A major road and an artificial waterway are barriers to the rapidly ... · A major road and an...

A major road and an artificial waterway are barriers to the rapidly declining western

ringtail possum, Pseudocheirus occidentalis

Kaori Yokochi BSc. (Hons.)

This thesis is presented for the degree of Doctor of Philosophy of The University of Western Australia

School of Animal Biology

Faculty of Science

October 2015

i

Abstract

Roads are known to pose negative impacts on wildlife by causing direct mortality,

habitat destruction and habitat fragmentation. Other kinds of artificial linear structures,

such as railways, powerline corridors and artificial waterways, have the potential to

cause similar negative impacts. However, their impacts have been rarely studied,

especially on arboreal species even though these animals are thought to be highly

vulnerable to the effects of habitat fragmentation due to their fidelity to canopies. In this

thesis, I studied the effects of a major road and an artificial waterway on movements

and genetics of an endangered arboreal species, the western ringtail possum

(Pseudocheirus occidentalis). Despite their endangered status and recent dramatic

decline, not a lot is known about this species mainly because of the difficulties in

capturing them. Using a specially designed dart gun, I captured and radio tracked

possums over three consecutive years to study their movement and survival along Caves

Road and an artificial waterway near Busselton, Western Australia. I studied the home

ranges, dispersal pattern, genetic diversity and survival, and performed population

viability analyses on a population with one of the highest known densities of P.

occidentalis. I also carried out simulations to investigate the consequences of removing

the main causes of mortality in radio collared adults, fox predation and road mortality,

in order to identify effective management options. A rope bridge was built to provide

this species with a safe passage across Caves Road in July 2013, and I present the

results from 270 days of monitoring of the rope bridge and factors influencing the

numbers of crossings.

No radio collared possums crossed the road successfully during my study, while two

were killed on the road. No collared possums crossed the waterway, except for one

accidentally falling into the waterway during a severe storm. None of the home ranges

included the road or waterway, suggesting that they both act as physical barriers for

possums. Even a 5 m wide firebreak was enough to limit the movements of some

possums where canopy connection was not available. Individuals in partially cleared

campsites mostly remained within groups of trees with continuous canopy connections.

Home ranges were small (males: 0.31 ± 0.044 ha, females: 0.16 ± 0.017 ha), and their

sizes were affected by sex and proximity to the waterway. These results highlight the

exceptionally sedentary and arboreal nature of this species.

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I used 12 species-specific microsatellite DNA loci to investigate the fine-scale spatial

genetic structure and the effects of a road and an artificial waterway on the population

of P. occidentalis. Spatial autocorrelation analyses identified positive genetic structure

over distances up to 600 m in continuous habitat. The artificial waterway was associated

with significant genetic divergence, while no significant genetic divergence was

detected across the road. However, this increasingly busy road may cause future

divergence, and road mortality can still contribute to loss of genetic diversity. Therefore,

providing safe passages to reconnect habitat is suggested to maximise genetic diversity

and prevent isolation of subpopulations.

Predation by red foxes (Vulpes vulpes) was the most common cause of mortality in adult

radio collared possums contributing to 70 % of all confirmed mortalities. Road

mortality also contributed to about 10 % of mortalities. A population viability analysis

revealed that the probability of this important population going extinct in 20 years was

alarmingly high (P = 0.921 with 95 % lower confidence interval of 0.903 and upper

confidence interval of 0.937). Removal of the effects of road mortality and fox

predation on adult and pouch young survival rates dramatically reduced the extinction

probability (P = 0.318 without road mortality and P = 0.004 without fox predation),

indicating that reducing both road mortality and fox predation is essential to ensure the

survival of this important population.

We monitored the rope bridge using motion sensor cameras and microchip readers for

270 days. Western ringtail possums started crossing the bridge 36 days after its

installation, which was remarkably sooner than expected or previously reported. It took

other possums and glider species in the eastern states of Australia seven to 17 months to

start crossing rope bridges across roads. After a period of habituation, multiple

individuals were found crossing the bridge every night at a rate of 8.87 0.59 complete

crossings per night, which was at least double of those reported on bridges built in

eastern Australia. The number of crossings increased over time and decreased on windy

or warm nights. Brightness of the moon also slightly reduced the crossings by the

possums. Longer monitoring and genetic analyses to test whether crossings result in

gene flow are necessary to assess the true conservation value of this bridge. However,

these early monitoring results suggest that rope bridges have the potential to be safe

crossing structures for this species.

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This study provides an example of an artificial linear structure other than a road having

similar or even greater impacts on wildlife than a road. It therefore highlights the need

for more research into the impacts of artificial structures such as waterways. The

population of P. occidentalis I studied has a high probability of extinction in the near

future and more effective management strategies, especially against the effects of fox

predation and road mortality, are urgently needed in order to ensure its survival.

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Table of contents Abstract ………………………………………………………………………...……… i

Acknowledgement ………………………………......………………………………... vi

Declaration and publications ……………………………………………..………... viii

1. General introduction……………………………….……………………………..... 1

1.1. Impacts of artificial linear structures ………………………...…….……….… 2

1.1.1. Impacts of roads ……………………………………………………….. 2 1.1.1.1. Habitat alteration, degradation and destruction .............................................. 2

1.1.1.2. Direct mortality ............................................................................................... 3

1.1.1.3. Habitat fragmentation ..................................................................................... 5 1.1.2. Impacts of artificial linear structures other than roads .............................. 7

1.2. Mitigation measures ............................................................................................. 8

1.2.1. Common measures ..................................................................................... 8

1.2.2. Wildlife crossing structures ....................................................................... 9

1.3. The western ringtail possum .............................................................................. 11

1.3.1. Biology and ecology ................................................................................ 11

1.3.2. Decline and management ......................................................................... 12

1.3.3. Locke Nature Reserve and surrounding campsites .................................. 14

1.4. Gaps in the knowledge ....................................................................................... 16

1.5. Research aims .................................................................................................... 17

1.6. Structure of the thesis ......................................................................................... 18

1.7. References .......................................................................................................... 19

2. An artificial waterway and a road restrict movements and alter home ranges of

the western ringtail possum ................................................................................... 33

Abstract .................................................................................................................... . 34

Introduction ............................................................................................................... 35

Materials and methods .............................................................................................. 36

Results ....................................................................................................................... 44

Discussion ................................................................................................................. 49

References ................................................................................................................. 55

Supplementary results ....………………………………………………………........62

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3. The western ringtail possum shows fine-scale population structure and limited

gene flow across an artificial waterway ................................................................ 63

Abstract ..................................................................................................................... 64

Introduction ............................................................................................................... 65

Materials and methods .............................................................................................. 66

Results ....................................................................................................................... 70

Discussion ............................................................................................................ ..... 76

References ............................................................................................................ ..... 82

Appendix 1 …………………………………………………………………...…… 88

4. A predicted sharp decline of a stronghold population of the western ringtail

possum calls for urgent reduction in fox predation and road mortality ……... 97

Abstract ..................................................................................................................... 98

Introduction ............................................................................................................... 99

Materials and methods ............................................................................................ 100

Results ..................................................................................................................... 110

Discussion .............................................................................................................. . 114

References ............................................................................................................... 122

Appendix 1 ……………………………………………………………………….. 129

5. A remarkably quick habituation and high use of a rope bridge by the western

ringtail possum ………………………………………………………………….. 139

Abstract ................................................................................................................... 140

Introduction ............................................................................................................. 141

Materials and methods ............................................................................................ 143

Results ................................................................................................................... 147

Discussion .............................................................................................................. . 150

References ............................................................................................................... 155

6. General discussion ................................................................................................. 161

6.1. Key findings ..................................................................................................... 162

6.2. Limitations ....................................................................................................... 166

6.3. Future research ................................................................................................. 168

6.4. Management applications ................................................................................ 170

6.5. Conclusion ....................................................................................................... 171

6.6. References ........................................................................................................ 172

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Acknowledgement

I simply would not have been able to complete this thesis without the generous help and

advice from many people. There were a number of frustrating and tough periods, but

support from a group of incredible people made the journey a great experience.

First of all, I would like to thank my supervisors Roberta Bencini and Jason Kennington.

I simply cannot thank you enough for your continuous guidance, encouragement,

support and advice throughout this long journey. Even though you are both extremely

busy with teaching, other research and many other students, you were always a short

walk/ e-mail/ phone call away and patiently answered many of my questions.

I would also like to thank Brian Chambers for his helpful advice and support in so many

different aspects of this project, including the fieldwork set up and data analyses. You

have no idea how helpful your practical advice on fieldwork and analyses were to me!

Robert Black kindly provided me with his expert guidance and patiently taught me the

life cycle analyses and population viability analysis. Thank you so much, Bob. I enjoyed

our hours-long discussions. Big thanks also go to Mike Johnson for sharing his valuable

advice at panel meetings and providing the final check up of this thesis. I would also

like to very gratefully acknowledge Paul de Tores and Judy Clark for their extensive

support, valuable advice and training at the initial stage of this project. I had the best

trainers to learn the possum catching and handling skills from. I would also like to thank

my thesis examiners for their thorough review of the thesis and valuable comments.

This was a large project involving over three years of continuous fieldwork, laboratory

analyses, and construction of a rope bridge, and many agencies and organisations

generously provided financial, in-kind and/or technical support. This project would

simply not exist without the extensive support from our main industry partner, Main

Roads Western Australia. I would especially like to thank Gerry Zoetelief and Alan

Grist for their continuous support. The School of Animal Biology at UWA and the

Western Australian Department of Parks and Wildlife also provided financial and

technical support throughout this study. We received financial support from Western

Power, the Satterley Property Group and the Holsworth Wildlife Research Endowment.

My gratitude goes to the DPaW Busselton office (John Carter) and the City of

Busselton (Will Oldfield) for generously providing me with local support during the

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fieldwork. I would like to thank the South West Aboriginal Land and Sea Council for

their understanding and support for this project. Owners and managers of the campsites

within my study area were incredibly generous and helpful. I would like to thank the

Peppermint Park Eco Village, Abundant Life Centre, Scripture Union Camp Geographe,

Christian Brethren Camp, and Legacy Camp for generously letting me wander around

the camp and catch possums in the middle of the night. Uta Wicke at the Possum Centre

in Busselton provided expert advice on the western ringtail possums. Thank you, Uta.

I could not have done my fieldwork without the help of over 100 volunteers. I am sorry

that I cannot name all of you here but I truly enjoyed meeting all of you and you made

the fieldwork extra enjoyable for me. Many of you came out to the bush more than once,

and special thanks go to my best friend Chihiro Hirota who braved the hot, cold, dry,

wet and windy conditions for countless times. I would also like to thank Kaarissa

Harring-Harris for helping me collect data from the bridge in 2014.

A big thank you also goes to my office mates and fellow postgraduates at the school.

You motivated me and made me think being stuck in front of a computer in the office

isn’t so bad. I knew I wasn’t alone on this PhD journey!

To my friends, thank you so much for bearing with me talking non-stop about possums

at one second and then trying to avoid talking about my thesis at all cost at next second.

Many of you got dragged out to the bush for a fieldwork too and I hope you enjoyed

meeting the possums.

To my Japanese and Australian families, thank you so much for encouraging me and

always being so supportive of this long journey. Knowing that you are always behind

me helped me tremendously throughout the study.

To my partner, Michael Galton-Fenzi, no words are sufficient to thank you for your

limitless support and confidence in me. You always cheered me up and pushed me

through when I was going through stressful times. You believed in me when I doubted

myself and made sure I focused on my project.

Last but not least, I would like to say a massive thank you to all the possums that were

involved in this study. I know they cannot read this, but they truly were the main

contributors to this thesis, and I sincerely hope that this thesis can help the future

survival of this unique and incredible species.

viii

Declaration and publications

This thesis is presented as a combination of scientific papers and chapters. All parts of

this thesis have been written by Kaori Yokochi with advice from the supervisors

(Roberta Bencini and W. Jason Kennington from UWA) and co-authors of the scientific

papers (Brian K. Chambers in Chapter 2 and 4, and Robert Black in Chapter 4, both

from UWA). My contributions (%) for each chapter of the thesis are outlined below.

Chapter 2: Yokochi K, Chambers BK, Bencini R (2015) An artificial waterway and

road restrict movements and alter home ranges of endangered arboreal marsupial.

Journal of Mammalogy. doi:10.1093/jmammal/gyv137

Conception and study design 60 %, data collection 90 %, data analyses 100 %,

interpretation of results 90 %

Chapter 3: Yokochi K, Kennington WJ, Bencini R. (in review) A narrow artificial

waterway is a greater barrier to gene flow than a major road for an endangered arboreal

specialist: the western ringtail possum (Pseudocheirus occidentalis). PLOS ONE

(currently under a revision after comments from reviewers)



Chapter 4: Yokochi K, Black R, Chambers BK, Bencini R. (unpublished*) A predicted

sharp decline of a stronghold population of the western ringtail possum calls for urgent

reduction in fox predation and roadkill.



*This chapter will be separated into two scientific papers for publications.

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Chapter 5: Yokochi K, Bencini R. (2015) A remarkably quick habituation and high use

of a rope bridge by an endangered marsupial, the western ringtail possum. Nature

Conservation 11: 79-94. doi:10.3897/natureconservation.11.4385



The contribution of the different co-authors in the papers/chapters is mainly associated

with the initial research directions, advice on data analysis when required, and editorial

input in the drafts on the papers and/or chapters. I have obtained permissions from all

co-authors to include these chapters in this thesis.

All procedures for sample and data collection in this thesis were approved by the

Animal Ethics Committee at The University of Western Australia (RA/3/100/539 and

RA/3/100/1213).

---------------------- Kaori Yokochi

Roberta Bencini (coordinating supervisor)

23 October 2015

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Chapter 1. General Introduction

The landscape we live in is covered with artificial linear structures such as roads,

railways, artificial waterways, and powerline corridors. These structures are essential

for our modern society, providing us with vital services such as transport, water supply,

drainage and electricity. However, they are not without cost to the natural environment,

and damage done by these structures can be so large that they can threaten many

wildlife species (Laurance et al. 2014).

In the last two decades, there has been an dramatic increase in the number of studies

investigating the impacts of roads on ecosystems, and a field dedicated to such studies

was developed and named “road ecology” (Forman 1998, Coffin 2007). Although

artificial linear structures other than roads can potentially pose similar impacts on

ecosystems, these structures have not been studied as much as roads to this date

(Benítez-López et al. 2010). Studies on arboreal species are especially rare, even though

they are thought to be highly vulnerable to the impacts of habitat fragmentation due to

their fidelity to canopies (Lancaster et al. 2011, Taylor et al. 2011).

The western ringtail possum (Pseudocheirus occidentalis, Thomas 1888) is a strictly

arboreal marsupial that mainly occurs in rapidly urbanised parts of the southwest of

Western Australia, the only mainland biodiversity hotspot in Australia (Myers et al.

2000). Despite its recent classification as an endangered species, many aspects of its

ecology and biology still remain unknown, mainly due to the difficulties in capturing

this elusive species (Department of Parks and Wildlife WA 2014, Woinarski et al.

2014).

This thesis focuses on assessing the negative impacts of a major road and an artificial

waterway on a population of western ringtail possums near Busselton, Western

Australia. The effect of a rope bridge as a mitigation measure is also investigated.

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1.1. Impacts of artificial linear structures

Construction of artificial linear structures normally involves clearing of land and

introducing new surface and infrastructure. For example, roads are often covered with

bitumen or gravels, powerline corridors are cleared areas lined with pylons with

interconnecting power cables, and artificial waterways are filled with water. These new

surfaces and land clearing, together with the traffic that runs on the surface,

significantly change habitat and availability of resources for wildlife. For example,

some raptor species have been found to benefit from roads because they prefer hunting

in cleared areas or scavenge on roadkills (Meunier et al. 2000). On the other hand,

where predators avoid habitats near roads, small prey species, such as the white footed

mouse (Peromyscus leucopus), may benefit from the presence of roads (Rytwinski and

Fahrig 2007). However, these cases seem to be rare exceptions, and the number of

documented negative impacts of roads on wildlife considerably outweighs the

documented positive impacts (Fahrig and Rytwinski 2009).

Although artificial linear structures can pose negative impacts on wildlife populations in

many different ways (Trombulak and Frissell 2000, Jaeger et al. 2005, Clevenger and

Wierzchowski 2006, Laurance et al. 2009), most of these impacts fall within three main

categories: habitat loss and degradation, direct mortality, and habitat fragmentation. The

vast majority of the studies on the negative impacts of artificial linear structures have

focussed on roads; therefore, below I discuss the impacts of roads first, followed by the

impacts of other types of artificial linear structures.

1.1.1. Impacts of roads

1.1.1.1 Habitat alteration, degradation and destruction

Land clearing prior to road construction inevitably results in removal and/or reduction

of resources required for the survival for wildlife inhabiting the area (Trombulak and

Frissell 2000, Grilo et al. 2010). Once the road opens to traffic, changes to the chemical

and physical properties of the habitat along the road can further impact wildlife

populations. For example, the greater amount of light penetrating into the vegetation

along a road may deter animals that avoid open areas, while increased light, pollution,

and soil compaction along a road may alter the species composition of vegetation,

resulting in altered animal abundances (Trombulak and Frissell 2000, Jaeger et al. 2005,

Bignal et al. 2007, Goosem 2007). Similarly, the increased level of noise along roads

3

can influence animal behaviours such as foraging, predator avoidance and courtship,

resulting in reduced reproduction and/or survival (Forman and Alexander 1998,

Kociolek et al. 2011, Shannon et al. 2014). For example, McClure et al. (2013)

experimentally demonstrated that noise of a road alone dramatically decreased the

abundance of birds. Roads can also benefit undesirable species. For example, roads

attract hunters and predators such as grey wolves (Canis lupus), resulting in a lowered

abundance of herbivores such as caribous (Rangifer tarandus caribou, Bowman et al.

2010). Introduced pest species, such as the cane toad (Bufo marinu), European red fox

(Vulpes vulpes), and feral cats (Felis catus) in Australia, are known to use roads as

dispersal corridors (May and Norton 1996, Seabrook and Dettmann 1996, Brown et al.

2006). Traffic on roads also aids dispersal of weed species and pathogens, such as the

dieback fungus (Phytophthora cinnamomi) that is threatening native tree species

throughout Australia (Goosem 2007, Cahill et al. 2008).

1.1.1.2. Direct mortality

Even before habitat loss and degradation caused by road construction affect wildlife

populations, the processes involved with road constructions, especially clearing of the

vegetation, can cause injuries and mortalities in slow-moving sedentary species

(Trombulak and Frissell 2000). Although the exact number of animals killed during

road constructions is unknown, the surface area of roads in the U.S.A. alone was

estimated to be approximately 4.8 million ha in 1996 (Trombulak and Frissell 2000),

indicating the magnitude of the impact of road constructions on wildlife.

Once the road opens to traffic, collisions between cars and animals pose a direct impact

on the survival of the wildlife populations by directly increasing the rate of mortality.

Millions of animals die on roads every year following collisions with vehicles. For

example, Hobday and Minstrel (2008) estimated the annual number of animals killed on

state roads in Tasmania to be up to 1,500,000, after accounting for carcass removal by

scavengers and possibility of animals dying away from roads after collisions. By

reviewing and updating results from previous studies, Erickson et al. (2005) estimated

the annual number of birds killed on roads in the United States of America to be

80,000,000. Additionally roadkills numbers might be greatly underestimated especially

for small animals because their carcasses disappear quickly (Santos et al. 2011). These

numbers indicate the enormity of the issue of road mortality throughout animal taxa and

regions.

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However, the negative impacts of road mortality on populations cannot be assessed only

by the numbers because some species may be numerous or highly reproductive enough

to overcome the impact of road mortality. The effect of road mortality is thought be

devastating especially for already fragmented small populations (Forman and Alexander

1998, Laurance et al. 2009), and many studies have supported this notion. For example,

Jones (2000) found that the increased level of road mortality following the widening

and sealing of a road in the South-west Tasmania World Heritage Area caused the local

extinction of a population of the vulnerable eastern quoll (Dasyurus viverrinus) and

halved the local population of the endangered Tasmanian devil (Sarcophilus harrisii). In

a nine-year study of a roadside population of the threatened Florida scrub jay

(Aphelocoma coerulescens), Mumme et al. (2000) found that the mortality of the

breeders caused by collisions with vehicles significantly exceeded the production of

yearlings near a highway, resulting in a roadside demographic sink.

Population Viability Analysis (PVA) is becoming a popular tool to assess the impacts of

road mortality on wildlife populations because researchers can incorporate

environmental and demographic stochasticities in predicting the population trend and

compare scenarios such as removal of road mortality (Akçakaya and Sjögren-Gulve

2000). Ramp and Ben-ami (2006) conducted a PVA on a population of swamp

wallabies (Wallabia bicolor) in Royal National Park near Sydney and concluded that

this population is likely to go extinct in the next 100 years based on current mortality

rates. However, they found that a 20 % reduction in the road mortality of females is

likely to reverse the decline. Similarly, a PVA on a population of the common wombat

(Vombatus ursinus) in Kosciuszko National Park in New South Wales, Australia

identified road mortality as a decisive parameter that determines the population trend

(Roger et al. 2011). The same PVA also identified landscape connectivity, which can be

negatively affected by the presence of roads, as a parameter that affects the population

viability. These examples highlight the strong impact that road mortality can pose on

threatened wildlife populations. In addition, road mortalities can further threaten

wildlife populations by continuously reducing the number of individuals in a population

and by preventing individuals from reaching other patches of habitats, which can lead to

lowered genetic diversity and the accumulation of genetic differences between isolated

patches (Jackson and Fahrig 2011).

5

1.1.1.3. Habitat fragmentation

Although the total landmass cleared for roads may not be as extensive as other

anthropogenic alteration of the land, such as clearing for agricultural land, roads can

limit movements of wildlife and act as a barrier between two separated habitats that

were previously connected (Forman and Alexander 1998, Corlatti et al. 2009). If severe,

this barrier effect can split a larger population into a number of smaller isolated

populations without inter-population migrations. Small isolated populations have a

higher risk of extinction because of their higher vulnerability to stochastic demographic

changes and catastrophic events such as severe weather, fire and diseases (Foley 1997).

For example, Berger (1990) followed the fate of 122 populations of bighorn sheep (Ovis

canadensis) and found that populations with less than 50 individuals went extinct within

50 years in all cases and that populations with more than 100 individuals lasted longer,

for up to 70 years. In addition, habitat fragmentation caused by anthropogenic barrier

effects can further increase the risk of local extinction by preventing immigration and

dispersal of juveniles, which are important sources of new individuals for declining

populations (Crooks and Sanjayan 2006, Stewart and Van der Ree 2006).

Once small isolated populations go extinct, the barrier effect can also prevent

recoloniastion of the empty habitats, resulting in a smaller number of subpopulations

contributing to a metapopulation (Foley 1997). A metapopulation follows a path of

extinction when the extinction rate of subpopulations surpasses the recolonisation rate

(Foley 1997); therefore, severed connectivity among subpopulations increases the risk

of extinction across larger landscapes (i.e. metapopulation extinction) especially for

already threatened species (Ovaskainen and Hanski 2003).

A limitation on movements is also problematic because it can prevent migration of

animals and reduce gene flow between these groups. When gene flow is reduced for an

extended time period, the isolated group of animals can experience an increased level of

inbreeding and genetic drift, which lowers the genetic diversity within the population

(Frankham et al. 2002). Low genetic diversity can result in the expression of deleterious

recessive alleles that are normally suppressed in genetically healthy populations,

resulting in lowered fitness of the population (Frankham et al. 2002, Corlatti et al.

2009). Low genetic diversity can also reduce a population’s ability to adapt to

environmental changes, such as climate change (Frankham et al. 2002). These genetic

consequences ultimately increase the risk of extinction.

6

Barrier effects caused by roads can be so severe that Cushman et al. (2010) concluded

that the habitat fragmentation effects caused by roads were more problematic than the

effects caused by agricultural or urban land use in western Massachusetts, U.S.A. In a

review on the genetic effects of roads on wildlife populations, Holderegger and Di

Giulio (2010) found that fragmentation of habitats by roads led to rapid declines in

genetic diversity within populations and increased genetic divergence between

populations in a wide range of species including invertebrates, amphibians and

mammals. In a study of flightless ground beetles (Carabus violaceus), Keller and

Largiadér (2013) concluded that habitat fragmentation caused by roads caused increased

genetic divergence, loss of genetic diversity, and possibly local extinctions. Genetic

divergence across roads have also been reported in the desert big horn sheep (Ovis

canadensis nelsoni, Epps et al. 2005), coyote (Canis latrans, Riley et al. 2006), bobcat

(Lynx rufus, Riley et al. 2006), bank vole (Clethrionomys glareolus, Gerlach and

Musolf 2000), common lizard species (Uta stansburiana, Plestiodon skiltonianus and

Sceloporus occidentalis, Delaney et al. 2010), timber rattlesnake (Crotalus horridus,

Clark et al. 2010), and red-backed salamanders (Plethodon cinereus, Marsh et al. 2008).

Arboreal animals are thought to be especially vulnerable to the effects of habitat

fragmentation because of their fidelity to canopies (Lancaster et al. 2011). However,

studies assessing the barrier effects of roads on arboreal species are still limited and

results are varying. For example, Van der Ree et al. (2010) found that roads did not

restrict the movement of squirrel gliders (Petaurus norfolcensis) as long as median

strips with tall trees were present. Wilson et al. (2007), Radespiel et al. (2008),

Goldingay et al. (2013), and Munguia-Vega et al. (2013) found that the movements of

lemuroid ringtail possums (Hemibelideus lemuroides), golden-brown mouse lemurs

(Microcebus ravelobensis), squirrel gliders, and black-tailed brush lizards (Urosaurus

nigricaudus) were restricted over roads but not to the point where they prevented

dispersal and gene flow. On the other hand, Lee et al. (2010) found that koalas

(Phascolarctos cinereus) that were separated by roads were genetically divergent.

7

1.1.2. Impacts of artificial linear structures other than roads

As presented in the sections above, roads can have multi-fold impacts on populations

and metapopulations because they can drive a threatened population to extinction by

processes like habitat loss and degradation that lower resource availability, vehicle

collisions that increases mortality, and barrier effects that prevent immigration. Roads

can then prevent recolonisation of the empty habitat, which affects the survival and

dynamics of metapopulations. Similarly, artificial linear structures other than roads,

such as railways, powerline corridors and artificial waterways can have significant

negative impacts on wildlife populations by causing habitat destruction, direct

mortalities and habitat fragmentation.

Habitat is often cleared and altered in order to construct these structures, just as with

roads; therefore, they can compromise the abundance and/or health of local wildlife

populations (Mahoney and Schaefer 2002, Clauzel et al. 2013). Collisions between

animals and trains occur on railways (Andreassen et al. 2005, de Oliveira et al. 2014),

birds die of electrocution when they collide with powerlines (Bevanger 1995, Jenkins et

al. 2010), and waterways can cause drowning of wildlife (Rautenstrauch and Krausman

1989, Peris and Morales 2004, García 2009). These artificial linear structures can also

restrict movements of wildlife and cause habitat fragmentation. For example,

Bhattacharya et al. (2003) found that bumblebees (Bombus and Xylocopa species)

avoided crossing a railway even though they had the ability to do so. St Clair (2003)

found that forest dependent birds were 50 % less likely to cross a natural river than a

busy highway in Banff National Park, and Marsh et al. (2007, 2008) detected significant

genetic divergences across narrow natural waterways in some terrestrial species. Natural

river systems have also been found to cause genetic divergence in some arboreal species

(Eriksson et al. 2004, Goossens et al. 2005, Jalil et al. 2008, Quéméré et al. 2010). The

barrier effects of artificial waterways can be similar or even greater than those of natural

waterways because cleared and reinforced banks that are often associated with artificial

waterways can further deter animals or prevent animals from climbing out of the

waterway (Coulon et al. 2006).

Given these potential threats, more research into the impacts of artificial linear

structures on Australian wildlife has been called since as early as 1990 (Andrews 1990),

and several studies have since revealed the negative impacts of powerline corridors. For

example, Goosem and Marsh (1997) and Wilson et al. (2007) found that power line

8

corridors restricted the movements of rainforest small mammals and strictly arboreal

lemuroid ringtail possums. However, research on the negative impacts of artificial

waterways on Australian wildlife is completely lacking despite the potentially high risk

these waterways can pose.

1.2. Mitigation measures

1.2.1. Common measures

To address these negative impacts, many different types of mitigation measures have

been implemented on roads and other artificial linear structures worldwide. Common

measures along roads include reducing speed limits, installing warning signs for drivers,

installing warning systems that alert drivers when animals are near the road, and

installing systems to deter animals from roadsides (Jones 2000, Huijser et al. 2008,

Laurance et al. 2009, Bond and Jones 2014). Exclusion fences have also been used

along roads, railways and canals to prevent the access of animals to the linear structures

(Carmichael 1991, Clevenger et al. 2001a, Laurance et al. 2009). Although the low costs

and the ease of implementation have attracted road authorities worldwide to the options

of reducing the speed limit and installation of road signs, these methods have been

found to be ineffective in actually lowering the vehicle speed or reducing the number of

collisions between vehicles and animals (Bissonette and Kassar 2008, Huijser et al.

2008). Installation of driver warning systems has increased in recent years; however, it

is expensive (e.g. US$ 40,000 – 96,000 per km, Huijser et al. 2008), and some systems

have been found to be ineffective for the traffic travelling at over 100 km/h (Gordon et

al. 2004). Fences have been found to reduce the number of roadkills if they are properly

installed and maintained (Clevenger et al. 2001a); however, they are expensive

(US$ 52,000 – 82,000 per km on both sides of a road, Huijser et al. 2008) and cannot be

used where vehicle need to access to properties along the road. Fences also do not

address the issue of severed connectivity and restricted gene flow. In fact, for the

species that avoid roads or for areas where road mortality is low, fences have been

found to increase the extinction risk of wildlife populations by increasing the effect of

habitat fragmentation (Jaeger and Fahrig 2004).

9

1.2.2. Wildlife crossing structures

Given the limitations of these common mitigation measures, wildlife crossing structures

have become popular in recent years because they can in theory reduce road mortalities

and maintain habitat connectivity by providing animals with safe passages across roads

(Foster and Humphrey 1995, Clevenger and Huijser 2011). Many underpasses and

overpasses have been constructed under and over roads worldwide, and a wide variety

of terrestrial species have been found to use them (Clevenger and Huijser 2011). The

most famous and well documented case would be the underpasses and overpasses

constructed across the Trans-Canada Highway in Banff National Park in Canada, which

have been monitored for over 13 years. Multiple carnivorous and herbivorous

mammalian species use these crossing structures (Clevenger and Waltho 2005), and

crossing of these underpasses by grizzly (Ursus arctos) and black bears (Ursus

americanus) has restored gene flow to a level that prevents genetic isolation (Sawaya et

al. 2014). On a Spanish motorway, over 17 mammalian and reptilian species used

overpasses and three different types of underpasses (Mata et al. 2008). In southeast

Queensland, Australia, at least five native species of mammals were found to cross

Compton Road using an overpass, and over 20 species of birds that avoid crossing the

road were found either flying above or utilising the vegetation on the overpass (Bond

and Jones 2008, Jones and Pickvance 2013). In southwest Western Australia, 15

mammalian, reptilian and bird species have been recorded to use underpasses to cross

roads and highways (Chambers and Bencini 2015).

