Spatial ecology and genetic population structure of the ...

Spatial ecology and genetic population structure of

the common brushtail possum (Trichosurus

vulpecula) within a New Zealand urban

environment

Amy Louise Adams

A thesis submitted for the degree of

Doctor of Philosophy

at the University of Otago, Dunedin,

New Zealand.

March 2013

ii

Abstract Invasive species are a major threat to global biodiversity due to the competitive and

predatory pressures they exert on indigenous species, and the alterations they cause to

native ecosystems. Within New Zealand, introduced mammalian species have had

significant impacts on native fauna and flora, and are responsible for population

declines and extinctions. One of the most devastating invasive species in New Zealand

is the Australian common brushtail possum (Trichosurus vulpecula Kerr1). The

behavioural flexibility and generalist lifestyle of this species enables it to exploit a wide

range of food sources and habitats, including urban areas. For an invasive species to be

effectively managed, knowledge regarding its spatial ecology and population genetics is

essential. Possums have not been studied within urban New Zealand environments, but

it is well documented that urban-based populations of animals can behave and use

resources differently than in their native habitats. This study aimed to investigate the

distribution, habitat use, and genetic population structure of possums within an urban

environment in New Zealand and provided new insights to inform control operations.

Within invaded environments, the distribution and availability of key resources

required by a species for survival will partly determine species distribution and habitat

use. WaxTags® were deployed in five urban habitats to collect presence/absence data

which were analysed using the software PRESENCE. Occupancy models revealed that the

probability of possum occupancy within the urban environment was influenced by the

type of habitat, supplementary food resources, and proximity to forest fragments. These

results indicate that possums do not use the urban habitat evenly at a broad scale.

Lightweight GPS collars were used to investigate the spatial ecology of urban possums.

The accuracy and performance of these collars were assessed through stationary field

tests within three residential habitat types. Fix rates and error around collected locations

were influenced by sky availability, vegetation complexity, distance to buildings, and

satellite configuration. Estimated error values were incorporated into subsequent spatial

analyses to generate more robust conclusions regarding habitat selection.

1 See Appendix 1 for authorities of all species mentioned in this thesis.

iii

GPS collars were deployed on 24 brushtail possums trapped within residential gardens.

Home ranges estimated using minimum convex polygons and Brownian bridges

revealed that sizes of home ranges varied considerably between individuals within the

urban landscape and in relation to possums in other environments, with males having

larger home ranges ( x = 4.86 ha) than females ( x = 2.42 ha). Resource utilisation

functions were used to assess resource selection at a finer scale within the urban

environment. The strongest selection was consistently for forest fragments and

residential areas composed of structurally-complex vegetation, and areas in closer

proximity to forest fragments. For urban possum management to be effective, control

needs to target these habitat types concurrently to minimise reinvasion potential.

Recently, molecular genetics have been incorporated into the management of invasive

species to identify potential reinvasion pathways, allowing the identification of

‘eradication units’. An attempt to eradicate possums from the Otago Peninsula is

currently underway. Potential reinvasion sources were investigated by determining the

population structure of possums from seven locations around Dunedin and within the

rural environment on the Otago Peninsula using twelve microsatellites. Population

clustering determined by STRUCTURE and TESS, coupled with differences in genetic

variation between populations, revealed a potential reinvasion pathway onto the Otago

Peninsula from residential suburbs at the base of the Peninsula. This implies that if an

urban buffer zone is well managed based on the fine-scale GPS information, the Otago

Peninsula should remain possum-free.

iv

Acknowledgements Firstly, I would like to extend my sincere thanks to my supervisor Dr. Yolanda van

Heezik (Zoology Department), and co-supervisors, Prof. Kath Dickinson (Botany

Department), and Dr. Bruce Robertson (Zoology Department) for their unwavering

support, encouragement, guidance, valuable input, and advice over the last few years.

Without this, this research would not have been possible.

This research was made possible with the generous financial assistance from the

Department of Zoology, the Federation of Otago Graduate Women (Brenda Shore

Award for Women), the Royal Society of New Zealand (Hutton Fund), and the Marples

& Marples Ecology Fund. I am also grateful to have been supported for three years by a

University of Otago Postgraduate Scholarship, which made this research opportunity

possible for me. This study was authorized by the University of Otago Animal Ethics

Committee (spatial analysis component: AEC D38/09; genetics component: ET 3/12).

This research was carried out with the intention of providing valuable spatial and

genetic data to the Otago Peninsula Biodiversity Group (OPBG) to assist in their

(hopefully successful!) eradication attempt of possums from the Otago Peninsula. This

data will highlight potential re-invasion pathways and habitat selection of urban

possums to enable successful implementation and management of an urban buffer zone.

As such, I would like to thank Rhys Millar and Rik Wilson from the OPBG, in which

earlier discussions with them regarding the possum eradication helped shape this

project to what it is today.

To all the landowners who answered my call for possums and the Dunedin City

Council (particularly Scott MacLean), I extend a massive thank-you for the generous

use of your properties throughout the course of this research. Without your backyards,

parks, or forest fragments, this research would not have been possible. I am very

grateful to previous possum students, Danilo Hegg and Charlotte Seifert, who taught

me how to trap, handle, collar, and radio-track possums. Their knowledge and

assistance was invaluable. A huge thank-you goes to Dave McPhee who retrieved my

un-cooperative possums after months of unsuccessful trapping efforts by myself. Alas,

not all possums were retrievable, with a couple still adorning very expensive necklaces!

v

Sarah Fisher, your enthusiastic attitude towards fieldwork (Chapter II) made working

on weekends bearable and I thank-you for your dedication and company on those hot

(and wet) summer days. I would also like to thank everyone who provided me with

possums (or ears) for the genetic component of this study including Jo Forrester, Danilo

Hegg, Dave McPhee, Rik Wilson, and various farm owners in Aramoana. You all made

my life that much easier.

To everyone who assisted me with data analyses, I would like to extend my sincere

gratitude. Firstly, to George Pickerell for assistance in identifying bite marks in

WaxTags®, and to Dr. Darryl MacKenzie and Dr. Konstanze Gebauer for advice on

site occupancy modelling (Chapter II). Secondly, to Alastair Neaves, Dr. Paul Denys,

and Mark Denham from the School of Surveying for advice on validation methodology,

subsequent valuable insights into data analyses, and the training and lending of GPS

equipment (Chapter III). I would also like to thank Sheena Townsend and Aviva Stein

for statistical advice. Thirdly, to Sarah MacDonald, Bevan Pelvin, and Dr. Tony Moore

for GIS support (School of Surveying; Chapter IV). A special mention must be made to

Dr. Mariano Rodríguez Recio for long discussions about spatial ecology methods,

advice, technical assistance, and continued support until analyses were completed.

Fourthly, I would like to thank Niccy Aitken and Dr. Andrea Taylor for information

about possum primers, Dr. Dianne Gleeson and Dr. Robyn Howitt (both from Landcare

Research) for supply of several possum primers, and Karen Judge and Dr. Tania King

for laboratory support and training (Chapter V). Also, a special thanks to Nicolas

Dussex who was subjected to ample genetics-related questions, but who always had

time to assist me. Thanks also go to the Department of Zoology technical staff

including Kim Garrett (I really loved the fishing trips in exchange for dealing with my

possums), Ken Miller, Murray McKenzie, Esther Sibbald, Vivienne McNaughton,

Nicky McHugh, Erik Liepins, Jonathon Ung, and the ever-so-helpful Ronda Peacock

and Wendy Shanks.

Thank-you is also warranted to all the people who make up the Department of Zoology

(especially the Genetics lab and the Spatial Ecology group), who have been a great

support to me during the last few years in various ways and have made the work

environment a friendly place. I would also like to extend a huge thank-you to all my

friends and family who have provided me with strength and support to make it through

vi

this challenge called a PhD and who have listened to countless possum stories and PhD

woes. Thanks for always being there for me and making these years enjoyable. George,

I thank-you for being a wonderful office mate and friend, and always lending an ear

and offering valuable advice. To Kim Harris, a fellow PhD student and friend, thank-

you for all our much needed coffee breaks, being my buddy at various workshops, and

for the valuable R codes. Sabrina Hock, I valued our friendship throughout your stay

here in New Zealand and appreciate your time in providing valuable comments on the

draft of this thesis once you had returned home. My paternal grandparents, John and

Shirley Adams, thank-you for your weekly letters and “greenie”. My maternal

grandparents, Roy and Marj Olsen, who sadly both passed away in 2012, thank-you for

your weekly care packages. To my partner Shane Tuffery, I thank-you for your

continued support, love, and patience. Thanks for keeping me sane and forcing me to

take time out! To my mum (Lynnette Adams), dad (Barry Adams), and sister (Kylie

Patterson): there are no words to communicate how deeply appreciative I am for your

unconditional love, support, and words of encouragement. So I extend to you, from the

bottom of my heart, two simple words: thank-you!

“Smooth seas do not make skillful sailors” (African Proverb)

vii

Table of Contents

Abstract ...........................................................................................................................ii

Acknowledgements ........................................................................................................ iv

Table of Contents ..........................................................................................................vii

List of Figures ................................................................................................................ xi

List of Tables ............................................................................................................... xiii

List of Abbreviations ................................................................................................... xiv

1 General introduction........................................................................................... 16

1.1 Biological invasions ......................................................................................... 17

1.1.1 Invasive mammalian species on islands....................................................... 18

1.1.1.1 New Zealand as a case study for biological invasions ......................... 18

1.1.2 Invasive mammalian species in urban habitats ............................................ 21

1.2 Urban ecology .................................................................................................. 22

1.3 Collecting data on animal behaviour and resource use .................................... 24

1.3.1 Wildlife tracking methods ............................................................................ 24

1.3.2 The Global Positioning System (GPS) for wildlife tracking ....................... 24

1.3.3 Advantages and limitations of GPS telemetry ............................................. 25

1.4 Analysing animal movement and resource use ................................................ 27

1.4.1 Home range estimates .................................................................................. 27

1.4.2 Resource selection........................................................................................ 29

1.5 Genetics as a tool for population structure and connectivity ........................... 31

1.6 Biology of the study species: Trichosurus vulpecula ....................................... 33

1.6.1 Description ................................................................................................... 33

1.6.2 Behaviour and social organisation ............................................................... 33

viii

1.6.3 Diet ............................................................................................................... 35

1.6.4 Reproduction ................................................................................................ 36

1.6.5 Distribution .................................................................................................. 37

1.7 Trichosurus vulpecula impacts on New Zealand’s flora and fauna ................. 39

1.8 Thesis structure and overall aims ..................................................................... 40

1.8.1 Author contribution ...................................................................................... 42

2 Predicting distribution patterns for common brushtail possums

(Trichosurus vulpecula) in an urban environment ........................................... 43

2.1 Introduction ...................................................................................................... 44

2.2 Methods ............................................................................................................ 47

2.2.1 Study area ..................................................................................................... 47

2.2.2 Possum surveys ............................................................................................ 49

2.2.3 Statistical analysis ........................................................................................ 52

2.2.4 Model evaluation.......................................................................................... 54

2.3 Results .............................................................................................................. 56

2.3.1 All urban habitats ......................................................................................... 57

2.3.2 Residential analysis ...................................................................................... 57

2.3.3 Estimated possum distribution ..................................................................... 57


2.4 Discussion ........................................................................................................ 63

2.4.1 Detection probability.................................................................................... 65

2.4.2 Limitations ................................................................................................... 66


2.4.4 Management implications ............................................................................ 67

3 An evaluation on the accuracy and performance of lightweight GPS

collars in an suburban environment.................................................................. 69

3.1 Introduction ...................................................................................................... 70

ix

3.2 Methods ............................................................................................................ 73

3.2.1 Study area ..................................................................................................... 73

3.2.2 Stationary collar tests ................................................................................... 73

3.2.3 Data analysis ................................................................................................ 75

3.3 Results .............................................................................................................. 77

3.4 Discussion ........................................................................................................ 83

3.4.1 Limitations ................................................................................................... 86

3.4.2 Management implications ............................................................................ 87

4 Understanding home range behaviour and resource selection of

invasive common brushtail possums (Trichosurus vulpecula), in urban

environments ....................................................................................................... 89

4.1 Introduction ...................................................................................................... 90

4.2 Methods ............................................................................................................ 94

4.2.1 Study area ..................................................................................................... 94

4.2.2 Possum trapping and handling ..................................................................... 95

4.2.3 Data filtering ................................................................................................ 96

4.2.4 Home range estimations and habitat use ...................................................... 97

4.2.5 Core areas of activity ................................................................................. 101

4.3 Results ............................................................................................................ 101

4.3.1 Home range sizes ....................................................................................... 102

4.3.2 Resource selection within home ranges ..................................................... 106

4.3.3 Core areas of activity ................................................................................. 112

4.4 Discussion ...................................................................................................... 112

4.4.1 Home ranges .............................................................................................. 113

4.4.2 Habitat selection ......................................................................................... 117

4.4.3 Limitations ................................................................................................. 121

4.4.4 Management implications .......................................................................... 122

x

5 Identifying the genetic population structure of an invasive pest species

for control purposes .......................................................................................... 124

5.1 Introduction .................................................................................................... 125

5.2 Methods .......................................................................................................... 130

5.2.1 Sampling .................................................................................................... 130

5.2.2 Microsatellite genotyping .......................................................................... 131

5.2.3 Data analyses.............................................................................................. 134

5.3 Results ............................................................................................................ 137

5.4 Discussion ...................................................................................................... 145

5.4.1 Management implications .......................................................................... 151

6 General discussion............................................................................................. 153

6.1 Key results and management implications ..................................................... 155

6.1.1 GPS technology.......................................................................................... 155

6.1.2 Control recommendations .......................................................................... 156

6.2 Recommendations for future research ............................................................ 159

6.3 Conclusions .................................................................................................... 161

References ................................................................................................................... 162

Appendices .................................................................................................................. 202

Appendix 1: List of species ........................................................................................ 203

Appendix 2: OPBG possum eradication information ............................................. 205

Appendix 3: Incremental graphs .............................................................................. 207

Appendix 4: Resource utilisation function equations ............................................. 211

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List of Figures Figure 2.1: Examples of the three residential habitat types ............................................ 49

Figure 2.2: Locations of the WaxTag® sampling .......................................................... 51

Figure 2.3: Determining the optimised threshold for converting continuous

probabilities into presences/absences .......................................................... 55

Figure 2.4: Detection of possums within five urban habitats ......................................... 56

Figure 2.5: Receiver operator curves for the final occupancy models ........................... 62

Figure 3.1: Mean fix success rates of lightweight GPS collars under different sky

availabilities in residential habitat ............................................................... 78

Figure 3.2: Mean location error associated with HDOP values ..................................... 80

Figure 3.3: Mean location error (µLE ± SD) for each HDOP value .............................. 80

Figure 4.1: Location of possum home ranges in Dunedin............................................ 103

Figure 4.2: Example of home ranges for a female and male possum ........................... 105

Figure 4.3: Mean use of habitats within possum home ranges in Dunedin .................. 108

Figure 4.4: Mean use of habitats within female and male possum home ranges ......... 109

Figure 4.5: Probability distribution map of possums in Dunedin ................................ 110

Figure 4.6: Probability distribution map for a female and male possum ..................... 111

Figure 5.1: Locations of the sampled possum populations .......................................... 129

Figure 5.2: Mean genetic relatedness estimates among possum populations .............. 138

Figure 5.3: Mean female and male genetic relatedness estimates ................................ 139

Figure 5.4: Principal coordinates analysis for possum populations around Dunedin

and on the Otago Peninsula ....................................................................... 140

Figure 5.5: Genetic and geographic distances among possum populations ................. 140

Figure 5.6: Bayesian inference of the number of population clusters in possums

around Dunedin and on the Otago Peninsula ............................................ 143

xii

Figure 5.7: Posterior estimates of individual admixture coefficients for possums

generated from STRUCTURE and TESS ................................................. 144

xiii

List of Tables

Table 2.1: Descriptions of the urban habitat types ......................................................... 48

Table 2.2: Models investigating site occupancy and detection probability of

possums within urban habitats ..................................................................... 58

Table 2.3: Models investigating site occupancy of possums in residential habitats ...... 59

Table 2.4: Urban possum occupancy estimates generated from the final model ........... 60

Table 2.5: Variables included in the final residential occupancy model, their

coefficients, and standard errors .................................................................. 60

Table 3.1: Fix success rates and location errors in residential habitats under

different sky availabilities .............................................................................. 78

Table 3.2: Models explaining the fix success rate of lightweight GPS collars .............. 79

Table 3.3: Models explaining the location error of lightweight GPS collars ................. 82

Table 4.1: Home range and core area estimates for possums in Dunedin .................... 104

Table 4.2: Description of the resource utilisation function for possums ...................... 107

Table 4.3: Home range sizes of possums in New Zealand and Australia .................... 115

Table 5.1: Details of the twelve microsatellite loci ...................................................... 132

Table 5.2: Summary of the genetic variation previously described in possum

populations throughout New Zealand........................................................ 133

Table 5.3: Genetic variation among possum populations around Dunedin and on

the Otago Peninsula ................................................................................... 138

Table 5.4: Pairwise values of Slatkin's lineralised FST among possum populations

around Dunedin and on the Otago Peninsula ............................................ 141

Table 5.5: Estimation of migration rates among possum populations around

Dunedin and on the Otago Peninsula ........................................................ 142

Table 5.6: Inferred STRUCTURE population cluster for possums ............................. 145

xiv

List of Abbreviations

2-D Two Dimensional

3-D Three Dimensional

Aa Aramoana

AIC Akaike’s Information Criterion

AICc Akaike’s Second-order Corrected Information Criterion

ANOVA Analysis of Variance

AUC Area Under the Curve

BB Brownian Bridge

BBMM Brownian Bridge Movement Model

BMV Brownian Motion Variance

BZ Buffer Zone

CAR Conditional Autoregressive

CBD Central Business District

CS Cape Saunders

DIC Deviance Information Criterion

DOP Dilution of Precision

FSR Fix Success Rate

GIS Geographic Information System

GPS Global Positioning System

HDOP Horizontal Dilution of Precision

HE Expected Heterozygosity

HO Observed Heterozygosity

HWE Hardy-Weinberg Equilibrium

LE Location Error

LMM Linear Mixed Model

MCMC Markov Chain Monte Carlo

MCP Minimum Convex Polygon

NZ New Zealand

OPBG Otago Peninsula Biodiversity Group

PB Portobello

PC Port Chalmers

xv

PCO Principal Coordinates Analysis

PCR Polymerase Chain Reaction

PDF Probability Density Function

Res 1 Residential 1

Res 2 Residential 2

Res 3 Residential 3

RMS Root Mean Square

ROC Receiver Operating Characteristic

RSF Resource Selection Function

RUF Resource Utilisation Function

SD Standard Deviation

SE Standard Error

TH Taiaroa Head

UD Utilisation Distribution

VDOP Vertical Dilution of Precision

VHF Very High Frequency

Chapter I: General introduction

16

Chapter I:

1 General introduction

A common brushtail possum (Trichosurus vulpecula) within an urban garden.


17

1.1 Biological invasions

Biological invasion describes the introduction of invasive exotic animal or plant species

into new areas outside their natural range where they have the potential to threaten

native biodiversity and alter ecosystems (Courchamp et al. 2003; Clavero & Garcia-

Berthou 2005). Invasive species, either through accidental or deliberate introductions,

are now considered to be one of the leading threats to global biodiversity causing

global, national, and local changes to biotas (Thomsen et al. 2011). There are four

stages required for an exotic species to successfully invade an ecosystem: (1) the

species needs to be transported to an area outside of its historical range; (2) once in a

new area, the exotic species must become established; (3) once established, inter-

specific interactions between the native community of the newly invaded area and the

invasive species occur; and (4) there is continual spread of the invasive exotic species

from the area of establishment (Williamson 1996; Shigesada & Kawasaki 1997; Shea &

Chesson 2002). Generally, for an invasive species to establish and experience

population growth, the invaded area must have resources for the species to exploit, have

niche opportunities, and have no, or reduced, natural enemies present (Williamson

1996; Shea & Chesson 2002).

Once an introduced species becomes established in a new environment, through biotic

interactions it can cause numerous ecological impacts at individual, population, and

ecosystem levels (Ebenhard 1988; Williamson 1996; Vitousek et al. 1997; Parker et al.

1999; Mack et al. 2000). Impacts include hybridisation, changes to the growth and

mortality rates of native species at individual and population levels, altered co-

evolutionary relationships such as predation and competition, transmission of diseases

and parasites, altered community composition, and modified ecological processes and

ecosystem functioning (e.g. resource and nutrient availability, and primary

productivity). Invasive species can also cause anthropogenic threats, including threats

to agricultural industries that provide livelihoods for many human populations.

Mammalian species often have a higher invasion success than other animal taxa and

can be damaging to native wildlife and ecosystems (Courchamp et al. 2003; Jeschke

2008; Vila et al. 2010). One of the leading causes of mammalian invasions is the

deliberate introduction of species, which has increased with the escalation of dispersal,


18

travel, and colonisation by humans globally (McKinney 2006). Reasons for deliberately

introducing mammalian species include cultural nostalgia, where European settlers re-

created a European landscape in the newly colonised area (Williamson 1996); the

establishment of recreational game and food sources (e.g. feral pigs (Sus scrofa), feral

goats (Capra hircus), and Himalayan thar (Hemitragus jemlahicus); Veblen & Stewart

1982; Conry 1988); economic gain (e.g. cattle for agriculture; Pimentel et al. 2005),

arctic foxes (Alopex lagopus) and red foxes (Vulpes vulpes) to establish a fur trade

(Bailey 1993)); as companion animals (e.g. dogs (Canis lupus familiaris) and cats

(Felis catus); Pimentel et al. 2005); and as biological control agents (e.g. ferrets

(Mustela furo) and stoats (M. erminea) in New Zealand, and red foxes in Australia to

control European rabbits (Oryctolagus cuniculus; Howard 1967)).

1.1.1 Invasive mammalian species on islands

Islands are particularly vulnerable to biological invasions and their subsequent

ecological impacts, especially exotic mammalian species, due to their simplified trophic

webs, high endemism rates, low diversification of native species, and subsequent niche

opportunities for invaders including availability of resources and fewer natural

predators, competitors, and parasites (Shea & Chesson 2002; Courchamp et al. 2003).

Furthermore, invasive mammals have a more pronounced impact on native species

which evolved on islands in the absence of mammals as they often lack adaptations to

cope with the presence of exotic mammalian predators (Atkinson 2001). Consequently,

native animal species can quickly reduce in numbers and range after exotic mammals

are introduced onto islands, primarily due to direct predation and competition for

resources (Elton 1958; Iverson 1978; Taylor 1979; Dickman 1996; Courchamp et al.

2003).

1.1.1.1 New Zealand as a case study for biological invasions

New Zealand, which is comprised of three main islands, has had 55 mammalian species

introductions since the mid-19th

Century, with 32 of these species successfully

becoming established (Veitch & Clout 2001). These exotic mammals have had

devastating impacts on New Zealand’s ecosystems and endemic species, which evolved

predominantly in the absence of land mammals, and thus mammalian predatory and


19

browsing pressures (Atkinson 2006). In the absence of mammals, large birds assumed

the dominant roles of herbivores (e.g. the six genera of extinct moa) and predators (e.g.

Haast’s eagle (Harpagornis moorei)), resulting in distinctive guilds and plant-herbivore

co-evolutionary relationships within New Zealand (King 2005; Atkinson 2006). Thus,

native animal species are extremely vulnerable to predation and competition imposed

by introduced mammals (Atkinson 2006). Since the introduction of mammalian

species, numerous native avian species have gone extinct, and there have been

reductions in population numbers and range distributions for many more birds, as well

as for native invertebrates and reptiles (Towns & Daugherty 1994; Clout 2001).

Mammalian species posing the most risk for New Zealand’s native fauna (primarily

through predation of eggs, fledglings/juveniles, and adults) include the Australian

common brushtail possum (Trichosurus vulpecula Kerr), cats, ferrets, stoats, weasels

(M. nivalis), hedgehogs (Erinaceus europaeus), rats (Rattus rattus, R. exulans, and R.

norvegicus), and the house mouse (Mus musculus) (Parkes & Murphy 2003).

Due to the impact invasive mammalian species are exerting on New Zealand’s native

species and ecosystems, New Zealand is one of the leaders in the management and

control of exotic species (Saunders & Norton 2001; Courchamp et al. 2003), and has

achieved significant reductions and eradications of invasive pest populations over the

last few decades (King 2005; Clout & Russell 2006). Traditionally, eradication efforts

were restricted to small, offshore islands in an attempt to create predator-free offshore

islands, which can then be used as refugia for populations of threatened native species

occurring on the mainland (Saunders & Norton 2001; Clout & Russell 2006). From

1900 to the 1970s, control of mammals was aimed at eradicating medium to large-sized

animals (>10 kg) from small offshore islands (5 - 219 ha) through traditional trapping

and shooting (Clout & Russell 2006). Successful eradication of smaller mammals

occurred from the 1980s, on ever-increasing island sizes due to thorough planning,

development of new baits, improved trapping devices, and technological developments

(Saunders & Norton 2001; Cromarty et al. 2002; Towns & Broome 2003; Clout &

Russell 2006). As such, 17 exotic mammalian species have successfully been

eradicated from 140 small offshore islands reaching over 10,000 ha in size (Saunders &

Norton 2001; Clout & Russell 2006).


20

Control of invasive mammalian species on mainland New Zealand remains a challenge

due to the large size of the islands: North, South, and Stewart Islands, and the continual

reinvasion pressure from surrounding uncontrolled areas (Saunders & Norton 2001). As

complete eradication is often not feasible on large islands, species management is

directed at controlling or removing invasive species from target areas (e.g. the

'Mainland Island approach', which can reach up to 6000 ha in size; Saunders & Norton

2001), especially in areas with high levels of endemic wildlife (Courchamp et al. 2003).

For example, the creation of mainland predator exclusion sanctuaries (e.g. Hurunui

Mainland Island (12,000 ha) in Canterbury, Maungatautari (3,400 ha) in the Waikato,

and Orokonui Ecosanctuary (250 ha) in Dunedin). Due to technological developments,

such as aerial toxic bait application, mainland control initiatives can now be

implemented over more extensive and remote areas (Saunders & Norton 2001).

However, to achieve predator control on large islands, control needs to be more

intensive, and maintained and monitored for longer than is required on offshore islands

(Saunders & Norton 2001). Additionally, these initiatives generally require an

associated education focus aimed at the community, which is achieving some success in

securing community involvement in subsequent conservation management strategies

(Saunders & Norton 2001).

Formulation and improvement of management and control methodologies to eradicate

or reduce population numbers of an invasive species to minimal levels on larger islands

requires the identification of where to effectively direct control efforts. Detailed

knowledge of the behaviour, spatial ecology (spatial patterns of individuals and

populations in relation to the spatial configuration of landscape features; Collinge

2001), and genetic population structure of invasive species within a specific habitat is

essential. A detailed understanding of how invasive species are distributed and move

across a landscape, how they use different habitats, and what resources or features they

select or avoid will consequently enhance the success rate of control strategies by

providing information for use in methodological design, such as optimal placement of

bait stations and trap lines. The continual development of wildlife tracking technology,

molecular genetics tools, and computational abilities enables the investigation of

invasive species, which can be coupled with developments in eradication techniques to

ultimately improve the probability of success of control operations.


21

1.1.2 Invasive mammalian species in urban habitats

Invasive species are also present within rapidly expanding urban environments. There

is increasing evidence that these habitats can support significant populations of both

native and exotic species, with some areas supporting higher native species richness

than surrounding rural areas (Blair 1996; Pickett et al. 2008; van Heezik et al. 2008a),

and in some cases providing refugia for threatened species (Mason 2000). Urban areas

are also primary sites for interactions between people and wildlife (Turner et al. 2004;

Miller 2005; Pickett et al. 2008). The ability of animal species to survive in urban

environments is dependent on whether they can modify their behaviour to adapt to

urban stresses, and utilise highly modified, fragmented habitats (Roitberg & Mangel

1997; McKinney 2002, 2006).

Research on the effects of invasive species in urban environments is largely lacking, yet

they are likely to be having negative biodiversity effects, causing reductions in

abundance, diversity, and fecundity of native species within cities, as they do elsewhere

(Brown et al. 1993; Innes et al. 1994; Sadlier 2000). The urban landscape is highly

fragmented, consisting of a mosaic of habitat types ranging from relatively undisturbed

patches of natural habitat, through to residential suburban areas interspersed with

fragments of remnant forest, to highly developed commercial or industrial areas. This

results in an irregular spatial distribution of resources which is a key factor in

determining the behaviour, movements, and space use of animals throughout a

landscape (MacArthur 1972; Taylor et al. 1993; McKinney 2002; Holt 2003; Guisan &

Thuiller 2005). Populations within urban areas can thus use resources and space

differently than in their more traditional habitats (Ditchkoff et al. 2006; Baker & Harris

2007). Due to the capability of invasive species to exploit fragmented, modified

environments, their impacts on native species are likely to extend from natural

vegetation fragments and into residential habitats. Hence, there is a need to understand

the spatial ecology of invasive species in urban habitats to help understand their

potential impacts on native fauna and flora to enable the formulation of effective

control strategies in these landscapes.


22

1.2 Urban ecology

Urban ecology is a relatively recent scientific discipline that has developed as a result

of a growing acknowledgment of the role that rapidly expanding urban areas worldwide

play in supporting biodiversity (Grimm et al. 2008). Increased urbanisation of human

populations have meant urbanization is the number one cause of habitat loss in many

countries, but despite the extreme modification of urban landscapes, evidence supports

the important role urban biodiversity plays in maintaining ecosystem function,

providing habitat for wildlife populations, in some cases providing refugia for

populations threatened elsewhere, and also providing the human population with

opportunities to interact with nature; this has been shown to be good for our

psychological and physical well-being (Gregory & Ballie 1998; Mason 2000; Mortberg

& Wallentinus 2000; Pickett et al. 2008; van Heezik et al. 2008a; Faeth et al. 2011). It

is important to gain a comprehensive understanding of how urbanisation affects animal

species for both conservation and management purposes (Czech et al. 2000; Davison et

al. 2009; Gehrt et al. 2009). Within urban environments, species compositions,

richness, evenness, and abundances can be drastically altered (Faeth et al. 2011). Urban

ecology investigates relationships between habitat characteristics and species

behaviour, examines community structure across a gradient of urbanisation, and

examines how habitat conversion alter species distributions and abundances (e.g. Blair

1996; Tigas et al. 2002; Kaneko et al. 2006; Marks & Bloomfield 2006; McKinney

2006; Adams 2008; Horn et al. 2011). Investigation of urbanisation impacts have

largely been directed at understanding how native species respond to habitat

modifications in order to increase conservation of urban populations or understanding

population dynamics of species that are causing human-wildlife conflict (e.g. Marks &

Bloomfield 2006; Baker et al. 2007; Adams 2008; Gehrt et al. 2009; Gese et al. 2012).

While urbanisation often negatively affects animal species causing population

displacements, declines, and extinctions, some species thrive in these environments

(Riley 2006). Urban-adapters can modify their behaviour to survive, such as changing

the timing of their activity patterns (McClennen et al. 2001; Tigas et al. 2002; Riley et

al. 2003; Ditchkoff et al. 2006) or exploiting novel food and den resources (Tigas et al.

2002; Riley 2006; Baker et al. 2007; Davison et al. 2009; Wright et al. 2012; Podgorski

et al. 2013) and are often exotic species. Some species even reach unusually high


23

densities within urban environments (Marks & Bloomfield 2006; Riley 2006;

Podgorski et al. 2013).

Despite urban areas increasing in their extent worldwide, knowledge regarding the

spatial ecology of invasive species in urban areas, which is required to successfully

manage urban-based populations, is sorely lacking (Magle et al. 2012). Both conserving

and controlling urban-based species is dependent on gaining a thorough understanding

of the processes, both anthropogenic and natural, which influence their spatial ecology

(Faeth et al. 2011). While there are some common themes in urban ecology, such as

alterations in animal behaviour and space use, generalisations within the literature

relating to species responses to urbanisation should be treated with caution due to the

large variations between and within species, and the differing levels of urbanisation

between locations (Gese et al. 2012). Although still in its infancy, this discipline can

greatly benefit from an interdisciplinary approach to improve our understanding of the

spatial ecology of species persisting in urban environments and to create effective

control strategies for invasive species.

A multidisciplinary spatial approach provides the opportunity to collect species-specific

information regarding distribution, behaviour, resource selection, and population

connectivity at both broad and fine scales using different techniques. Collected data can

be collated and examined to provide a more in-depth understanding of species

requirements across the urban environment, which cannot be gained by analysing

information at only one scale. Additionally, information can be examined at both

population and individual levels, providing insights into variation between individuals

within a population and to produce generalisations regarding the population as a whole

across the entire landscape. This thesis investigates the spatial distribution, habitat use,

and genetic population structure of the invasive Australian common brushtail possum

(T. vulpecula) inhabiting urban environments in New Zealand to enhance the success of

both urban-based control strategies and an eradication programme within adjacent rural

habitat.


24

1.3 Collecting data on animal behaviour and resource use

1.3.1 Wildlife tracking methods

Traditionally, data used to evaluate distribution, abundance, behaviour, and resource

selection of animal species have been collected using methods such as direct field

observations (Sanderson 1966), observations of animal presence including hairs, scats,

and footprints (e.g. Gompper et al. 2006; McKelvey et al. 2006; Sulkava 2007), mark-

recapture (e.g. Beamesderfer & Rieman 1991), site occupancy (e.g. Reunanen et al.

2002; Gormley et al. 2011), distance sampling (e.g. Buckland et al. 2001), spool-and-

line tracking (e.g. Anderson et al. 1988; Key & Woods 1996), radio-tracking via Very

High Frequency (VHF) radio-tags (e.g. Carey et al. 1990; Tew & Macdonald 1994;

Tchamba et al. 1995; Samuel & Fuller 1996; Bradshaw et al. 1997; Chamberlain et al.

2002; Dahle & Swenson 2003), and satellite tracking (Higuchi et al. 1996; Seegar et al.

1996). These methods are often time-consuming, expensive, and labour intensive.

Additionally, many animal species, including mammals, are elusive, evading direct

observation or are unobtainable due to the remoteness and inaccessibility of the land.

This can result in limited information being able to be obtained with the use of the

above methods (MacKenzie et al. 2002; Gu & Swihart 2004; MacKenzie et al. 2006).

1.3.2 The Global Positioning System (GPS) for wildlife tracking

In 1995, the development of fully operational Global Positioning System (GPS)

technology enabled the use of GPS telemetry to investigate habitat and resource

selection, use of space, and animal movement patterns of large animals in more detail

and at a higher accuracy (e.g. Rodgers et al. 1996; Galanti et al. 2000; Belant &

Follmann 2002; Creel et al. 2005; Dussault et al. 2005; Horne et al. 2007; Storm et al.

2007). GPS receivers include attachments such as collars, harnesses, backpacks, or are

simply glued or taped on to the animal (Samuel & Fuller 1996). These devices operate

by searching for satellites at pre-determined time intervals and if greater than three

satellite signals are acquired, they will store the GPS location (known as positional

fixes or fixes) of the animal (Rodgers et al. 1996). This location is calculated by the

receiver, which uses the continual satellite signals transmitted from 24 satellites that

orbit the Earth twice a day, with each orbit following a near-identical path (El-Rabbany

2006; Samama 2008). If a position is successfully collected, the accuracy of the


25

resulting location can range from centimetres to kilometres (Capaccio et al. 1997;

Hulbert & French 2001; Villepique et al. 2008). Information collected by GPS receivers

include dates, times, geographical co-ordinates, and performance information, such as

number of satellites present and dilution of precision (DOP) values. Receivers are

retrieved via recapturing tagged individuals, through an automatic release when the

battery is nearly drained, or remote activation of a built-in release system (Merrill et al.

1998). Upon retrieval of the receiver, positional data stored in the built-in memory of

the GPS receiver is obtained by downloading the data using appropriate computer

software (Rodgers et al. 1996; Sawyer et al. 2007).

Due to the size and weight of GPS receivers, GPS-tracking of wildlife to investigate

resource selection, movements, and behaviour has been traditionally limited to large

mammals including moose (Alces alces; Moen et al. 2001; Dussault et al. 2005),

wolves (Canis lupus; Merrill et al. 1998), grizzly bears (Ursus arctos; Nielson et al.

