the distribution of honey possums?

Honey possums were captured at all sites trapped consistently. At two other fragmented sites (Sites F1 and F12) and one unfragmented site (Site UF9), no honey possums were captured, which was most likely a result of a much lower trapping effort (F1- four trapping sessions, F12 one trapping session and UF9 one trapping session, all other sites trapped consistently over the period from spring 2006 to summer 2008, excluding the winter months). Assuming equal trapability at all sites and ignoring lower sampling effort at 3 sites (see above) Fisher’s exact test showed that there was no significant difference between fragmented and unfragmented sites in relation to the presence or absence of honey possums within the study area (n=1224, P>0.05).

3.3.3 Is there a difference between fragmented and unfragmented sites for the number of honey possums caught?

An average number of honey possums caught per trapping session was calculated. The majority of sites (Sites F 1, F2, UF6, UF7, F8, UF9, UF10, F11, and F12) fell into the category of having a honey possum average (average number of honey possums caught per trapping session) of between zero and one. Site F3 had an average of between one and two, and Sites F4 and F5 had the greatest honey possum average of all the trapping sites with an average between two and three (Figure 3.4).

Figure 3.4 Average number of honey possums trapped per session in the Gnangara study area at Sites 1 – 12. F1, F2, F3, F4, F5, F8, F11, F12= fragmented habitat, UF6, UF7, UF9, UF10= unfragmented habitat.

One way analysis of variance showed a significant difference between all sites for the overall number of honey possums caught (F=3.61, df=11, P=0.0008), the number of males caught (F=2.59, df=11, P=0.011), and the number of adults caught (F=2.29, df=11, P=0.023). There was no significant difference between all sites for the number of females caught (F=1.55, df=11, P=0.142), or for the number of juveniles caught (F=1.57, df=11, P=0.136). When sites were grouped into fragmented and unfragmented habitat however, a significant difference was found between the two habitat types, for the overall number of honey possums caught (F=3.78, df=9, P=0.046), for the number of females caught (F=4.52, df=9, P=0.03), and for the number of juveniles caught (F=2.78, df=9, P=0.09). These results (Figure 3.5) indicate that while there is a difference in capture rates between sites for males, it is not necessarily being caused by fragmentation, although a significant difference between fragmented and unfragmented sites was shown for females.

Figure 3.5 Comparisons showing the number of honey possums trapped at each site, the number of males caught at each site, the number of adults caught at each site, the number of honey possums caught in fragmented vs. unfragmented habitat, the number of females caught in fragmented vs. unfragmented habitat, and the number of juveniles caught in fragmented vs. unfragmented habitat that were found to have significant differences.

a) A comparison of the overall number of honey possums found at each trapping site. Significant differences were found between all sites for the overall number of honey possums caught at each site.

b) A comparison of the overall number of males caught at each trapping site. Significant differences were found between sites.

c) A comparison of overall number of adults caught at each trapping site. Significant differences were found between sites.

d) A comparison of overall number of honey possums caught in fragmented and unfragmented habitat. A significant difference was found between the habitat types.

e) A comparison of overall number of females caught in fragmented and unfragmented habitat. A significant difference was found between the habitat types.

f) A comparison of overall number of juveniles caught in fragmented and unfragmented habitat. A significant difference was found between the habitat types.

Site/habitat isolation (defined in Table 3.1) and the size of the habitat containing the trapping sites (defined in Table 3.1: <100ha, 100-150ha, 150-200ha, or >200ha) were also analysed to determine any correlations with the average number of honey possums caught per trapping session at each site. The results showed no correlation of site/habitat isolation with average number of honey possums caught (r= 0.36, P> 0.05). The size of the habitat containing the trapping site (Figure 3.6) was however, strongly, and, negatively correlated with the average number of honey possums caught (r= -0.59, P= 0.05).

Figure 3.6 Average number of honey possums caught per trapping session and size of habitat in which animals were caught. The size of the habitat containing the trapping site was strongly and negatively correlated with the average number of honey possums caught (r= -0.59, P= 0.05).

A 2x2 contingency table using Fisher’s exact test resulted in no significant difference (P=0.579) between the proportion of males and females caught in fragmented and unfragmented sites, with males representing 60% of honey possums trapped in both fragmented and unfragmented habitat. Adult honey possums were trapped the most frequently, however a 2x2 contingency table using Fisher’s exact test revealed no significant difference (P=0.412) for the number of adults and juveniles caught in fragmented and unfragmented habitat.

3.4 Discussion 3.4.1 What is the pattern of occurrence of honey possums?

This study was carried out in a highly fragmented landscape consisting of patches of remnant bushland surrounded by thousands of hectares of pine plantation. In general, this study showed that although the landscape of the Gnangara mound is highly fragmented, honey possums have maintained a distribution throughout. Honey possums were trapped at most of the sites within the study area. If these honey possums are not persistent populations (previous to pine plantation establishment approximately 60 years ago), and are a result of dispersal, this is surprising given the fragmented nature of the habitat and the isolation of trapping sites.

Previous studies in Fitzgerald River National Park recorded only limited movement of honey possums with most individuals moving less than 30 metres (even over several months), with no evidence of marked dispersal (Garavanta et al. 2000) but in Scott National Park, movement distances were highly variable, ranging from 0m to 368.3m (Bradshaw et al. 2007). Differences in habitat such as extensive open burnt areas and clumped food sources were suggested as an explanation for the more extensive movement at Scott River. The Fitzgerald River National Park study was conducted in areas of more continuous and mature vegetation, unlike the fragmented and sparse habitat in the Gnangara study area.

In this study in Gnangara, there were three sites where honey possums were not recorded. As trapping efforts were not exhaustive (and at two of the three sites trapping efforts were lower than the rest of the sites) it cannot be said that honey possums are absent from any of the sites, but (ignoring the lower trapping effort) it could be that they are in much lower numbers or are utilising the habitat differently to other near-by sites.

The sites that recorded no honey possum captures were Site F1, Site UF9 and Site F12. The lack of captures at Site F1 was perplexing as it was chosen as a replicate pair for Site F2, which did record honey possum captures. Site F1 had similar habitat variables to Site F2 such as similar size, shape and isolation of the habitat patch and contained a large proportion of Banksia sessilis a known preferred honey possum food plant (Saffer 1998; Wooller et al. 1983; Wooller et al. 2000). The presence of B. sessilis was a major factor in choosing Site F1 to trap, just as it was for Site UF10, which only recorded a single honey possum capture. Although it

48 was expected that we would trap honey possums at these sites due to the presence of B.sessilis, we may have overlooked the structure of such vegetation and how honey possums would utilise it. For example, B. sessilis can grow in quite dense thickets. To feed on these, honey possums may move through the dense branches from plant to plant. If this was the case, it is therefore not likely that they would be trapped in pit-traps as they would not have the need to travel along the ground.

The recapture rate of honey possums was extremely low in this study when compared to rates recorded from other long-term mark-recapture studies. In this study, recaptures only accounted for 6.7% of all honey possum captures, whereas Garavanta et al. (2000) recorded recapture rates of between 33-42% in areas of mature vegetation. Similar to Garavanta et al. (2000), Everaardt (2003) also recorded high recapture rates of between 15-30% in areas long unburnt (greater than 40 years). However, Everaardt (2003) also recorded recapture rates of 7- 24% in areas burnt less than ten years prior. The trapping sites in the present study area have similar recently burnt fire ages of less than 14 years, with the recapture rate also being similar at 6.7% to those reported by Everaardt (2003). A high turnover of animals may suggest several scenarios, for example a high population density (so the chances of recapture are very low), high dispersal, trap shyness, or very poor survival. It is unlikely that there is a very high population density in this study area however, as a capture rate higher than 1.59% would be expected. Trap shyness would also be questionable as Garavanta et al. (2000) recorded high recapture rates for honey possums caught in mature, long unburnt vegetation, indicating that the species does not exhibit trap shyness elsewhere. However, the possibilities of high dispersal and very poor survival may both explain the low recapture rate recorded in this study. Patterns of population subdivision and dispersal are discussed in Chapters 4 and 5 using genetic markers.

3.4.2 Does fragmentation affect the distribution of honey possums?

Within this study area, habitat fragmentation does not appear to affect the distribution of honey possums. There was no significant difference found between fragmented and unfragmented sites in relation to the presence or absence of honey possums. It is possible that honey possum populations operate at very small spatial scales with small patches acting as self sustaining sites, but occasionally dropping out altogether. It is also possible that there is frequent loss and gain, with short term persistence of populations suggested by the presence

49 of juveniles at most sites and the absence of honey possums at others. An alternative explanation regarding the lack of significant difference in the distribution of honey possums in fragmented and unfragmented habitat is that the pine plantations are not a fragmented habitat for them. This would suggest that honey possums are capable of utilising the pine plantations for facilitating movement quite effectively.

3.4.3 Is there a difference between fragmented and unfragmented sites for the number of honey possums?

Although there was no significant difference between fragmented and unfragmented sites for the presence or absence of honey possums throughout the study area, there was a significant difference found between the average number of honey possums caught in fragmented and unfragmented habitat. It could be expected that the size of a habitat fragment would have an effect on honey possum numbers, as larger fragments usually contain greater habitat diversity than smaller ones (Saunders et al. 1991). Smaller fragments are also more likely to be negatively impacted by edge effects (Saunders et al. 1991), and therefore honey possum numbers should be lowest in the smallest fragments. Population sizes of Gehyra variegata (Geckkonidae) in remnants in the Western Australian wheatbelt were highly and positively correlated with fragment size despite this being a lizard species that is very mobile in fragmented landscapes (Hoehn et al. 2007; Sarre 1998)). Honey possums however, did not appear to follow this pattern. Although there was a significant difference in the average number of honey possums caught in fragmented and unfragmented habitat in this study, captures were higher in fragmented habitat, followed by habitat with corridors, and then unfragmented habitat. This suggests the possibility that the honey possums that were caught in the fragments were not part of locally persistent populations, but moving through the habitat uninhibited by habitat fragmentation.

It was previously thought that honey possums are relatively sedentary animals (Bradshaw and Bradshaw 2002; Garavanta 1997; Garavanta et al. 2000; Wooller et al. 2000), with some indication that larger movements may be possible (Bradshaw et al. 2007). The suggestion of relatively sedentary behaviour was made based on results from mark-recapture studies in areas of relatively dense, mature vegetation and so it may be possible that honey possums had very little need to move long distances. If however, limited dispersal capability is characteristic of this species in all habitat types, and given its small size, movement through areas containing

50 little or no cover may be restricted. If this is the case, honey possums may not be able to readily move through the surrounding matrix of pine plantation within the study area, and habitat fragmentation would affect the number of honey possums caught. As honey possum captures were highest in fragmented habitat and there were very few recaptures, it is likely that honey possums are able to utilize the pine plantation matrix for movement. In a study on movement patterns between remnants in a highly fragmented landscape in South Australia, Marchesan & Carthew (2008) found that the yellow-footed antechinus (Antechinus flavipes) are capable of moving through non-native elements of the landscape including pine plantation and roads. While most movements were made within patches, male A .flavipes made regular movements between fragments, apparently undeterred by the matrix (Marchesan & Carthew, 2008). As honey possums like A.flavipes are agile and highly arboreal, movement through the pine plantation may be relatively unrestricted for honey possums as well.

It has been suggested that the social behaviour of some marsupials may also be likely to impact their dispersal ability through fragmented landscapes, with dispersal being impeded in more social species. In a study on habitat corridors, Lindenmayer & Nix (1993) found that more solitary arboreal marsupials were most frequently recorded in wildlife corridors, as opposed to animals with a more colonial social structure. Cale (2003) found that habitat fragmentation is likely to disrupt social neighbourhoods of white-browed babblers (Pomatostomus superciliosus), a social species that lives in groups of up to 13 birds. In the Western Australian wheatbelt Cale (2003) found that fragmentation resulted in lower levels of social interaction that reduced productivity. Given that honey possums are generally not very social animals (Bradshaw & Bradshaw 2002; Wooller et al. 2004) it could be that this aspect of their ecology may improve their ability to disperse in a fragmented environment.

Garavanta et al. (2000) estimated from trapping records that home ranges averaged 0.13 ha for males and 0.07 ha for females. It was suggested that males move further to search for females in oestrous, and also possibly that larger females behaviourally excluded males from habitat (Garavanta et al. 2000; Wooller et al. 2000). In another study on movement patterns of honey possums on the south coast of Western Australia, Bradshaw & Bradshaw (2002) proposed that honey possum home ranges did not significantly differ between the sexes. Using trapping data and radio-tracking, it was determined that males (0.03 ha) and females (0.01 ha) had similar home ranges, but males had significantly larger utilization areas (0.79 ha) than female honey possums (0.14 ha). Utilization areas were calculated as radio-tracking was not

51 considered adequate for home range estimation alone based on data being gathered over short time spans (Bradshaw & Bradshaw 2002). In the Gnangara study area, a higher percentage of males were caught in both fragmented and unfragmented habitat than females, and there was a significant difference between the number of females caught in fragmented and unfragmented habitat. This result supports the observation by Garavanta et al. (2000) and Bradshaw & Bradshaw (2002) that male honey possums exhibit greater movement patterns than females. The notion is also supported by the highly promiscuous breeding strategy (e.g. large teste to body size ratio, largest mammal sperm) of the male honey possum (Russell & Renfree 1989).

In both fragmented and unfragmented habitats, adults were trapped more frequently than juveniles, with no female juveniles being caught in unfragmented sites. In unfragmented habitat adults represented 92% of captures and juveniles 8% of captures and in fragmented habitat adults represented 82% and juveniles 18% of captures. The higher capture rate for adult honey possums was also recorded by Everaardt (2003) with 79% adults compared to 21% juveniles. In comparison to the study areas on the south coast (see Bradshaw & Bradshaw 2002; Garavanta et al. 2000), this northern study area is more sparsely vegetated. It may be possible that the survival of juveniles to adulthood is hindered as a result of this sparsely vegetated habitat. For example, if food resources are limiting, honey possums would have to travel further to obtain adequate sustenance, and the battle for survival is greater for a juvenile animal in the growth and development stage of its life. In a study by Bladon et al. (2002) of 23 juvenile eastern pygmy possums (Cercartetus nanus) that were marked while still with their mother, only three survived to maturity. In this same study, juvenile capture rates were also low with minimal juvenile and sub-adult recruitment into the population (Bladon et al. 2002).

This study has provided results that contrast with what is previously known of the movements of honey possums. The majority of published research on honey possums has been from studies undertaken in the south coastal regions of Western Australia (see Bradshaw & Bradshaw 2002; Garavanta et al. 2000; Wooller et al. 2000;), with very little on the ecology of the species in the drier and fragmented landscapes north of Perth that form a large part of the range of this species. Although honey possums have not previously been recorded as having great dispersal powers (Garavanta et al. 2000), the species has maintained a distribution throughout a highly fragmented landscape. If the honey possums captured within the

52 fragments are not from a persistent population prior to fragmentation then the trapping results appear to reflect that the Gnangara pine plantation matrix is not hindering movement and dispersal in and out of habitat patches is possible.

Chapter 4: Habitat fragmentation and honey possum dispersal: a genetic approach 4.1 Introduction 4.1.1 Habitat fragmentation and dispersal capabilities

When habitat is destroyed, a patchwork of habitat fragments may be left behind. These fragments are often isolated from one another by a highly modified or degraded landscape. This reduction in habitat area and quality, as well as connectivity between fragments is a major influence on a species’ ability to persist in modified landscapes (Banks et al. 2005). Habitat fragmentation is a major threat to biodiversity worldwide and one of the greatest contributors to extinction (Groombridge 1992; Henle et al. 2004; Lindenmayer and Fischer 206).