Although these crossing structures can benefit many species of varying taxa, not a

single type of wildlife crossing structure serves all species, and some species show

strong preference towards particular types of crossing structure. For example, in Banff

National Park, grizzly bears, wolves (Canis lupus), deer (Odocoileus species), and elk

(Cervus elaphus) preferred large open underpasses, while black bears and cougars

(Puma concolor) preferred small confined underpasses possibly due to their affinity to

cover (Clevenger and Waltho 2005). In northeast New South Wales, Australia, larger

animals such as macropods, canids, and large lizards used overpasses more than

underpasses to cross roads, while small animals such as bandicoots, rodents and frogs

preferred underpasses (Hayes and Goldingay 2011). These preferences shown by

different species highlight the importance of installing appropriate types of crossing

structures based on the target species’ known biological and ecological characteristics.

10

Having an appropriate type of crossing structures is particularly important for strictly

arboreal species like the lemuroid ringtail possum and the Herbert River ringtail possum

(Pseudochirulus herbertensis) in tropical Queensland, Australia. These possum species

rarely descend to the ground and would not use underpasses; however, Goosem et al.

(2005) recorded the use of rope bridges by these possums. In other parts of the world,

rope bridges and gliding poles have been built to facilitate movements of arboreal

animals such as lemurs, opossums, monkeys, dormice, squirrels, and flying squirrels

(Norwood 1999, Mass et al. 2011, Minato et al. 2012, Kelly et al. 2013, Teixeira et al.

2013, Sonoda 2014). Within Australia, these crossing structures have been built for

glider and possum species as well as koalas. However, monitoring of the use of these

structures by the target species is still limited to a handful of cases (Weston et al. 2011,

Goldingay et al. 2013, Russell et al 2013, Taylor and Goldingay 2013, Soanes et al.

2013), and an assessment of factors influencing the use of these structures is lacking.

As well as benefitting native wildlife populations, wildlife crossing structures can also

benefit introduced or pest species. For example, in Australia, introduced species such as

red foxes, feral cats, rabbits (Oryctolagus cuniculus), black rats (Rattus rattus) and

house mice (Mus musculus) have been recorded to use underpasses and overpasses

(Bond and Jones 2008, Chambers and Bencini 2015). The use of crossing structures by

these introduced species may have negative effects on native wildlife as easier and safer

dispersal of introduced species across roads can result in increased competition and

predation pressure on native wildlife populations that may already be under increased

pressure from the presence of artificial linear structures. Several researchers have also

suggested that predators, including introduced predators, may learn to use wildlife

crossing structures as prey traps because the concentrated abundance of prey species

near and within a confined crossing structure could act as an easy hunting ground for

predators (Hunt et al. 1987, Little et al. 2002). However, this hypothesis is not

supported by available data. In their review, Little et al. (2002) found no evidence of

increased frequencies of predation in or near crossing structures. Dickson et al. (2005)

also found that cougars in Southern California did not use underpasses to “trap” prey

species. Similarly, Ford and Clevenger (2010) analysed predation data collected over 31

years and found no evidence of increased predation on ungulates near underpasses and

overpasses in Banff National Park. Although predators do not seem to use crossing

structures as prey traps, avoidance of crossing structures by prey species may occur if

predators frequently use them (Little et al. 2002). Some prey species, such as many

11

mammalian species (reviewed by Apfelbach et al. 2005), toads (Bufo species, Flowers

and Graves 1997), and blue tits (Cyanistes caeruleus, Amo et al. 2008), are known to

identify chemical cues (i.e. odours) of their predators and avoid areas with the cues.

This predator avoidance behaviour may be causing prey species to avoid crossing

structures used by predators, as observed in North America by Foster and Humprey

(1995) and Clevenger et al. (2001b). In Australia, whether predator avoidance behaviour

is displayed by native prey species at crossing structures is unclear. For example, foxes,

cats and multiple individuals of their prey species used an underpass at Slaty Creek in

Victoria (Abson and Lawrence 2003) and underpasses near Mandurah, Western

Australia (Chambers and Bencini 2015). This apparent lack of predator avoidance by

Australian species could be because these introduced predators and native prey species

did not co-evolve (Little et al. 2002, Mata et al. 2015). In support of this notion, several

native prey species, such as the southern brown bandicoot (Isoodon obesulus), common

brushtail possum (Trichosurus vulpecula), and Australian bush rats (Rattus fuscipes)

have been found not to recognise or react to scent of foxes and cats (Banks 1998, Mella

et al. 2011).

Wildlife crossing structures can also be costly to install (Can$1,750,000 for an

underpass in Banff National Park and €5,600,000 for an overpass in Netherlands,

Huijser et al. 2008), which can deter road construction agencies and relevant

government authorities from implementing them. However, the benefit they can provide

to wildlife and motorists is currently thought to outweigh the large cost (Mata et al.

2008, Polak et al. 2014).

1.3. The western ringtail possum

1.3.1. Biology and ecology

The western ringtail possum, also called ngwayir by the Noongar indigenous people of

southwest Australia, is a medium-sized nocturnal marsupial weighing up to 1.3 kg

(Wayne et al. 2005). These possums are strongly arboreal and strictly folivorous, mostly

feeding on leaves and some flowers of selected tree species (Jones et al. 1994a). They

prefer tree hollows as rest sites, but they can also construct nests called “dreys” from

vegetative materials especially where tree hollows are not available (Ellis and Jones

1992). They have also been observed to rest in understorey vegetations and abandoned

12

rabbit burrows where tree hollows are sparse (Jones et al. 1994a, Clarke 2011). In

coastal regions of southwest Western Australia, these possums are strongly associated

with peppermint trees (Agonis flexuosa), with up to 99.6 % of their diet comprising of

A. flexuosa leaves and the majority of their dreys made of materials gathered from A.

flexuosa trees (Ellis and Jones 1992, Jones et al. 1994a).

They can reproduce all year around if the environment is favourable, but normally have

two peak breeding periods: April to June and October to December in the A. flexuosa

dominated coastal region, and May to June and October to November in the jarrah

(Eucalyptus marginata) dominated inland region (Jones et al. 1994b, Wayne at al.

2005). The birth of a single young is most common, but mothers can give birth to twins

and raise them to maturity on rare occasions (Jones et al. 1994b). Juveniles can reach

sexual maturity within a year after birth, and the possums are thought to live for 4 to 5

years on average in the wild (Ellis and Jones 1992, Wayne et al. 2005).

1.3.2. Decline and management

Fossil records indicate that P. occidentalis historically occupied most of the southwest

corner of Western Australia, ranging from southeast of Geraldton (400 km North of

Perth) to the southern edge of the Nullarbor Plain (Figure 1; Woinarski et al. 2014).

After European settlement in the 1830s, local extinctions of these possums were

recorded as early as in the 1920s, and by the 1980s, they were seen only in patchy areas

in the southern coastal strip between Bunbury and Albany and in inland riparian habitat

in the Upper Warren and Perup area (Jones et al. 2004, Clarke 2011, Woinarski et al.

2014). Wilson (2009) found that populations of P. occidentalis in the coastal Bunbury

region (“Gelorup region”) and coastal Busselton region were genetically distinct from

each other, even though these regions were only 30 km apart. The genetic

distinctiveness of the Albany population was not assessed in Wilson’s study; hence it

remains unknown to this date. The Upper Warren area once held a large genetically

distinct population; however, regular spotlighting surveys in the area have shown a

dramatic decline of the species between the 1990s and the early 2000s, and no P.

occidentalis has been recorded during spotlight surveys since 2009 (Wilson 2009,

Wayne et al. 2012). Currently P. occidentalis is seen in very fragmented patches

between Bunbury and Augusta and in Albany. The total area it currently occupies is

estimated to be less than 500 km2 (Woinarski et al. 2014).

13

Figure 1 A map of historical and current ranges of Pseudocheirus occidentalis (adapted from

Woinarski et al. 2014). The dark shade represents the historical range without recent records and the

lighter shades are historical ranges that experienced rapid declines. Red crosses and green circles are

locations of records before 1993 and between 1993 and 2012, respectively. Blue circles are locations

where the species has been translocated successfully.

Pseudocheirus occidentalis is classified as Vulnerable by the International Union for

Conservation of Nature (Morris et al. 2008) and by the Australian Environment

Protection and Biodiversity Conservation Act 1999 (Department of the Environment,

Water, Heritage and the Arts 2013). However, these classifications are considered

outdated, and the Action Plan for Australian Mammals 2012 calls for its classification to

be changed to critically endangered (Woinarski et al. 2014). As a result, its

classification was recently changed to Endangered by the Western Australian

government (Department of Parks and Wildlife WA 2014). The main threatening

processes are habitat destruction and fragmentation caused by urbanisation and logging,

predation by introduced predators such as red foxes and feral cats, and altered fire

regimes (Wayne et al. 2006, Woinarski et al. 2014). Road mortalities are also common

for this species (Trimming et al. 2009).

Perth

Busselton

Albany

Upper Warren/ Perup

Bunbury/ Gelorup

Augusta

Australia

14

Several management options, including translocations and fox control programs have

been attempted since the early 1990s to stop the decline of P. occidentalis. Due to their

highly specialised requirements and susceptibility to predation, translocations have had

a low success rate (de Tores et al. 2004, Clarke 2011). The effectiveness of extensive

fox control programs routinely conducted in Western Australia has not been identified

for P. occidentalis to this date mainly due to the difficulty in capturing these possums

and estimating their numbers (de Tores et al. 2004, Woinarski et al. 2014). The likely

effectiveness of other management options such as reduction of road mortalities has also

not been assessed.

The A. flexuosa dominated southern part of the Swan Coastal Plain now remains their

core habitat; however, this coastal region is currently one of the fastest developing

regions in Australia (Australian Bureau of Statistics 2014) and the increasing number of

large-scale developments is further threatening the persistence of this species

(Woinarski et al. 2014). Opportunistic surveys suggest a recent rapid decline of P.

occidentalis on the Swan Coastal Plain, but the current status and future direction of the

populations in this region has not been formally investigated with population viability

analysis (Woinarski et al. 2014). The coastal habitat in the Busselton region has a

population with the highest known density of P. occidentalis (Jones et al. 1994a, 2007).

This area has also been identified as an area likely to be highly affected by new road

developments (Laurance et al. 2014). Many artificial linear structures are already

present in this area; however, their negative impacts on P. occidentalis have never been

investigated. Long-term studies on home ranges, dispersal pattern and basic

demographic rates, including reproductive and survival rates of wild western ringtail

possums are lacking in this important region. The exception is a study conducted by

Jones et al. (1994b) who estimated home ranges from 10 weeks of monitoring and

observed dispersal of five individuals.

1.3.3. Locke Nature Reserve and surrounding campsites

Within the Busselton region, the highest densities of P. occidentalis have been recorded

at Locke Nature Reserve and the surrounding campsites (Jones et al. 2007). Locke

Nature Reserve is a 200 ha reserve that provides mostly continuous A. flexuosa canopy

with swampy open areas in the southern part. The Western Australian Department of

Parks and Wildlife manages the reserve, and no recreational activities are permitted

within its boundaries. Monthly baiting with sodium monofluoroacetate is conducted

15

within the reserve as part of the Western Shield Program, which aims to control

introduced predators, such as red foxes (de Tores et al. 2004).

The area to the north of the reserve is Crown land leased to religious and youth groups

that use it as campsites. In these campsites, parts of the vegetation have been cleared for

recreational purposes and the canopy connections are more limited than in the nature

reserve, but the remaining vegetation is still dominated by A. flexuosa. The area to the

east of the reserve is a privately owned caravan park, and canopy cover in this park is

limited except for the southern end of the park and northern end along the road where a

strip of A. flexuosa trees has been kept as a road reserve.

Caves Road runs between the nature reserve and campsites on its north (Figure 1). Its

existence as a narrow dirt road was recorded as early as the 1930s, and it was sealed in

the 1960s to become the current single carriageway road. This 15 m wide road connects

popular tourist destinations in the region. The recorded daily traffic volume on this road

was 6,000 cars in 2008, but it can be up to 15,000 cars during the peak tourist season in

summer (Main Roads WA 2009, G. Zoetelief, Pers. Comm.). With the cleared verges

and no overhanging trees across the road, the total canopy gap across the road is 25 m.

The speed limit on this road is 90 km h-1, and dead western ringtail possums have

regularly been seen on this road. Trimming et al. (2009) identified this section of Caves

Road as a roadkill hotspot for P. occidentalis. However, the true impact this road poses

on this population has never been assessed.

In addition to Caves Road, an artificially reinforced, straightened and widened section

of the Buayanyup River runs between Locke Nature Reserve and the caravan park on its

east. This 30 m wide artificial waterway was built in the 1930s to act as drainage in this

historically flood-prone area. Banks on both sides are kept clear to provide public access

and for maintenance, resulting in a 45 m gap in the canopy. This waterway is not used

for transportation, and it contains water all year. As P. occidentalis is not known to

swim voluntarily, this linear structure could potentially act as a barrier; however, its

impact on the P. occidentalis population is not known.

16

Figure 2 A map of the study area near Busselton, Western Australia. Red lines and blue lines

indicate the edges of Caves Road and an artificial waterway, respectively. Areas enclosed with black

rectangles are study blocks in which data and samples from Pseudocheirus occidentalis were

collected.

1.4. Gaps in the knowledge

As discussed above, the impacts of artificial linear structures on wildlife, especially the

impacts of artificial linear structures other than roads on arboreal species, are largely

unknown. Our understanding of important aspects of the biology, ecology and current

status of the endangered P. occidentalis is also lacking. Listed below are the key

questions that need to be resolved:

a) Do artificial linear structures, including those other than roads, act as barriers to

strictly arboreal species such as P. occidentalis?

b) If artificial linear structures, including those other than roads, act as barriers to

strictly arboreal species, does the barrier effect result in genetic divergence?

17

c) Do rope bridges provide these strictly arboreal animals with safe crossings of

artificial linear structures?

d) What are the home ranges of P. occidentalis in its core habitat and what influences

their size?

e) What are the basic demographic parameters such as reproductive and survival rates of

P. occidentalis in its core habitat?

f) What are the dispersal patterns of P. occidentalis and do dispersals result in fine scale

genetic structure?

g) Is the stronghold P. occidentalis population suffering from lowered genetic health?

h) What is the current state of the stronghold population of P. occidentalis in its core

habitat?

i) What is the likely future direction of the P. occidentalis population in its core habitat?

j) What are the estimated impacts of known threats on P. occidentalis in its core habitat,

including road mortality and fox predation?

1.5. Research aims

The first retro-fitted rope bridge in Western Australia was built across Caves Road in

2013 to provide arboreal animals with a safe passage across the road. It was constructed

by Main Roads Western Australia as a part of this PhD project to investigate some of

the research questions outlined above. I formulated six specific research objectives to be

addressed in this thesis. They are:

a) to assess the impact of a road and an artificial waterway on the movements of P.

occidentalis;

b) to investigate the genetic impacts of a road and an artificial waterway on P.

occidentalis;

c) to gain more information on home ranges of P. occidentalis in A. flexuosa dominated

habitat;

18

d) to assess the general genetic health and fine-scale genetic structure within a

population of P. occidentalis

e) to investigate the current status and predict the future direction of a population of P.

occidentalis in its core habitat using population viability analysis and assess the likely

effectiveness of two potential management options: removal of fox predation and road

mortality; and

f) to monitor the use of a newly constructed rope bridge and assess whether it provides

P. occidentalis with safe passage across Caves Road and to determine which factors

affect the use of the bridge.

1.6. Structure of the thesis

This thesis is presented as a series of scientific papers in accordance with section 10. 28

– 35 of the Postgraduate and Research Scholarship Regulations at The University of

Western Australia. It addresses each of above aims using a population of P. occidentalis

at Locke Nature Reserve and surrounding campsites.

There are six chapters in this thesis, consisting of:

Chapter 1 (this chapter) that provides a general introduction,

Chapter 2 (data chapter) that addresses the above objectives (a) and (c),

Chapter 3 (data chapter) that addresses the above objectives (b) and (d),

Chapter 4 (data chapter) that addresses the above objective (e),

Chapter 5 (data chapter) that addresses the above objective (f), and

Chapter 6 that provides a general discussion, overall conclusions, future research

directions and management implications in terms of the conservation of P. occidentalis

and management of the negative impacts of roads and artificial waterways.

Some parts of the introduction and method sections overlap among four data chapters

because each chapter is presented as a standalone paper. Chapters 2 to 5 are written as

scientific papers with multiple authors; therefore plural pronouns are used. Chapters 2

and 5 have been published in peer-reviewed journals, and Chapter 3 has been submitted

19

and is currently under review for publication. Two scientific papers will be produced

from Chapter 4 and submitted to peer reviewed journals.

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33

Chapter 2

An artificial waterway and a road restrict movements and alter home ranges of the

western ringtail possum

This chapter has been published in the Journal of Mammalogy as:

Yokochi K, Chambers B, Bencini R (2015) An artificial waterway and road restrict movements and

alter home ranges of endangered arboreal marsupial. Journal of Mammalogy

doi:10.1093/jmammal/gyv137

34

An artificial waterway and a road restrict movements and alter home ranges of an

endangered arboreal marsupial

Kaori Yokochi, Brian K. Chambers, Roberta Bencini

Abstract

Artificial linear structures can cause habitat fragmentation by restricting movements of

animals and altering home ranges. The negative impacts of these linear structures,

especially of those other than roads, on arboreal species have been rarely studied even

though these species can be greatly affected because of their fidelity to the canopy. We

studied the home ranges of an endangered arboreal marsupial, the western ringtail

possum (Pseudocheirus occidentalis), with a focus on the impacts of a road and an

artificial waterway on their movement. We radiotracked18 females and 19 males for 3

years along a major road and an artificial waterway near Busselton, Western Australia,

and estimated home ranges using the a-local convex hull (a-LoCoH) estimator. No

possum crossed the road successfully during the monitoring period while one crossed

the waterway. Males had a mean home range size of 0.31 ± 0.044 (SE) ha, almost

double that of the females at 0.16 ± 0.017 ha. Possums near the waterway had larger

home ranges (0.30 ± 0.048 ha) than those near the road (0.19 ± 0.027 ha), and the size

increased with proximity to the waterway, probably due to the greater availability of

nearby canopy connections and the lower availability of preferable foliage. These

results demonstrate that both the road and waterway represent significant physical

barriers to possums, and the artificial waterway influenced home ranges more severely

than the road. This suggests that linear infrastructure other than roads can affect

movements of strictly arboreal animals, and negative impacts of these structures need to

be assessed and mitigated by reconnecting their habitat, just as those of roads.

35

Introduction

Artificial linear structures can pose negative effects on wildlife populations by causing

habitat destruction, lowered habitat quality, and habitat fragmentation due to barrier

effects (Forman and Alexander 1998). Severed or restricted movements and gene flow

across artificial linear structures can further increase the risk of extinction in threatened

wildlife species by lowering their fitness and adaptability (Forman and Alexander 1998,

Frankham et al. 2002, Epps et al. 2005). Habitat fragmentation can also restrict dispersal,

which further increases the possibility of local extinction (Forman and Alexander 1998).

Studies of the impacts of roads on wildlife have become more common in recent years

(Clevenger and Wierzchowski 2006), and Forman (2000) suggested that road networks

have ecological impacts on 20% of the land of the United States. Other linear

infrastructure, such as artificial waterways, can also cause habitat fragmentation and

restrict the movement of animals; however, studies on negative impacts of these types

of infrastructure are rare compared to those of roads. Such studies are lacking especially

on arboreal species even though they are thought to be highly vulnerable to the impact

of habitat fragmentation due to their fidelity to canopies (McAlpine et al. 2006). To

investigate the impacts of different types of artificial linear structures on the movement

of arboreal animals, we studied the movements and home ranges of western ringtail

possums (Pseudocheirus occidentalis Thomas 1888) near an existing road and an

artificial waterway that was built to ease flooding in the area.

Pseudocheirus occidentalis has experienced a dramatic decline in its numbers and range

due to factors such as destruction and fragmentation of habitat and predation by

introduced predators (de Tores et al. 2008). This species was recently classified as

critically endangered in Australian national action plans for mammals and is expected to

decline further in the future due to these continuing threats as well as climate change

(Molloy et al. 2014, Woinarski et al. 2014). Despite its endangered status, many aspects

of its ecology and biology still remain unknown mainly due to the difficulty of

capturing these strongly arboreal animals (de Tores et al. 2004, Wayne et al. 2005a,

Woinarski et al. 2014). These nocturnal folivores occupy a limited geographic range in

the southwest of Western Australia, a major biodiversity hotspot on mainland Australia

(Myers et al. 2000). The peppermint tree (Agonis flexuosa) dominated woodlands of the

Swan Coastal Plain between Bunbury and Dunsborough are their core habitat because A.

flexuosa is their preferred food source and they are known to build nests (“dreys”) using

materials mainly from this tree species (Ellis and Jones 1992, Jones et al. 1994a, de

36

Tores et al. 2004). However, the southwest region currently has the highest road density

among all the regions in Western Australia except for the Perth Metropolitan region

(Main Roads Western Australia 2014), and it is one of the most rapidly growing regions

in Australia with up to 5.3% annual increase in its human population (Australian Bureau

of Statistics 2014). This rapid urbanization will contribute to further loss and

fragmentation of P. occidentalis habitat. Wayne et al. (2006) found that the abundance

of P. occidentalis in noncoastal jarrah (Eucalyptus marginata) dominated forests was

negatively associated with habitat loss caused by forest fragmentation. The negative

impacts of habitat fragmentation by roads on other possum species have also been

documented (Wilson et al. 2007, Lancaster et al. 2011); however, little is known about

the impacts of roads on P. occidentalis, let alone the impacts of artificial waterways.

Pseudocheirus occidentalis is known to be sedentary and strongly reluctant to traverse

on the ground (Jones et al. 1994b), and Wilson et al. (2007) found that similarly

arboreal lemuroid ringtail possums (Hemibelideus lemuroides) avoided crossing a 5 to

20 m wide forestry dirt road and a power-line corridor at ground level. Given this

information, we predicted that a major busy road and a 30 m wide artificial waterway

would prevent P. occidentalis from crossing or further expanding their home ranges.

Although these linear structures may present barriers on one side of possums’ home

ranges, the available vegetation along and away from these linear structures would

enable possums to extend their home ranges into different directions; therefore, we also

expected that the size of home ranges of P. occidentalis would not differ near and away

from the road or waterway. In the process of testing these hypotheses, we also aimed to

gain basic information on their home ranges, such as differences due to sex,

reproductive season, and land use of their habitat.

Materials and methods

Study area

This study was conducted in Locke Nature Reserve and surrounding campsites, 9 km

west of Busselton, Western Australia (33° 39′ S 115° 14′ E). This 200 ha reserve was

managed by the Western Australian Department of Parks and Wildlife with no

recreational activities by the public permitted. Its A. flexuosa-dominated habitat was

known to support a high density of P. occidentalis (de Tores and Elscot 2010). The

public used the campsites surrounding the reserve throughout the year with a peak in

37

summer. They saw P. occidentalis regularly but they did not directly interact with the

animals due to the strongly nocturnal and arboreal nature of the species.

Caves Road, a 15 m wide single carriageway providing a 25 m gap between vegetation

canopies, separated the nature reserve in the south from campsites in the north (Figure

1). The traffic volume on this road was highly seasonal and reached up to 15,000

vehicles per day during the peak holiday season (G. Zoetelief, Main Roads Western

Australia, pers. comm.). On the eastern edge of the reserve, an artificially reinforced,

straightened, and widened part of the Buayanyup River (“artificial waterway”) ran from

south to north, separating the reserve from a campsite. This 30 m wide artificial

waterway was built in the 1930s to act as drainage in this historically flood-prone area

and contained water throughout the year. Banks on both sides of the waterway were

kept clear of vegetation to provide public access and access for maintenance, which

resulted in a 45 m wide gap between canopy-level vegetation. Neither of these artificial

linear structures had canopy connections across them, meaning that there was no

connection among branches of trees that would allow possums to cross the clearings

without descending to the ground.

Four 200 × 200 m study blocks (1A, 1B, 2A, and 2B) were set up so that 1A and 2A fell

in the nature reserve, and 1B and 2B fell in campsites. 1A and 1B were directly opposite

each other and separated by Caves Road, and 2A and 2B were directly opposite each

other and separated by the waterway (Figure 1). The canopy connection in the campsites

was limited compared to the nature reserve, in which canopy cover was mostly

continuous except for a few firebreaks and a swampy area in the southern part of 2A. In

1A, there was a 5 m wide firebreak running parallel to the road without any canopy

connection and a 4 m firebreak running perpendicular to the road with at least two

sections with canopy connection that would allow P. occidentalis to cross the firebreak

without descending to the ground (Table 1). In 2A, there was a 4 m wide firebreak

running parallel to the waterway and a 2 m wide track running perpendicular to the

waterway, both with more than three sections with canopy connections.

38

Figure 1 A map of the study area near Busselton, Western Australia. Solid lines represent borders of

Caves Road (west to east) and an artificial waterway (north to south). 1A, 1B, 2A, and 2B are 200 ×

200 m blocks where data from radio-collared Pseudocheirus occidentalis were collected. 1A and 2A

are inside Locke Nature Reserve, and 1B and 2B are within campsites.

Table 1 Characteristics of study blocks and firebreaks at the study site near Busselton, Western

Australia. Barrier is the type of artificial linear structure adjacent to the block, and direction of a

firebreak is its direction against the closest barrier. Canopy connection is the number of sections with

canopy connections that would allow possums to cross the firebreak without descending to the

ground.

Firebreaks

Block Barrier Land use Direction Width (m) Canopy connection

1A Road Nature reserve Parallel 5 0

Perpendicular 4 2

2A Waterway Nature reserve Parallel 4 > 3

Perpendicular 2 > 3

1B Road Campsite Canopy connection was limited throughout.

2B Waterway Campsite Canopy connection was limited throughout.

39

Data collection

Pseudocheirus occidentalis was captured near and away from the road and waterway in

all 4 blocks using a specially modified tranquilizer dart gun with darts containing a dose

of 11-12 mg/kg of Zoletil 100 (Virbac Australia, Milperra, Australia) following a

method developed by P. de Tores and reported by Clarke (2011). Initially, three adult

males and three adult females in each block (i.e., 24 animals in total) were fitted with

VHF radio collars with a mortality function (AVM Instrument Company, Ltd., Colfax,

California, or Biotrack, Wareham, United Kingdom). However, the number of

monitored animals fluctuated throughout the monitoring period between March 2010

and March 2013 due to mortality and failure of some transmitters. When we failed to

pick up signals from a radio collar, we expanded the search area to check whether the

individual had moved out of the range. We also searched for the particular individual

near its last known location with spotlights. If we failed to locate and recapture the

animal after a collar stopped transmitting or if an animal died, another adult of the same

sex was captured and collared in the same block. Fifty-two adult individuals were

monitored in total during 3 years.

Collared animals were located during the day and/or night with an average time span

between locations of 6.4 ± 0.23 days. Each animal’s location was determined by homing

in using a three element Yagi antenna (Sirtrack, Havelock North, New Zealand) and an

R-1000 telemetry receiver (Communications Specialists Inc., Orange, California).

Pseudocheirus occidentalis is sedentary and homing on individuals did not cause them

to move away from the researchers, so it was possible to record coordinates of each

animal’s locations using a handheld GPS unit (Mobile Mapper Pro, Magellan

Navigation, Inc., Santa Clara, California). We recorded the species and visually

estimated the height of every tree in which a collared possum was observed and

calculated the proportion of A. flexuosa among the trees utilized by the possums. We

also calculated the average height of the trees in the nature reserve, campsites, along

Caves Road, and along the artificial waterway. Using Wilcoxon rank sum tests, the

average height of A. flexuosa was compared between those in the nature reserve and

those in campsites, between those within the thin strip along the road in block 1A and

those outside of the strip in 1A, and between those within the thin strip along the

waterway in block 2A and those outside of the strip in 2A. All procedures for handling

animals were approved by the Animal Ethics Committee at The University of Western

40

Australia (RA/3/100/539 and RA/3/100/1213). We conducted our fieldwork following

the Australian code of practice for the care and use of animals for scientific purposes

(National Health and Medical Research Council 2004), which complies with the

American Society of Mammalogists guidelines (Sikes et al. 2011).

Estimation of home ranges

Out of 52 individuals, 37 had enough data to be used for robust home range analyses

(block 1A: 5 females and 7 males, 1B: 4 females and 5 males, 2A: 5 females and 5

males, 2B: 4 females and 2 males) after the assessment of incremental plots of estimated

home range area against the number of locations using Ranges 8 (Kenward et al. 2008).

This lack of data was due to mortality events and an unexpectedly large number of

AVM collars failing prematurely. Both day and night locations were pooled for each

individual in order to include foraging and resting locations into the estimation of its

overall home range size. The average number of locations recorded for each individual

was 62.6 ± 5.78 (ranging from 26 to 156). Time to independence, which is the time span

required between location records in order to achieve temporal independence, was

estimated using Ranges 8. For 12 individuals, time to independence was estimated to be

over 21 days, which was impractical especially if we were to have an adequate number

of locations for robust analyses. To estimate home ranges accurately, having an

adequate number of locations is more important than achieving independence between

them (Reynolds and Laundre 1990, Rooney et al. 1998, Kernohan et al. 2001). Given

that P. occidentalis tends to go back to the same rest sites at dawn on consecutive days

and that only one location was recorded on any day or night, we considered the existing

sampling interval of approximately six days to be acceptable for the estimation of home

ranges for this study.

There are two methods that have been commonly used to estimate home ranges in

recent studies, kernel density estimators (KDEs) and local convex hull (LoCoH)

estimator. KDE, described by Worton (1989), is currently the most commonly used

method; however, several authors reported that it tends to overestimate or fragment

home ranges especially when their shapes are complex (Getz and Wilmers 2004, Wilson

et al. 2007, Cumming and Cornélis 2012, Kie 2013). LoCoH, especially the a-LoCoH

method, has been reported to represent home ranges more accurately when there are

“sharp” features like barriers although the process of implementation is more

complicated than KDEs (Getz et al. 2007, Cumming and Cornélis 2012). Our study area

41

included potential sharp barriers such as a major road and an artificial waterway;

therefore, we employed the a-LoCoH method to estimate the home ranges of P.

occidentalis.

We used the tlocoh package (Lyons et al. 2013) in R version 3.0.1 (R Development

Core Team 2013) without the time component s to estimate home ranges. We estimated

the a value for each individual as described by Getz et al. (2007) and Lyons et al. (2013)

and calculated 25%, 50%, and 95% isopleths of individual home ranges. Estimated

home ranges were then visualized on ArcGIS 10 (ESRI 2012). We used the 95%

isopleth as an estimate of home range to match the published literature, although the

appropriateness of this value as a representation of a home range has been questioned

when using KDE (Börger et al. 2006, Fieberg and Börger 2012). The 25% isopleth was

regarded as the core home range.