2002; Gau et al. 2004), elephants (Loxodonta africana; Galanti et al. 2000; 2006), lions

(Panthera leo; Valeix et al. 2009), and chacma baboons (Papio cynocephalus ursinus;

Pebsworth et al. 2012). More recently, smaller species have been able to be fitted with

GPS receivers due to the miniaturisation of batteries and other technological

components. Examples include ocelots (Leopardus pardalis; Haines et al. 2006), feral

pigeons (Columba livia; Rose et al. 2005), feral cats (Recio et al. 2010), and the

Australian common brushtail possum (Blackie 2004; Dennis et al. 2010).

1.3.3 Advantages and limitations of GPS telemetry

GPS telemetry has numerous advantages over traditional VHF radio-tracking. Unlike

VHF telemetry, animal positions are continuously collected over any pre-configured

time interval without the presence of a fieldworker (Rumble & Lindzey 1997). This

enables data to be collected from any area, including inaccessible or remote areas,

under any weather conditions, and at any time (Rodgers et al. 1996; Rumble & Lindzey

1997). The ability to select time intervals for data collection often means that more

spatial data (positional fixes) are collected compared to VHF studies (Cain et al. 2005;

Land et al. 2008). For example, Rumble et al. (2001) collected more data from four

GPS collars on elk to analyse behaviour and habitat use than three technicians did using

VHF collars on ten times the number of elk. Collected GPS data are more indicative of


26

natural behaviour as animals are not disturbed by the close proximity of fieldworkers as

with VHF radio-tracking (Blackie 2004; Cooke et al. 2004). GPS location data are also

generally more accurate (reported errors of 30 m) than location data acquired via

triangulation of VHF radio-signals, which can have errors of up to 500 m (Marzluff et

al. 1994; Rempel et al. 1995; Zimmerman & Powell 1995; Frair et al. 2010). The

advantages of GPS telemetry can facilitate investigation of animal behaviour,

movement, space use, and resource selection at fine ecological scales.

Despite all of the advantages of GPS telemetry, the performance and accuracy of

receivers when deployed on animals can be limited by blocked satellite signals, which

leads to missed positional fixes or a lower accuracy of collected positional fixes (Frair

et al. 2004). Of particular relevance to subsequent analyses using GPS-collected data is

that of location (or positional fix) error. Only locations under an acceptable error level

should be used in analyses, meaning filtering of the location data is required (Lewis et

al. 2007). A common filtering method of GPS fixes is to use the information collected

by the receiver on the number of satellites present and/or the dilution of precision

(DOP) for each stored positional fix to filter out positional fixes below or above a

certain threshold (Moen et al. 1996; D'Eon & Delparte 2005). This is based on the

assumption that positional fixes are more accurate with lower DOP values and with

more satellites present when the positional fix was acquired (D'Eon et al. 2002; D'Eon

& Delparte 2005). However, this filtering method can result in the discarding of

accurate data, while outliers are retained (D'Eon & Delparte 2005; Lewis et al. 2007).

Hence, alternative post-processing methods may be more suitable to filter location data,

such as applying correction factors (Frair et al. 2004) or using characteristics of animal

movement (turning angles, speed; Bjorneraas et al. 2010) to identify large location

errors.

GPS telemetry is also limited by the high costs associated with the corresponding

technologies required (Hebblewhite & Haydon 2010). Such costs generally limit the

number of animals able to be tracked, due to the number of GPS devices

accommodated by research budgets. This then limits the ability to make robust

conclusions from the data collected to answer research questions (Samuel & Fuller

1996; Cooke et al. 2004; Girard et al. 2006). However, costs associated with GPS

technology are continually decreasing and with appropriate research questions and


27

study designs, GPS telemetry can be extremely beneficial in the study of animal

movement, behaviour, and resource selection (Lizcano & Cavelier 2004).

1.4 Analysing animal movement and resource use

A common way to investigate space use of animals from collected GPS locations

throughout time is by home range analyses (Garton et al. 2001). Home ranges are

defined as ‘the area traversed by the individual in its normal activities of food

gathering, mating and caring for young’ (Burt 1943), and are associated with finding

resources for survival and reproduction including food, water, shelter, and mates (Krebs

& Davies 1997). Thus, home ranges reflect the relationship between the availability and

abundance of essential resources required by a species within the environment and

consequent animal movements based on the animal’s understanding of the environment

i.e. its cognitive map (Powell 2000; Borger et al. 2008). For example, large, over-

lapping home ranges can indicate that resources are patchily distributed or available at

low densities within the local environment (Borger et al. 2008). Animals will use

specific areas within their home ranges for different behaviours which again reflects the

abundance and spatial distribution of resources (Marzluff et al. 2001; Faeth et al. 2011;

Powell & Mitchell 2012). Within urban-environments, human disturbance and habitat

modification act to modify the behaviour, movements, space use, and home ranges of

animals, largely due to the patchy distribution of resources (Tigas et al. 2002;

Podgorski et al. 2013). Some appreciation of how the use of space by urban-based

animals has been modified as a result of the environment can be gained by comparing

home range sizes between rural and urban environments (Gehrt et al. 2009).

Knowledge of home range sizes, space utilisation, and resource selection by animals

can then be used in the management of urban populations.

1.4.1 Home range estimates

Describing the home range of a species can be difficult as range size varies depending

on the method used to collect location data from individuals (e.g. radio tracking,

spotlighting, GPS telemetry) and the time frame that these locations are collected over

(Jolly 1976; Ward 1978; Ward 1984; Powell & Mitchell 2012). Additionally, a variety

of techniques exist that calculate home range sizes for animals but which vary in


28

accuracy and suitability, and it is recommended that more than one method is used

(Harris et al. 1990; White & Garrott 1990; Kernohan et al. 2001). Due to the number of

methods available to researchers, home ranges are often calculated with little

understanding of how they are being derived and what they mean (Powell & Mitchell

2012). Traditionally, minimum convex polygons (MCPs) have been used to estimate

home ranges by connecting the outer locations collected for an individual to calculate

the maximum area of use (Mohr & Stumpf 1966). However, MCPs provide no

information on the intensity of space use and are very sensitive to peripheral locations

which may result in the home range including large areas that are not (or rarely)

frequented by an individual (Harris et al. 1990; Laver & Kelly 2008).

A more recent approach to calculating home ranges is by creating a utilisation

distribution (UD), which is a two-dimensional probability density of space use by an

animal over a given timeframe. In addition to estimating the size of the home range,

UDs also estimate the intensity of use within the home range by the animal and are

referred to as probability density functions (PDFs; Van Winkle 1975; Worton 1989;

Kranstauber et al. 2012). Unlike the MCP, where the boundaries simply represent the

outermost set of locations, the UD boundary is produced using the entire distribution of

locations (Kernohan et al. 2001). UDs use location data to describe the intensity of use

for each location by assessing the probability that the animal occurs there as determined

from the density of animal locations within the home range (Van Winkle 1975;

Kernohan et al. 2001; Marzluff et al. 2001). As UDs represent the intensity of space

use, they can be used to determine core areas of use, thus be used to identify important

resources for the animal (Samuel et al. 1985; Marzluff et al. 2001). UDs are becoming

more favoured in spatial ecology studies compared to MCPs, as they can depict

numerous cores of activity, are less sensitive to outliers, eliminate unused areas, and are

not reliant on outer locations to anchor their corners (Kernohan et al. 2001; Hemson et

al. 2005).

A relatively recent approach at examining animal movements and space use is that of

mechanistic home range models (Moorcroft et al. 2006; Moorcroft & Barnett 2008).

These spatially-explicit mechanistic models predict an animal’s use of space by

integrating information on the sequence of animal locations and the time spent between

each location (Horne et al. 2007; Kranstauber et al. 2012). This space use is derived


29

using correlated random walks that are applied between successive locations to estimate

the movement path of the animal and develop two-dimensional probability density

functions (PDF; Moorcroft et al. 1999, 2006; Horne et al. 2007). Additionally,

ecological, behavioural, and environmental factors, as well as the presence of

conspecifics, can be incorporated into mechanistic models to enable examination of

important resources or factors impacting animal behaviour and movements (Moorcroft

et al. 1999; Kernohan et al. 2001; Marzluff et al. 2001; Moorcroft et al. 2006;

Moorcroft & Barnett 2008). Mechanistic models provide higher predictive and

explanatory power than previous descriptive models (Moorcroft et al. 1999; Marzluff et

al. 2001).

One example of a mechanistic model which estimates home ranges and portrays animal

movements is the use of Brownian bridges (Bullard 1999; Horne et al. 2007). The

Brownian bridge movement model (BBMM) is an advancement over traditional kernel

UD approaches, as it models the movement paths of animals while eliminating non-

used areas which would traditionally be included in the home range (Bullard 1999;

Horne et al. 2007). This is achieved by using Brownian bridges, which are continuous-

time stochastic models of movement, that estimate the probability of an animal being at

a location conditioned upon the distance and time between successive locations, and the

mobility (speed of movement) of the animal (Bullard 1999; Horne et al. 2007). These

methods are likely to be superior to traditional methods as they can reveal more

accurate depictions of individual home ranges, ultimately providing more insights into

space use by animals, which in turn will increase the effectiveness of control efforts for

invasive species.

1.4.2 Resource selection

Resource distribution is widely considered as the main variable influencing animal

distribution, movements, and space use (Turchin 1998; Powell 2000; Adams 2001;

Borger et al. 2008; Roshier et al. 2008). As limiting resources (food, shelter, and

conspecifics) are generally distributed heterogeneously across a landscape, animal

movements and selection of habitats and resources are often non-random (Borger et al.

2008). Selection of habitats is therefore regarded as a behavioural choice where an

animal purposefully selects a particular area/s over other available areas (Boyce &


30

McDonald 1999). Within a home range, areas are not all used equally, and typically

each animal has core areas of use. These areas are generally more intensively exploited,

reflecting a localised abundance and quality of desirable resources (Boyce & McDonald

1999; Benhamou & Riotte-Lambert 2012). Habitat and resource selection therefore

assumes that an animal is intentionally selecting a particular habitat or resource and that

this selection results in an increase to the individual’s survival, reproduction, and/or

fitness (Thomas & Taylor 2006). Identification of home ranges and core areas, and the

resources they contain, is required to implement successful conservation or control

measures. Resource selection studies are widely used to investigate how species use

space in relation to resource availability, habitat type, topography, and landscape

features (e.g. Koehler & Hornocker 1991; Barton et al. 1992; Boyce & McDonald

1999; Rettie & Messier 2000; Grinder & Krausman 2001; Manly et al. 2002; Apps et

al. 2004; Dussault et al. 2005; Moorcroft et al. 2006).

Traditionally, habitat or resource use by animals has been investigated using resource

selection functions (RSFs; Boyce & McDonald 1999; Manly et al. 2002), which

indicate resources within a landscape that are disproportionately used compared to their

availability (Boyce & McDonald 1999; Erickson et al. 2001). Selection is investigated

by comparing used habitats and/or resources to those that are available (Manly et al.

2002). An RSF is then created using data collected from either a used versus unused

(presence/absence) or a used versus available (presence/availability) study design

(Boyce & McDonald 1999; Boyce et al. 2002; Manly et al. 2002). The main criticism

surrounding RSFs is the arbitrary definition of habitat and resource availability

(Aebischer et al. 1993). Defining available habitat and resources is especially difficult

in urban environments due the highly heterogeneous nature of the landscape (Marks &

Bloomfield 2006). More recently, resource utilisation functions (RUFs) have been

developed and are beginning to be implemented in resource selection studies to

investigate the intensity of resource use (Marzluff et al. 2004; Horne et al. 2007;

Rittenhouse et al. 2008; Long et al. 2009). RUFs entail the creation of a UD, where the

intensity (height) of the UD can be related to landscape variables within the home range

of an individual through the use of multiple regression adjusted for spatial

autocorrelation (Marzluff et al. 2004; Millspaugh et al. 2006). The inclusion of a UD

enables space to be treated as continuous, instead of discrete as in RSFs, increasing the

accuracy of resource use in selection studies (Marzluff et al. 2004). Again, the


31

increased accuracy of these methods at depicting resource use by a species can be used

in management decisions for invasive species to ultimately enhance the success of

control campaigns.

1.5 Genetics as a tool for population structure and connectivity

Molecular genetics analyses have been valuable in the conservation and management of

threatened animal species (e.g. Paetkau et al. 1997; Spong et al. 2000; Hrbek et al.

2005; Robertson 2006; Robertson et al. 2007; Jamieson 2011; Grueber et al. 2012). For

example, individuals can be selected based on their genetic make-up for translocations

to minimise the probability of inbreeding and reduced genetic variation which would

render the translocated population vulnerable to environmental changes (Spong et al.

2000; Jamieson 2011; Grueber et al. 2012). Additionally, based on gene flow patterns

between populations, reserves or protected areas can be established or enhanced to

ensure the continued exchange of migrants, thus genes, between populations or to re-

populate depleted populations (Hrbek et al. 2012). Such analyses have only recently

been incorporated into the management of invasive species, but are proving to be

extremely valuable in understanding populations and designing subsequent control for

them (see Robertson & Gemmell 2004; Abdelkrim et al. 2005; Gleeson et al. 2006;

Rollins et al. 2006). For example, the geographic source of invasions has been

identified using genetic markers (Pinceel et al. 2005; Carvalho et al. 2009).

Genetic analyses using polymorphic genetic markers, such as microsatellites, are used

to identify population structure and connectivity of populations, which reflect

movement and/or dispersal patterns (Neigel 1997; Balloux & Lugon-Moulin 2002;

Abdelkrim et al. 2007). Gene flow (i.e. the number of alleles exchanged) between

populations is a reflection of genetic structuring within populations and is usually

evident to some degree within a species (Balloux & Lugon-Moulin 2002). The genetic

population structure of introduced species is impacted by historical processes, life

histories (such as the mating system of the species), and environmental barriers to gene

flow (Donnelly & Townson 2000; Gerlach & Musolf 2000; Palsson 2000; Tiedemann

et al. 2000). Founding populations are often genetically less diverse than the original

population, having a limited representation of alleles due to the often small number of

individuals involved in the introduction event (Nei et al. 1975; Clegg et al. 2002).


32

Further losses in genetic variation occur through population bottlenecks after

colonisation, through genetic drift which causes loss of alleles and changes in allele

frequencies, limited migratory options, and inbreeding (Chakraborty & Nei 1977;

Allendorf & Luikart 2007). Furthermore, habitat can have important implications for

dispersal and hence genetic structure of populations. For example, many urban-adapted

animal populations have lower genetic diversity compared to rural counterparts because

of founder effects and restricted gene flow in the highly fragmented urban landscape

(e.g. Wandeler et al. 2003; Rutkowski et al. 2006; Evans et al. 2009). Additionally,

isolation by distance is a main cause of genetic differentiation between populations;

populations that are geographically distant are genetically more different than

populations that are geographically close (Wright 1946; Hardy & Vekemans 1999).

Gaining an understanding of the genetic structure of invasive species populations can

improve the development and effectiveness of implemented control or eradication

measures (Hampton et al. 2004; Taylor et al. 2004). Genetic tools are increasingly

being utilised to identify possible reinvasion pathways in association with areas

undergoing control of a target species, which reveals the potential for eradication

failure (Robertson & Gemmell 2004; Abdelkrim et al. 2005). This is because

“eradication units” (populations spatially close enough for migration to occur) can be

determined, identifying all populations requiring simultaneous eradication to avoid

recolonisation (Robertson & Gemmell 2004). For example, molecular genetics was

used to infer population structure in Norway rats (R. norvegicus), leading to their

ongoing, successful eradication from sub-Antarctic South Georgia (Robertson &

Gemmell 2004). Alternatively, genetic variation (thus gene flow) between populations

can highlight areas conducive to reinvasion that can then be prioritised in ongoing

control efforts to minimise the potential of reinvasion occurring in the

controlled/eradicated area. Knowledge of the population structure, coupled with

knowledge surrounding habitat use and movement patterns of a species, can be used to

successfully identify areas within an environment associated with high probability of

presence for that species to formulate effective control initiatives. As such, molecular

genetics is being incorporated into numerous studies to improve pest management,

including control of feral cats in Hawai’i (Hansen et al. 2007), and management of

European starlings (Sturnus vulgaris) in Western Australia (Rollins et al. 2006). Results

from such studies have shown how genetic analyses can be utilised to minimise the risk


33

of eradication failure and are thus recommended to be incorporated into the formulation

of successful and realistic control strategies for invasive species (Abdelkrim et al. 2005;

Gleeson et al. 2006).

1.6 Biology of the study species: Trichosurus vulpecula

Introduced mammalian herbivores are a serious threat to New Zealand’s native biota

(Nugent et al. 2001). One of the most widespread and devastating mammalian species

to have been deliberately introduced to New Zealand is the Australian common

brushtail possum (Trichosurus vulpecula; hereafter referred to as possum).

1.6.1 Description

The possum belongs to the family Phalangeridae, which consists of 28 species of

nocturnal tree-dwelling marsupials, including brushtail possums and cuscuses (Groves

2005). Species from the Phalangeridae family are native to Tasmania, Australia, New

Guinea, and the islands east of the Solomons and west of the Celebes (Kerle 1984;

Groves 2005).

Male and female possums do not differ significantly in size or weight; adults range

between 650 - 930 mm in total length, with body mass ranging between 1.4 - 6.4 kg

(Cowan 2005). However, the size of possums within New Zealand generally increases

in a southerly direction, with individuals being significantly heavier in the South Island

compared to possums in the North Island (Cowan 2005). This pattern is correlated to

cooler annual temperatures in the South Island, with possums conforming to

Bergmann’s Rule (larger individuals of a species occur within colder environments;

Yom-Tov et al. 1986; Cowan 2005).

1.6.2 Behaviour and social organisation

Possums are generally arboreal, and have opposable first and second digits on their

forefeet, as well as long, prehensile tails to enable grasping of branches (Wilkinson &

Dickman 2006). However, they also spend some time on the ground, with this time

increasing in areas of fox control in Australia (Pickett et al. 2005), and in New Zealand,

which lacks the main ground-based predators of Australia: foxes, dingoes (Canis


34

familiaris), carpet pythons (Morelia spilota), and lace monitors (Varanus varius)

(Cowan & Clout 2000). Possums also have the ability to swim, but generally avoid

entering water sources (Cowan 2005; Cowan et al. 2007).

Possums are primarily solitary, with social interactions generally only occurring

throughout the breeding season or when adult females have dependent offspring (Jolly

1976; Kerle 1984; MacLennan 1984). However, there is an extensive overlap of home

ranges between individuals, with den sites typically being the only space defended

(Crawley 1973; Green 1984; Cowan 2005). When interactions between individuals

occur, the older, larger individuals are generally dominant over the younger, smaller

possums (Jolly 1976; Biggins & Overstreet 1978; Day et al. 2000). When possums with

similar dominance statuses coincide, they will mutually avoid using an area together

(Winter 1976).

Den sites of possums in Australia generally occur above ground, due to the presence of

ground-based predators (Cowan 1990, 2005). Hollow tree trunks or branches, epiphyte

or vine thickets amongst tree branches, or spaces under roofs are typical possum dens

(Cowan 1989, 2005). Ground-based den sites in New Zealand include hollows within

fallen trees, in burrows of other species, under logs, tree roots, wood piles, dense

thickets of vegetation, and rock outcrops (Cowan & Clout 2000; Cowan 2005; Glen et

al. 2012; Rouco et al. 2013). Individuals generally move between five to ten different

den sites, usually switching between dens every couple of nights (Cowan 2005). Males

tend to use more den sites than females, with dens often being utilised by more than one

individual, but which are not inhabited at the same time (Green & Coleman 1986;

Cowan 1989; Paterson et al. 1995).

Possums will emerge from their dens about 30 minutes after sunset, having been active

in their dens for one to two hours beforehand (Ward 1978; MacLennan 1984; Cowan

2005). However, activity is suppressed by strong winds and heavy rains, which can

delay emergence from dens by up to five hours (Ward 1978). After emerging,

individuals generally stay in the vicinity of their den, either sitting, grooming or moving

around (Ward 1978; MacLennan 1984). Individuals are usually the most active between

11:00 pm and 2:30 am (Paterson et al. 1995). Possums spend about one to two hours

per night feeding (10 - 15% of time), with the rest of the time spent grooming (10 -


35

15% of time), sitting (45% of time), and moving about (20 - 30% of time). Feeding

occurs in two to three different sessions, at two to four sites throughout the night, and is

separated by sitting and moving between feeding sites (Ward 1978; MacLennan 1984).

It is thought that the low proportion of time spent feeding per night is not a reflection of

food availability, but instead is limited by their ability to detoxify anti-herbivore

chemicals present within vegetation and/or their ability to extract nutrients and energy

from consumed food (Nugent et al. 2000). Males will return to their dens after females,

who return one to two hours before sunrise in winter, but just before sunrise in summer

(Ward 1978).

1.6.3 Diet

Possums are generalist and opportunistic folivores, with foliage accounting for 50 -

95% of their diet (Kerle 1984; Smith & Lee 1984; Nugent et al. 2000). In Australia,

possum diet is comprised mostly of Eucalyptus leaves, but they will also eat foliage

from a range of other plant species (Fitzgerald 1984; Kerle 1984; Evans 1992).

Possums have clear preferences for different plant species with these preferences

differing between locations (Green 1984; Cowan 2005). Preferences are influenced by

the plant species available in an area, the abundance of the available plant species,

presence of non-foliar foods such as fruits, and the age, toxicity, and digestibility of

leaves (Fitzgerald 1976; Coleman et al. 1985; Cowan 2005). This selectivity often

results in individuals returning to the same tree night after night until it has been

completely defoliated (Leutert 1988; Payton 2000). The variation in the concentrations

of secondary metabolites and nutrient content between individual trees may contribute

to the selectivity exhibited by possums (Cowan 2001).

Many of the plant species browsed by possums, especially Eucalyptus, have low

nutritional value and possums have a limited ability to remove adequate nutrients and

energy from consumed food (Nugent et al. 2000). To help compensate for this, possums

have a low metabolic rate, and the hind-gut mixes and digests food, enabling small food

particles that are more easily digested to be selectively retained (Hume 1999; Nugent et

al. 2000). Nevertheless, these adaptations do not fully compensate for a small gut

capacity. Possums therefore require additional high-nutrient and/or high-energy food

sources to supplement their foliage diet. For example, they are known to frequently


36

consume a variety of non-foliar food sources including flowers, fruits, human food

scraps, roses, and vegetables from private gardens, which tend to have a higher nutrient

and energy value than tree foliage (Bolton & Ahokas 1997; Pass et al. 1999; Matthews

et al. 2004; Harper 2005). When available, these food sources (especially fruits) are

generally preferred over foliage, and can increase reproductive success and the carrying

capacity of possums in an area (Kerle 1984; Evans 1992; Nugent et al. 2000; Ramsey et

al. 2002).

In New Zealand, possum diet is comprised largely of foliage from over 80 native forest

canopy tree species, with the bulk of the diet comprising a number of dominant canopy

tree species (e.g. northern and southern rātā (Metrosideros robusta and M. umbellata),

kāmahi (Weinmannia racemosa), and kohekohe (Dysoxylum spectabile)), and small tree

species or shrubs (e.g. pāte (Schefflera digitata), māhoe (Melicytus ramiflorus), fuchsia

(Fuchsia excorticata), and five-finger (Pseudopanax arboreus)) (Payton 2000). When

available, the diet is supplemented with fruits, flowers, epiphytes, vines, buds, bark,

fungi, ferns, grasses, vegetable crops, invertebrates, bird eggs, and chicks (Warburton

1978; Bolton & Ahokas 1997; Pass et al. 1999; Nugent et al. 2000; Cowan 2005; Glen

et al. 2012). Possums are also known to scavenge on carcasses, including rabbits, pig,

and deer (Cowan 2005).

1.6.4 Reproduction

The reproductive strategies of possums are poorly known; they may be polygynous

(males mate with more than one female; Sarre et al. 2000; Taylor et al. 2000), but are

not monogamous (Taylor et al. 1999). Depending on local population dynamics and

environmental conditions, females will mature between one to two years of age, while

males mature at two years of age (Tyndale-Biscoe & Renfree 1987). The majority of

females give birth during autumn, although if females have good body condition and

there is a good food supply they can give birth again during spring (Kerle 1984). Joeys

remain with their mother for between seven and nine months, after which time they

disperse (Clout & Efford 1984; Efford 1998; Cowan & Clout 2000; Cowan 2005).

Males tend to move and disperse more frequently and further than females, who tend to

establish home ranges adjacent to their mother’s (Clout & Gaze 1984; Ward 1985; Pech


37

et al. 2010; Blackie et al. 2011). Dispersing possums move an average distance of five

km and can move up to three km per night, with several moves possibly being made

before establishing a home range (Cowan & Rhodes 1993; Cowan et al. 1996; Cowan

et al. 1997). However, when females do disperse, they often travel larger distances than

males, with two females being recorded to have dispersed 32 km and 41 km

respectively in rural habitat (Efford 1991; Cowan & Clout 2000).

Research on the life expectancy of possums in the wild is lacking. The maximum

lifespan in New Zealand is estimated to be 14 years, with survival rates showing a

decrease beyond seven years of age (Brockie et al. 1981; Coleman & Green 1984;

Efford 2000).

1.6.5 Distribution

Within Australia, possums have the widest natural distribution of all marsupials; they

are widespread throughout Tasmania and the south-western, eastern, and arid central

areas where they occupy any area with trees, and are most common in forested or

wooded locations, with abundance highest in south-eastern dry woodlands (Kerle 1984;

Harper 2005). However, possums have disappeared from over half of their original

natural range in Australia (Kerle et al. 1992; Kerle 2004; Paull & Kerle 2004).

Densities of possums in the natural habitats where they remain in Australia are

generally low, at less than one individual per hectare, but they can reach up to four per

hectare in forested habitats (Kerle 1984; Kerle 2004; Matthews et al. 2004; Paull &

Kerle 2004). Low densities and declining populations can be attributed to predation

from animals such as dingoes, foxes, carpet pythons, powerful owls (Ninox strenua),

and wedge-tailed eagles (Aquila audax), disease, human modifications to the

environment (habitat disturbance and loss), and competition for food and shelter with

other marsupials (Kerle et al. 1992; Clout & Erickson 2000; Efford 2000; Kerle 2004).

Because possums are behaviourally flexible and show a wide tolerance to

environmental conditions, they are also found outside of their natural habitats, and in

particular have adapted to urban environments (Kerle et al. 1992; Kerle 2001; Cowan

2005). Urban habitats contain novel resources such as abundant food supplies, both

vegetation and human-subsidised foods, and artificial den sites which possums are able


38

to exploit (Statham & Statham 1997; Kerle 2001). Due to the availability of these

additional resources, which are not present in natural environments, possums can reach

high densities in urban habitats in Australia ranging from five to seven individuals per

hectare (Kerle 2001; Eyre 2004; Matthews et al. 2004; Hill et al. 2008).

The first successful introduction of possums to New Zealand was in 1858 with the aim

of establishing a fur industry (Pracy 1974). At least 127 separate liberation events of

both Australian and New Zealand-bred possums have occurred since then, carried out

by the New Zealand government, acclimatisation societies, and private individuals

(Pracy 1974; Clout & Erickson 2000). Originally, it was viewed that the presence of

possums in New Zealand was beneficial due to the provision of a fur and skin trade,

thus in 1911 they were protected under the Animals Protection Act 1908 (McDowall

1994). However, as possum densities and distributions increased, their negative impact

on the native flora and fauna became apparent, and the government reversed the

protection of possums in 1947 and sanctioned possum control initiatives (Clout &

Erickson 2000). The major habitat that possums occupy in New Zealand is native and

exotic forests, but are also found in sand dunes, montane scrublands, sub-alpine

grasslands, tussock grasslands, drylands, swamps, farmland, orchards, and urban

gardens. Today, possums occupy more than 90% of New Zealand, with dispersal into

previously uninhabited areas still continuing (Green 1984; Cowan 2005).

While densities of possums vary greatly within New Zealand, they can reach densities

of up to 20 times that found in their native habitats in Australia (Coleman et al. 1980;

Efford 2000; Rouco et al. 2013). The success of possums in New Zealand has been

attributed to several factors. Firstly, they are able to tolerate a wide range of

environments and implement an opportunistic and generalist feeding strategy, feeding

on a wide range of foliage and non-foliar food items (Clout & Erickson 2000).

Secondly, they have experienced a release from predators (foxes, dingoes, carpet

pythons, lace monitors, wedge-tailed eagles, and powerful owls), competitors (other

arboreal marsupials and gliders for food and shelter), and parasites (Smith & Lee 1984;

Clout & Erickson 2000; Cowan 2005). Thirdly, forests in New Zealand lack chemical

defences; more species are palatable and have higher nutrient levels compared to forest

tree species in Australia, especially in eucalypt forests which have low levels of

phosphorus and nitrogen (Brockie 1992; Cork 1996; Clout & Erickson 2000). Fourthly,


39

there was an unoccupied niche in New Zealand’s native forest ecosystems due to the

absence of herbivorous mammalian species, as well as compromised long-evolved

plant-herbivore relationships due to the extinction of the dominant avian herbivores

(Fleming 1979; Caughley 1989).

1.7 Trichosurus vulpecula impacts on New Zealand’s flora and fauna

Within New Zealand, possums are known to prey upon various native species,

including birds and their chicks and eggs (e.g. kōkako (Callaeas cinereus wilsoni), kiwi

(Apteryx spp.), North Island saddleback (Philesturnus carunculatus rufusater), kererū

(Hemiphaga novaeseelandiae), and fantail (Rhipidura fuliginosa)) (Brown et al. 1993;

McLennan et al. 1996; Sadlier 2000). There is also evidence that possums prey on

native land snails (Powelliphanta spp.), and various invertebrate species including stick

insects (Phasmatodea spp.), beetles (Coleoptera spp.), cicadas (Hemiptera spp.), and

wētā (Stenopelmatidae and Rhaphidophorioae spp.) (Meads 1976; Cowan & Moeed

1987; Owen & Norton 1995; Sadlier 2000). It is also thought that competition occurs

between possums and native New Zealand bird species for nest/den sites and food

resources due to dietary overlap, however there is currently no direct evidence for this

competition (Green 1984; Owen & Norton 1995; Sadlier 2000). Urban environments

are becoming increasingly recognized as important for supporting significant

populations of native wildlife, and the negative impacts of possums on New Zealand

native wildlife are highly likely to extend to this landscape. It is therefore important to

gain an insight into the spatial ecology of possums within urban environments to

formulate successful control strategies for these habitats.

Native forest communities within New Zealand can be extensively damaged and

modified by possums; the total population of possums can consume up to 21,000 tonnes

of vegetation per day, which can cause canopy dieback (Green 1984; Payton 2000;

Cowan 2005). Within forests, possums will selectively browse on preferred tree

species, often returning to the same tree until it is completely defoliated, generally

resulting in the death of the tree (Meads 1976; Rose et al. 1992; Payton 2000). This

level and selectivity of browsing also affects the regeneration ability of individual trees

and succession within forest stands, inhibiting fruiting and/or flowering of trees.

Consequently, the community composition of forests changes as non-preferred plant


40

species use the newly available canopy space created as palatable plant species become

greatly reduced or extinct locally (Green 1984; Cowan 1992; Rogers & Leathwick

1997; Nugent et al. 2000; Payton 2000).

Additionally, possums pose a considerable threat to New Zealand’s agricultural

industry, as they are vectors of bovine tuberculosis (Tb, Mycobacterium bovis), creating

a significant risk for Tb transmission to dairy cattle, beef, and deer (Ekdahl et al. 1970;

O'Neil & Pharo 1995; Coleman & Caley 2000). The industry is also adversely affected

by possums eating pasture, damaging crops, and killing poplars (Populus spp.), which

are planted to prevent soil erosion (Thomas et al. 1984).

1.8 Thesis structure and overall aims

In this study, I use several methods at differing scales to investigate the distribution and

habitat use of possums throughout an urban environment. Research on possums

inhabiting urban environments in New Zealand is currently lacking, due to the focus on

the ecological and economic threats possums pose to New Zealand’s forestry and rural

habitats. However, there is increasing evidence that urban areas can support significant

populations of native wildlife, and it is important to gain an understanding of the spatial

ecology of invasive pest species in these habitats, where they are also likely to be

having negative impacts to native fauna and flora.

This thesis has been written as a series of four stand-alone, but inter-related chapters

intended for publication, as well as a general introduction and discussion.

Consequently, some degree of repetition between chapters was inevitable, but has been

minimised where possible through the use of cross-referencing between chapters.

Chapter II and Chapter III have been published, while Chapter V is under review at the

time of thesis submission.

Chapter II aims to assess the spatial distribution of possums across an urban

environment. Presence-absence data and site occupancy methodology incorporating

incomplete detectability were used to test the hypothesis that in urban environments,

possums would have the highest occupancy within forest fragments (their natural

habitat) due to foraging and denning opportunities, followed by residential suburbs that


41

are comprised of structurally-complex vegetation, while amenity habitat would have the

lowest occupancy. Habitat characteristics from the five distinct urban habitat types that

comprise most of the city area (forest fragments, amenity parks, and residential

suburbs) were incorporated into these analyses to investigate how they influence the

probability of possum occupancy within residential areas.

The primary aim of Chapter III was to determine which environmental and technical

factors influence the accuracy and performance of lightweight GPS collars within the

urban environment. Stationary GPS collar tests were used to test the hypotheses that the

performance and accuracy of collars would decrease with increased vegetation

complexity, distance to buildings, and canopy cover (decreased sky availability), and

increase as the number of satellites increased and HDOP decreased. The study also

aimed to quantify the location error surrounding GPS locations collected within the

urban environment: this information was then incorporated into subsequent spatial

analyses to provide more robust results.

Chapter IV uses GPS data collected from possums captured in residential suburbs to

examine the spatial ecology and habitat selection of urban possums at the scale of the

individual possum. This chapter tested the hypotheses that (i) home ranges of possums

within urban areas would be larger than those in forest or rural habitats due to the

irregular spatial distribution of resources in the heterogeneous urban landscape,

especially well-established, intact stands of vegetation which possums use extensively

to forage and den in. The patchy nature of the habitat would require individuals to

travel further to locate such resources; (ii) possums would select forest fragments and

residential areas comprised of structurally-complex and diverse vegetation, while

avoiding residential habitats with simple or no vegetation; and (iii) possums would not

establish home ranges independently of forest fragments within the urban environment.

Home ranges were investigated using Brownian bridges, incorporating the estimated

location error obtained in Chapter III. Resource utilisation functions (RUFs) were

applied to investigate habitat selection with the inclusion of environmental attributes

obtained from a habitat map of Dunedin. Identification of habitats and landscape

configurations preferred by urban possums provided an insight into which resources

urban possums are using. This information can assist in the decision-making process for

urban-based management strategies. Specifically, information from this research will


42

inform the Otago Peninsula Biodiversity Group’s (OPBG) campaign to eradicate

possums from the Otago Peninsula.

To enhance the long-term success of the OPBG’s possum eradication, Chapter V

examines the genetic population structure, thus connectivity, of possums in and around

Dunedin and on the Otago Peninsula. The primary aim of this chapter was to identify

potential dispersal pathways from the mainland onto the Otago Peninsula, and thus

potential sources of re-invasion following the eradication. Genetic variation from

twelve microsatellite loci was used to test the hypothesis that possum populations on

the Western and Eastern sides of the Otago Harbour would be genetically differentiated

due to isolation by distance and that one reinvasion pathway would exist onto the Otago

Peninsula through the residential areas at the base of the Otago Peninsula. By

identifying potential reinvasion pathways, as well as understanding the habitat selection

by a species, potential management units can be better defined, and management

strategies are more likely to maximise long-term eradication success.

The findings of this thesis are summarised, discussed, and placed in a wider context in

Chapter VI. In this chapter, on the basis of the combined results from the spatial

ecology of possums gained from GPS telemetry and the population structure of

possums around Dunedin and on the Otago Peninsula obtained through molecular

genetics, I provide general recommendations for urban management of possums, as

well as specific recommendations in order to maintain the OPBG’s vision of a possum-

free Otago Peninsula.

1.8.1 Author contribution

I am the first author of all resulting manuscripts as I contributed to the design of the

methodologies, collected the data, performed all data analyses, and wrote the

manuscripts. Yolanda van Heezik, Kath Dickinson, and Bruce Robertson helped

conceive this PhD project and supervised me throughout the design, data collection,

and data analysis stages, as well as providing editorial comments on my thesis chapters.

As my co-authors, they also revised resulting manuscripts. Mariano Recio will be an

additional co-author for the manuscript produced from Chapter IV, as he gave advice

and assisted in the statistical analyses.