The effects of habitat fragmentation on native fauna may be better understood by combining demographic analyses with genetic analyses (Tallmon et al. 2002). For example, using the high information content of multiple, hypervariable microsatellite loci to assign individuals to a population where its genotype had the greatest probability of occurring, Eldridge et al. (2001) demonstrated that a population of rock wallabies (Petrogale lateralis), originated from a recent dispersal event. Previous to that analysis, most evidence had suggested that Petrogale are relatively sedentary and dispersal between habitat patches is infrequent (Eldridge et al. 2001). This example reflects the difficulties in detecting dispersal by demographic analyses alone, and the ability of genetic techniques to further investigate species’ dispersal capabilities.

The honey possum, Tarsipes rostratus is another species where previous research has suggested a relatively sedentary nature (Garavanta 1997; Garavanta et al. 2000; Wooller et al. 2000). Long term mark recapture studies in Fitzgerald River National Park on the south coast of Western Australia have estimated from trapping records that home ranges averaged 0.13 ha for males and 0.07 ha for female honey possums (Garavanta et al. 2000). In that study Garavanta et al. (2000) reported that honey possums generally did not move greater than 30 metres. In another study on movement patterns of honey possums on the south coast of Western Australia in Scott National Park, Bradshaw & Bradshaw (2002) proposed that honey possum home ranges did not differ significantly between the sexes. Using trapping data and radio-tracking, it was determined that males (0.03 ha) and females (0.01 ha) had similar home ranges, but males had significantly larger utilization areas (0.79 ha) than female honey

54 possums (0.14 ha). The difference in the area used by honey possums in Scott National Park is an indication that although they showed relatively restricted movement in Fitzgerald River National Park (in areas of mature, long unburnt vegetation), they can exhibit different movement patterns in different habitat. This example shows the problems in making assumptions on dispersal capabilities based on mark- recapture studies alone or in assuming that data from one habitat type are transferrable to another.

4.1.2 Plantations and fragmented habitat

The natural vegetation cover of every continent (except for Antarctica) has been modified considerably since the development of agriculture (Saunders et al. 1991). In Australia, the majority of clearing has been for agricultural production, however, commercial plantation forestry also accounts for a significant amount of clearing. Plantation forestry is expanding into areas of high forest production value that often have high biodiversity values as well (Beeton et al. 2006). Within these plantations there are areas not suitable for planting, and these are usually left as remnant fragments of native vegetation. Native fauna that are unable to use the plantations may be present in these remnant patches of native vegetation (Lindenmayer et al. 1999).

The Gnangara pine plantation north of Perth, Western Australia, consists of over 18,000 ha of Maritime pine (Pinus pinaster), with patches of remnant bushland scattered throughout. Part of this study included trapping in eight of these remnant patches to investigate the presence and distribution of honey possums within a fragmented landscape. The trapping results from this study (Chapter 3) indicated that honey possums were not only present in both the fragmented and the nearby unfragmented sites within the study area, but were probably also moving through the matrix of the pine plantation. Although many aspects of the ecology of the honey possum lend themselves to dispersal capability (e.g. relatively solitary lives, do not build nests etc), I expected a lack of understorey in the pine plantation would impede the species’ ability to move through the matrix undetected by predators. It may be however, that the plantation is dense enough to allow the honey possum to undertake movements predominantly above ground.

This study was aimed at gaining a more detailed insight into a population of honey possums in Gnangara where previous studies elsewhere (e.g. Bradshaw & Bradshaw 2002; Bradshaw et al.

2007; Garavanta 1997; Garavanta et al. 2000; Wooller et al. 2000) have generated contradictory or inconclusive data in relation to dispersal capabilities. Much of the knowledge of honey possum dispersal capabilities was obtained from long-term studies in dense, mature vegetation of Fitzgerald River National Park (e.g. Garavanta et al. 2000; Wooller et al. 2004) and in Scott National Park (e.g. Bradshaw & Bradshaw 2002) on the south coast of Western Australia. These areas consist of more continuous and mature vegetation rather than the fragmented and sparse habitat in the Gnangara study area. Here I add to the standing knowledge of honey possum distribution and dispersal patterns from studies in a distinctively different habitat on the northern Swan Coastal Plain.

As discussed in Chapter 2 of this study, phylogeography can contribute to conservation management by helping us understand the historical responses of species to landscape changes (Moritz & Faith 1998) and provide the first step towards understanding a population’s genetic structure. Previous results from this study have shown that overall phylogeographic structuring was shallow for honey possums, and it is probable that the species has maintained recent genetic connectivity across its whole geographic range (Chapter 2). A landscape genetic perspective however, offers further insights into genetic connectivity not provided by traditional phylogeography studies (Koscinski et al. 2009). Landscape genetics has emerged as a tool that combines population genetics, landscape ecology, geography, and spatial statistics to examine how landscape features affect gene flow at multiple spatial and temporal scales (Manel et al. 2003). Effective distances can better explain patterns of differentiation in populations, especially in heterogeneous landscapes where barriers to dispersal may be common (Koscinski et al. 2009).

Previously, knowledge of dispersal capabilities of honey possums has been limited to studies involving mark-recapture techniques (Garavanta et al. 2000) or home range movement studies using a combination of mark-recapture and radio-tracking (Bradshaw & Bradshaw 2002). These have been very effective in determining short to medium range movements, but are still relatively broad in that they cannot assess what is happening at a genetic level. Phylogeographic results reported in Chapter 2 suggest that honey possums have maintained genetic connectivity throughout their range. To add to this result, microsatellite markers were developed to assess genetic structuring using nuclear markers and build on the existing knowledge of honey possum dispersal obtained from demographic data, given that these types of markers have immense value in conservation management (Sarre & Georges 2009).

Assignment tests provide the most direct method to determine the population origin of individuals (Manel et al. 2005) and were used here to examine population structure of honey possums within the Gnangara landscape.

In this chapter I report on:

i) the distribution of genetic diversity within and between local populations, and

ii) use that diversity in assignment tests designed to determine whether there is any evidence of population structuring at several spatial scales: locally amongst fragments, between fragmented and un-fragmented sites and between northern and south coast populations

4.2 Methods 4.2.1 Laboratory Analysis

A total of 160 individuals were genotyped for this study, including 26 from the unfragmented study areas at Yanchep National Park and Yeal Nature Reserve (16 males and 10 females) and 65 from the fragmented remnant patches within the Gnangara pine plantation (37 males and 28 females) (See Chapter 3). Sixty nine individuals from 25 locations across south-western Australia, including 23 individuals from 5 locations on the south coast of Western Australia were also genotyped to compare individuals and population structure from the southern extent of Tarsipes’ range. Samples were genotyped at eight microsatellite loci, as described in Appendix 1 – Microsatellite Development. Material for genetic analysis was taken from ear punch samples collected and stored in alcohol as described in Chapter 3, or, from Western Australian Museum tissue collections (collection samples described in Chapter 2).

4.2.2 Genetic Diversity of Populations Several possible population combinations based on geography, habitat subdivision and proximity were explored during the analysis (see also 4.2.4 below) and basic population genetic descriptors derived. The average number of alleles, the observed heterozygosity, and the average gene diversity for assumed populations were calculated using FSTAT (Goudet

1995). Differentiation among populations was assessed by calculating pairwise FST (Weir &

Cockerham 1984), using FSTAT and GENEPOP (Raymond & Rousseau 1995). GENEPOP was also used to test for agreement with Hardy-Weinberg equilibrium (HWE) using exact tests, as well as genotypic linkage disequilibrium at each pair of loci.

4.2.3 Assignment Tests

Genotypic data were used to assign individuals probabilistically to source population sites. Assignments were carried out using the program STRUCTURE Version 2.3 (Pritchard et al. 2000) in which estimations were made of an individuals’ site of origin. STRUCTURE estimates the posterior probability (Q) that each honey possum was born at each of K candidate sites. Analyses were performed with several different assumptions about population number (K) (Table 4.1) using the Bayesian clustering method implemented in STRUCTURE.

Table 4.1 Population combinations used in genetic analysis to determine overall population structure within the Gnangara study area. F = fragmented site, UF = unfragmented site, GSS = Gnangara Biodiversity study trapping sites carried out by the Western Australian Department of Environment and Conservation – see Figure 5.3 for trapping locations.

K= Pop 1 Pop 2 Pop 3 Pop4 Pop5 2 Gnangara South Coast

3 Gnangara Gnangara South Coast Unfragmented Fragmented (F4, F5) 4 Yanchep Gnangara Yeal South Coast Unfragmented Fragmented Unfragmented (GSS, F2, F3, (F4, F5) (UF6, UF7) F8, UF10, F11) 5 Yanchep Gnangara Gnangara Yeal South Unfragmented Fragmented Fragmented Unfragmented Coast (GSS, F2, F3, F4 (isolated) F5 (isolated) (UF6, UF7) F8, UF10, F11)

I ran four assignment tests using different population models (Table 4.1) each model is justified below.

K = 5 this model assumes development of the pine plantation isolated fragmented populations F4 & F5 and populations in Yeal Nature Reserve and Yanchep National Park. F4 and F5 may have diverged, e.g. small population size has lead to drift or inbreeding, but that all populations in the separate continuous native vegetation of blocks of Yanchep National Park and Yeal Nature Reserve remained connected (within blocks) and therefore undifferentiated. The South Coast sites were amalgamated and assumed to be isolated by distance. I assumed they were all connected though this may not be true, particularly for sites east of Albany.

K = 4 assumes fragmented sites will be different but share some common features, e.g. each has the same ancestral genotype from initial isolation. Or common perturbance patterns, e.g. fire, might also impart some common patterns to both fragments.

K = 3 similar to K=4 but further assumes some more recent potential for connectedness between Yeal Nature Reserve and Yanchep National Park, e.g. from historical vegetation connections north or south of the pine plantation that may have maintained connectedness longer than to fragments.

K = 2 treats Gnangara as a single population, e.g. gene flow can occur through pine forest to fragments and between Yeal Nature Reserve and Yanchep National Park, but assumes there will be some difference from the south coast: partly corresponding with major vegetation tracks for the high rainfall zone of the west coast and the southeast coastal zone (Hopper & Gioia 2004, cf Chapter 2).

The assumption of vegetation being connected may not be totally justified for the south coast particularly between Albany, Hopetoun and Ravensthorpe. Of these models, K= 2 and K = 5 are clearly the most plausible.

The length of the burn-in was set at 50,000 iterations followed by 100,000 Markov Chain Monte Carlo repetitions. Individuals were then assigned to the population in which their posterior probability was highest.

4.3 Results 4.3.1 Genetic Diversity of Populations

Between 5 and 21 honey possums were genotyped per Gnangara population grouping with the average number of alleles per locus in each grouping (K=4) ranging between 4.0 and 5.88. The average gene diversity among the four Gnangara population groupings was 0.673 ± 0.06, ranging between 0.633 and 0.699 (Table 4.2). The average observed heterozygosity for the population groupings was 0.522 ± 0.10, ranging between 0.480 and 0.614 (Table 4.2). When individuals were combined into just two populations, the average number of alleles per locus in the Gnangara and south coast populations were 7.57 ± 1.32 and 5.71 ± 1.06 respectively. The average gene diversity for Gnangara was 0.694 ± 0.07 and 0.648 ± 0.08 for the south coast population; and the average observed heterozygosity was 0.517 for Gnangara and 0.554 for south coast (Table 4.3).

Exact tests revealed that the Gnangara populations were not in HWE, with significant departures at several loci in all four populations. Three loci showed significant deviation from HWE in all populations. However, further analysis revealed that when samples were combined into just two populations, Gnangara and South Coast, both populations overall, were in HWE. No pairwise tests for genotypic linkage disequilibrium among the eight loci were significant, indicating that the loci appear to be segregating more or less independently and departure from HWE is most likely a result of null alleles within the small populations (Blouin et al. 1996).

The population differentiation tests indicated very little differentiation among populations

(Table 4.4). All population pairs were very similar, with no FST values significantly different (P<0.001 after Bonferroni adjustment) from zero. Samples from southern coastal populations were also included in the pairwise FST tests to compare the populations in the Gnangara area to populations from elsewhere within Tarsipes’ range. Although the Gnangara populations were more similar to each other than to the south coast populations, FST values were low (Table 4.4) and again were not significantly different (P<0.001) from zero. When the five south coast populations were pooled into a single south coast population however, pairwise FST values were significantly greater than 0 between Gnangara Fragmented F4 and South Coast

(FST= 0.1310; P<0.005 after Bonferroni adjustment), Gnangara Fragmented F5 and South Coast

(FST=0.0948; P<0.005 after Bonferroni adjustment), and Yeal Unfragmented and South Coast

(FST=0.1052; P<0.005 after Bonferroni adjustment). Pairwise FST (0.0796) was also significant (P<0.05) when analysis was run as K=2, Gnangara and South Coast.

Table 4.2 Summary statistics for 8 microsatellite loci in four populations of Tarsipes rostratus from Gnangara, Western Australia. Between 5 and 21 honey possums were genotyped per Gnangara population grouping. Statistics show the mean sample size per locus, the average number of alleles at each locus, the average observed heterozygosity and the average gene diversity at each locus.

Population N A HO HE

Yanchep Unfragmented/Connected 29.25 ± 0.84 5.88 ± 1.14 0.614 ± 0.11 0.663 ± 0.07

Gnangara Fragmented F4 (isolated) 13.75 ± 1.32 5.5 ± 1.02 0.489 ± 0.09 0.633 ± 0.07

Gnangara Fragmented F5 (isolated) 15.0 ± 1.52 5.88 ± 0.85 0.480 ± 0.09 0.699 ± 0.06

Yeal Unfragmented 7.63 ± 1.0 4.0 ± 0.53 0.584 ± 0.12 0.694 ± 0.06

N= mean sample size per locus; A= average number of alleles at each locus; HO= average observed heterozygosity; HE= average gene diversity at each locus (Nei 1987). All estimates are ± standard error.

Table 4.3 Summary statistics for 8 microsatellite loci in two populations of T. rostratus from the Gnangara study area and the south coast of Western Australia. Statistics show the mean sample size per locus, the average number of alleles at each locus, the average observed heterozygosity and the average gene diversity at each locus.

Population N A HO HE

Gnangara population 66.43 ± 4.80 7.57 ± 1.32 0.517 ± 0 0.694 ± 0.07

South coast population 19.0 ± 1.23 5.71 ± 1.06 0.554 ± 0 0.648 ± 0.08

Table 4.4 Pairwise FST values for four populations of T. rostratus from Gnangara, Western Australia and five populations from south coastal locations, Western Australia. None were significantly > 0 (see text).

Yanchep Frag HP4 Frag HP5 Yeal Albany Augusta Hopetoun Ravensthorpe Walpole Frag F4 0.0305 Frag F5 0.0003 0.0019 Yeal 0.0014 0.0037 0.0274 Albany 0.0473 0.0993 0.0828 0.0985 Augusta 0.0370 0.1359 0.0664 0.0836 0.0213 Hopetoun 0.0852 0.1244 0.0886 0.1045 0.1106 0.0435 Ravensthorpe 0.0695 0.1302 0.0670 0.0736 0.1182 0.0226 0.0515 Walpole 0.0142 0.0966 0.0393 0.0744 0.0579 0.0744 0.0084 0.0315

South Coast combined 0.0610 0.1310 0.0948 0.1052

4.3.2 Assignment Tests

Overall, assignment test results obtained in STRUCTURE revealed very little population structure within the Gnangara study area (Figure 4.1). The analysis using K=5 produced the least amount of structuring and the lowest probabilities of assigning individuals to source populations (Table 4.5). For example, when trapping site origin was used as the basis for assumed populations (K=5) the highest probability assigned to any individual belonging to a source population was only 49% (an individual trapped in unfragmented habitat of Yeal Nature Reserve, being assigned to Population 4). When populations were condensed into K=4, posterior probabilities increased, but overall structuring was still very low (Figure 4.1). Probabilities increased again when assignment tests were run with K=3 (Figure 4.1) however, these results still indicated that overall structure was weak, even with the inclusion of south coast populations.