We estimated home range sizes during the breeding (April to July and September to

November) and non-breeding seasons (rest of the year), as observed in this study and

also by Jones et al. (1994b). Studied animals were found to stay in the same area over

consecutive breeding seasons, so data over three years were pooled for both seasons to

increase sample sizes. After an assessment of incremental plots, home ranges in the

breeding season were estimated for 8 females and 6 males, and those in non-breeding

season were estimated for 11 females and 8 males. All the home range sizes were log-

transformed to fit normality.

Factors influencing home range size

We assessed the effects of the type of the closest barrier, distance from the closest

barrier, sex, and habitat land use on the size of home ranges using a generalized linear

model in JMP 10 (SAS Institute Inc. 2012; Table 2a). We separated possums into two

groups according to the type of their closest barrier (road or waterway). We then

calculated the distance from the closest barrier for each individual as the shortest

distance between Caves Road or the waterway and the central point of the 25% isopleth

home range. To assess whether the distance to the barrier influenced home ranges

differently for two kinds of barriers, we added the interactive model between type of

barrier and distance to the barrier. We categorized animals in blocks 1A and 2A as the

“nature reserve group,” and animals in 1B and 2B as the “campsite group.” We also

included number of locations and body weight in the modelling to ensure home range

42

estimates were not affected by the duration of monitoring or the size of the animals. We

assessed whether reproductive season had an effect on the size of home range using

linear mixed models with standard least square personality while setting individual

identity as a random effect (Table 2b). We added an interactive model between sex and

breeding season in the analysis to assess whether breeding seasons affected home range

differently for males and females. For all the modelling analyses, a normal distribution

and identity link function were used.

For each set of candidate models, we ranked models based on their corrected Akaike

Information Criterion (AICc) values. We regarded models that ranked higher than the

null model and with ΔAICc of less than 2.0 as having a strong support, and those with

ΔAICc between 2.0 and 7.0 as having weak support from our data (Burnham and

Anderson 2002). When a model was found to have support from our data, we assessed

the directionality and significance of the effects of factors based on parameter estimates

and their 95% confidence intervals.

43

Table 2 Candidate models and corresponding hypotheses on a) overall home range size and b)

reproductive seasonal home range size of Pseudocheirus occidentalis in Busselton, Western

Australia. For the linear mixed model (b), the identity of the animal (ID) was included in all models

as a random factor and sex and reproductive season were fixed factors.

Variables used in model Hypothesis tested

a) Overall home range size (Generalised linear model)

Sex Males would have larger home ranges than females.

Barrier type The type of the closest barrier would not affect the

home range size.

Distance to barrier Proximity to the closest barrier would not affect

home range size.

Type x Distance Type of the closest barrier or the distance from it

would not affect home range.

Habitat Possums in campsites would have larger home

ranges than those in the nature reserve.

Body weight Weight of possums would not affect home range

size.

Number of locations Location record number would not affect home

range size.

Null Home range size would vary randomly.

b) Reproductive seasonal home range size (Linear mixed model)

Sex Males would have larger home ranges than females,

but there would be no effect of reproductive

season.

Season Home range would be larger during breeding season,

but there would be no effect of sex.

Sex + Season Males would have larger home ranges than females,

and home ranges would be larger during breeding

season for both sexes.

Sex x Season Males’ home range would be larger than females’,

and it would expand more than female’s during the

breeding season.

Null (ID factor only) Home range size would vary with individuals.

44

Results

Movements across the road and waterway

Eight radio-collared individuals were located within 20 m of road edges for over 98% of

their location records; however, none of them was found on the other side of its usual

side on Caves Road during three years of monitoring (Figure 2). One male and one

female were killed on the road 115 and 311 days after they were collared, respectively.

Neither of them had been located on the other side of the road before the mortality

events. One male that had been living on trees adjacent to the artificial waterway

crossed the waterway from 2B to 2A 35 days after being collared and never crossed the

waterway again for the rest of 210 days of monitoring.

Home ranges of P. occidentalis did not included Caves Road or the artificial waterway

even in the individuals that lived adjacent to them (Figures 3a and 3b), and the firebreak

without canopy connection also seemed to restrict movements of some possums

(Figures 2 and 3a). In 1A, all five individuals observed within the thin strip of trees

between Caves Road and the west to east firebreak had narrow, elongated home ranges

along the road compared to those on the other side of the road or firebreak (Figure 3a).

By contrast, in 2A, where there were canopy connections, all four individuals observed

within the thin strip of trees between the artificial waterway and the north to south

firebreak had home ranges that included the firebreak and vegetation on the other side

(Figure 3b). Visual inspections of the home ranges also revealed that where canopy

connections were limited within campsites, the core of the home ranges of possums

overlapped with groups of trees with continuous canopy (Figure 3b).

In all locations, A. flexuosa accounted for more than half of the trees on which radio-

collared possums were observed but the proportion of possums observed on A. flexuosa

within the nature reserve was higher away from the road or waterway than along the

road or waterway (Table 3). On average, A. flexuosa trees were significantly taller in the

campsites than in the nature reserve (P < 0.001), away from the road than along the road

(P = 0.001), and away from the waterway than along the waterway (P < 0.001; Table 3).

45

Figure 2 Location records of Pseudocheirus occidentalis along Caves Road near Busselton, Western

Australia. Solid lines are the borders of Caves Road, and dotted lines are firebreaks. Different

colours represent different individuals. Locations from individuals in campsites north of the road

(1B) are marked with triangles and those from individuals in the nature reserve on the south (1A) are

marked in circles.

Figure 3 Examples of estimated home ranges of Pseudocheirus occidentalis along Caves Road and

an artificial waterway near Busselton, Western Australia. Home ranges of a) two individuals in a

nature reserve south of the road (1A), two individuals in campsites north of the road (1B), and b)

three individuals in a nature reserve west of the artificial waterway (2A), and two individuals in a

campsite east of the waterway (2B) are presented. Solid lines are approximate edges of canopy

covers adjacent to the road or waterway, and dotted lines are firebreaks. Areas with different shades

represent the 95% (light), 50% (medium), and 25% isopleths (dark) of home ranges.

46

Table 3 Proportion of Agonis flexuosa among trees on which radio-collared Pseudocheirus

occidentalis was observed and the average height of A. flexuosa in different locations near Busselton,

Western Australia. N is the number of total trees recorded. Values in parentheses are SE values.

Differences in the average heights were significant in all three comparisons (P < 0.05).

Location N % A. flexuosa Height (m)

Nature reserve 944 84.5 8.33 (0.10)

Campsite 270 86.3 9.06 (0.16)

1A

Away from road 332 87.6 8.90 (0.16)

Along road 69 73.4 7.92 (0.32)

2A

Away from waterway 349 87.0 8.00 (0.15)

Along waterway 25 59.5 6.30 (0.24)

Factors influencing the size of the home range

When we examined individual factors, sex had the strongest influence on home range

size followed by the type of the closest barrier, both of which ranked higher than the

null model and had significant effects (Table 4a). A model for the interaction between

the type of and distance to the closest barrier also had stronger support from our data

than the null model. None of the other factors such as land use and body weight had

support from our data. The number of location records also did not have an effect on

home range size, indicating that the number of location records collected was enough to

conduct a robust estimation of home ranges.

The average home range size of males was 0.31 ± 0.044 (s.e.) ha, almost twice the size

of the females’ at 0.16 ± 0.017 ha. The average home range size near the waterway

(0.30 ± 0.048 ha, n = 16) was about 1.5 times larger than that near Caves Road (0.19 ±

0.027 ha, n = 21). Given the strong influences of sex and the type of the closest barrier

indicated by their AICc values and 95% confidence intervals, a model combining these

two factors was added to the analysis. This model had the strongest support among all

models investigated, suggesting that both sex and the type of closest barrier affected the

home range sizes of possums (Table 4a). When we assessed the effect of reproductive

season on home range size, all three models including reproductive season had weaker

47

support than the null model, indicating that reproductive season was not a significant

predictor of home range size for either sex (Table 4b).

Table 4 Results of a) generalized linear model analysis on overall home range size and b) mixed

linear model analysis on reproductive seasonal home range size of Pseudocheirus occidentalis in

Busselton, Western Australia. Parameter estimates are presented only for models that ranked higher

than the null models or had at least weak support (ΔAICc > 7.0). * denotes parameter estimates with

95% confidence intervals outside of zero. In the case of categorical variables, parameter estimates (

s.e.) are for the categories presented in parentheses (e.g. the parameter estimate of the sex model is

for females). AICc: corrected Akaike Information Criterion.

Model AICc ∆AICc Parameter estimates

a) Overall home range size (Generalized linear model)

Sex + Barrier type 0.56 - Sex: -0.142 0.036 (female)*

Barrier: 0.117 ± 0.036 (waterway)*

Sex 7.31 6.76 -0.127 ± 0.040 (female)*

Barrier type x Distance 7.48 6.93 Barrier: 0.117 ± 0.039 (waterway)*

Distance: -0.001 ± 0.001

Barrier x Distance: -0.003 ± 0.001*

Barrier type 11.26 10.70 0.098 ± 0.043 (waterway)*

Null 13.80 13.24

Habitat 14.25 13.69

Body weight 15.18 14.62

Number of locations 15.85 15.29

Distance to barrier

16.00

15.44

b) Reproductive seasonal home range size (Linear mixed model)

Sex 18.63 - -0.212 ± 0.065 (female)*

ID (null) 21.45 2.81

Season + Sex 25.53 6.89 Sex: -0.211 ± 0.065 (female)*

Season: 0.031 ± 0.028 (breeding)*

Reproductive season 28.15 9.52

Season x Sex 33.71 15.08

48

To further investigate the interaction between the type of and distance to the closest

barrier, the effects of distance to the barrier on home range size were assessed

separately for each group. Only the sex and null model had strong support (ΔAICc <

2.0) for possums near Caves Road, suggesting that the distance to the road was not a

significant predictor of the home range size of possums (Table 5a). By contrast, the

home range sizes of possums near the artificial waterway showed a significant negative

relationship with the distance to the waterway (Table 5b).

Table 5 Results of generalized linear model analysis on home range size of Pseudocheirus

occidentalis near a) Caves Road and b) an artificial waterway in Busselton, Western Australia.

Parameter estimates are presented only for models that ranked higher than the null models or had at

least weak support (ΔAICc > 7.0). * denotes parameter estimates with 95% confidence intervals

outside of zero. For the sex factor, parameter estimates ( s.e.) are for females. AICc: corrected

Akaike Information Criterion.


a) Caves Road

Sex 6.32 - -0.114 0.052*

Null 7.92 1.60

Sex + Distance 8.69 2.37 Sex: -0.109 ± 0.051*

Distance: 0.001 ± 0.002

Distance

9.68

3.37

0.002 ± 0.002

b) Artificial waterway

Sex + Distance -4.51 - Sex: -0.128 ± 0.041*

Distance: -0.003 ± 0.001*

Sex -1.79 2.72 -0.179 ± 0.045*

Distance -0.50 4.01 -0.005 ± 0.001*

Null 6.17 10.68

49

Discussion

Effects of the road on movement and home ranges

None of the collared possums was observed to successfully cross Caves Road in three

years of monitoring, and both of the individuals that tried to cross the road were killed

by vehicles. This lack of crossings indicates that the road was acting as a physical

barrier to P. occidentalis, as expected. Although the possibility of possums crossing the

road and returning to the original side between monitoring dates cannot be eliminated

from our data, it is unlikely given the strong arboreal nature of this species. A similar

lack of road crossings has also been reported in other arboreal animals, such as eastern

chipmunks (Tamias striatus, Ford and Fahrig 2008) and squirrel gliders (Van der Ree et

al. 2010). Russell et al. (2009) found that a close relative of P. occidentalis, the common

ringtail possum (Pseudocheirus peregrinus), was frequently killed on roads in Sydney,

Australia. This arboreal folivorous species is naïve on the ground just like P.

occidentalis, and this characteristic was thought to be one of the main contributing

factors to its high frequency of road mortality. This suggests that P. occidentalis may

also be experiencing a high level of road mortality on roads with no canopy connections,

such as Caves Road, and highlights the multiple manners in which roads can negatively

affect P. occidentalis.

The proximity to the road did not affect the size of home ranges overall, as expected.

Many vertebrate species expand their home ranges when the population densities and

availability of food, refuge, or mates are low (Maher and Lott 2000). Pseudocheirus

occidentalis in our study area depends heavily on A. flexuosa for food and refuge, so it

is likely that their home range sizes are influenced by the availability and quality of A.

flexuosa foliage as well as population density. Harring-Harris (2014) studied the

population density of P. occidentalis along Caves Road, along the artificial waterway,

and within the non-edge habitat of Locke Nature Reserve and found that the density of

the possums did not differ in these three areas. However, in the same study, Harring-

Harris (2014) found that the water content of A. flexuosa leaves was higher along the

road and waterway than in the non-edge habitat and that nitrogen content of the leaves

was also slightly higher along the road. This and our results suggest that the higher

water and nitrogen content of the A. flexuosa foliage along the road did not result in

reduced home range size of P. occidentalis. One possible explanation for this lack of

difference could be that the quality of foliage away from the road was already sufficient

50

to sustain P. occidentalis in small home ranges, and the additional levels of water and

nitrogen in foliage along the road did not change the behaviour of possums. Our study

area is located within the most pristine habitat for P. occidentalis (Jones et al. 1994a, de

Tores et al. 2004), and the home ranges estimated in this study were much smaller than

those in a less ideal inland habitat studied by Wayne et al. (2000): 5.01 ha for males and

1.26 ha for females. These large differences in home range sizes suggest that the quality

of habitat does affect the home range sizes of P. occidentalis; however, the increase in

water and nitrogen content closer to Caves Road was possibly not large enough to alter

their home range size.

It is also possible that other factors that increase the home range size of possums may

have masked the effects of the higher quality foliage near the road. While roads may

provide higher levels of water and nutrients to nearby soil due to runoff, they can also

pose adverse effects on nearby vegetation by processes such as air and soil pollution and

soil compaction (Trombulak and Frissell 2000). For example, Bignal et al. (2007) found

that air pollution near roads increased defoliation in oak trees (Quercus petraea) and

Smith et al. (2001) found that soil compaction reduced the root growth of young A.

flexuosa. Although Smith et al. (2001) did not study trees near roads, their results

suggest that A. flexuosa is vulnerable to a known impact of roads. The proportion of A.

flexuosa trees within the thin vegetation strip along the road in 1A was found to be

slightly lower than that away from the road, and A. flexuosa trees in this strip were

shorter than those outside of the strip (Table 2). Whether these characteristics were

caused by the presence of the road and whether they actually resulted in smaller

quantity of A. flexuosa foliage is uncertain. An assessment of the levels of pollutants

and soil properties as well as the quantity of A. flexuosa foliage near the road would be

required to provide us with more insight into the negative impacts of roads on A.

flexuosa trees and, as a result, P. occidentalis.

During our monitoring period, possums were regularly seen on the vegetation at the

edge of the road foraging, grooming, socializing, and resting, and this suggests that they

avoided crossing the road not because of environmental cues such as noise or light but

because there was no canopy connection. Wilson et al. (2007) observed a similar trend

in the lemuroid ringtail possum on a roadside. This suggests that the roadside vegetation

is an important habitat for P. occidentalis as seen in other arboreal species studied by

Downes et al. (1997). Preservation of such habitat could prove beneficial to this

51

endangered species especially if mitigation measures against the impact of habitat

fragmentation are provided. Therefore, as suggested by several authors (Goosem et al.

2005, Laurance et al. 2009, Goldingay et al. 2013), providing a safe passage for arboreal

animals to cross artificial linear structures, such as a wildlife crossing structure, would

be an important step toward reducing and reversing their negative impacts. In support of

this, as soon as a rope bridge was installed across Caves Road after the end of this study,

possums started using it almost immediately and with a high frequency (Yokochi and

Bencini 2015).

Effects of the artificial waterway on movement and home ranges

One male crossed the waterway during three years of monitoring; however, this

crossing seemed to be a rare event caused by severe weather. The study area

experienced strong gusts up to 55.4 km/h during the week it crossed the waterway

(Australian Bureau of Meteorology 2014), and it is likely that the male fell in the water

during this storm and subsequently swam to the other side. This individual never

returned to the original side of the waterway after the crossing event, suggesting that the

artificial waterway was also acting as a barrier for the possums, as we expected.

Contrary to our expectation, possums living closer to the waterway had larger home

ranges than those away from it. The density of the possums was found to be similar near

and away from the waterway, and the water contents in A. flexuosa foliage were slightly

higher near the waterway possibly because of the proximity to the permanent water

source (Harring-Harris 2014). Therefore, it seems that factors other than population

density or water content in foliage caused the increased home range size. One possible

cause of the home range expansion in P. occidentalis near the waterway is the lower

availability of A. flexuosa foliage for food and refuge. We found that A. flexuosa trees

within the vegetation strip next to the waterway were about 2 m shorter than those

outside of the strip in the nature reserve, and the proportion of A. flexuosa was 25%

lower inside the strip than outside (Table 2). Given the high dependence of P.

occidentalis on A. flexuosa, it is possible that possums along the waterway had to

expand their home ranges in order to have access to a greater number of A. flexuosa.

The availability of canopy connections across the firebreak along the waterway would

also have allowed possums to cross the firebreak in search for more food and refuge. All

four individuals that were recorded on the narrow strip of the trees along the waterway

incorporated the vegetation on the other side of the firebreak into their home ranges

52

(e.g., Figure 3b). This availability of canopy connections may also have caused them to

have larger home ranges because they included the 2 m wide firebreak in their home

ranges in order to get to the other side. Further investigations into the quantity of A.

flexuosa leaves available near the waterway and other possible factors influencing the

home range sizes, such as levels of other nutrients in foliage, are needed to identify

what is causing this expansion of home ranges.

Both the road and the artificial waterway acted as a barrier to the movement of P.

occidentalis; however, possums near the waterway had larger home ranges than those

near the road. Brearley et al. (2010) also showed that impacts of barriers and urban

edges on the squirrel glider changed depending on the types of the urban edges present,

such as road edges or residential edges. It is therefore important that we do not assume

that all barriers have uniform impacts on home ranges of wildlife.

Effects of the firebreaks

Surprisingly, a 5 m wide firebreak in the nature reserve along Caves Road with no

canopy connection restricted the movements of some possums, and possums observed

in the thin strip of vegetation between Caves Road and this firebreak had home ranges

that were elongated along the road. The home ranges of possums on the opposite side of

the road were not as elongated as those in 1A, indicating that this distinct home range

shape was not caused by the presence of the road alone. Possums living next to the

waterway in 2A did not have elongated home ranges even though there was a 4 m wide

firebreak along the waterway, probably because of the canopy connections across the

firebreak. Both the high quality of habitat and lack of canopy connections seem to have

prevented the possums on the roadside in 1A from expanding their home ranges across

the firebreak. Similar distinctive long and thin home ranges have also been seen in other

arboreal marsupials, such as bobucks (Trichosurus cunninghami) and squirrel gliders

(Petaurus norfolcensis) living in linear roadside remnants (Van der Ree and Bennett

2003, Martin et al. 2007). Possums in campsites with limited canopy connection showed

a similar behavior as they tended to stay within groups of trees with continuous canopy

and traveled only occasionally to other trees across cleared patches. This confirms the

strong unwillingness of P. occidentalis to traverse on the ground unless they are driven

by the lack of resources and highlights their high susceptibility to the effects of habitat

fragmentation.

53

Other factors influencing home range

As we expected, males had larger home ranges than females, confirming the trend

shown in previous studies on P. occidentalis (Jones et al. 1994b, Wayne et al. 2000,

Clarke 2011) and in other arboreal marsupials (White 1999, Martin et al. 2007, Cruz et

al. 2012, Law et al. 2013). However, reproductive season did not influence home range

sizes of P. occidentalis, indicating that their home ranges during the non-breeding

season are already large enough for individuals to find mates or extra resources during

the breeding season. The breeding season of the possums coincide with the season when

peppermint foliage is more abundant or nutritious (Jones et al. 1994a, 1994b, Wayne et

al. 2005b), which would supply enough resources during this energetically demanding

period. Our study area is considered to be a pristine habitat for P. occidentalis and it

supports one of their highest known densities (Jones et al. 2007); therefore, the results

may differ in other areas where the habitat is not ideal or where P. occidentalis occurs

in lower densities.

Whether a possum lived in the nature reserve or campsites did not affect its home range

size overall, and this is probably because the high quality foliage, high density of

possums, and their strong fidelity to canopies prevented them from expanding home

ranges despite the low density of trees in campsites. Although we did not have data on

the density of A. flexuosa in the study area, it was evident that the density was lower in

the campsites due to the presence of irrigated grass areas and structures such as toilet

blocks and kitchens. The lower density of trees in campsites could result in each tree

having more access to water, nutrients, and sunlight. Harring-Harris (2014) found that

the moisture content of A. flexuosa leaves was higher in the campsite. As P. occidentalis

obtains most of their water from consuming leaves, this makes the leaves in campsites

more favorable to the possums than to those in the reserve. This proposition is

supported by the observation by the same author that the density of P. occidentalis in

campsites north of Caves Road was about four times higher than that in Locke Nature

Reserve. We found that possums in campsites remained within a group of trees with

continuous canopy most of the time, again highlighting their unwillingness to descend

to the ground. Wright et al. (2012) found that another arboreal marsupial species, the

Virginia opossum (Didelphis virginiana), had smaller home ranges in an urban

environment than rural habitat due to the greater availability of food and refuges in the

urban areas. Due to the more specialized diet of P. occidentalis compared to D.

54

virginiana, the effects of higher quality foliage, higher population density, and

unwillingness to leave the canopy might have been just enough to counterbalance the

effect of low density of trees, thus resulting in the lack of difference in home range sizes

between campsites and nature reserve.

Although we did not detect differences in home range size between the nature reserve

and campsites within our study site, home ranges estimated in jarrah forests (Wayne et

al. 2000) and those of translocated possums (Clarke 2011) were almost 10 times larger

than our estimates, indicating that the home range of this species can vary greatly

depending on habitats and circumstances. For the first time, we estimated the home

ranges of wild P. occidentalis in its core habitat based on long-term monitoring of

multiple individuals, and home ranges of P. occidentalis in this study were expected to

be smaller than those in other habitats.

Conclusion

Results from this study update and add to the essential ecological information required

for the management of endangered P. occidentalis in its core habitat. Both a major road

and an artificial waterway were acting as physical barriers to possums in our study, and

individuals closer to the waterway had larger home ranges probably due to nearby

canopy connections and the lower availability of their preferred foliage. These results

indicate that permanent artificial linear structures other than roads can have a similar or

greater impact on the movement and home range of strictly arboreal mammals, and their

impact needs to be assessed and mitigated similarly to those of roads. Even a seemingly

harmless scale of linear clearing, such as a firebreak that offered no canopy connections

across it, limited the movements and home ranges of P. occidentalis; therefore,

providing passages at the canopy level is essential when clearing is likely to affect the

movement of this endangered species. Vegetation adjacent to linear structures can have

a high conservation value for arboreal animals like P. occidentalis, and these vegetation

strips need to be reconnected by means such as wildlife crossing structures to mitigate

negative impacts of habitat fragmentation.

55

Acknowledgements

We thank the School of Animal Biology at The University of Western Australia, Main

Roads Western Australia, the Western Australian Department of Parks and Wildlife,

Western Power, and the Satterley Property Group for providing funding and technical

support for this project. We gratefully acknowledge P J. de Tores for providing valuable

advice, support and training throughout the initial part of this study. We would also like

to thank the City of Busselton, Peppermint Park Eco Village, Camp Geographe,

Abundant Life Centre, Christian Brethren Campsites and Possum Centre Busselton Inc.

for their support and over 100 volunteers who helped us in the field.

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Supplementary results

As the collar weight was found to be a significant factor influencing the survival of

radio collared possums (see Chapter 4), I tested whether it influenced the home range

sizes of possums. When I incorporated the collar weight model into the generalised

linear model (Table 4), it was clear that the collar weight had no influence on the home

range sizes of the radio-collared possums (Table A1).

Table A1 Results of generalized linear model analysis on home range size of Pseudocheirus

occidentalis in Busselton, Western Australia. Parameter estimates are presented only for models that

ranked higher than the null models or had at least weak support (ΔAICc > 7.0). * denotes parameter

estimates with 95% confidence intervals outside of zero. In the case of categorical variables,

parameter estimates ( s.e.) are for the categories presented in parentheses (e.g. the parameter

estimate of the sex model is for females).AICc: corrected Akaike Information Criterion.


Sex + Barrier type 0.56 - Sex: -0.142 0.036 (female)*

Barrier: 0.117 ± 0.036 (waterway)*

Sex 7.31 6.76 -0.127 ± 0.040 (female)*

Barrier type x Distance 7.48 6.93 Barrier: 0.117 ± 0.039 (waterway)*

Distance: -0.001 ± 0.001

Barrier x Distance: -0.003 ± 0.001*

Barrier type 11.26 10.70 0.098 ± 0.043 (waterway)*

Null 13.80 13.24

Habitat 14.25 13.69

Body weight 15.18 14.62

Number of locations 15.85 15.29

Collar weight 15.97 15.41

Distance to barrier 16.00 15.44

63

Chapter 3

A narrow artificial waterway is a greater barrier to gene flow than a major road for an endangered arboreal specialist: the western

ringtail possum (Pseudocheirus occidentalis)

This chapter is currently being revised for a publication with PLoS ONE.

64

A narrow artificial waterway is a greater barrier to gene flow than a major road

for an endangered arboreal specialist: the western ringtail possum (Pseudocheirus

occidentalis)

Kaori Yokochi, W. Jason Kennington, Roberta Bencini

Abstract

The fragmentation of habitats by roads and other artificial linear structures can have a

profound effect on the movement of arboreal species due to their strong fidelity to

canopies. Here, we used 12 microsatellite DNA loci to investigate the fine-scale spatial

genetic structure and the effects of a major road and a narrow artificial waterway on a

population of the endangered western ringtail possum (Pseudocheirus occidentalis) in

Busselton, Western Australia. Using spatial autocorrelation analysis, we found positive

genetic structure in continuous habitat over distances up to 600 m. These patterns are

consistent with the sedentary nature of P. occidentalis and highlight their vulnerability

to the effects of habitat fragmentation. Pairwise relatedness values and Bayesian cluster

analysis also revealed significant genetic divergences across an artificial waterway,

suggesting that it was a barrier to gene flow. By contrast, no genetic divergences were

detected across the major road. While studies often focus on roads when assessing the

effects of artificial linear structures on wildlife, this study provides an example of an

often overlooked artificial linear structure other than a road that has a significant impact

on wildlife dispersal leading to genetic subdivision.

65

Introduction

Roads and other artificial linear structures, such as railways, powerline corridors, and

artificial waterways are thought to inhibit movements of animals, leading to the

fragmentation of populations, increased inbreeding, and loss of genetic diversity

(Forman and Alexander 1998). In a review on the genetic effects of roads on wildlife

populations, Holderegger and Di Giulio (2010) found that fragmentation of habitats by

roads quickly decreased genetic diversity within populations and increased genetic

divergence between populations in a wide range of species including invertebrates,

amphibians and mammals. Clark et al. (2010) and Epps et al. (2005) also found that

relatively recently built roads limited the dispersal and increased genetic divergence of

timber rattle snakes (Crotalus horridus) and bighorn sheep (Ovis canadensis nelsoni).

Since inbreeding and reductions in genetic diversity increase the risk of extinction of

isolated populations (Frankham 2005), it is crucial to consider the impacts of artificial

linear structures when developing management strategies for threatened species.

Strictly arboreal species are thought to be more vulnerable than the majority of

terrestrial species to the impacts of artificial linear structures without canopy

connections because many of them tend to avoid descending to the ground, (Lancaster

et al. 2011, Taylor et al. 2011). The western ringtail possum (Pseudocheirus

occidentalis Thomas 1888) is a medium-sized nocturnal marsupial endemic to the

southwest of Western Australia, the only international biodiversity hotspot on mainland

Australia (Myers et al. 2000). This species is likely to be susceptible to the negative

impacts of artificial linear structures due to their known sedentary nature and strong

fidelity to canopies (Jones et al. 1994b, Chapter 2: Yokochi et al. 2015). Studies on their

movements suggest that their dispersal range is small (Jones et al. 1994b). They also

have small home ranges (< 0.5 ha), and a road and an artificial waterway have been

found to restrict their movements (Chapter 2: Yokochi et al. 2015).

Over the last few decades P. occidentalis has gone through a dramatic decline in

numbers and range due to anthropogenic factors such as habitat destruction and

fragmentation and the impact of introduced predators (Burbidge and McKenzie 1989,

Woinarski et al. 2014). The Bunbury – Busselton region in the southwest of Western

Australia is one of the last strongholds for this species. However, it is one of the fastest

growing regions in Australia (Australian Bureau of Statistics 2014), and suitable habitat

for the possums is disappearing due to the rapid urbanisation. Despite its endangered

66

conservation status, relatively little is known about the population structure of P.

occidentalis (Woinarski et al. 2014). The only genetic studies done to date are a

phylogenetic study using mitochondrial DNA, which supported their status as a single

species, and a broad-scale population genetic study that identified three distinct

populations within the current range of the species based on microsatellite markers

(Wilson 2009).

In this study, we used microsatellite markers to investigate whether the previously

reported small home ranges and limited dispersal in P. occidentalis are supported by the

presence of fine-scale genetic structure. We also investigated whether a road and

artificial waterway without canopy connection were associated with genetic divergences.

Given the limited movements across artificial linear structures, a strong reluctance to

traverse on the ground, and lack of evidence that the species voluntarily swims (Chapter

2: Yokochi et al. 2015), we predicted that there would be genetic differentiation across

both the road and artificial waterway.


Study site

This study was conducted in Locke Nature Reserve and surrounding campsites, 9 km

west of Busselton, Western Australia (33° 39' 32'' S; 115° 14' 26'' E), where the habitat

dominated by peppermint trees (Agonis flexuosa) is supporting a high density of P.

occidentalis (de Tores and Elscot 2010, Jones et al. 1994a). We set up seven 200 m x

200 m study blocks, 1A, 1B, 1-2C, 1D, 2A, 2B and 2D, chosen so that they were small

enough to fall within boundaries of campsites, large enough to contain a sufficient

number of individuals for sampling, and far enough from each other within continuous

habitat to prevent individuals from including multiple blocks in their home ranges

(Figure 1). Caves Road, running from east to west, separated the nature reserve in the

south from campsites in the north with no canopy connection (Figure 1). A record of

this road as a narrow gravel road exists as early as the 1930s, and it became a sealed 15

m wide single carriageway approximately 50 years ago in the 1960s. With the cleared

verges on both sides and no branches in contact across it, this unfenced road provided a

25 m canopy gap. The recorded traffic volume on Caves Road was about 6,000 vehicles

per day in 2008 (Main Roads Western Australia 2009). However, the traffic volume on

this road is highly seasonal and it can be up to 15,000 vehicle per day during the peak

holiday season in summer (G. Zoetelief, Main Roads WA, Pers. Comm.). On the eastern

67

edge of the reserve, an artificially altered part of the Buayanyup River (“artificial

waterway”) ran from north to south, separating the reserve in the west from a campsite

in the east (Figure 1). This 30 m wide waterway was built approximately 80 years ago

in the 1930s to prevent flooding in the area, and contained water all year around. With

cleared paths on both banks and no branches overhanging, this artificial waterway

provided a permanent 45 m gap in the canopy.