Chapter II: Site occupancy of urban possums

43

Chapter II:

2 Predicting distribution patterns for common

brushtail possums (Trichosurus vulpecula) in

an urban environment

Experimental set up of a WaxTag® used to determine possum presence.

A refined version of this chapter has been published as:

Adams, A.L., Dickinson, K.J.M., Robertson, B.C., van Heezik, Y. (2013). Predicting

summer site occupancy for an invasive species, the common brushtail possum

(Trichosurus vulpecula), in an urban environment. PLoS ONE 8: e58422.

doi:10.1371/journal.pone.0058422.


44

2.1 Introduction

Within and across environments, the spatial distribution of animal species reflect the

availability and distribution of species-specific resources and the ability of species to

reach and exploit them (MacArthur 1972; Taylor et al. 1993; Brown et al. 1996; Ostfeld

& Keesing 2000; Ganzhorn 2002; McKinney 2002; Holt 2003). Moreover, species

distributions and occupancy of habitat patches is influenced by landscape structure,

which determines habitat availability and connectivity between resources (Saunders et

al. 1991; Reunanen et al. 2002). Landscape structure is especially significant in

fragmented habitats where important resources and habitat patches become

heterogeneously distributed across the environment (Taylor et al. 1993; Mackey &

Lindenmayer 2001; Fahrig 2003; Guisan & Thuiller 2005). Fragmentation of

landscapes results in an overall loss of natural habitat, both spatially and structurally,

and species which are sensitive to urbanisation ('urban-avoiders'; McKinney 2002) may

be limited to small patches of natural habitat isolated within a matrix of modified

habitats (Saunders et al. 1991; Bender et al. 1998). Spatial isolation of patches can limit

movements between patches by animals that are unable to disperse across the modified

matrix (McKinney 2002; Reunanen et al. 2002; Hobbs et al. 2009). Furthermore, edge

effects can reduce the quality of the habitat within the patch with changes in

microclimate, soil conditions, plant composition, and species interactions (Turner 1989;

Saunders et al. 1991; Fahrig 2003). The degree of fragmentation within a landscape and

the nature of the matrix will influence the amount of time individuals spend outside of

their preferred natural habitat, therefore altering their normal distribution patterns

(Murcia 1995; Fahrig 2003). The impacts of habitat fragmentation on animal

distributions are particularly significant in urban landscapes, which are becoming

increasingly prevalent as the world’s human population continues to grow (McKinney

2002, 2006; United Nations 2011). In urban environments, the ecosystem has been

transformed into hybrid systems consisting of fragments of original habitat that are

often small and embedded within a matrix of highly-modified and human-dominated

habitat (Hobbs et al. 2009).

Urban environments can also provide novel resources and habitats. Species that are

sufficiently behaviourally and biologically flexible to exploit resources within modified

matrix habitats are known as ‘urban-adapters’ and are often generalist species


45

(McKinney 2002; Wandeler et al. 2003; McKinney 2006). For example, red foxes

(Vulpes vulpes) exploit anthropogenic food sources and alter spatial behaviours to adapt

to urban environments in numerous European cities (e.g. Harris 1981; White et al.

1996), North America (e.g. Adkins & Stott 1998) and Australia (e.g. Marks &

Bloomfield 1999). Other examples of mammalian ‘urban-adapters’ include Eurasian

badgers (Meles meles), squirrels (Sciurus carolinensis, S. vulgaris), and the stone

marten (Martes foina), with their distributions generally reflecting the spatial

distribution and availability of resources (Luniak 2004). Species such as Northern

raccoons (Procyon lotor), which require forest fragments, particularly for shelter, can

also frequent and exploit resources in the urban matrix, such as vegetable gardens and

garbage (McKinney 2002). Invasive species, which tend to be opportunistic foragers

and behaviourally flexible, are usually more successful at adapting to and thriving in

urban habitats (Sol et al. 2012).

An invasive urban-adapter in New Zealand is the possum (see Chapter I; Kerle 1984;

Harper 2005). Introduced into New Zealand from Australia in 1858, it has a wide

distribution throughout both countries that includes urban environments (Pracy 1974;

Kerle 1984; Cowan 2005). In New Zealand, possums are an invasive pest species,

primarily occupying forest habitats where they exist at densities far higher than in their

native Australian range (Kerle 1984; Statham & Statham 1997; Efford 2000; Cowan

2005), where densities are constrained by the palatability and nutritional content of

forest vegetation (Freeland & Winter 1975; Clout & Erickson 2000; DeGabriel et al.

2009). Although primarily folivorous, possum distribution is also influenced by the

distribution and availability of nutrient and energy-rich, non-foliar food resources (e.g.

fruits and flowers) which are a sought-after supplementary food source (Warburton

1978; Cowan 1990; Nugent et al. 2000; Kerle 2001).

Little is known about the distribution of possums throughout urban habitats, but it is

likely to be mainly influenced by the distribution of food sources (McDonald-Madden

et al. 2000). In Australia, possums can exist at higher densities in urban areas than in

native forests (Eyre 2004; Matthews et al. 2004; Hill et al. 2008), where they move

between native remnants and adjacent residential areas, exploiting nutritionally

valuable food sources in private gardens (e.g. cultivated garden plants, vegetable

gardens, fruit trees, compost, food scraps), human-subsidised food left out intentionally,


46

and artificial shelter resources (e.g. roof awnings, floor spaces; Statham & Statham

1997; Matthews et al. 2004; Harper 2005; Harper et al. 2008). Although studies suggest

that urban possums do not establish a home range independently of forest fragments

(Statham & Statham 1997; Matthews et al. 2004; Harper 2005; Harper et al. 2008).

In contrast, New Zealand forests support possum densities comparable to those found in

Australian urban areas due to the greater palatability and nutritional quality of the

vegetation (Brockie 1992; Clout & Erickson 2000). Possums in New Zealand cause

severe damage to native vegetation, they compete with native birds for resources, and

they eat birds and their eggs (see Chapter I; Cowan 1990; Sadlier 2000). They are

therefore likely to have the same negative impacts on avian diversity and community

structure in at least some components of the urban landscape. However, the highly

palatable and productive nature of New Zealand forests (Clout & Erickson 2000) may

mean that there is less incentive than in Australia for possums to move out of urban

forest fragments to occupy other less densely vegetated habitats. It is therefore

important to determine the extent to which invasive adaptable species, such as possums,

have established throughout the heterogeneous urban landscape to evaluate potential

impacts on native biodiversity and to guide effective urban-based management

strategies, which need to be based on a comprehensive understanding of the distribution

of the species across non-traditional habitats.

This study used site occupancy methodology to assess the distribution of invasive

possums and determine environmental factors influencing their occupancy throughout

an urban landscape. This research was conducted during summer when impacts on

urban avian populations will be the greatest due to greater movements by possums

(Cowan & Clout 2000) and the vulnerability of nesting adults, eggs, and nestlings to

possum predation. Occupancy was evaluated across five urban habitat types: forest

remnants, amenity parks, and residential areas representing typical variation in garden

size and structure. It was hypothesised that in urban environments (1) possums would

have the highest probability of occupancy within forest fragments (tree stands greater

than one hectare) due to the availability of their primary dietary item, plant material

(which includes leaves, fruits, and flowers); (2) amenity areas would have the lowest

proportion of occupancy by possums due to less opportunities for foraging and denning

(absence of mature vegetation); and (3) within residential areas, possums would display


47

higher occupancy in areas composed mainly of properties with large, well-established

gardens due to the availability of their primary dietary item, plant material, as well as in

areas with supplementary food sources, due to the higher nutritional and energy value

of these non-foliar food items.

2.2 Methods

2.2.1 Study area

Field work was conducted within the city boundaries of Dunedin (~120,000

inhabitants; Statistics New Zealand 2006), New Zealand (NZ; 45°52´S, 170°30´E). The

22,500 ha urban area included a green town belt featuring remnant forest fragments

totalling 145 ha, stand-alone vegetation fragments, amenity parks, and residential areas

of differing levels of urbanisation characterised by housing density and vegetation

characteristics (Table 2.1; Fig. 2.1; Freeman & Buck 2003). Five distinguishable habitat

types representing a gradient of urban land use were defined on a GIS-based habitat

map of Dunedin City (Freeman & Buck 2003): forest fragments, amenity spaces,

Residential 1 (Res 1), Residential 2 (Res 2), and Residential 3 (Res 3; See Table 2.1 for

further details).


48

Table 2.1: Descriptions of the five urban habitat types in which possums were

surveyed in, Dunedin, NZ (Freeman & Buck 2003).

Habitat

Type Habitat Description

Forest

fragment

Structure-rich tree stands composed of both exotic and native tree species

forming closed canopies ranging from one hectare to 24 ha in area (mean =

4 ha), which are surrounded by modified residential landscapes. Fragments

have a similar distribution throughout Res 1 and Res 2 habitats, but are

largely absent from Res 3 habitat.

Amenity Amenity spaces including council recreational parks or playgrounds,

playing fields, and golf courses in which grass is mown regularly with edges

varying from houses and roads (n = 7), to bare edges with scattered trees

and shrubs (n = 14), to completely enclosed by mature trees (n = 9).

Res 1 Residential areas with greater than one third of the property size comprised

of mature, structurally-complex gardens containing an assortment of lawns,

hedges, shrubs, and large established trees. Green cover totals 70%, with a

mean housing density of 11.6/ha (SD = 1.98, n = 14; van Heezik et al.

2008a).

Res 2 Residential areas with greater than one third of the property size comprised

of structurally-less complex gardens dominated by lawns. Green cover

ranges between 42 - 50%, with a mean housing density of 12.52/ha (SD =

2.27, n = 20 suburbs; van Heezik et al. 2008a).

Res 3 Residential areas with no garden or where less than one third of the property

is dominated by flowerbeds or lawn. Green cover totals 30%, with a mean

housing density of 28.6/ha (SD = 3.14, n = 6 suburbs; van Heezik et al.

2008a).


49

Figure 2.1: Examples of the three residential habitat types in Dunedin: (A) Res 1;

(B) Res 2; and (C) Res 3. See Table 2.1 for habitat definitions. Photos

sourced from Dunedin City Council (2007).

2.2.2 Possum surveys

Occupancy data were collected by surveying thirty randomly generated locations in

each of the five habitats (n = 150; Fig. 2.2) using ArcGIS 9.3.1 software (ESRI 2009)

and the GIS-based habitat map of Dunedin City (Freeman & Buck 2003). Fifteen

locations were randomly chosen from each habitat type to be sampled within one of

two weekly sampling periods over summer which coincided with favourable weather

conditions (mostly fine with no heavy rain or strong winds; Paterson et al. 1995):

possums are most likely to be active, providing the highest detection probabilities,


50

during fine weather. Weekly intervals were selected in accordance to the national

standardised WaxTag® protocol, as possums are more likely to be detected over a

period of seven nights, during which time individuals should occupy any given part of

their home range (Thomas et al. 2007; National Possum Control Agencies 2010).

Following the national protocol for WaxTag® use, possum presence was detected by

placing three peanut butter-flavoured WaxTags® and a lure at each of the 150 sites

(Fig. 2.2) and leaving them for seven consecutive nights (Thomas et al. 2007; National

Possum Control Agencies 2010). WaxTags® were nailed 70 cm above the ground to a

randomly chosen tree which were haphazardly distributed throughout properties (or

post in Res 3 gardens which lacked trees) and were spaced 10 m apart to enable

independence (Ogilvie et al. 2006; National Possum Control Agencies 2010). A flour

and icing sugar blaze (5:1 ratio) was smeared below the WaxTags® to act as a visual

and olfactory lure (National Possum Control Agencies 2010). Contagion, an occurrence

where an individual is detected at more than one sampling location due to actively

seeking out the monitoring device for the bait (Bamford 1970), was prevented by the

use of the above spacing and the unpalatable nature of the wax (Meenken 2005;

Thomas et al. 2007). The use of three independent WaxTags® at each site acted as

repeat surveys within the same visit. The possibility of incurring a false-presence error

was minimised as WaxTags® were specifically used to target possums, as well as using

one trained observer to interpret results (MacKenzie et al. 2006). Habitat

characteristics, considered as likely influences on occupancy probability, were recorded

for each location. This included presence of supplementary food sources consisting of

any type of fruit tree (fruiting and non-fruiting), vegetable gardens (presence was

recorded if any vegetables were seen growing on the property), and un-covered

compost heaps. Connectivity was assessed by calculating the percentage of area of

forest fragments within a 500 m buffer mapped around each site, which reflected the

average nightly movements of possums within the urban environment (see Chapter VI

and Statham & Statham 1997) using ArcGIS 9.3.1 software (ESRI 2009).


51

Figure 2.2: Locations of the 150 WaxTag® sites across five urban habitat

types, Dunedin, New Zealand. See Table 2.1 for habitat

descriptions.


52

2.2.3 Statistical analysis

Bite marks from WaxTags® were classified to animal species (Thomas 2011) using

possum presence/absence data to generate naїve occupancy estimates (probability of

occupancy excluding detection probability). Habitat occupancy ( ) and detectability

(p) were further investigated using a single-season analysis within the software

PRESENCE version 3.0 using logistic regression models and which incorporates

incomplete detectability, producing more reliable estimates (see MacKenzie et al. 2003;

Hines 2006; MacKenzie et al. 2006 for further details). Incorporation of incomplete

detectability helps overcome generation of biased estimates caused by false-absences

which occur when a species is recorded as absent while present but unobserved within a

habitat (Tyre et al. 2003; MacKenzie et al. 2006). Due to the one surveyor, one

sampling method, and one season, detection probability in all models is assumed to

have remained constant.

The initial analysis to investigate occupancy of possums within an urban environment

used a simple spatially-explicit habitat model that assumed and p were constant

(designated as “.” in models) over all sampling periods and habitats: (.).(.) p An

alternative model including habitat type as a covariate, (.),)( phabitat was then

constructed because possums are known to use forest fragments more heavily than

other habitat types, based on the availability of their main dietary component, plant

material (Nugent et al. 2000).

To further investigate possum presence in an urban setting, the presence/absence

dataset was sub-sampled to include only the three residential habitat types (Res 1, Res

2, Res 3; n = 90). This enabled possum occupancy to be examined in relation to the

type of residential habitat and habitat characteristics as covariates (supplementary food

items; area (%) of forest fragment within a buffer). Forest and amenity habitats were

excluded because these variables contained all zeros for supplementary food sources,

which causes non-convergence in PRESENCE models. Covariate data (χ) are easily

incorporated into analyses to estimate the probability of occupancy at site i )( i by

using a logit function of m habitat covariates:


53

mmi ...)(logit 110

where β represents the estimated beta parameters.

Firstly, occupancy was modelled as a function of residential habitat type to determine if

the type of residential habitat affects the presence of possums. This was followed by

modelling residential habitat types while varying the other habitat covariates. This

allowed each covariate to be tested separately to examine their importance in

influencing possum presence, while the detection probability was held constant:

psi(residential habitat + habitat covariate),p(.). Lastly, models were created using

combinations of the different habitat covariates.

Models were compared based on the relative difference in Akaike’s second-order

corrected Information Criterion (AICc) values corrected for small sample sizes

(Burnham & Anderson 2002). The model with the smallest AICc value represents the

model nearest to reality, thus best describes the observed data (Burnham & Anderson

2004).

For both modelling situations, PRESENCE often warned that models had failed to

converge. Non-convergence of models indicates that the maximum-likelihood

algorithm has not converged. Model convergence occurs when the programme fits the

data to a starting model and keeps improving the model by refitting the data until the

model cannot be further improved. Therefore, a model that does not converge means

that no model can be found which explains the data with any degree of confidence as

parameterization fails to converge on reasonable estimates. However, in PRESENCE

these warnings can be ignored when model convergence is accurate to greater than two

significant digits (Hines 2011), therefore all models were considered valid. In models

where occupancy estimates near 1 or 0, standard errors could be quite large. This is

acceptable as when real parameters are 1 or 0, their associated standard errors are

undefined, thus large standard errors of the estimated parameters are expected (Hines

2011).


54

2.2.4 Model evaluation

Models designed for predictive purposes should be evaluated (Fielding & Bell 1997;

Manel et al. 2001), where a model has a good predictive ability if it can distinguish

between occupied and unoccupied sites (Pearce & Ferrier 2000). To evaluate the

predictive ability of the logistic regression models selected as ‘best’, the threshold-

independent area under the curve (AUC) of Receiver Operating Characteristic (ROC)

plots were calculated. ROC plots are a threshold optimisation method which determines

the cross-point of sensitivity (true positives) and specificity (false positives) plotted

against numerous thresholds using the dichotomous presence-absence data produced

from the continuous probabilities generated by the models (Fig. 2.3; Fielding & Bell

1997; Barbosa et al. 2003; Jimenez-Valverde & Lobo 2007). A series of sensitivity and

specificity values are generated by varying the decision threshold, which are then

plotted as ROC curves (Metz 1978). Sensitivity is the number of observed presences

that are predicted as such by the model, while specificity is the number of observed

absences predicted by the model (Fielding & Bell 1997). The area under the curve

produced indicates the overall capability of the model to predict the data that was used

to create it and is a threshold independent indicator of the model’s classification ability

(Fielding & Bell 1997; Pearce & Ferrier 2000). The AUC values vary between 0 and 1,

and are used to evaluate the performance of a model in its capability to correctly

differentiate between locations where species are present and absent (Pearce & Ferrier

2000). AUC values of 1 indicate the model has perfect classification, with values

between 0.7 and 0.9 representing models which are reasonably useful at classification.

However, AUC values below 0.7 represent models with some predictive differentiation,

but the model is randomly producing similar proportions of correct and incorrect

predictions. An AUC value of 0 indicates the model has poor accuracy (no correct

classifications) being worse than random (Pearce & Ferrier 2000; Hirzel et al. 2006;

Cianfrani et al. 2010). ROC analyses were performed in the “PresenceAbsence”

package (Freeman & Moisen 2008) in R version 14.1 (R Development Core Team

2011).

The AUC is threshold-independent unlike more traditional model evaluating methods

which require a classification threshold to be selected to classify sites as occupied or

unoccupied based on whether the predicted probability is higher or lower than the


55

threshold value (Altman et al. 1994; Pearce & Ferrier 2000). Having a threshold-

independent method overcomes the often arbitrary selection of a threshold, which can

cause biases in accuracy interpretation, and the use of such threshold independent

methods over the last decade has increased in ecological research (see Fielding & Bell

1997; Pearce & Ferrier 2000; Osborne et al. 2001; Luck 2002).

Figure 2.3: Determining the corrected classification rates for all possible cut-off points

at 0.1 intervals by estimating the optimal threshold for converting

continuous probabilities into presences/absences for (A) all urban habitats

and (B) three residential habitats using the final model for each. The

identification of the cross-point between specificity and sensitivity

resulted in an optimised threshold of 0.44 with a 73% correct

classification rate for all habitat types and an optimised threshold of 0.33

and a 67% correct classification rate for the three residential habitats.

(A)

(B)


56

2.3 Results

Possums were detected at least once at 59 sites (n = 150 sites; Fig. 2.4), giving a naїve

overall occupancy estimate of 0.39 across all habitats. The total number of possum

detections over the three WaxTag® surveys ranged from 52 in forest fragment habitat

to 0 in Res 3 (Fig. 2.4). Naїve occupancy estimates ranged from 0.93 in forest

fragments, 0.43 in amenity space, 0.40 in Res 1, 0.20 in Res 2, and 0 in Res 3.

Properties in all three residential habitats within this study had the same percentage

(20%) of properties containing fruit trees, and about half of these within each habitat

were fruiting during the sampling period. Proportions of properties containing

vegetable gardens varied between residential habitat types: 30% of Res 1, 43% of Res

2, and 10% of Res 3 properties. Res 1 and Res 2 had a similar proportion of properties

containing some form of un-covered compost: 27% and 23% respectively, while only

0.7% of Res 3 properties had compost heaps.

Figure 2.4: Number of sites possums were detected at within each urban habitat

type (n = 30 per habitat) and number of times possums were detected

within each habitat type (n = 90 sites per habitat type), Dunedin, NZ.

See Table 2.1 for habitat classifications.

0

10

20

30

40

50

60

Forest Amenity Res 1 Res 2 Res 3

Nu

mb

er

Habitat Type

Sites Detected

Times Detected


57

2.3.1 All urban habitats

When all habitat types were included, the model with the most support was where

occupancy varied as a function of habitat type (Table 2.2). Occupancy of possums ( )

was highest in forest fragments, followed by amenity, Res 1, and Res 2, with lowest

occupancy in Res 3 habitat (Table 2.2). Detection probability was constant across all

five habitats (Table 2.2).

2.3.2 Residential analysis

When forest and amenity habitats were excluded, eight potential models varying in

their combinations of covariates were fitted to the observed data (Table 2.3). Habitat

type, supplementary food items, and percentage area of forest fragment within 500 m of

a sampled location all improved model fit (Table 2.3). In the top-ranking model, Res 1

had the highest occupancy estimate for possums followed by Res 2 then Res 3 habitats

(Table 2.4). Therefore, possums are likely to be found in residential areas with

structurally-complex vegetation, supplementary food items, and greater proximity to

forest fragments (Table 2.5). Again, detection probability was constant across all three

residential habitats (Table 2.4).

2.3.3 Estimated possum distribution

The current level of occupancy of possums throughout the Dunedin urban environment

was then determined using the estimates generated from the top models (Table 2.4).

The modelling predicted that all urban forest fragments are occupied by possums, while

about half of the amenity and Res 1 habitats will have possums present (Table 2.4).

Presence declined in Res 2 habitats, but due to the size of this habitat category, a

considerable area was still estimated to be occupied by possums (499.5 ha; Table 2.4).

58

54

Table 2.2: Comparison of habitat models using Akaike's second-order corrected Information Criterion (AICc) to obtain site occupancy

(ᴪ ± 1 SE) and detection probabilities (p ± 1 SE) for possums in five urban habitat types, Dunedin, NZ. See Table 2.1 for

habitat classifications.

Model Description AICc ∆i wi

Model

Likelihood K ᴪ(Forest) ᴪ(Amenity) ᴪ(Res 1) ᴪ(Res 2) ᴪ(Res 3) p

psi(Habitat),p(.) 369.43 0.00 1.00 1.00 6

1.00

(0.00)

0.50

(0.11)

0.46

(0.10)

0.23

(0.08)

<0.0001

(0.00)

0.49

(0.05)

psi(.),p(.) 434.16 64.73 0.00 0.00 2 0.47

(0.05)

0.47

(0.05)

0.47

(0.05)

0.47

(0.05)

0.47

(0.05)

0.46

(0.05)

∆i = AICc differences, wi = Akaike weights; K = number of parameters; (.) = constant probability

Chap

ter II: Site o

ccup

ancy

of u

rban

possu

ms

58

Table 2.3: The model set investigating site occupancy for possums within three residential habitats,

Dunedin, NZ. Models are ranked based on Akaike’s second-order corrected Information

Criterion (AICc).

Model Description AICc ∆i wi

Model

Likelihood K Deviance

psi(Residential+Food+Area),p(.) 146.93 0.00 0.63 1.0000 6 133.92

psi(Residential),p(.) 147.96 1.03 0.37 0.5979 4 139.49

psi(.),p(.) 163.30 16.37 0.00 0.0003 2 159.16

psi(Area),p(.) 164.95 18.02 0.00 0.0001 3 158.67

psi(Food),p(.) 169.51 22.58 0.00 0.0000 2 165.37

psi(Food+Area),p(.) 171.61 24.68 0.00 0.0000 3 165.33

psi(Residential+Area),p(.) 173.24 26.31 0.00 0.0000 5 162.53

psi(Residential+Food),p(.) 173.77 26.84 0.00 0.0000 5 163.06

psi(Residential),p(Residential) 178.31 32.39 0.00 0.0000 4 170.39

∆i = AICc differences, wi = Akaike weights; K = number of parameters; (.) = constant probability

Chap

ter II: Site o

ccup

ancy

of u

rban

possu

ms

59


60

Table 2.4: Estimated proportions and amount of area occupied by possums for the two

model sets across five different urban habitats, Dunedin, NZ. See Table 2.1

for habitat classifications.

Analysis Habitat

Type

Area

(ha)

Proportion of

Occupied

Area ( )

Absolute amounts

of Occupied Area

(ha)

Detectability

(p)

All Habitats Forest 145 1.00 (0.00) 145.0 0.49 (0.05)

Amenity 919 0.50 (0.11) 459.5 0.49 (0.05)

Res 1 302 0.46 (0.10) 138.9 0.49 (0.05)

Res 2 1921 0.23 (0.08) 441.8 0.49 (0.05)

Res 3 381 0.00 (0.00) 0.0 0.49 (0.05)

Total

3668

1185.2

Residential

Habitats

Res 1 302 0.45 (0.14) 135.9 0.44 (0.08)

Res 2 1921 0.26 (0.18) 499.5 0.44 (0.08)

Res 3 381 0.00 (0.00) 0.0 0.44 (0.08)

Total

2604

635.4

Table 2.5: Variables included in the final model for occupancy

probability across the three residential habitat types, their

coefficients (β), and standard errors (SE).

Variable β SE

Res 1 40.68 1.46

Res 2 39.17 1.55

Res 3 -41.32 1.46

Food 1.64 0.74

% Area Forest 36.37 23.39


61


Perfect predictive ability (AUC = 1.0 ) is achieved when the proportion of true positives

equals one, while the proportion of false positives equals zero for all threshold values.

This is signified when the ROC curve mirrors the left hand and top axes (Pearce &

Ferrier 2000). In this study, the predictive ability of the best models both showed

moderately good accuracy. The (.))( phabitat model examining possum presence

across all five urban habitat types had an AUC of 0.84 (SE = 0.03; Fig. 2.5) indicating

that the model could correctly discriminate between the presence and true absence of

possums 84% of the time. The .)%forest)p(foodψ(habitat model investigating

occupancy of possums across residential habitat types had an AUC of 0.77 ± 0.05 (Fig.

2.5); thus the model had a 77% success rate at discriminating between possum presence

or absence.


62

Figure 2.5: Receiver operator curve plots for the best model modelling possum

occupancy across (A) all five urban habitats and (B) the three residential

urban habitats. The diagonal line represents an indiscriminate model

(AUC = 0.50).

(A)

(B)

Sensitivity

Sensitivity

1-S

pec

ific

ity

1-S

pec

ific

ity


63

2.4 Discussion

Habitat occupancy of possums in the urban Dunedin landscape during summer reflected

the heterogeneous distribution of resources and patch connectivity, emphasising the

influence that landscape structure and resulting resource availability have on the

distribution of this species (Dunning et al. 1992). Highest occupancy rates in natural

habitats (nearly all forest fragments sampled were occupied) reflect the traditional

distribution of possums throughout forests, which contain important resources

including den sites and their primary dietary item, plant material (leaves, fruits, and

flowers), which makes up 50-90% of their total diet (Kerle 1984; Nugent et al. 2000).

Possum occupancy declined outside of forest fragments, decreasing with increased

modification of matrix habitat. Amenity habitat had the second highest occupancy

estimate which is most likely explained by the surrounding characteristics of this open,

green space. Possums were detected in seven of the nine amenity locations that were

completely enclosed by complex vegetation, but in only three of the seven amenity

locations surrounded by housing and roads, and three of the fourteen locations that had

bare edges with a scattering of trees or shrubs. The presence of mature, complex

vegetation around amenity space is likely acting as an available source of food and

shelter. Urban parks in Australia containing trees also have associated high possum

densities which are thought to be related to foraging opportunities from both

surrounding vegetation and human food scraps (McDonald-Madden et al. 2000). The

behavioural flexibility of possums enables individuals to exploit both novel food

sources (tree foliage, fruit trees, food scraps, and garden plants including their flowers)

and novel den sites (trees, wood piles, roofs, chimneys, spaces in and under buildings)

available in private residential gardens (Statham & Statham 1997; Harper 2005).

However, these results indicate their distribution across residential areas is constrained

by the amount and structure of the vegetation. Within residential areas, possums were

most likely to occur in areas with large gardens situated close to forest fragments,

containing structurally-complex vegetation, and some source of supplementary food.

Res 1 and 2 habitats have similar-sized gardens, but they vary in vegetation

characteristics, with resources becoming patchier in Res 2 habitats due to the lower

proportion of vegetation cover and the simpler nature of the gardens (van Heezik et al.


64

2008a). This was reflected in lower occupancy rates: 23% of Res 2 habitat compared to

46% in Res 1 habitat.

Possums residing in urban bush fragments in Australia also frequent gardens of

surrounding residential properties which contain a high diversity of exotic plant

species, including shrubs, trees, and their fruits and flowers. These are of higher

nutritional value and less toxic than Eucalyptus species (Attiwill & Leeper 1987),

which constitute a major part of possum diet in their native range (Freeland & Winter

1975; Kerle 1984; Harper et al. 2008). Native forest species in New Zealand typically

have greater nutritional and energy value than native vegetation in Australia and they

lack chemical defence mechanisms (Clout & Erickson 2000; Cowan 2001).

Nevertheless, even though forest fragments in urban New Zealand consist of vegetation

that is more palatable and nutritionally valuable than native vegetation fragments in

Australia (Brockie 1992), possums in New Zealand still used residential gardens to

supplement their diet with highly nutritious and energy rich food items.

The presence of fruit trees primarily, but also vegetable gardens and compost heaps,

within residential areas all influenced possum presence. The presence of fruits in other

New Zealand environments are known to influence the distribution of possums:

seasonal fruiting in forested environments results in an increase in the number of

possums eating often large quantities of fruit which can account for up to 42% of their

diet (Coleman et al. 1985; Cowan 1990; Nugent et al. 1997), while possums in rural

landscapes are known to travel up to 1600 m to reach preferred dietary items, such as

apples (Jolly 1976; Green & Coleman 1986; Brockie et al. 1997). Fruit, vegetables, and

compost heaps all act as a supplementary food source, providing possums with dietary

items high in energy and nutrients to help compensate for their limited ability to extract

enough nutrients and energy from foliage alone (Nugent et al. 2000; Harper 2005).

Even though the proportions of these supplementary food sources were similar across

Res 1 and Res 2 habitats, occupancy rates were quite different, suggesting that general

habitat characteristics and levels of housing density and green cover are more important

in determining occupancy. Despite appropriate food supplies being available in Res 3

habitat, albeit at lower densities, these areas may remain unoccupied because they are

situated far from forest fragments and are therefore not well connected to possum’s

traditional habitat. The virtual absence of structurally-complex vegetation used for both


65

food and den sites in Res 3 habitat may also be a limiting factor. Due to the ability of

possums to use virtually any structure in the urban environment as a den (e.g. Harper

2005), dens could not be quantified in this study, and thus could not be included as a

limiting factor in the occupancy models.

Res 1 and Res 2 habitats were typically located in suburbs in which possums had

shorter distances to travel from forest fragments to access food resources in gardens.

Low density housing with private gardens containing greater plant diversity are often

characteristics found in high socio-economic neighbourhoods (van Heezik, unpublished

data), which also tend to be located close to public green spaces (Hope et al. 2003;

Strohbach et al. 2009). This clustering of vegetatively diverse gardens close to forest

fragments may facilitate the movement of possums between forest fragments and

residential habitats. Similar trends have been documented in other urban areas with

Baker & Harris (2007) showing that the use of private gardens by mammalian species

within residential areas of Great Britain decreased as urbanisation increased due to

smaller garden sizes, increased human disturbance, and increased distance to natural or

semi-natural areas. The degree of connectivity between habitat patches and the

presence of preferred habitat has been shown to significantly affect species occupancy

by affecting dispersal across less preferred habitat types (Villard et al. 1999; Reunanen

et al. 2002).

Occupancy of possums within urban habitats, especially those with high housing

densities, may be further limited by a number of factors: predation from introduced

mammals (Lepczyk et al. 2004; Ditchkoff et al. 2006), in this case dogs, increased

noise (i.e. children, pets and traffic; Warren et al. 2006), and pollutants (Ditchkoff et

al. 2006). While possums were not detected in Res 3 habitat, it cannot be concluded

that they were absent. Species may occupy habitats in low densities, resulting in very

low probabilities of being detected using the detection method employed during the

sampling period (Wintle et al. 2005).

2.4.1 Detection probability

Possums were not always detected when they were present in a habitat; detection

probabilities less than one indicate false absences. The naїve site occupancy estimates


66

underestimated occupancy by 3 - 7%, as imperfect detectability of the species was not

accounted for (MacKenzie & Bailey 2004; MacKenzie et al. 2006). However, because

detectability of a species at a site can be influenced by numerous factors including

sampling effort (e.g. type and timing of the survey), weather conditions, animal

behaviour, local density of the species, random chance, and observer experience (Tyre

et al. 2003; Bailey et al. 2004; Gu & Swihart 2004), it is important to incorporate the

probability of detection into data analysis to avoid misleading and inaccurate

conclusions both of occupancy rates and effects of variables (Tyre et al. 2003;

MacKenzie et al. 2006).

2.4.2 Limitations

One limitation of this study is that occupancy was only examined during one season.

This may affect detectability which may differ during months when dispersal occurs,

during the breeding season when possums become more social, or in relation to

seasonal food supply. Occupancy may also be affected as it is possible that possums

may use different parts of their home ranges with different intensities during different

seasons of the year. However, we were interested in whether they used non-fragment

habitat at all and due to possums typically having stable home ranges (Brockie et al.

1997; Efford 2000), surveying was conducted in summer when they would have the

highest detectability (due to favourable weather conditions). Additionally, summer

coincides with when they would be having the highest impacts on native breeding bird

populations, as nesting adults, eggs, and nestlings are all vulnerable to possum

predation. Due to the importance of supplementary food sources within the residential

models, occupancy of possums within residential habitats may decline in winter when

fruit and vegetables are not readily available, and possums may less frequently leave

forest fragments. Occupancy is predicted to be similar in spring, due to the availability

of supplementary food sources and increased movements of possums coinciding with

their breeding season (Kerle 1984). To reach a more robust estimation of occupancy

across urban habitats, sampling over more seasons should be taken into consideration.


67


The ROC plots showed that the best fit models had moderate predictive abilities. These

values tend to be lower than AUC values obtained in other studies where models have

good predictive abilities. These include 0.96 for rufous bristlebirds (Dasyornis

broadbenti) in forest/woodland/shrubland habitat (Gibson et al. 2004), 0.94 for sambar

deer (Rusa unicolor) in forest/grassland habitat (Gormley et al. 2011), 0.9 for American

black bears (Ursus americanus) in primarily hardwood habitat (Long et al. 2011), 0.88

for Yellow-cheeked crested gibbons (Nomascus gabriellae) in forest habitat (Gray et al.

2010), and 0.65 for fishers (Martes pennanti) in primarily hardwood habitat (Long et al.

2011). However, Tyre (2003) show from simulations that even under ideal conditions,

the AUC can be as low as 0.8. Results from this study indicate that the “best” models

do not represent all of the environmental factors that interact to influence possum

occupancy in urban habitats. Imperfect discrimination of models is often due to other

relevant, but unmeasured or unobtainable, habitat characteristics or processes, either

biological or environmental (Tyre et al. 2003), making it difficult to determine

accurately all the factors influencing occupancy (Fielding & Bell 1997; MacKenzie et

al. 2003; Bailey et al. 2004). The models selected here as best describing possum

occupancy are therefore the best occupancy models in terms of the habitat data

collected. Additionally, the model’s ability to discriminate between positive and

negative locations is influenced by the fact that species do not always occupy all

available habitat (Fielding & Bell 1997). To further improve confidence in the process

of evaluating habitat models, it is recommended to obtain an independent data set from

sites not used in the original study which models were based on in order to attain an

unbiased assessment for the predictive power of the model (Fielding & Bell 1997;

Pearce & Ferrier 2000).

2.4.4 Management implications

Effective management of an invasive species requires knowledge of its distribution

throughout a landscape to indicate where control should be focussed and the intensity

of control required. Data from this study show that in a New Zealand urban landscape,

despite the high nutritional quality of fragment vegetation, possums still occur across

residential areas that support structurally-complex vegetation. These vegetation

characteristics are also associated with higher native bird diversity and abundance (van


68

Heezik et al. 2008b), indicating that negative impacts of possums on birds will extend

across large parts of the city. Their extensive distribution across urban habitats means

control measures aimed at possums will fail if they are restricted to the possum’s

traditional habitat (forest fragments) without regard to surrounding residential areas and

movement of individuals between these two habitats. While invasive species

management can become less cost-effective in habitats where their probability of

occurrence is low (Hauser & McCarthy 2009), community involvement in possum

surveillance and control is an option in residential areas and which is already being

carried out in some areas of New Zealand, and has the potential to achieve biodiversity

gains.