With the exception of K=2, all other analyses resulted in no individuals being assigned to a population within the Gnangara study area with greater than a 91% probability and the highest posterior probability assigned to any individual within the south coast population was 61%. Although pooling the individuals into just two populations produced higher overall posterior probabilities for assignments (highest Gnangara assignment was 97% and highest south coast assignment was 92%), there were still discrepancies within the results. For example, five out of 23 individuals from the south coast were assigned to the Gnangara population with >90% posterior probability, and 12 out of 82 individuals that were trapped at Gnangara were assigned to south coast with >90% probability. For the pre-defined Gnangara population 50% of individuals were assigned to Gnangara and 50% to south coast. The pre-defined south coast population resulted in the assignment of 58% of individuals, with 42% being assigned to Gnangara (Table 4.5).

Figure 4.1 Assignment of individual honey possums into assumed source populations using STRUCTURE. Colour blocks represent probability individuals are assigned into pre-defined populations.

K=5 (Red=Yanchep Unfragmented, Green=Gnangara Fragmented F4, Blue=Gnangara Fragmented F5, Yellow=Yeal Unfragmented, Pink=South Coast)

K=4 (Red=Yanchep Unfragmented, Green=Gnangara Fragmented, Blue= Yeal Unfragmented, Yellow= South Coast)

K=3(Red=Gnangara Unfragmented, Green=Gnangara Fragmented, Blue= South Coast)

K=2(Red=Gnangara, Green=South Coast)

Table 4.5 Proportion of membership of honey possum individuals in pre-defined populations in each of the clusters: K=5, K=4, K=3, K=2 as assigned by STRUCTURE.

K=5 Assigned Population Gnangara Gnangara Assumed Population Yanchep Fragmented Fragmented Yeal Unfragmented South Coast No. of Unfragmented Frag4 Frag5 individuals

Yanchep Unfragmented 0.179 0.188 0.202 0.243 0.188 35

Gnangara Fragmented F4 0.216 0.211 0.191 0.191 0.190 18

Gnangara Fragmented F5 0.205 0.189 0.196 0.238 0.173 19

Yeal Unfragmented 0.196 0.216 0.190 0.192 0.205 10

South Coast 0.203 0.230 0.207 0.109 0.252 23

K=4 Assigned Population Yanchep Gnangara Yeal No. of Assumed Population Unfragmented Fragmented Unfragmented South Coast individuals

Yanchep Unfragmented 0.224 0.303 0.238 0.236 35

Gnangara Fragmented 0.222 0.266 0.313 0.199 37

Yeal Unfragmented 0.213 0.241 0.321 0.225 10

South Coast 0.341 0.091 0.200 0.368 23

K=3 Assigned Population Gnangara Gnangara No. of Assumed Population Unfragmented Fragmented South Coast individuals

Gnangara Unfragmented 0.307 0.391 0.302 45

Gnangara Fragmented 0.359 0.348 0.294 37

South Coast 0.330 0.213 0.457 23

K=2 Assigned Population No. of Assumed Population Gnangara South Coast individuals

Gnangara 0.499 0.501 82

South Coast 0.426 0.574 23

4.4 Discussion 4.4.1 Genetic subdivision within and between populations

Information on genetic population structure can be useful in investigating dispersal capabilities of a species (Fredsted et al. 2005). When combined with demographic data, population genetics can provide greater detail about the relationship between landscape features and microevolutionary processes (Manel et al. 2003) and therefore provide insight into the distribution patterns of honey possums in the landscape. Using eight polymorphic microsatellite loci, the results of tests of linkage disequilibrium, HWE and F-statistics from this study all suggest that honey possums are not divided into subpopulations within the Gnangara pine plantation study area, but more likely function genetically as a single population. The lack of spatial genetic structure supports the results obtained from the trapping component of this study (Chapter 3) and is consistent with phylogeographic analysis based on mtDNA data which also suggested wide spread dispersal (see Chapter 2) homogenising genetic structure.

Under the assumption of four populations within the study area (Yanchep unfragmented/connected, fragmented/isolated F4, fragmented/isolated F5 and Yeal unfragmented) exact tests revealed that all populations significantly departed from HWE. There are several causes for departures from HWE including population subdivision, inbreeding, or null alleles (Blouin et al. 1996). However, when individuals from the Gnangara study area were pooled into a single representative population, there was no deviation from HWE detected, suggesting an effect of small population sizes (Blouin et al. 1996). No linkage disequilibrium was detected between loci under any assumptions about population structure (K=5, K=4, K=3, or K=2), suggesting no impacts of inbreeding or population subdivision, further supporting the idea of a single Gnangara population. There is no geographic pattern related to isolation, fragmentation or any other landscape structure in the Gnangara area.

Observed heterozygosity (HO) overall was lower than expected heterozygosity (HE) for all assumed populations in the study area, and HO was slightly lower in the fragments. When individuals from the Gnangara study area were pooled into a single representative population, both HE and HO were very similar to those from the south coast population. The F-statistics supported the results of tests of linkage disequilibrium, indicating that the Gnangara study area comprises a single honey possum population, with gene flow being maintained

71 throughout the landscape. There is no evidence that fragmentation and the replacement of native vegetation by pine forest impedes gene flow or causes any other pattern of divergence.

4.4.2 Assignment tests and population structure

Understanding patterns of gene flow requires a detailed knowledge of how landscapes can influence population structure (Manel et al. 2003). Estimates of divergence between populations are useful for understanding historical gene flow and relationships between populations, however performing assignment tests can determine recent migration and gene flow history (Kane & King 2009). Assignment tests revealed little to no population structuring associated with the assumed fragmented and unfragmented population representations (Figure 4.1). Assignment test results more likely inferred a single Gnangara population of honey possums, indicating that fragmented patches are not isolated habitat and that the pine forest matrix surrounding native vegetation fragments does not inhibit honey possum dispersal.

Genetic data revealed a similar outcome for Antechinus agilis populations in a Pinus radiata plantation near Tumut, New South Wales (Banks et al. 2005). Antechinus agilis populations were not isolated in vegetation remnants: there was a high level of population connectivity despite the fragmented habitat system (Banks et al. 2005). Similarly, Lindenmayer et al. (2005) found no significant relationship between population recovery of Rattus fuscipes and patch size and isolation in forest fragments in a radiata pine plantation. Although the honey possum is more of a feeding specialist, feeding solely on nectar and pollen (Russell & Renfree 1989), than Antechinus or bush rats, it appears that in terms of movement it may have the same generalist dispersal traits allowing gene flow to be maintained through the vegetation matrix supplied by a pine plantation. Tallmon et al. (2002) using combined demographic and genetic data showed that the matrix surrounding forest fragments was not an impermeable barrier to vole dispersal and populations maintained high levels of heterozygosities due to nuclear gene flow. The gecko, Gehyra variegata also disperses readily through fragmented landscapes (e.g. Hoehn et al. 2007; Sarre et al. 1995). For example, Sarre (1998) shows that G. variegata has persisted despite habitat fragmentation in south-western Australia, because it is a habitat generalist and readily colonizes remnants indicating it also readily uses the modified matrix between remnants.

Fragmentation is not necessarily a cause for concern in management of populations in fragmented and even heavily modified landscapes – provided the modified landscape can support dispersal. Equally there is no blanket conclusion as in the same habitat geckos can be very restricted (e.g. in contrast to G. variegata, movement of Oedura reticulata between remnants is negligible, Sarre et al. 1995) and Wayne et al. (2006) showed that the western ringtail possum, (Pseudocheirus occidentalis) is quite sensitive to habitat fragmentation and disturbance. It is also important that limited dispersal and historical patterns of population growth and range shift can also generate very high levels of genetic structuring in undisturbed forest systems (frogs, Geocrinia species, Driscoll 1998).

Species that cannot use all landscape elements may be at the greatest risk of extinction, however recognizing the complexity of the interaction of ecological traits in species’ responses to fragmentation is the key to understanding the impacts of habitat fragmentation (Driscoll & Weir 2005). It is critical to understand how species respond biologically to spatial heterogeneity of the landscape as the interplay between selection and gene flow strongly influences biotic processes linked to adaptation (Manel et al. 2010). The Gnangara study area is a fragmented environment where food sources are sparsely distributed and fire is a relatively common occurrence within the landscape. Within this landscape, it is likely that selection pressure over many generations of honey possums has led to the species adapting to local conditions and increasing their dispersal capabilities. Although the pine plantations do not provide food resources for honey possums, they do apparently provide enough connectivity and shelter for movement into areas where food plants are present. While results from this study do not suggest that habitat fragmentation does not affect honey possum persistence in the landscape, it does indicate that pine plantations do not completely fragment honey possum habitat and in this case do not appear to greatly impede honey possums dispersing and maintaining gene flow.

Both the demographic and genetic results from this study so far have indicated that honey possums are continuously distributed throughout this fragmented landscape. The results do not provide any evidence of genetic subdivision or population structuring within or between populations, indicating wide spread dispersal. It may be possible to determine other variables that may be influencing honey possum dispersal, distribution or density within the landscape. When geographical locations of individuals are known but population limits are unclear, spatial

73 autocorrelation or regression methods can identify genetic boundaries, as they do not assume population structuring (Manel et al. 2005). The next chapter will use spatial autocorrelation and regression to investigate scale and environmental variables, such as fire and habitat suitability, that may be influencing honey possum dispersal within the Gnangara study area.

Chapter 5: The effect of fire and other habitat variables on honey possum dispersal in a fragmented landscape 5.1 Introduction 5.1.1 Fire in the landscape

Fire has played, and continues to play, a key ecological role in the Australian landscape (Burrows & Wardell-Johnson 2003; Driscoll et al. 2010; Gill 1981; Lindenmayer et al. 2008; Parr & Andersen 2006). Sedimentary deposits from south-west Australia indicate that fire was present before the arrival of Aboriginal people and since then has remained a constant feature in the environment (Hassell & Dodson 2003). Fire scars on grasstrees in south-west Australia have revealed that after the arrival of Aboriginal people and before the arrival of Europeans, fire was present at an average interval of 4 years, suggesting relatively frequent use of fire by Aboriginal people (Lamont et al. 2004).

At the time of European settlement, fire was commonly seen as a negative influence on ecosystems and so a widespread practice for conservation management was to exclude fire from ecologically significant areas (Parr & Andersen 2006). By the early 1900s a fire-exclusion policy was in place in south-western Australia. However, after a major bushfire in 1961, low- intensity, prescribed burns were introduced back into the landscape (Lamont et al. 2004). Generally, fire is now recognized as a driver of ecological processes and increased patchiness and heterogeneity are promoted as the best way to prescribe burn in fire-prone conservation areas (Bradstock et al. 1995; Burrows & Wardell-Johnson 2003; Driscoll et al. 2010; Parr & Andersen 2006).

Fire regime including frequency, intensity and season can influence the floristics and structure of vegetation communities (Driscoll et al. 2010; Fisher et al. 2009; Puglisi et al. 2005; Van Dyke et al. 2007). For example, in Bold Park, an isolated patch of native vegetation in Perth, Western Australia, frequent fire decreased native cover within the Banksia woodland and allowed a shift from woody, perennial, native, re-sprouter species to herbaceous, perennial, introduced species (Fisher et al. 2009). This changed fuel types and increased fuel loads and consequent responses to fire (Fisher et al. 2009). Changes in fire regimes, leading to differences in vegetation composition can then further contribute to a decrease in local species diversity (Van Dyke et al. 2007).

5.1.2 Fauna responses to fire

Just as vegetation communities differ in their responses to changing fire regimes, the responses of fauna species to fire are also diverse (Lindenmayer et al. 2008; Monamy & Fox 2000; Pons & Clavero 2010; van der Ree and Loyn 2002; Wilson et al. 2001). Many small mammal species follow repeatable, succession, recolonisation stages after fire events, with different species needing particular structural habitat requirements (Catling et al. 2001; Driscoll et al. 2010; Fox 1982; Fox & McKay 1981; Monamy & Fox 2000; Sutherland & Dickman 1999). But not all fauna species follow the same response patterns in all habitat types. For example, Lindenmayer et al. (2008) reported noticeable variation in responses among mammal groups following a wildfire in Booderee National Park, New South Wales. Unexpectedly, Lindenmayer et al. (2008) did not observe the succession patterns of small mammals following wildfire documented by other studies (e.g. Sutherland & Dickman 1999), with responses also not following from life-history attributes as predicted by Friend (1993).

Species’ responses to fire are complex and involve many variables other than habitat structure and time since last burnt. Vegetation structure does not just depend on time since fire, but also on the severity of the initial disturbance and environmental conditions at the time of the fire (Pons & Clavero 2009). For example, Pons & Clavero (2009) highlight the slow recovery of bird communities in burnt mountain shrublands in the Pyrenees. At 1800m, the mean annual temperature is 9°C colder than at sea-level on the same latitude, resulting in slower post-fire regeneration and possibly delaying the response of the avifauna (Pons & Clavero 2009). This study illustrated the need to recognise environmental conditions such as altitude and weather in planning prescribed burning regimes, not just assessing them on resultant vegetation structure alone.

Cercartetus nanus, the eastern pygmy possum, is a food specialist, gaining the majority of its nutritional requirements from nectar and pollen, similar to honey possums. The species therefore could be particularly prone to food limitation, post-fire (Tulloch & Dickman 2007). Sutherland et al. (2006) however, showed that C. nanus are present in substantial numbers in burnt habitat as soon as 12 months post-fire. Rather than following a succession pattern similar to that of other small mammals, C. nanus has a greater capacity than other pollen and nectar feeding mammals and birds to adapt to post-fire conditions because they can more readily locate patchily distributed resources (Sutherland et al. 2006; Tulloch & Dickman 2007).

Conversely, there are fauna species that appear to have limited ability to adapt to burnt habitat conditions and are considered to be fire-sensitive. For example, populations of Antechinus minimus maritimus (swamp antechinus) at inland sites within the eastern Otway Ranges became locally extinct after a 1983 wildfire and had not recolonised the sites 15 years following the fire (Wilson et al. 2001). Some bird species have also been found to be fire sensitive, such as the threatened Mallee emu-wren (Brown et al. 2009). Vegetation structure is a key influence in local distribution and abundance of the Mallee emu-wren, with time since the habitat was last burnt the overriding factor in determining densities (Brown et al. 2009).

5.1.3 Tarsipes rostratus: a fire sensitive species?

As a species’ response to fire can vary significantly, it is clear that a diverse fire regime may be needed to maximise biodiversity conserved (Parr & Andersen 2006). The marked contrasts in species’ responses also suggest that land managers may need to tailor management actions to the needs of particular elements of the biota, e.g. if they are of particular conservation concern, rather than use indicator species responses to predict those of other species present within the landscape (Lindenmayer et al. 2008). Alternatively, where evidence suggests that particular elements of the biota are not sensitive to altered burning regimes, fire management can be directed at those species in the landscape that are fire-sensitive (Parr & Andersen 2006). If the broad management goal is to avoid population extinctions as a result of adverse fire regimes, emphasis should be placed on developing an understanding of species responses, knowledge of how spatial and temporal fire patterns influence the biota, and understanding interactions of fire regimes with other processes that can modify species responses to fire (Driscoll et al. 2010).

Garavanta et al. (2000) suggested that the sedentary behaviour exhibited by Tarsipes rostratus may impede that species ability to undergo effective dispersal after disturbance events. Tarsipes is a food specialist, depending solely on nectar and pollen of flowers, favouring flowers on plants belonging to the families Proteaceae and Mrytaceae (Russell & Renfree 1989). Due to its small size and high carbohydrate diet (Bradshaw & Bradshaw 1999) Tarsipes may be particularly prone to food limitation when flowers are limited, especially after a fire event. It is therefore quite plausible that the honey possum could be a fire-sensitive species, and prescribed burning regimes may need to be designed around this species’ response to fire.