Figure 1 A map of a study area near Busselton, Western Australia. Red lines represent the borders

of Caves Road (west to east) and blue lines represent the borders of an artificial waterway (north to

south). 1A, 1B, 1-2C, 1D, 2A, 2B and 2D are 200 m × 200 m study blocks where samples from

Pseudocheirus occidentalis were collected. 1A, 1-2C and 2A were inside Locke Nature Reserve, and

1B, 1D, 2B and 2D were within partially cleared campsites.

68

Sample collection and microsatellite genotyping

Between March 2010 and November 2012, a total of 145 adult possums were captured

using a specially modified tranquiliser dart gun with darts containing a dose of 11 - 12

mg/kg of Zoletil 100® (Virbac Australia, Milperra, NSW Australia). Sample sizes

within each block ranged from 10 to 32 (1-2C: n = 12, 1A: n = 26, 1B: n = 32, 1D: n =

10, 2A: n = 31, 2B: n = 22, 2D: n = 12). Sample collection followed the methods

developed by P. de Tores and reported by (Clarke 2011). A thin slice of ear tissue was

removed from each animal under anaesthesia using Isoflurane, and each tissue was

stored in either dimethyl sulfoxide solution (Seutin et al. 1991) or 90 % ethanol until

DNA was extracted. Coordinates of each capture location were recorded using a

handheld GPS unit (Mobile Mapper Pro®, Magellan Navigation, Inc. California USA).

All procedures for capturing and handling animals followed the Australian code of

practice for the care and use of animals for scientific purposes (National Health and

Medical Research Council 2004), and were approved by the Animal Ethics Committee

at The University of Western Australia (RA/3/100/539 and RA/3/100/1213). Permits to

access Locke Nature Reserve (CE003434) and to capture the possums for scientific

purposes (SF008419) were obtained from Western Australian Department of Parks and

Wildlife.

DNA was extracted from each sample using Qiagen DNeasy Blood and Tissue kit

(Qiagen, Venlo, Netherlands) following the manufacturer’s instructions. Concentration

and quality of each DNA sample were then determined using a NanoDrop ND-1000

spectrophotometer (Thermo Fisher Scientific Inc., Massachusetts, USA). We used 12

species-specific microsatellite markers (A1, A106, A119, A122, A127, A2, A6, B104,

C111, D104, D113, and D114) following PCR conditions described by Wilson et al.

(2009). Genotypes at each locus were determined using an ABI 3700 Genetic Analyzer

with a GeneScan-500 LIZ dye size standard (Applied Biosystems Inc., California, USA).

Data analysis

The presence of null alleles was assessed for each locus using Microchecker v.2.2.3

(Van Oosterhout et al. 2004). Genetic diversity within each block was quantified by

calculating allelic richness (AR) and Nei (1978)’s estimator of gene diversity (H) within

Fstat v.2.9.3 software package (Goudet 2002). Deviations from random mating were

assessed using randomization tests, with results characterized with the inbreeding

coefficient (FIS) statistic. Significantly positive FIS values indicate a deficit of

69

heterozygotes relative to a random mating model, while negative results indicate an

excess of heterozygotes. Genotypic disequilibrium was tested between each pair of loci

within each study block. FIS values and tests for deficits in heterozygotes and genotypic

equilibrium were calculated using the Fstat v.2.9.3. Tests for differences in genetic

diversity and FIS among study blocks were performed using Friedman’s ANOVA and

Wilcoxon rank tests with the R v.3.0.1 statistical package (R Depelopment Core Team

2013).

We performed a Spatial Autocorrelation (SA) analysis on the samples collected from

the nature reserve only (blocks 1A, 1-2C and 2A, n = 69) to investigate how the genetic

similarity of individuals changed over geographical distance within continuous

vegetation (i.e. whether a fine-scale genetic structure was present without the presence

of artificial linear structures). The result from this analysis would also tell us whether

distances that were the same as the widths of the road and artificial waterway were large

enough to cause genetic divergence in continuous vegetation. SA analyses were

conducted using GenAlEx v.6.501 (Peakall and Smouse 2012) with the results

presented in two different ways. Firstly, mean genetic correlation coefficients (r) were

calculated and plotted over a range of distance classes increasing at 100 m intervals to

obtain autocorrelograms. Secondly, because estimates of r are influenced by the size of

distance classes (Peakall et al. 2003), we also performed Multiple Distance Class

(MDC) analyses to calculate and plot r for a series of increasing distance class sizes.

Decreasing r with increasing distance interval class indicates significant positive spatial

structure, and the distance interval class at which r is no longer greater than zero

represents the limit of detectable genetic structure (Peakall et al. 2003). Tests for

statistical significance were carried out by random permutation and calculating the

bootstrap 95% confidence limits of r using 1000 replicates. We also conducted 2-

Dimensional Local Spatial Autocorrelation (2DLSA) analysis using GenAlEx. This

analysis investigates uniformity of spatial autocorrelation over the study area by

estimating local autocorrelation (lr) by comparing each individual with its nearest

neighbours (Double et al. 2005). Calculations of lr were made using the nearest five

individuals with statistical significance determined using permutation tests.

Population structure across the whole study area was assessed using pairwise FST values

and Bayesian clustering analysis. Pairwise FST values and tests for genetic

differentiation between blocks were calculated using Fstat. The Bayesian clustering

analysis was performed using Structure v.2.3.4 (Pritchard et al. 2000). This method

70

identifies genetically distinct clusters (K) based on allele frequencies across all loci.

Analyses were based on an ancestry model, which assumes admixture and correlated

allele frequencies with study blocks used as prior information about the origin of the

samples. A burn-in period of 100,000 and 1,000,000 Markov Chain Monte Carlo

(MCMC) iterations were used for 10 replicate runs for each number of clusters (K)

ranging from 1 to 10. We then determined the most likely value of K using the ∆K

method of Evanno et al. (2005) implemented in Structure Harvester v.0.6.93 (Earl and

von Holdt 2012). Each individual’s average membership to K clusters from 10 replicate

runs was calculated and re-organised using Clumpp v.1.1.2 (Jakobsson and Rosenberg

2007) and visualised using Distruct v.1.1 (Rosenberg 2004).

The effects of artificial linear structures were then examined by comparing relatedness

values between pairs of individuals on the same side of the road or waterway with

relatedness values between pairs of individuals on opposite sides of the road or

waterway. The effect of the road was examined without data from individuals on the

other side of the waterway (2B and 2D) to remove the effect of the waterway, and the

effect of the waterway was examined without data from those on the other side of the

road (1B and 1D) to remove the effect of the road. Pairwise relatedness values were

calculated using the method of Queller and Goodnight (1989) with GenAlEx. We tested

for differences in pairwise relatedness values using Mann-Whitney U test in R.

Results

Genetic variation within blocks

Two loci were identified as having null alleles (locus A119 in block 1B and locus A1 in

blocks 2A and 2D). Since there was no consistent pattern in the presence of null alleles

(i.e. the loci with null alleles were not the same across study blocks), all loci were

retained for further analysis. No FIS values were significantly different from zero

indicating that the groups of possum in all blocks were in Hardy-Weinberg Equilibrium.

There was no evidence of genotypic disequilibrium between any pair of loci within any

study block after correcting for multiple comparisons. There were also no significant

differences in allelic richness (χ2 = 8.73, P = 0.189, d.f. = 6), gene diversity (χ2 = 8.55, P

= 0.200, d.f. = 6) or FIS (χ2 = 4.12, P = 0.660, d.f. = 6) among study blocks (Table 1).

71

Table 1 Genetic variation among P. occidentalis within each study block. Standard errors are in parentheses. N is the mean sample size per locus, AR is the mean allelic richness based on a sample size of 10 individuals, H is the mean gene diversity, and FIS is the inbreeding coefficient. No FIS values were significantly different from zero.

Block N AR H FIS

1-2C 12 3.5 (0.3) 0.62 (0.03) – 0.02

1A 26 3.4 (0.3) 0.57 (0.04) – 0.02

1B 32 3.7 (0.3) 0.60 (0.03) 0.01

1D 10 3.3 (0.3) 0.56 (0.05) 0.00

2A 31 3.7 (0.3) 0.61 (0.04) 0.04

2B 22 3.6 (0.3) 0.60 (0.03) – 0.01

2D 12 3.9 (0.3) 0.64 (0.03) 0.06

Fine-scale genetic structure within continuous habitat

Fine-scale spatial genetic structure was detected within continuous habitat. In SA

analysis, the genetic correlation coefficient (r) was significantly positive up to 100 m

and it intercepted zero at 347 m (Figure 2a). The MDC analysis showed significantly

positive r values over distances up to 600 m (Figure 2b). All size class bins were well

represented, with a minimum of 95 and 515 pairwise comparisons per bin for the SA

analyses and MDC analysis respectively. The 2DLSA analysis revealed clusters of

positive genetic correlation in all three blocks, indicating that positive genetic structure

was not confined to one particular area within the nature reserve (Figure 3).

72

Figure 2 (a) A correlogram plot and (b) a multiple distance class plot based on 69 Pseudocheirus

occidentalis in continuous habitat in Locke Nature Reserve near Busselton, Western Australia.

Dotted lines (a) and small blue markers (b) represent upper and lower 95 % confidence intervals

around zero. Red circle markers on the solid line (a) and red markers (b) are the genetic correlation

values (r) that differ significantly from zero based on bootstrap resampling (P values are shown in

parentheses).

73

Figure 3 Plot of two-dimensional local spatial autocorrelation analyses of Pseudocheirus

occidentalis sampled in Locke Nature Reserve near Busselton, Western Australia.

Red lines represent the borders of Caves Road (west to east) and blue lines represent the borders of

an artificial waterway (north to south). Markers represent geographical locations of the local spatial

autocorrelation analyses with significantly positive (solid symbols) or non-significant values (open

symbols) based on five nearest neighbours. Coordinates are based on GDA 94 projection (zone 50).

Population structure across the whole study area and the effect of artificial linear

structures

Significant genetic divergences were observed between 14 (67 %) pairs of blocks.

These pairs of blocks occurred on the same and opposite sides of Caves Road, as well as

the same and opposite sides of the artificial waterway (Table 2). Pairwise FST values

ranged from 0.017 to 0.079, with the highest FST occurring between blocks 1D and 2D,

which were separated by both the road and waterway (Table 2).

Population structure across the whole study area was also evident with the Bayesian

clustering analysis. Analysis of the Structure results using the ΔK method clearly

identified K = 3 as the most likely number of genetic clusters (Figure 4). A bar plot of

74

individuals’ memberships to each cluster showed that most individuals from the western

side of the artificial waterway (blocks 1-2C, 1A, 1B, 1D and 2A) were predominantly

assigned to cluster 1 (shown in pink in Figure 5), whereas those from the eastern side

(blocks 2B and 2D) were predominantly assigned to clusters 2 (yellow) and 3 (blue)

respectively (Figure 5).

Table 2 Pairwise FST values (below diagonal) and P-values from tests of differentiation (above

diagonal) between blocks. Significant divergences are highlighted in bold text. The adjusted

significance level for multiple comparisons is 0.0024. P-values were obtained after 2,100

permutations.

1-2C 1A 1B 1D 2A 2B 2D

1-2C - 0.0010 0.0019 0.0010 0.0081 0.0005 0.0095

1A 0.050 - 0.0010 0.0148 0.0033 0.0005 0.0005

1B 0.050 0.035 - 0.0081 0.0033 0.0005 0.0005

1D 0.056 0.016 0.033 - 0.0033 0.0005 0.0005

2A 0.033 0.012 0.017 0.023 - 0.0005 0.0005

2B 0.060 0.023 0.061 0.040 0.034 - 0.0019

2D 0.041 0.061 0.047 0.079 0.036 0.045 -

Figure 4 Summary of ∆K estimates (Evanno et al. 2005) for varying numbers of genetic clusters (K)

derived from the STRUCTURE analysis of 145 adult Pseudocheirus occidentalis from Busselton,

Western Australia.

0

1

2

3

4

5

6

1 2 3 4 5 6 7 8 9 10

Del

ta K

K

75

Figure 5 Summary of the Bayesian clustering analysis assuming three admixed populations of

Pseudocheirus occidentalis in Busselton, Western Australia.

Each column represents an individual’s estimated membership to three genetic clusters represented

by different colours. Vertical dotted lines separate individuals sampled from different blocks.

Vertical bold solid lines represent the presence of Caves Road and an artificial waterway.

The pairwise relatedness analysis also indicated that the individuals separated by the

artificial waterway were significantly less related to each other than the individuals on

the same side of the waterway (Figure 6, P = 0.002) indicating a significant genetic

divergence across the waterway. However, the relatedness between individuals from

different blocks on the same side of the road did not differ significantly from that

between individuals from opposite sides of the road (Figure 6, P = 0.219) indicating

there was no detectable genetic divergence across the road. Pairwise relatedness (RS)

values between individuals from the same blocks were significantly higher than RS

values between individuals from different blocks on the same or opposite sides of the

road or the waterway (Figure 6, P < 0.001).

76

Figure 6 Mean pairwise relatedness values (Queller and Goodnight 1989) for Pseudocheirus

occidentalis individuals sampled from the same block (“Within”), different blocks on the same side

of an artificial barrier (“Same”) and opposite sides of an artificial barrier (“Opposite”).

Different letters below the bars indicate significant differences found between the mean pairwise

relatedness values with the Mann-Whitney U test. Error bars represent the 95% confidence intervals

determined by bootstrap resampling.

Discussion

The fine-scale population structure detected in this study confirms that dispersal in P.

occidentalis is limited. The maximum distance at which a significantly positive genetic

structure was detected in continuous habitat with SA and MDC analyses was 600 m.

This finding is consistent with the sedentary nature of this species observed in previous

telemetry studies (Clarke 2011, Jones et al. 1994b, Chapter 2: Yokochi et al. 2015).

Fine-scale population structure was also evident with the pairwise relatedness values,

which were significantly higher between individuals from the same blocks than between

77

individuals from different blocks. Furthermore, the fine-scale genetic structuring was

observed equally in all study blocks within the nature reserve, indicating that this

genetic structuring was not restricted to one part of the nature reserve. Similar patterns

of positive fine-scale genetic structuring have been reported in other small to medium

sized mammals, such as the Australian bush rat (Rattus fuscipes, Peakall et al. 2003),

the brush-tailed rock-wallaby (Petrogale penicillata, Hazlitt et al. 2004), the Eurasian

badger (Meles meles, Pope et al. 2006), the southern hairy-nosed wombat (Lasiorhinus

latifrons, Walker et al. 2008), and the squirrel glider (Petaurus norfolcensis, Goldingay

et al. 2013). All of these species are thought to have short dispersal distances due to

their philopatry or small body size, both of which may have contributed to the positive

genetic structuring found in this P. occidentalis population.

SA analysis on another species of possum, the common brushtail possum (Trichosurus

vulpecula) found positive spatial structure up to distances of 896 m and 454 m for males

and females, respectively (Stow et al. 2006). Male-biased dispersal of up to 10 km was

also recorded for this species in field studies (Clout and Efford 1984). Another arboreal

marsupial, the koala (Phascolarctos cinereus) has also been recorded to disperse up to

10.6 km (Dique et al. 2003) with a spatial autocorrelation analysis showing no evidence

of limited dispersal (Lee et al. 2010). In this study, we could not investigate the fine-

scale genetic structure of males and females separately due to small sample sizes. Such

analyses are required to test the hypothesis that dispersal in P. occidentalis is driven by

males, which is based on observations of only a few individuals (Jones et al. 1994b).

Nevertheless, the presence of fine-scale population structure at short geographical

distances (300 - 400 m) highlights the exceptionally high level of philopatry in P.

occidentalis even compared to other arboreal marsupials, highlighting their high

susceptibility to the impacts of habitat fragmentation.

In support of our prediction about the effect of the artificial waterway, both the pairwise

relatedness and Bayesian cluster analyses indicated that possums separated by the

artificial waterway were genetically distinct, indicating that it was acting as a barrier to

gene flow. The waterway was much narrower than the spatial scale at which positive

genetic structure was detected within continuous vegetation, and this rules out the

possibility that the genetic divergence across the waterway was purely due to

geographical distance. Thus, it appears that even a 30 m wide artificial waterway is

sufficient to restrict gene flow in P. occidentalis, enhancing population structure over

short distances.

78

On the other hand, Caves Road did not seem to have an apparent effect on the genetic

connectivity of P. occidentalis, according to the results from pairwise relatedness and

Bayesian cluster analyses, even though it was found to be restricting the movements of

possums in a previous study (Chapter 2: Yokochi et al. 2015). Caves Road is a busy

road without canopy connection. However, it is not fenced and possum roadkills have

been recorded in the area (Trimming et al. 2009), indicating that some possums try to

cross it. One to ten migrants per generation are considered to be enough to maintain

genetic homogeneity (Mills and Allendorf 1996), and it is possible that the small

number of possums that successfully crossed the road had been sufficient to maintain

enough gene flow to prevent genetic divergence. The gap in the vegetation across the

road was 20 m narrower than that across the artificial waterway, which may also have

contributed to the apparently higher permeability of the road. Moreover, Caves Road

was bituminised only 40 to 50 years ago, with significant increases in traffic volumes

occurring only recently, so its barrier effect may not have had sufficient time to result in

genetic divergences detectable with the microsatellite loci used in this study. What we

detect in the genetic structure of animals today reflects historic rather than present

events, and depending on the circumstances, it could take tens and hundreds of

generations for the effects of habitat fragmentation to become detectable (Keyghobadi

2007). Therefore, we still should not disregard the risk of this road causing genetic

divergence within this important population of P. occidentalis.

Another possible explanation for the difference in the genetic effect of the road and

waterway is the difference in effective population size of possums along these linear

structures. A population with a smaller effective population size suffers a greater effect

of genetic drift, which results in a greater rate of genetic divergence (Marsh et al. 2008).

While this remains a possibility, a previous study has shown that the density of P.

occidentalis within Locke Nature Reserve does not differ near Caves Road or the

artificial waterway (Harring-Harris 2014). Our data also indicate that the levels of

genetic diversity and inbreeding were not different among blocks. Therefore, it is

unlikely that the difference in the level of genetic divergence between these barriers was

caused by the effective population size alone.

In a study investigating genetic divergence among red-backed salamanders (Plethodon

cinereus) occurring on different sides of roads and streams, Marsh et al. (2008, 2007)

found that 2–7 m wide streams caused small, but significant genetic divergence.

Amongst the roads, only one 104 m wide highway caused apparent genetic divergence

79

out of six roads examined. The other five roads (13 – 47 m) were at least 30 years older

than the highway, but much narrower and not as busy. Quéméré et al. (2010) also found

that the primary causes of genetic structuring in the golden-crowned sifaka (Propithecus

tattersalli) were a river and geographical distances rather than a road. On the other hand,

Estes-Zumpf et al. (2010) found that none of the roads or creeks they examined caused

genetic divergence in the pygmy rabbit (Brachylagus idahoensis) possibly due to the

greater mobility of this species. These results, together with our own, indicate that

different types of linear barriers can have different levels of permeability to the

movement of different species, and the permeability of barriers also depends on the

mobility of the species. Therefore, it is important to assess the negative impacts of a

wider range of artificial linear structures especially in areas where endangered sedentary

species occur.

FST values also suggested that the groups of possums on the east of the waterway were

genetically different from those on the west. The samples from block 2B were

significantly different from the all other blocks, probably due to the presence of the

waterway and the mostly cleared land between blocks 2B and 2D. Block 2D was

significantly different from all other blocks except 1-2C. There is a road bridge across

the waterway north of these two blocks (see Figure 1), and it is possible that some P.

occidentalis have used this bridge to cross the waterway in the past, resulting in the non-

significant pairwise FST between blocks 2D and 1-2C. It should also be noted that the

sample sizes within these blocks (N = 12 for both) were considerably smaller than the

others, lowering the reliability of the FST value. The pairwise FST values showed

inconclusive results across the road. For example, FST values between blocks 1A and 1B

and blocks 1-2C and 1D were found to be significant; however, FST values between

blocks 1B and 2A and blocks 1D and 2A were non-significant despite the presence of a

road and greater distances between these blocks. This inconsistency may have been

caused by the small sample sizes in some of the study blocks and/or the fine scale of the

genetic divergence examined in this study. Individual-level analyses, such as pairwise

relatedness analyses and Bayesian cluster analyses, have been suggested to be more

appropriate than population-level analyses, such as estimated paiwise FST, for studies

examining fine-scale genetic divergence because combining individuals within

populations can lead to loss of fine scale information (Broquet et al. 2006). Given the

small geographical scale of this study, the clear results shown by both of individual-

80

level analyses are likely to be more robust than the inconsistent results shown by a

population-level analysis.

All blocks had high levels of genetic variation and did not deviate from Hardy-

Weinberg equilibrium, suggesting that the possums in these blocks are not experiencing

a high level of inbreeding despite the presence of two barriers. This is probably because

blocks were still connected with other patches of habitat outside our study area and they

were not completely isolated. The different cluster membership in the Bayesian cluster

analysis for blocks 2B and 2D may also be explained by these connections to other

patches. Blocks 2B and 2D were isolated by the waterway from the other study blocks,

and were separated from each other by a mostly cleared campsite. Given the

exceptionally highly sedentary and arboreal nature of this species (Chapter 2: Yokochi

et al. 2015), the limited canopy connection between these two blocks (Figure 1) may

have been enough to cause this differentiation between neighbouring blocks. However,

2D had continuous canopy covers to the habitat east of the study area via a road reserve

(Figure 1), possibly altering the allele frequencies in this area compared to those on the

western side of the waterway. Block 2B was the most isolated among all blocks because

the area to the east and south of this block was mostly cleared farmland (Figure 1), and

this isolation seems to be reflected in its memberships to mostly yellow and blue genetic

clusters (Figure 5) rather than mostly red and blue like the other blocks. To confirm this,

DNA samples from possums in the area east and south of the campsite would need to be

analysed; however, this was outside of the scope of our study.

Even though possums in our study area did not seem to suffer from a low genetic

diversity or a high level of inbreeding at the time of sample collection, the barrier

effects of the waterway and road still need to be mitigated to maximise their ability to

disperse, given the current increasing pressure of habitat clearing. Caves Road has not

caused a detectable genetic divergence among possums yet, but it is restricting their

movements and causing mortality (Chapter 2: Yokochi et al. 2015), which also reduces

genetic diversity over generations (Jackson and Fahrig 2011). The number and range of

P. occidentalis is expected to decline dramatically due to the impacts of climate change,

even without these barrier effects (Molloy et al. 2014). To restore connectivity, effective

mitigation measures that provide possums with a safe passage across the waterway and

road should be considered. Yokochi and Bencini (2015) found that P. occidentalis

quickly habituated to a rope bridge built across Caves Road after the end of this study

and multiple individuals frequently crossed it, highlighting the potential of this wildlife

81

crossing structure as an effective mitigation measure. Therefore, research into the

capability of these rope bridges of restoring gene flow as well as reducing road

mortality in P. occidentalis is warranted.

Conclusion

Both telemetry data from a previous study (Chapter 2: Yokochi et al. 2015) and genetic

data from this study show that P. occidentalis have limited dispersal making them

highly susceptible to the impacts of habitat fragmentation. Indeed, we found evidence to

suggest that an artificial waterway has been limiting the gene flow of P. occidentalis,

resulting in genetic divergence within spatial scales of hundreds of metres. By contrast,

a busy road does not seem to have had a detectable impact on population structure,

though future impacts cannot be ruled out. While studies investigating negative effects

of artificial linear structures on population structure tend to focus on roads, our study

provides an example where an artificial waterway poses significant genetic impacts on

an endangered arboreal species. We therefore urge for more research to be conducted on

the impacts of artificial linear structures other than roads. Our study shows that

mitigation measures should be considered for a wide range of artificial linear structures

that divide wildlife habitats.

Acknowledgments

We would like to thank the School of Animal Biology at The University of Western

Australia, Main Roads Western Australia, the Western Australian Department of Parks

and Wildlife, Western Power, the Satterley Property Group and the Holsworth Wildlife

Research Endowment for providing funding and support for this project. We gratefully

acknowledge Mr. Paul J. de Tores for providing valuable advice, support and training

for the fieldwork. Yvette Hitchen at Helix Molecular Solutions Pty Ltd provided

technical assistance with DNA extractions and conducted PCR and scoring of genotypes

for this project. We would also like to thank the City of Busselton, Peppermint Park Eco

Village, Camp Geographe, Abundant Life Centre, Christian Brethren Campsites,

Legacy Camp and Possum Centre Busselton Inc. for their support and over 100

volunteers who helped us in the field.

82

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Conservation 11: 79-94. doi:10.3897/natureconservation.11.4385

Yokochi K, Chambers B, Bencini R (2015) An artificial waterway and road restrict

movements and alter home ranges of endangered arboreal marsupial. Journal of

Mammalogy: doi:10.1093/jmammal/gyv137

88

Appendix 1

Table A1 Multilocus genotypes of the 145 adult Pseudocheirus occidentalis used in the study. The

microsatellite loci (A1, A127, A106, A6, A2, D113, A122, D104, C111, A119, D114 and B104) are

described in Wilson et al. (2009).