Chapter III: Lightweight GPS collar validation

69

Chapter III:

3 An evaluation on the accuracy and

performance of lightweight GPS collars in an

suburban environment

One of the lightweight GPS collars used in this research.

A refined version of this chapter has been published as:

Adams, A.L., Dickinson, K.J.M., Robertson, B.C., van Heezik, Y. (2013). An

evaluation of the accuracy and performance of lightweight GPS collars in

an suburban environment. PLoS ONE 8(7):

http://dx.plos.org/10.1371/journal.pone.0068496.


70

3.1 Introduction

The development of Global Positioning System (GPS) technologies in the mid-1990s

has enabled the use of GPS telemetry to investigate habitat and resource selection,

space use, and movement patterns of wildlife (Rodgers 2001; Hebblewhite & Haydon

2010). GPS telemetry overcomes many of the disadvantages of traditional VHF (Very

High Frequency) radio-tracking, as more accurate locations can be continuously

collected regardless of season, time of day, weather conditions, and terrain, without the

need for fieldworkers (see Chapter I for detailed explanations; Rodgers et al. 1996;

Rumble & Lindzey 1997). It also avoids the problem of animals modifying their

behaviour due to proximity to humans (Rodgers et al. 1996; Rumble & Lindzey 1997).

As such, GPS radio-tracking is increasingly being utilised for selection studies at fine

habitat scales (Rempel et al. 1995).

Despite the clear advantages, the GPS receiver is subject to two types of error: receivers

may fail to acquire the necessary satellite signals over a pre-defined time schedule

(missed data; termed fix success rate (FSR)), and locations acquired may be spatially

inaccurate (referred to as location error (LE)). LE can result in misclassification of

habitats and/or resources in selection studies, leading to poor management decisions

regarding species and/or habitat management (Moen et al. 1997; Frair et al. 2004;

D'Eon & Delparte 2005). These two types of error are influenced by numerous

environmental and technological factors that can affect signal transmission from

satellites to receivers (Frair et al. 2010).

The main environmental factors affect FSR and LE of GPS collars by obstructing or

reflecting the transmission of satellite signals and include topography (D'Eon et al.

2002; Frair et al. 2004; Cain et al. 2005) and vegetation characteristics, particularly

those associated with species composition and structural complexity (principally stem

density, life form, canopy height and cover) (Rempel et al. 1995; Rempel & Rodgers

1997; Dussault et al. 1999; D'Eon et al. 2002; Di Orio et al. 2003; Frair et al. 2004;

Recio et al. 2011). Built structures within modified environments are also expected to

affect the performance of GPS devices (Rose et al. 2005). These features act to

decrease the FSR and increase the LE of collected fixes (Rempel & Rodgers 1997;


71

Rumble & Lindzey 1997; Hulbert & French 2001; Stokely 2005; Zweifel-Schielly &

Suter 2007; Samama 2008).

Technological factors influencing the FSR and LE include the number of satellites

present and their geometric configuration (Moen et al. 1996; Di Orio et al. 2003). A

three-dimensional (3-D) positional fix is obtained when signals from four or more

satellites are used, while two-dimensional (2-D) positional fixes are acquired from three

satellites (Rempel et al. 1995; Di Orio et al. 2003). Generally, 3-D fixes are more

accurate than 2-D fixes due to the higher number of satellites used to acquire the fix

(Rempel et al. 1995; Di Orio et al. 2003). The geometric configuration of available

satellites is represented by the Dilution of Precision (DOP; e.g. horizontal (HDOP) or

vertical (VDOP)). Low DOP values are acquired when satellites are spaced widely

apart, resulting in smaller triangulation errors and more accurate positional fixes (i.e.

low LEs) than fixes associated with high DOP values (representing poor satellite

geometry; Rempel et al. 1995; Moen et al. 1997; El-Rabbany 2006). Other technical

factors influencing FSR and LE include weakening GPS batteries (Gau et al. 2004),

differences in collar brands, malfunctioning electronics, errors in satellite clocks, and

multipath signals. The latter occurs when the GPS collar receiver acquires multiple

satellite signals due to reflection off nearby surfaces (Rempel et al. 1995; Hofmann-

Wellenhof et al. 2001; El-Rabbany 2006; Graves & Waller 2006).

The magnitude of error surrounding a GPS receiver in any given environment can be

estimated and related to environmental and technical variables (Cargnelutti et al. 2007).

Therefore, researchers using GPS radio-telemetry on wildlife should first evaluate the

error associated with their GPS device within their specific study habitat (Cagnacci et

al. 2010). The performance of GPS collars designed for large animals (see Chapter I for

examples) has been widely evaluated in both field trials (i.e. on the target animals) and

stationary tests performed at known locations (Rempel et al. 1995; Moen et al. 1996; Di

Orio et al. 2003; Cain et al. 2005). However, only a handful of studies have examined

the performance of lightweight GPS collars recently developed for use on small-to-

medium sized animals (Rose et al. 2005; Blackie 2010; Dennis et al. 2010; Recio et al.

2011). Lightweight GPS collars require their own testing, as the smaller technological

GPS components may result in differences in how these GPS collars operate compared

to larger collars (Dennis et al. 2010).


72

Most research evaluating the performance of GPS collars of varying sizes has been

conducted in non-urban environments (Rempel et al. 1995; Moen et al. 1997; D'Eon

2003; Di Orio et al. 2003; Cain et al. 2005; Graves & Waller 2006; Lewis et al. 2007;

Dennis et al. 2010; Recio et al. 2011). Only one study has investigated the performance

of a small GPS receiver (29-36 g for deployment on feral pigeons (Columba livia))

within an urban environment in the central part of a major industrial city (Basel,

Switzerland; Rose et al. 2005). Urban environments are highly heterogeneous: on a

broad scale, landscape components include industrial, commercial, and residential areas

as well as parks, reserves, and waste land, while on a fine scale there can be significant

patchiness within these landscape components, with buildings and vegetation varying in

height and density. Vegetative surfaces have already been shown to affect GPS error by

blocking or reflecting satellite signals within natural habitats (Hulbert & French 2001),

and this multipath error is likely to be greater in urban environments due to the

presence of buildings. A large proportion of the urban landscape is residential (between

20% and 26% of large cities (Loram et al. 2007) and 36% in a smaller city (Mathieu et

al. 2007)), and these suburban habitats have been shown to support significant

populations of wildlife (McKinney 2002, 2006; Pickett et al. 2008; Goddard et al.

2010). Evaluation of the performance and accuracy of GPS collars within suburban

areas is therefore necessary to provide information about the error associated with

collected fixes to enable data correction, or the implementation of appropriate buffers in

selection studies that occur in suburban habitat (Frair et al. 2010). Incorporating error

information within a study site can help minimise the potential of habitat/resource

misclassification, and reduce incorrect conclusions regarding resource selection, and

subsequent poor management decisions.

This study aimed to evaluate the main environmental and technical factors causing

error (FSR and LE) in lightweight GPS collars using stationary tests across a typical

suburban environment as a precursor to research on resource selection of possums.

Consequently, the error estimate would be used to produce a buffer size representing

the location error around collected positional fixes specifically for resource selection

studies within suburban habitats. Three main environmental variables were predicted a

priori to influence GPS collar performance in this environment: suburban areas which

differ in the complexity including the vertical structure and density of vegetation

present within a garden; the degree of canopy closure (referred to here as sky


73

availability); and proximity to buildings. It was hypothesised that the (i) FSR would

decrease as the complexity of vegetation within different residential urban areas

increases, whereas LE would increase; (ii) FSR would increase whereas LE would

decrease with increasing sky availability as more satellites will be available to generate

positional fixes; and (iii) FSR would decrease with increasing proximity to buildings

whereas LE would increase due to the reflection of satellite signals off buildings.

Technical factors predicted a priori to influence collar performance included the

number of satellites and satellite geometry (HDOP). It was predicted that LE would

decrease as the number of satellites increased and HDOP decreased.

3.2 Methods

3.2.1 Study area

Collar performance was tested in suburban gardens of Dunedin, New Zealand (NZ;

45°52´S, 170°30´E), with properties typically consisting of detached single or double

storey houses, where property sizes range between 0.018 ha and 0.282 ha in size

(median = 0.061; mean = 0.075 ± 0.01 SE; van Heezik, unpublished data). Houses are

typically surrounded by vegetation, usually on all sides, which covers between 15% and

95% of the total area of the property (median = 62%; van Heezik, unpublished data).

To gain an understanding of factors influencing collar performance in the suburban

environment, sample sites were randomly selected from properties where subsequent

GPS collaring of possums was to occur, with the assumption that possums were living

independently of forest fragments. Suburban habitats fall into three distinguishable

categories (Res 1, Res 2, Res 3; see Chapter II for habitat descriptions) according to

variations in housing densities, garden structures, and vegetation complexity. The

properties selected represented this spectrum of suburban residential development and

would be typical of suburbs in most cities.

3.2.2 Stationary collar tests

Three 120 g Wildlife GPS data-logger collars (Sirtrack Electronics, Havelock North,

New Zealand, http://www.sirtrack.com) equipped with a 12-channel GPS receiver

Trimble iQ to be later deployed on possums within a suburban environment were

tested. At each site, collars were configured to acquire a location every 15 minutes in

one 24 hour period, which encompasses two complete satellite constellation cycles


74

incorporating all possible satellite configurations (97 possible fixes; El-Rabbany 2006;

Samama 2008). Data were stored in a built-in memory capable of storing 40,000 fixes

until collar retrieval, and included information such as the date, time, longitude,

latitude, number of satellites present, and HDOP for each successful fix.

Firstly, to verify that all test collars were operating similarly, the three collars were

deployed simultaneously at a known survey mark under open sky to assess the

performance and accuracy of each collar. Differences between the FSR and LE values

between the three collars were quantified using a one-way analysis of variance

(ANOVA).

The collars were then evaluated under different environmental conditions within the

three suburban habitat types. Three gardens were randomly chosen in each of the three

suburban habitats and were separated by at least 600 m. Vegetation in these gardens

was evaluated to confirm they fell within the categories (Res 1, Res 2, Res 3) defined in

Table 2.1 (Chapter II). In each garden, sites representing four categories of sky

availability were tested: 0 - 25%, 26 - 50%, 51 - 75%, and 76 - 100% sky availability (n

= 36 sites; 4 sites per garden). Sky availability was assessed using a convex spherical

densitometer, which provides relative estimates of percent coverage (Lemmon 1956),

with coverage including both vegetation and built structures. To determine the impact

of buildings on FSR and LE, the distance to the nearest building from the stationary

collar was measured at each of the four sites within each garden: distances ranged from

4 m to 34 m and were equally distributed throughout the four sky availability

categories. The collars were left at each of the four sites within a garden for 24 hours on

a 30 cm high block representing the height of possums when on the ground. For

optimal reception of signals from satellites, the GPS collar was placed with the

antennae pointing directly upright. The ‘true’ geographic co-ordinates of each site were

determined using the average of five locations recorded from a Trimble R7 GNSS using

only co-ordinates that were obtained with more than seven satellites present, with

precision criteria of < 0.015 m horizontally and < 0.020 m vertically.


75

3.2.3 Data analysis

The FSR for each site was calculated by dividing the number of collected fixes for each

site by the maximum number of fixes expected for a 24 hour period (97 fixes). Spatial

accuracy (LE) was calculated for each collected positional fix by calculating the

Euclidean distance between each of the collected positional fixes and the corresponding

‘true’ collar location determined using the Trimble R7 GNSS as follows:

5.022LE yx

where x and y are the differences between the collected and the ‘true’ x- and y co-

ordinates respectively (Rempel et al. 1995).

Outlier LE values, which occur in all GPS receivers (Frair et al. 2010), were identified

as fixes falling outside of three times the standard deviation of the mean LE value for

each site, and were subsequently removed from modelling. The root mean square

(RMS) of the LE (LERMS) was then calculated within each vegetation and sky

availability categories, and globally for all sites (n sites) as a measure of the average

location error across all study sites (Denys 2010):

5.02

n

2

2

2

1RMS

n

LE...LELELE

The LERMS estimate can assist in the selection of buffer sizes around collected

positional fixes in resulting spatial analyses for data collected from GPS collars

deployed in field trials on animals. Additionally, the arithmetic mean (µLE) and median

(µ1/2LE) of the LE under each sky availability class were calculated for comparison

with previous studies. One-way ANOVAs were performed to determine if the FSR and

LE differed between gardens within the three suburban habitats. A two-way ANOVA

was performed to determine if LE differed between type of fix (2-D, which is

calculated from three satellite signals, or 3-D, which is calculated from four or more


76

signals) due to the possibility that environmental conditions might affect the proportion

of 3-D fixes (which could have consequences for precision) and sky availability.

A model selection approach was used to identify the predictor variables hypothesised a

priori that have the greatest impact on FSR and LE separately. For both analyses, data

were screened using a Spearman pairwise correlation coefficient (|r| > 0.6 cut-off value

(Hosmer & Lemeshow 2000)) to avoid correlated predictor variables within the same

model. This test revealed that HDOP and the number of satellites were correlated

(Spearman’s pairwise correlation test; r = -0.6, p < 0.0001), thus these two variables

were not placed together in the same model.

The influence of sky availability, vegetation complexity (as a function of suburban

habitat type), and distance to the nearest building on FSR was assessed using fixed

effects logistic regression to model the probability of successful locations per site (Frair

et al. 2004). Eight models representing alternative hypotheses, including the null and

global models, were fitted to the standardised data.

LE was modelled using the same standardised environmental predictors as FSR

(vegetation complexity, sky availability, and distance to the nearest building), as well as

technical predictors including HDOP and the number of satellites present for each

positional fix. Linear Mixed Models (LMMs) were used (Gelman & Hill 2007) to

model the global and null models and all combinations of predictor variables, with

models including a random intercept for site to control for the non-independence of the

collected positional fixes at each site (Skrondal & Rabe-Hesketh 2004; Hebblewhite et

al. 2007). Additionally, to evaluate the differences in the generated location errors

(which were logarithm transformed to meet the assumption of normality) between 2-D

and 3-D positional fixes, linear regression analysis was performed.

For both analyses, model selection was based on the relative difference in Akaike’s

second-order corrected Information Criterion (AICc) values, corrected for small sample

sizes (Burnham & Anderson 2002). The information-theoretic approach involves the

development of a set of working hypotheses or models, from which the best model is

selected using Akaike’s Information Criterion (AIC), in this case corrected for small

samples sizes (AICc). This approach is effective in achieving strong inferences from


77

data analyses (Burnham & Anderson 2002). For the LE analysis, I model averaged the

coefficients for the fixed effects predictor variables (sky availability, vegetation

complexity, HDOP) for the top model set comprising the models with ∆AIC ≤ 2

(Burnham & Anderson 2002). All modelling was performed in the statistical software

programme R (R Development Core Team 2011).

3.3 Results

All three collars had a FSR of 1 (100% of the positional fixes were collected) under

clear sky at the selected survey mark. There were no significant differences in the

accuracy (LE) of the three Sirtrack lightweight GPS collars when simultaneously

deployed at the survey mark (F2,288 = 1.2, p = 0.27), with the LE ranging between < 1.0

m and 106.3 m (median = 16.9 m).

FSRs did not differ between gardens with differing vegetative complexity independent

of sky availability ( x 90.6%; F2,33 = 0.1, p = 0.93). The FSR decreased as the amount

of clear sky decreased, ranging from 97% when there was high sky availability, to 80%

when there was low sky availability (Fig. 3.1; Table 3.1). Across all gardens, 64% of

the positional fixes obtained were 3-D fixes.


78

Figure 3.1: The average fix success rate (FSR) of lightweight GPS collars (± 1

SE) under four sky availability classes conducted in suburban

habitats of Dunedin, NZ.

Table 3.1: The FSR (± SD), root mean square of location errors (LERMS),

and the mean (µLE ± SD) and median (µ1/2LE) LEs generated

from data collected from GPS collars during stationary field

tests under four sky availability classes across three suburban

habitat types (n = 36) in Dunedin, NZ. Outliers represent fixes

with a LE > 3 SDs from the mean LE of all fixes.

Outliers

Sky

Availability

(%) FSR (%)

LERMS

(m) µLE (m)

µ1/2LE

(m)

Mean No.

outliers

LERMS

(m)

0-25 81.3 ± 13.6 40.3 23.8 ± 17.0 19.6 5.9 ± 2.8 35.8

26-50 89.6 ± 7.0 32.9 23.0 ± 20.6 16.3 6.4 ± 3.3 29.1

51-75 94.9 ± 1.5 30.9 21.2 ± 13.7 17.8 5.4 ± 3.9 24.7

76-100 96.7 ± 0.6 28.2 16.0 ± 14.2 12.9 7.0 ± 3.0 20.7

0

20

40

60

80

100

0-25 26-50 51-75 76-100

Su

cc

es

sfu

l F

ix R

ate

(%

)

Sky Availability (%)


79

Of the eight potential models varying in their combinations of the predictor variables

fitted to the collected FSR data, the top-ranked model included sky availability only

(Table 3.2); with FSR decreasing with decreased sky availability (increasing canopy

cover; coefficient = -0.12, SE = 0.05).

Table 3.2: Ranking of the models based on the Akaike’s second-order corrected

Information Criterion (AICc) explaining the FSR obtained by lightweight

GPS collars under different suburban habitats and sky availability classes

during stationary field tests (n = 36) conducted in Dunedin, NZ.

Model Description K AICc ∆i wi Model

Likelihood

Sky Availability 3 221.2 0.00 0.50 1.00

Sky Availability + Vegetation complexity 4 223.6 2.35 0.15 0.31

Sky Availability + Distance to buildings 4 223.6 2.36 0.15 0.31

Null model 2 224.7 3.50 0.09 0.17

Sky availability + Vegetation complexity +

Distance to buildings 5 226.1 4.86 0.04 0.09

Distance to buildings 3 226.6 5.35 0.03 0.07

Vegetation complexity 3 227.0 5.74 0.03 0.06

Vegetation complexity + Distance to buildings 4 229.0 7.73 0.01 0.02

∆i = AICc differences, wi = Akaike weights; K = number of parameters

LE values were significantly different between gardens of differing vegetation

complexity (F2,3243 = 59.5, p < 0.001), with larger LEs obtained in gardens with

complex, mature vegetation (Res 1; x 33 m) than properties characterised by lawn

and flower beds (Res 3; x 28 m). This produced an overall average of 30.1 m for all

suburban areas. Additionally, LE tended to decrease with increasing sky availability

(Table 3.1, Fig. 3.2). The calculated µLE also increased with increasing HDOP values,

with maximum values being reached for HDOP > 10, indicating large, inaccurate

values (Fig. 3.3). The large variation in µLE for each HDOP value and associated large

standard deviations shows that while LE decreases with increasing HDOP, the ranges

also include some positional fixes with similar accuracy to those associated with lower

HDOP values (Fig. 3.3).


80

Figure 3.2: The root mean square of location error (LERMS) associated with

lightweight GPS collars under four sky availability classes

conducted in suburban habitats (n = 36) in Dunedin, NZ.

Figure 3.3: Mean location error (µLE ± SD) for each HDOP value.

0

10

20

30

40

0-25 26-50 51-75 76-100

LE

RM

S (m

)

Sky Availability (%)

-50

0

50

100

150

200

250

300

350

400

0 1 2 3 4 5 6 7 8 9 10 11 12 13

Me

an

Lo

cati

on

Err

or

(µL

E)

HDOP


81

The magnitude of LE differed depending on whether three satellites (2-D) or four or

more satellites (3-D) were available to generate the positional fix, with a significant

difference occurring between the type of positional fix obtained (i.e. 2-D or 3-D) and

sky availability (F1,3243 = 15.0, p < 0.001): a higher proportion of 2-D fixes occurred in

the 0 - 25% sky availability class compared to the other three sky availability classes.

The LE associated with 3-D positional fixes (LERMS = 26.2 m), which accounted for

64% of all positional fixes obtained, were significantly smaller than those associated

with 2-D positional fixes (LERMS = 35.5 m; F1,3243 = 39.3, p < 0.001). After outlier

removal, LERMS decreased to 21.5 m for 3-D fixes and 30.7 m for 2-D fixes, indicating

that outliers occur in both fix types. A similar number of outliers in relation to LE were

obtained within all four sky availability classes (Table 3.1). Filtering the dataset to

remove these outliers improved the LERMS values by several metres for each sky

availability class (Table 3.1). HDOP values for 2-D fixes ranged from 1.2 – 12.7

(median = 3.7; 95% of fixes < 12.4), while the HDOP of 3-D fixes ranged from 1 –

12.7 (median = 2.3; 95% of fixes < 5.0). Additionally, linear regression analysis of the

logarithm transformed LE and associated HDOP values verified that LE values

increased with increasing HDOP values (coefficient = 0.05, SE = 0.002, p < 2.0-16

).

However, HDOP only explained 14% of the variation within LE (R2 = 0.14).

Among the top six models explaining variation in LE, the two that were within ΔAIC ≤

2 values of each other included the predictors sky availability, vegetation complexity,

and HDOP (Table 3.3). Model averaging of the fixed-effects within these top two

models showed that HDOP had the strongest effect on LE (coefficient = 0.13, SE =

0.001), followed by sky availability (coefficient = 0.05, SE = 0.03), and vegetation

complexity (coefficient = 0.03, SE = 0.03). Therefore, LE increased with increasing

HDOP, canopy cover (i.e. reduced sky availability), and vegetation complexity.

Distance to buildings, which are characteristic of urban environments, was not present

in the top models indicating that this factor was not important in influencing the

variation in LE (Table 3.3).

82

Table 3.3: Ranking of models based on the Akaike Information Criterion (AIC) explaining the location error

(LE) obtained by lightweight GPS collars in different suburban habitat types and sky availability

classes during stationary field tests (n = 36) conducted in Dunedin, NZ.

Model Description K AIC ∆i wi Model

Likelihood

Sky availability + HDOP 5 1724.59 0.00 0.61 1.00

Vegetation complexity + HDOP 5 1726.29 1.70 0.26 0.43

Sky availability + Distance to buildings + HDOP 6 1729.40 4.81 0.05 0.09

Vegetation complexity + Distance to buildings + HDOP 6 1729.57 4.98 0.05 0.08

Vegetation complexity + Sky availability + HDOP 6 1730.94 6.35 0.03 0.04

Vegetation complexity + Sky availability + Distance to

buildings + HDOP

7 1735.45 10.86 0.003 0.004

∆i = AIC differences, wi = Akaike weights; K = number of parameters

Chap

ter III: Lig

htw

eight G

PS

collar v

alidatio

n


83

3.4 Discussion

This is the first study evaluating the performance and accuracy of lightweight collars in

suburban environments. Sky availability had the largest effect on FSR, while LE was

affected by sky availability, vegetation complexity within different suburban habitats,

and HDOP. Despite the complexity of structures and vegetation within suburban areas,

values for FSR and LE were comparable to those obtained in less built-up

environments. This produced a mean LE estimate of 30.1 m for suburban habitat types.

Within a suburban environment, FSR increased with increasing sky availability, which

is probably due to fewer objects blocking or reflecting satellite signals (D'Eon &

Delparte 2005; Sager-Fradkin et al. 2007). For example, high canopy closure can result

in a 50% reduction in the FSR (Jiang et al. 2008) due to poor satellite visibility, which

also generates a higher proportion of 2-D fixes (D'Eon et al. 2002; Lewis et al. 2007;

Hansen & Riggs 2008). There was also evidence that variation in vegetation complexity

characteristic of the differing suburban habitat types, and distance to buildings

influenced FSR. However, these two habitat variables were relatively less important

compared with sky availability. The overall average FSR of 90.6% in this stationary

suburban study was slightly inferior to overall average FSRs of stationary studies

investigating GPS performance in less built-up habitats with variations in sky

availability; e.g. 92.1% in New Zealand farmland habitat (Dennis et al. 2010), 93% and

99% for two brands of collars in forest habitats (Di Orio et al. 2003), 92.8% in

mountainous habitat (Jiang et al. 2008), and an average FSR of 94.8% from a review of

35 articles (Cain et al. 2005). By testing the collars in identical conditions, this study

provided evidence that manufacturing differences did not exist between the individual

Sirtrack lightweight GPS collars (also see Blackie (2010) and Recio et al. (2011)).

As hypothesised, the accuracy of positional fixes within the suburban environment was

dependent on a combination of technical (satellite geometry (HDOP)) and

environmental (sky availability and vegetation complexity) variables. Location error

increased with decreasing sky availability and increasing HDOP, with LE varying

between suburban habitat types of differing vegetation complexity as predicted.

However, distance to buildings did not significantly influence LE despite the potential

for satellite signals to be reflected off building surfaces. The error obtained in this study


84

could be a reflection of the suburban habitats containing only low buildings, which may

not be significantly affecting satellite signals.

After filtering outliers, the LERMS decreased for all sky availability classes, with an

overall trend of smaller error values as sky availability increased. It is therefore

important to apply a preliminary filter to collected GPS datasets to remove these fixes

(Frair et al. 2010; Recio et al. 2011). The range of average LE values (µLE = 16.0 m to

23.8 m) that were obtained in this study are comparable to those reported in non-urban

New Zealand environments, including 7.9 m to 14.0 m in habitat with no/low

vegetation, 8.8 m to 11.4 m in tussock habitats, 31.0 m to 51.7 m within forest habitats

(Recio et al. 2011), and 9.0 m to 12.3 m in a farmland environment (Dennis et al.

2010). Within a forest habitat, Blackie (2004) attained accurate positional fixes from

collared possums and concluded that the canopy cover associated with forests in New

Zealand is unlikely to be so dense as to majorly impair the performance of lightweight

GPS collars for possums. Similar ranges of average LEs have also been observed in

natural habitats outside of New Zealand (e.g. Rempel et al. 1995; D'Eon et al. 2002; Di

Orio et al. 2003; Cain et al. 2005; Lewis et al. 2007). This could be a reflection of the

suburban habitats containing only low buildings which may not be significantly

affecting satellite signals. From these filtered results, it is recommended that

researchers use a single conservative buffer of 24 m around collected fixes in urban

habitat and resource selection studies.

LE increased when the number of satellites used to obtain a positional fix decreased as

predicted. Other studies in a variety of habitats have also documented an increase in

error with higher HDOP values (e.g. Moen et al. 1997; Rempel & Rodgers 1997);

higher HDOP values are usually associated with poor satellite configurations, such as

when only three satellites are available, or when satellites are clustered (Jiang et al.

2008). Additionally, 2-D fixes, which made up 36% of all fixes in this study, were less

accurate than 3-D fixes, and were largely associated with decreased sky availability.

This result is slightly higher than proportions obtained in other habitats (30.2% in

farmland (Dennis et al. 2010); 31% in boreal forests (Dussault et al. 1999); and 31.4%

in mountainous habitat (Jiang et al. 2008)), and is a reflection of satellite geometry and

number of satellites available (e.g. Rempel et al. 1995; Edenius 1997; Lewis et al.

2007). In less built-up habitats, poor satellite configurations are associated with reduced


85

sky availability caused by dense canopy cover, high terrain, or physical objects

masking the sky (Moen et al. 1996; Rempel & Rodgers 1997; Hulbert & French 2001;

Stokely 2005; Zweifel-Schielly & Suter 2007). Differences in these environmental

conditions, which influence the number of satellites available to the GPS device, will

also result in differences in the precision of acquired 3-D fixes (Moen et al. 1996; Recio

et al. 2011). In the suburban environment, poor satellite configurations due to reduced

sky availability are likely to be associated with vegetation cover within individual

gardens, particularly in areas containing large proportions of complex, mature

vegetation, especially tall trees (i.e. Res 1). However, the results also indicate that

accurate positional fixes can be obtained for large HDOP values and for 2-D fixes.

Therefore, by using traditional filtering methods, such as discarding 2-D fixes (D'Eon et

al. 2002) or fixes with an associated HDOP ≥ 9 (e.g. Lewis et al. 2007), accurate

locations will also be discarded. Additionally, the removal of all 2-D fixes can reduce

the dataset significantly. Accurate fixes were also discarded when using these filters in

this study, and therefore it is recommended to utilise more recent filtering approaches,

which incorporate species-specific assumptions regarding unrealistic speeds and

distances travelled between consecutive locations (Bjorneraas et al. 2010) to filter

collected datasets from suburban environments (see Chapter IV).

Distance to buildings was not an important predictor variable affecting the FSR or LE

in this study, which did not support the hypothesis. This finding is similar to results

from Rose et al. (2005) who documented that LE decreased significantly in an area with

high storage buildings, but was similar in all other urban locations tested. They also

found that FSR decreased as the amount of open sky decreased (Rose et al. 2005),

although there was no mention of whether sky obstruction was due to building presence

or vegetation. Therefore, the non-significant result reported in this study may be a

reflection of the density and height of the surrounding buildings. This research was

performed in private gardens of low-rise suburban areas of the city (i.e. one to two

storey buildings) as these areas typically make up a large proportion of urban

landscapes: 36% of the urban landscape in Dunedin is residential, which is mostly

comprised of one or two storey housing (Mathieu et al. 2007). Additionally, it was

performed in suburban areas as possums were known to be distributed throughout these

areas (see Chapter II) and in a subsequent study possums were trapped within

residential gardens to determine their use of these habitats (see Chapter IV). The low


86

heights of buildings within these suburbs may not be high enough to reflect or block

satellite signals. The opposite may be true for high-rise areas of the city, for example

the Central Business District (CBD) and/or industrial sectors, which contain a greater

proportion of tall buildings.

3.4.1 Limitations

This study evaluated only stationary lightweight GPS collars and did not incorporate

the impact that animal behaviour and movement might have on FSR and LE values.

Signal acquisition of collars has been shown to be affected by animal behaviour and

body position in that different activities, particularly denning and foraging, can result in

the orientation of the antennae being horizontal relative to the sky, leading to lower

FSRs and number of 3-D fixes (D'Eon & Delparte 2005; Augustine et al. 2011). For

example, D’Eon (2003) reported that the main source of lost data (i.e. a low FSR) is

associated with animal activity, while Lewis et al. (2007) reported a reduction of 11%

in the FSR when collars were deployed on live animals. However, in stationary tests the

antenna is always directly vertical to the sky, maximising signal reception, as antennae

on collars are designed in an upright position to optimise signal acquisition (Moen et al.

1996; Moen et al. 1997; Dussault et al. 1999; Bowman et al. 2000; D'Eon & Delparte

2005; Graves & Waller 2006).

Researchers should therefore consider the behaviour of their study species as well as

environmental and technical factors when evaluating the performance of their collars in

the field. In particular, smaller animals have more opportunities to utilise areas which

decrease sky visibility, such as tree hollows, which can influence the probability of

missed or inaccurate fixes (Dennis et al. 2010). Possums forage both vertically and

horizontally, which may affect the FSR and LEs: these are likely to improve with

increased positional height in trees due to fewer layers of vegetation blocking

acquisition of satellite signals. By evaluating collar performance close to the ground, I

was taking a conservative approach in calculating collar error, as error is expected to be

greater at ground-level due to canopy cover. Additionally, animal behaviour can affect

the type of positional fix acquired (i.e. 2-D or 3-D fixes, with denning and foraging

behaviours having the potential to increase the number of 2-D fixes). This can result in

a large proportion of the dataset being removed if all 2-D fixes or fixes with an


87

associated HDOP ≥ 9 are filtered out. Therefore, I recommend the use of filtering

datasets based on recently developed filters that incorporate species-specific movement

information (Bjorneraas et al. 2010).

While this study did not evaluate the performance of GPS collars on live possums, the

stationary test evaluated the error associated with lightweight collars within a built-up

suburban environment allowing examination of how vegetation characteristics of

private gardens and proximity of buildings associated with suburban habitats influence

collar performance for subsequent resource selection analyses (see Chapter IV).

Additionally, by developing an error estimate independent of animal behaviour for

suburban environments, these results have a general applicability for use in other

studies conducted in similar environments.


Determining the performance and accuracy of GPS collars in a study location is

important for making accurate conclusions from the spatial data and appropriate

decisions regarding species and habitat management. A relatively large LE was found

to be associated with lightweight GPS collars within suburban habitats. LE of this size

is less important for determining home range sizes, but is likely to have significant

impacts on habitat and resource selection analyses, and should be accommodated

through the use of buffers reflecting habitat-specific LEs around each positional fix. It

is recommended that a 24 m buffer be utilised around collected positional fixes for

habitat and resource selection studies within suburban habitats. However, because

urban environments are highly heterogeneous at a fine scale, large buffers around

collected fixes based on large LEs can include multiple habitats or resources, and it can

be difficult to accurately identify which habitats and/or resources animals are using.

Additionally, overlapping buffers, due to their large size, can be problematic when

trying to differentiate the predictive landscape features of available and used areas

(White et al. 2005). Unless the error of GPS devices can be reduced through better

technology, these results suggest that conclusions about resource and habitat selection

in heterogeneous suburban environments should be made with caution. I therefore

recommend that researchers in urban environments should consider analysing GPS data

using other recently developed techniques that are not reliant on buffer sizes, such as


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mechanistic modelling employing Brownian bridges (Horne et al. 2007; Sawyer et al.

2009). These methods are not reliant on buffer sizes, but instead incorporate the

location error directly into the analysis (see Chapter IV; Horne et al. 2007).

Chapter IV: Spatial ecology of urban possums

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Chapter IV:

4 Understanding home range behaviour and

resource selection of invasive common

brushtail possums (Trichosurus vulpecula) in

urban environments

A sedated possum having just been GPS collared in an urban garden, Dunedin.


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4.1 Introduction

Home ranges, spatial distribution, and behaviour of animals within an environment are

rarely random. Instead, space use, distribution, and resource selection of animals are

affected by their age and sex (Harris et al. 1990; Laver & Kelly 2008), foraging strategy

(Robinson & Holmes 1982; Turchin 1991), reproductive status (San Jose & Lovari

1998; Ciuti et al. 2006), local availability of resources (e.g. mates, food, shelter;

MacArthur 1972; Ostfeld & Keesing 2000; Ganzhorn 2002), intra- and inter-specific

competition and interactions (Rosenzweig 1991; Hughes et al. 1994; Blackie et al.

2011), predation risk (Ferguson et al. 1988; Lima & Dill 1990; Hughes et al. 1994), and

population density (Manly et al. 2002). Habitat configuration and composition also

impact these characteristics (Manly et al. 2002; Gillies & St Clair 2010).

Urban landscapes have traditionally been regarded as unsuitable for native wildlife

species to persist in (McKinney 2002, 2006). Yet, urban habitats, which continue to

expand globally, are now recognised as having the ability to support significant

populations of both native and exotic wildlife (e.g. McKinney 2002; Ditchkoff et al.

2006; Pickett et al. 2008; van Heezik et al. 2008a). Successful urban species are

typically sufficiently tolerant of human disturbance, opportunistic, and behaviourally

flexible to exploit resources within these highly fragmented and modified habitats.

These characteristics also describe invasive species (McKinney 2002; Wandeler et al.

2003; McKinney 2006). However, in order to meet their life-history requirements,

populations within urban areas often behave and use space differently compared to

populations within their natural ranges due to differences in the spatial distribution of

resources (Marzluff et al. 2001; Ditchkoff et al. 2006; Baker & Harris 2007). For

example, urban racoons (Procyon lotor) display an aggregated distribution in urban

landscapes in response to the distribution and availability of food items, unlike in rural

habitats where resources, and thus spatial distribution of individuals, are more spread

out (Prange et al. 2004). Additionally, due to the availability of artificial food sources,

mammalian species in urban areas often have smaller home ranges and greater

population densities (Smith & Engeman 2002; Prange et al. 2004).

Research on the spatial ecology of invasive species in urban environments is largely

lacking, as management of invasive species has mostly been a rural or forestry issue


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(Nogales et al. 2004; Clout & Russell 2006). However, controlling exotic species is

becoming an issue within urban areas due to the negative impacts they exert on

biodiversity, the environment, and humans. For example, urban foxes (Vulpes vulpes)

in European cities carry a zoonotic risk, rabies (Wandeler et al. 2003), while invasive

common house geckos (Hemidactylus frenatus) out-compete native mourning geckos

(Lepidodactylus lugubris) resulting in population declines (Petren & Case 1996). An

understanding of the spatial cognition map that animals continuously update with

respect to the distribution of required resources (i.e. their home range; Powell &

Mitchell 2012) is necessary to inform the management of populations of invasive

species, including optimal device (traps, baits) placement, and optimal timing, duration,

and frequency of control operations to prevent re-invasion (Norbury et al. 1998; Cowan

& Clout 2000; Cameron et al. 2005).