A long-term mark-recapture study within Fitzgerald River National Park, ≈ 500 km southeast of the current study area, showed movements of honey possums are limited with no evidence of marked dispersal in areas of long unburnt, mature vegetation (Garavanta et al. 2000). Capture rates of honey possums were low in areas recently burnt, increased over the following 10 years and continued to increase slowly for up to 15-20 years at which time capture rates peaked. Although rates then declined, honey possums were still present in vegetation unburnt for 60 years (Everaardt 2003). Similar trapping patterns were found in the Scott National Park 500 km west of Fitzgerald River, with trapping rates in burnt areas lower than those recorded in unburnt sites (Bradshaw et al. 2007). Radio tracking by Bradshaw & Bradshaw (2002) however showed males particularly, moved a lot further than mark-recapture data from trapping indicated, suggesting there may have been unrecognised movements in earlier mark- recapture studies.

The demographic results from this study (see Chapter 3) suggest that habitat fragmentation does not appear to substantially influence honey possum presence, density or occurrence in the landscape. The genetic results (see Chapter 4) suggest also that fragmentation is not reducing the genetic diversity of honey possums or restricting gene flow. This chapter therefore investigates what other factors might be driving honey possum distribution and density in my study area. Specifically, this study is aimed at identifying habitat variables within the landscape, particularly fire age and habitat condition, that may predict honey possum density in the study area. I used spatial autocorrelation to identify any fine-scale genetic structure within and among populations, which will be discussed in relation to honey possum dispersal in a fragmented and fire-affected landscape.

5.2 Methods

This study was conducted in and around the Gnangara Pine Plantation, approximately 60 km north of Perth, Western Australia. Details of trapping including maps, marking etc. are outlined in Chapter 3. Data from trapping sites 1-12 are presented in this chapter. Occurrence data from an additional nine sites (13-21 from a concurrent biodiversity study carried out by the Western Australian Department of Environment and Conservation - site locations are shown in Figure 5.3) are also included to further document the distribution of honey possums within the

78 study area. Sites 13-21 were used in all analyses as described below, excluding the Principal Components and regression analyses.

I carried out a rapid habitat assessment at each of the original 12 trapping sites at the conclusion of the trapping study. Variables measured included fire age (provided by the Western Australian Department of Environment and Conservation- the study area is subject to a notional seven year rotational burn regime by the DEC but fire intervals can be shorter or longer); disturbance level, vegetation condition, habitat condition, under-storey condition, over-storey condition, canopy height, and level of weed infestation (Table 5.1). The vegetation condition scale used in the rapid assessment was based on that developed by Keighery (1994). The habitat variables measured during the habitat assessments (based on habitat assessments from Bush Forever (Government of Western Australia 2000)) were scored, with details of the categories outlined in Table 5.1. These variables were used to ascertain if honey possum density could be predicted for each trapping site.

Table 5.1 Habitat variables measured during a rapid habitat assessment at each of the original 12 trapping sites at the conclusion of the trapping study. Variables measured included fire age, disturbance level, vegetation condition, habitat condition, under-storey condition, over-storey condition, canopy height, and level of weed infestation.

Over- Under- Over- Degree Veg Habitat storey storey storey Sene- Patch of conditio conditio conditio conditio canopy scence Hollows Feral Site size isolation n n n n height class Dieback present Logs Dist Weeds animals

1 2 3 2 3 2 3 1 1 4 0 0 0 1 4

2 3 3 2 2 2 2 1 1 1 0 1 0 0 0

3 2 2 2 2 2 2 1 1 1 0 0 0 1 0 4 1 4 2 2 2 2 1 1 1 0 0 1 1 0 5 1 4 2 2 2 2 1 1 1 0 0 1 1 4 6 4 1 3 4 3 3 2 2 2 1 0 0 0 0 7 4 1 2 2 2 2 2 1 1 0 0 0 0 1 8 2 2 2 2 2 2 2 1 1 0 0 1 1 4 10 4 1 2 2 2 3 1 1 1 0 0 1 1 0 11 4 4 2 3 2 3 1 1 1 0 0 1 1 0 12 4 4 2 3 3 3 2 2 4 0 1 1 1 0

Patch size: 1=<100ha, 2=100-150ha, 3=150-200ha, 4=>200ha; degree of isolation: 1=unfragmented, 2=corridor present, 3=completely isolated habitat <150m from continuous habitat 4=completely isolated >500m from continuous habitat; Vegetation condition, habitat condition, overstorey condition, understorey condition: 1=pristine, 2=excellent, 3=very good, 4=good, 5=degraded (Keighery 1994); overstorey canopy height: 1=<5m, 2=5-15m, 3=15- 30m, 4=>30m; Senescence class: 1=all crowns live, 2=some crowns declining, 3=50% crowns declining, 4=75% crowns declining, 5=most declining; dieback: 1=no evidence, 2=active, 3=old or inactive, 4=unknown (e.g. isolated Banksia deaths, fire); hollows: 0=absent, 1=present; logs: 0=absent, 1=present; disturbance: 0=none, 1, 2, 3, 4, 5=major; weed infestation: 0=none, 1=low, 2=medium, 3=high; ferals: 0=none, 1=fox tracks, 2=cat tracks, 3=pigs, 4=other

There were three questions associated with this study:

1. Do honey possums maintain gene flow throughout the landscape?

Phylogeographic analyses and assignment tests were used earlier in this study (Chapters 2 and 4) to determine gene flow within the landscape and investigate dispersal capabilities in relation to habitat fragmentation (Chapters 2 and 4). These genetic analyses of honey possums showed very little structure. For example, phylogeographic analyses (at a regional scale) revealed very little geographic structuring of honey possum populations across their range, and assignment tests (at a landscape scale) indicated that the honey possums present within the Gnangara study area were functioning as a single population, with habitat fragmentation having very little impact on genetic diversity. Here, spatial autocorrelation (Peakall & Smouse 2006) was used to investigate dispersal at a finer scale and add to the earlier analyses of genetic structure at regional and landscape scales. Based on Garavanta et al. (2000) and Bradshaw & Bradshaw (2002) I expected there would be fine scale genetic structuring of honey possums within the study area corresponding to fire ages of trapping sites. Results from this analysis may have important implications for aspects of prescribed burning regimes such as size and patchiness of burns, in relation to genetic connectivity of honey possums within the landscape.

I performed spatial autocorrelation analyses using the technique developed by Smouse & Peakall (1999) to investigate genetic similarity of honey possums over small distances. This analysis was aimed at determining distances at which honey possums maintain gene flow in relation to source populations potentially recolonising burnt habitat. GENALEX 6.3 (Peakall & Smouse 2006) was used to calculate an autocorrelation coefficient (r) for each distance class using a pairwise genetic and geographic distance matrix. The pairwise genetic distance was calculated from eight loci of 82 individuals trapped throughout the Gnangara study area, with the associated pairwise geographic distance matrix calculated from the X- and Y- coordinates of the site location at which the animal was trapped. Distance classes were set at 2 km intervals from 0-14 km, and then >14 km. The calculated autocorrelation coefficient (r) was plotted as a function of distance to produce autocorrelograms, illustrating the genetic similarity between pairs of individuals within a specified geographic distance class.

As the capacity to detect spatial genetic structure from a single correlogram depends on the interaction between the true extent of genetic structure (Peakall & Smouse 2006), the distance class sizes chosen and the number of samples per distance class, a multiple distance class analysis was also calculated. In this analysis, r was calculated for increasing distance classes ranging from the minimum distance between sites, to the maximum distance for sampling.

2. Does the spatial pattern of fire affect the distribution of honey possums?

To investigate if the spatial pattern of fire affects the distribution of honey possums throughout the study site, fire ages of trapping sites and adjoining areas were obtained from DEC, as well as GIS maps containing year since last burnt. Maps were manipulated using ArcGIS 9.3 (Esri) to compare the average number of honey possums caught per trapping site in relation to the year since last burnt and habitat conditions of the trapping sites. Three maps were developed for the trapping sites including fire age- years since last burnt, average number of honey possums caught, and habitat condition of honey possum trapping sites. The fire age map uses a category of 1-3 years as the time since last burnt, to allow for comparisons over the entire trapping period from spring 2006 through to autumn 2008. For example, at the beginning of the trapping period, trapping site UF6 had not been burnt for 10 years, by the end of the trapping period it was 12 years since the last fire, so the site was therefore mapped with a fire age category of 10-12 years since last burnt.

3. Do habitat variables predict honey possum densities?

To investigate this, principal component analysis was used to transform eight possibly correlated variables (including fire age, and vegetation/habitat quality variables, see Table 5.4) into a smaller number of uncorrelated principal components. The resulting principal components were then used in a regression analysis to predict honey possum densities within the landscape. Principal component analysis was carried out using data from trapping sites 1- 12 only. The additional nine sites from the GSS study were mapped only to indicate the distribution pattern of honey possums in the landscape in relation to the spatial pattern of fire.

5.3 Results 5.3.1 Do honey possums maintain gene flow throughout the landscape?

Spatial autocorrelation demonstrated that dispersal is not limited on the fine spatial scale between sampling sites in the Gnangara study area. The autocorrelogram (Figure 5.1) shows that no fine scale genetic patterns are evident within the population, with r never deviating significantly from zero. Multiple distance class analysis showed similar results to the autocorrelogram, indicating dispersal is not limited at this spatial scale (Figure 5.2).

Figure 5.1 Correlogram plot of the genetic correlation coefficient (r) among all honey possum individuals as a function of distance showing no significant genetic structure from 2-20 km. The permuted 95% confidence interval (dashed lines) and the bootstrapped 95% confidence error bars are also shown.

Figure 5.2 Multiple distance class correlogram of the genetic correlation coefficient (r) as a function of distance showing no significant genetic structuring at multiple distances from 0-2 through to 0-20 km. The permuted 95% confidence interval (red lines) and the bootstrapped 95% confidence error bars are also shown. U = upper, L = lower 95% CI

5.3.2 Does the spatial pattern of fire affect the distribution of honey possums?

Fire ages (years since last burnt) varied among the honey possum trapping sites and across the whole study area. Fire ages varied between 1-3 years since last burnt and 37-39 years since last burnt (Figure 5.3). The fragmented trapping sites ranged from 4-6 years since last burnt to 13-15 years since last burnt, and the unfragmented trapping sites ranged from 1-3 years to 37- 39 years since last burnt (Table 5.2). With the exception of site F1 honey possums were present in all of the fragments trapped, and were trapped in recently burnt areas (1-3 years since last burnt) as well as long unburnt areas (37-39 years since last burnt)(Figure 5.4).

Figure 5.3Fire ages, years since last burn, of honey possum trapping sites within the Gnangara Pine Plantation, Western Australia (original data and polygons provided by J. Kuehs and T. Sonnerman, DEC). In this and subsequent figures, Sites 1-12 represent original trapping sites described throughout this study, sites 13-21 are additional sites that were part of an additional biodiversity study being carried out by DEC, trapped using comparable protocols and provide additional data in relation to distribution of honey possums throughout the study area. The central white area of the figure represents an area of continuous pine plantation. The surrounding polygons represent remnant native vegetation.

Figure 5.4 Average number of honey possums caught per honey possum trapping session within the Gnangara Pine Plantation, Western Australia (original data and polygons provided by J. Kuehs and T. Sonnerman, DEC). Sites 13-21 are additional sites that were part of a biodiversity study being carried out in parallel by the DEC. Trapping methods were comparable and trapping sessions were undertaken in the same time period as sites 1-12. The central white area of the figure represents an area of continuous pine plantation. The surrounding polygons represent remnant native vegetation.

Figure 5.5 Approximate habitat condition of honey possum trapping sites (and surrounding area) within the Gnangara Pine Plantation, Western Australia (original data and polygons provided by J. Kuehs and T. Sonnerman, DEC). Values shown for polygons are broad and approximate as they are inferred from trapping sites located within the area. The central white area of the figure is represents an area of continuous pine plantation. The surrounding polygons represent remnant native vegetation.

Table 5.2 A comparison of the average number of honey possums caught in relation to fire age and habitat condition of each Gnangara trapping site including Gnangara Sustainability Strategy trapping sites.

Site Fire Age Habitat Condition Average number of Year Since Last Burnt (1-5) honey possums caught 0, 0.1-0.5, 0.6-1.0, 1.0- 1.5, 1.6-2.0, 2.1-2.5 1 10-12 3 0 2 10-12 2 0.1-0.5 3 4-6 2 1.6-2.0 4 7-9 2 2.1-2.5 5 7-9 2 2.1-2.5 6 10-12 4 1.6-2.0 7 4-6 2 0.6-1.0 8 13-15 2 0.1-0.5 9 1-3 3 0 10 4-6 2 0.1-0.5 11 4-6 3 0.6-1.0 12 4-6 3 0 13 1-3 3 0.1-0.5 14 4-6 3 0.1-0.5 15 37-39 3 0.1-0.5 16 4-6 2 0.1-0.5 17 1-3 3 0.1-0.5 18 1-3 3 0.1-0.5 19 19-21 2 0.1-0.5 20 1-3 3 0.1-0.5 21 4-6 3 0.1-0.5

Habitat Condition- 1=Pristine, 2=Excellent, 3=Very Good, 4=Good, 5=Degraded

5.3.3 Do habitat variables predict honey possum densities?

Eight variables were measured to determine if there were any correlations between site variables that might predict honey possum presence and density. Principal component analysis was used to transform eight possibly correlated variables into a smaller number of uncorrelated principal components. There were two principal components (PC1 and PC2) with an eigenvalue greater than one (Table 5.3).The first principal component (PC1) had an eigenvalue of 3.137 and accounted for 39.2% of the variability in the data, and the second

89 principal component (PC2) had an eigenvalue of 2.344 and accounted for 29.3% of the variability in the data. Table 5.4 shows the component loadings with the vegetation variables (vegetation, habitat, and overstorey condition) significantly and positively correlated with PC1 (r=0.80, 0.92, and 0.80 respectively, P< 0.5). PC2 was significantly and positively correlated with disturbance and weeds (r=0.85, 0.80, P<0.05), as well as significantly, and negatively correlated with fire age (r=-0.83, P< 0.5). A principal component plot was examined to determine the relationships among the trapping sites (Figure 5.6).

As PC1 and PC2 had eigenvalues greater than one, the PCA scores were used in a multiple regression model with the average number of honey possums caught per trapping session at each site as the dependent variable to determine whether habitat properties could predict honey possum density. The multiple regression analysis was not significant for either PC1 or PC2 (r²= 0.158, P=0.201; r²=0.016, P=0.699 respectively), indicating that the variables associated with both principal components were not predicting honey possum density.

The average number of honey possums caught per trapping site generally peaked around 8 years since last burnt, with densities lower in areas less than about 3 years since last burnt, and areas greater than 12 years since last burnt (Figure 5.7). Comparisons of fire age groups (2, 5, 8 and ≥11 years post fire) found no significant difference between groups using Kruksal- Wallis analysis of variance models (3 df, Chi2 = 6.16, p = 0.104) but there was a significant difference using Anova (F3,18 = 8.27, p = 0.001). Regression analysis showed no significant relationship (r2 =0.05, P=0.35) between the average number of honey possums caught and the fire age of a site as you would expect if there is a rise then decline in trap success rate.

TABLE 5.3 Principal component analysis was used to transform eight possibly correlated habitat variables into a smaller number of uncorrelated principal components. Eigenvalues and % variance explained for each principal component, showing PC1 and PC2 with eigenvalues >1 Explained Variance (Eigenvalues) Value PC 1 PC 2 PC 3 PC 4 PC 5 PC 6 PC 7 PC 8 Eigenvalue 3.137 2.344 0.912 0.600 0.498 0.261 0.182 0.066 % of Var. 39.216 29.295 11.402 7.500 6.226 3.264 2.274 0.823 Cum. % 39.216 68.511 79.913 87.413 93.639 96.903 99.177 100.000

Table 5.4 Component loadings showing correlations between initial variables and the principal components. Habitat condition, vegetation condition and over-storey condition are positively correlated with PC1; disturbance and weeds are positively correlated with PC2, and fire age is negatively correlated with PC2.