Individual Block Microsatellite loci

A1 A127 A106 A6 A2 D113

1 CR1-2C 162 162 249 249 211 211 232 232 162 172 231 231

2 CR1-2C 162 172 246 249 211 219 240 242 168 168 231 231

3 CR1-2C 162 175 249 249 211 237 232 242 166 168 231 231

4 CR1-2C 162 172 246 249 211 237 232 232 168 172 227 231

5 CR1-2C 162 172 249 249 229 237 232 242 168 168 227 231

6 CR1-2C 162 162 249 249 211 227 232 232 168 172 227 227

7 CR1-2C 172 175 246 246 211 229 232 232 168 172 227 227

8 CR1-2C 172 172 246 249 237 237 232 232 162 172 227 231

9 CR1-2C 162 172 246 249 211 237 232 242 168 172 227 231

10 CR1-2C 162 172 249 249 211 229 232 242 168 168 227 231

11 CR1-2C 162 172 246 246 219 229 240 240 162 168 222 231

12 CR1-2C 162 175 246 249 211 211 232 232 166 168 227 231

13 CR1A 172 172 249 249 211 211 232 249 168 168 227 231

14 CR1A 162 172 246 249 211 211 232 242 166 172 227 231

15 CR1A 162 175 246 249 211 237 232 232 168 172 231 231

16 CR1A 162 175 249 249 211 211 232 232 168 168 227 231

17 CR1A 162 170 246 249 211 237 232 232 168 172 227 231

18 CR1A 170 175 246 249 211 237 242 242 168 172 227 231

19 CR1A 162 172 246 249 211 211 232 240 168 172 227 231

20 CR1A 172 172 246 246 211 211 232 232 172 172 227 231

21 CR1A 162 175 246 249 211 229 232 232 168 172 227 231

22 CR1A 162 162 249 249 211 211 232 244 172 172 231 231

23 CR1A 162 175 249 249 211 211 232 247 168 172 231 231

24 CR1A 162 162 249 249 211 211 232 247 168 168 227 227

25 CR1A 162 162 249 249 237 237 232 232 172 172 227 227

26 CR1A 162 175 249 249 211 211 232 242 172 172 227 235

27 CR1A 162 175 249 249 211 229 240 242 168 168 227 227

28 CR1A 162 175 249 249 211 229 232 240 172 172 227 231

29 CR1A 162 172 249 249 211 211 232 247 168 172 227 227

30 CR1A 162 162 249 249 211 229 240 247 166 168 227 227

31 CR1A 172 175 249 249 211 211 232 242 172 172 227 231

32 CR1A 175 175 246 249 211 211 232 232 168 168 227 227

33 CR1A 172 175 246 249 211 211 232 242 168 172 227 227

34 CR1A 162 172 249 249 211 211 232 232 168 172 231 231

89

35 CR1A 172 172 249 249 211 237 232 232 168 168 227 231

36 CR1A 172 172 246 249 211 229 232 242 168 172 231 231

37 CR1A 162 162 246 249 211 211 232 232 168 168 231 231

38 CR1A 162 175 249 249 211 211 232 242 172 172 227 231

39 CR1B 175 175 246 249 211 229 232 242 166 172 231 235

40 CR1B 162 170 249 249 211 229 232 242 172 172 227 231

41 CR1B 162 175 249 249 211 221 232 232 168 172 227 231

42 CR1B 162 172 246 249 225 227 232 240 168 168 231 231

43 CR1B 162 172 249 249 227 229 242 242 168 172 227 227

44 CR1B 162 170 246 249 211 229 232 232 172 172 231 231

45 CR1B 172 172 246 246 211 229 232 240 168 172 227 231

46 CR1B 162 162 249 249 211 237 232 232 172 172 231 231

47 CR1B 162 170 246 249 229 229 232 244 172 172 231 235

48 CR1B 164 172 246 249 211 229 232 247 168 172 227 231

49 CR1B 170 172 249 249 211 229 232 232 168 172 227 231

50 CR1B 162 172 246 249 211 211 232 247 162 172 227 231

51 CR1B 172 175 249 249 211 221 232 240 168 172 222 231

52 CR1B 170 172 249 249 211 227 242 242 172 172 222 227

53 CR1B 162 175 246 249 211 229 232 232 162 172 227 227

54 CR1B 162 172 249 249 229 237 232 242 172 172 227 231

55 CR1B 162 162 246 249 211 211 232 240 172 172 231 231

56 CR1B 175 175 246 249 227 237 232 242 168 172 227 231

57 CR1B 175 175 249 249 229 229 232 242 166 172 227 235

58 CR1B 172 172 246 246 211 237 232 240 172 172 231 231

59 CR1B 162 172 246 249 229 229 232 240 168 172 227 227

60 CR1B 162 172 246 249 227 237 232 242 172 172 222 227

61 CR1B 162 172 249 249 211 229 232 244 172 172 231 231

62 CR1B 162 172 249 249 229 237 232 232 172 172 231 231

63 CR1B 162 162 246 249 229 229 232 242 172 172 227 227

64 CR1B 162 170 246 246 237 237 232 242 172 172 227 231

65 CR1B 162 162 246 246 237 237 232 232 172 172 227 231

66 CR1B 162 172 249 249 211 227 232 242 168 172 227 231

67 CR1B 162 172 249 249 211 227 232 242 162 172 231 231

68 CR1B 162 172 249 249 211 237 232 247 172 172 231 231

69 CR1B 170 175 246 249 229 229 232 244 162 172 227 227

70 CR1B 162 162 246 249 211 229 232 247 162 172 227 231

71 CR1D 172 175 249 249 211 237 242 247 168 172 231 231

72 CR1D 175 175 246 249 211 221 232 232 172 172 227 231

73 CR1D 162 172 249 249 211 237 232 242 172 172 227 227

74 CR1D 162 162 249 249 211 211 232 247 168 172 227 231

75 CR1D 172 175 249 249 211 211 232 244 162 168 227 231

76 CR1D 162 175 246 249 221 229 232 247 168 172 231 231

90

77 CR1D 162 172 246 249 211 211 232 242 168 172 227 231

78 CR1D 162 172 249 249 211 229 232 238 162 168 227 227

79 CR1D 170 170 249 249 211 211 232 238 168 172 227 227

80 CR1D 172 172 249 249 211 211 232 242 172 172 227 231

81 CR2A 170 172 249 249 211 211 232 242 166 168 227 231

82 CR2A 175 175 246 249 211 229 232 247 168 168 227 231

83 CR2A 175 175 249 249 211 211 232 242 166 168 227 231

84 CR2A 162 172 246 246 211 237 232 242 168 168 222 227

85 CR2A 162 162 249 249 211 227 232 232 168 168 227 231

86 CR2A 162 175 249 249 211 211 244 247 172 172 227 227

87 CR2A 162 162 249 249 211 229 240 247 166 168 227 227

88 CR2A 162 162 249 249 211 229 232 232 172 172 231 235

89 CR2A 162 162 249 249 211 211 232 247 172 172 231 231

90 CR2A 162 175 249 249 211 227 232 247 162 172 227 231

91 CR2A 162 162 249 249 211 211 232 240 166 172 227 227

92 CR2A 162 162 246 249 211 211 242 247 168 172 227 231

93 CR2A 162 162 246 249 211 237 232 247 172 172 227 231

94 CR2A 162 162 249 249 211 227 232 247 168 172 227 227

95 CR2A 175 175 249 249 211 229 232 232 168 172 227 227

96 CR2A 172 172 246 249 237 237 232 232 168 172 222 231

97 CR2A 162 175 249 249 229 237 232 247 168 172 227 231

98 CR2A 162 170 249 249 211 229 232 232 168 168 227 231

99 CR2A 162 175 249 249 227 237 232 247 162 172 227 227

100 CR2A 162 162 249 249 211 229 232 232 168 172 227 231

101 CR2A 162 162 249 249 211 229 232 232 172 172 231 231

102 CR2A 162 162 249 249 227 229 240 247 166 172 227 227

103 CR2A 162 162 249 249 211 227 232 247 162 172 227 231

104 CR2A 162 175 246 249 237 237 232 242 168 168 231 231

105 CR2A 175 175 246 249 211 237 238 247 166 168 231 231

106 CR2A 162 175 246 249 237 237 232 232 168 172 231 231

107 CR2A 162 170 249 249 211 227 232 232 162 168 227 227

108 CR2A 162 172 249 249 229 237 232 247 172 172 231 231

109 CR2A 162 162 249 249 229 237 232 242 162 168 231 231

110 CR2A 172 175 249 249 211 229 244 247 172 172 227 231

111 CR2A 172 175 246 249 211 211 242 247 168 172 227 231

112 CR2B 162 162 246 249 211 229 232 242 166 172 227 231

113 CR2B 172 175 246 249 211 211 232 249 166 168 231 231

114 CR2B 162 170 246 249 227 229 240 240 168 172 227 231

115 CR2B 162 175 249 249 211 229 232 249 168 172 227 231

116 CR2B 162 172 249 249 211 211 232 249 172 172 222 227

117 CR2B 162 162 246 249 211 211 242 249 168 172 222 227

118 CR2B 172 175 249 249 211 211 232 244 168 168 227 227

91

119 CR2B 162 172 246 249 211 227 232 232 168 168 227 231

120 CR2B 162 162 246 246 229 229 232 240 168 172 227 227

121 CR2B 175 175 246 249 211 211 232 232 166 168 231 231

122 CR2B 162 170 249 249 211 211 232 244 166 168 227 231

123 CR2B 162 175 246 249 211 211 232 232 166 172 222 227

124 CR2B 162 175 249 249 211 229 232 249 172 172 227 231

125 CR2B 162 172 246 246 211 211 232 232 168 168 222 231

126 CR2B 162 162 246 249 211 211 232 232 168 168 231 231

127 CR2B 162 172 249 249 211 227 232 242 166 168 231 231

128 CR2B 170 172 249 249 211 227 232 244 168 172 227 231

129 CR2B 172 172 246 249 211 211 232 249 166 172 227 231

130 CR2B 162 172 249 249 211 237 249 249 168 172 222 227

131 CR2B 172 175 249 249 227 229 232 232 168 172 227 231

132 CR2B 162 175 249 249 211 227 232 240 168 172 227 227

133 CR2B 162 162 246 246 229 229 240 240 168 172 227 227

134 CR2D 162 162 246 249 229 229 240 240 166 172 222 231

135 CR2D 162 162 246 249 211 229 232 240 168 168 222 231

136 CR2D 162 162 246 249 211 229 240 240 168 172 222 231

137 CR2D 162 162 246 246 211 237 232 247 166 168 222 231

138 CR2D 162 162 249 249 229 233 232 242 168 172 227 231

139 CR2D 162 162 246 246 211 239 240 240 168 168 222 231

140 CR2D 170 170 249 249 211 229 232 244 168 168 227 231

141 CR2D 162 172 246 249 227 229 232 240 172 172 227 227

142 CR2D 162 175 249 249 229 229 232 240 164 166 227 227

143 CR2D 162 162 246 246 229 229 240 242 168 168 227 227

144 CR2D 162 162 246 249 211 229 242 242 168 168 222 231

145 CR2D 164 175 249 249 211 211 232 240 172 172 227 227

92

Individual Block Microsatellite loci

A122 D104 C111 A119 D114 B104

1 CR1-2C 209 212 205 209 207 211 283 283 269 273 277 277

2 CR1-2C 209 212 206 213 207 211 279 279 273 273 277 277

3 CR1-2C 209 212 205 209 207 211 279 289 250 273 266 273

4 CR1-2C 205 205 205 213 211 211 279 291 250 254 270 277

5 CR1-2C 205 209 209 213 207 207 279 279 250 273 266 277

6 CR1-2C 205 212 205 213 207 211 279 283 254 257 266 277

7 CR1-2C 205 209 213 213 211 211 279 279 250 257 273 273

8 CR1-2C 205 210 213 213 207 211 279 291 250 250 270 277

9 CR1-2C 205 205 205 213 207 211 279 279 254 257 270 273

10 CR1-2C 205 210 205 213 211 211 279 279 257 273 266 273

11 CR1-2C 209 212 213 213 211 211 279 279 273 273 266 277

12 CR1-2C 205 205 205 205 207 211 289 291 250 273 273 277

13 CR1A 205 209 205 205 211 211 279 291 250 257 270 273

14 CR1A 205 212 205 209 207 211 279 279 254 257 273 277

15 CR1A 212 212 205 213 207 211 291 291 269 273 273 273

16 CR1A 210 212 205 205 207 207 291 291 250 273 266 270

17 CR1A 212 212 205 205 207 211 291 291 254 269 273 273

18 CR1A 205 212 205 205 207 211 279 291 250 254 273 273

19 CR1A 205 212 205 213 207 211 291 291 269 273 270 273

20 CR1A 205 212 205 213 207 211 279 291 257 269 270 277

21 CR1A 205 212 205 205 207 211 279 279 250 254 273 273

22 CR1A 205 206 209 213 207 211 279 291 269 269 273 277

23 CR1A 205 212 209 213 207 211 291 291 254 273 273 273

24 CR1A 205 212 205 213 207 211 279 291 254 269 270 273

25 CR1A 205 212 205 205 207 207 283 291 269 269 273 277

26 CR1A 205 210 204 205 207 207 279 291 273 277 266 273

27 CR1A 210 212 205 213 211 211 279 291 250 273 270 273

28 CR1A 210 212 205 205 207 207 279 291 250 257 266 273

29 CR1A 205 210 205 213 207 207 279 291 250 257 266 273

30 CR1A 205 205 205 213 207 211 279 291 250 254 273 277

31 CR1A 205 210 205 205 207 207 279 291 250 257 273 273

32 CR1A 210 212 204 213 211 211 279 289 250 250 273 277

33 CR1A 210 212 205 213 211 211 279 279 250 257 270 277

34 CR1A 206 209 205 209 207 211 279 291 257 269 273 273

35 CR1A 210 212 205 205 207 211 279 291 257 269 273 277

36 CR1A 205 209 205 205 211 211 279 291 250 257 273 273

37 CR1A 206 212 206 206 211 211 291 291 269 269 273 273

38 CR1A 205 205 204 205 211 211 279 289 250 250 273 277

39 CR1A 205 209 205 205 207 211 279 283 250 269 270 270

93

40 CR1A 205 205 205 213 207 207 283 283 269 273 273 277

41 CR1B 209 212 213 213 207 207 289 291 250 273 273 273

42 CR1B 209 209 205 209 207 211 289 289 250 277 273 273

43 CR1B 205 205 205 213 207 211 279 283 250 250 273 277

44 CR1B 205 212 205 205 207 211 279 291 254 254 273 273

45 CR1B 205 210 205 213 207 211 283 291 250 257 273 273

46 CR1B 212 212 205 205 211 211 279 279 250 269 273 277

47 CR1B 205 212 205 209 207 211 279 291 254 257 273 277

48 CR1B 209 212 205 205 207 207 289 291 250 257 266 273

49 CR1B 205 210 205 205 207 211 279 279 254 257 273 273

50 CR1B 205 210 205 205 207 207 279 279 257 269 273 273

51 CR1B 209 212 209 213 207 207 289 291 250 257 273 277

52 CR1B 205 205 205 209 207 211 279 279 250 254 266 273

53 CR1B 205 212 205 205 211 211 279 279 250 273 270 273

54 CR1B 212 212 205 205 207 207 279 279 257 269 270 273

55 CR1B 209 212 205 205 207 219 279 279 269 273 270 273

56 CR1B 209 212 209 213 211 211 291 291 250 250 273 273

57 CR1B 209 212 205 205 207 211 279 291 250 269 270 273

58 CR1B 212 212 205 209 207 207 291 291 250 250 273 273

59 CR1B 205 209 205 209 211 211 279 279 250 257 270 277

60 CR1B 205 212 209 213 207 211 279 279 250 254 266 277

61 CR1B 205 209 205 213 207 211 279 279 257 273 270 277

62 CR1B 205 212 205 205 207 211 279 291 250 269 273 277

63 CR1B 205 212 205 205 207 211 279 279 250 254 273 277

64 CR1B 212 212 205 205 207 211 279 279 254 254 273 277

65 CR1B 205 212 205 205 207 211 279 279 254 254 273 273

66 CR1B 209 212 205 209 207 211 279 283 250 257 270 273

67 CR1B 209 210 205 205 207 207 279 279 257 273 273 273

68 CR1B 210 212 205 205 207 211 279 279 257 269 273 273

69 CR1B 205 212 201 205 211 211 279 279 250 254 270 273

70 CR1B 205 205 205 205 207 207 279 291 269 269 273 277

71 CR1B 205 212 205 205 211 211 291 291 250 250 273 277

72 CR1B 212 212 205 213 207 211 279 291 250 250 273 273

73 CR1B 206 210 205 205 211 211 279 279 257 269 266 277

74 CR1B 205 206 205 205 207 211 279 283 250 269 273 277

75 CR1D 205 212 205 205 207 211 279 291 250 257 266 273

76 CR1D 205 212 205 205 207 207 279 283 250 250 273 273

77 CR1D 205 205 205 205 211 211 279 283 257 269 277 277

78 CR1D 205 212 205 205 207 211 279 291 254 254 273 277

79 CR1D 205 209 205 209 211 211 283 283 250 254 273 277

80 CR1D 205 206 205 205 211 211 279 283 257 257 277 277

81 CR1D 205 210 205 213 207 211 279 279 254 257 266 273

94

82 CR1D 209 209 206 209 207 207 279 291 250 257 266 270

83 CR1D 209 209 205 205 211 211 279 291 250 250 273 277

84 CR1D 205 209 205 205 207 211 279 279 250 269 273 277

85 CR2A 209 212 205 213 207 211 289 291 257 273 270 273

86 CR2A 210 210 205 213 207 207 289 291 250 269 273 277

87 CR2A 205 205 205 205 211 211 279 291 250 273 273 273

88 CR2A 205 212 205 205 207 211 289 291 250 250 273 277

89 CR2A 205 209 206 213 207 207 279 279 250 254 273 277

90 CR2A 209 212 206 209 207 211 279 279 250 269 273 273

91 CR2A 205 210 205 209 211 211 279 291 254 273 266 270

92 CR2A 205 209 205 205 211 211 279 279 254 269 273 277

93 CR2A 205 212 205 209 207 211 279 283 273 277 273 277

94 CR2A 209 212 205 205 211 211 289 291 257 273 273 273

95 CR2A 205 212 205 209 211 211 279 291 250 269 273 273

96 CR2A 210 212 205 213 207 207 279 289 250 250 273 273

97 CR2A 205 210 205 205 207 211 279 291 250 254 273 277

98 CR2A 210 210 205 205 207 207 279 279 254 254 266 273

99 CR2A 205 212 205 209 207 211 279 289 269 273 273 273

100 CR2A 205 210 205 213 207 207 279 291 254 254 273 277

101 CR2A 205 205 205 205 207 211 289 291 250 254 273 273

102 CR2A 205 205 205 205 207 211 279 291 250 273 273 273

103 CR2A 205 212 205 206 207 207 279 279 250 273 273 273

104 CR2A 205 205 205 205 207 211 279 291 250 273 277 277

105 CR2A 205 205 205 209 211 211 291 291 250 250 266 277

106 CR2A 205 212 205 209 207 211 279 279 273 273 273 277

107 CR2A 205 209 205 213 207 211 283 291 261 269 270 273

108 CR2A 212 212 209 209 207 211 279 279 269 277 273 273

109 CR2A 209 212 205 213 207 207 279 283 250 273 270 273

110 CR2A 210 212 210 210 207 207 279 289 269 269 273 277

111 CR2A 205 209 205 206 207 211 283 291 257 257 270 273

112 CR2A 205 210 205 213 207 211 289 291 254 257 270 270

113 CR2A 205 212 205 209 207 211 291 291 250 257 273 273

114 CR2A 205 212 205 205 207 211 283 291 250 273 273 273

115 CR2A 205 212 205 205 211 211 283 291 250 250 266 273

116 CR2B 205 215 205 205 211 211 291 291 250 257 273 273

117 CR2B 205 212 205 205 211 211 283 291 250 273 266 273

118 CR2B 209 209 205 209 211 211 291 291 254 273 273 273

119 CR2B 205 205 209 209 207 211 279 283 250 273 270 273

120 CR2B 209 212 205 205 211 211 291 291 254 273 273 273

121 CR2B 205 205 205 209 207 211 279 291 250 250 273 277

122 CR2B 209 210 205 213 207 211 289 291 257 273 270 273

123 CR2B 205 209 213 213 211 211 289 291 254 257 273 273

95

124 CR2B 205 215 206 206 211 211 291 291 250 250 273 273

125 CR2B 205 210 205 209 211 211 279 291 254 273 273 277

126 CR2B 205 212 205 209 207 211 279 283 254 273 273 277

127 CR2B 205 209 205 205 207 211 289 291 250 254 273 277

128 CR2B 205 209 205 209 207 211 291 291 254 269 273 277

129 CR2B 205 215 205 209 211 211 279 291 257 257 270 273

130 CR2B 209 215 205 205 211 211 291 291 257 273 273 273

131 CR2B 205 212 205 205 207 211 291 291 250 250 273 273

132 CR2B 205 209 205 213 211 211 289 291 269 277 266 270

133 CR2B 205 209 205 205 207 211 279 283 254 254 270 281

134 CR2B 205 210 205 213 207 211 279 289 250 269 273 273

135 CR2B 209 212 205 213 207 211 279 283 254 273 273 273

136 CR2B 210 212 213 213 207 211 279 283 250 273 266 273

137 CR2B 210 212 205 205 207 211 283 291 273 273 270 273

138 CR2D 205 212 205 209 211 219 291 291 250 254 266 277

139 CR2D 209 212 213 213 207 211 283 291 250 254 273 273

140 CR2D 210 212 205 209 207 211 279 291 254 257 266 277

141 CR2D 205 210 205 205 211 211 279 289 250 254 273 277

142 CR2D 205 212 205 205 207 211 279 279 250 273 270 277

143 CR2D 205 205 205 205 207 207 279 289 254 273 273 281

144 CR2D 209 212 205 205 211 211 279 291 250 250 270 270

145 CR2D 210 210 205 205 207 207 279 289 257 273 266 273

97

Chapter 4

A predicted sharp decline of a stronghold population of the western ringtail possum calls for urgent reduction in fox predation

and road mortality

This chapter will be separated into two scientific papers for publications: one on the negative effect

of radio collars on the survival of possums and the other on the population viability analysis.

98

A predicted sharp decline of a stronghold population of the western ringtail

possum calls for urgent reduction in fox predation and road mortality

Kaori Yokochi, Robert Black, Brian K. Chambers, Roberta Bencini

Abstract

The western ringtail possum (Pseudocheirus occidentalis) was recently classified as

endangered; however, knowledge of its life cycle or population viability is very limited.

For the first time, we projected the near future change in a stronghold population of this

species in Busselton, Western Australia, using Population Viability Analyses (PVA)

based on survival and fecundity data collected over three years of continuous

monitoring. We found that this population has experienced a decline in recent years, and

models of the population predicted a 92.1 % probability of extinction within 20 years.

Fox predation was the most common cause of mortality in radio collared adult possums

and contributed to 16 cases out of 23 confirmed mortalities. Road mortality accounted

for the mortalities of two collared possums. Removal of the effects of fox predation on

adult and pouch young survival rates dramatically reduced the probability of extinction

to 0.4 %. Removal of the effects of road mortality also reduced the probability of

extinction to 31.8 %. These simulated results indicate that these management options

are likely to effectively slow down the decline of this population. However, this

population was predicted to decline even in the fox removal scenario, suggesting that

conservation efforts to increase other demographic parameters, such as fecundity rate,

are also necessary to stop the decline. Our results emphasise the alarmingly poor

outlook for this species and call for an urgent and more intense management of its

threatening processes. In the process of PVA, we unexpectedly found that the weight of

radio collars affected the survival of adult possums even though the collars used were

only up to 2.7 % of the animal’s body weight, about half of the limit set by the current

recommendation. Therefore, we call for more research on the impacts of collars on P.

occidentalis and other strictly arboreal specialist, and review of the current

recommended limit. In the mean time, researchers should use the lightest possible

transmitters on P. occidentalis.

99

Introduction

Predicting the future trend of wildlife populations can provide researchers and managers

with valuable information on how to ensure the survival of populations and/or species.

Population viability analysis (PVA) has become a popular tool to make such predictions

because it can provide quantitative estimates of population trends, such as probability of

extinction, sensitivity of the population to a change in vital rates, and predicted

effectiveness of management strategies, while incorporating uncertainties such as

demographic and environmental stochasticity into the projections (Akçakaya and

Sjögren-Gulve 2000). Detailed information on survival and fecundity of the target

species is required for an effective PVA; however, this information is hard to obtain for

some species especially if they are rare and/or elusive.

The western ringtail possum (Pseudocheirus occidentalis) is one such species because

its capture rate by conventional trapping is extremely low due to their arboreal and

folivorous nature (Wayne et al. 2005a). Because of this, studies on its population

viability have never been conducted despite its recent classification as endangered

(Department of Parks and Wildlife WA 2014a). Its numbers and range have declined

dramatically in recent years. The causes of this decline have been postulated to be

destruction and fragmentation of habitats, predation by introduced European foxes

(Vulpes vulpes) and feral cats (Felis catus), and road mortality (Trimming et al. 2009,

Clarke 2011, Woinarski et al. 2014). The species has a restricted geographic range in

the southwest of Western Australia, and the Busselton region holds one of the few

stronghold populations left for this species (Jones et al. 1994a). However, this coastal

region is currently one of the fastest developing regions in Australia (Australian Bureau

of Statistics 2009), and an increasing number of large-scale developments threaten the

persistence of this species. Furthermore, the southwest of Western Australia has been

experiencing drier and hotter conditions for the last decade, and these conditions are

predicted to worsen in the next 50 years due to climate change (Indian Ocean Climate

Initiative 2012). This change is likely to impact P. occidentalis adversely as this species

is not well adapted to dry, hot climates. Yin (2006) found that P. occidentalis is unable

to lose heat effectively at temperatures above 35 °C because evaporative heat loss

through fur licking is the main strategy against overheating. In coastal regions, western

ringtail possums are highly dependent on the foliage of peppermint trees (Agonis

flexuosa), from which they obtain most of their water intake (Jones et al. 1994b).

However, during extended period of hot weather, the possums have been observed to

100

descend to the ground to drink water and to seek refuge in understorey vegetation,

which increases the risk of predation by ground dwelling predators such as foxes (Jones

et al. 1994b, Department of Parks and Wildlife WA 2014b). In inland habitat dominated

by jarrah (Eucalyptus marginata), long-term spotlight surveys have revealed up to 99 %

decline in the detection rate of P. occidentalis between 1998 and 2009 (Wayne et al.

2012). A decline of this species in coastal regions has also been suggested but not

confirmed due to a lack of systemised and continuous monitoring of populations

(Woinarski et al. 2014).

By fitting multiple individuals from a population in Busselton with radio collars and

monitoring them for over three years, we were able to estimate survival and fecundity

rates of P. occidentalis and to construct its life cycle graph and transition matrices for

the first time to build population viability models. These models allowed us to identify

the causes of mortality and factors influencing their survival, and as a result, estimate

the recent state and future direction of the population. We also simulated the effects of

removal of foxes and road mortalities on the population. Based on available information

on this species and region, we hypothesised that the future projection of a P.

occidentalis population in Busselton would show a declining trend. Because fox

predation and roads are known to cause mortality in the possums, we also expected that

removing fox predation and road mortality would ease the predicted decline.


Study area

We studied a population of P. occidentalis in Locke Nature Reserve and surrounding

campsites, located about 9 km West of Busselton, Western Australia (33°39'S 115°14'E).

This area supports one of the highest densities of P. occidentalis (Jones et al. 2007, de

Tores and Elscot 2010). The 200 ha reserve provides mostly continuous canopy cover

except for a few fire breaks and a swampy area in the southern part. The Western

Australian Department of Parks and Wildlife manages the reserve, and no recreational

activities are permitted inside. The reserve is baited monthly with kangaroo meat

containing sodium monofluroacetate as part of the Western Shield program

implemented to eradicate or reduce the fox population (de Tores et al. 2004). These

baits are unlikely to directly affect P. occidentalis due to this species’ strictly arboreal

nature and folivorous diet. Campsites are located to the north of the reserve across the

15 m wide Caves Road and to the west across an artificially widened and straightened

101

part of the Buayanyup River, which is a 30 m wide “artificial waterway” (Figure 1).

Due to the presence of irrigated grass areas for recreational use, the canopy connection

in the campsites is limited compared with the nature reserve. We set up seven 200 m x

200 m blocks (1A, 1B, 1-2C, 1D, 2A, 2B and 2D) in this area to study the P.

occidentalis population (Figure 1).

Figure 1 A map of the study area near Busselton, Western Australia. Red lines represent the edges

of Caves Road and blue lines represent the edges of a 30 m wide artificial waterway. Areas enclosed

by black lines are 200 m x 200 m study blocks where Pseudocheirus occidentalis were caught and

monitored.

Data collection

117 female and 97 male possums were captured in study blocks using a specially

modified tranquiliser dart gun with darts containing a dose of 11-12 mg/kg of Zoletil

100® (Virbac Australia, Milperra, NSW Australia) following a method developed by P.

102

de Tores and reported by Clarke (2011). We assessed their reproductive status by

measuring testes for males and checking the pouch for young and evidence of

reproduction, such as elongated teats and enlarged mammary glands for females. When

a pouch young was present, its body length including its head, presence of fur, and its

ability to open its eyes were recorded in order to estimate its age and therefore its birth

month.

We initially fitted VHF radio collars with a mortality function (AVM Instrument

Company, Ltd., Colfax, California USA, or Biotrack, Wareham, UK) to three adult

females and three adult males in each of four blocks: 1A, 1B, 2A and 2D (24 individuals

in total). All collars used in our study had the same design: a brass loop band acting as

an antenna and a transmitter and a battery coated with epoxy resin. We recorded

survival of each collared animal once weekly or fortnightly at an average time span

between monitoring of 9.33 0.37 (s.e.) days by homing in on the signal from the radio

collars and by directly sighting each animal at night. We monitored the possums on a

total of 130 occasions from March 2010 to July 2013. We also recorded the presence of

any companions, such as mates and/or young at foot at every sighting. The number of

monitored animals fluctuated throughout the study period due to mortality and failure of

some transmitters. If an animal died or we failed to locate and recapture the animal after

a transmitter failure, another animal of the same sex was captured and collared in the

same block. Fifty-three individuals (22 females and 31 males) weighing between 795.2

and 1307.0 g were monitored in total. The weight of collars varied between 15.8 and

22.6 g (1.2 – 2.7 % of body weight), depending on the model of the collar and the size

of the individual as larger individuals required longer bands.

When a collared animal was found dead, the likely cause of death was established from

available evidence. For example, the cause of mortality was recorded as road mortality

if a body was located on the road and it showed obvious signs of a motor vehicle

collision (e.g. the carcass was flattened) or a sign of blunt trauma identified through post

mortem examination by a veterinarian. The cause of mortality was recorded as a

predation event if the body had obviously been torn apart, eaten or buried. In cases of

suspected predation events, swab samples of saliva were taken from tooth marks on the

collar or chewed parts of the body. We used a pool of DNA markers that are specific to

the red fox, cat, dog and the western quoll (Dasyurus geoffroii) to genetically identify

the predator species from the swab samples. We performed melt-curve analyses, which

utilises a specific dye with melting temperature that varies depending on the length of

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the DNA fragments. By assessing at which temperature the dye disappears, scientists

can identify which species-specific marker amplified the particular DNA fragment

(Berry and Sarre 2007). We cannot be completely certain whether the saliva was left on

the body because the predator preyed on the possum or because it scavenged on an

already deceased possum; however, foxes and cats are known to hunt and prey on

possum species, including P. occidentalis (Kinnear et al. 2002, Clarke 2011, Yokochi,

personal observation), so we assumed that the mortality was caused by direct predation.

Post mortems were also performed on presumably predated carcasses to confirm the

signs of predation if bodies were still present and intact.

Survivorship analysis

Using survival data from radio telemetry monitoring, we constructed candidate models

with factors that potentially influenced the survival rate of adult possums. We

conducted a survivorship analysis using the known fate model with logit link function in

program MARK v.7.1 (White 1999). Factors incorporated in candidate models were sex,

type of closest barrier (road or waterway), habitat type (nature reserve or campsite),

year, life cycle season, average rainfall, body weight and weight of collars. We counted

a year from May to April, and life cycle seasons were set as May to October (season 1)

and November to April (season 2) so that two peaks in breeding cycles of P.

occidentalis fell at the beginning of these seasons (Jones et al. 1994b, Chapter 2). We

obtained rainfall data from a weather station at Busselton Regional Airport, which was

located approximately 15 km from the study site (Australian Bureau of Meteorology

2014). We included the weight of collars in the modelling analysis to check the

assumption that collars do not affect the survival of studied animals (Pollock et al.

1995).

Candidate models that represented our data the best were identified using corrected

Akaike Information Criterion (AICc) values. We considered models with ∆AICc values

of less than 2.0 to have strong support and those with ∆AICc values between 2.0 and 7.0

to have weak support (Burnham and Anderson 2002), compared with the best fitting

model. In models with strong support, we identified the directionality of the effects

from their parameter estimates and checked for their significant divergence from zero

using 95 % confidence intervals.

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As the collar weight was found to be the most influential factor for the survival rate (see

Results), we re-ran the same survivorship modelling analysis with data only from

possums wearing collars lighter than 19.9 g, to minimise the effect of collar weight on

the estimated survival rates. If a heavy collar on an individual was replaced with a light

collar during the study period, only the data after switching to a light collar for that

particular individual were included in the analysis. Survival data from 15 females and

19 males over 105 weeks satisfied this criterion. From this survivorship analysis with

light collars, we then estimated the survival rate of adult females in seasons 1 and 2 in

2010, 2011 and 2012 (six estimates in total) by model averaging sex, year and season

models. 95 % confidence intervals for estimated survival rates were calculated using the

profile likelihood method within program MARK.

Life cycle matrices

We investigated the life cycle of females only because pooling both sexes in matrix-

based PVA can lead to an underestimation of the extinction risk by ignoring the

demographic stochasticity in sex ratio (Brook et al. 2000a). We focused on females

because they are the reproductively limiting sex and we could estimate their fecundity

parameters from our data while this could not be done for males. In favourable

conditions, female possums in the Busselton region can reproduce twice a year at

around the beginning of each life cycle season (Jones et al. 1994b, Yokochi, personal

observation). None of the radio collared females reproduced twice in a life cycle season.

After each of two breeding peaks, the young stays in its mother’s pouch for

approximately three months and become an independent juvenile at the approximate age

of 6 to 7 months (Jones et al. 1994b, Yokochi, personal observation). In captivity,

females have been observed to start breeding as early as 305 days old (Ellis and Jones

1992). The average life span of P. occidentalis in the wild is thought to be 4 to 5 years

(Wayne et al. 2005b), and the oldest known age of collared females at the end of our

monitoring period was 5 years old, at which stage, the animal was still reproductively

active.

Based on these life history characteristics, we constructed a Leslie life cycle graph and

transition matrix with six-monthly time steps up to five years of age, for each of two life

cycle seasons in each of three monitoring years (i.e. six seasonal matrices in total). A

Leslie matrix can be constructed from observed demographic data using 1) fx = mx. P0,

where fx is a fecundity parameter, mx is the number of female young produced by a

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female in one time step (i.e. six months), and P0 is the probability of a female pouch

young surviving to become an independent juvenile (Ebert 1999). This fx = mx. P0,

model had one juvenile stage and 9 adult stages (Figure 2). An estimated rate of change

in the population size during one time step (λasympt “asymptotic lambda”) can be

calculated from this matrix, then the same population information can be restated by

constructing a second matrix using 2) fx = mx. λasympt (Ebert 1999).

Figure 2 A Leslie life cycle graph for female Pseudocheirus occidentalis in Busselton, Western

Australia where fx = mx. P0. The time interval for each step is six months. fx is a fecundity

parameter, mx is the number of female young produced by a female in one time step, P0 is the

survival rate of female pouch young, PJ is the survival rate of female juveniles, and PA is the survival

rate of female adults. J refers to the juvenile stage (0.5 to 1 year old), A1 (1 to 1.5 years old adult),

A2 (1.5 to 2 years old), A3 (2 to 2.5 years old), A4 (2.5 to 3 years old), A5 (3 to 3.5 years old), A6 (3.5

to 4 years old), A7 (4 to 4.5 years old), A8 (4.5 to 5 years old) and A9 (5 to 5.5 years old).