Recent developments in GPS technology have lead to the use of GPS telemetry to

collect data required to analyse animal movements and resource selection at a fine-

scale. Advantages of GPS telemetry enables the continuous collection of location data

from free-living animals over great distances and periods of time (see Chapter I for

detailed explanations; Rodgers et al. 1996; Manly et al. 2002; Hebblewhite & Haydon

2010), which can be used to calculate the size, shape, and structure of home ranges and

core areas of activity, as well as analysing movement patterns and resource selection

(Harris et al. 1990; Kernohan et al. 2001). This spatial information can then be used to

inform decisions regarding species management (Harris et al. 1990), to optimise both

conservation and control initiatives. Location data have previously been analysed using

resource selection functions (RSFs) which compare the use and availability of different

resources within an animal’s home range (Erickson et al. 2001; Manly et al. 2002). The

creation of RSFs is based on the assumption that the quality of the habitat is correlated

positively with the probability the animal or species will occur there (Gibson et al.

2004) and distinguish resources that are being disproportionately used compared to

their availability. However, habitat availability is subjective to what the researcher

decides is available to the animal, but which may in fact not be available for the animal

to reach or use (White & Garrott 1990). Defining availability proves to be even more

difficult in urban landscapes due to the highly heterogeneous nature of the landscape

(Marks & Bloomfield 2006). Calculation of RSFs do not directly incorporate telemetry

error into the analysis, which can result in misclassification of habitat, especially in


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heterogeneous habitats, and thus can produce erroneous conclusions about habitat

selection resulting in inappropriate management decisions (Withey et al. 2001;

Millspaugh et al. 2006). It is possible to account for error in RSFs in the form of

buffers, but buffers can be quite large and include numerous habitat types making it

difficult to accurately determine which habitat or resources are being selected by the

animal. The application of buffers is particularly challenging in urban landscapes,

which are very patchy in terms of resources and habitat types. Additionally, RSFs

provide no indication of the intensity of space use by the animal (Manly et al. 2002;

Marzluff et al. 2004). To improve on this, utilisation distributions (UDs) can be

employed, which incorporate the likelihood of occurrence at each location within a

home range, creating a probability density function (PDF) which quantifies the relative

use of space (Van Winkle 1975; Silverman 1986; Kernohan et al. 2001). Habitat

variables can then be related to the intensity of animal use at a location (height of the

UD which corresponds to relative use x 100) through multiple regression using

resource utilisation functions (RUFs; Marzluff et al. 2004; Millspaugh et al. 2006).

A relatively recent approach to UD estimation is the mechanistic Brownian bridge

method, which is recommended when analysing serially correlated GPS locations (see

Chapter III; Bullard 1999; Horne et al. 2007). The Brownian bridge movement model

(BBMM) is a ‘continuous-time stochastic movement model’ that estimates the

probability of use within a home range from time-specific GPS data, while accounting

for the uncertainty of movement between locations (Horne et al. 2007; Lewis et al.

2007; Sawyer et al. 2009). The UD created by the BBMM is reliant on GPS data

collected at relatively frequent intervals, knowledge of the location error associated

with the collected GPS fixes, and an estimate of the animal’s average mobility rate as

described by the Brownian motion variance (BMV) parameter, σ2

m (Horne et al. 2007).

The BBMM approach coupled with RUFs have several advantages over traditional

RSFs: (1) by incorporating location error into the analysis, BBMMs produce more

robust results, which is particularly desirable for heterogeneous environments (Horne et

al. 2007); (2) the UD is estimated based on an individual’s movement trajectory instead

of collected point locations (Kranstauber et al. 2012); (3) creation of UDs through

Brownian bridges is thought to be more appropriate when relatively frequent intervals

are used to collect GPS data and when investigating resource selection of species that

exhibit small home ranges (Horne et al. 2007; Long et al. 2009); (4) spatial


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autocorrelation in the UD is accounted for in RUFs (Marzluff et al. 2004; Long et al.

2009; Kranstauber et al. 2012); and (5) RUFs produce both individual and population-

level models allowing investigation of individual variation in resource selection

(Marzluff et al. 2004).

The possum is an example of a behaviourally flexible, opportunistic, invasive species in

New Zealand. Possums pose a significant threat to New Zealand’s native fauna and

flora, as well as to agricultural and forestry industries (see Chapter I). Consequently,

research on the spatial ecology of possums has largely been concentrated in forest

(Clout & Gaze 1984; Ward 1985; Green & Coleman 1986; Blackie 2004) and rural

(Jolly 1976; Brockie et al. 1997; Blackie 2004) environments in an attempt to control

and mitigate the negative impacts of this invasive species. However, possums are also

well established in urban habitats, both in Australia (Statham & Statham 1997; Harper

2005; Harper et al. 2008) and New Zealand (see Chapter II; Adams et al. 2013). Yet,

given that their native habitat is forest, it is not clear whether possums can survive

purely in residential gardens (i.e. independently of forest fragments). Currently, control

within urban areas of New Zealand is largely targeted at remnant forest fragments. Yet

the behavioural flexibility and opportunistic diet of possums enables them to occupy a

wide range of urban habitats, where they have the potential to serve as a source of

reinvasion for surrounding controlled rural and forested habitats. Possums also have

negative impacts on native biodiversity, which are likely to extend to populations

inhabiting urban environments. Control of possums within the urban environment is

necessary and should benefit from an increased knowledge of their spatial ecology and

behaviour obtained through more efficient data collection and improved

methodological technologies.

This research used GPS and Geographic Information Systems (GIS) technologies to

investigate the spatial ecology and identify key habitat requirements of possums at a

fine-scale in urban areas, aiming to inform the urban management of possums in New

Zealand. Specifically, information will inform the Otago Peninsula Biodiversity

Group’s (OPBG) strategy to control possum numbers on the Otago Peninsula to help

prevent reinvasion of possums from adjoining urban areas through the establishment of

an urban buffer zone (see Appendix 2). In this study, parameters hypothesised to

influence possum habitat use and/or distribution included urban habitat cover, distance


94

to nearest forest fragment, distance to nearest road, and habitat patchiness (i.e. the

number of different habitat types within a sampled area where a single habitat type

represents continuous habitat). It was hypothesised that: (1) home ranges of possums

within urban areas would be larger than those in forest or rural habitats due to the

irregular spatial distribution of resources in the heterogeneous urban landscape,

especially well-established, intact stands of vegetation which possums use extensively

to forage and den in. This is because the patchy nature of the urban environment would

require individuals to travel further to locate such resources, such as has been

documented in dryland ecosystems (Glen et al. 2012); (2) within individual urban home

ranges, possums would select habitats composed mainly of remnant forest fragments or

residential areas with large, structurally-complex gardens (Res 1 and Res 2) due to the

availability of plant material utilised as a food source and den sites; (3) possums would

avoid residential habitats which lacked structurally-diverse vegetation (Res 3); (4)

possums would not establish home ranges independently of forest fragments within the

urban environment due to irregular resource distribution within other urban habitat

types, particularly large stands of trees which are the primary source of dens and food

for possums; (5) possums would avoid roads which incur a high mortality risk; and (6)

possums would prefer continuous habitat rather than patchy/edge habitats due to the

irregular distribution of resources, especially large stands of trees and structurally-

complex vegetation which are used as dens and their primary food source, within urban

environments.

4.2 Methods

4.2.1 Study area

Field work was conducted in residential gardens within the city boundaries of Dunedin,

New Zealand (NZ; 45°52´S, 170°30´E) to determine if possums can live in residential

gardens independent of forest fragments. Urban properties range from large,

structurally-complex gardens through to small, simple flowerbeds or lawns (see

Chapter II for habitat descriptions). Vegetation composition and structure within these

gardens is heterogeneous and extremely variable in composition and structure and

includes native and exotic tree and shrub species, fruit trees, vegetable gardens, and

lawns. Trapping locations were attained from responses to advertisements placed in the

local newspapers (The Star and Otago Daily Times) and the University of Otago


95

fortnightly bulletin (Otago Bulletin) requesting notification of possum presence in

private gardens, as recommended by Harper (2005). Trapping was primarily

concentrated in the residential suburbs at the base of the Otago Peninsula as this area is

intended to be the location of the OPBG’s urban buffer zone (see Appendix 2), as well

as in city suburbs that were purely residential, to avoid trapping possums on the edges

of forest fragments: in this way it would be possible to evaluate whether urban possums

can live independently of forest fragments. All field work was completed under animal

ethics approval AEC D38/09.

4.2.2 Possum trapping and handling

At each trapping location, two collapsible mesh-wire cage traps (Grieve Wrought Iron,

Christchurch) were left tied open for a week with apples and Ferafeed pellets

(Connovation, New Zealand, http://www.connovation.co.nz), designed as a pre-feed for

possums, scattered in and around traps allowing possums to become accustomed to

them so as to increase subsequent capture rates. Traps were then set using apples as

bait, while lures included pre-feed pellets scattered at the front of the traps and a flour

and icing sugar blaze (5:1 ratio) smeared on tree trunks above traps. Live trapping

occurred on two occasions: September 2010 through to February 2011 and September

2011 to December 2011 to increase sample sizes, as urban possums were more difficult

to re-trap than rural possums (this resulted in some collars remaining on possums for up

to four months after the GPS battery had been depleted). Trapping occurred in spring

and summer, as more favourable weather conditions increase the likelihood of possums

being active every night, and spring coincides with greater possum movements and so

maximum estimates of home ranges would be obtained (Cowan & Clout 2000). For

accurate estimation of home ranges, it is suggested that at least 20 animals are required,

with a minimum of 50 locations acquired for each individual (Otis & White 1999;

Seaman et al. 1999; Garton et al. 2001; Millspaugh et al. 2006). Within a small time

frame of a week, Blackie (2004) revealed that a minimum of 40 GPS locations per

possum are sufficient to determine a stable home range.

On capture, possums were anaesthetised with an intramuscular injection of 0.3 - 0.9 ml

of ketamine (100 mg ml-1

) and 0.5 - 1.0 ml of Domitor (Medetomidine hydrochloride, 1

mg ml-1

), dependent on assessment of individual weights. Once sedated, possums were


96

weighed and an evaluation of sex, age (juvenile, young adult, adult) via tooth wear

following Winter’s (1980) diagrams, and health status was completed. Adult possums

weighing greater than 2.5 kg were fitted with a 120 g Wildlife GPS datalogger collar

(Sirtrack electronics, Havelock North, New Zealand, http://www.sirtrack.com) in

accordance to the requirement that collars weigh less than 5% of an animal’s body mass

(Animal Care and Use Committee 1998). GPS collars were equipped with a 12-channel

GPS receiver Trimble iQ. Collared possums were made identifiable by ear-tagging

(Web Ear Tags #3, Department of Conservation, New Zealand). An intramuscular

injection of 0.5 - 1.0 ml of Antisedan (Atipamezone hydrochloride, 5 mg ml-1

) reversed

the effect of the Domitor.

Collars were programmed to acquire and store the spatial location (longitude and

latitude), date, time, quality of the locational fix (horizontal dilution of precision,

HDOP), and number of satellites present at 15 minute intervals. This enabled data to be

collected across seven to twelve consecutive days. Additionally, collars contained a

VHF (Very High Frequency) transmitter enabling the use of radio-telemetry to locate

den sites every second day to investigate the types of dens being utilised in the urban

environment. After 14 days, re-capturing of possums began via the use of Timms

possum kill traps (KBL Rotational Moulders, Palmerston North, New Zealand,

http://www.kbl.co.nz) or with the assistance of a Dunedin City Council Pest Manager if

possums proved elusive due to trap shyness. Kill traps were baited with apple, while

pre-feed pellets and a flour and icing sugar blaze acted as lures. Additional lures were

used for elusive possums including aniseed, cinnamon, rose, and/or eucalyptus oils,

curry powder, ferafeed smooth in a tube paste (Connovation, New Zealand,

http://www.connovation.co.nz), and Lanes Ace possum lure composed of secretions

from possum glands (http://www.lanesacelures.co.nz).

4.2.3 Data filtering

Upon collar retrieval, GPS data were screened to remove daytime fixes, which are

representative of den sites, since only locations collected while possums were active

were required. Additionally, the data were filtered to remove positional fixes with low

accuracy. Traditionally, filters based on the HDOP values have been implemented

(Lewis et al. 2007), but a large proportion of accurate fixes have been shown to be


97

removed using this method (see Chapter III; Lewis et al. 2007; Dennis et al. 2010;

Recio et al. 2011). Therefore, the positional fixes were filtered using a non-movement

method which removes locations based on species-specific assumptions of how the

species typically moves: locations requiring unrealistic speeds and distances travelled

between consecutive fixes were identified and removed (Bjorneraas et al. 2010). This

approach applies two filters to remove outlier and unrealistic locations using a moving

window encompassing the four previous and four consecutive locations to each

collected location. It then determines whether the distance of the location to the

previous point exceeds first the median and then the mean distance of each of the steps

taken along the moving window. Firstly, to remove large errors based on knowledge of

possum behaviour (careful, deliberate climbers; McKay & Winter 1989), fixes were

discarded if they were farther away than preset distances which were deemed not

possible for possums to travel during the programmed fix interval (15 min). A lower

distance threshold filter was then applied using the mean distance of locations. If a fix

was located farther than the preset distances for the median (23 m/min (345 m in 15

min; which corresponded with the 99.9th percentile of data from collared possums in

this study)) and mean (9 m/min (135 m in 15 min; which corresponded with the 99.4th

percentile)), then the fix was removed. A third filter was applied to remove spike

locations, which represent individuals travelling at high speeds to a location and

returning back to the area of the original location (speed-angle filter). The speed-angle

thresholds were set to 6 m/min (90 m in 15 min) and 160°. The latter value corresponds

with a notable increase in angle frequency compared to frequencies of any other angle

value within the dataset, as in GPS telemetry, an excess of angles near 180° are likely

to be locational errors rather than animal movement (Hurford 2009). Missing fixes can

be ignored in this filtering approach due to using speed and turning angle estimates

(Bjorneraas et al. 2010). Filtering reduced the original data set by 5%, giving an

average of 185 fixes per possum (SE ± 15.4 fixes; range: 57 – 321). Filtered GPS data

were plotted onto the urban Dunedin habitat map (Freeman & Buck 2003) for visual

inspection within ArcGIS 9.2 (ESRI 2009).

4.2.4 Home range estimations and habitat use

To identify whether home ranges of the possums were fully revealed, incremental

analyses were performed in Ranges 8.0 (Kenward et al. 2008), where the presence of an


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asymptote indicates the number of locations required to describe a home range (Harris

et al. 1990). All but one possum exhibited an asymptote; one female showed signs of

reaching an asymptote and had a home range size similar to the sizes of other females

in this study (Appendix 3). Thus all possums were included in analyses.

Two techniques were used to estimate home ranges of possums: the 100% minimum

convex polygon (MCP) method and Brownian bridges. The use of MCPs was primarily

to allow comparisons with previous studies investigating home ranges of possums that

have been calculated using MCPs (Harris et al. 1990). MCPs have been frequently used

due to their ease of calculation, where a MCP is established around all the collected

locations of an animal to determine home range size (Mohr & Stumpf 1966; Harris et

al. 1990; Seaman et al. 1999). Possum MCPs were constructed in the “adehabitat”

package (Calenge 2006) in R version 14.1 (R Development Core Team 2011).

The disadvantages of MCPs (potential to include large areas which are seldom used due

to the sensitivity of MCPs to outer locations and no indication of the intensity of use;

Harris et al. 1990) can be overcome by employing probabilistic methods. Brownian

bridges were selected for their ability to provide more reliable estimates of home ranges

and provide information about the intensity of space use throughout the home range.

The Brownian bridge movement model (BBMM) was used to create a 95% UD for

individual possums to investigate home ranges and resource selection in an urban

environment. The BBMM requires a sequence of time-specific location data, the

estimated location error of the GPS data, and the cell size of the output UD grid (Horne

et al. 2007; Sawyer et al. 2009). The location error (30 m) was determined from

stationary tests of the collars (see Chapter III). The BMV uses the numbered GPS

points to construct Brownian bridges between any two locations (Horne et al. 2007). By

maximising the likelihood of obtaining the Brownian bridges between the odd-

numbered locations, which are assumed to be independent from the Brownian bridges

between the even-numbered locations, the BMV can be estimated (Horne et al. 2007).

The BBMM assumes that the location errors have a bivariate normal distribution, which

was upheld in this study, and random animal movement between consecutive locations

(Horne et al. 2007). As time between consecutive locations increases, the more likely

that the assumption of animals moving randomly is violated (Horne et al. 2007).

However, as locations in this study were only 15 minutes apart, and Horne et al. (2007)


99

and Sawyer et al. (2009) successively applied the BBMM to location data collected at 7

hour and 2.5 hour intervals respectively, this assumption was considered reasonable.

The 95% UDs were calculated using the “BBMM” package (Nielsen et al. 2011) in R

version 14.1 (R Development Core Team 2011), with a cell size of 10 m x 10 m

suitable to enable extraction of landscape variables but which did not create a huge

raster surface as large datasets can cause problems in the models.

The probabilities of the UD were then regressed against a set of environmental

variables to determine resource selection: (1) six urban habitat types (Res 1, Res 2, Res

3, forest fragments, amenity, and other (all other habitat types present in the study

area); see Chapter II for habitat descriptions); (2) distance to the nearest road, (3)

distance to the nearest forest fragment, and (4) landscape richness (the number of

habitat categories present in a sampled area as an indication of continuous or patchy

landscape). To obtain habitat values, an urban habitat map (Freeman & Buck 2003) was

converted into a series of habitat raster layers, with a 2 m x 2 m cell size, using the

Spatial Analyst FocalMean function. Distance to roads and to the nearest forest

fragment rasters were calculated using the habitat map and the Spatial Analyst

Euclidean Distance function. A landscape richness raster was calculated using the

Spatial Analyst FocalVariety function. All calculations were conducted in ArcMap 9.2

(ESRI 2009). Habitat cover and landscape metrics were then measured in each 10 m x

10 m grid cell of the 95% UD using a moving-window approach with the Intersect

Point Tool in Hawth’s Tools v3.24 for ArcGIS (Beyer 2004). This extracted the relative

use of each cell and the measure of the associated variables for subsequent evaluation

of resource selection.

To assess resource selection (defined as the intensity of space use at each UD grid cell)

within individual home ranges (third-order selection; Johnson 1980), RUFs were

calculated for individual possums using multiple regression in the “ruf” package

(Marzluff et al. 2004; Millspaugh et al. 2006) in R version 14.1 (R Development Core

Team 2011). Predictor variables were tested for collinearity using a Spearman’s

pairwise correlation coefficient, with an |r| > 0.6 as a conservative cut-off value

(Hosmer & Lemeshow 2000), for each individual to avoid strongly correlated variables

within the same model. Each RUF produces unstandardised resource coefficients for

each habitat variable. Spatial autocorrelation is accounted for in the ‘ruf’’ package by


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the application of a Matern correlation function, which is a function of distance

between pixels (Handcock & Stein 1993; Marzluff et al. 2004). The use of Matern

models in modelling of continuous spatial processes is increasing due to their capability

to encompass a large array of spatial dependencies and their flexibility (Handcock &

Stein 1993; Marzluff et al. 2004).

The population-level RUF was calculated by averaging the unstandardised coefficients

for each habitat variable and computing their variances according to Marzluff et al.

(2004; equation 2; see Appendix 4), which quantifies the uncertainty in the average

coefficient value for the animals in the study. Variables to remain in the final model

were determined using t-statistics (α ≤ 0.05) (Zar 1984; Long et al. 2009). The average

unstandardised coefficients of the significant habitat variables where used to map the

predicted probability of use by possums across the urban Dunedin landscape within

ArcGIS 9.2 (Marzluff et al. 2004; ESRI 2009; Kertson & Marzluff 2011).

Standardised partial regression coefficients for individual models were then calculated

according to Marzluff et al. (2004; equation 1; see Appendix 4). The standardised

coefficients for the significant habitat variables were averaged across individuals

enabling the importance of each variable to be ranked (Zar 1984; Marzluff et al. 2004;

Long et al. 2009). Conservative variance estimates for these average standardised

coefficients, including intra- and inter-animal variation, were calculated according to

Marzluff et al. (2004; equation 3; see Appendix 4). The standardised regression

coefficients were then used to construct 95% confidence intervals for each individual to

determine the number of possums whose use was significantly correlated with each

habitat variable (Marzluff et al. 2004; Kertson & Marzluff 2011).

The predictive ability of the model was evaluated using a previously collected dataset

from possums in the same urban environment (van Heezik et al., unpublished data).

Following the methods outlined in Howlin et al. (2003), the slope and R2 of the

regression were used to measure the model’s predictive ability. The number of

locations and predicted probability of use were extracted from 100 m x 100 m raster

cells (this size encompasses the location error both in residential habitats (see Chapter

III) and that obtained under forests; Recio et al. 2011) from the resulting predictive

map. The observed selection for each cell was calculated by dividing the number of


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locations per cell by the total number of locations within the study area (Howlin et al.

2003). The expected values of selection for each cell (values from the map) were scaled

to sum to one. These data were grouped into five equally-sized bins based on the

percentiles of the estimated probabilities of use (24 values/bin; Howlin et al. 2003). The

observed and predicted selections of each bin were calculated by summing the

probabilities of use for all units per bin, respectively (Howlin et al. 2003). A simple

linear regression was used to compare the observed and predicted selection values to

determine the map’s predictive ability (Howlin et al. 2003). A model is considered to

have good predictive abilities if the regression slope is positive, with the 95%

confidence interval including 1 but excluding 0 (Johnson et al. 2000; Howlin et al.

2003). A positive or negative slope with 0 included in the confidence interval is

indicative of poor predictive abilities (Johnson et al. 2000; Howlin et al. 2003). Greater

predictive abilities are also associated with higher R2 values in regression (Howlin et al.

2003).

4.2.5 Core areas of activity

Compositional analyses were used to compare the habitat composition of the core areas

of activity (50% UD) to the habitats available within the 95% UD within individuals’

home ranges (Aebischer et al. 1993), as the sample unit in these analyses are the

individual animals rather than individual locations (Otis & White 1999). To determine

if possums displayed no selection for core habitat type (i.e. random habitat use),

weighted compositional analyses using Wilk’s lambda tests were applied for all

possums, and for female and male possums separately, by applying log-ratios between

available and used proportions of habitats (Aebischer et al. 1993; Otis & White 1999;

Millspaugh et al. 2006). Invalid log-ratio transformations were avoided by replacing

habitat proportions of zero with 0.001 (Aebischer et al. 1993). One individual, which

only used one habitat, was dropped from the analysis. Calculations were performed in

R version 14.1 (R Development Core Team 2011) in the “adehabitat” package using

1000 iterations (Aebischer et al. 1993; Calenge 2006).

4.3 Results

Thirty-two possums were trapped and collared throughout the combined study period,

giving a capture rate of 5% (n = 657 trap-nights). Data were successfully retrieved from


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24 of the 32 possums fitted with a GPS collar; 8 males and 11 females in spring 2010,

and 3 males and 2 females in spring 2011. Data were recorded for 5 to 18 days ( x =

8.4; SE ± 0.6 days) before the GPS battery failed. There were no significant differences

in body weight between the sexes (Mann-Whitney U-test, U = 42, P = 0.08; Table 4.1).

The number of den sites recorded ranged between three and five for both sexes ( x =

3.5; SE ± 0.1), with there being no significant difference between sexes (Mann-

Whitney U-test, U = 50.5, P = 0.16). Den sites ranged from roofs of houses,

macrocarpa (Cupressus macrocarpa) hedges, burrows underneath woodpiles, hollows

within trees, and under or within muehlenbeckia (Muehlenbeckia genus) covered

shrubby thickets or gorse (Ulex europaeus).

Male possums travelled between 263 m and 1070 m per night ( x = 623 m; SE ± 77 m),

while female possums travelled between 229 m and 745 m per night ( x = 478 m; SE ±

48 m). Cumulative distances travelled per night by male possums were significantly

larger than distances travelled by females (Mann-Whitney U-test, U = 38, P = 0.05).

4.3.1 Home range sizes

Analysis of home ranges collected from the 24 possums showed a large variability in

size (Fig. 4.1; Table 4.1). The size of the home range area of the 100% MCPs ranged

from 0.77 ha to 10.78 ha ( x = 4.26 ha; median = 3.57 ha; SE ± 0.98 ha) for males, and

0.55 ha to 4.72 ha ( x = 1.53 ha; median = 0.94 ha; SE ± 0.35 ha) for females (Table

4.1). Home range sizes tended to be larger using the Brownian bridges UD approach,

which can be expected due to the inclusion of the location error surrounding the GPS

collars. The overall mean home range size for all possums was 3.54 ha (SE ± 0.45 ha),

compared with 2.78 ha (SE ± 0.55 ha) for MCPs. Males had significantly larger home

range sizes (Mann-Whitney U-test, U = 29, P = 0.01) than females (Table 4.1).

Examples of Brownian bridge home ranges are presented in Figure 4.2.


103

Figure 4.1: Habitat map of the urban Dunedin area (large frame) showing the

possum home ranges (black) in relation to forest fragments

(green), residential and built-up areas (dark grey), and

amenity/shrub/grasslands (light grey). White areas depict water

bodies. Some home ranges are overlapping.


104

Table 4.1: Home range and core area estimates for GPS tracked possums within an

urban environment, Dunedin, NZ, as determined by 100% minimum

convex polygons (MCP) and Brownian bridges (BB).

Home range (ha) Core area (ha)

Sex Body weight

(kg) No. of fixes

MCP

100% BB 95% BB 50%

F 2.5 231 1.15 1.68 0.35

F 3.6 187 1.62 2.48 0.66

F 2.6 187 0.65 1.48 0.41

F 2.7 240 0.84 1.72 0.41

F 3.3 182 1.58 3.17 1.00

F 2.5 145 4.72 4.51 0.70

F 4.0 267 0.55 1.21 0.34

F 2.8 79 0.85 2.02 0.58

F 3.3 217 1.65 2.50 0.48

F 2.8 143 3.62 5.04 1.42

F 3.9 64 0.94 2.38 0.68

F 3.4 321 0.88 1.57 0.35

F 3.9 173 0.82 1.74 0.45

Mean ± SE

(n = 13)

3.17

± 0.15

187.4

± 19.7

1.53

± 0.35

2.42

± 0.09

0.60

± 0.33

M 3.5 209 2.75 4.21 1.11

M 3.4 93 1.01 2.28 0.66

M 2.5 241 10.59 8.99 1.73

M 2.5 306 0.72 1.51 0.29

M 2.5 308 6.54 7.31 1.84

M 2.5 57 3.35 4.57 0.88

M 2.8 166 1.31 2.20 0.55

M 2.9 160 1.92 2.99 0.74

M 2.6 312 8.29 7.14 0.69

M 2.6 142 4.24 5.29 1.23

M 2.8 207 6.12 7.00 1.71

Mean ± SE

(n = 11)

2.80

± 0.11

200.1

± 24.06

4.26

± 0.98

4.86

± 0.75

1.04

± 0.16

All possums

Mean ± SE

(n = 24)

2.99

± 0.10

193.2

± 15.8

2.78

± 0.55

3.54

± 0.45

0.80

± 0.10


105

Figure 4.2: Home range size indicated by a 95% utilisation distribution

(superimposed over the urban habitat map) of (A) a female and (B)

a male possum within an urban environment, Dunedin, NZ,

generated using Brownian bridges. See Table 2.1 for descriptions of

habitat classes.

(B)

(A)


106

4.3.2 Resource selection within home ranges

The population-level RUF is presented in Table 4.2: two habitat variables (distance to

roads and landscape richness) were not statistically significant and were not included in

the final model. Individual possums varied considerably in their use of resources, with

the average absolute value of the standardised coefficients indicating the relative use of

each resource by all possums (Table 4.2). The highest ranking variable was forest

fragments, followed by Res 2, then distance from forest fragments (Table 4.2). Most

possums used forest fragments and Res 2 significantly, corresponding with the

importance of these variables in the RUF. The variables forest fragments and distance

to nearest forest fragment were correlated for 13 possums, thus data from only 11

possums were used to construct the coefficient estimate for distance to nearest forest

fragment. From these 11 individuals, nine possums significantly selected areas close to

forest fragments (Table 4.2). A similar number of possums significantly used and

avoided amenity and other habitat variables, whereas Res 1 was not significantly used

by many possums (Table 4.2).

Possum use of the different habitats can also be visualised by calculating the average

height of the UD (actual use; Marzluff et al. 2004) for each habitat type (Figs. 4.3, 4.4).

Only possums that had a given habitat type available within their home range were

included in this analysis (e.g. 18 possums had forest fragments within their home

range). Again, this shows that possums used forest fragments and residential areas with

vegetation present more than residential areas with structurally-simple gardens (Res 3;

possums did not use this habitat at all), or amenity/other habitats (Fig. 4.3). This overall

pattern was also evident when analysing the sexes separately (Fig. 4.4).

Due to the inclusion of inter-possum variation, the variation around the standardised

coefficients is larger than that for the unstandardised coefficients (Marzluff et al. 2004;

Table 4.2). Inter-possum variation is included in calculations as the use of particular

resources can vary between individual animals due to differences in behaviour and/or

other features within a landscape, and this enables inference from this study sample to

all possums in the population (Marzluff et al. 2004).

107

Table 4.2: Parameter coefficient estimates (β) and relative ranking of habitat variables for a population level

resource utilisation function (RUF) for possums (n = 24) across an urban environment, Dunedin, NZ.

No. possums with

use significantly

associated with

variable

Variable β SE

Standardised

β

Standardised

β SE + -

Relative

Rankb

Intercept 1.79 0.23

Forest fragment 0.002143 0.000483 0.00393 0.006582 15 3 1

Res 1 0.000633 0.000125 0.00080 0.000308 4 1 6

Res 2 0.001891 0.000462 0.00381 0.006539 18 3 2

Amenity 0.002926 0.001342 0.00360 0.005976 7 5 4

Other 0.005078 0.001895 0.00294 0.005873 4 3 5

Distance to Remnant -0.00158 0.000499 0.00362 0.001736 2 9 3

Distance to Roads NSa

Landscape Richness NSa

a NS: Not significant (p ≥ 0.05)

b Ranking based on absolute values of the standardised coefficients (β)

Chap

ter IV: S

patial eco

logy o

f urb

an p

ossu

ms


108

Figure 4.3: Mean use of habitat types (± SE) found within possum home ranges,

Dunedin, NZ. Numbers above means represent number of possums that

had each habitat type available within their home range.

0

0.1

0.2

0.3

0.4

0.5

Forest Fragment

Res 1 Res 2 Amenity Other

Me

an

use

(h

eig

ht

of

UD

)

Habitat Type

18 22

9 13

9


109

Figure 4.4: Mean use of habitat types (± SE) found within (A) female and (B)

male possum home ranges, Dunedin, NZ. Numbers above means

represent number of possums that had each habitat type available

within their home range.

The model validation showed that the RUF performed at an acceptable level of

prediction regarding possum resource selection across an urban environment (R2 = 80;

Slope = 0.19; 95% CI: 0.004, 0.11). An overall map representing the predicted

probability of possum use is shown in Figure 4.5, while Figure 4.6 compares the habitat

used with the predicted probability of use for two individuals, where the clusters of

observed GPS fixes match up with areas predicted to have a high probability of use.

-0.1

0.1

0.3

0.5

0.7

Forest Fragment

Res 1 Res 2 Amenity Other Me

an

use

(h

eig

ht

of

UD

)

9

12

4

6

3

-0.1

0.1

0.3

0.5

0.7

Forest Fragment

Res 1 Res 2 Amenity Other

Me

an

use

(h

eig

ht

of

UD

)

Habitat Type

9 10

5

7 6

(A)

(B)


110

Figure 4.5: Predicted probability of use by possums throughout urban Dunedin,

NZ, generated from a population-level resource utilisation function

(RUF).


111

Figure 4.6: Examples of the habitat classification (maps on the left) within the

home ranges of a (A) female and (B) male possum, and results of the

predicted relative probability of use based on a population-level

resource utilisation function (RUF; maps on the right), Dunedin, NZ.

See Table 2.1 for descriptions of habitat classes.

(B)

(A)


112

4.3.3 Core areas of activity

Core areas for male possums were significantly larger than those of females (Mann-

Whitney U-test, U = 32.5, P = 0.02; Table 4.1). The comparison between core area use

and overall habitat availability within home ranges showed that possums were not

selecting for certain habitat types within their core areas (i.e. no one habitat was

selected (random selection); λ = 0.83, χ2 = 4.47, p = 0.36 or p = 0.42 by randomisation),

with no overall significant differences occurring between utilised habitats. When

examining habitat and core use between sexes, females exhibited a non-random

selection of core areas (λ = 0.35, χ2 = 12.60, p = 0.01 or p = 0.05 by randomisation),

with the ranking matrix ranking habitat use within the core area as: Res 2 > forest

fragment > other > Res 1 > amenity. Males however exhibited a random selection of

core areas (λ = 0.56, χ2 = 6.38, p = 0.18 or p = 0.36 by randomisation). There was high

variability in the habitats contained within individual core areas: Res 2 was absent from

only three core areas compared to Res 1 habitat, which was present in only eight core

areas. Forest fragment habitat was present in most core areas (17 possums), while less

than half of the possums tracked contained amenity and other habitats within their core

areas. Differences between sexes could be related to the breeding season, where

females could be selecting for areas with ample resources to provide young, while it is

thought that males move around in search of females (Cowan & Clout 2000).

4.4 Discussion

In New Zealand, possums have successfully invaded diverse habitats including urban

landscapes, with behaviour, movement, and diet varying within each environment

(Nugent et al. 2000). Urban areas are a complex mosaic of habitat types providing a

range of resources for wildlife. Residential areas appear to provide sufficient resources

in the form of gardens and amenity playing fields to support a population of possums

independent of their traditional forest habitat, which is limited to urban forest

fragments. Possums are true urban adapters, in that they show behavioural flexibility

and opportunism in their ability to use resources that are closely associated with

humans in a highly modified landscape, and a high degree of tolerance to disturbance.

This study addressed the question of whether there are sufficient resources in these

areas, and in other urban green spaces, such as amenity playing fields, to support a

population of possums independent of the traditional forest habitat that can still be


113

found in urban forest fragments. This kind of information is necessary to ascertain the

extent of their potential impacts to native biodiversity and to effectively implement

control operations. GPS telemetry technology within urban environments has largely

had a conservation focus, where home range sizes and resource selection have been

examined to determine areas beneficial to the studied species. This study aimed to use

GPS telemetry, which has previously been applied to study the movements and habitat

selection of rural possums (Blackie 2004; Dennis et al. 2010), for the purpose of

investigating the spatial ecology of urban-based possums in an effort to optimise

possum control within a highly heterogonous, urban environment.

4.4.1 Home ranges

As in previous studies, home ranges of tracked possums varied among individuals and

between sexes. Male possums had larger home ranges and core areas than females

within the urban environment, which has been observed in both Australia and New

Zealand throughout a variety of different habitat types: native forests, pine plantations,

farmland, scrublands, dry grasslands, swamps, and urban areas (Table 4.3; Dunnet

1956; Winter 1963; Clout 1977; Warburton 1977; Ward 1978; Hocking 1981; Allen

1982; Triggs 1982; Green 1984; Kerle 1984; Green & Coleman 1986; Brockie et al.

1987; Statham & Statham 1997; Ball et al. 2005; Pech et al. 2010; Glen et al. 2012;

Rouco et al. 2013). Female possum home ranges were similar in size ( x = 1.53 ha) to

those in urban Australia ( x = 2.03 ha; Statham & Statham 1997; x = 1.02 ha; Harper

2005), but larger than females within urban/wooded grassland habitat (1.1 ha; Dunnet

1956). Male home ranges ( x = 4.28 ha) differed compared to range sizes within urban

Australia ( x = 8.61 ha; Statham & Statham 1997; x = 1.19 ha; Harper 2005), and were

larger than possum home ranges in urban/wooded grasslands ( x = 3.0 ha; Dunnet

1956). In New Zealand, males had larger urban home ranges than those in a modified

forest/urban environment ( x = 1.5 ha; Winter 1963), while female urban home ranges

were slightly smaller ( x = 2.7 ha; Winter 1963).