Component Loadings (correlations between initial variables and principal components) Variable PC 1 PC 2 PC 3 PC 4 PC 5 PC 6 PC 7 PC 8 VEG COND 0.797 -0.007 -0.087 -0.548 0.054 -0.190 0.119 0.067 HABITAT COND 0.916 0.100 0.310 0.000 0.125 -0.003 -0.029 -0.197 UNDER-STOREY COND 0.678 0.519 0.430 0.000 -0.092 0.183 -0.171 0.123 OVER-STOREY COND 0.797 -0.007 -0.087 0.548 0.054 -0.190 0.119 0.067 CANOPY HEIGHT 0.660 -0.068 -0.685 0.000 0.044 0.297 -0.017 -0.011 DISTURBANCE -0.153 0.849 -0.350 0.000 0.179 -0.216 -0.236 -0.014 WEEDS -0.332 0.804 0.109 0.000 0.401 0.143 0.222 0.013 FIRE AGE -0.012 -0.832 0.112 0.000 0.523 0.011 -0.136 0.051

F11 UF10 UF9, F12

F4, F5

F8 F3 F1

UF6 UF7 F2

FIGURE 5.6 Principal Component Plot representing the relationships between principal components, with radiating weighting lines representing variables correlated with each principal component. Trapping sites are overlayed to show the variables affecting each site.

Figure 5.7 General pattern of honey possum captures- the average number of honey possums caught per trapping session in habitat of differing fire ages. Regression analysis shows no significant relationship between the average number of honey possums trapped per session and fire age (r2 =0.05, df=19, P=0.35).

5.4 Discussion 5.4.1 Maintaining gene flow

Spatial autocorrelation analysis has varied applications in wildlife management from conservation (Blouin et al. 2010) to pest control (Pople et al. 2007); with the ability to detect genetic structure of populations at a fine scale (Underwood et al. 2007). Within the Gnangara study area, spatial autocorrelation analyses revealed no fine scale genetic structuring of the honey possum across distances up to 20 km. Using spatial autocorrelation analysis Row et al. (2010) showed that habitat degradation is having a strong effect on the population structure of eastern foxsnakes in south-western Ontario, and is limiting dispersal across the region. Schrey et al. (2011) also used spatial autocorrelation to characterize the effect of fire on the genetic diversity and genetic differentiation of the Florida sand skink within a site with a well documented and maintained controlled fire regime. Spatial autocorrelation analyses revealed fine scale genetic structure and that individuals rarely disperse >1 km, indicating that infrequent fire may be beneficial to the species (Schrey et al. 2011). Unlike these examples, the Gnangara result is more consistent with random movement across the landscape: very different from the studies above but consistent with the lack of genetic structure reported across the whole of species range in Chapters 2 and 4.

5.4.2 Fire and habitat quality

Within the Gnangara study area, honey possums were present in habitats with very varied fire histories. The fire ages of sites where honey possums were trapped ranged between 1-3 years since last burnt and 37-39 years since last burnt, indicating that this species is not especially sensitive to fire and may even survive fires (Fig 5.5). The Western Australian Department of Environment and Conservation’s (DEC) rotational burning regime has resulted in a mosaic of patches of habitat with varying fire ages within the study area. Trapping results from this study showed that generally, honey possum densities were low in recently burnt habitat (1-3 years since last burnt), gradually increased to a peak at 7-9 years since last burnt, and then gradually decreased again with longer unburnt habitat. Although this general pattern suggests that honey possums appear to favour areas that have not been burnt for around eight years, there was no significant difference found for the average number of honey possums caught in habitat of differing fire ages indicative of high variance within and between sites.

Fire and other disturbances cluster together as impacts on habitat quality (Table 5.3, Fig. 5.4) and distinct from vegetation condition variables: neither vegetation quality (measured as PCA1), or disturbance (PCA2 including fire) predicted honey possum density. These trapping results have shown that within the Gnangara study area honey possums utilise habitat with varying fire ages, highlighting their capacity to cope with patchy, mosaic burning and heterogeneity within the landscape. The capacity to rapidly invade burnt areas, their extensive use of these landscapes and maintained gene flow as shown by the spatial autocorrelation analyses, are all consistent with fire not being a major factor influencing distribution or density when it occurs at the spatial scales reported here.

Honey possum densities in this study followed a similar general pattern to the capture rates obtained by Everaardt (2003) in Fitzgerald River National Park with low numbers in recently burnt patches, followed by a gradual increase to a peak, and then a decrease in numbers as fire age increased. Although a similar general pattern was found, the apparent favoured fire age was considerably different. In Gnangara, honey possum densities peaked at an average fire age of 8 years since last burnt, whereas honey possum capture rates were the highest at 20 years post fire at Fitzgerald River (Everardt 2003).

Fire has a long history in influencing Australian environments (Burrows & Wardell-Johnson 2003; Gill 1981; Kemp 1981) and it is quite possible that honey possums, like several other species (e.g. bats- Law 1995; honeyeaters- Pyke 1985: pygmy possums Tulloch & Dickman 2007) have evolved a response of resource tracking to deal with fire. For example, Cercartetus nanus (eastern pygmy possum) is a nectar-feeding marsupial species comparable in size and predicted dispersal capacity to honey possums. In a study on the effects of food and fire on the demography of C. nanus, there was a rapid response to artificial energy supplements with a local increase in numbers and some evidence of an improvement in condition (Tulloch & Dickman 2007). It is very possible that the honey possum (like the eastern pygmy possum), continuously monitors shifts in resources, e.g. using olfactory cues or predictable successional changes post fire, (cf seasonal shifts in sugar sources in Leadbeaters possum, Smith 1984) and has adapted well to the spatial variation in the availability of ephemeral resources in fluctuating environments.

Radio-tracking in the Scott National Park provided evidence of honey possums utilising much greater areas than previously reported in Fitzgerald River National Park by Garavanta et al.

(2000) based on trapping data alone (Bradshaw & Bradshaw 2002; Bradshaw et al. 2007). Within the study area at Scott National Park, radio-tracking also showed individuals were routinely moving from unburnt habitat into burnt habitat to feed in summer (Bradshaw et al. 2007) consistent with a resource tracking capacity rather than a simple avoidance of burnt areas. Habitat differences between the study areas at Fitzgerald River National Park and Scott National Park may explain the differences in daily movements: e.g. the presence of extensive open burnt areas in Scott National Park (Bradshaw et al. 2007). Differences in resource availability generated by variance in fire impact, habitat quality and sparse vegetation cover may also be why honey possums were trapped at Gnangara sites in habitat with extremely varied fire histories with no fine scale genetic structuring across distances up to 20 km. In a similar study in the fragmented habitat of a softwood plantation in NSW, Banks et al. (2005) discovered that patch occupancy of Antechinus agilis was primarily influenced by habitat quality but population size was influenced by forest type.

The results in this study highlight the substantial differences between sites across the geographic range of honey possums and how the species is able to respond to such differences. For example, the structural differences in habitat, and inconsistent, clumped food sources in Scott National Park may account for honey possums utilising greater areas there than reported by Garavanta et al. (2000) in Fitzgerald River National Park (Bradshaw et al. 2007). Differences in plant assemblages, flowering ages of plant species, and overall productivity of the systems may also account for differences in honey possum densities in the Gnangara study area and previous study areas of the Western Australian south coast. Understanding what those links might be is critical. A study on fire frequency in Banksia woodland in Bold Park, Western Australia also highlighted the importance of vegetation structure and fire regimes and the possibility of directional shifts in vegetation structure. Fisher et al. (2009) demonstrated a link between an altered vegetation structure, fire frequency, and plant invasion indicating that the area’s average fire age of four years since last burnt was too frequent. The four-year fire frequency exceeded plant species’ capacities to replenish the canopy and soil stored seed banks, and sustain underground storage organs (Fisher et al. 2009). As Banksia woodlands are a main habitat for the honey possum in the Gnangara area, this is an important finding and highlights the importance of a minimum of a 7- 9 year rotational prescribed burn regime in this habitat type.

Understanding how individuals move through the landscape is fundamental in predicting impacts of landscape alterations (Row et al. 2010). The Gnangara study area has previously been subject to landscape alterations in the form of habitat fragmentation, and continues to be subjected to prescribed fire regimes. Regardless of these alterations, honey possums occur across the whole landscape and they have maintained gene flow throughout the Gnangara study area at both a local and a landscape scale. Within fragmented environments, the short term response of organisms depends largely on the standing genetic variation within populations. As populations are generally small and relatively isolated within fragmented landscapes, they can be increasingly subject to genetic drift (and inbreeding), consequently depleting genetic variation and lowering their evolutionary potential (Bijlsma & Loeschcke, 2005). Collectively however, both demographic and genetic results from this study show that it is likely that honey possums have managed to persist in this patchy habitat and have adapted to the effects of fragmentation and fire or have a landscape use pattern, particularly resource tracking that generates widespread dispersal and population mixing.

Chapter 6: The dispersal of honey possums, Tarsipes rostratus, in relation to habitat fragmentation and fire: conclusions and management implications

6.1 Movements and dispersal

This study was carried out in the Gnangara and Yanchep pine plantation to investigate the patterns of distribution and dispersal of honey possums in relation to habitat fragmentation and fire history. The study combined both demographic and genetic data to gain an accurate and in-depth understanding of the dispersal capabilities of the species. Several questions were posed: How does habitat fragmentation and fire impact on the dispersal of honey possums? And how can results from this study be used to manage fire-affected fragmented landscapes for honey possum conservation?

Mitochondrial DNA, STRUCTURE analyses and spatial autocorrelation approaches are consistent in this study, suggesting widespread dispersal at both very local and the broadest geographic scales. Results showed very little phylogeographic structure across the geographic range of Tarsipes rostratus, and the phylogeny did not appear to reflect the historical, biogeographical tracks of south-west flora. At a local scale in the Gnangara study area, habitat fragmentation did not appear to affect the distribution of honey possums. Results also showed that there did not appear to be any physical barriers associated with the pine plantations to movement through the matrix. Microsatellite analyses also revealed very little population structure in the Gnangara study area, indicating that gene flow has been maintained through the fragmented landscape. These results contrast somewhat with previous mark-recapture data (Garavanta et al. 2000) which suggested limited dispersal (generally less than 30 metres) and radio tracking data (Bradshaw & Bradshaw 2002) which allowed relatively long movements following flower availability but still a maintained core range with limited dispersal.

For managers the challenge is to understand whether these are:

i) differences imposed in some unknown way by fragmentation as earlier studies sites were conducted in largely unfragmented landscapes,

ii) consequences of the wetter cooler habitats across the south coast,

iii) caused by differences in vegetation structure, e.g. more continuous overstorey may mean less movement across the ground generating less selection against movement by predators,

iv) a result of differences in the tools used to assess dispersal where genetic approaches may pick up the genetic consequences of multiple paternity (Bryant 2004) and extensive movement by males that are missed by trapping as males make short term/long distance moves that are generally missed ( cf Bradshaw & Bradshaw 2002); or

v) where trapping techniques cause shifts in behaviour so animals move less.

Future research on the dispersal capabilities of the honey possum should concentrate on these potential explanations for the differences found between my study and the long-term studies on the south coast of Western Australia. It would be beneficial to explore in more detail the influence of habitat structure on the movements of honey possums throughout their entire range.

6.2 Fire and vegetation quality

Results from the Gnangara study area have also shown that honey possums utilise habitat with varying fire ages and habitat conditions, highlighting their capacity to cope with patchy, mosaic burning and heterogeneity in the landscape. The data from my study suggest little impact of fire, based on the variance in trap success rate but the mean effect could indicate peaks in abundance 7-8 years post fire and possibly later at about 20 years- the latter comparable to some data from Fitzgerald River. It is therefore difficult to suggest a desirable fire interval, scale or annual timing. While the data presented in this study suggests little impact of fire, managers should consider impacts of climate change such as a potentially drier climate and higher fire frequency. With increasing pressure from urbanisation, an increase in the frequency of fire may have a greater impact on species persisting in fragmented environments.

The Gnangara area has been managed for both timber production and fauna & flora conservation but its fragmented nature may have generated a much smaller average size of fire events and much more heterogeneity in fire age across the landscape – attributes that may be important if vegetation condition follows similar patterns. Understanding how much vegetation condition (which will reflect age, isolation, edge effects, fire and grazing impacts) is linked to fragmentation and disturbance is critically important.

Vegetation condition although affected by land use, is largely determined by climate. Changes in climate may result in variability of resources in the landscape and this variability may be important in maintaining gene flow for certain species in fragmented populations in these landscapes (Blaum et al. 2012). Anthropogenic land and resource use changes related to climate change however can also impact the survival of wildlife (Forrest et al. 2012). For small- ranging species (whether naturally small or small due to anthropogenic pressures) habitat fragmentation is particularly detrimental irrespective of the additional pressure of climate change (Hof et al. 2011). In fragmented landscapes the persistence of species and the maintenance of genetic diversity within and between populations, depends on dispersal capabilities of organisms across matrix areas (Blaum et al. 2012). For the honey possum, an endemic species to south-western Australia, the concurrent processes of climatic change and habitat fragmentation is an area that requires more attention. For example if the combined effects of habitat loss and fragmentation, and climate change are greater than the effects of each threat individually, current conservation management strategies may be inefficient or ineffective Mantyka-Pringle et al. 2012).

6.3 Management How can results from this study be used to manage fire-affected fragmented landscapes for honey possum conservation?

Tarsipes rostratus is the only species in the Family Tarsipedidae and it is endemic to south- western Western Australia. Honey possums primarily occur in habitat areas of high plant species richness (Russell & Renfree 1989; Wooller et al. 2004). They are not considered to be a threatened species and are relatively common within their range, but they are dependent on coastal heath and Banksia woodland habitat types (Bradshaw et al. 2007). Lindenmayer et al. (2011) argue that a greater recognition is needed in conservation biology to go beyond the

100 traditional focus of endangered species and it is important for land managers to take steps to conserve some common species to ensure that they do not become uncommon or rare. It could be argued that honey possums are one such species that could demand a greater focus from conservation. While this study has shown that they appear to have coped well with disturbance in terms of genetic connectivity, special consideration to their conservation may be warranted if a key aim of conservation biology is to prevent species from declining or becoming extinct (Lindenmayer et al. 2011).

Another important aspect to consider in prioritising conservation efforts is if its phylogenetic position (Bininda-Emonds et al. 2007); a monotypic family representing a monophylogenetic clade (Kirsch et al. 1997; Phillips & Pratt 2008) justifies special consideration, for example, consistent with the arguments for conservation of phylogenetic diversity (Faith 1992) and the setting of biodiversity conservation priorities (Rosauer et al. 2009).

One of the overall outcomes of this research was to use the results from the study to build plans for habitat connectivity for long-term sustainable populations that are under continuing threat from habitat clearing and development, and fire. Land managers need to decide how much a species like the honey possum should drive land management decisions. It is not a weed species that can cope with almost any habitat quality- it has disappeared from fragments in truly urban landscapes, declined with declines in vegetation condition but it copes well with even relatively high levels of disturbance.

To better understand the response of honey possums to continued disturbance it may be beneficial to investigate processes influencing social systems. For example, disturbance can affect key drivers of the evolution of social systems such as resources, demography and genetic structure. Documenting the adaptive response of species to disturbance is likely to be important in our understanding of the evolution of social systems and the ecological responses to disturbance (Banks et al. 2012). While this study has shown that honey possums in the Gnangara area have continued to utilise a fragmented and fire-affected landscape, the long- term conservation of this species would benefit from further research on the evolution of social systems of other honey possum populations in different regions and habitat types. This is highlighted by the differences in dispersal capabilities found in this study compared with previous mark-recapture studies (e.g. Garavanta et al. 2000; Bradshaw & Bradshaw 2002) on the south coast of Western Australia.