The basic survivorship and fecundity parameters required in our life cycle transition

matrices were mx, P0, survival rate of a juvenile female (PJ), and survival rate of an adult

female (PA). For each of six life cycle seasons, we calculated mx by averaging the

numbers of female pouch young present in the pouches of each adult female caught in

the season. We estimated P0 by dividing the number of surviving offspring of radio

collared females at the point of independence by the number of newborns observed with

adult females in either capture or radio tracking data in the season. Because of the lack

of data on the sex ratio of newborns, we assumed that P0 was the same for males and

females, as observed in a closely related species, common ringtail possums (P.

peregrinus, How et al. 1984). No information on PJ of P. occidentalis was available

because juveniles were too small to be collared and once independent, they could no

longer be observed with their collared mothers. PJ is thought to be lower than P0 or PA

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due to juveniles’ naivety and need to traverse over greater distances in unknown

territories for dispersal, which increases the risk of road mortality and predation (A.

Wayne, J. Clarke, and U. Wicke, Pers. Comm.). How et al. (1984) also found that PJ of

P. peregrinus was lower than their P0 or PA. Although P. peregrinus is a much more

common species than P. occidentalis, these two species share similar biological

characteristics, including being arboreal folivores, having two breeding peaks per year,

and having an average life span of 4 to 5 years (Thomson and Owen 1964, How et al.

1984, Woinarski et al. 2014); therefore, we used the PJ value of 0.367 reported by How

et al. (1984) as an estimate of PJ for all six seasons in our analyses. PA for each of six

seasons was estimated from the survivorship analysis described earlier.

We used the Leslie matrices with fx = mx. P0 to obtain the estimate of λasympt and the

elasticity for each element in the matrix. Elasticity values for eight PA were added to

obtain a single elasticity value for PA, and elasticity values for nine fx were added to

obtain an elasticity value for P0 (Ebert 1999). Using the λasympt value, we then

constructed a fx = mx. λasympt matrix and calculated the stable age distribution (Cx) for

the season. This process was repeated for each of six seasons to obtain a set of six fx =

mx. λasympt matrices and six lists of Cx values. We used the “lambda”, “elasticity”, and

“stable.stage” functions in the popbio package v.2.4 (Stubben et al. 2012) within R

v3.0.1 (R Foundation for Statistical Computing 2013, available at http://www.R-

project.org) for these calculations.

Demographic and environmental stochasticity projection

The fx = mx. λasympt transition matrix, starting female population size, a vector of starting

numbers of females in 11 life stages, and a matrix of the variances of fx (i.e. var(fx))

were necessary to conduct a population projection to estimate demographic stochasticity

for each of six seasons, using the “multiresultm” function in the popbio package for R.

We estimated the starting female population size in our PVA based on the most recent

adult population estimate in Locke Nature Reserve, which was 353 (de Tores and Elscot

2010). Given that 117 out of 214 possums (54.7 %) we captured were females, we

calculated that 193 out of 353 adults would be females. We averaged the six sets of Cx

values derived from fx = mx. λasympt transition matrices to obtain one set of Cx values.

We then estimated the proportion of female adults in the studied population to be

64.0 % by summing the Cx values of nine adult stages. Based on this percentage, we

calculated the total starting female population size (i.e. number of adults, juveniles, and

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pouch young) to be (193 / 0.64) = 302. In each of six life cycle seasons, the starting

number of females in each life stage was calculated by multiplying 302 by the

corresponding Cx value for the stage. Because the λasympt is constant, the var(fx), or the

variance of mx. λasympt, was calculated as λasympt 2* var(mx), where var(mx) is the variance

of mx in each season. Using these parameters, we ran 1,000 simulations of population

projection over 20 years. We applied the “multiresultm” function on the starting

numbers of the population, and continued to apply it on the newly created set of

numbers until it reached 20 years (i.e. 40 six-monthly time steps). In each of 1,000

simulations, a six-monthly rate of change in population size after adding demographic

stochasticity (λdemog) was calculated as -1 (Dorai-Raj 2015). We performed all these

calculations and projections in R v3.0.1. The R script for these steps is presented in

Appendix 1.

Impacts of fox predation and road mortality

To assess how fox predation was affecting the population, we estimated new sets of PA

values through the same survivorship analyses, while assuming individuals that were

killed by foxes had survived for the remainder of the monitoring period. We are aware

that individuals may die of other causes even if they escaped fox predations. However,

given that the majority of mortalities of P. occidentalis were presumed to be caused by

fox predation in the studied population, the manipulation of the mortality values was

kept simple for the simulation purposes. As pouch young are completely reliant on their

mothers for their survival in the wild, the rate of increase in PA following the removal of

fox predation was also applied to P0. PJ for this assessment remained the same as the

original analyses because causes of mortality were unknown for juveniles. We then

conducted the same set of projection processes with the new sets of PA and P0 values.

We also conducted the same analyses for the scenario of removal of road mortality by

assuming all adult possums that were killed by vehicles had survived for the remainder

of the monitoring period. For each scenario, r and P(ext) were calculated and compared

with the estimates from the actual field data using profile likelihood 95 % confidence

intervals.

The processes and assumptions involved with our projections and simulations are

summarised in Figure 3 and Table 1.

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Table 1 A list of assumptions and their justifications made while performing population viability

analyses (PVA) of a stronghold population of Pseudocheirus occidentalis in Busselton, Western

Australia. PA, PJ, and P0 are survival rates of adult, juvenile and pouch young, respectively.

Assumption Source, justification and comments 1) Possums died of fox predation if fox

saliva was present.

Scavenging is possible but unable to distinguish in the field. Foxes are known to actively prey on P. occidentalis.

2) The trend of the female population represents the trend of the whole population.

Females are the reproductively limiting sex. Brook et al. (2000a)

3) PA is unaffected by collar weight when collars are < 20 g.

PA for 19.9 g collar is within 95 % C.I. of PA for 15.8 g collar.

4) PJ of P. occidentalis is the same as that of P. peregrinus.

The two species are closely related and share many biological characteristics. How et al. (1984)

5) P0 is the same for males and females.

How et al. (1984)

6) Population is in stable age distribution.

Although unlikely, this was assumed, as we could not assess this with our data.

7) The population size of adults is 353. de Tores & Elscot (2010) 8) Population goes extinct if the

number of females falls below two.

Conservative value set for PVA. Likely to underestimate the extinction risk if the real threshold is higher than two.

9) No catastrophes (severe weather events, fires or diseases) for the next 20 years.

This was assumed as data are unavailable. Likely to underestimate the extinction risk.

10) No migration for the next 20 years.

Unlikely but assumed as data unavailable. Likely to overestimate the extinction risk.

11) In a fox predation removal scenario, if a possum does not die of fox predation, it survives till the end of study period.

70 % of mortality was caused by fox predation. Likely to overestimate the effect of fox predation removal.

12) In a road mortality removal scenario, if a possum does not die of road mortality, it survives till the end of study period.

Likely to overestimate the effect of road mortality removal. Also assumed by Ramp & Ben-Ami (2006)

13) Removal of fox predation or road mortality does not affect PJ.

Juveniles are likely to be affected by fox predation and road mortality, but mortality data on juveniles are unavailable.

109

Figure 3 A flow chart showing the process of the population viability analyses. PY and BoM stand

for pouch young and Australian Bureau of Meteorology (2014), respectively. PA, PJ, and P0 are

survival rates of adult, juvenile and pouch young, respectively. fx is fecundity parameter, Cx is stable

age distribution value, λasympt is asymptotic lambda, λproj is projected change in population size, and

P(ext) is probability of extinction. Numbers in grey diamonds are assumptions made in the step.

Numbers in yellow diamonds are assumptions relevant only in simulated scenarios. Refer to Table 1

for corresponding assumptions. Assumption 2 is not shown as it applies to the whole process.

110

Results

Causes of mortality and survivorship analysis

We confirmed mortalities of 23 radio collared possums in three years, and the number

of mortalities ranged between 2 and 6 in each six monthly season (Table 2). Fox

predation was the most common cause of mortality, contributing to up to 100 % of

mortalities in each season. Road mortality contributed to two mortalities in total. Other

causes of mortality included one cat predation, one predation in which the predator

species could not be genetically determined, one general condition loss and two

unknown cause. Possums wearing heavy radio collars (> 19.9 g) contributed to 48 % of

mortalities even though only 36% of monitored possums were wearing heavy collars.

Table 2 The number and causes of confirmed mortalities of radio collared Pseudocheirus

occidentalis and in Busselton, Western Australia. Season 1 is from May to October and season 2 is

from November to April. “Unknown Predator” is the number of mortalities caused by predation

events where the predator species could not be identified. Numbers in parentheses are numbers of

possums wearing collars that were heavier than 19.9 g. “Radio collared” is the total number of

possums monitored during the whole study period.

Season Fox Cat Unknown Predator Road Other Total

2010-1 3 (3) 0 1 (1) 1 (1) 0 5 2010-2 3 (3) 0 0 1 (0) 0 4 2011-1 5 (2) 0 0 0 1 (0) 6 2011-2 1 (0) 0 0 0 1 (0) 2 2012-1 2 (0) 0 0 0 0 2 2012-2 2 (0) 1 (0) 0 0 1 (1) 4 Total 16 (8) 1 (0) 1 (1) 2 (1) 3 (1) 23 (11) Radio collared 53 (19)

111

When we performed a survivorship analysis, the weight of the collars was found to be

the most influential factor affecting the survival of adult possums, with an increase in

the collar weight decreasing their survival (Table 3). The null model had the second

strongest support from our data, but its support was a quarter of that for the collar

weight. All the other candidate models ranked lower than the null model and none of

their parameters had a significant effect on the survival of possums. Given the strong

effect of the collar weight on the survival of possums, we projected the estimated six-

monthly survival rate of possums over the range of collar weights used in this study

(15.8 – 22.6 g), based on the candidate model with collar weight as a factor. The

estimated six monthly survival rate of possums wearing the lightest collar was 0.897

(95 % confidence intervals: 0.783, 0.954) and it fell by 15 % to 0.763 (0.665, 0.838)

when the collar weight was increased to 19.9 g. The survival rate fell by a further

15.5 % to 0.623 (0.406, 0.800) with the heaviest collar.

Table 3 Results of a known fate model analysis on the survival of adult Pseudocheirus occidentalis

near Busselton, Western Australia. The variable “LC (Life Cycle) season” refers to May to October

(season 1) and November to April (season 2), “Rainfall” is the average rainfall, “Habitat” is whether

the individual was located in Locke Nature Reserve or a campsite, and “Barrier type” refers to the

closest barrier (road or artificial waterway). In the case of categorical variables, parameter estimates

( s.e.) are for the categories presented in parentheses (e.g. the parameter estimate of the sex model

is for females). * indicates parameters with 95 % confidence intervals outside of zero.

Variable AICc ∆AICc AICc weight Parameter estimates

Collar weight 265.0

0.48 -0.244 0.11*

Null 267.8 2.7 0.12

Body weight 268.5 3.5 0.08 0.002 ± 0.00

Sex 268.7 3.6 0.08 0.480 ± 0.46 (female)

LC season 268.7 3.6 0.08 -0.478 ± 0.46 (season I)

Rainfall 269.2 4.1 0.06 -0.079 ± 0.10

Habitat 269.7 4.6 0.05 -0.172 ± 0.52 (reserve)

Barrier type 269.7 4.7 0.05 0.080 ± 0.46 (road)

Year 275.2 10.2 0.00 -0.233 ± 1.16 (2010)

0.009 ± 1.17 (2011)

-0.193 ± 1.19 (2012)

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In the survivorship analyses, after the removal of data from individuals wearing heavy

collars, the null model had the strongest support from data; however, differences in

AICc values were small and all factors except for the year had strong support from our

data (∆AICc < 2.0, Table 4). However, in all models, 95 % confidence intervals of the

parameter estimates included zero, indicating that their effects were not significant.

Six-monthly PA values for females estimated by model averaging sex, life cycle season

and year models were very similar over three years varying between 0.845 and 0.863

(Table 5). Six-monthly P0 fluctuated the most (0.333 – 1.000) out of three parameters

estimated, and mx varied between 0.234 and 0.447 over three years.

Table 4 Results of a known fate model analysis on the survival of adult Pseudocheirus occidentalis

with collars less than 20 g near Busselton, Western Australia. The variable “LC (Life Cycle) season”

refers to May to October (season 1) and November to April (season 2), “Rainfall” is the average

rainfall, “Habitat” is whether the individual was located in Locke Nature Reserve or a campsite, and

“Barrier type” refers to the closest barrier (road or artificial waterway). In the case of categorical

variables, parameter estimates ( s.e.) are for the categories presented in parentheses (e.g. the

parameter estimate of the sex model is for females). 95 % confidence intervals of all parameters

included zero.

Variable AICc ∆AICc AICc weight Parameter estimates

Null 133.5 0.23

Rainfall 133.7 0.2 0.21 -0.177 ± 0.13

Sex 134.6 1.1 0.13 0.599 ± 0.64 (female)

Barrier type 134.7 1.2 0.13 -0.617 ± 0.72 (road)

Body weight 135.3 1.8 0.10 -0.001 ± 0.00

LC season 135.4 1.8 0.09 -0.266 ± 0.63 (season I)

Habitat 135.5 2.0 0.09 0.088 ± 0.64 (reserve)

Year 139.2 5.7 0.01 0.442 ± 1.53 (2010)

0.179 ± 1.20 (2011)

-0.111 ± 1.21 (2012)

113

Table 5 Six-monthly demographic parameters and results of a Leslie transition matrix analysis of

female Pseudocheirus occidentalis in Busselton, Western Australia. Season 1 is from May to

October, and season 2 is from November to April. PA is the survival rate of adults per six months, PJ

is the survival rate of juveniles per six months, P0 is the survival rate of pouch young per six months,

and mx is the number of female young produced by a female in six months. λasympt (the estimated

rate of change in population size in six months) and elasticity values were calculated from Leslie

transition matrices using these demographic parameters. Elasticity values for P0 and PJ are presented

together because they were identical. Numbers in parentheses are 95 % confidence intervals or

variances in case of mx. Subscripted numbers are the sample sizes or the ranges of sample sizes in

case of PA.

Six monthly demographic parameters Matrix analysis results Season PA P0 mx λasympt Elasticity PJ (P0) PA 2010-1 0.849 0.500 0.447 0.852 0.167 0.665 (0.656, 0.943) 2-4 (0.205, 0.795) 12 (0.240) 41

2010-2 0.863 0.333 0.263 0.746 0.144 0.712 (0.607, 0.945) 6-9 (0.000, 0.747) 6 (0.185) 30

2011-1 0.847 0.778 0.417 0.908 0.180 0.639 (0.669, 0.938) 8-17 (0.490, 1.000) 9 (0.193) 18

2011-2 0.862 1.000 0.234 0.868 0.168 0.664 (0.711, 0.940) 14-18 (1.000, 1.000) 4 (0.067) 8

2012-1 0.845 0.500 0.250 0.774 0.152 0.696 (0.662, 0.938) 10-13 (0.062, 0.938) 6 (0.214) 8

2012-2 0.859 0.750 0.375 0.894 0.174 0.652 (0.702, 0.941) 12-15 (0.260, 1.000) 4 (0.143) 8

PVAs on observed and simulated data

The λasympt values for all six life cycle seasons were smaller than one and indicated that

the number of female P. occidentalis in this population would decrease by between 9

and 25 % in each season if the population was in a stable age distribution (Table 5). The

elasticity values for the survival rate of adults (PA) were about four times greater than

those of pouch young (P0) or juveniles (PJ ) in all life cycle seasons, suggesting that

changes in PA alter the rate of population changes in female P. occidentalis more than

those in P0 or PJ do. According to our PVA, the studied population has over 92 %

probability of going extinct in the next 20 years (Table 6).

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In a scenario where the road mortality effect on survival rates of adults and pouch

young was removed from our dataset, λproj was smaller than one, indicating that the

population would still be in decline; however, the predicted rate of decline was smaller

than the decline predicted from our original data, and P(ext) dropped to 32 % (Table 6).

In a scenario where fox predation effect was removed, the population was also projected

to decline, but the rate of decline was even smaller than the “no road mortality” scenario

and P(ext) in the next 20 years was almost zero (0.4 %, Table 6).

Table 6 Results from population viability analyses of a Pseudocheirus occidentalis population in

Busselton, Western Australia based on an observed dataset and two management scenarios. Analyses

were run using data from females only. “No road” and “No fox” are scenarios where road mortality

and fox predation were removed from the estimation of adult and pouch young survival rates. Six

monthly λproj is the projected change in the number of females over six months, and P(ext) is the

projected probability of the number of females dropping to below two in 20 years. Numbers

presented in brackets are 95 % lower and upper confidence intervals.

Scenario λproj P(ext)

Observed

0.827 (0.788, 0.882)

0.921 (0.903, 0.937)

No road

0.882 (0.837, 0.882)

0.318 (0.290, 0.347)

No fox 0.891 (0.882, 0.907) 0.004 (0.001, 0.009)

Discussion

Current and future decline of P. occidentalis

Our population projections suggest that the studied population of P. occidentalis is

declining and has an alarmingly high risk of extinction within the next 20 years. These

projections were built upon many assumptions (Table 1 and Figure 3), so the rate of

decline should not be accepted as a precise prediction. However, data from other studies

seem to support this recent steep decline. In early 2009, the density of P. occidentalis

within continuous vegetation at Locke Nature Reserve was estimated to be 5.84 ha-1

from four months of monitoring (de Tores and Elscot 2010). Four and a half years later

in a recent study, Harring-Harris (2014) estimated the density of the possums within

continuous vegetation at this reserve to be 1.03 ha-1 from one month of monitoring. This

reduction in the density gives us an estimated population decline of 0.825 per six

115

months, which is close to our λproj estimate of 0.827 per six months. Although these

density estimates were based on the numbers of possums detected by different operators,

both studies were conducted intensively in short time periods and employed a distance

sampling method, which is thought to be a more accurate method than other

conventional methods to estimate a density of this elusive species (de Tores and Elscot

2010, Finlayson et al. 2010). Therefore, the two estimates are comparable and provide

strong support for our projections of an alarming decline in this population.

Although the projected decline of the studied population from our PVA was similar to

the recent decline observed by Harring-Harris (2014), the future population projection

in this study may be optimistic compared with the reality. One of the assumptions made

in this analysis was that environmental conditions will remain the same for the next 20

years. Although 2010 - 2013 had a lower rainfall and greater number of days with a

maximum temperature of 35 °C or above compared to previous years (Australian

Bureau of Meteorology 2014, summarised in Figure 4), the Busselton region is expected

to experience even less rain and more frequent hot days in the future due to climate

change (Indian Ocean Climate Initiative 2012). This drier and hotter climate is likely to

negatively impact the survival of P. occidentalis as these possums struggle to cope with

dry conditions with ambient temperatures above 35 °C (Yin 2006). Other assumptions

included no catastrophes, such as severe weather events, fires or diseases. These events

could happen in the next 20 years, especially in view of the predicted changes in climate,

and would dramatically accelerate the possums’ decline. The accuracy of the probability

of extinction estimated from PVA is debatable (Brook et al. 2000b, Fieberg and Ellner

2000, Coulson et al. 2001, Ball et al. 2003, McCarthy et al. 2003, Schödelbauerová et al.

2010), so it should not be taken as a precise prediction; however, it does provide

indications of the general direction in which the population is headed. In the case of this

stronghold population of P. occidentalis, the future outlook is poor even when we take

these uncertainties into account.

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Figure 4 Number of days with a maximum temperature of or above 35°C and annual rainfall

between 1998 and 2013 in Busselton, Western Australia. Bars represent the number of hot days and

the line graph represents the change in annual rainfall. The monitoring period for this study (2010 to

2013) is highlighted by dark shaded bars and markers on the line graph. Upper and lower dotted

lines represent the average annual rainfall and average number of hot days, respectively. Climate

data were obtained from a weather station at Busselton Regional Airport (Australian Bureau of

Meteorology 2014).

Our results have serious implications for the entire species because the studied

population is thought to be one of the largest and densest populations left, and it is

located in the most pristine habitat for the species (Jones et al. 1994a, 2007). Therefore,

it is possible that some of other remaining populations of this endangered species are

experiencing an even greater decline than that reported here. Based on the spotlight

count data of P. occidentalis between 1997 and 2004 in the inland jarrah-dominated

Upper Warren region (Wayne et al. 2012), the finite rate of their population change can

be calculated as 0.453 per six months. This value is about a half of the rate estimated in

this study, suggesting that the Upper Warren population declined at twice the rate of the

stronghold population we studied. As a result, no possums have been sighted in most of

the inland survey areas after 2006, even though the Upper Warren population used to be

a large, genetically diverse population (Wilson 2009). This rapid decline supports the

notion that P. occidentalis in other parts of its remaining range may be experiencing a

greater decline and calls for more urgent and effective conservation efforts to be

implemented to ensure this species’ survival. Long-term monitoring of P. occidentalis

populations in other parts of its remaining range, such as in the Albany region, is also

urgently needed to assess their population trends.

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all (

mm

)

Num

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117

Two management scenarios

Presumed predation by foxes was the most common cause of mortality in radio collared

adult P. occidentalis, and the simulated removal of fox predation in PVA resulted in a

slower rate of decline and a dramatically lower probability of extinction in the next 20

years, as we expected. This result suggests that the removal or reduction of foxes in the

area is likely to be effective in slowing down the decline of P. occidentalis. Locke

Nature Reserve is baited monthly with baits containing sodium monofluroacetate toxin

to control foxes (de Tores et al. 2004, C. Forward, pers. comm.); however, fox predation

is still having a significant negative impact on the study population. Due to public safety

concerns, baiting cannot be conducted in campsites or along roads, which makes it

extremely hard to eradicate foxes from the study area because foxes are currently free to

roam between the baited nature reserve and unbaited campsites. The farmland south of

the reserve could also act as source of foxes without effective fox control measures.

Reducing and maintaining a low density of foxes in the whole region with the aim of

eventual eradication is essential to ensure the survival of P. occidentalis as a species,

and more effective baits and/or methods to control foxes on a broader scale need to be

explored. We also recorded one instance of predation by a cat in the area, so cats need to

be controlled concurrently with foxes because their number can increase as the number

of foxes decreases (Glen and Dickman 2005, de Tores and Marlow 2012).

Two radio collared adult possums were killed by vehicles on the road, and the simulated

removal of road mortality in PVA resulted in a slower rate of population decline and a

lower probability of extinction, as expected. Although the removal of road mortality

did not seem to ease the decline of possums as much as the removal of fox predation,

mortalities on roads need to be minimised to maximise the chance of this species

persisting. One of the most common methods to reduce road mortality is installing

fences along the road; however, it is impractical for this species in our study area

because of the arboreal nature of the possums and the need for public accesses to the

campsites along Caves Road. Road mortalities need to be reduced by providing

possums with safe ways to cross roads by means such as rope bridges. In a previous

study, Caves Road was found to limit the movements of P. occidentalis (Yokochi et al.

2015: Chapter 2). Isolated populations have a higher risk of extinction because of its

higher vulnerability to stochastic demographic changes and catastrophic events such as

severe weather, fire and diseases (Foley 1997). If no action was taken to increase the

connectivity, the isolation caused by the road may further threaten the survival of this

118

population. A high level of connectivity between subpopulations also encourages gene

flow, dispersal and immigration, which can prevent further decline of the population.

Therefore, installing wildlife crossing structures across the road is likely to benefit this

declining population in multiple ways.

We conducted PVAs on both of these scenarios by changing only the adult and pouch

young survival rates because data for juvenile mortality were not available. Removal of

fox predation and road mortality is also likely to increase the survival rate of juveniles

because at this stage individuals are thought to be most vulnerable to these threats.

Therefore, the actual positive effects of removal of foxes and road mortality may be

greater than those predicted in this study. On the contrary, some assumptions made

during the modelling processes may have caused overestimation of the positive impacts

that removal of these threats would have on the studied population. For example, we

simulated the removal of threats by assuming that individuals that were killed by the

particular threat would survive for the remainder of the monitoring period; however, it

is possible that these individuals would die of other causes during the monitoring period.

This is especially the case for the simulation of the removal of road mortality because

two road-killed individuals that were assumed to have survived could have instead died

of the most common cause of mortality, fox predation. We are not alone in making this

assumption in a simulation of reduction in road mortality (Ramp and Ben-Ami 2006);

however, to what degree this uncertainty affected the outcome of our simulations cannot

be known from our data, and caution should be paid when interpreting our results.

Nonetheless, increased adult and pouch young survival rates due to the removal of

common threats still resulted in projected rates of population change (λproj) of less than

one, suggesting that this population is likely to keep declining even without fox

predation or road mortality. One possible explanation for this lack of impact of the

simulated management of threats could be that the observed adult survival rates were

already high (0.85 – 0.86 per six months). Because of the high base survival rates,

removal of fox predations and removal of road mortalities only resulted in 9 % and 4 %

increase in survival rates, respectively. The high elasticity value of the adult survival

rates meant that these small changes were enough to significantly lower the population

extinction risk in the near future (i.e. 20 years); however, they were still not enough to

change the population trend. Therefore, conservation efforts to increase other

demographic rates, such as fecundity rate, need to be undertaken concurrently with the

reduction in fox predation and road mortality. By comparing reproductive trend in

119

different habitats, Jones et al. (1994b) suggested that improved habitat quality can

increase the fecundity rate of P. occidentalis. However, which particular habitat

components and attributes encourage survival and reproduction of P. occidentalis is still

largely unknown and this population is already thought to be living in the most pristine

environment in its range; therefore, more research is needed on preferred macro- and

microhabitats of this species.

Impacts of collar weight

We unexpectedly found that the weight of the collars strongly affected the survival of

the possums. While adult possums wearing 15.8 g radio collars had a 90% chance of

survival for six months, those with 20 g collars only had a 76 % chance, and those

wearing 22.6 g collars had a less than 63 % chance of survival. Individuals wearing

collars over 20 g accounted for half of mortalities caused by predation by foxes even

though only 36 % of the monitored individuals wore heavy collars. This suggests that

heavier collars may have somehow increased the chance of possums being predated. It

is possible that the extra weight of collars slowed movements of possums and/or forced

them to come down to the ground, thus making them more vulnerable to predation.

Several researchers have examined the effects of radio collars on the survival of small

mammalian species, but their results are contradictory (Withey et al. 2001). For

example, wearing radio collars reduced the body condition of European badgers (Meles

meles; Tuyttens et al. 2002) and biased the sex-ratio of water voles (Arvicola terrestris;

Moorhouse and Macdonald 2005); however, collars did not affect the short term

survival of house mice (Mus domesticus; Pouliquen et al. 1990), yellow-necked mice

(Apodemus flavicollis; de Mendonça 1999) or root voles (Microtus oeconomus;

Johannesen et al. 1997). Studies into the effects of radio collars on arboreal mammals

seem to be lacking, and this study is the first instance where the negative effect of collar

weight on the survival of an arboreal marsupial species has been tested and

demonstrated. This result is extremely alarming because the weight of the heaviest

collar used in this study was about half of the recommended maximum collar weight set

in guidelines that are commonly used by wildlife researchers (Animal Ethicks Infolink

n.d., Sikes and Gannon 2011). These guidelines recommend researchers to fit radio

collars of up to 5 % (10 % in case of Sikes and Gannon 2011) of the animals’ body

weight, which would be approximately 50 g (or 100 g) for an average 1000 g P.

occidentalis.

120

We reduced the effect of collar weights on our projections by omitting data from

possums wearing collars heavier than 20 g (i.e. more than 2 % of their body weight);

therefore, the results from our PVA should not have been affected by the effects of the

collars. However, this means that putting heavy collars on P. occidentalis is likely to

accelerate the decline of this species; therefore, researchers must be cautious when

monitoring this species in the future. The trap success rate of these possums is

extremely low due to their highly arboreal nature and folivorous diet (Wayne et al.

2005a); therefore, we will still have to rely on radio transmitters to monitor them. The

weight and size of transmitters might decrease as lighter and smaller batteries become

available with advances in technology, which, in turn, will decrease the burden of

collars on animals. However, for now we must ensure that we use the lightest and

smallest collars available on this species in order to minimise the negative impact of the

collars. The current recommended maximum weight of radio collars appears to be

excessive for western ringtail possums, and possibly for other small to medium sized

arboreal marsupials. This study was not designed to investigate the effects of the collar

weight on animals, so more well-designed studies are needed on the impacts of collars

on arboreal mammals; however, until then, we strongly suggest changing the maximum

collar weight to 2 % of the body weight for arboreal marsupials especially if long-term

monitoring is expected.

Limitations and future research directions

As discussed above, many assumptions were made in the process of performing our

PVA (Table 1). One of the assumptions made was that if fox saliva was found on the

collar or carcass of a dead possum, the death was due to fox predation. We are aware

that the fox could have scavenged on the carcass of a possum that had died of a different

cause; therefore, the observed number of “fox predation” event may be an overestimate.

One could try to distinguish between predation and scavenging events by the presence

of subcutaneous haemorrhage on a carcass because trauma caused while the prey is

alive would cause subcutaneous haemorrhaging (Miller et al. 1985). However, this

observation is not possible if the carcass is not intact at discovery, which was the case

for many recorded mortalities in this study. Given this lack of data and the knowledge

that foxes do actively prey on P. occidentalis (Kinnear et al. 2002, Clarke 2011,

Yokochi, personal observation), we assumed that foxes actively predated on radio

collared P. occidentalis, and readers should be aware that the estimated impacts of

removal of fox predations may have been overestimated as a result.

121

Another assumption in our PVA was that the studied population would experience no

migration for the next 20 years. This assumption is very unlikely to be true because the

study area was connected to other known P. occidentalis habitats (Chapter 3). However,

there were no data available on the rate of migration from/ to this population, so

migration was not incorporated in our models. Given the rapid rate of decline estimated

in our PVA (λproj of 0.827 per six months) and the starting female population size of

302, at least 52 female immigrants per six months would be required to stop the

population decline. Given the highly arboreal and sedentary nature of P. occidentalis in

this population (Yokochi et al. 2015: Chapter 2), the actual migration rate is unlikely to

be high enough to change the population trend. Nonetheless, immigration is an

important source of new individuals for a declining population (Foley 1997); therefore,

this assumption is likely to have resulted in an overestimation of extinction risk.

Research on the migration rates of P. occidentalis is needed to eliminate the uncertainty

of this assumption from future PVA.

The juvenile survival rate (PJ ) used in our analyses was based on data from a similar,

but different ringtail possum species in a different habitat 40 years ago; therefore, it was

only a general estimation. Having accurate parameter estimates is important in PVA

because they are the basis of the model construction and they can alter the resulting

projections significantly (Taylor 1995). This PJ value was the best estimation available

to us at the time of this study, especially given that PJ is thought to be lower than PA or

P0 in P. occidentalis (Pers. Comm. A. Wayne, J. Clarke, and U. Wicke). Elasticities of

PJ were also lower than those of adult survival rate (PA), suggesting that having an

accurate PA is more important in accurately projecting this population than PJ.