The hypothesis that purely-urban possums would have larger home ranges than in other

New Zealand environments due to the irregular distribution of natural resources was

not supported. Larger home ranges have been documented in dry grassland ecosystems

compared with forest systems due to den sites and food sources being sparsely


114

distributed in this environment (Glen et al. 2012). In this study, both sexes had

considerably smaller home ranges in the urban environment compared to other New

Zealand environments, including pasture/remnant forests (males: x = 30 ha; females:

x = 31 ha; Brockie et al. 1987), dry grasslands (36.19 – 54.07 ha; Rouco et al. 2013),

and podocarp-mixed broadleaf forests (males: 2.5 - 65 ha; females: 2.2 - 45.8 ha; Ward

1978; Green 1984; Green & Coleman 1986; Blackie et al. 2011), with the exception

being pine plantations (males: 0.4 - 1.4 ha; females: 0.2 - 1.4 ha; Table 4.3; Clout 1977;

Warburton 1977). However, urban home ranges for both sexes in this study were

comparable to the size of ranges of possums within Eucalyptus forests in Australia

(males: 1.1-8.3 ha; females: 0.9-6.5 ha; How 1981; Kerle 1984; Cowan & Clout 2000;

Table 4.3). Comparisons between studies must be treated cautiously as direct

comparisons and detailed analyses of possum home ranges is difficult because

estimates have been collected using a variety of methods across a variety of landscapes

(Table 4.3).


115

Table 4.3: Home range sizes of possums in different New Zealand and Australian

habitat types generated by a range of methods (T = trapping; R = radio-

tracking; G = GPS tracking; S = spotlighting). Based on the tables

produced in Cowan & Clout (2000) and Rouco et al. (2013). Where one

estimate is provided, home ranges were not examined separately for the

sexes.

Mean home

range (ha)

Habitat Female Male Method Reference

New

Zealand

Modified forest, urban 2.7 1.5 T Winter 1963

Pasture/scrub/remnant forest 0.9 1.4 R Paterson et al. 1995

Pasture/scrub/willows 31.0 29.9 R Brockie et al. 1987

Pasture/scrub/remnant forest 0.9 3.1 T Jolly 1976

Mixed farmland/Beech forest 6.0 R Ball et al. 2005

Drylands 3.3 7.1 R Glen et al. 2012

Grassland/shrubland 36.2 -54.1 T Rouco et al. 2012

Pine plantation 0.7 0.7 T Warburton 1977

Pine/scrub/forest areas 1.3 1.9 T Triggs 1982

Nothofagus forest 3.1 G Pech et al. 2010

Podocarp/broadleaf forest 0.5 G Blackie et al. 2011

Podocarp-mixed broadleaf forest 0.5 0.8 T+S+R Crawley 1973

Podocarp-mixed broadleaf forest 2.6 3.9 R Ward 1978

Podocarp-mixed broadleaf forest 18.3 24.6 R Green 1984

Green & Coleman

1986

Australia Urban 1.9 10.9 T+R

Statham & Statham

1997

Urban 1.02 1.2 R Harper 2005

Urban, wooded grassland 1.1 3.0 T Dunnet 1956

Eucalyptus woodland 0.9 1.1 T Kerle 1984

Open Eucalyptus forest 1.7 3.7 T Winter 1976

Eucalyptus forest 1.8 6.3 R Allen 1982

Eucalyptus forest 4.7 7.4 T How 1981

Eucalyptus rain forest 4.2 7.0 S Hocking 1981

Home ranges, spatial distribution, and behaviour of animals within an environment are

rarely random. In possums, population densities, age, female distribution, dominance

status, and predator densities have all been shown to influence possum home range

sizes (Cowan & Clout 2000; Efford 2000; Blackie 2004; Harper 2005; Blackie et al.

2011), and many of these factors will have been quite different between the different


116

landscapes studied. Resource availability throughout a landscape is thought to be a

main contributor to the differences in home range sizes between habitats (McNab 1963;

Krebs & Davies 1997; Borger et al. 2008), with larger ranges indicating that required

resources for that species are more patchily distributed throughout that environment,

e.g. in rural environments (Green & Coleman 1986; Cowan & Clout 2000). Possums

residing on the exterior of forests adjacent to pasture have larger home ranges as

individuals travel from the forest to exploit sought-after pastures and crops, resulting in

larger home ranges (Crawley 1973; Green 1984; Green & Coleman 1986; Blackie

2004). Although I expected home ranges to be relatively large in residential

environments because traditional resources are patchily and thinly distributed, the

smaller urban home ranges of possums in this study suggest that there are ample

resources at a fine-scale (e.g. supplementary food items and den sites; see Chapter II;

Harper 2005) that individuals can encounter and exploit, without travelling large

distances. In contrast, large home ranges would be expected if resources were sparse

and spread unevenly throughout urban habitats, resulting in larger movements of

possums to encounter and exploit sought-after resources.

Smaller home ranges compared to rural counterparts is also evident in other urban-

based mammalian species due to distribution of food and the ability of species to

exploit these often novel food sources (Tigas et al. 2002; Riley 2006; Baker et al. 2007;

Davison et al. 2009; Wright et al. 2012; Podgorski et al. 2013). Additionally, smaller

home ranges can be attributed to the generally constant food availability all year within

urban environments, compared to seasonal availability within other environments

(Podgorski et al. 2013). This may also explain the constant home range sizes of some

species across seasons (Gehrt et al. 2009). Home range sizes in this study were

relatively similar across all tracked individuals. However, some species can also exhibit

large variations in home range sizes within urban environments. For example, home

ranges of coyotes (Canius latrans) are twice the size in heavily developed areas

compared to those residing in less-developed urban areas (Gese et al. 2012), with some

individuals being completely reliant on natural habitat patches, while others are totally

independent (Gehrt et al. 2009).

Differences in radio-tracking methods can also influence home range sizes, which may

partly reflect the differences in home range size between studies (Table 4.3). Harper


117

(2005) reported this as a main factor contributing to size differences between their

findings and those of Statham & Statham (1997), both of which were conducted in

urban Australian environments. Seasonal differences, especially during the breeding

season where males tend to have the greatest movements (Cowan & Clout 2000), are

also known to affect home range sizes of possums, and can vary considerably compared

to their annual home range (Ward 1978). Home range size in this study should reflect

the maximal home range sizes because tracking coincided with the breeding season,

when possums are known to extend their home ranges (Paterson et al. 1995), but

comparisons with other studies should be made with caution.

Within the literature, there is considerable debate surrounding what is the most suitable

method for estimating home ranges (Laver & Kelly 2008). While MCPs are regarded as

facilitating comparisons between studies, caution must be applied when interpreting

results between different studies. Differences in tracking methodologies used and the

period over which data were collected between studies, as well as the algorithms

employed to calculate home ranges, can create significant variation in sizes between

studies (Lawson & Rodgers 1997; Cowan & Clout 2000; Blackie 2004). For example,

home ranges calculated using radio telemetry are generally much larger than ranges

generated from live-trapping (Ward 1984). The use of the two home range estimators in

this study generated different home range size estimates, although the variability

between individuals and sexes remained largely consistent, with the trend of smaller

female home ranges also being evident in other environments (Crawley 1973; Jolly

1976; Ward 1978; Clout & Gaze 1984; Green 1984; Blackie 2004). The larger home

ranges of males may be, at least partly, due to their polygynous mating system, where

movements are related to female distribution (Cowan & Clout 2000; Gerlach & Musolf

2000; Sarre et al. 2000; Taylor et al. 2000).

4.4.2 Habitat selection

The use of GPS data, in association with landscape covariates, has enabled the

identification of habitat features that influence possum movements and habitat selection

within the urban environment. Knowledge of resource selection in a species enables

predictions of space use to be generated across the entire landscape. Possums in non-

urban habitats do not use their home ranges uniformly, preferring micro-habitats that


118

offer better shelter and food resources (le Mar 2002; Blackie 2004), and urban possums

behaved similarly. Similar trends have also been found in urban-based species of

conservation concern. Many of these species, like possums, are primarily located in

natural fragments and tend to avoid heavily urbanised and populated areas (see Chapter

II; Kaneko et al. 2006; Riley 2006; Gehrt et al. 2009). Despite this, residential areas

have been shown to be preferred and used extensively by a variety of opportunistic

mammalian species, due to the provision of food and shelter, including badgers (Meles

meles; Davison et al. 2009), striped skunks (Mephitis mephitis; Rosatte et al. 2011),

coyotes (Tigas et al. 2002; Davison et al. 2009), bobcats (Lynx rufus; Tigas et al. 2002),

and red foxes (Baker et al. 2007).

As hypothesised, possums selected forest fragments within the urban environment, and

in Dunedin these fragments are dominated by native forest species. This preference

remains when analysing female and male possums separately, however standard errors

are quite large due to the small sample sizes and results must be treated with caution.

Selection for forest fragments is also observed in Australian urban areas, where

possums were most active within native Eucalypt forest remnants, but frequented

adjacent residential properties to exploit nutritious food sources (e.g. fruits, flowers,

vegetables, and human and pet food scraps; Harper 2005). Requirements for den sites,

which are most commonly tree hollows in Australia, and the availability of their main

dietary item (vegetation) are thought to drive this preference (le Mar 2002; Harper

2005; Harper et al. 2008). The preference for forest fragments in New Zealand may

also reflect the often greater nutritional value and palatability of New Zealand’s native

forest species compared to native forest species in Australia, especially Eucalyptus

species, which often have high toxicity levels and low nutritional value (Attiwill &

Leeper 1987; Brockie 1992; Clout & Erickson 2000; Cowan 2001).

Distance from forest fragments was also shown to be an important landscape feature

influencing possum habitat use (consistent with modelling in Chapter II). In this study,

possums were more likely to be found in habitats close to forest fragments: this

preference may reflect a strategy to reduce travel costs, which can involve risks

crossing roads, and most likely explains to some degree their selection of other habitats.

The use of other urban habitat types by possums may therefore be influenced by the

distance from forest fragments, with the possibility that areas closer to forest fragments


119

are considered as more “available” due to associated movement costs. For example, in

forest/rural habitats, a threshold distance has been reported for possums beyond which

it is not energetically advantageous for individuals to travel from forests into preferred

pasture habitats (Coleman et al. 1985).

For all possums, Res 2 was the second-most used urban habitat type in the RUF (this

trend also remained when analysing the sexes separately). Willingness to move out of

forest fragments and into residential gardens may reflect the constraints imposed by the

digestive system of possums. Due to a small body size and a largely fibrous diet,

individuals must supplement their diet of foliage with additional nutrient and energy-

rich food items (Evans 1992; Nugent et al. 2000; Kerle 2001). In New Zealand rural

areas, possums mostly eat foliage from canopy species and supplement this with items

such as fruits and flowers (Nugent et al. 2000). Within Australian urban areas, possums

are known to repeatedly visit residential gardens to exploit ornamental flowers and

shrubs, fruits, vegetables, and compost (Kerle 2001; Matthews et al. 2004; Harper

2005), and anecdotal accounts from property owners indicate that possums exploit

these resources within urban areas in New Zealand (also see Chapter II). The presence

of a range of plant composition and structure within gardens may also be promoting

possum use, as it provides food and shelter opportunities. The resources provided by

Res 2 habitat appeared to be adequate to support possums.

Despite being comprised of properties with large gardens containing mature,

structurally-complex vegetation, Res 1 habitat emerged as least selected by possums.

Res 1 areas form only a small proportion of the total amount of residential habitat in

Dunedin (11.6% of the residential area; see Chapter II), and are often situated close to

forest fragments. Only five possums were trapped in or near these areas, and of those

five, four showed a positive association with this habitat. A trapping bias towards Res 2

habitats was evident in this study: 19 of the 24 possums were trapped in Res 2 gardens.

This trapping bias could be obscuring use of Res 1 habitat by possums as

approximately half of Res 1 habitat is occupied by possums in Dunedin (Chapter II;

Adams et al. 2013).

Possums in this study did not use Res 3 habitat, which makes up 14.6% of the total

residential area, and is characterised by high housing densities and an absence of


120

mature, structurally-complex vegetation (i.e. trees, shrubs). I did not receive any

notifications from the public in Res 3 areas in response to advertisements. This finding

supports the distribution patterns of possums in the broader-scale site occupancy study

(see Chapter II). The absence of possums from Res 3 habitat indicates there were limits

to the ability of possums to exploit parts of the residential landscape. For example,

predation from dogs (Lepczyk et al. 2004; Ditchkoff et al. 2006), where assuming pet

ownership rates are constant across suburbs, density of pets will increase with increased

housing density, which holds true for cat ownership in Dunedin (van Heezik et al.

2010). Other limiting factors may include: lack of food and dens due to minimal

vegetation, little connectivity between forest fragments, pollutants (Ditchkoff et al.

2006), and increased noise (i.e. children, pets, traffic; Warren et al. 2006).

The degree to which amenity habitat was used depended on the presence of surrounding

vegetation: possums were less frequently associated with parks completely surrounded

by housing and/or roads than those surrounded by borders of complex vegetation (e.g.

large trees; see Chapter II). The high densities of possums found within Australian

urban parks are thought to be due to foraging opportunities from human food leftovers

and surrounding tree vegetation for food and dens (McDonald-Madden et al. 2000).

Possums in this study also showed some preference for the other habitat category, albeit

it was the least preferred habitat type in this study. This may be due to the types of

habitat constituting the category, including shrublands and grasslands, which provide

foraging and denning opportunities.

Six possums (four females and two males) had home ranges completely independent of

forest fragments, indicating that possums have the ability to live purely within

residential habitats. All six possums were limited to gardens containing mature,

structurally-complex vegetation, which provided opportunities for shelter, foraging, and

refugia from human disturbance and predators.

Contrary to expectations, roads were not avoided as hypothesised, despite the increased

probability of vehicles causing mortalities within urban areas (Forman & Alexander

1998). Possum non-avoidance of roads has been documented in other studies, even

though collisions with cars were the leading cause of mortality for urban possums in

Tasmania (Statham & Statham 1997). Road avoidance is also evident in other urban-


121

based species (e.g. Tigas et al. 2002; Baker et al. 2007). Continuous habitats were also

not preferred over patchy habitats as hypothesised. Utilisation of a mosaic of habitat

types (high landscape richness within home ranges) reflects the behavioural flexibility

of this species and their ability to exploit resources across the urban landscape (Statham

& Statham 1997; Harper 2005; Harper et al. 2008). Therefore, roads and habitat edges

do not appear to be barriers to possum movements throughout urban habitats.

The use of numerous types of habitats within core areas of individual home ranges in

relation to habitats available (no preference for certain habitats) may be attributable to

the high variability in the habitats contained within individual core areas, which is

probably a reflection of the behavioural flexibility of possums. Additionally, it may be

a function of the short time frame of data collection (average of 8.4 days). Core areas

may be shifting all the time depending on which den is being used (possums typically

have between five and ten den sites; Cowan 2005). The seemingly random preference

of core habitat types in this study, over this time frame, is likely to reflect the quantity

and quality of favourable resources within the differing urban habitats (Boyce &

McDonald 1999), with individuals establishing core areas where they can exploit the

most resources (e.g. dens, food).

4.4.3 Limitations

Conclusions in this study are inferred from possums tracked during one season, hence

seasonal variation was not considered. Additionally, because only five individuals were

tracked in the second year, all individuals were pooled for analyses. Therefore, inter-

annual variation was also not considered. The spatial ecology of possums may differ

between seasons and years in response to factors such as resource availability and

distribution, and population densities. Future research should extend on this study by

investigating how the spatial ecology and habitat selection of possums in urban

environments varies between seasons and years. Further improvements to the battery

life of GPS collars will also increase the amount of location data able to be collected.

A second limitation was the sample size. In this study, sample sizes were constrained

by two factors: the high costs of the GPS collars, which usually limits the number of

individuals able to be tracked (Garton et al. 2001), and the low trapping and re-trapping


122

success of possums. Additionally, the use of the collars was limited by the transmitter’s

battery life (average of 8.4 days). Future studies could investigate using longer periods

of time between fixes and programming the location acquisition schedule to switch off

during the day when possums are inactive. Both of these would result in a longer period

of time that data can be collected over, generating more representative results for home

range and selection analyses.

Another limitation was the broad habitat categories used, as determined by the scale of

the habitat map available. As a consequence, very fine-scale resource use of possums

was unable to be investigated within residential gardens. This limitation is evident in

other urban-based telemetry studies due to the heterogeneity of the urban landscape and

the difficulty of extracting fine-scale detail across the entire landscape (Marks &

Bloomfield 2006). Information at a very fine-scale would increase knowledge of the

resources being utilised by possums in gardens and thus help identify key habitat

features (such as the precise habitat and vegetation composition of properties) and

resources (such as specific plant species or presence of supplementary food sources)

influencing possum presence in residential habitats. This knowledge would then

enhance development of control strategies. This, however, can only be achieved with a

more detailed habitat map which, for example, could discriminate between vegetation

types within private gardens. However, GPS errors associated with such a

heterogeneous habitat can obscure information at this fine-scale, thus such analyses are

likely to be limited until further improvements are made to GPS receivers or to spatial

analysis methods.


By gaining accurate information on the spatial ecology and habitat selection of invasive

species, more successful and cost-effective control strategies can be implemented

(Parkes & Murphy 2003; Blackie et al. 2011). Because impacts of urbanisation on

animal behaviour varies both between and within species, management decisions

surrounding urban populations should be based on information collected on the local

population within a study area (Davison et al. 2009). The types of habitats most

preferred by possums in this study (forest fragments and residential gardens containing

structurally-complex vegetation) are also associated with the highest densities and


123

species richness of native avian species (van Heezik et al. 2008a; van Heezik et al.

2010). Possums are likely to have similar negative impacts on these urban bird

populations as in their more traditional habitats. Due to their ability to establish home

ranges independently of forest fragments, these impacts have the potential to extend

across a large proportion of the city (see Chapter II). This has implications for control

efforts within urban areas, which are currently largely restricted to remnant fragments

or landowners undertaking trapping efforts in their own gardens. If control continues to

be directed solely at fragments, operations are likely to be unsuccessful, due to the

reinvasion potential from neighbouring residential areas, and will fail to protect wildlife

found outside fragments. Systematic control should therefore target both remnants and

residential areas containing mature, diverse vegetation. Additionally these findings

have important implications for the possum eradication occurring on the Otago

Peninsula, due to the large natural buffer at the base of the Otago Peninsula. The low

probability of possum presence in this area implies a low likelihood of possum

movement from the city suburbs to the suburbs at the base of the Otago Peninsula. It is

therefore likely that if appropriate trapping is aimed at habitats with high possum use,

the eradication of possums from the Otago Peninsula will have long-term success. To

further ensure the success of eradication programmes, the public should be informed

about the purpose of the control scheme. Community involvement is an important

component of the OPBG’s eradication initiative where they have encouraged

landowners to actively participate by trapping in their own backyards. Cities are the

ideal landscape to extend the successes of community-based control evident elsewhere,

which has the potential to enhance native biodiversity and human-wildlife experiences

within urban environments.

Chapter V: Genetic population structure in possums

124

Chapter V:

5 Identifying the genetic population structure of

an invasive pest species for control purposes

The interface between urban and rural habitats where an urban buffer zone will be

implemented at the base of the Otago Peninsula by the OPBG.

A refined version of this chapter has been submitted to Biological Invasions as:

Adams, A.L., van Heezik, Y., Dickinson, K.J.M., Robertson, B.C. (in submission).

Identifying eradication units in an invasive mammalian pest species.


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5.1 Introduction

Biological invasions of mammalian species are regarded as one of the major causes of

global biodiversity loss and environmental change (Vitousek et al. 1997; Clout 2002;

Courchamp et al. 2003; Jeschke 2008), and on islands they are a primary cause of

wildlife declines and extinctions (Courchamp & Sugihara 1999; Burbidge & Manly

2002; Courchamp et al. 2003). In order to mitigate the effects of mammalian invaders,

eradication (complete removal of a species from an area) or control (suppression of the

population number of a species to a low level) strategies are implemented to conserve

and restore natural ecosystems and wildlife (Myers et al. 2000). For example, various

worldwide offshore islands have either had all, or some of, the following species

eradicated: feral goats (Capra hircus), feral pigs (Sus scrofa), cats (Felis catus),

European rabbits (Oryctolagus cuniculus), Norway and/or black rats (Rattus norvegicus

and R. rattus), and house mice (Mus musculus) (Veitch & Bell 1990; Bell 2002;

Nogales et al. 2004; Campbell & Donlan 2005; Clout & Russell 2006; Howald et al.

2007; Donlan & Wilcox 2008; Howald et al. 2009; Oppel et al. 2010). Following

successful eradication of invasive mammals, populations of threatened native species

often recover (North et al. 1994; Towns et al. 2001; Jones et al. 2005; Smith et al.

2006), highlighting the conservation benefits of control efforts. However, control

initiatives are associated with significant economic and often ecological costs, and can

fail, especially if research on the ecology and behaviour of the target species is lacking

(Rodriguez et al. 2006; MacKay et al. 2007). Other causes for eradication failure

include: (1) poor planning and execution including inappropriate bait delivery, gaps in

application, and use of baits when food within the habitat is plentiful; (2) detection and

avoidance of eradication tools; (3) reinvasion from surrounding uncontrolled areas,

especially when trying to eradicate part of a sink population; and (4) eradication

methods cease before all individuals are killed, leaving survivors in the environment

(Myers et al. 2000; Clout & Russell 2006; Rodriguez et al. 2006; Abdelkrim et al.

2007; MacKay et al. 2007). For example, 7% of eradication programmes on New

Zealand offshore islands have resulted in failure (Courchamp et al. 2003) due to a

combination of these reasons. The probability of eradication failure can be reduced by

developing effective eradication or control initiatives by gaining an understanding of

the invasive species’ behaviour, spatial distribution, and population genetics (Hampton

et al. 2004; Gleeson et al. 2006; Bellingham et al. 2010; Towns et al. 2013).


126

A primary cause of eradication failure is the reinvasion of the controlled area by the

target species residing in neighbouring areas, or from adjacent islands and/or the

mainland via migration (Abdelkrim et al. 2005). Areas that are geographically close

enough and enable migration of the target species (i.e. populations that are not

genetically isolated) are known as “eradication units” (Robertson & Gemmell 2004).

Rapid recolonisation of the targeted area, and hence eradication failure, will occur if

eradication is directed at only part of the population within an eradication unit

(Robertson & Gemmell 2004). To ensure the long-term success of eradication

programmes, populations within defined eradication units must be eradicated at the

same time (Robertson & Gemmell 2004; Abdelkrim et al. 2005). Eradication units can

be identified by analysing the gene flow and subsequent genetic population structure of

a target species within a particular area of interest (Robertson & Gemmell 2004;

Abdelkrim et al. 2005).

Molecular genetics tools are increasingly being utilised to determine the genetic

population structure of target species (Balloux & Lugon-Moulin 2002; Manel et al.

2003; Gleeson et al. 2006; Frankham et al. 2010). These tools are used to infer gene

flow between geographic locations by revealing population dynamics more effectively

than direct observations (Hampton et al. 2004; Robertson & Gemmell 2004; Abdelkrim

et al. 2005; Rollins et al. 2006). Genetic similarity between geographically isolated

populations indicates significant levels of gene flow, while differentiation between

neighbouring populations implies low migration rates (Balloux & Lugon-Moulin 2002).

Information regarding gene flow can be used to identify possible reinvasion pathways

and thus potential for eradication failure, and to classify population units for

management and conservation (Neigel 1997; Robertson & Gemmell 2004; Abdelkrim

et al. 2005). Globally, arbitrary boundaries have been previously selected on the

mainland for both management and eradication schemes in the absence of knowledge

on the spatial and genetic structure of invasive populations which can increase chances

of eradication failure due to reinvasion potential from interconnected populations in

surrounding areas (Saunders & Bryant 1988; Brussard et al. 1998; Myers et al. 1998;

Saunders & Norton 2001; Abdelkrim et al. 2010; King et al. 2011). However, by

gaining an understanding of gene flow for a particular species within an area targeted

for eradication, reinvasion potential and pathways can be incorporated into subsequent

management decisions, maximising the probability of long-term eradication success.


127

Due to the uniqueness of New Zealand’s native animals, there is a desire for New

Zealand to achieve a pest-free status in terms of invasive mammalian species

(Bellingham et al. 2010; Towns et al. 2013). As such, New Zealand is renowned as

being leaders in pest management and techniques, with over 250 eradications of

vertebrates having been attempted (Krajick 2005; Clout & Russell 2006; Rauzon 2007;

Towns et al. 2013). The ability to eradicate invasive mammals over larger areas and in

more complex ecosystems is likely to be achieved as management techniques improve

(Towns et al. 2013). Possums, one of New Zealand’s most damaging invasive pest

species, were deliberately introduced from both mainland Australian and Tasmanian

populations, and their spread around the country was assisted by extensive liberations

of New Zealand-bred possums (Pracy 1974; Triggs & Green 1989). Current possum

populations were founded by repeated introductions and liberations, which often

consisted of a small number of individuals at a time, and through natural expansion

(Pracy 1974; Clout & Erickson 2000; Cowan 2005). Established populations within

New Zealand exchange migrants, but this is limited by the geographic distance and

physical barriers between populations (Taylor et al. 2004). In Australia, possum

populations exhibit high levels of genetic variability both between and within

populations (Taylor & Cooper 1998; Clinchy et al. 2004), and this level of genetic

diversity has been retained in New Zealand possum populations (Taylor & Cooper

1998; Lam et al. 2000; Taylor et al. 2000; Clinchy et al. 2004; Taylor et al. 2004):

North and South Islands are slightly less genetically diverse than possums in Australia,

with South Island populations being less genetically differentiated, with a lower allelic

diversity than North Island populations (Triggs & Green 1989; Taylor et al. 2004). The

high levels of genetic variability reported in New Zealand possum populations may

therefore relate back to the multiple introductions of possums from several locations

within Australia (Taylor et al. 2004).

To date, molecular genetics have not been used to investigate connectivity between

possum populations to assist in developing possum eradication or control operations on

either islands or on the mainland, including urban areas, in New Zealand. By using

molecular genetics, genetic population structure at a local scale can be identified and

incorporated into area-specific management decisions, as the degree of genetic

differentiation between populations reflects the connectivity between populations. An

eradication programme targeting possums has been developed for the Otago Peninsula


128

(Dunedin, New Zealand) by the Otago Peninsula Biodiversity Group (OPBG), and is to

be implemented in sectors (see Appendix 2). The Otago Peninsula is linked to the

mainland at the base of the Otago Harbour with three potential dispersal pathways for

possum movement onto the Peninsula: (1) dispersal onto the Otago Peninsula through

the suburbs at the base of the Otago Harbour; (2) dispersal across the Otago Harbour

from Port Chalmers to Portobello via Quarantine Island; or (3) dispersal across the

Otago Harbour from Aramoana to Taiaroa Head (Fig. 5.1). This study examines the

population genetic structure of the invasive possum in and around Dunedin city and the

Otago Peninsula using genetic variation at twelve nuclear microsatellite loci to

determine whether potential dispersal pathways exist from mainland areas onto the

Otago Peninsula. As possums are reluctant swimmers (Cowan 2005; Cowan et al.

2007), it is hypothesised that only one reinvasion pathway onto the Otago Peninsula

would be evident: dispersal from the residential areas at the base of the Otago Harbour.

By identifying reinvasion pathways, this study aims to determine potential management

units to minimise the possibility of possum reinvasion onto the Otago Peninsula.


129

Figure 5.1: Locations of the seven hypothesised possum populations around

Dunedin and on the Otago Peninsula, NZ. Population codes are

defined in Table 5.3. Arrows and numbers depict the three possible

reinvasion pathways (see text).


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5.2 Methods

5.2.1 Sampling

Possums were sampled from seven locations in and around Dunedin City and on the

Otago Peninsula (Aramoana (Aa), Port Chalmers (PC), City (Western suburbs in

relation to the Otago Harbour), Buffer Zone (BZ; suburbs bordering the rural habitat of

the Otago Peninsula where a buffer zone is proposed to be implemented and maintained

after possum eradication by the OPBG), Portobello (PB), Cape Saunders (CS), and

Taiaroa Head (TH); Fig. 5.1). Possums are able to swim, albeit reluctantly, generally

only entering water to avoid predators (Cowan 2005; Cowan et al. 2007), and therefore

all distances between populations assume no swimming unless stated otherwise.

Possums from Aa, PC, City, and BZ were trapped using Timms possum kill traps (KBL

Rotational Moulders, Palmerston North, New Zealand, http://www.kbl.co.nz) within

Dunedin City Council-operated forest fragments or within private gardens accessed

from responses to advertisements in newspapers and flyers (see Chapter IV). Adult

possums from the Otago Peninsula (PB, CS, TH) were supplied by the OPBG at the

same time my own trapping occurred. Sampling was restricted to adult possums, as

determined by body weight (greater than 2.5 kg) and tooth wear (Class 3 or higher;

Winter 1980; Cowan & White 1989), to provide a representation of the breeding

population within each sampling location due to the potentially large dispersal distances

of juvenile possums before establishing a home range (average dispersal distance is 5

km, which is less than the distances between sampling locations; Efford 1991; Cowan

& Rhodes 1993; Cowan et al. 1996; Cowan et al. 1997). Individuals were sexed where

possible, but this information was not available for samples collected from other

sources (i.e. farmers, the OPBG contractors). Genetic samples were obtained from 30

dead adult possums from each location (n = 210) by taking a small piece of ear tissue

using ‘V’ cattle ear notch pliers (Shoof International, Cambridge, New Zealand,

http://www.shoof.co.nz) and stored in 70% ethanol at 4°C in the laboratory prior to

DNA extraction. The GPS location for each possum was also recorded at the time of

sampling to enable distance comparisons.


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5.2.2 Microsatellite genotyping

Genomic DNA was extracted from all samples using a small piece of ear tissue and a

5% Chelex protocol (Walsh et al. 1991). Samples were genotyped at twelve

polymorphic microsatellite loci (Tv14, Tv16, Tv19, Tv27, Tv53, Tv54, Tv58, Tv64,

TvM1, Tv5.64, Tv_PnMs16 and Tv_B38.1; Table 5.1) previously characterised for

possums to assess genetic population structure. Eight of these loci have been previously

documented as having high heterozygosity (0.11 - 0.83) and a large number of alleles

(4 - 15) within New Zealand possum populations (Table 5.2; Taylor & Cooper 1998;

Lam et al. 2000; Taylor et al. 2000; Taylor et al. 2004). For comparison, Australian

possum populations exhibit higher levels of heterozygosity (0.50 – 0.90) and allelic

richness (5 – 21 alleles) for the same loci (Taylor & Cooper 1998; Taylor et al. 2004).

Polymerase chain reaction (PCR) was used for multiplex amplification of the twelve

microsatellite loci using a Qiagen Type-it Microsatellite PCR kit. Forward primers of

Tv5.64, Tv_PnMs16, and Tv_B38.1 required the inclusion of a M13 tag, while the

remaining primers were already 5’ end labelled with fluorophores (6-FAM, VIC, PET

or NED; Table 5.1; Schuelke 2000). Independent PCR reactions were carried out for

the two to three loci associated with each M13 tag, with two separate multiplex

reactions being performed (Table 5.1). Each 2 µL PCR reaction contained

approximately 50 ng of template DNA (dried), 1 µL of Qiagen Type-it microsatellite

PCR mix, 0.32 µM of each end-labelled locus-specific forward primer, and 0.32 µM of

each locus-specific reverse primer. When M13 tags were used, 0.08 µM of each locus-

specific forward, and 0.32 µM of each locus-specific reverse and an M13 tag were used

in separate PCR reactions. Thermal cycling consisted of an initial denaturation step at

95°C for four minutes, followed by 35 cycles of 94°C for 30 seconds, 60°C for 45

seconds, and 72°C for 45 seconds, and a final extension step of 72°C for 40 minutes.

After PCR amplification, 25 µL of MilliQ H20 was added to the PCR product, followed

by combining 10 µL of each dye-labelled PCR product for each multiplex reaction. A

size standard was then added to 2 µL of the combined mix which consisted of 0.2 µL

GeneScanTM

LIZ®500 size marker and 7.8 µL Hi-Di Formamide. Amplifications were

stored at -20°C for up to 24 hours prior to genotyping on an Applied Biosystems

3730XL DNA Analyzer (Applied Biosystems Inc).


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Table 5.1: Details of the twelve polymorphic microsatellite loci used on adult possums

from around Dunedin and the Otago Peninsula, NZ (n = 210).

Locus Primers (5’ → 3’)

Dye

label A

Allele size

range (bp)

Multiplex 1

Tv16*

F: GAGGCTACCATTAGACGCAA VIC 12 114-146

R: ACCCAAATGAACAGAAAGGC

Tv19* F: CCTCCTCCCCATCCTTCCTG

VIC 12 254-294 R: GTTCAATTGCAGGGCTATGG

Tv27* F: AGTGGAACCACATGTCAGGGC

NED 10 163-193 R: GCCAAGAAAACCCCAAATGG

Tv53* F: GGGAGTAGTTGTCTGAGTTCCC

6-FAM 15 222-272 R: CCCTGGAGTTTGACAACCTG

Tv54*ª F: GGGAGGCATAAAGTGCCAGA

NED 3 87-119 R: TGACCGACACTGACGACCCC

Tv58* F: GCACCCAAGGACCCCCAAGA

6-FAM 9 124-168 R: CCATATCACAGTGCTTGGCG

Tv64* F: AGGGAGACTGAGTGCGTTTG

PET 14 138-199 R: AGACAGGAAAATTTGTGCCC

TvM1† F:CTGATCGATACCTCGCCTTCGAATCGG

NED 11 224-252 R: CATGACACCTGGGCACTCAGGACT

Multiplex 2

Tv14‡

F: CACCTAACCATCATCCTCTC PET 8 70-111

R: CTCACCCATCAGAACCTAGC

Tv5.64ᴪ

F: TTTATCCCTACTAGAGGTAGGT VIC 12 221-303

R: CCCTCTCCATCTGCTCCTC

Tv_PnMs16ᴪ

F: CCACCCCAATTAGATTAGCTC 6-FAM 15 220-254

R: GGATGGTTTGTGACAATTTGC

Tv_B38.1 ᴪ

F: CACGTTGTCTCCCACCCC VIC 15 61-97

R: CCAGTAAAGATTTGGAAGAGG

* Taylor & Cooper, 1998; † Lam et al., 2000; ‡ Martin et al., 2007; ᴪ Niccy Aitken, pers. comm.

ª Sex-linked (see text)

A = Total number of alleles at each locus


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Table 5.2: Summary of the genetic variation previously described in

possum populations throughout New Zealand and

surrounding offshore islands.

Location A HE

Wanganui, Taranaki* 4.6 0.72

Hohotaka, Manawatu-Wanganui‡ 6.0 0.77

Waipoua, Auckland* 5.6 0.78

Shannon, Wellington* 6.1 0.78

Orongorongo, Wellington* 6.2 0.76

Orongorongo, Wellington‡ 9.0 0.77

Hawke’s Bay, Gisborne* 6.5 0.83

Hawke’s Bay, Gisborne* 11.8 0.83

Hawke’s Bay, Gisborne† 3.6 0.54

Banks Peninsula, Canterbury* 4.0 0.59

Nelson, Marlborough* 4.4 0.69

Hokitika, Westland* 5.1 0.65

Pigeon Flat, Otago* 5.1 0.72

Kawau Island* 5.2 0.72

Codfish Island* 1.9 0.23

Stewart Island* 2.1 0.32

Chatham Island* 2.2 0.42

* Taylor et al. 2004; † Taylor et al. 2000; ‡ Lam et al. 2000

A = allelic richness; HE = expected heterozygosity

Alleles were visually scored using the programme Gene Mapper (Applied Biosystems

Inc). Banding profiles of the single alleles for each locus were produced, which aided in

the determination of heterozygotes and homozygotes when there was a potential that

two alleles differed by only one repeat unit (which creates the presence of stutter

bands). Recorded peak sizes were binned into allele sizes using the programme

FlexiBin, which uses a simple algorithm to employ automated binning (Amos et al.

2007). This binning procedure was implemented because peak sizes are often shorter or

longer than the repeat size of a microsatellite locus (i.e. two base pairs for a

dinucleotide repeat), and forcing them into the exact allele size can result in allele

scoring errors (Amos et al. 2007). Samples that failed to amplify at four or more loci (n

= 1) were excluded from the data set.


134

Genotyping errors can result due to a number of reasons including non-amplification of

alleles (null alleles or allelic dropout), human or laboratory errors, and allele mis-

scoring due to when alleles differ by only one repeat unit (mis-score heterozygotes as

homozygotes) or the presence of ‘stutter bands’ (Litt et al. 1993; Johansson et al. 2003;

Dakin & Avise 2004; Hoffman & Amos 2005). To determine the accuracy of

genotyping, 21 randomly selected samples across all locations (10%) were repeat-

genotyped at all twelve loci (Hoffman & Amos 2005). Error rate per allele was

calculated by dividing the number of mismatched alleles by the total number of alleles

(Hoffman & Amos 2005).