101

South-western Australia is a highly fragmented landscape with anthropogenic activities such as agriculture, forestry and urban development all competing with natural ecosystems for valuable resources. Biodiversity conservation in forestry and agricultural landscapes is important however, as these industries depend on healthy soils, nutrient cycling and other ecosystem services provided by biodiversity (Fischer et al. 2006). In order to maintain biodiversity, ecosystem function, and resilience in production landscapes (such as in south- western Australia), landscapes should include structurally characteristic patches of native vegetation, corridors and stepping stones between them, a structurally complex matrix, and buffers around sensitive areas (Fischer et al. 2006). Management plans for species and habitats should aim to develop resilient landscapes where the evolutionary potential of species and populations can be conserved. There needs to be explicit consideration of genetic diversity and the processes that support ongoing evolutionary processes in biodiversity management (Sgrò et al. 2010). Given that results from this study have suggested that honey possums have maintained dispersal capabilities at current fragmentation levels in south- western Australia, land management should ensure that at least current levels of connectivity are maintained. Connections should include structurally characteristic patches of native vegetation, corridors and stepping stones between them, and a structurally complex matrix to allow for continued dispersal of the honey possum, an iconic, endemic species.

102

REFERENCES:

Attiwill, P.M. (1994). Ecological disturbance and the conservative management of eucalypt forests in Australia. Forest Ecology and Management 63, 301-346.

Avise, J.C. (1998). The history and purview of phylogeography: a personal reflection. Molecular Ecology 7, 371-379.

Avise, J.C. (2009). Phylogeography: retrospect and prospect. Journal of Biogeography 36, 3-15.

Banks, S.C., Blyton, M.D.J., Blair, D, McBurney, L and Lindenmayer, D.B. (2012). Adaptive responses and disruptive effects: how major wildfire influences kinship-based social interactions in a forest marsupial. Molecular Ecology 21, 673-684.

Banks, S.C., Finlayson, G.R., Lawson, S.J., Lindenmayer, D.B., Paetkau, D, Ward, S.J. and Taylor, A.C. (2005). The effects of habitat fragmentation due to forestry plantation establishment on the demography and genetic variation of a marsupial carnivore, Antechinus agilis. Biological Conservation 122, 581-597.

Banks, S.C., Ward, S.J., Lindenmayer, D.B., Finlayson, G.R., Lawson, S.J. and Taylor, A.C. (2005). The effects of habitat fragmentation on the social kin structure and mating system of the agile antechinus, Antechinus agilis’. Molecular Ecology 14, 1789-1801.

Beeton, R.J.S., Buckley, K, Jones, G.J., Morgan, M.D., Reichelt, R.E. and Trewin, D (2006). Australia State of the Environment 2006. Independent report to the Australian Government Minister for the Environment and Heritage. Department of Environment and Heritage, Canberra.

Bermingham, E and Moritz, C (1998). Comparative phylogeography: concepts and applications. Molecular Ecology 7, 367-369.

Berry, O, Tocher, M.D., Gleeson, D.M. and Sarre, S.D. (2005). Effect of vegetation matrix on animal dispersal: genetic evidence from a study of endangered skinks. Conservation Biology 19, 855-864.

103

Berry, O, Tocher, M.D. and Sarre, S.D. (2004). Can assignment tests measure dispersal? Molecular Ecology 13, 551-561.

Bijlsma, R and Loeschcke, V (2005). Environmental stress, adaptation and evolution: an overview. Journal of Evolutionary Biology 18, 744-749.

Bininda-Emonds, O.R.P., Cardillo, M, Jones, K.E., MacPhee, R.D.E., Beck, R.M.D., Grenyer R, Price, S.A., Vos, R.A., Gittleman, J.L. and Purvis, A (2007). The delayed rise of present-day mammals. Nature 446, 507-512.

Bladon, R.V., Dickman, C.R. and Hume, I.D. (2002). Effects of habitat fragmentation on the demography, movements and social organization of the eastern pygmy-possum (Cercartetus nanus) in northern New South Wales. Wildlife Research 29, 105-116.

Blackmore, C. J., Peakall, R and Heinsohn, R (2011). The absence of sex-biased dispersal in the cooperatively breeding grey-crowned babbler. Journal of Animal Ecology, 80, 69–78.

Blaum, N., Schwager, M., Winchmann, M.C. and Rossmanith, E (2012). Climate induced changes in matrix suitability explain gene flow in a fragmented landscape – the effect of interannual rainfall variability. Ecography 35, 650-660.

Bleby, K, Reaverley, A, Valentine, L and Wilson, B (2009). ‘Chapter 7: Fire regimes’ in Biodiversity values and threatening processes of the Gnangara groundwater system, eds BA Wilson and LE Valentine, Department of Environment and Conservation.

Blouin, M.S., Parsons, M, Lacaille, V and Lotz S (1996). Use of microsatellite loci to classify individuals by relatedness. Molecular Ecology 5, 393-401.

Blouin, M.S., Phillipsen, I.C. and Monsen, K.J. (2010). Population structure and conservation genetics of the Oregon spotted frog, Rana pretiosa. Conservation Genetics 11, 2179-2194.

104

Bradshaw, S.D. and Bradshaw, F.J. (1999). Field energetics and the estimation of pollen and nectar intake in the marsupial Honey possum, Tarsipes rostratus, in heathland habitats of South-Western Australia. Journal of Comparative Physiology B 169, 569-580.

Bradshaw, S.D. and Bradshaw, F.J. (2002). Short-term movements and habitat use of the marsupial honey possum (Tarsipes rostratus). Journal of Zoology, London 258, 343-348.

Bradshaw, S.D. , Dixon, K.W., Hopper, S.D., Lambers, H, Turner, S.R. (2011). Little evidence for fire-adapted plant traits in Mediterranean climate regions. Trends in Plant Science 16, 69-76.

Bradshaw, S.D., Phillips, R.D., Tomlinson, S, Holley, R.J., Jennings, S and Bradshaw, F.J. (2007). Ecology of the Honey possum, Tarsipes rostratus, in Scott National Park, Western Australia. Australian Mammalogy 29, 468-493.

Bradstock, R.A., Keith, D.A. and Auld, T.D. (1995). ‘Fire and conservation: imperatives and constraints on managing for diversity’ in Conserving biodiversity: threats and solutions, eds RA Bradstock, TD Auld, DA Keith, RT Kingsford, D Lunney & DP Silvertsen, Surrey Beatty and Sons, Sydney, pp.323-333.

Bryant, K.A. (2004). The mating system and reproduction in the honey possum, Tarsipes rostratus: a life-history and genetic perspective. Ph.D. thesis, Murdoch University.

Brown, S, Clarke, M and Clarke, R (2009). Fire is a key element in the landscape-scale habitat requirements and global population status of a threatened bird: The Mallee Emu-wren (Stipiturus mallee). Biological Conservation 142, 432-445.

Burrows, N and Abbott, I (2003). ‘Fire in south-western Australia: synthesis of current knowledge, management implications and new research directions’, in ‘Fire in ecosystems of south-west Western Australia: impacts and management, eds I Abbott & N Burrows, Backhuys Publishers, Leiden.

Burrows, N & Wardell-Johnson, G (2003). ‘Fire and plant interactions in forested ecosystems of south-west Western Australia’ in Fire in ecosystems of south-west Western Australia: impacts and management, eds I Abbott & N Burrows, Backhuys Publishers, Leiden, pp. 225-268.

105

Byrne, M (2007). Phylogeography provides an evolutionary context for the conservation of a diverse and ancient flora. Australian Journal of Botany 55, 316-325.

Caccetta, P and Dunne, R (2010). A methodology to estimate the future extent of dryland salinity in the southwest of Western Australia. Journal of Environmental Quality 39, 26-34.

Cale, P.G. (2003). The influence of social behaviour, dispersal and landscape fragmentation on population structure in a sedentary bird. Biological Conservation 109, 237-248.

Carreon-Marttinez, L and Heath, D.D. (2010). Revolution in food web analysis and trophic ecology: diet analysis by DNA and stable isotope analysis. Molecular Ecology 19, 25–27.

Catling, P.C., Coops, N.C. and Burt, R.J. 92001). The distribution and abundance of ground- dwelling mammals in relation to time since wildfire and vegetation structure in south-eastern Australia. Wildlife Research 28, 555-564.

Christensen, P.E.S. (1980). The biology of Bettongia pencillata Gray, 1837, and Macropus eugenii (Desmarest, 1817) in relation to fire. Forests Department of Western Australia.

Clement, M, Posada, D and Crandall, K (2000). TCS: a computer program to estimate gene genealogies. Molecular Ecology 9, 1657-1660.

Cooper, M.L. (2000). Random amplified polymorphic DNA analysis of southern brown bandicoot (Isoodon obesulus) populations in Western Australia reveals genetic differentiation related to environmental variables. Molecular Ecology 9, 469–479.

Cooper, S.J.B., Harvey, M.S., Saint, K.M., Main, B.Y. (2011). Deep phylogeographic structuring of populations of the trapdoor spider Moggridgea tingle (Migidae) from southwestern Australia: evidence for long-term refugia within refugia. Molecular Ecology 20, 3219–3236.

Delgado, J.A., Serrano, J.M., Lopez, F and Acosta, F.J. (2008). Seed size and seed germination in the Mediterranean fire-prone shrub Cistus ladanifer. Plant Ecology 197(2), 269-276.

106

Driscoll, D.A. (1998). Genetic structure of the frogs Geocrinia lutea and Geocrinia rosea reflects extreme population divergence and range changes, not dispersal barriers. Evolution 52, 1147- 1157.

Driscoll, D.A. and Weir, T (2005). Beetle responses to habitat fragmentation depend on ecological traits, habitat condition, and remnant size. Conservation Biology 16, 182-194.

Driscoll, D.A., Lindenmayer, D.B., Bennett, A.F., Bode, M, Bradstock, R.A., Cary, G.J., Clarke, M.F., Dexter, N, Fensham, R, Friend, G, Gill, M, James, S, Kay, G, Keith, D.A., MacGregor, C, Russell-Smith, J, Salt, D, Watson, J.E.M., Williams, R.J., York, A (2010). Fire management for biodiversity conservation: Key research questions and our capacity to answer them. Biological Conservation 143, 1928-1939.

Edwards, D.L and Roberts, J.D (2011). Genetic diversity and biogeographic history inform future conservation management strategies for the rare sunset frog (Spicospina flammocaerulea). Australian Journal of Zoology 59, 63-72.

Edwards, D.L., Roberts, J.D. and Keogh, J.S. (2007). Impact of Plio-Pleistocene arid cycling on the population history of a southwestern Australian frog. Molecular Ecology 16, 2782-2796.

Eldridge, M.D.B., Kinnear, J.E. and Onus M.L. (2001). Source population of dispersing rock wallabies (Petrogale lateralis) identified by assignment tests on multilocus genotypic data. Molecular Ecology 10, 2867-2876.

Everaardt, A.N. (2003). The impact of fire on the honey possum Tarsipes rostratus in the Fitzgerald River National Park, Western Australia. Ph.D. thesis, Murdoch University.

Excoffier, L, Laval, G and Schneider, S (2005). Arlequin ver. 3.0: An integrated software package for population genetics data analysis. Evolutionary Bioinformatics Online 1, 47-50.

Faith, D.P. (1992). Conservation evaluation and phylogenetic diversity. Biological Conservation 61, 1-10.

107

Felsenstein, J (1985). Confidence limits on phylogenies: an approach using the bootstrap. Evolution 39, 783-791.

Fischer, J, Lindenmayer, D.B. and Manning, A.D. (2006). Biodiversity, ecosystem function, and resilience: ten guiding principles for commodity production landscapes. Frontiers in Ecology and the Environment 4, 80-86.

Fisher, J.L., Loneragan, W.A., Dixon, K, Delaney, J and Veneklaas, E.J. (2009). Altered vegetation structure and composition linked to fire frequency and plant invasion in a biodiverse woodland. Biological Conservation 142, 2270-2281.

Folmer, O, Black, M, Hoeh, W, Lut,z R and Vrijenhoek, R (1994) DNA primers for amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates. Molecular Marine Biology and Biotechnology, 3, 294–299.

Forrest, J.L., Wikramanayake, E., Shrestha, R., Areendran, G., Gyeltshen, K., Maheshwari, A., Mazumdar, S., Naidoo, R., Thapa, G.J. and Thapa, K (2012). Conservation and climate change: Assessing the vulnerability of snow leopard habitat to treeline shift in the Himalaya. Biological Conservation 150, 129-135.

Fox, B.J. (1982). Fire and mammalian secondary succession in an Australian coastal heath. Ecology 63, 1332-1341.

Fox, B.J. and McKay, G.M. (1981). Small mammal responses to pyric successional changes in eucalypt forest. Australian Journal of Ecology 6, 29-41.

Fredsted, T, Pertoldi, C, Schierup, M.H. and Kappeler, P.M. (2005). Microsatellite analyses reveal fine-scale genetic structure in grey mouse lemurs (Microcebus murinus). Molecular Ecology 14, 2363-2372.

Friend, G.R. (1993). Impact of fire on small vertebrates in mallee woodlands and heathlands of temperate Australia: a review. Biological Conservation 65, 99-114.

108

Friend, G.R., Smith, G.T., Mitchell, D.S. and Dickman C.R. (1989). Influence of pitfall and drift fence design on capture rates of small vertebrates in semi-arid habitats of Western Australia. Australian Wildlife Research 16, 1-10.

Friend, G & Wayne, A (2003). ‘Relationships between mammals and fire in south-west Western Australian ecosystems: what we know and what we need to know’, in Fire in ecosystems of south-west Western Australia: impacts and management, eds I Abbott & N Burrows, Backhuys Publishers, Leiden, pp. 363-380.

Fu, Y.X. (1997). Statistical tests for neutrality of mutations against population growth, hitchhiking and background selection. Genetics 147, 915-925.

Garavanta, C.A.M. (1997). A mark-recapture study of the social organization of the honey possum Tarsipes rostratus in the Fitzgerald River National Park, Western Australia. Ph.D. thesis, Murdoch University.

Garavanta, C.A.M., Wooller, R.D. and Richardson, K.C. (2000). Movement patterns of honey possums, Tarsipes rostratus, in the Fitzgerald River National Park, Western Australia. Wildlife Research 27, 179-183.

Gill, A.M. (1981). ‘Adaptive responses of Australian vascular plant species to fire’, in Fire and the Australian biota, eds AM Gill, RH Groves & IR Noble, Australian Academy of Science, Canberra, pp. 243-272.

Gill, A.M. and Bradstock, R (2003). ‘Fire regimes and biodiversity: a set of postulates’, in Fire ecology, policy and management issues, eds G Cary, D Lindenmayer & S Dovers, CSIRO Publishing, Collingwood, pp. 15-25.

Goudet, J (1995). FSTAT (Version 1.2): A computer program to calculate F-statistics. Journal of Heredity 86, 485-486.

Gouw, G, Stewart, B.A. and Daniels, S.R. (2006). Phylogeographic structure of a freshwater crayfish (Decapoda: Parastacidae: Cherax preissii) in south-western Australia. Marine and Freshwater Research 57, 837-848.

109

Government of Western Australia, (2000). Bush Forever Volume 2: Directory of Bush Forever sites. Department of Environmental Protection, Perth, Western Australia.

Government of Western Australia, (2009). Draft Gnangara Sustainability Strategy. Government of Western Australia.

Groom, P.K., Froend, R.H., Mattiske, E.M. and Koch, B (2000). Myrtaceous shrub species respond to long-term decreasing groundwater levels on the Gnangara Groundwater Mound, northern Swan Coastal Plain. Journal of the Royal Society of Western Australia 83, pp. 75-82.