Nevertheless, studies into the survival of juveniles are needed so that a more accurate PJ

can be incorporated into future analyses. Our limited observational data may have

caused particularly low values of mx (the number of female young produced by a female

in one time step). Our data were the best estimate available at the time of this study

because the only other available data on the reproductive rate of wild female P.

occidentalis were those estimated by Jones et al. (1994a) based on one-year-long

monitoring of only three females. Demographic parameter estimates based on larger

sample sizes and longer monitoring periods would increase the accuracy of the

projections. However, the species is extremely difficult to capture and therefore

obtaining larger samples size would represent a serious challenge. Data on demographic

122

parameters of males would also give us a more thorough picture of the status of this

population by enabling us to incorporate both sexes into the projections.

Conclusion

Using PVA, we predicted that a stronghold population of the endangered western

ringtail possum would decline with a high probability of extinction within the next 20

years if no action is taken. Fox predation was the most common cause of mortality in

this population, followed by road mortality, and removal of these known threats

dramatically reduced the projected probability of extinction. However, this population

was predicted to decline even with the removal of these threats, suggesting that

conservation efforts to increase other demographic parameters, such as fecundity rate,

need to be implemented concurrently with mitigation of the impacts of fox predation

and road mortality. Our results highlight the poor outlook for this species and call for

the urgent implementation of conservation strategies. The current recommended

maximum weight of radio collars is also excessive for this species, and we recommend

using collars that are as light as possible, up to 2 % of the animals’ body weight, for this

species and possibly also for other arboreal specialists.

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Appendix 1

Below I present the R script used in the population viability analyses of Pseudocheirus

occidentalis. This script was developed by Robert Black and Kaori Yokochi, based on

Elbert (1999). An example set of transition life cycle matrices required is given first,

followed by the script.

Table A1 “WRP-F-5yr 2010-1nf.txt”. A fx = mx. P0 transition life cycle matrix of female

Pseudocheirus occidentalis in the first life cycle season of 2010.

J A1 A2 A3 A4 A5 A6 A7 A8 A9 J 0 0.088 0.088 0.088 0.088 0.088 0.088 0.088 0.088 0.088

A1 0.367 0 0 0 0 0 0 0 0 0 A2 0 0.863 0 0 0 0 0 0 0 0 A3 0 0 0.863 0 0 0 0 0 0 0 A4 0 0 0 0.863 0 0 0 0 0 0 A5 0 0 0 0 0.863 0 0 0 0 0 A6 0 0 0 0 0 0.863 0 0 0 0 A7 0 0 0 0 0 0 0.863 0 0 0 A8 0 0 0 0 0 0 0 0.863 0 0 A9 0 0 0 0 0 0 0 0 0.863 0

Table A2 “WRP-F-5yr 2010-1nf ml.txt “. A fx = mx. λ transition life cycle matrix of female

Pseudocheirus occidentalis in the first life cycle season of 2010.

PY J A1 A2 A3 A4 A5 A6 A7 A8 A9 PY 0 0 0.196 0.196 0.196 0.196 0.196 0.196 0.196 0.196 0.196 J 0.333 0 0 0 0 0 0 0 0 0 0

A1 0 0.367 0 0 0 0 0 0 0 0 0 A2 0 0 0.863 0 0 0 0 0 0 0 0 A3 0 0 0 0.863 0 0 0 0 0 0 0 A4 0 0 0 0 0.863 0 0 0 0 0 0 A5 0 0 0 0 0 0.863 0 0 0 0 0 A6 0 0 0 0 0 0 0.863 0 0 0 0 A7 0 0 0 0 0 0 0 0.863 0 0 0 A8 0 0 0 0 0 0 0 0 0.863 0 0 A9 0 0 0 0 0 0 0 0 0 0.863 0

130

Table A3 “WRP-F-5yr 2010-1h ml v.txt”. A var(fx) matrix of female Pseudocheirus occidentalis in

the first life cycle season of 2010.

PY J A1 A2 A3 A4 A5 A6 A7 A8 A9 PY 0 0 0.067 0.067 0.067 0.067 0.067 0.067 0.067 0.067 0.067 J 0 0 0 0 0 0 0 0 0 0 0

A1 0 0 0 0 0 0 0 0 0 0 0 A2 0 0 0 0 0 0 0 0 0 0 0 A3 0 0 0 0 0 0 0 0 0 0 0 A4 0 0 0 0 0 0 0 0 0 0 0 A5 0 0 0 0 0 0 0 0 0 0 0 A6 0 0 0 0 0 0 0 0 0 0 0 A7 0 0 0 0 0 0 0 0 0 0 0 A8 0 0 0 0 0 0 0 0 0 0 0 A9 0 0 0 0 0 0 0 0 0 0 0

R script for PVA

library(popbio)

library(binom)

###2010

##Season1

#fx=mxp0

wrp2010.1nf <- read.table("WRP-F-5yr 2010-1nf.txt", header=T, row.names =1)

wrp2010.1nf.mat <- as.matrix(wrp2010.1nf)

adl <- c("J", "A1", "A2", "A3", "A4", "A5", "A6", "A7", "A8", "A9")

lambda(wrp2010.1nf.mat)

#Use lambda value to construct fx=mx.lambda matrix

#fx=mx.lambda matrix saved as "WRP-F-5yr 2010-1nf ml.txt"

#fx=mx.lambda

wrp2010.1nfml <- read.table("WRP-F-5yr 2010-1nf ml.txt", header=T, row.names

=1)

wrp2010.1nfml.mat <- as.matrix(wrp2010.1nfml)

adl <- c("PY", "J", "A1", "A2", "A3", "A4", "A5", "A6", "A7", "A8", "A9")

stable.stage(wrp2010.1nfml.mat)

lambda(wrp2010.1nfml.mat)

#Check if lambda value is the same as fx=mxp0. If not, check for errors in

matrices.

##Season2

#fx=mxp0



adl <- c("J1", "A1", "A2", "A3", "A4", "A5", "A6", "A7", "A8", "A9")



#fx=mx.lambda


=1)






matrices.

###2011

#Season1

131

#fx=mxp0



adl <- c("J", "A1", "A2", "A3", "A4", "A5", "A6", "A7", "A8", "A9")



#fx=mx.lambda


=1)






matrices.

##Season2

#fx=mxp0



adl <- c("J1", "A1", "A2", "A3", "A4", "A5", "A6", "A7", "A8", "A9")



#fx=mx.lambda


=1)






matrices.

###2012

#Season1

#fx=mxp0



adl <- c("J", "A1", "A2", "A3", "A4", "A5", "A6", "A7", "A8", "A9")



#fx=mx.lambda


=1)






matrices.

##Season2

#fx=mxp0



adl <- c("J1", "A1", "A2", "A3", "A4", "A5", "A6", "A7", "A8", "A9")



#fx=mx.lambda


=1)






matrices.

132

################################################################

##############Demographic stochasticity#########################

#Load fx=mx.lambda matrices

t11 <- read.table("WRP-F-5yr 2010-1nf ml.txt", header = TRUE, row.names = 1)






t11 <- as.matrix(t11)






#Load var(fx) matrices

v11 <- read.table("WRP-F-5yr 2010-1h ml v.txt", header = TRUE, row.names = 1)






v11 <- as.matrix(v11)






##2010-1

xx <- splitA(t11, r=1, c= 3:11)

t11T <- xx$T

t11F <- xx$F

reps <- 1000 # number of trajectories

tmax <- 40 # length of the trajectories 40 = 20 years

totalpop <- matrix(0,tmax,reps) # initializes totalpop matrix to store

trajectories

nzero <- c(79,47,19,19,19,19,20,20,20,20,20) # starting population size,

example only, calculated by [Starting N*Cx] for each stage.

#Cx was obtained from [stable.stage] in the previous section.

for (j in 1:reps)

{

n <- nzero

for (i in 1:tmax)

{

n <- multiresultm(n,t11T, t11F, varF= v11)

totalpop[i,j] <- sum(n)

}

}

#set up variable for years to totalpop > 0

gzyr <- rep(0, reps)

# set up variable for calculation of instantaneous rate of change, r

r <- rep(0, reps)

# calculate r from starting values (nzero)

for(i in 1:reps){

# get row numbers with values > 2

gz <- which(totalpop[,i] >= 2)

# get largest row number

gzm <- max(gz)

# get totalpop value at last step > 0

# calculate time step in years that has last totalpop > 0

gzyr[i] <- gzm/2

# calculate r

133

r[i] <- (log(totalpop[gzm,i]) - log(sum(nzero)))/gzyr[i]

}

# calculate lambda per 6 months

lambda.yr <- exp(r)

l6.11 <- sqrt(lambda.yr)

mean(l6.11) #lambda demog as mean

ql6 <- quantile(l6.11, prob = c(0.025, 0.5, 0.975))

ql6 #lambda demog as median and CI

##2010-2

xx <- splitA(t12, r=1, c= 3:11)

t12T <- xx$T

t12F <- xx$F


tmax <- 40 # length of the trajectories


trajectories


example only

for (j in 1:reps)

{

n <- nzero

for (i in 1:tmax)

{



}

}

# end example from multiresultm {popbio}




r <- rep(0, reps)


for(i in 1:reps){




gzm <- max(gz)



gzyr[i] <- gzm/2

# calculate r


}

lambda.yr <- exp(r)


mean(l6.12)


ql6

##2011-1

xx <- splitA(t21, r=1, c= 3:11)

t21T <- xx$T

t21F <- xx$F




trajectories


example only

for (j in 1:reps)

134

{

n <- nzero

for (i in 1:tmax)

{



}

}





r <- rep(0, reps)


for(i in 1:reps){




gzm <- max(gz)



gzyr[i] <- gzm/2

# calculate r


}

lambda.yr <- exp(r)


mean(l6.21)


ql6

##2011-2

xx <- splitA(t22, r=1, c= 3:11)

t22T <- xx$T

t22F <- xx$F




trajectories


example only

for (j in 1:reps)

{

n <- nzero

for (i in 1:tmax)

{



}

}





r <- rep(0, reps)


for(i in 1:reps){




gzm <- max(gz)


135


gzyr[i] <- gzm/2

# calculate r


}

lambda.yr <- exp(r)


mean(l6.22)


ql6

##2012-1

xx <- splitA(t31, r=1, c= 3:11)

t31T <- xx$T

t31F <- xx$F




trajectories


example only

for (j in 1:reps)

{

n <- nzero

for (i in 1:tmax)

{



}

}





r <- rep(0, reps)


for(i in 1:reps){




gzm <- max(gz)



gzyr[i] <- gzm/2

# calculate r


}

lambda.yr <- exp(r)


mean(l6.31)


ql6

##2012-2

xx <- splitA(t32, r=1, c= 3:11)

t32T <- xx$T

t32F <- xx$F




trajectories

136


example only

for (j in 1:reps)

{

n <- nzero

for (i in 1:tmax)

{



}

}

matplot(totalpop, type = 'l', log="y",

xlab = 'Time (0.5 years)', ylab = 'Total population')





r <- rep(0, reps)


for(i in 1:reps){




gzm <- max(gz)



gzyr[i] <- gzm/2

# calculate r


}

lambda.yr <- exp(r)


mean(l6.32)


ql6

##################################################################

##############Environmental stochasticity#########################

# provide initial age class distribution, a vector of same number of ages

# as in transition matrices

init <- 302 #Starting N

# set number of simulations

jj <- 1000

# number of years in each simulation

nyears <- 20

# assign minimum total number for "extinction"

extinctTN <- 2

# set up vector for counter for time to extinction

extinction <- rep(0, jj)

# set up vector for calculated little r

r <- rep(0,jj)

# set up vector for nyears in units of time-step of matrices (= 0.5 years)

Year <- seq(0,nyears,0.5)

# set up matrix to save Total from each of jj simulations of Years

simTotal <- matrix(rep(0, jj*length(seq(0,nyears,0.5))), nrow = jj, byrow =

TRUE)

137

# establish loop for j simulations: in this case jj repeats

for(j in 1:jj){

# set up matrices for results

# 41 rows in this case for nyears = 20

out <- matrix(rep(0, 2*length(Year)), nrow = length(Year), byrow = TRUE)

colnames(out) <- c("Year", "Total")

# put Year in first column of out

out[, "Year"] <- Year

# put init in columns 2 in first row of out

out[1, 2] <- init

# set up vector for year of matrices to record the random sampling sequence

sy <- rep(0, nyears)

# initialize counter for saving each matrix x vector multiplication

k <- 2

# establish loop for years of simulation

for(i in 1:nyears){

# sample a pair of matrices with replacement

s <- sample(1:3, 1, replace = TRUE)

# determine which pair of matrices to use

sy[i] <- s

if(s == 1) {

t1 = l6.11

t2 = l6.12

}

if(s == 2) {

t1 = l6.21

t2 = l6.22

}

if(s == 3) {

t1 = l6.31

t2 = l6.32

}

# for season 1 (i.e., t1)

# multiply the matrix by the vector of numbers per age class

# and save the result in the output matrix (i.e., out)

l1 <- sample(t1,1,replace = TRUE)

out[k, 2] <- round(l1 * out[k-1, 2], digits = 0)

# test the size of the total (here 2) and if <extnctTN record j, and stop

= break

if(out[k,2] < extinctTN) {

extinction[j] <- k

break

}

# repeat for season 2 (i.e., t2)

l2 <- sample(t2,1,replace = TRUE)

# print(l2)

out[k+1, 2] <- round(l2 * out[k, 2], digits = 0)

# print(out)

if(out[k+1,2] < extinctTN) {

extinction[j] <- k+1

break

}

# increment k

k <- k+2

} # end of nyears loop

# Save totals in out in simtotal

simTotal[j,] <- out[,2]

#Calculate r without using "extinction" values?

if(out[(nyears*2)+1,2] < extinctTN) {

r[j] <- (log(out[extinction[j],2]) - log(out[1,2])) / out[extinction[j],1]

}else{

r[j] <- (log(out[(nyears*2)+1,2]) - log(out[1,2])) / out[(nyears*2)+1,1]

}

138

} # end of j loop

#calculating 6monthly lambda

lambda.yr <- exp(r)

l6 <- sqrt(lambda.yr)

ql6 <- quantile(l6, prob = c(0.025, 0.5, 0.975))

ql6

qr <- quantile(r, probs=c(0.025,0.5,0.975))

qr

exp(qr)

## determine proportion of jj simulations extinct at each half year

# set up vector to hold number of >= extinctTN

pgt <- rep(0, length(simTotal[1,]))

# loop to extract pgt

for(ii in 1:length(simTotal[1,])){

pgt[ii] <- length(subset(simTotal[,ii], simTotal[,ii] >= extinctTN))

}

pgt #lists the numbers of surviving simulations at each time step

pextinct <- (jj -pgt)/jj

#set up matrix for profile likelihood CIs

cis <- matrix(rep(0, 2*length(simTotal[1,])), nrow = length(simTotal[1,]),

byrow = TRUE)

for(yy in 1:length(simTotal[1,])) {

pci <- binom.profile(jj-pgt[yy], jj)

cis[yy,] <- pci$confidenceIntervals[2, c(1,3)]

}

# cis

pextinct #lists proportion of extinct simulations at each time step. i.e. the

last value is P(ext) for nyears

cis[(nyears*2)+1,] #95% profile likelihood CI

#####End script#####

139

Chapter 5

A remarkably quick habituation and high use of a rope bridge by an endangered marsupial, the western ringtail possum

This chapter has been published in the Nature Conservation as:

Yokochi K, Bencini R (2015) A remarkably quick habituation and high use of a rope bridge by an

endangered marsupial, the western ringtail possum. Nature Conservation 11: 79-94

10.3897/natureconservation.11.4385

140

A remarkably quick habituation and high use of a rope bridge by an endangered

marsupial, the western ringtail possum

Kaori Yokochi, Roberta Bencini

Abstract

Rope bridges are being increasingly installed worldwide to mitigate the negative

impacts of roads on arboreal animals. However, monitoring of these structures is still

limited and an assessment of factors influencing the crossing behaviours is lacking. We

monitored the use of a rope bridge near Busselton, Western Australia by the endangered

western ringtail possums (Pseudocheirus occidentalis) in order to identify the patterns

of use and factors influencing the crossings. We installed motion sensor cameras and

microchip readers on the bridge to record the crossings made by individual animals, and

analysed these crossing data using generalised linear models that included factors such

as days since the installation of the bridge, breeding season, wind speed, minimum

temperature and moonlight. Possums started investigating the bridge even before the

installation was completed, and the first complete crossing was recorded only 36 days

after the installation, which is remarkably sooner than arboreal species studied in other

parts of Australia. The possums crossed the bridge increasingly over 270 days of

monitoring at a much higher rate than we expected (8.87 ± 0.59 complete crossings per

night). Possums crossed the bridge less on windy nights and warm nights probably due

to the risk of being blown away and heat stress on warmer days. Crossings also

decreased slightly on brighter nights probably due to the higher risk of predation.

Breeding season did not influence the crossings. Pseudocheirus occidentalis habituated

to the bridge very quickly, and our results demonstrate that rope bridges have the

potential as an effective mitigation measure against the negative impacts of roads on

this species. More studies and longer monitoring, as well as investigating whether

crossings result in the restoration of gene flow are then needed in order to further assess

the true conservation value of these crossing structures.

141

Introduction

Roads can act as a barrier to movement and gene flow in wildlife populations and cause

genetic isolation and fragmentation. This barrier effect can result in demographic and

genetic issues such as lowered migration, dispersal abilities, fitness, and adaptability,

which increases the risk of population extinction (Forman and Alexander 1998). To

mitigate these impacts, an increasing number of wildlife crossing structures are being

installed worldwide because they have the potential to prevent road mortality and

habitat fragmentation by providing animals with safe passages across roads (Clevenger

and Wierzchowski 2006).

Arboreal species can be especially affected by roads because of their fidelity to canopies

and naivety on the ground (Lancaster et al. 2011). Many rope bridges, or canopy bridges,

have been installed worldwide to mitigate negative impacts on arboreal species,

including several opossum, monkey, dormouse and squirrel species (Norwood 1999,

Teixeira et al. 2013, Sonoda 2014). In the eastern parts of Australia, rope bridges have

been built for gliders, possums, and koalas (Phascolarctos cinereus); however,

monitoring of the use of these structures by the target species is still limited to a handful

of cases (Weston et al. 2011, Goldingay et al. 2013, Soanes et al. 2013), and assessment

of factors influencing the use of these structures is lacking.

In Western Australia a rope bridge was installed on Caves Road near Busselton in 2013.

The bridge was targeted to provide safe crossing for the western ringtail possum

(Pseudocheirus occidentalis), a nocturnal, folivorous, arboreal marsupial endemic to

southwest Western Australia (Figure 1a). In a national action plan for Australian

mammals in 2012, this species was classified as critically endangered due to a

continuing dramatic decline in its numbers and range (Woinarski et al. 2014). Habitat

destruction, habitat fragmentation and introduced predators such as red foxes (Vulpes

vulpes) and feral cats (Felis catus) are thought to be the main causes of their decline

(Department of Environment, Water, Heritage and Arts 2008, Morris et al. 2008). The

Busselton region is considered to be one of few strongholds left for this species possibly

because it still has a relatively high abundance of the species’ main food source, the

peppermint tree (Agonis flexuosa in the Myrtaceae Family). However, this area is also

subject to rapid and large-scale developments, which threaten the persistence of the

species (Jones et al. 1994a, Australian Bureau of Statistics 2014). These possums are

highly sedentary and have home ranges as small as 0.31 ha in high density areas

142

(Chapter 2: Yokochi et al. 2015). They show a high fidelity to canopies, and Yokochi et

al. (Chapter 2: 2015) found that Caves Road, a 15 m wide road was restricting their

movements and home ranges. Trimming et al. (2009) also found this road to be a

roadkill hotspot for this species (Figure 1b).

Figure 1 Photographs of the western ringtail possum (Pesudocheirus occidentalis). a) A possum at

Locke Nature Reserve, b) A possum roadkill in Busselton, Western Australia

We monitored the use of this bridge by P. occidentalis and other fauna to identify the

patterns of use and factors influencing the crossings. In previous studies, animals have

been observed to show reluctance towards wildlife crossing structures for a certain

period of time before they habituated to them and started using them regularly (Gagnon

et al. 2011). For example, possums and gliders in eastern Australia started using rope

bridges after 7 to 17 months of bridge constructions, and the number of crossings

increased over time until it reached an asymptote (Weston et al. 2011, Goldingay et al.

2013, Soanes et al. 2013). Therefore, we expected that P. occidentalis would avoid

using the rope bridge for a certain period of time before it starts crossing, and that the

number of crossings would increase over time and eventually reach an asymptote.

143

Several arboreal marsupials increase their activity ranges or change their movement

patterns during the breeding season in search of mates and additional resources (Gentile

et al. 1997, Broome 2001, Loretto and Vieira 2005). Other arboreal folivorous species

have been observed to be less active on well lit nights with low temperatures,

presumably to avoid the risk of predation and heat loss (Laurance 1990, Starr et al. 2012,

Rode- Margono and Nekaris 2014). Greater wind speed also decreased the number of

common brushtail possums (Trichosurus vulpecula) observed in open pasture (Paterson

1993). Wind speed did not influence the detection rate of P. occidentalis in a forest

habitat (Wayne et al. 2005); however, the rope bridge in this study was completely

exposed to the wind over the road, and strong wind could deter P. occidentalis from

crossing the open bridge. Given this information, we also predicted that P. occidentalis

would cross the bridge more during their breeding seasons and less on well lit, cold

and/or windy nights.

Materials and Methods

Study area and rope bridge

In July 2013, a rope bridge was constructed across Caves Road about 9 km west of

Busselton, Western Australia (33° 39' 32" S; 115° 14' 26" E) to connect peppermint

trees in Locke Nature Reserve to those in a campsite across the road. Caves Road is a 15

m wide major road connecting popular tourist destinations in the region. The recorded

daily traffic volume on this road was 6,000 cars in 2008, but it could vary up to 15,000

cars in the peak tourist season (Main Roads WA 2009, G. Zoetelief, Pers. Comm.).

Locke Nature Reserve and its surrounding campsites are known to support the highest

density of P. occidentalis in the Swan Coastal Plain, a region dominated by A. flexuosa

vegetation, which is an ideal habitat for the possums (Jones et al. 1994a, Jones et al.

2007). Another possum species, the common brushtail possum, has also been observed

in the nature reserve at a low density (Clarke 2011, Yokochi unpublished data).

The rope bridge was supported by an approximately 8.5 m tall wooden pole with a

concrete foundation and two metal stay wires on each side of the road. The bridge was

300 mm in width and approximately 26.5 m in length. Two steel wires running between

poles with nettings of marine grade ropes in between provided a flat surface for

possums to cross (Figure 2). We employed the flat design over a box design because the

box design was found to be unnecessary (Weston et al. 2011). One large rope extending

144

from the top of each pole provided a passage between the bridge and surrounding trees,

together with the metal stay wires that were in contact with nearby trees.

Figure 2 A rope bridge installed on Caves Road near Busselton, Western Australia. a) Two stay

wires and a rope extending from the pole of a rope bridge to nearby trees on South side of Caves

Road, b) Close up of the bridge showing one of the sensors and microchip reader on the North side

(taken by an infrared camera on the bridge)

Monitoring

We captured 44 female and 53 male western ringtail possums within two 200 m x 200

m blocks on the north and south sides of the rope bridge site on Caves Road from

March 2010 to April 2014 (Figure 3). To capture the possums, we used a specially built

tranquiliser gun with darts containing a nominal dose of 11-12 mg/kg of Zoletil 100®

(Virbac Australia, NSW Australia) following a method developed by P. de Tores and

reported by Clarke (2011). A Trovan Unique ID100 Implantable Transponder (Trovan,

Ltd., U.K.) was inserted subcutaneously between the shoulder blades of each captured

possum.

145

Thirty days after the installation of the rope bridge, an infrared camera (BuckEye Cam

Orion camera, BuckEye Cam, Ohio USA), a microchip reader (LID650N / ANT612

system, Dorset Identification B.V., Aalten, Netherlands), and a pair of optical sensors

were set up on each end of the bridge (monitoring system set up by Faunatech Austbat,

Victoria Australia). When an animal moved past and blocked one of the sensors, this

triggered the camera to take three consecutive photos and activated the microchip reader

for a period of 30 seconds. Date and time were recorded on every photograph taken, and

the microchip readers recorded the date, time and microchip code of individuals that

used the bridge. Unfortunately, the microchip readers malfunctioned regularly, so we

used photographic data from 270 nights of monitoring from August 2013 to May 2014

for further analyses.

Figure 3 A map of the study area near Busselton, Western Australia. Black rectangles represent the

areas where Pseudocheirus occidentalis were captured for tagging, and the thick red line represents

the rope bridge across Caves Road.

146

A crossing was regarded “partial” if an animal was recorded on one side of the bridge

only and returned back to its original side. A crossing was regarded “complete” if an

animal was recorded leaving one side and then arriving on the other side within 10

minutes. We recorded the simultaneous crossing by two and three adults as two and

three crossings respectively; however, a crossing by a pair of mother and young was

counted as a single crossing. Species, type, and direction of the crossings were obtained

from photographic data to calculate the number of complete crossings of the bridge by P.

occidentalis on each night.

Data analyses

We used generalised linear models with a negative binomial distribution and log link

function to identify the factors influencing the number of crossings per night because

the crossing data were discrete and overdispersed (Byers et al. 2003). Based on our

hypotheses, we constructed candidate models with variables such as days since the

bridge installation, breeding season, daily minimum temperature, fraction of the moon

lit, and daily maximum wind speed (Table 1). We set the breeding season as April to

July and September to November, which are the known breeding peaks for P.

occidentalis in the Busselton region (Chapter 2, Jones et al. 1994b). We obtained data

from the Australian Bureau of Meteorology (2014) on daily minimum temperature and

maximum wind speed at Busselton Regional Airport, which is approximately 15 km

from the study site. Data on the fraction of the moon illuminated at 10 pm in Western

Australia were obtained from The United States Navy Observatory (2014).

We ran generalized linear models using the package MASS v.7.3-35 (Ripley et al. 2014)

on R version 3.0.1 (R Development Core Team 2013), and ranked the models based on

Akaike Information Criterion (AIC) values. The model with the lowest AIC value was

chosen as the best fit for the data. We considered models with ΔAIC values (the

difference between the AIC value of the model and that of the highest ranking model) of

less than 2 to have strong support, those with ΔAIC values between 2 and 7 to have

weak support and those with ΔAIC values of greater than 7 to have no support from our

data (Burnham and Anderson 2002). We also generated 95 % confidence intervals for

each of the parameters to check for directionality and significant divergence from zero.

147

Table 1 Candidate models used to analyse variables affecting the number of crossings of a rope

bridge by Pseudocheirus occidentalis. A generalised linear model with negative binomial regression

was used to compare these candidate models. “+” denotes additive effects of variables and “*”

denotes additive and interactive effects of variables.

Model Hypothesis tested

Time Crossings will increase over time.

Breeding Crossings will increase in breeding seasons.

Min temp Crossings will decrease on cold nights.

Moon Crossings will decrease on well lit nights.

Wind Crossings will decrease on windy nights.

Time + Breeding Crossings will increase over time and in breeding seasons.

Time + Min temp Crossings will increase over time but decrease on cold nights.

Time + Moon Crossings will increase over time but decrease on well lit nights.

Time + Wind Crossings will increase over time but decrease on windy nights.

Min temp * Moon Crossings will decrease on cold nights if the moon is bright.

Null The number of crossings varies randomly.

Results

Within a week of installation of the poles, an author (KY) observed two western ringtail

possums on stay wires investigating the poles. This was even before the metal wires and

rope nettings were installed between the poles (i.e. before the installation of the bridge

was completed). Three separate partial crossings by P. occidentalis were recorded on 16

photos on the first night of monitoring on the north end of the bridge. The first complete

crossing from north to south was recorded on the 6th night of monitoring, only 36 days

after the installation of the bridge had been completed. During 270 nights of monitoring,

cameras recorded 664 complete crossings from north to south and 636 complete

crossings from south to north, totalling 1300 crossings. The number of complete

crossings increased gradually over time (Figure 4), and P. occidentalis completely

crossed the bridge at least three times a night for the last 100 nights of monitoring. The

rate of crossings was 8.87 ± 0.59 (s.e.) complete crossings per night for the last 30

nights of monitoring. No other species, including common brushtail possums, was

captured on cameras other than several birds, including Australian magpies (Cracticus

tibicen), tawny frogmouths (Podargus strigoides), common bronzewings (Phaps

148

chalcoptera), silvereyes (Zosterops lateralis), and red wattlebirds (Anthochaera

carunculata) resting on the bridge.

Figure 4 Weekly averages of the number of complete crossings by Pseudocheirus occidentalis on a

rope bridge installed over Caves Road near Busselton, Western Australia. The thick line shows the

weekly averages and thin vertical lines represent standard errors of the means.

Figure 5 Photographs of mother and young Pseudocheirus occidentalis crossing the road using the

rope bridge near Busselton, Western Australia.

149

Microchip readers malfunctioned regularly, and not all possums using the bridge were

microchipped, so only five microchipped individuals were recorded on eight nights. The

north reader recorded one female partially crossing the bridge four times on one night,

and the same possum was also photographed on the bridge with her young on multiple

occasions. After gaining independence, her young was recorded crossing the bridge on

its own. Other mothers and their young as well as pairs of a male and a female were also

regularly captured by the cameras while crossing the bridge together (Figure 5).

The number of crossings by P. occidentalis had a strong positive correlation with time

since bridge installation (Table 2). At the same time, the number of crossings decreased

on nights with greater maximum wind speed. The number of crossings was found to

increase on colder nights although this effect was not as strong as that of wind (ΔAIC =

1.4). The fraction of the moon lit had a considerably weaker negative effect on the

number of crossings compared with wind speed and minimum temperature (ΔAIC =

4.9). The correlation between the number of crossings and breeding season was even

weaker (ΔAIC = 5.4) and 95 % confidence interval of its parameter estimate included

zero, indicating that the breeding season did not affect the number of crossings. The

interaction effect between the moonlight and minimum temperature also had no support

(ΔAIC = 159.5).

150

Table 2 Generalised linear model analysis on number of crossings of a rope bridge by

Pseudocheirus occidentalis. AIC stands for Akaike Information Criterion. Numbers in brackets are

95 % confidence intervals for the parameter estimates. “Time” is the number of days since the

installation of the rope bridge, “Wind” is the daily maximum wind speed, “Tmin.” is the daily

minimum temperature, and “Moon” is the fraction of the moon lit. For the breeding season variable,

the estimate is for the non-breeding season.