5.2.3 Data analyses

Genetic variation, including heterozygosity and allelic richness, at all twelve

microsatellite loci was initially explored using GenAlEx 6 (Peakall & Smouse 2006).

Additionally, the performance of loci as population genetic markers was determined by

testing for evidence of deviation from Hardy-Weinberg Equilibrium (HWE). Deviation

from HWE was assessed using GENEPOP 4.0.10 (Raymond & Rousset 1995), with the

dememorization number, number of batches, and the number of iterations per batch all

set to 1000. A Bonferroni correction (P = 0.05/number of tests) was applied to account

for Type I errors due to the use of numerous independent statistical tests on the data.

FSTAT 2.9.3 (Goudet 2001) was used to generate allelic richness. A one-way analysis of

variance (ANOVA) and Tukey’s tests were conducted to assess differences in allelic

richness among populations using SPSS 15.0 (SPSS Inc 2006). Genetic relatedness of

individuals was estimated in GenAlEx 6 (Peakall & Smouse 2006) by estimating the

average pairwise relatedness among populations, with the permutation and bootstrap

numbers both set to 999 (Queller & Goodnight 1989).

Several techniques were used to examine the spatial genetic structure in possums.

Genetic relationships among populations were visualised in GenAlEx 6 using principal

coordinates analysis (PCO). Genetic structure was also examined using a Mantel test

(Mantel 1967) to test for isolation by distance between the seven locations using

GenAlEx 6 (Peakall & Smouse 2006). A matrix of Nei’s genetic distances between

locations was generated and compared against a matrix of geographic distances using

9,999 permutations. An isolation by distance pattern is expected in a continuous


135

population that has restricted dispersal, as a linear increase in genetic differentiation

occurs with increasing geographic distance between individuals (Wright 1943).

Geographic distances were estimated from the GPS locations of individual possums

overlaid onto the Dunedin urban habitat map (Freeman & Buck 2003) in ArcGIS 9.2

(ESRI 2009). Slatkin’s lineralised FST was used to evaluate the pairwise differentiation

between populations in ARLEQUIN 3.5.1.3 (Excoffier & Lischer 2010) to evaluate the

genetic differentiation between populations. The significance of the FST values was

tested using 30,000 permutations. Recent migration rates between possum populations

were estimated using the Bayesian approach employed in BayesAss 3.0 (Wilson &

Rannala 2003). The programme was run with a Bayesian Markov Chain Monte Carlo

(MCMC) length of 3,000,000 iterations, a burn-in period of 200,000 iterations, and

various delta values for migration rates (m), allele frequencies (P), and inbreeding

values (F) to achieve acceptable acceptance rates. Convergence was checked using

Tracer 1.5 (Rambaut & Drummond 2009).

The number of population clusters (K) present in the sample of possums was inferred

using two Bayesian clustering methodologies. Firstly, K was inferred using the software

STRUCTURE 2.3, which utilises a Bayesian MCMC approach to cluster individuals into

the most appropriate population using genotypic information across multiple loci

(Pritchard et al. 2000). STRUCTURE 2.3 was chosen as it works well with a moderate

number of loci (Pritchard et al. 2000) and low genetic differentiation between

populations (Latch et al. 2006; Waples & Gaggiotti 2006). K was tested for between 1

and 7, where 7 would indicate a distinct population for each geographic location. A

mixed ancestry (admixture) model with correlated allele frequencies between

populations was used, as in cases of subtle population structure this is considered best

(Falush et al. 2003), while sampling locations were used as prior information

(LOCPRIOR). The parameters r (recombination rate) and alpha (the Dirichlet

parameter representing the degree of admixture) were visually checked to ensure

convergence and that STRUCTURE was not detecting false structure when using loc

priors. Initial exploration of the data both with and without loc priors revealed that the

same clustering was evident in both models (data not shown). The analysis was run

with a burn-in of 100,000 iterations followed by 300,000 repetitions of MCMC chains

for three iterations. Analysis of population clusters was run for the entire data set for K


136

= 1 - 7 (11 loci), and for females (n = 55) and males (n = 40) separately for K = 1 - 4

(representing the four sampling locations where sex data was collected). The probable

value of K was selected by calculating ∆K, an ad hoc statistic for identifying clusters

(Evanno et al. 2005). Individuals were assigned to a population cluster based on a

threshold of q ≥ 0.8, which characterises individuals with high ancestry (Lecis et al.

2006; Bergl & Vigilant 2007; Nsubuga et al. 2010).

The second Bayesian approach used was TESS 2.3.1 (Durand et al. 2009), which also

uses a Bayesian clustering algorithm to determine population genetic structure (Chen et

al. 2007). However, TESS incorporates a spatially-explicit model by including the exact

geographical co-ordinate for each individual as informed priors (Durand et al. 2009;

Francois & Durand 2010). Therefore, TESS can help distinguish between clinal versus

clustering structure in continuous populations (Chen et al. 2007), which may be the

case for possums from Aramoana down through the City and up to Taiaroa Head, due

to their average dispersal distances. The TESS algorithm incorporating the spatial

coordinates of individuals was run using the conditional autoregressive (CAR)

Gaussian model of admixture with an interaction parameter of 0.6 as described in Chen

et al. (2007). The model was run for 50,000 iterations following a burn-in period of

10,000 MCMC iterations for Kmax = 2 through to Kmax = 7, with 100 replicates for each

Kmax. The 20% lowest likelihood runs for each Kmax were exported to CLUMPP 1.1.2 to

average individual membership coefficients, which corrects for discrepancies between

runs (label-switching of population clusters; Jakobsson & Rosenberg 2007). Results

were visualised using distruct 1.1 (Rosenberg 2004). Again, individuals were assigned

to the population based on the proportion of membership (q ≥ 0.8). K was selected

based on when the Q-matrix of posterior probabilities for individuals revealed no

additional clusters and by plotting the deviance information criterion (DIC) against K

and selecting the K that was associated with stabilisation of the DIC curve (i.e. reached

a plateau; Durand et al. 2009; Francois & Durand 2010).

Assignment tests were used to obtain further information regarding the distinctiveness

of each population. Self-classification of individuals to their original location was

achieved using partially-Bayesian methods (Rannala & Mountain 1997; Paetkau et al.

2004) in GENECLASS 2 (Piry et al. 2004) using the “leave one out option” and 10,000


137

simulated individual genotypes (Cornuet et al. 1999). This was completed for all seven

populations, for Aa and TH populations separately, and for PC and PB populations

separately, to demonstrate population separation by distance.

5.3 Results

Eleven of the twelve loci were used in the analysis of possum population structure

(Table 5.1): Tv54 was dropped from analyses as it was found to be sex-linked (Andrea

Taylor, pers. comm. 2012). The scoring error rate across the eleven loci was low (0.026

± 0.020 (SD) errors per allele, n = 21 samples). Of the 210 possums sampled across all

seven populations, 209 adults were successfully genotyped at more than eight of the 11

loci. Samples that failed to amplify at four or more loci (n = 1) were excluded from

further data analyses.

Across the 11 loci over all seven populations, a total of 136 alleles were scored (Table

5.1). The mean number of alleles varied among populations: lowest occurred in TH

(6.1) and highest in City (9.0; Table 5.1). Allelic richness was also lowest in TH (6.0)

and highest in City (8.6; Table 5.3), with the Tukey’s tests only showing significance

between TH and City (P = 0.028). Observed heterozygosity (HO) was similar across all

populations ranging from 0.52 (BZ) to 0.68 (TH; Table 5.3). However, there was a

significant heterozygote deficiency in all seven populations relative to HWE

expectations (Table 5.3). In terms of genetic relatedness, individuals were unrelated in

City ( x = 0.0094, 95% CI: 0.005, -0.005; Fig. 5.2), while the highest relatedness

among individuals occurred in TH ( x = 0.04, 95% CI: 0.009, -0.009; Fig. 5.2). All

other populations exhibited similar levels of relatedness (Fig. 5.2). When analysing

female and male relatedness for the four populations where sex was recorded, females

generally exhibited a higher relatedness between individuals (Fig. 5.3).

Out of the 77 tests for HWE at the 11 loci, 52 deviated from HWE proportions at the P

< 0.05 significance level, with 21 remaining significant after a standard Bonferroni

correction had been applied. As no loci had a consistent departure from HWE over all

populations, all loci were included in subsequent analyses.


138

Table 5.3: Population information and genetic diversity for possum populations around

Dunedin and the Otago Peninsula that amplified at eleven microsatellite

loci (n = 209 adults).

Population Code n A AC HO HE

Aramoana Aa 30 7.7 (2.7) 7.3 (2.4) 0.54 (0.18)* 0.72 (0.07)

Port Chalmers PC 30 8.1 (2.6) 7.7 (2.4) 0.60 (0.10)* 0.74 (0.07)

City City 30 9.0 (2.5) 8.6 (2.2) 0.60 (0.12)* 0.78 (0.05)

Buffer Zone BZ 29 7.4 (1.7) 7.1 (1.7) 0.52 (0.12)* 0.73 (0.11)

Portobello PB 30 7.4 (1.4) 7.1 (1.3) 0.62 (0.14)* 0.74 (0.05)

Cape Saunders CS 30 7.4 (1.9) 7.0 (1.7) 0.57 (0.09)* 0.74 (0.06)

Taiaroa Head TH 30 6.1 (1.4) 6.0 (1.3) 0.68 (0.09)* 0.70 (0.08)

n = sample size; A = mean number of alleles scored per locus (SD); AC = mean allelic richness per

locus (SD); HO = observed heterozygosity (SD), HE = expected heterozygosity (SD), * significant

deficiency of heterozygotes after Bonferroni correction (P < 0.05).

Figure 5.2: Mean pairwise genetic relatedness estimates for seven possum

populations from around Dunedin and the Otago Peninsula. Error bars

are the 95% CI as determined by bootstrapping. Population codes are

outlined in Table 5.3.

0.000

0.020

0.040

0.060

Aa PC City BZ PB CS TH

r

Population


139

Figure 5.3: Mean pairwise genetic relatedness estimates for (A) females and (B) males

from four possum populations from around Dunedin and the Otago

Peninsula. Error bars are the 95% CI as determined by bootstrapping.

Population codes are outlined in Table 5.3.

PCO of the microsatellite data showed a distinction between possum populations on the

Western and Eastern sides of the Otago Harbour, with a clear separation between Aa

and TH populations (Fig. 5.4). The first three principal axes collectively explained

59.1% of the total genetic diversity across all seven populations (PC1: 24.1%; PC2:

18.1%; PC3: 16.9%). These results corroborated well with the mantel test, which

showed a significant isolation by distance pattern (Fig. 5.5, R2

= 0.79, P = 0.01).

-0.06

-0.02

0.02

0.06

r

(A)

-0.06

-0.02

0.02

0.06

Aa PC City BZ

r

Population

(B)


140

Figure 5.4: Principal coordinates analysis showing genetic relationships of possums

among five populations from two spatially separate locations either side

of the Otago Harbour. Population codes are outlined in Table 5.3.

Figure 5.5: The relationship between Nei’s genetic distance and the geographic

distance among seven possum populations around Dunedin and the

Otago Peninsula.

-0.60

-0.40

-0.20

0.00

0.20

0.40

0.60

0.80

-0.60 -0.40 -0.20 0.00 0.20 0.40 0.60 0.80

PC

2

PC1

Aa

PC

PB

CS

TH

R² = 0.7926

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

0 10 20 30 40 50

Ne

i's

Pa

irw

ise G

en

eti

c

Dis

tan

ce

Geographic Distance (km)


141

Pairwise FST values ranged from 0.007 to 0.107 (Table 5.4). Following correction for

multiple tests, 16 of the 21 pairwise FST values showed significant, moderate

differentiation (Table 5.4). The highest FST value was between Aa and PB (37 km apart

via land), with the next two highest values being between Aa and TH (42.5 km apart

via land, FST = 0.10), and Aa and CS (40 km apart, FST = 0.089; Table 5.4). This again

indicates an isolation by distance pattern of genetic structure. Of interest, PC and City

are not significantly differentiated, exhibiting very little differentiation (FST = 0.007), in

comparison with City and BZ (FST = 0.051) which are significantly differentiated

(Table 5.4).

Table 5.4: Pairwise values of Slatkin's lineralised FST (asterisk denotes significance P <

0.05; bold values denote significance following a standard Bonferroni

correction) among seven population of possums around Dunedin and the

Otago Peninsula. Population codes are defined in Table 5.3.

Aa PC City BZ PB CS TH

Aa — 0.01799* 0.02877* 0.08430* 0.10749* 0.08915* 0.10263*

PC

— 0.00704 0.06760* 0.07946* 0.06949* 0.08513*

City

— 0.05059* 0.05289* 0.04836* 0.06750*

BZ

— 0.03943* 0.03218* 0.06247*

PB

— 0.01795* 0.01955*

CS

— 0.01305

TH —

The proportions of immigrants (m) into each population are shown in Table 5.5

indicating an absence of migration between most populations (due to the high

proportion of individuals within each population being derived from the same

population and the low levels of migration between populations). The mean posterior

probabilities of migration rates showed that most individuals originated from the

population they were sampled in for all populations (range: m = 0.68 – 0.93), with Aa

having the highest proportion of non-migrants (Table 5.5). Gene flow was typically

highest between geographically close populations under the assumption that possums

did not swim (Table 5.5; Fig. 5.1). There was evidence of asymmetrical migration

occurring between populations from PC into Aa (m = 0.23), from City into PC (m =

0.10) and into BZ (m = 0.06), from BZ into PB (m = 0.05), and from TH into PB (m =


142

0.22; Table 5.5). In contrast, migration rates from these populations in the opposite

direction were low, tending to be < 0.02 (Table 5.5). The results also suggest that there

was asymmetrical migration occurring from both PB and TH into CS, but both had

associated large standard deviations (Table 5.5). All other population pairings had more

symmetrical migration rates, but all had relatively large standard deviations, thus

requiring caution when interpreting the results.

Table 5.5: Estimates of migration rates (proportion of individuals) among the seven

possum populations sampled around Dunedin and the Otago Peninsula

generated using BayesAss 3.0. Bold values denote significant differences

between directions of migration. Population codes are defined in Table 5.3.

Migrated Into

Migrated

From Aa PC City BZ PB CS TH

Aa 0.9346

(0.0233)

0.0099

(0.0099)

0.0111

(0.0103)

0.0122

(0.0115)

0.0131

(0.0121)

0.0097

(0.0094)

0.0093

(0.0091)

PC 0.2381

(0.0326)

0.6804

(0.0203)

0.0403

(0.0213)

0.0111

(0.0106)

0.0108

(0.0109)

0.0097

(0.0096)

0.0096

(0.0095)

City 0.0091

(0.0089) 0.0977

(0.0284)

0.7878

(0.0301)

0.0627

(0.0248)

0.0185

(0.0162)

0.0134

(0.0129)

0.0108

(0.0104)

BZ 0.0244

(0.0166)

0.0100

(0.0095)

0.0135

(0.0125) 0.8653

(0.0418)

0.0517

(0.0219)

0.0246

(0.0262)

0.0105

(0.0102)

PB 0.0114

(0.011)

0.0092

(0.0090)

0.0101

(0.0098)

0.0191

(0.0169) 0.8843

(0.0665)

0.0541

(0.0651)

0.0116

(0.0121)

CS 0.0093

(0.0090)

0.0091

(0.0088)

0.0097

(0.0094)

0.0265

(0.0180)

0.1812

(0.0943) 0.7469

(0.0948)

0.0173

(0.0204)

TH 0.0093

(0.0090)

0.0091

(0.0088)

0.0094

(0.0091)

0.0105

(0.0101) 0.2177

(0.0772)

0.0592

(0.0703) 0.6850

(0.0178)

Values depict the means of the posterior distributions of the migration rate into each population with the

standard deviation in parentheses. Migration rates were estimated as the proportion of individuals in

column populations that originated from populations in rows. Values along the diagonal are the

proportions of individuals within a population derived from that population.


143

Bayesian clustering analysis performed in STRUCTURE 2.3 (Pritchard et al. 2000)

clearly identified two clusters based on the ad-hoc ∆K statistic for all possums in the

sample (Figs. 5.6 & 5.7). One cluster represents Aa/PC/City (Western cluster) and the

second cluster represents TH/CS/PB/BZ (Eastern cluster). This remained at two clusters

when examining males alone, but increased to three clusters when examining females

only. TESS Bayesian clustering analyses also suggested the existence of two clusters

(Fig. 5.7). With a cut-off threshold of q ≥ 0.80, 88% of the 209 possums could be

allocated to one of the two clusters for the STRUCTURE runs, with 25 possums unable

to be assigned to a cluster (Table 5.6). Assignment rose to 93% for the TESS runs, with

ten individuals remaining unassigned to a cluster, while five individuals sampled in the

Western cluster (from City) were mis-assigned to the Eastern cluster. This level of

membership again suggests population structure and no/low gene flow occurring

between populations on adjacent sides of the Otago Harbour.

Figure 5.6: Bayesian inference of the number of population clusters (Kmax) in

possums sampled around Dunedin and the Otago Peninsula (n =

209). K was estimated using the distribution of ∆K.

0

10

20

30

40

50

60

0 2 3 4 5 6 7

∆K

Number of clusters (Kmax)


144

Figure 5.7: Posterior estimates of individual admixture coefficients (Q-matrices)

adjusted for label-switching for seven possum populations around

Dunedin and the Otago Peninsula generated from (A) STRUCTURE and

(B) TESS for Kmax = 2 - 5 (n = 209). Each colour represents a different

cluster with each column representing an individual. Vertical black bars

separate sampling sites.

Km

ax

Km

ax

Population


145

Table 5.6: Number of individual possums with q ≥ 0.8 cluster membership

from each of the seven populations assigned to each of two

clusters inferred using Bayesian clustering (programme

STRUCTURE). Unassigned individuals had q ≤ 0.8. Population

codes are defined in Table 5.3. Bold values indicate the most

likely inferred population of origin.

Inferred Cluster

Population n I II Assigned Unassigned

Aa 30 0.020 0.980 30 0

PC 30 0.073 0.927 30 0

City 30 0.260 0.740 17 13

BZ 29 0.761 0.239 17 12

PB 30 0.976 0.024 30 0

CS 30 0.945 0.055 30 0

TH 30 0.997 0.003 30 0

The assignment tests generated using GENECLASS 2 reinforce the clustering results from

the Bayesian population structuring. Assignment tests correctly assigned most

individuals to the area where they were trapped (59.8%). When examining the two

furthest populations only (Aa and TH: 42.5 km apart via land; 400 m apart across

water, Fig. 5.1), 100% of individuals were correctly assigned to their population of

origin. Assignment rates were similar when comparing PC and PB populations, which

had a 96.7% correct assignment rate (28.9 km apart via land; 1.2 km apart across water;

Fig. 5.1). Of the incorrectly assigned individuals, 82% were assigned to a neighbouring

population. The level of unassigned individuals in City and BZ may be an indication of

these areas receiving individuals from outside of the sampling area.

5.4 Discussion

Eleven polymorphic microsatellite loci were investigated to examine the genetic

diversity, population genetic structure, and gene flow among seven possum populations

within and surrounding an urban environment adjacent to an area undergoing possum

eradication in New Zealand. This research is the first to examine the genetic population

structure of possums within an urban landscape, as well as applying molecular genetics


146

to assist in the management of an urban buffer zone to ensure long-term success of an

adjacent eradication. This study revealed that a moderate level of genetic variation

exists within possums around Dunedin and on the Otago Peninsula, with allelic richness

and heterozygosity being similar across all seven populations. This level of genetic

variation is comparable with other New Zealand possum populations which exhibit

moderate amounts of genetic diversity (see section 5.2.2), with variation tending to be

higher in North Island populations (Taylor & Cooper 1998; Taylor et al. 2000; Clinchy

et al. 2004; Taylor et al. 2004). Allelic richness in this study was similar to that reported

in Australian populations (see section 5.2.2), but other rural-based South Island possum

populations have significantly lower allelic richness compared to Australian

populations, which may reflect the number of founding individuals in an area (Taylor et

al. 2004; Stow et al. 2006). Heterozygosity loss is generally indicative of a population

slowly expanding from very few individuals, which may not be true for invading

species, especially those experiencing multiple introduction events (Dlugosch & Parker

2008; Frankham et al. 2010). Invasive populations can therefore have similar (or even

higher) levels of genetic variation in comparison to populations from their native origin.

This can be seen in introduced European rabbits within Australia (Zenger et al. 2003),

greenfinches (Carduelis chloris) in New Zealand (Merila et al. 1996), and brown anole

(Anolis sagrei) in America (Kolbe et al. 2004). The high level of genetic diversity

retained in New Zealand possum populations, both within this study and in previous

studies, implies that large genetic bottlenecks did not occur during possum

establishment in New Zealand (Taylor et al. 2004), possibly due to a large founding

population resulting from numerous liberations (Pracy 1974; Clout & Erickson 2000).

A moderate level of genetic variation existed among possum populations in this study,

but all populations exhibited a significant heterozygote deficiency, which could be due

to inbreeding (mating between relatives) within populations (Frankham et al. 2010).

The analysis of genetic relatedness provides some support for this, where all but one

population (City) showed elevated levels of relatedness amongst individuals.

Inbreeding in some of these populations could be expected due to their isolation from

other populations (i.e. populations on the Otago Peninsula) and the typically small

dispersal (sampled areas were separated by more than the average dispersal distance of

possums; Efford 1991; Cowan & Clout 2000) and movement distances (see Chapter

IV) of possums.


147

Urban and rural possum populations in this study exhibited similar levels of genetic

variation. For many species, genetic diversity tends to be lower in urban animal

populations compared to populations in rural habitats (Wandeler et al. 2003; Takami et

al. 2004; Desender et al. 2005; Rutkowski et al. 2006; Noel et al. 2007; Huck et al.

2008; Evans et al. 2009; Gardner-Santana et al. 2009), and is thought to be due to a

combination of the founder effect, behavioural changes, and the highly modified,

fragmented nature of the urban environment acting to restrict gene flow (Wandeler et

al. 2003; Rutkowski et al. 2006; Huck et al. 2008; Gardner-Santana et al. 2009). For

possums, genetic diversity may be retained due to their behavioural flexibility, which

has enabled them to adapt to and exploit many areas of the urban environment, so that

they are not isolated to fragments of their natural habitat (see Chapters II and IV).

European blackbirds (Turdus merula) that have adapted to urban habitats (Moller et al.

2012) also show comparable levels of genetic diversity between urban and forest

populations (Partecke et al. 2006).

The results of the PCO, mantel test, and pairwise FST values all support an isolation by

distance pattern with genetic similarity between possum populations increasing with

decreasing geographic distance. Isolation by distance can be caused by the dispersal

abilities of the species, or barriers to dispersal, including natural events (e.g. volcanic

eruptions, glaciations) or natural (i.e. water bodies, mountain ranges, habitat

type/connectivity) and artificial (i.e. urbanisation, roads) features (Wright 1943;

Gerlach & Musolf 2000; Lowe et al. 2004; Broquet et al. 2006; Schmuki et al. 2006;

Delaney et al. 2010). The low FST values between Aa/PC and PC/City on the Western

side of the Otago Harbour and between PB/CS, PB/TH, and CS/TH on the Eastern side

are most likely indicative of ongoing gene flow between neighbouring populations. The

pattern of genetic similarity and differentiation between possum populations observed

in this study may therefore partly be a reflection of the generally small dispersal

distances of possums in New Zealand (Cowan & Rhodes 1993; Cowan et al. 1996;

Cowan et al. 1997). Similar isolation by distance patterns, where the majority of

dispersal occurs between neighbouring populations, has also been reported in other

Australian marsupials, including the brush-tailed rock wallaby (Petrogale penicillata;

Hazlitt et al. 2006), quokka (Setonix brachyurus; Sinclair 2001), and the mountain

pygmy possum (Burramys parvus; Mitrovski et al. 2007). These species may have poor

dispersal abilities even in continuous habitat (Hazlitt et al. 2006).


148

One exception to the pattern of similarity between neighbouring possum populations is

the significant genetic differentiation between the City and BZ populations, which are

separated by approximately 7 km between sampled individuals. In contrast, the City

and PC populations, which are approximately 13 km apart, are not significantly

differentiated. Often within urban areas, there are patches of suitable habitat surrounded

by sub-optimal habitat that can limit the dispersal distances of urban-based animals

(Krummel et al. 1987; Murcia 1995; Etter et al. 2002; McKinney 2002). Consequently,

populations become geographically isolated and ultimately genetically differentiated

due to low migration rates and resulting genetic drift (Whitlock et al. 2000). The

differentiation between City and BZ populations may be due to the heavily urbanised

area (comprised largely of industrial and Res 3 habitat) at the base of the Otago

Harbour that separates the sampled suburbs of the City and the BZ (City to the West,

BZ to the East), where possum occupancy is very low in Res 3 habitat (see Chapter II).

Therefore, the level of urbanisation may be limiting possum dispersal between the City

and the BZ, reducing gene flow. This is reinforced by the predictive map from Chapter

IV, which indicates low probability of use by possums in this area (Fig. 4.4).

Additionally, possums in residential suburbs were found to move an average of 550 m a

night, with an average home range size of 2.87 ha (see Chapter IV). These relatively

small movements may further explain the differentiation between the two urban

populations. The spatial isolation of habitat patches by modified and/or unsuitable

habitat has also impeded the dispersal of other Australian marsupials (Pope et al. 1996;

Eldridge et al. 2001; Jones et al. 2004; Mitrovski et al. 2007; Walker et al. 2008).

Conversely, the habitat available to possums extending from the City to PC has higher

possum occupancy (see Chapter II), and appears to be conducive to possum use (Fig.

4.4), which may facilitate dispersal, hence gene flow, between these areas. These

results may also possibly be a reflection of the small sample sizes obtained for each

population and the high rates of unassigned individuals within the City and BZ

populations. Therefore, results should be treated with caution. Larger sample sizes

which display the same pattern as above will help reinforce the conclusions made here.

Nearly all possum populations had relatively low, symmetrical migration rates

indicative of populations having a high level of non-migrants. Population pairings that

did have significant migration rates also tended to have more genetically similar

populations. These populations were neighbouring populations, which again indicates


149

gene flow occurring between populations in close proximity. This trend supports

previous analyses where populations farther apart from one another receive fewer

migrants than neighbouring populations (Wright 1943; Broquet et al. 2006; Hazlitt et

al. 2006; Mitrovski et al. 2007). Results from GENECLASS 2 support this pattern, as the

majority of individuals assigned as migrants originated from neighbouring populations.

Interestingly, there appears to be an asymmetrical migration pattern occurring from the

City to the BZ and onto the Otago Peninsula. Due to the high levels of genetic

variation, the level of unassigned individuals, and the non-relatedness of individuals

within this population, City appears to be a sink population (e.g. King et al. 2000), with

the possibility that City is receiving migrants (asymmetrical migration) from

surrounding (un-sampled) mainland areas. Therefore, this could be creating a pressure

on the City population, causing migration onto the Otago Peninsula. In contrast, on the

Otago Peninsula, in which possum populations exhibit relatedness, there does not seem

to be pressure for individuals to migrate back onto the mainland. This could have

potential implications in regards to the OPBG’s possum eradication operation, as this

suggests a potential avenue for reinvasion from urban areas. Such asymmetrical

migration is also apparent on the mainland from City to PC and Aa. Despite the

potential for PC and Aa populations to also be receiving migrants from surrounding

inland areas, these populations exhibited relatedness amongst individuals, with

relatedness being higher amongst females. This result may partly be a reflection of the

sampling strategy, as multiple possums were often sampled from the same farms in

these locations, which is likely to increase the likelihood of relatedness, especially due

to female philopatry (Clout & Efford 1984; Ji et al. 2001; Stow et al. 2006). Results

from the analysis examining relatedness amongst females and males do however need

to be treated with caution, due to low sample sizes.

Although pairwise FST values revealed significant genetic differentiation among

populations, both Bayesian clustering techniques implemented in this study provided

evidence of geographic structuring of possums into two distinct clusters: a Western

cluster (Aa, PC, City populations) and an Eastern cluster (TH, CS, PB, BZ

populations). This clustering indicates that there is a potential reinvasion pathway onto

the Otago Peninsula through the suburbs of the BZ area (Arrow 1 in Fig. 5.1). These

results also indicate that the Otago Harbour is a barrier to possum dispersal, preventing

reinvasion of possums onto the Otago Peninsula from Aa and PC. Individual


150

assignment tests also provide evidence that reinvasion across the Otago Harbour is

unlikely, with a 100% correct assignment rate when analysing possums from Aa and

TH, and a 96.7% correct assignment rate for possums between PC and PB. Population

structure within a species generally relates to habitat suitability and connectivity, and

the species’ dispersal abilities (Piertney et al. 1998; Wood & Pullin 2002; Jones et al.

2004; Pilot et al. 2006). The presence of two population clusters in this study may again

be attributed to the dispersal abilities of possums and the probable unsuitable urban

habitat between the City and BZ populations, affecting dispersal. Similar trends have

been found in Tasmania where intense urbanisation has resulted in limited gene flow,

and separate population clusters, in Tasmanian devils (Sarcophilus harrisii; Jones et al.

2004).

When analysing the two sexes separately (Aa, PC, City, BZ populations only),

population structuring suggested two clusters for males and three clusters for females.

This pattern could reflect the dispersal patterns of possums in New Zealand, which tend

to be male-biased (Clout & Efford 1984; Efford 1991; Cowan et al. 1996; Taylor et al.

2000; Ji et al. 2001; Stow et al. 2006). Females are typically philopatric; they establish

home ranges that overlap with, or are next to, their mother’s home range (Clout &

Efford 1984; Ji et al. 2001; Stow et al. 2006); males however tend to travel further

away from their natal location before establishing a home range (Efford 1991; Ji et al.

2001; Stow et al. 2006). This male-biased dispersal is common in many mammalian

(Goudet et al. 2002) and marsupial species (Jones et al. 2004), and a male-female

difference in population clusters has been documented for feral cats (Hansen et al.

2007). When analysing all possums together, a lower level of population structuring

can be expected due to the higher rates of male dispersal, which would overshadow the

more localised dispersal of females. However, these results are based on a very low

sample size, as sex information was not available for all possums included in this study,

and therefore should be interpreted cautiously. In future, a larger sample size will

provide more confidence in the differences of population clustering between males and

females.


151


A key factor influencing the success of implemented control strategies is dispersal from

adjacent, uncontrolled areas (Cowan & Clout 2000; Abdelkrim et al. 2005).

Management strategies for controlling possums (and other invasive species) throughout

New Zealand can be improved by incorporating molecular genetics into the preliminary

decision-making process, especially via the identification of potential reinvasion

pathways (Robertson & Gemmell 2004; Abdelkrim et al. 2005; Rollins et al. 2006).

Additionally, molecular genetics can be an important tool in ongoing control efforts by

utilising assignment tests to identify whether individuals caught in the controlled area

after operations are survivors or recolonisers (Robertson & Gemmell 2004).

This study revealed that microsatellites are extremely useful in assessing the genetic

population structure of possums within New Zealand. Possum populations on the Otago

Peninsula are a separate cluster to those on the mainland, with the only likely potential

reinvasion pathway being on land through the suburbs of Dunedin City. These findings

have important implications for the eradication operation developed by the OPBG, as

they suggest that minimal trapping is required along the Otago Peninsula once possums

have initially been removed from the area and long-term success will be dependent on

the implementation, monitoring, and maintenance of an urban buffer zone at the base of

the Otago Peninsula between the suburbs and rural areas (the buffer zone). Another

implication from these results is the direction of eradication. Eradication efforts are

often too expensive and labour extensive to occur over the entire target area at once and

like the possum eradication on the Otago Peninsula, they are completed in stages.

Based on the reinvasion pathway from the suburbs at the base of the Peninsula, the

eradication project may benefit from sequential possum eradication beginning at the

Southern end of the Otago Peninsula. Establishing the urban buffer zone will

effectively put a “cork in the bottle” and prevent further possum movement onto the

Peninsula. Eradication can then focus on the possums already on the Peninsula without

migrants adding to the population. In terms of the urban area, control will need to be

more intensive and ongoing due to the area being a likely sink population, receiving

migrants from other populations and across a wider area.


152

Additionally, large water bodies appear to be barriers to possum dispersal and

movements. This could have implications to other areas undergoing possum control,

where water bodies may act as natural barriers defining areas where control can be

implemented with minimal reinvasion potential.

This study highlights that mainland eradications of invasive mammalian populations

can be successful if planning and control efforts are based on well-informed

information regarding the target species. Using molecular genetics to optimise the long-

term success of control initiatives will ultimately benefit native biodiversity and reduce

operational costs. For New Zealand, this is positive news in the quest for reaching a

pest-free country and conserving the native biodiversity.

Chapter VI: General discussion

153

Chapter VI:

6 General discussion

A common brushtail possum (Trichosurus vulpecula) in urban New Zealand.


154

Globally, biological invasions are a major threat to native species, ecosystems, and

ecosystem functioning (Courchamp et al. 2003; Clavero & Garcia-Berthou 2005;

Thomsen et al. 2011). In particular, islands are especially vulnerable to invasions due to

niche opportunities, availability of resources, and absence (or reductions) of natural

competitors, predators, and parasites (Shea & Chesson 2002; Courchamp et al. 2003).

New Zealand is an example of an island ecosystem where invasive species have had

devastating impacts on the native fauna and flora, as well as ecosystem composition

and functioning. While numerous control operations have successfully been

implemented in New Zealand in an effort to mitigate the impacts of invaders, the

majority occur on offshore islands and within more traditional habitat types (Saunders

& Norton 2001; Courchamp et al. 2003). There is mounting evidence that urban

environments can support significant populations of wildlife, including exotic species

(Blair 1996; McKinney 2002; Ditchkoff et al. 2006; Riley 2006; Pickett et al. 2008;

Goddard et al. 2010). It is therefore likely that the negative biodiversity impacts caused

by invasive species extend into urban environments and therefore control efforts should

include these landscapes as well as within traditional environments.

Due to the rapid global expansion of urban areas, it is important to gain a

comprehensive understanding of how urbanisation impacts animal species for both

conservation and management purposes, because species compositions, abundances,

behaviour, and movements can be drastically altered in these environments (Czech et

al. 2000; Davison et al. 2009; Gehrt et al. 2009; Faeth et al. 2011). Both conserving and

controlling urban species is dependent on gaining a thorough understanding of the

processes, both anthropogenic and natural, which influence their spatial ecology (Faeth

et al. 2011). However, knowledge regarding the spatial ecology and connectivity of

invasive species for use in management within urban areas is lacking (Magle et al.

2012).

Formulation of effective control strategies for invasive species is enhanced by gaining

species-specific information regarding behaviour, movements, resource selection, and

population structure within specific locations (Nathan et al. 2008; Patterson et al. 2008).

A comprehensive picture of the dynamics and impacts of invasive species can be

achieved by collecting information at several scales and at both the individual and

population levels using multiple, complementary spatial methods. In order to expand on


155

the successes New Zealand has already achieved in controlling and eradicating

populations of invasive animals, more multi-disciplinary approaches are required to

gain necessary fine-scale information. Developments in technology will enable the

collection of very fine-scale data and improved analyses will provide more accurate

conclusions. The present research benefits from using an interdisciplinary approach to

improve our understanding of the spatial ecology, habitat use, and population

connectivity of the invasive possum within an urban landscape in New Zealand, with an

overall aim of informing the urban management of this species, and providing location-

specific information to a conservation group (the OPBG) to assist in their possum

eradication on the Otago Peninsula. Hence, some results of this research are specific to

possums in the studied area, while others have a more general management application

to other urban habitats.

6.1 Key results and management implications

6.1.1 GPS technology

The development of GPS telemetry has great potential for remotely collecting a large

dataset of locations for a wide range of wildlife species to investigate distributions,

movements, and habitat and resource selection. This study is the first in New Zealand

to use lightweight GPS collars to track possums within an urban environment from a

management viewpoint. The use of such collars is an appropriate and successful

method to determine the short-term spatial ecology and habitat use of possums within

urban habitats (Chapters III and IV), and the performance of the collars in the urban

environment was similar to that of comparable collars in other environments (Di Orio et

al. 2003; Cain et al. 2005; Jiang et al. 2008; Blackie 2010; Dennis et al. 2010). With the

ability to collect up to 321 locational fixes from an individual over an average of 8

days, the GPS collars allowed the collection of finer-scale data in comparison to

traditional tracking methods, such as live-trapping and VHF radio-tracking, and their

use is recommended when examining fine-scale spatial patterns (Clout & Gaze 1984;

Ward 1985; Green & Coleman 1986; Brockie et al. 1997).