Groombridge, B (1992). Global Biodiversity, Chapman & Hall, London.

Gruber, B and Henle, K (2008). Analysing the effect of movement on local survival: a new method with an application to a spatially structured population of the arboreal gecko Gehyra variegata. Oecologia 154(4), 679-690.

Hassell, C.W. and Dodson, J.R. (2003). ‘The fire history of south-west Western Australia prior to European settlement in 1826-1829’, in Fire in Ecosystems of South-west Western Australia: Impacts and Management, eds I Abbott and N Burrows, Backhuys Publishers, Netherlands, pp. 71-85.

Hayward, M.W., de Tores, P.J., Dillon, M.J. and Fox, B.J. (2003). Local population structure of a naturally occurring metapopulation of the quokka (Setonix brachyurus Macropodidae: Marsupialia). Biological Conservation 110, 343-355.

He, T., Lamont, B. B. and Downes, K. S. (2011). Banksia born to burn. New Phytologist, 191, 184–196.

Henle, K, Davies, K.F., Kleyer, M, Margules, C and Settele, J (2004). Predictors of species’ sensitivity to fragmentation. Biodiversity and Conservation 13, 207-251.

Henle, K, Lindenmayer, D.B., Margules, C.R., Saunders, D.A. and Wissel, C (2004). Species survival in fragmented landscapes: where are we now? Biodiversity and Conservation 13, 1-8.

110

Hoehn, M, Sarre, S.D. and Henle, K (2007). The tales of two geckos: does dispersal prevent extinction in recently fragmented populations? Molecular Ecology 16, 3299-3312.

Hof, C., Levinsky, I., Araújo, M.B. and Rahbek, C. (2011). Rethinking species’ ability to cope with rapid climate change. Global Change Biology 17, 2987-2990.

Hopper, S.D. (1979). Biogeographical aspects of speciation in the southwest Australian flora. Annual Review of Ecology and Systematics 10, 399-422.

Hopper, S.D. and Burbidge, A.H. (1989). Conservation status of Banksia woodlands on the Swan Coastal Plain. Journal of the Royal Society of Western Australia 71, 115-116.

Hopper, S.D. and Gioia, P (2004). The southwest Australian floristic region: evolution and conservation of a global hot spot of biodiversity. Annual Review of Ecology, Evolution and Systematics, 35, 623-650.

Horwitz, P, Bradshaw, D, Hopper, S, Davies, P, Froend, R & Bradshaw, F (2008). Hydrological change escalates risk of ecosystem stress in Australia’s threatened biodiversity hotspot. Journal of the Royal Society of Western Australia 91, 1-11.

Kane, N.C. and King, M.G. (2009). Using parentage analysis to examine gene flow and spatial genetic structure. Molecular Ecology 18, 1551-1552.

Keighery, B.J. (1994). Bushland plant survey: A guide to plant community survey for the community. Wildflower Society of Western Australia (Inc), Nedlands, Western Australia.

Kemp, E.M. (1981). ‘Tertiary palaeogeography and the evolution of Australian climate’ in Ecological Biogeography of Australia, ed A Keast, Kluwer Academic Publishers Group, The Netherlands, pp. 3-21.

Kimura, M (1980). A simple method for estimating evolutionary rate of base substitutions through comparative studies of nucleotide sequences. Journal of Molecular Evolution 16, 111- 120.

111

Kirsch, J.A.W., Springer, M.S. and Lapointe, F.J. (1997). DNA-hybridisation Studies of Marsupials and their Implications for Metatherian Classification. Australian Journal of Zoology 45, 211–280.

Knight, E.H. and Fox, B.J. (2000). Does habitat structure mediate the effects of forest fragmentation and human-induced disturbance on the abundance of Antechinus stuartii? Australian Journal of Zoology 48, 577-595.

Koscinski, D, Yates, A.G., Handford, P and Lougheed, S.C. (2009). Effects of landscape and history on diversification of a montane, stream-breeding amphibian. Journal of Biogeography 36, 255-265.

Krauss, J, Bommarco, R, Guardiola, M, Heikkinen, R.K., Helm, A, Kuussaari, M, Lindborg, R, Öckinger, E, Pärtel, M, Pino, J, Pöyry, J, Raatikainen, K.M., Sang, A, Stefanescu, C, Teder, T, Zobel, M and Steffan-Dewenter, I (2010). Habitat fragmentation causes immediate and time- delayed biodiversity loss at different trophic levels. Ecology Letters 13, 597–605.

Lada, H, Thomson, J.R., Mac Nally, R and Taylor A.C. (2008). Impacts of massive landscape change on a carnivorous marsupial in south-eastern Australia: inferences from landscape genetics analysis. Journal of Applied Ecology 45, 1732-1741.

Lambeck, R.J. (1997). Focal species: a multi species umbrella for nature conservation. Conservation Biology 11, 849-856.

Lamont, B.B., Wittkuhn, R and Korczynskj, D (2004). Ecology and ecophysiology of grasstrees. Australian Journal of Botany 52(5), 561-582.

Law, B.S. (1995). The effect of energy supplementation on the local abundance of the common blossom bat, Syconycteris australis, in south-eastern Australia Oikos 72, 42-50.

Lindenmayer, D.B., Cunningham, R.B. and Peakall, R (2005). The recovery of populations of bush rat Rattus fuscipes in forest fragments following major population reduction. Journal of Applied Ecology 42, 649-658.

112

Lindenmayer, D.B., Cunningham, R.B. and Pope, M.L. (1999). A large-scale experiment to examine the effects of landscape context and habitat fragmentation on mammals. Biological Conservation 88, 387-403.

Lindenmayer, D.B. and Fischer, J. (2006). Tackling the habitat fragmentation panchreston. TRENDS in Ecology and Evolution 22, 127-132.

Lindenmayer, D.B. and Nix, H.A. (1993). Ecological principles for the design of wildlife corridors. Conservation Biology 7, 627-630.

Lindenmayer, D.B., MacGregor, C, Welsh, A, Donnelly, C, Crane, M, Michael, D, Montague- Drake, R, Cunningham R.B., Brown, D, Fortescue, M, Dexter, N, Hudson, M & Gill, A.M. (2008). Contrasting mammal responses to vegetation type and fire. Wildlife Research 35, 395-408.

Lindenmayer, D.B., Wood, J.T.,McBurney, L, MacGregor, C, Youngentob, K, and Banks, S.C. (2011). How to make a common species rare: A case against conservation complacency. Biological Conservation 144, 1663-1672.

Manel, S, Gaggiotti, O.E. and Waples, R.S. (2005). Assignment methods: matching biological questions with appropriate techniques. Trends in Ecology & Evolution 20, 136-142

Manel, S, Joost, S, Epperson, B.K., Holderegger, R, Storfer, A, Rosenberg, M.S., Scribner, K.T., Bonin, A and Fortin, M.J. (2010). Perspectives on the use of landscape genetics to detect genetic adaptive variation in the field. Molecular Ecology 19, 3760-3772.

Manel, S, Schwartz, M.K., Luikart, G and Taberlet, P (2003). Landscape genetics: combining landscape ecology and population genetics. TRENDS in Ecology and Evolution 18, 189-197.

Mantyka-Pringle, C.S., Martin, T.G. and Rhodes, J.R. (2012). Interactions between climate and habitat loss effects on biodiversity: a systematic review and meta-analysis. Global Change Biology 18, 1239-1252.

113

Marchesan, D and Carthew, S.M. (2008). Use of space by the yellow-footed antechinus, Antechinus flavipes, in a fragmented landscape in South Australia. Landscape Ecology 23, 741- 752.

Mitrovski, P, Heinze, D.A., Broome, L, Hoffman, A.A. and Weeks, R (2007). High levels of variation despite genetic fragmentation in populations of the endangered mountain pygmy- possum, Burramys parvus, in alpine Australia. Molecular Ecology 16, 75-87.

Monamy, V and Fox, B.J. (2000). Small mammal succession is determined by vegetation density rather than time elapsed since disturbance. Austral Ecology 25, 580-587.

Moritz, C (1999). Conservation units and translocations: strategies for conserving evolutionary processes. Hereditas 130, 217-228.

Moritz, C (2002). Strategies to protect biological diversity and the evolutionary processes that sustain it. Systematic Biology 51, 238-254.

Moritz, C and Faith, D.P. (1998). Comparative phylogeography and the identification of genetically divergent areas for conservation. Molecular Ecology 7, 419-429.

Myers, N, Mittermeier, R.A., Mittermeier, C.G., da Fonseca, G.A.B. and Kent, J (2000). Biodiversity hotspots for conservation priorities. Nature 403, 853-858.

Nei, M and Kumar, S (2000). Molecular Evolution and Phylogenetics, Oxford University Press, New York.

Nelson, E.C. (1981). ‘Phytogeography of southern Australia’ in Ecological Biogeography of Australia, ed A Keast, W. Junk, The Hague, pp. 733-759.

Parolo, G, Ferrarini, A and Rossi, G (2009). Optimization of tourism impacts within protected areas by means of genetic algorithms. Ecological Modelling 220, 1138-1147.

Parr, C.L. and Andersen, A.N. (2006). Patch mosaic burning for biodiversity conservation: a critique of the pyrodiversity paradigm. Conservation Biology 20, 1610-1619.

114

Pausas, J.G. and Keeley J.E. (2009). A burning story: The role of fire in the history of life. BioScience 59, 593-601.

Peakall, R and Lindenmayer, D (2006). Genetic insights into population recovery following experimental perturbation in a fragmented landscape. Biological Conservation 132, 520-532.

Peakall, R and Smouse, P.E. (2006). GENALEX 6: genetic analysis in Excel. Population genetic software for teaching and research. Molecular Ecology Notes 6, 288-295.

Pearse, D.E. and Crandall, K.A. (2004). Beyond Fst: Analysis of population genetic data for conservation. Conservation Genetics 5, 585-602.

Pestell, A.J.L., Cooper, S.J.B., Saint, K & Petit, S (2008). Genetic structure of the western pygmy possum, Cercartetus concinnus Gould (Marsupialia: Burramyidae) based on mitochondrial DNA. Australian Mammalogy 29, 191-200.

Pestell, A.J.L. and Petit, S (2007). Methods and ethical considerations of pitfall trapping for the western pygmy possum (Cercartetus concinnus Gould) (Marsupialia: Burramyidae), with observations on capture patterns and nest sites. Wildlife Research 34, 296-305.

Phillips, M.J. and Pratt, R.C. (2008). Family-level relationships among the Australasian marsupial “herbivores” (Diprotodontia: Koala, wombats, kangaroos and possums). Molecular Phylogenetics and Evolution 46, 594-605.

Pons, P and Clavero, M (2010). Bird responses to fire severity and time since fire in managed mountain rangelands. Animal Conservation 13, 294-305.

Pope, L.C., Storch, D, Adams, M, Moritz, C and Gordon, G (2001). A phylogeny for the genus Isoodon and a range extension for Isoodon obesulus peninsulae based in mtDNA control region and morphology. Australian Journal of Zoology 49, 411-434.

Pople, A.R., Phinn, S.R., Menke, N, Grigg, G.C., Possingham, H.P. and McAlpine, C (2007). Spatial patterns of kangaroo density across the South Australian pastoral zone over 26 years:

115 aggregation during drought and suggestions of long distance movement. Journal of Applied Ecology 44, 1068-1079.

Primack, R.B. (1998). Essentials of Conservation Biology, Sinauer Associates, Sunderland.

Pritchard, J.K., Stephens, M and Donnelly, P (2000). Inference of Population Structure Using Multilocus Genotype Data. Genetics 155, 945-959

Puglisi, L, Adamo, M.C. & Baldaccini, N.E. (2005). Man-induced habitat changes and sensitive species: a GIS approach to the Eurasian Bittern (Botaurus stellaris) distribution in a Mediterranean wetland. Biodiversity and Conservation 14, 1909-1922.

Pyke, G.H. (1985). Seasonal patterns of abundance of insectivorous birds and flying insects. Emu 85, 34-39.

Raymond M, & Rousset F, (1995). GENEPOP (version 1.2): population genetics software for exact tests and ecumenicism. Journal of Heredity 86, 248-249.

Rosauer, D, Laffan, S.W., Crisp, M.D., Donnellan, S.C. and Cook, L.G. (2009). Phylogenetic endemism: a new approach for identifying geographical concentrations of evolutionary history. Molecular Ecology 18, 4061-4072.

Row, J.R., Blouin-Demers, G and Lougheed, S.C. (2010). Habitat distribution influences dispersal and fine-scale genetic population structure of eastern foxsnakes (Mintonius gloydi) across a fragmented landscape. Molecular Ecology 19, 5157-5171.

Russell, E.M. and Renfree, M.B. (1989). ‘Tarsipedidae’, in Fauna of Australia: Mammalia, eds DW Walton & BJ Richardson, AGPS, Canberra, pp. 769-782.

Saitou, N & Nei, M (1987). The neighbour-joining method: a new method for reconstructing phylogenetic trees. Molecular Biology and Evolution 4, 406-425.

116

Saenz-Agudelo, P, Jones, G.P., Thorrold, S.R. and Planes, S (2009). Estimating connectivity in marine populations: an empirical evaluation of assignment tests and parentage analysis under different gene flow scenarios. Molecular Ecology 18, 1765–1776.

Saffer, V.M. (1998). A comparison of foodplant utilization by nectar-feeding marsupials and birds in the Fitzgerald River National Park, Western Australia. Ph.D. thesis, Murdoch University.

Sarre, S.D. (1998). Demographics and population persistence of Gehyra variegata (Gekkonidae) following habitat fragmentation. Journal of Herpetology 32, 153-162.

Sarre, S.D. and Georges, A (2009). Genetics in conservation and wildlife management: a revolution since Caughley. Wildlife Research 36, 70-80.

Sarre, S, Smith, G.T. and Meyers, J.A. (1995). Persistence of two species of gecko (Oedura reticulata and Gehyra variegata) in remnant habitat. Biological Conservation 71, 25-33.

Saunders, D.A., Hobbs, R.J. and Margules, C.R. (1991). Biological consequences of ecosystem fragmentation: a review. Conservation Biology 5, 18-32.

Schrey, A.W., Fox, A.M., Mushinsky, H.R. and McCoy, E.D. (2011). Fire increases variance in genetic characteristics of Florida Sand Skink (Plestiodon reynoldsi) local populations. Molecular Ecology 20, 56-66.

Sgrò, C.M., Lowe, A.J. and Hoffmann, A.A. (2011). Building evolutionary resilience for conserving biodiversity under climate change. Evolutionary Applications 4, 326-337.

Simmons, J.M., Sunnucks, P, Taylor, A.C. and van der Ree, R (2010). Beyond roadkill, radiotracking, recapture and FST—a review of some genetic methods to improve understanding of the influence of roads on wildlife. Ecology and Society 15, 9.

Sinclair, E.A. (2001). Phylogeographic variation in the quokka, Setonix brachyurus (Marsupialia: Macropodidae): implications for conservation. Animal Conservation 4, 325-333.

117

Smith, A (1984). Diet of Leadbeaters Possum, Gymnobelideus leadbeateri (Marsupialia). Australian Wildlife Research 11, 265-273.

Smouse, P.E. and Peakall, R (1999). Spatial autocorrelation analysis of individual multiallele and multilocus genetic structure. Heredity 82, 561-573.

Soulé, M.E. (1985). What is conservation biology? BioScience 35, 727-734.

Spencer, P.B.S., Rhind, S.G. and Eldridge, M.D.B. (2001). Phylogeographic structure within Phascogale (Marsupialia: Dasyuridae) based on partial cytochrome b sequence. Australian Journal of Zoology 49, 369-377.

Sutherland, E.F. and Dickman, C.R. (1999). Mechanisms of recovery after fire by rodents in the Australian environment: a review. Wildlife Research 26, 405-419.