Model variables AIC ∆AIC AIC weight Parameter estimates

Time + Wind 1291.9 - 0.60 Time: 0.007 (0.006, 0.008)

Wind: -0.011 (-0.019, -0.004)

Time + Tmin. 1293.3 1.4 0.30 Time: 0.007 (0.006, 0.008)

Tmin.: -0.027 (-0.046, -0.008)

Time + Moon 1296.8 4.9 0.05 Time: 0.007 (0.006, 0.008)

Moon: -0.211 (-0.411, -0.011)

Time + Breeding 1297.3 5.4 0.04 Time: 0.007 (0.006, 0.008)

Breeding: -0.141 (-0.283, 0.001)

Time 1299.1 7.2 0.02 0.007 (0.006, 0.008)

Wind 1430.0 138.1 0.00 -0.022 (-0.032, -0.013)

Breeding 1448.7 156.8 0.00 -0.150 (-0.254, 0.055)

Null 1448.7 156.8 0.00

Tmin. 1449.2 157.3 0.00 -0.017 (-0.045, 0.010)

Moon 1449.6 157.7 0.00 -0.022 (-0.032, -0.013)

Moon x Tmin. 1451.4 159.5 0.00 Moon: -0.618 (-1.622, 0.381)

Tmin. : -0.036 (-0.086, 0.013)

Moon. Tmin.: 0.042 (-0.041, 0.125)

Discussion

As expected, the use of the rope bridge by P. occidentalis increased over time; however,

the possums started crossing the bridge much sooner and at a much higher rates than we

expected. They started investigating the bridge even before the installation was

completed, and the first complete crossing was recorded only 36 days after the

installation, which is remarkably shorter than seven months – the shortest time elapsed

before other possum and glider species started to use rope bridges in other parts of

Australia (Weston et al. 2011, Goldingay et al. 2013, Soanes et al. 2013).

151

The rate of crossings was also considerably higher than those previously reported for

other possums and gliders. Possums and gliders crossed the Pacific Highway in New

South Wales using rope bridges at a rate of 0.02-0.08 crossings per night per species

(Goldingay et al. 2013). On the Hume Highway in Victoria, squirrel gliders (Petaurus

norfolcensis) used one of the rope bridges at a rate of 2.47 crossings per night after

habituation (Soanes et al. 2013). In Queensland, the pooled crossing rate of three

possum species was up to one crossing per 150 minutes (i.e. 4.8 crossings per 12 hours,

Weston et al. 2011). The crossing rate of P. occidentalis recorded in this study (8.87

crossings per night) is considerably higher than these previously reported rates, and it

did not reach a clear asymptote during the monitoring period. This high rate could be

due to the high density of the species in the study area and/or their particular lack of

avoidance behaviour towards unfamiliar objects such as the new rope bridge (Wayne et

al. 2005, Jones et al. 2007, Clarke 2011, Yokochi, K. personal observation). Moreover,

possum species studied by Weston et al. (2011) lived in rainforests that generally have

greater canopy cover than our study site, and their fidelity to a dense canopy may have

made these possums more reluctant to cross exposed bridges. Uneven numbers of

crossings by P. occidentalis in different directions suggest that some individuals crossed

the bridge and remained on the other side. Use of the bridge by two generations of

possums is also an encouraging sign that it will be used over generations and that it will

be able to help increase gene flow across the road. These results suggest that P.

occidentalis can learn to use this type of wildlife crossing structure very quickly, and

show that rope bridges have the potential to be a very effective mitigation measure

against the negative impacts of roads on this critically endangered species.

The number of bridge crossings decreased on windy nights, as expected. Being exposed

to strong wind on the bridge may have discouraged possums from crossing due to the

higher risk of being blown away. A higher risk of heat loss could be another reason for

possums to cross the bridge less on windy nights (McCafferty et al. 2011); however,

this is unlikely to be the case given that the number of crossings actually increased on

colder nights, contrary to our expectation. It seems that heat loss is not a big problem for

P. occidentalis unlike other arboreal mammals studied by Laurance (1990), Starr et al.

(2012), and Rode-Margono and Nekaris (2014). These researchers studied species in

tropical regions such as Northern Queensland, Cambodia, and West Java, and their

study species might have been more susceptible or less adapted to cold conditions

compared with P. occidentalis. On the other hand, the lower number of crossings by P.

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occidentalis on warmer nights may be due to their susceptibility to overheating as they

are prone to overheat and known to suffer physiologically at an ambient temperature of

35 °C or above (Yin 2006). In the study area, days with higher minimum temperatures

generally experienced higher maximum temperature, which might have placed the

possums under heat stress. Several mammalian species have been observed to decrease

their food intake and activity under heat stress in order to reduce their heat production

(Terrien et al. 2011). P. occidentalis may employ similar behavioural coping

mechanisms and thus reduce their activity, including bridge crossings, on warmer nights.

Moonlight had a weak effect on the number of crossings, and fewer crossings were

recorded on brighter nights. Whether this trend is caused by possums generally reducing

their activities on bright nights or possums being discouraged to cross the exposed

bridge on brighter nights cannot be known from our data. Wayne et al. (2005) reported

that the moon or wind had no effect on the number of possums seen by spotlighting in a

forest; however, possums are likely to act differently in a completely exposed

environment such as on a rope bridge compared to an environment with greater cover

from predators such as the canopy in a forest. Native owl species, such as the masked

owl (Tyto novaehollandiae) are thought to be present in the region (Clarke 2011), and

they prey on similar sized possum species in New South Wales (Kavanagh 1996).

Therefore, it is possible that P. occidentalis reduced their activities on the exposed rope

bridge on bright nights in order to reduce the risk of predation by birds of prey.

Contrary to our expectation, the number of crossings did not increase during the

breeding seasons. Home ranges of P. occidentalis in the same area also did not change

during the breeding seasons (Chapter 2: Yokochi et al. 2015), suggesting that P.

occidentalis do not expand their areas of activities to search for mates or extra resources

during the breeding season. A longer monitoring period would be required to assess the

effect of breeding season on the crossing behaviour more thoroughly because only two

breeding seasons could be monitored and the rate of crossings did not reach an

asymptote in this study.

Malfunction of the microchip readers made it impossible for us to identify all

individuals using the bridge; however, the data still revealed that at least five different

individuals used the bridge and that these individuals were using the bridge regularly.

We must be cautious when interpreting the number of crossings on this bridge because a

few individuals contributed to many of the crossings. At the same time, however, this

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also means that those individuals incorporated the bridge into their regular movement,

which yet again suggests their high adaptability to this type of structure. To identify

exactly how many individuals are benefitting from the bridge, we need to improve the

monitoring system or develop a more reliable way of identifying individuals.

Multiple years of monitoring of the rope bridge in this study will also be necessary to

investigate the long-term seasonal and yearly changes in the use of this bridge by P.

occidentalis as well as to identify the asymptotic rate of crossing. Gagnon et al. (2011)

found that the elk (Cervus elaphus) adapted and habituated to terrestrial crossing

structures over years, and some factors, such as season, time of the day and length of

monitoring, that influenced the crossing frequencies in the first year of monitoring,

became insignificant after four years. Given the remarkably quick habituation shown by

our study species, we may be able to identify the factors influencing their long term

crossing behaviours in less than four years.

We also need to study the use of rope bridges in other areas in order to further assess the

effectiveness of these structures as a wildlife crossing structure for P. occidentalis. Only

one bridge was installed for this study due to financial constraints, and crossing patterns

and characteristics are likely to differ in other areas with different population densities,

habitats, and road characteristics or even for different kinds of artificial linear structures.

For instance, it took P. occidentalis 18 months before it was recorded on another bridge

installed across a newly constructed highway in Bunbury, located only 60 km away

from the study area (B. Chambers, Pers. Comm.). This is possibly due to the lower

density of the species in the area, recent disturbance caused by the road construction,

and the greater length of the bridge (Bencini and Chambers 2014). Although it is

probably unnecessary for the rope bridge in our study because of its high crossing rate,

alteration of the design would be possible for the bridge in Bunbury or future bridges if

the possums do not appear to habituate to them. A design to reduce the exposure and the

effects of wind and moonlight may encourage possums to start using the bridges. In

another study conducted in the same study area in Busselton, we found that an artificial

waterway nearby was causing greater genetic divergence among P. occidentalis than

Caves Road (Chapter 3); therefore, installation and monitoring of a rope bridge across

this waterway is strongly recommended given the willingness of the possums to utilise

these crossing structures in this area. Crossing behaviours of P. occidentalis is also

likely to differ in areas where more competitive arboreal species, such as common

154

brushtail possums, exist in higher densities than at our study area because they are

thought to limit the activities of P. occidentalis (Clarke 2011).

Using individual based analyses such as parentage testing and Bayesian cluster analysis,

Sawaya et al. (2014) found that grizzly (Ursus arctos) and black bears (Ursus

americanus) using terrestrial crossing structures were breeding on the other side of a

highway and achieved enough gene flow to avoid genetic isolation. A similar

investigation into whether the crossings of the bridge by possums result in reproduction

on the other side and restore the gene flow is essential in order to assess the true

conservation value of rope bridges (Corlatti et al. 2009). We also need to assess whether

the rope bridge provides a safe passage for dispersing juveniles, therefore assisting the

restoration of gene flow. A study focusing on this life stage needs to be conducted, as

well as genetic investigations to assess the change in the level of gene flow before and

after the bridge construction.

Conclusion

Roads pose negative impacts on wildlife and their impacts need to be mitigated by

providing safe passages especially for threatened arboreal species. The critically

endangered P. occidentalis habituated to a rope bridge remarkably quickly, and the

bridge is now regularly used by multiple individuals at a high rate every night. These

results show the high potential of rope bridges as an effective mitigation measure

against the negative impacts of roads on this species. More studies and longer

monitoring, as well as genetic investigations into whether crossings by animals result in

the restoration of gene flow are needed in order to assess the true conservation value of

these crossing structures.

Acknowledgements

We would like to thank Main Roads Western Australia, the Western Australian

Department of Parks and Wildlife, Western Power, the School of Animal Biology at

The University of Western Australia and the Satterley Property Group for technically

and financially supporting this study. We also would like to acknowledge Mr Paul J. de

Tores for his invaluable advice, support and training throughout the earlier stage of this

study, and Dr Brian K. Chambers for his helpful advice and support in the analyses of

data. We would also like to thank the City of Busselton, Abundant Life Centre, and

Possum Centre Busselton Inc. for their support and over 100 volunteers, including

155

Kaarissa Harring-Harris, who helped us in the field braving long hot, cold and/or wet

days and nights.

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Chapter 6. General discussion

This thesis was set out to fill the gaps in our knowledge of the biology, ecology and

current status of the endangered western ringtail possums (Pseudocheirus occidentalis)

and our knowledge of the negative impacts of artificial linear structures on wildlife,

especially on arboreal species. I studied a population of P. occidentalis near Busselton,

Western Australia to fulfil the six main aims of this thesis, which were:

a) to assess the impact of a road and an artificial waterway on the movements of P.

occidentalis;

b) to investigate the genetic impacts of a road and an artificial waterway on P.

occidentalis;

c) to gain more information on home ranges of P. occidentalis in A. flexuosa dominated

habitat;

d) to assess the general genetic health and fine-scale genetic structure within a

population of P. occidentalis;

e) to investigate the current status and predict the future direction of a population of P.

occidentalis in its core habitat using a population viability analysis and assess the

likely effectiveness of two potential management options: removal of fox predation

and road mortality; and

f) to monitor the use of a newly constructed rope bridge and assess whether it provides

P. occidentalis with safe passage across Caves Road and to determine which factors

affect the use of the bridge.

Here, I provide an overview of key findings of this thesis that filled these gaps and

discuss the overall results to produce conclusions and management implications.

Limitations of this study and possible future research directions are also presented.

162

6.1. Key findings

As expected, radio-telemetry results confirmed that both Caves Road and an artificial

waterway were barriers to the movements of P. occidentalis and that the possums were

not expanding their home ranges across the road or waterway (Chapter 2). Their average

home ranges were 0.31 0.04 (s.e.) ha for males and 0.16 0.02 ha for females, which

are smaller than those of other similar-sized arboreal marsupials in Australia, such as

common ringtail possums (Pseudocheirus peregrinus: 0.64 ha for females and 1.03 ha

for males, Lindenmayer et al. 2008) and lemuroid ringtail possums (Hemibelideus

lemuroids: 0.47 ha for females and 0.43 ha for males, Wilson et al. 2007). Spatial

autocorrelation analyses also showed positive fine-scale genetic structuring over

distances up to 100 - 600 m within Locke Nature Reserve, confirming a small range of

dispersal in this species, even compared to other arboreal marsupials (Stow et al. 2006,

Lee et al. 2010, Chapter 3). These results highlight the exceptionally high level of

philopatry in P. occidentalis.

Surprisingly, movements of the possums were restricted even by a 5 m wide firebreak

where canopy connection was not available (Chapter 2). Possums in partially cleared

campsites mostly remained within groups of trees with continuous canopy, and travelled

only occasionally to other trees across cleared patches. These results highlight their

strong fidelity to canopy and reluctance to traverse on ground level.

A number of possums were seen foraging, grooming and resting on trees on the road

verges (Chapter 2). This suggests that the presence of traffic was not the reason why

possums did not cross the road. Instead, the exceptionally strong sedentary and arboreal

nature of these possums is thought to have prevented them from crossing a road without

canopy connections. It is also likely that this sedentary and arboreal nature contributed

to the avoidance of crossing the artificial waterway.

Although both road and waterway were barriers to the movements of possums,

significant genetic divergence was detected across the waterway only, contrary to my

expectation that both linear structures would pose a barrier to gene flow (Chapter 3).

With spatial autocorrelation analysis, I confirmed that this genetic divergence was likely

to have been caused by the barrier effect of the waterway, not the geographical distance.

The older age and greater width of the waterway may have discouraged possums from

crossing it for a longer period of time, contributing to the greater genetic divergence

across this structure than the road. Given that roadkills of P. occidentalis have been

163

regularly recorded on Caves Road (Trimming et al. 2009, Chapter 2), it seems that

possums do try to cross the road occasionally. It appears there have been sufficient

successful crossings to maintain enough gene flow to prevent significant genetic

differentiation. This study therefore provides an example of an artificial waterway

having a greater genetic impact on a threatened species than a major road, highlighting

the need for more studies into these often forgotten types of artificial linear structures.

Although a significant genetic divergence was not detected across the road, it is clearly

restricting the movements of possums and causing direct mortality, which reduces the

effective population size and can eventually result in lowered genetic diversity (Jackson

and Fahrig 2011). Therefore, mitigation measures, in the form of a rope bridge, were

implemented and studied as part of this PhD project. Their effectiveness, studied in

Chapter 4, will be discussed later.

The studied population is thought to be one of few remaining large populations of this

endangered species (Jones et al. 1994, 2007). However, PVA on females predicted that

this population is in decline with a high probability of extinction in the next 20 years

(Chapter 4). Predation by foxes (Vulpes vulpes) was the main cause of mortality

accounting for almost 70 % of adult mortalities, followed by road mortality (9 %). One

case of predation by a cat (Felis catus) was also recorded. Changes in adult survival

rates contributed to the population changes the most, and the simulated removal of the

fox predation from adult and pouch young survival rates reduced the probability of

extinction to almost zero. The simulated removal of road mortalities from adult and

pouch young survival also reduced the probability of extinction significantly. Locke

Nature Reserve was baited monthly with meat baits containing sodium

monofluroacetate to control foxes (de Tores et al. 2004, C. Forward, pers. comm.). My

results indicate that the current level of fox baiting is not sufficient to ensure the

survival of this population. They also suggested that focusing on increasing the

fecundity and survival rates of adults is likely to have the largest effect in slowing down

the decline in population size.

Although a high level of inbreeding was not detected in this population (Chapter 3), the

restricted movements of possums by two linear structures and shrinking population size

may contribute to demographic and genetic problems in the future, such as increased

susceptibility to environmental stochasticity and lower genetic diversity (Chapter 2 and

4). This will further accelerate the decline of this important population. Therefore, an

urgent and more intense management of its threatening processes is needed. A dramatic

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reduction in fox predation and road mortalities and a measure to mitigate the habitat

fragmentation caused by artificial linear structures are minimum requirements to ensure

the survival of this population.

A rope bridge was built across Caves Road in 2013, and I assessed its ability to mitigate

the negative impacts of road mortality and habitat fragmentation on P. occidentalis. By

monitoring the bridge using motion sensor cameras and microchip readers, I found that

P. occidentalis started crossing the rope bridge remarkably quickly with the first

crossing recorded only 36 days after installation (Chapter 5). This habituation time is

exceptionally short compared to seven months - the shortest habituation time of other

possum and glider species to rope bridges in other parts of Australia (Weston et al. 2011,

Goldingay et al. 2013, Soanes et al. 2013). By the end of 270 days of monitoring,

multiple individuals were crossing the bridge every night at a rate of 8.87 ± 0.59

complete crossings per night, which again was remarkably higher than the rate of other

species, which ranged from 0.02 to 4.80 crossings per night (Weston et al. 2011,

Goldingay et al. 2013, Soanes et al. 2013). Pseudocheirus occidentalis crossed the

bridge less on windy nights and warm nights, probably due to the risk of being blown

away and heat stress, by which they are known to be affected (Yin 2006). Crossings

also decreased slightly on brighter nights probably due to the higher risk of predation.

These results demonstrate that rope bridges can provide a safe way of crossing a road

for P. occidentalis and have the potential to be an effective mitigation measure.

One unexpected finding in this thesis was the way radio-collars influenced the survival

of the possums (Chapter 4). The known fate model estimated the adult survival rate of

0.897 per six months with the lightest collar (15.8 g), but the survival rate dropped to

0.623 per six months with the heaviest collar (22.6 g). The heaviest collar used in this

study was less than half of this maximum recommended weight set in guidelines that are

commonly used by wildlife researchers (Animal Ethics Infolink n.d., Sikes and Gannon

2011), yet it seemed to have decreased the survival rate of adult possums by 30 %

compared to the lighter collar. Several studies have been conducted to assess the effects

of radio collars on the survival of small mammalian species; however, their results are

contradictory (Withey et al. 2001). This is the first instance where the negative effect of

collar weight on the survival of an arboreal marsupial species was tested and recorded,

although it was not part of the original aims of the study. The mechanism behind the

increased mortality in individuals with heavier collars is not known as this study was

not set out to test the effect of radio collars on possums; however, it is possible that the

165

extra weight of collars slowed movements of possums and/or forced them to come

down to the ground level, thus making them more vulnerable to predation. Nonetheless,

the current recommended maximum weight of radio collars seems excessive for P.

occidentalis, and possibly for other small to medium sized arboreal marsupials.

This project confirmed that the stronghold population of endangered P. occidentalis is

in a rapid decline and under significant pressure from fox predation and road mortality.

By monitoring movements and estimating home ranges of P. occidentalis, I also

demonstrated its highly sedentary and arboreal nature. These characteristics can make

this species highly vulnerable to the impact of habitat fragmentation. Indeed, a busy

road was found to restrict their movement, as seen in many other arboreal species

(Wilson et al. 2007, Radespiel et al. 2008, Lee et al. 2010a, Lancaster et al. 2011,

Goldingay et al. 2013, Munguia-Vega et al. 2013). A 45 m wide artificial waterway was

also found to restrict their movements, and significant genetic divergence was observed

across the waterway, making this study the first to clearly demonstrate the capability of

an artificial waterway to fragment subpopulations of arboreal wildlife. The predicted

rapid decline, coupled with the demonstrated barrier effects of the artificial linear

structures, implies that this population is at a particularly high risk because habitat

fragmentation can accelerate the extinction process of an already declining population

by isolating it from surrounding subpopulations. Small isolated populations have a

higher risk of extinction because of their higher vulnerability to stochastic demographic

changes and catastrophic events (Foley 1997). The barrier effect prevents immigration,

which would normally act as an important source of new individuals for a small

population (Crooks and Sanjayan 2006, Stewart and Van der Ree 2006). The barrier

effect also restricts gene flow, which can result in lowered genetic diversity over

generations especially in a small population (Frankham et al. 2002). Lower genetic

diversity can then lead to lower adaptability and make the population even more

vulnerable to a changing environment. The significant genetic divergence observed

across the waterway in this study may be an indication that this genetic isolation is

already happening across the waterway. Isolation of this stronghold population is a

threat to P. occidentalis in the wider Busselton area as habitat fragmentation can prevent

recolonisation, resulting in the increased risk of extinction of the whole metapopulation

(Foley 1997). A rope bridge was installed across the studied road to investigate whether

it can provide safe passages for the possums. The quick adaptation and high use of the

bridge by the possums signal the high potential of this crossing structure as a mitigation

166

measure of the barrier effect. However, establishing safe crossings is only the first step

in mitigating the negative impact of artificial linear structures, and a reduction in road

mortality and an increase in habitat connectivity need to be demonstrated before this

bridge is deemed as an effective mitigation measure.

6.2. Limitations

Although this thesis adds to our knowledge on the endangered P. occidentalis and

addresses many questions that were previously unanswered, there were limitations

involved, which are mainly related to the inherent difficulty in capturing this elusive

species. Below, I present the main limitations of my thesis.

a) I confirmed that P. occidentalis has a limited dispersal range by identifying positive

fine-scale genetic structure within the nature reserve. However, the dispersal patterns of

females and males could not be assessed separately due to a small sample size. I was

therefore, unable to determine whether the dispersal of P. occidentalis is primarily

driven by one sex.

b) Movement and survival of juveniles could not be monitored as they were too light to

be fitted with radio collars. These data would be useful in directly monitoring their

dispersal and constructing more accurate population models.

c) Radio-collars affected survival rates of adult possums. Data from adults wearing

heavy collars were omitted from estimations of the survival rates to be used in PVA, but

this further reduced the sample size. The collar weight did not seem to affect the home

range sizes of adult possums (Chapter 2 Appendix); however, the possibility that

wearing collars affected movements and survival of the possums cannot be ruled out

because movement and survival data of possums wearing no collar were unavailable.

d) PVA in this thesis was performed using the data from females only because data on

fecundity of males were lacking. To perform PVA on males, we need to know the

number of young a male sires per breeding season and the survival rate of those young,

but this could not be achieved within the timeframe of a PhD project.

e) The current PVA can only provide an indication of the general trend to which the

population of P. occidentalis is heading and the result should be used cautiously (e.g.

for comparing the effectiveness of fox and road mortality removals). Demographic

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parameters estimated from larger sample sizes and a longer monitoring period would

improve the accuracy of the population projection (Fieberg and Ellner 2000).

f) Although I studied an important population of P. occidentalis in its core habitat, the

results may not reflect the overall state of the species. Home ranges, dispersal patterns,

genetic health and demographic rates of the possums are likely to differ in different

habitats, and their relationship with artificial linear structures may also differ. Other

artificial linear structures of different type, width and properties (e.g. wider highways,

fenced roads, railways and powerline corridors) are also likely to have different degrees

of impacts on P. occidentalis.

g) This thesis reports a remarkably short habituation time and a high rate of crossings of

the rope bridge by P. occidentalis. However, it is likely that the pattern of use would

differ depending on the habitat and the type of linear structure. For example, it took P.

occidentalis 18 months before crossing another bridge installed across a newly

constructed highway (B. Chambers, Pers. Comm.), possibly because of the lower

density of the species in the area, recent disturbance caused by the road construction,

and the greater length of the bridge (Bencini and Chambers 2014). The possums may

not habituate to a rope bridge across the artificial waterway so quickly either because it

is more exposed than a bridge across a road. A second bridge across the waterway was

initially planned for this study, but it could not be installed due to an unexpected

financial issue. Because the study area did not have a dense population of other arboreal

mammalian species, it is unclear whether rope bridges benefit other species. The use of

a bridge by P. occidentalis may also be influenced by the presence of other arboreal

species.

j) Although some factors influencing the rate of crossing on the rope bridge were

identified from monitoring data, the monitoring period was shorter than a year, due to

delays in the installation of the bridge. At the end of the monitoring period, the rate of

crossings had not reached an asymptote, and I did not have enough data to investigate

the change in the rate of crossings over several breeding seasons or years. Some factors

that are influential now may become uninfluential once the rate of crossings reaches an

asymptote (Gagnon et al. 2011).

h) I found that at least five different individuals were using the rope bridge based on the

data from motion sensor cameras and microchip readers. However, the microchip

readers malfunctioned regularly and I could not identify any more individuals crossing

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the bridge. Therefore, information such as the number of individuals benefitting from

the bridge, the age distribution of the individuals crossing the bridge, or the presence of

a sex bias in the individuals crossing the bridge could not be obtained.

6.3. Future research

To address the limitations discussed above and further improve our understanding of the

biology, ecology and management options for P. occidentalis, impacts of artificial linear

structures on arboreal animals and mitigation measures against them, the following

research should be conducted in future studies.

1) This thesis provides an example of an artificial waterway causing a greater genetic

impact on a threatened species than a major road. More studies assessing the negative

impacts of these often forgotten types of artificial linear structures on wildlife are

needed, and mitigation measures need to be implemented and their use studied where

wildlife is affected.

2) Road mortality contributed to about 10% of mortality in adult P. occidentalis in the

studied population. Unfortunately the rope bridge was installed later than expected, thus

negating the opportunity to investigate or detect a reduction in road kills after its

installation. The effectiveness of the rope bridge in reducing the number of road

mortalities needs to be assessed by monitoring changes in road mortality before and

after the rope bridge installation and in control sites. If no change is detected, other

options of mitigating road mortality need to be considered and monitored for their

effectiveness. Installing arboreal species resistant fencing structures (Vic Roads 2012)

together with the rope bridge may discourage possums from crossing the road on the

ground level; therefore may contribute to reduction of road mortality.

3) The effectiveness of the rope bridge in increasing gene flow needs to be assessed.

Crossings of a road by animals do not always result in gene flow (Riley et al. 2006), and

gene flow needs to be confirmed by assessing the short-term genetic changes (e.g.

paternity testing) and/or long-term genetic changes (e.g. lowered genetic divergence).

Paternity testing would also provide us with the male fecundity data needed for more

accurate PVA. Monitoring of changes in home ranges before and after the installation of

a rope bridge should also be conducted to see if possums cross the bridge to expand

their home ranges across the road.

169

4) A longer monitoring period of the rope bridge is necessary to find out the asymptotic

rate of crossings and factors influencing the crossing of the bridge by the possums in the

long term. A reliable monitoring system is also necessary to identify individuals and

establish how many individuals are benefitting from the bridge, whether a particular sex

or age group is more likely to cross, and whether the individuals crossing the bridge are

breeding on the other side.

5) More rope bridges need to be installed and the use of these bridges needs to be

monitored and compared to identify other factors influencing the crossing patterns of P.

occidentalis, such as type of artificial linear structures, density, habitat type, history of

anthropogenic disturbances and co-existing species. The studied artificial waterway

would be a good candidate location as the density of P. occidentalis is high and genetic

divergence has already been confirmed across this linear structure.

6) PVA on other populations of P. occidentalis needs to be performed to investigate if

the declining trend is uniform across its range. Home ranges, dispersal patterns and

genetic health can also be assessed at the same time to improve our knowledge of this

species across its range.

7) A study on fine-scale population structure of males and females needs to be

conducted to assess whether dispersal in P. occidentalis is driven by one sex. Direct

monitoring of juveniles would also provide us with information on their dispersal

patterns and tell us whether they disperse across the artificial linear structures. It would

also enable us to obtain demographic parameters for the juvenile stage and increase the

reliability of PVA.

8) To achieve monitoring of juveniles, more effective and less burdensome ways of

monitoring their movements and survival need to be developed. Lighter collars with

flexible bands that allow growth of the animals could be developed to deploy on

juveniles. The monitoring period would also need to be shortened to reduce the impacts

of collars. The effects of wearing radio collars on movements and survival of the

possums also need to be investigated further. Studies on the effect of the weight of

collars on the survival of other similar arboreal species should also be conducted to see

whether the negative impact is common in this type of animal.

170

6.4. Management implications

Given the large impact of fox predation and the poor outlook for the study population,

more effective fox control methods need to be developed and implemented urgently in

the wider area. Once implemented, the outcome needs to be monitored and its

effectiveness needs to be assessed regularly. Other measures to increase the

reproductive and survival rates, especially of adults, need to be developed and employed

to ensure the survival of this important population. Cats are now known to prey on P.

occidentalis in the reserve, so cats need to be controlled concurrently with foxes

because their number can increase as the number of foxes decreases (Glen and Dickman

2005, de Tores and Marlow 2012).

My thesis has confirmed the high vulnerability of P. occidentalis to the effects of

habitat fragmentation. The artificial waterway has caused a greater genetic divergence

among the possums than a major road. Its negative impact therefore needs to be

mitigated before it causes genetic problems. The capability of a rope bridge in

mitigating negative impacts of artificial linear structures still needs to be established

fully by monitoring the reduction in road mortality and increase in gene flow. However,

if it is effective, its installation across the artificial waterway is recommended.

The current recommended maximum weight of collars set in guidelines that are

commonly used by wildlife researchers (5 % of body weight: Animal Ethics Infolink

n.d., Sikes and Gannon 2011) is excessive for P. occidentalis, as radio-collars of less

than half of the recommended maximum weight were estimated to reduce the adult

survival rate per six months by 30 % compared to the lightest collar used. Radio-

telemetry is the only practical and effective way of monitoring this elusive species in

many cases, so researchers must ensure that they use the lightest collars possible. The

lightest possible collars should be used for P. occidentalis and possibly other vulnerable

arboreal marsupials, especially if long-term monitoring is being carried out.

Artificial linear structures other than roads have the potential to cause similar or even

greater negative impacts on wildlife than roads. Their impacts should be assessed,

especially where species that are likely to be vulnerable to habitat fragmentation occur,

and impacts need to be mitigated if they are significant. In case of arboreal species, rope

bridges can be easily built across other types of artificial linear structures, such as

railways, powerline corridors and artificial waterways. Therefore, the installation of

171

these structures should be actively considered once their effectiveness has been

established.

6.5. Conclusion

The large population of P. occidentalis in Busselton, Western Australia is under

pressure from fox predation and road mortality. Population projections predicted a

rapidly decline and there is a high probability of extinction in the next 20 years. On top

of these pressures, both a major road and an artificial waterway acted as physical

barriers to P. occidentalis. The barrier effect of the artificial waterway was also

associated with a significant genetic divergence between subpopulations. On the other

hand, no genetic divergence was detected across the road, though future impacts cannot

be ruled out. Potential impacts of artificial linear structures other than roads on wildlife

tend to be forgotten; however, this thesis presents an example where an artificial

waterway has caused a greater genetic impact on a population of an endangered arboreal

species than a major road. My results highlight the need for more research into the

negative impacts of artificial linear structures other than roads. Pseudocheirus

occidentalis showed a remarkably quick habituation to a rope bridge across the road

with multiple individuals crossing the bridge every night. This shows that rope bridges

can provide P. occidentalis with safe passages across roads and have the potential to

mitigate the negative impacts of roads. Longer monitoring and assessment of its ability

to restore and maintain gene flow are required to assess the true conservation values of

these structures. Once the effectiveness of rope bridges has been established, the

installation of similar structures across artificial linear structures other than roads should

be considered where vulnerable arboreal animals occur.

172

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