Sutherland, E.F., Lunney, D, Matthews A and Recher, H.F. (2006). ‘Post-fire observations of the eastern pygmy-possum Cercartetus nanus in Nadgee Nature Reserve and Ku-ring-gai Chase National Park, New South Wales’ in The Biology of Australian Possums and Gliders, eds RL Goldingray & SM Jackson, Surrey Beatty & Sons, Chipping Norton.

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

Tamura, K, Dudley, J, Nei, M and Kumar, S (2007). MEGA4: Molecular Evolutionary Genetics Analysis (MEGA) software version 4.0. Molecular Biology and Evolution 24, 1596-1599.

Tallmon, D.A., Draheim, H.M., Mills, L.S. and Allendorf, F.W. (2002). Insights into recently fragmented vole populations from combined genetic and demographic data. Molecular Ecology 11, 699-709.

Tulloch, A.I. and Dickman, C.R. (2007). Effects of food and fire on the demography of a nectar- feeding marsupial: a field experiment. Journal of Zoology 273, 382-388.

118

Underwood, J.N., Smith, L.D., Van Oppen, M.J.H. and Gilmour, J.P. (2007). Multiple scales of genetic connectivity in a brooding coral on isolated reefs following catastrophic bleaching. Molecular Ecology 16, 771-784. van der Ree, R and Loyn, R.H. (2002). The influence of time since fire and distance from fire boundary on the distribution and abundance of arboreal marsupials in Eucalyptus regnans- dominated forest in the Central Highlands of Victoria. Wildlife Research 29, 151-158.

Van Dyke, F, Schmeling, J.D., Starkenburg, S, Yoo, S.H. and Stewart, P.W. (2007). Responses of plant and bird communities to prescribed burning in tallgrass prairies. Biodiversity and Conservation 16, 827-839.

Walter, C and Stützel, H (2009). A new method for assessing the sustainability of land-use systems (I): Identifying the relevant issues. Ecological Economics 68, 1275-1287.

Wayne, A.F., Cowling, A, Lindenmayer, D.B., Ward, C.G., Vellios, C.V., Donnelly, C.F. and Calver, M.C. (2006). The abundance of a threatened arboreal marsupial in relation to anthropogenic disturbances at local and landscape scales in Mediterranean-type forests in south-western Australia. Biological Conservation 127, 463-476.

Weir, B.S. and Cockerham, C.C. (1984). Estimating F-statistics for the analysis of population structure. Evolution 38, 1358-1370.

Wilson, B.A., Aberton, J.G. and Reichl, T (2001). Effects of fragmented habitat and fire on the distribution and ecology of the swamp antechinus (Antechinus minimus maritimus) in the eastern Otways, Victoria. Wildlife Research 28, 527-536.

Wooller, R.D., Richardson, K.C., Garavanta, C.A.M., Saffer, V.M., Anthony, C and Wooller, S.J. (1998). The influence of annual rainfall upon capture rates of a nectar-dependent marsupial. Wildlife Research 25, 165-169.

Wooller, R.D., Richardson, K.C., Garavanta, C.A.M., Saffer, V.M. and Bryant, K.A. (2000). Opportunistic breeding in the polyandrous honey possum, Tarsipes rostratus. Australian Journal of Zoology 48, 669-680.

119

Wooller, R.D., Richardson, K.C., Saffer, V.M., Garavanta, C.A.M., Bryant, K.A., Everaardt, A.N. and Wooller, S.J. (2004). ‘The honey possum Tarsipes rostratus: an update’ in The Biology of Australian Possums and Gliders, eds RL Goldingray & SM Jackson, Surrey Beatty & Sons, Chipping Norton, pp. 312-317.

Wooller, R.D., Russell, E.M., Renfree, M.B. and Towers, P.A. (1983). A comparison of seasonal changes in the pollen loads of nectarivorous marsupials and birds. Australian Wildlife Research 10, 311-317.

Wooller, R.D., Russell, E.M. and Renfree, M.B. (1984). ‘Honey possums and their foodplants’, in Possums and Gliders, eds AP Smith & ID Hume, Surrey Beatty, Sydney, pp. 439-443.

Wooller, R.D., Renfree, M.B., Russell, E.M., Dunning, A, Green, S.W. and Duncan, P (1981). Seasonal changes in a population of the nectar-feeding marsupial Tarsipes spencerae (Marsupialia: Tarsipedidae). Journal of Zoology 195, 267-279.

Zenger, K.R., Eldridge, M.D.B. and Johnston, P.G. (2005). Phylogenetics, population structure and genetic diversity of the endangered southern brown bandicoot (Isoodon obesulus) in south-eastern Australia. Conservation Genetics 6, 193-204.

120

Appendix 1: Microsatellite development Introduction

Microsatellites are short sequences of tandemly repeated DNA motifs of 1-6 base pairs (Zane et al., 2002; Kofler et al., 2008). These sequences are generally highly polymorphic and are present in both coding and non-coding genes (Zane et al., 2002). The high degree of polymorphism and hypervariability of microsatellites is the main reason they have proven to be such useful genetic markers, especially in the fields of population genetics and conservation (Zane et al., 2002). Microsatellites have the potential to provide valuable information on genetic variation of species at both the population and the individual level (Manel et al., 2003; Sarre and Georges 2009). This study characterized microsatellite loci in the honey possum (Tarsipes rostratus) to investigate dispersal and genetic variation within a fragmented landscape.

Tarsipes rostratus is the only species within the family Tarsipedidae and is endemic to south- west Western Australia. They are restricted to southern coastal sandplain heaths and open low woodlands with heath understorey (Wooller et al., 2004), and primarily occur in habitat areas of high plant species richness (Russell and Renfree, 1989; Wooller et al., 2004). South-western WA has high levels of vegetation clearing and habitat is highly fragmented. As honey possum habitat largely coincides with the highest populated and most developed region of Western Australia, honey possums are potentially competing for that habitat with processes such as agriculture, forestry, and further development. It may therefore prove important to investigate dispersal capabilities at a finer level to understand current and historical gene flow across Tarsipes’ range. It is widely recognised that by investigating genetic variation of populations, we may be able to better ascertain or predict the limits of dispersal, and better monitor the status of the species in highly fragmented and increasingly developed regions (Zenger and Johnston, 2001; Jones et al., 2003; Mills and Spencer, 2003; Mitrovski et al., 2007).

Previously, knowledge of dispersal capabilities of Tarsipes has been limited to studies involving mark-recapture techniques (Garavanta et al., 2000; Everaardt, 2003) or home range movement studies (Bradshaw and Bradshaw, 2002). These have been very effective in determining short to medium range movements, but are still relatively broad in that they cannot assess what is happening at a genetic level. This study will develop microsatellite markers to assess gene flow and build on the existing knowledge of honey possum dispersal

121 obtained from demographic data given that these types of markers have immense value in conservation management (Sarre & Georges 2009).

Methods

Sequences that contained microsatellites were isolated using the FIASCO (Fast Isolation by AFLP of Sequences Containing repeats) method (Zane et al. 2002), with some minor additions and modifications (Beveridge and Simmons, 2004). DNA was extracted from tail tissue of one male T.rostratus using the Spin-Column protocol with the DNeasy Blood and Tissue Kit (QIAGEN). The tissue sample was transferred to a microcentrifuge tube containing 180µL ATL tissue lysis buffer and 20µL proteinase K. The tube was then incubated on a rocking platform at 56°C overnight until the tissue had completely lysed. DNA was extracted and precipitated from the sample with 200µL AL lysis buffer and 200µL 100% ethanol and centrifuged at >6000g for 1 min. The contents were then washed with 500µL AW1 wash buffer, centrifuged at >6000g for 1 min, and washed again with 500µL AW2 wash buffer and centrifuged at 20,000g for 3 mins. 100µL AE elution buffer (10mM Tris-Cl, 0.5mM EDTA; pH 9.0) was applied and centrifuged at 6000g for 1 min, and the process repeated to increase the final DNA concentration in the eluate.

DNA was then digested with the restriction enzyme Mse I (Invitrogen) and amplified fragment length polymorphism (AFLP) adaptors (Invitrogen) were ligated onto the digested DNA. After the ligation of the adaptors, the DNA was diluted to 1:10 to carry out a polymerase chain reaction (PCR) using AFLP adaptor-specific primers, Mse I-A (Invitrogen) to amplify the fragments and yield enough DNA for the next step. The genomic DNA fragments were then enriched as described by Glenn et al. (2000) to obtain fragments containing microsatellite sequences. Two mixtures of biotinylated oligonucleotides were used for enrichment, a set containing (AC)12 (AG)12 and a set containing (AAAC)6 (AAAG)6 (AATC)6 (AATG)6 (Invitrogen). Dynabeads (Invitrogen) were then used to capture the enriched DNA, which was amplified by a PCR to provide adequate DNA for cloning. A TOPO TA cloning kit (Invitrogen) was used to clone the enriched DNA, with 96 recombinant clones from the dinucleotide enrichment, and 96 clones from the tetranucleotide enrichment lifted for further amplification. A PCR was then carried out using M13 forward and M13 reverse primers, and using a nonradioactive detection method based on a protocol by Glenn et al. (2000), the PCR products were screened for the

122 presence of microsatellite repeats. The insert size was also estimated by running the PCR products out on an agarose gel, as clones were selected for sequencing based on an insert size of at least 500bp with a strong positive result in the hybridisation. Clones that appeared to differ in size were selected to reduce the chance of sequencing multiple copies of the same clone. Of the positive clones, PCR products for 64 dinucleotide and 40 tetranucleotide clones were cleaned up using a Mo Bio UltraClean DNA Purification kit (Mo Bio Laboratories) and sequenced using BigDye™ Terminator automatic sequencing (Macrogen). Sequences were aligned and edited manually.

Primers were designed for 22 sequences containing perfect repeat microsatellites using Primer 3. These primers were synthesised unlabelled by Geneworks and tested for amplification in a gradient PCR from 49°C to 60°C. The resulting PCR products were analysed by agarose gel electrophoresis to check amplification had taken place and that the bands were in the expected size range. Of the 22 primers tested, primers for 9 of the tetranucleotide clones and 2 of the dinucleotide clones were labelled with a fluorescent dye on the 5´ end. Polymorphism of the microsatellites was tested with Tarsipes rostratus DNA samples in a PCR using the diluted labelled primers. The PCR products were analysed on an ABI3730 Sequencer and sized using Genescan-500 ROX internal size standard and GeneMarker software (version 1.7).

Results

Of the 11 loci characterized, 8 were found to be polymorphic with between 3 and 14 alleles when tested with 105 Tarsipes rostratus individuals. Details of microsatellites including number of alleles and allele sizes are given in Table 1. The observed and expected heterozygosities for each locus were calculated using GENEPOP (Raymond and Rousset 1995) and are shown in Table 1. Each locus was also tested for conformance to Hardy-Weinberg equilibrium using GENEPOP (Raymond and Rousseau 1995). Exact tests revealed that three loci (Di-F7, Te-C7, Te-B7) showed significant deviation from Hardy Weinberg equilibrium. No pairwise tests for genotypic linkage disequilibrium among the eight loci however, were significant.

123

Table 1. Characterization of microsatellite loci in the honey possum, Tarsipes rostratus

Locus Primer Sequence (5’-3’) Repeat Motif No. of Alleles Allele Size HO / HE

Di F7 CCAGTCTGGGTGAGTGAAGC (TG)10 4 202-219 0.052 / 0.523

Te A6 GAAATATTACTGGCAAGC (AAAAAC)10 3 186-194 0.421 / 0.442

Te C7 CTGCATGAACCTGGGAAAGT (TGAA)7 7 173-194 0.462 / 0.768

Te B7 TAGCCATGTAACTTTGGGCAAG (CAAA)13 11 110-154 0.463 / 0.778

Te A1 GATATGCTCCGAAGATTG (CTTT)3 (TTTG)6 9 105-147 0.592 / 0.672

Te E4 TTGATTGCCTAAATGAATGCAC (GATT)7 9 150-190 0.567 / 0.506

Te F1 CCAGAGGGGTTCTGTGACTC (GTTT)10 14 137-174 0.907 / 0.893

Te C1 CTTGTGGAGAGCCACATTGA (GTTT)8 TTTG 9 199-229 0.776 / 0.851

HO, observed heterozygosity; HE, expected heterozygosity

124

References:

Beveridge, M & Simmons, LW 2004, ‘Microsatellite loci for Dawson’s burrowing bee (Amegilla dawsoni) and their cross-utility in other Amegilla species’, Molecular Ecology Notes, vol 4, pp. 379-381.

Bradshaw, SD & Bradshaw, FJ 2002, ‘Short-term movements and habitat use of the marsupial honey possum (Tarsipes rostratus)’, Journal of Zoology, London, vol 258, pp. 343-348.

Everaardt, AN 2003, The impact of fire on the honey possum Tarsipes rostratus in the Fitzgerald River National Park, Western Australia. Ph.D. thesis, Murdoch University.

Garavanta, CAM, Wooller, RD & Richardson, KC 2000, ‘Movement patterns of honey possums, Tarsipes rostratus, in the Fitzgerald River National Park, Western Australia’, Wildlife Research, vol 27, pp. 179-183.

Glenn, TC, Cary, T, Dust, M, Hauswaldt, S, Prince, K, Ramsdell, C & Shute, I 2000, Microsatellite Isolation. http://www.uga.edu/srel/DNA_Lab/Msat_Easy_Isolation_2000.rtf.

Jones, ME, Peatkau, D, Geffen, E & Moritz C 2003, ‘Microsatellites for the Tasmanian devil (Sarcophilus laniarius)’, Molecular Ecology Notes, vol 3, pp. 277-279.

Kofler, R, Schlötterer, C, Luschützky, E & Lelley, T 2008, ‘Survey of microsatellite clustering in eight fully sequenced species sheds light on the origin of compound microsatellites’, BMC Genomics, vol 9, p. 612.

Manel, S, Schwartz, MK, Luikart, G & Taberlet, P 2003, ‘Landscape genetics: combining landscape ecology and population genetics’, TRENDS in Ecology and Evolution, vol 18, pp. 189- 197.

Mills, HR & Spencer, PBS 2003, ‘Polymorphic microsatellites identified in an endangered dasyurid marsupial, the dibbler (Parantechinus apicalis)’, Molecular Ecology Notes, vol 3, pp. 218-220.

125

Mitrovski, P, Heinze, DA, Broome, L, Hoffman, AA & Weeks, R 2007, ‘High levels of variation despite genetic fragmentation in populations of the endangered mountain pygmy-possum, Burramys parvus, in alpine Australia’, Molecular Ecology, vol 16, pp. 75-87.

Raymond M, & Rousset F, 1995, ‘GENEPOP (version 1.2): population genetics software for exact tests and ecumenicism’, Journal of Heredity, vol 86, pp. 248-249.

Russell, EM & Renfree, MB 1989, ‘Tarsipedidae’, in Fauna of Australia: Mammalia, eds DW Walton & BJ Richardson, AGPS, Canberra, pp. 769-782.

Sarre, SD & Georges, A 2009, ‘Genetics in conservation and wildlife management: a revolution since Caughley’, Wildlife Research, vol 36, pp. 70-80.

Wooller, RD, Richardson, KC, Saffer, VM, Garavanta, CAM, Bryant, KA, Everaardt, AN & Wooller, SJ 2004, ‘The honey possum Tarsipes rostratus: an update’ in The Biology of Australian Possums and Gliders, eds RL Goldingray & SM Jackson, Surrey Beatty & Sons, Chipping Norton, pp. 312-317.

Zane, L, Bargelloni, L & Patarnello, T 2002, ‘Strategies for microsatellite isolation: a review’, Molecular Ecology, vol 11, pp. 1-16.

Zenger, KR & Johnston, PG 2001, ‘Isolation and characterization of microsatellite loci in the southern brown bandicoot (Isoodon obesulus), and their applicability to other marsupial species’, Molecular Ecology Notes, vol 1, pp. 149-151.

126