This study was the first to evaluate GPS collar performance in a suburban environment.

Similar environmental and technical factors affected collar performance in the suburban

environment as in other habitats (Rempel et al. 1995; Moen et al. 1997; D'Eon 2003; Di


156

Orio et al. 2003; Lewis et al. 2007; Dennis et al. 2010; Recio et al. 2011), despite the

presence of buildings that have the potential to block signal acquisition. As in other

environments, these factors have the potential to cause large error values to be acquired

by the GPS collars. Hence, I recommend that data filtering techniques using species-

specific movement information is required for data collected from animals within urban

habitats (see Chapter IV; Bjorneraas et al. 2010; Frair et al. 2010). Error rates obtained

from my validation experiment (Chapter III) were incorporated into subsequent habitat

selection analyses to produce more robust results (Chapter IV), as well as being able to

be applied to analyses conducted in comparable landscapes globally. Use of such error

estimates are already being incorporated into other spatial ecology research to generate

more robust conclusions (e.g. Recio 2011).

The main limitation of GPS telemetry on small-to-medium sized animals is that of the

battery weight and life. This is particularly relevant to management and conservation in

New Zealand where the most damaging invasive species and the most threatened native

species are small. However, ongoing advances in this technology (e.g. miniaturisation

of electronic components and increases in battery longevity) are occurring at a rapid

rate, and will eventually overcome these limitations (Cagnacci et al. 2010). These

advances, as well as cost reductions of devices, will enable very fine-scale data

regarding spatial ecology and resource selection to be collected across more individuals

within a study, and on other smaller taxa, such as reptiles and birds.

6.1.2 Control recommendations

Previous research into the spatial ecology and control of possums in New Zealand has

largely been focussed on native habitats and at a coarser scale (due to VHF radio-

tracking and spotlighting methods). However, resource use and movements of

individuals within urban environments can differ from those in natural habitats

(Ditchkoff et al. 2006; Baker & Harris 2007), and so it is important to understand the

spatial distribution and resource use of urban possums to be able to adequately select

areas requiring control. Within urban areas, current council-led possum control is

typically directed at forest remnants. Complementary analyses of both presence-

absence data at a broad scale (Chapter II) and GPS telemetry data at a fine scale

(Chapter IV) revealed that possums are utilising both native (forest fragments) and non-


157

native (residential gardens) habitats within urban environments and have the ability to

live independently of forest fragments within residential properties comprised of

structurally-complex and diverse vegetation. This behaviour is similar to that of other

opportunistic mammalian species inhabiting urban areas, which show preferences

towards residential areas and exploit the often novel resources (such as shelter and

food) located in these habitats (Tigas et al. 2002; Baker & Harris 2007; Davison et al.

2009; Rosatte et al. 2011; Podgorski et al. 2013). The features of these residential

gardens have also been shown to support native bird populations (van Heezik et al.

2010), thus possums are likely to be having negative biodiversity impacts by preying on

native birds, chicks, and eggs, in both forest fragments and residential areas where

possum distribution overlaps with native bird populations (Brown et al. 1993;

McLennan et al. 1996; Sadlier 2000). Additionally, residential areas can act as a source

for reinvasion into remnants that are subject to control operations around the city.

Therefore, I recommend that while control should continue to be targeted at forest

fragments due to the higher use by possums (Chapters II and IV) and greater densities

of native birds, these fragments should not be treated as isolated units separated from

adjacent residential areas. Instead, control efforts need also to be directed at residential

areas comprised of structurally-complex, mature vegetation to increase the success of

possum control within urban environments. The urban environment can also act as a

source for reinvasion into other adjacent habitats, affecting the success of control

operations in these areas (Cowan & Clout 2000; Abdelkrim et al. 2005).

Within Dunedin, the aim of the OPBG conservation group is to work alongside the

community in a collaborative manner to eradicate animal pests, especially possums,

from the Otago Peninsula to protect the native biodiversity on the Peninsula.

Identification of potential reinvasion pathways is therefore crucial for designing a long-

term, successful eradication (Robertson & Gemmell 2004; Abdelkrim et al. 2005;

Rollins et al. 2006). The genetic analyses of possum populations around Dunedin and

on the Otago Peninsula showed a significant isolation by distance pattern (i.e. the

majority of dispersal occurs between neighbouring populations) and revealed that two

population clusters exist: an Eastern Otago Peninsula cluster and a Western Dunedin

cluster (Chapter V). These two clusters indicate the existence of a potential reinvasion

pathway from the residential suburbs at the base of the Otago Peninsula to the rural

habitats on the Otago Peninsula (Chapter V). Such separation of populations has also


158

been documented in other species where intense urbanisation divides populations and

limits gene flow and dispersion (Eldridge et al. 2001; Jones et al. 2004; Mitrovski et al.

2007; Walker et al. 2008). Isolation by distance patterns are also evident in many other

Australian marsupials (Sinclair 2001; Hazlitt et al. 2006; Mitrovski et al. 2007). In

terms of the OPBG’s eradication operation, to ensure long-term success of possum

eradication on the Otago Peninsula, it will be imperative to monitor and maintain an

appropriate urban buffer zone. The spatial ecology, habitat use, and genetic population

connectivity of an invasive species is a key factor in determining placement of traps

and/or poison stations. Minimal trapping will be needed along the Otago Peninsula after

the initial eradication of possums, due to the presence of one likely terrestrial

reinvasion pathway through the BZ and the low connectivity between BZ and City

populations (Chapters II, IV, and V). To prevent reinvasion onto the Peninsula, possum

control will need to be concentrated within the BZ. Additionally, control of urban

possums in general should be focussed in forest fragments and residential properties

with mature, structurally-diverse vegetation and supplementary food sources (Chapters

II and IV) rather than spread out evenly across the entire urban landscape.

This research shows the potential and value of combining different tools to collect

information across over multiple scales to gain a more comprehensive understanding of

the dynamics of a species. For invasive species, this knowledge can be incorporated

into the decision-making process for formulating successful control strategies. For

example, this research gave broad-scale insights into urban possum populations with

site occupancy methods showing that the probability of urban possum occupancy is

influenced by habitat type, supplementary food, and distance from forest fragments,

and which varied across different residential suburbs. Additionally, molecular genetics

tools examined possum movements and connectivity over a large scale. This revealed

the existence of two population clusters, a Western mainland cluster and an Eastern

Peninsula cluster, providing insights into the separation of populations and direction of

movements (i.e. reinvasion pathways). On a finer scale, the GPS tracking provided

insights into the mechanisms and ecology of the large scale population patterns. Urban

possums have small home ranges and the strongest selection was for forest fragments,

but individuals are still able to exist in sufficiently vegetated gardens, and in areas in

close proximity to fragments. Combining information from all three methods, there

appears to be a separation between possums within the BZ and City areas, where the


159

heavily urbanised part of the city appears to be acting as a barrier to possum movement

both within the urban area and between the Eastern and Western sides of the Otago

Harbour.

A reduction in the overall costs associated with control operations and optimisation of

long-term success of such efforts on mainland New Zealand can be achieved by

combining similar multidisciplinary investigations into the genetic population structure

of the species targeted for control with information regarding the spatial ecology of the

species to determine where they are, what resources they are exploiting, and optimal

placement of control devices.

6.2 Recommendations for future research

This research focused on the spatial ecology and habitat use across spring and summer

when possums are the most active. Variation between seasons and across years was not

examined, and more research relating seasonal trends to resource availability would

strengthen the conclusions made here. Additionally, since large variations were

sometimes found between individuals and sexes, further GPS tracking of more urban

possums should be undertaken. A larger sample size was not obtained in this study due

to time limitations and the difficulty of trapping and re-trapping possums within the

urban environment. As a result of the low trapping success (5%) and the limited

number of GPS collars I had access to, data were only collected for one season split

over two years to acquire a substantial sample size for one season to produce more

robust results. This research was limited to Dunedin, and provided location-specific

data to a local possum control operation. While results are likely to apply to other urban

environments within New Zealand since Dunedin is a typical heterogeneous urban

landscape, GPS tracking should be extended to additional urban areas in future. This

would extend the scope of inference to all urban New Zealand environments, and also

the assessment of likely possum impacts on native bird populations within these

habitats.

It would also be of interest to determine dispersal patterns of juvenile possums to

identify how far they are travelling from their natal range, how this differs from

dispersal within more traditional habitats, and identification of any features within the


160

urban environment acting to limit dispersal and/or distances. Future miniaturisation of

batteries, hence devices, will enable juveniles to be tracked and these questions to be

addressed. However, molecular genetics could be utilised to address these questions by

sampling juveniles within and around urban areas.

Interpretation of resource selection of urban possums was also limited by the scale of

the habitat map available. With the development of a very fine-scale habitat map (i.e.

depicting resources available in private gardens), the GPS data collected in this study

could be further examined to identify landscape features and resources influencing

possum movements and habitat use at a very fine-scale. For example, what components

within individual gardens make them more attractive to possum use? However, caution

will need to be exerted when analysing spatial data in combination with a fine-scale

map in highly heterogeneous habitats, such as urban environments, due to the telemetry

error surrounding collected location points and the associated large buffer sizes. I

recommend the use of mechanistic modelling techniques that are not dependent on

buffer sizes, but these still may not overcome the difficulties in predicting habitat and

resource selection in a patchy landscape at a very fine-scale.

Another particular issue that would be interesting to pursue is that of how urban

residents view urban possum control and their attitudes towards this. Because urban

environments often have high human populations, the success of control operations in

these habitats can be largely dependent on social attitudes and public willingness to be

involved. Community-based control initiatives have been successfully created both

independently by community groups (Saunders & Norton 2001), and in rural areas in

conjunction with conservation groups (for example, community trapping on the Otago

Peninsula in conjunction with the OPBG), but these may not work within highly-

populated urban areas. Gauging the level of public enthusiasm for possum control and

people’s willingness to participate in such initiatives and combining this information

with spatial ecology data would likely help develop cost-effective, successful control

strategies for these areas.


161

6.3 Conclusions

This research is the first to incorporate the use of multiple tools at different scales to

examine the spatial ecology, habitat use, and genetic population structure of an invasive

species within an urban New Zealand environment. The occupancy and molecular

genetics tools examines possum movements and population structure over a broad

scale, while GPS tracking examined the spatial ecology and habitat use across a fine

scale providing insights into possum ecology and mechanisms behind the broad scale

population patterns. Using all three methods, this study identified clear patterns of

habitat use and spatial distribution of possums that can be used to develop a more

effective management strategy for this invasive pest within urban landscapes.

Furthermore, results can be utilised by the conservation group, the OPBG, to enhance

the long-term success of their possum eradication from adjacent rural areas. This multi-

disciplinary methodology approach is recommended in future urban studies to

determine patterns and processes occurring within animal populations persisting in

these highly heterogeneous habitats. Such an approach has the potential to be adopted

by other conservation groups around New Zealand, and globally, to enhance the

understanding of the target species and local populations, thereby improving the

development and effectiveness of management initiatives. If subsequent control

strategies are successful, it is likely that with more targeted management, populations

of native species will increase both in areas of complete eradication, as well as in urban

areas.

References

162

References

Abdelkrim, J., Pascal, M., Calmet, C. & Samadi, S. (2005). Importance of assessing

population genetic structure before eradication of invasive species: Examples

from insular Norway rat populations. Conservation Biology, 19, 1509-1518.

Abdelkrim, J., Pascal, M. & Samadi, S. (2007). Establishing causes of eradication

failure based on genetics: Case study of ship rat eradication in Ste. Anne

archipelago. Conservation Biology, 21, 719-730.

Abdelkrim, J., Byrom, A.E. & Gemmell, N.J. (2010). Fine-scale genetic structure of

mainland invasive Rattus rattus populations: Implications for restoration of

forested conservation areas in New Zealand. Conservation Genetics, 11, 1953-

1964.

Adams, A.L. (2008). Impacts of urbanisation on avian communities in Dunedin, New

Zealand. Honours dissertation, University of Otago.

Adams, A.L., Dickinson, K.J.M., Robertson, B.C. & van Heezik, Y. (2013). Predicting

summer site occupancy for an invasive species, the common brushtail possum

(Trichosurus vulpecula), in an urban environment. Plos One, 8, e58422.

doi:58410.51371/journal.pone.0058422.

Adams, E.S. (2001). Approaches to the study of territory size and shape. Annual

Review of Ecology and Systematics, 32, 277-303.

Adkins, C.A. & Stott, P. (1998). Home ranges, movements and habitat associations of

red foxes Vulpes vulpes in suburban Toronto, Ontario, Canada. Journal of

Zoology, 244, 335-346.

Aebischer, N.J., Robertson, P.A. & Kenward, R.E. (1993). Compositional analysis of

habitat use from animal radio-tracking data. Ecology, 74, 1313-1325.

Allen, N.T. (1982). A study of the hormonal control, chemical constituents, and

functional significance of the paracloacal glands in Trichosurus vulpecula

(including comparisons with other marsupials). MSc Thesis. University of

Western Australia.

Allendorf, F.W. & Luikart, G. (2007). Conservation and the genetics of populations.

Blackwell Publishing, UK.

Altman, D.G., Lausen, B., Sauerbrei, W. & Schumacher, M. (1994). Dangers of using

optimal cutpoints in the evaluation of prognostic factors. Journal of the

National Cancer Institute, 86, 829-835.

Amos, W., Hoffman, J.I., Frodsham, A., Zhang, L., Best, S. & Hill, A.V.S. (2007).

Automated binning of microsatellite alleles: Problems and solutions. Molecular

Ecology Notes, 7, 10-14.

References

163

Anderson, T.J.C., Berry, A.J., Amos, J.N. & Cook, J.M. (1988). Spool-and-line

tracking of the New Guinea spiny bandicoot, Echymipera kalubu (Marsupialia,

Peramelidae). Journal of Mammalogy, 69, 114-120.

Animal Care and Use Committee (1998). Guidelines for the capture, handling, and care

of mammals as approved by the American Society of Mammalogists. Journal of

Mammalogy, 79, 1416-1431.

Apps, C.D., McLellan, B.N., Woods, J.G. & Proctor, M.F. (2004). Estimating grizzly

bear distribution and abundance relative to habitat and human influence.

Journal of Wildlife Management, 68, 138-152.

Atkinson, I.A.E. (2001). Introduced mammals and models for restoration. Biological

Conservation, 99, 81-96.

Atkinson, I.A.E. (2006). Introduced mammals in a new environment. Biological

invasions in New Zealand (eds R.B. Allen & W.G. Lee), pp. 49-66. Springer-

Verlag, Berlin.

Attiwill, P.M. & Leeper, G.W. (1987). Forest soils and nutrient cycles. Melbourne

University Press, Victoria, Australia.

Augustine, B.C., Crowley, P.H. & Cox, J.J. (2011). A mechanistic model of GPS collar

location data: Implications for analysis and bias mitigation. Ecological

Modelling, 222, 3616-3625.

Bailey, E.P. (1993). Introduction of foxes to Alaskan Islands - History, effects on

avifauna, and eradication. pp. 1-53. United States Department of the Interior

Fish and Wildlife Service, Resource Publication, Washington.

Bailey, L.L., Simons, T.R. & Pollock, K.H. (2004). Estimating site occupancy and

species detection probability parameters for terrestrial salamanders. Ecological

Applications, 14, 692-702.

Baker, P.J. & Harris, S. (2007). Urban mammals: What does the future hold? An

analysis of the factors affecting patterns of use of residential gardens in Great

Britain. Mammal Review, 37, 297-315.

Baker, P.J., Dowding, C.V., Molony, S.E., White, P.C.L. & Harris, S. (2007). Activity

patterns of urban red foxes (Vulpes vulpes) reduce the risk of traffic-induced

mortality. Behavioral Ecology, 18, 716-724.

Ball, S.J., Ramsey, D., Nugent, G., Warburton, B. & Efford, M. (2005). A method for

estimating wildlife detection probabilities in relation to home-range use:

Insights from a field study on the common brushtail possum (Trichosurus

vulpecula). Wildlife Research, 32, 217-227.

Balloux, F. & Lugon-Moulin, N. (2002). The estimation of population differentiation

with microsatellite markers. Molecular Ecology, 11, 155-165.

Bamford, J. (1970). Evaluating opossum poisoning operations by interference with non-

toxic baits. Proceedings of the New Zealand Ecological Society, 17, 118-125.

References

164

Barbosa, A.M., Real, R., Olivero, J. & Vargas, J.M. (2003). Otter (Lutra lutra)

distribution modeling at two resolution scales suited to conservation planning in

the Iberian Peninsula. Biological Conservation, 114, 377-387.

Barton, R.A., Whiten, A., Strum, S.C., Byrne, R.W. & Simpson, A.J. (1992). Habitat

use and resource availability in baboons. Animal Behaviour, 43, 831-844.

Beamesderfer, R.C. & Rieman, B.E. (1991). Abundance and distribution of northern

squawfish, walleyes, and smallmouth bass in John Day Reservoir, Columbia

River. Transactions of the American Fisheries Society, 120, 439-447.

Belant, J.L. & Follmann, E.H. (2002). Sampling considerations for American black and

brown bear home range and habitat use. Ursus, 13, 299-315.

Bell, B., D. (2002). The eradication of alien mammals from five offshore islands,

Mauritius, Indian Ocean. Turning the tide: The eradication of invasive species

(eds C.R. Veitch & M.N. Clout), pp. 40-45. IUCN SSC Invasive Species

Specialist Group, Gland, Switzerland and Cambridge, UK.

Bellingham, P.J., Towns, D.R., Cameron, E.K., Davis, J.J., Wardle, D.A., Wilmshurst,

J.M. & Mulder, C.P.H. (2010). New Zealand island restoration: Seabirds,

predators, and the importance of history. New Zealand Journal of Ecology, 34,

115-136.

Bender, D.J., Contreras, T.A. & Fahrig, L. (1998). Habitat loss and population decline:

A meta-analysis of the patch size effect. Ecology, 79, 517-533.

Benhamou, S. & Riotte-Lambert, L. (2012). Beyond the Utilization Distribution:

Identifying home range areas that are intensively exploited or repeatedly visited.

Ecological Modelling, 227, 112-116.

Bergl, R.A. & Vigilant, L. (2007). Genetic analysis reveals population structure and

recent migration within the highly fragmented range of the Cross River gorilla

(Gorilla gorilla diehli). Molecular Ecology, 16, 501-516.

Beyer, H.L. (2004). Hawth's Analysis Tools for ArcGIS.

www.spatialecology.com/htools.

Biggins, J.G. & Overstreet, D.H. (1978). Aggressive and nonaggressive interactions

among captive populations of the brush-tail possum, Trichosurus vulpecula

(Marsupialia: Phalangeridae). Journal of Mammalogy, 59, 149-159.

Bjorneraas, K., Van Moorter, B., Rolandsen, C.M. & Herfindal, I. (2010). Screening

global positioning system location data for errors using animal movement

characteristics. Journal of Wildlife Management, 74, 1361-1366.

Blackie, H., M. (2004). High-resolution spatiotemporal behaviour and ecology of

brushtail possums (Trichosurus vulpecula), characterised using global

positioning devices. MSc Thesis, University of Auckland.

Blackie, H.M. (2010). Comparative performance of three brands of lightweight global

positioning system collars. Journal of Wildlife Management, 74, 1911-1916.

References

165

Blackie, H.M., Russell, J.C. & Clout, M.N. (2011). Maternal influence on philopatry

and space use by juvenile brushtail possums (Trichosurus vulpecula). Journal of

Animal Ecology, 80, 477-483.

Blair, R.B. (1996). Land use and avian species diversity along an urban gradient.

Ecological Applications, 6, 506-519.

Bolton, R.M. & Ahokas, J.T. (1997). Mixed function oxidases in an Australian

marsupial, the brushtail possum (Trichosurus vulpecula). Archives of

Environmental Contamination and Toxicology, 33, 83-89.

Borger, L., Dalziel, B.D. & Fryxell, J.M. (2008). Are there general mechanisms of

animal home range behaviour? A review and prospects for future research.

Ecology Letters, 11, 637-650.

Bowman, J.L., Kochanny, C.O., Demarais, S. & Leopold, B.D. (2000). Evaluation of a

GPS collar for white-tailed deer. Wildlife Society Bulletin, 28, 141-145.

Boyce, M.S. & McDonald, L.L. (1999). Relating populations to habitats using resource

selection functions. Trends in Ecology & Evolution, 14, 268-272.

Boyce, M.S., Vernier, P.R., Nielsen, S.E. & Schmiegelow, F.K.A. (2002). Evaluating

resource selection functions. Ecological Modelling, 157, 281-300.

Bradshaw, C.J.A., Boutin, S. & Hebert, D.M. (1997). Effects of petroleum exploration

on woodland caribou in northeastern Alberta. Journal of Wildlife Management,

61, 1127-1133.

Brockie, R. (1992). A living New Zealand forest. David Bateman, Auckland.

Brockie, R.E., Bell, B., D. & White, A.J. (1981). Age structure and mortality of possum

Trichosurus vulpecula populations from New Zealand. Proceedings of the 1st

Symposium on Marsupials in New Zealand (ed. B. Bell, D.), pp. 63-83. Zoology

Publications from Victoria University of Wellington 74, Wellington.

Brockie, R.E., Fairweather, A.A.C., Ward, G.D. & Porter, R.E.R. (1987). Field biology

of Hawke's Bay farmland possums, Trichosurus vulpecula. pp. 40. Department

of Scientific and Industrial Research, Lower Hutt, New Zealand.

Brockie, R.E., Ward, G.D. & Cowan, P.E. (1997). Possums (Trichosurus vulpecula) on

Hawke's bay farmland: Spatial distribution and population structure before and

after a control operation. Journal of the Royal Society of New Zealand, 27, 181-

191.

Broquet, T., Ray, N., Petit, E., Fryxell, J.M. & Burel, F. (2006). Genetic isolation by

distance and landscape connectivity in the American marten (Martes

americana). Landscape Ecology, 21, 877-889.

Brown, J.H., Stevens, G.C. & Kaufman, D.M. (1996). The geographic range: Size,

shape, boundaries, and internal structure. Annual Review of Ecology and

Systematics, 27, 597-623.

References

166

Brown, K., Innes, J. & Shorten, R. (1993). Evidence that possums prey on and

scavenge birds' eggs, birds and mammals. Notornis, 40, 169-177.

Brussard, P.F., Reed, J.M. & Tracy, C.R. (1998). Ecosystem management: What is it

really? Landscape and Urban Planning, 40, 9-20.

Buckland, S.T., Anderson, D.R., Burnham, K.P., Laake, J.L., Borchers, D.L. &

Thomas, L. (2001). Introduction to distance sampling. Oxford University Press,

Oxford, UK.

Bullard, F. (1999). Estimating the home range of an animal: A Brownian bridge

approach. MSc Thesis, University of North Carolina.

Burbidge, A.A. & Manly, B.F.J. (2002). Mammal extinctions on Australian islands:

Causes and conservation implications. Journal of Biogeography, 29, 465-473.

Burnham, K.P. & Anderson, D.R. (2002). Model selection and multimodel inference: A

practical information-theoretic approach, 2nd edn. Springer, New York.

Burnham, K.P. & Anderson, D.R. (2004). Multimodel inference - understanding AIC

and BIC in model selection. Sociological Methods & Research, 33, 261-304.

Burt, W.H. (1943). Territoriality and home range concepts as applied to mammals.

Journal of Mammalogy, 24, 346-352.

Cagnacci, F., Boitani, L., Powell, R.A. & Boyce, M.S. (2010). Animal ecology meets

GPS-based radiotelemetry: A perfect storm of opportunities and challenges.

Philosophical Transactions of the Royal Society B-Biological Sciences, 365,

2157-2162.

Cain, J.W., Krausman, P.R., Jansen, B.D. & Morgart, J.R. (2005). Influence of

topography and GPS fix interval on GPS collar performance. Wildlife Society

Bulletin, 33, 926-934.

Calenge, C. (2006). The package "adehabitat" for the R software: A tool for the

analysis of space and habitat use by animals. Ecological Modelling, 197, 516-

519.

Cameron, B.G., van Heezik, Y., Maloney, R.F., Seddon, P.J. & Harraway, J.A. (2005).

Improving predator capture rates: Analysis of river margin trap site data in the

Waitaki Basin, New Zealand. New Zealand Journal of Ecology, 29, 117-128.

Campbell, K. & Donlan, C.J. (2005). Feral goat eradications on islands. Conservation

Biology, 19, 1362-1374.

Capaccio, S., Lowe, D., Walsh, D.M.A. & Daly, P. (1997). Real-time differential

GPS/Glonass trials in Europe using all-in-view 20 channel receivers. Journal of

Navigation, 50, 193-208.

Carey, A.B., Reid, J.A. & Horton, S.P. (1990). Spotted owl home range and habitat use

in southern Oregon coast ranges. Journal of Wildlife Management, 54, 11-17.

References

167

Cargnelutti, B., Coulon, A., Hewison, A.J.M., Goulard, M., Angibault, J.M. &

Morellet, N. (2007). Testing global positioning system performance for wildlife

monitoring using mobile collars and known reference points. Journal of Wildlife

Management, 71, 1380-1387.

Carvalho, D.C., Oliveira, D.A.A., Santos, J.E., Teske, P., Beheregaray, L.B., Schneider,

H. & Sampaio, I. (2009). Genetic characterization of native and introduced

populations of the neotropical cichlid genus Cichla in Brazil. Genetics and

Molecular Biology, 32, 601-607.

Caughley, G. (1989). New Zealand plant-herbivore systems: Past and present. New

Zealand Journal of Ecology, 12, 3-10.

Chakraborty, R. & Nei, M. (1977). Bottleneck effects on average heterozygosity and

genetic distance with the stepwise mutation model. Evolution, 31, 347-356.

Chamberlain, M.J., Conner, L.M. & Leopold, B.D. (2002). Seasonal habitat selection

by raccoons (Procyon lotor) in intensively managed pine forests of central

Mississippi. American Midland Naturalist, 147, 102-108.

Chen, C., Durand, E., Forbes, F. & Francois, O. (2007). Bayesian clustering algorithms

ascertaining spatial population structure: A new computer program and a

comparison study. Molecular Ecology Notes, 7, 747-756.

Cianfrani, C., Le Lay, G., Hirzel, A.H. & Loy, A. (2010). Do habitat suitability models

reliably predict the recovery areas of threatened species? Journal of Applied

Ecology, 47, 421-430.

Ciuti, S., Bongi, P., Vassale, S. & Apollonio, M. (2006). Influence of fawning on the

spatial behaviour and habitat selection of female fallow deer (Dama dama)

during late pregnancy and early lactation. Journal of Zoology, 268, 97-107.

Clavero, M. & Garcia-Berthou, E. (2005). Invasive species are a leading cause of

animal extinctions. Trends in Ecology & Evolution, 20, 110.

Clegg, S.M., Degnan, S.M., Kikkawa, J., Moritz, C., Estoup, A. & Owens, I.P.F.

(2002). Genetic consequences of sequential founder events by an island-

colonizing bird. Proceedings of the National Academy of Sciences, USA, 99,

8127-8132.

Clinchy, M., Taylor, A.C., Zanette, L.Y., Krebs, C.J. & Jarman, P.J. (2004). Body size,

age and paternity in common brushtail possums (Trichosurus vulpecula).

Molecular Ecology, 13, 195-202.

Clout, M. (2001). Where protection is not enough: Active conservation in New

Zealand. Trends in Ecology & Evolution, 16, 415-416.

Clout, M.N. (1977). The ecology of the possum (Trichosurus vulpecula Kerr) in Pinus

radiata plantations. PhD Thesis, University of Auckland.

References

168

Clout, M.N. & Efford, M.G. (1984). Sex differences in the dispersal and settlement of

brushtail possums (Trichosurus vulpecula). Journal of Animal Ecology, 53, 737-

749.

Clout, M.N. & Gaze, P.D. (1984). Brushtail possums (Trichosurus vulpecula Kerr) in a

New Zealand beech (Nothofagus) forest. New Zealand Journal of Ecology, 7,

147-155.

Clout, M.N. & Erickson, K. (2000). Anatomy of a disastrous success: The brushtail

possum as an invasive species. The brushtail possum: Biology, impact and

management of an introduced marsupial (ed. T.L. Montague), pp. 1-9. Manaaki

Whenua Press, Lincoln, New Zealand.

Clout, M.N. (2002). Biodiversity loss caused by invasive alien vertebrates. Zeitschrift

Fur Jagdwissenschaft, 48, 51-58.

Clout, M.N. & Russell, J.C. (2006). The eradication of mammals from New Zealand

islands. Assessment and Control of Biological Invasion Risks (eds F. Koike,

M.N. Clout, M. Kawamichi, M. De Poorter & K. Iwatsuki), pp. 127-141. IUCN,

Gland, Switzerland.

Coleman, J. & Caley, P. (2000). Possums as a reservoir of bovine Tb. The brushtail

possum: Biology, impact and management of an introduced marsupial, pp. 92-

104. Manaaki Whenua Press, Lincoln, New Zealand.

Coleman, J.D., Gillman, A. & Green, W.Q. (1980). Forest patterns and possum

densities within podocarp/mixed hardwood forests on Mt Bryan O'Lynn,

Westland. New Zealand Journal of Ecology, 3, 69-84.

Coleman, J.D. & Green, W.Q. (1984). Variations in the sex and age distributions of

brush-tailed possum populations. New Zealand Journal of Zoology, 11, 313-

318.

Coleman, J.D., Green, W.Q. & Polson, J.G. (1985). Diet of brushtail possums over a

pasture-alpine gradient in Westland, New Zealand. New Zealand Journal of

Ecology, 8, 21-35.

Collinge, S.K. (2001). Spatial ecology and biological conservation - Introduction.

Biological Conservation, 100, 1-2.

Conry, P.J. (1988). Management of feral and exotic game species on Guam.

Transactions of the Western Section of the Wildlife Society, 24, 26-30.

Cooke, S.J., Hinch, S.G., Wikelski, M., Andrews, R.D., Kuchel, L.J., Wolcott, T.G. &

Butler, P.J. (2004). Biotelemetry: A mechanistic approach to ecology. Trends in

Ecology & Evolution, 19, 334-343.

Cork, S.J. (1996). Optimal digestive strategies for arboreal herbivorous mammals in

contrasting forest types: Why koalas and colobines are different. Australian

Journal of Ecology, 21, 10-20.

References

169

Cornuet, J.M., Piry, S., Luikart, G., Estoup, A. & Solignac, M. (1999). New methods

employing multilocus genotypes to select or exclude populations as origins of

individuals. Genetics, 153, 1989-2000.

Courchamp, F. & Sugihara, G. (1999). Modeling the biological control of an alien

predator to protect island species from extinction. Ecological Applications, 9,

112-123.

Courchamp, F., Chapuis, J.L. & Pascal, M. (2003). Mammal invaders on islands:

Impact, control and control impact. Biological Reviews, 78, 347-383.

Cowan, P. & Clout, M. (2000). Possums on the move: Activity patterns, home ranges

and dispersal. The brushtail possum: Biology, impact and management of an

introduced marsupial (ed. T.L. Montague), pp. 24-34. Manaaki Whenua Press,

Lincoln, New Zealand.

Cowan, P., Sarre, S. & Aitken, N. (2007). The genetics of possum dispersal. Kararehe

Kino Vertebrate Pest Research Issue 10, pp. 5-6. Landcare Research Manaaki

Whenua, Lincoln, New Zealand.

Cowan, P.E. & Moeed, A. (1987). Invertebrates in the diet of brushtail possums,

Trichosurus vulpecula, in lowland podocarp broadleaf forest, Orongorongo

Valley, Wellington, New Zealand. New Zealand Journal of Zoology, 14, 163-

177.

Cowan, P.E. (1989). Denning habits of common brushtail possums, Trichosurus

vulpecula, in New Zealand lowland forest. Australian Wildlife Research, 16, 63-

78.

Cowan, P.E. & White, A.J. (1989). Evaluation of a tooth-wear age index for brushtail

possums, Trichosurus vulpecula. Australian Wildlife Research, 16, 321-322.

Cowan, P.E. (1990). Fruits, seeds, and flowers in the diet of brushtail possums,

Trichosurus vulpecula, in lowland podocarp/mixed hardwood forest,

Orongorongo Valley, New Zealand. New Zealand Journal of Zoology, 17, 549-

566.

Cowan, P.E. (1992). The eradication of introduced Australian brushtail possums,

Trichosurus vulpecula, from Kapiti Island, a New Zealand nature reserve.


Cowan, P.E. & Rhodes, D.S. (1993). Electric fences and poison buffers as barriers to

movements and dispersal of brushtail possums (Trichosurus vulpecula) on

farmland. Wildlife Research, 20, 671-686.

Cowan, P.E., Brockie, R.E., Ward, G.D. & Efford, M.G. (1996). Long-distance

movements of juvenile brushtail possums (Trichosurus vulpecula) on farmland,

Hawke's Bay, New Zealand. Wildlife Research, 23, 237-244.

Cowan, P.E., Brockie, R.E., Smith, R.N. & Hearfield, M.E. (1997). Dispersal of

juvenile brushtail possums, Trichosurus vulpecula, after a control operation.

Wildlife Research, 24, 279-288.

References

170

Cowan, P.E. (2001). Advances in New Zealand mammalogy 1990-2000: Brushtail

possum. Journal of the Royal Society of New Zealand, 31, 15-29.

Cowan, P.E. (2005). Brushtail possum. The handbook of New Zealand mammals (ed.

C.M. King), pp. 56-80. Oxford University Press, Melbourne.

Crawley, M.C. (1973). A live trapping study of Australian brushtailed possums,

Trichosurus vulpecula (Kerr), in the Orongorongo Valley, Wellington, New

Zealand. Australian Journal of Zoology, 21, 75-90.

Creel, S., Winnie, J., Maxwell, B., Hamlin, K. & Creel, M. (2005). Elk alter habitat

selection as an antipredator response to wolves. Ecology, 86, 3387-3397.

Cromarty, P.L., Broome, K.G., Cox, A., Empson, R.A., Hutchinson, W.M. &

McFadden, I. (2002). Eradication planning for invasive alien animal species on

islands: The approach developed by the New Zealand Department of

Conservation. Turning the tide: The eradication of invasive species (eds C.R.

Veitch & M.N. Clout), pp. 85-91. IUCN SSC Invasive Species Specialist

Group, Gland, Switzerland and Cambridge, UK.

Czech, B., Krausman, P.R. & Devers, P.K. (2000). Economic associations among

causes of species endangerment in the United States. Bioscience, 50, 593-601.

D'Eon, R.G., Serrouya, R., Smith, G. & Kochanny, C.O. (2002). GPS radiotelemetry

error and bias in mountainous terrain. Wildlife Society Bulletin, 30, 430-439.

D'Eon, R.G. (2003). Effects of a stationary GPS fix-rate bias on habitat selection

analyses. Journal of Wildlife Management, 67, 858-863.

D'Eon, R.G. & Delparte, D. (2005). Effects of radio-collar position and orientation on

GPS radio-collar performance, and the implications of PDOP in data screening.

Journal of Applied Ecology, 42, 383-388.

Dahle, B. & Swenson, J.E. (2003). Seasonal range size in relation to reproductive

strategies in brown bears Ursus arctos. Journal of Animal Ecology, 72, 660-667.

Dakin, E.E. & Avise, J.C. (2004). Microsatellite null alleles in parentage analysis.

Heredity, 93, 504-509.

Davison, J., Huck, M., Delahay, R.J. & Roper, T.J. (2009). Restricted ranging

behaviour in a high-density population of urban badgers. Journal of Zoology,

277, 45-53.

Day, T., O'Connor, C. & Matthews, L. (2000). Possum social behaviour. The brushtail

possum: Biology, impact and management of an introduced marsupial (ed. T.L.

Montague), pp. 35-46. Manaaki Whenua Press, Lincoln, New Zealand.

DeGabriel, J.L., Moore, B.D., Foley, W.J. & Johnson, C.N. (2009). The effects of plant

defensive chemistry on nutrient availability predict reproductive success in a

mammal. Ecology, 90, 711-719.

References

171

Delaney, K.S., Riley, S.P.D. & Fisher, R.N. (2010). A rapid, strong, and convergent

genetic response to urban habitat fragmentation in four divergent and

widespread vertebrates. Plos One, 5, e12767.

doi:12710.11371/journal.pone.0012767.

Dennis, T.E., Chen, W.C., Koefoed, I.M., Lacoursiere, C.J., Walker, M.M., Laube, P.

& Forer, P. (2010). Performance characteristics of small global-positioning-

system tracking collars for terrestrial animals. Wildlife Biology in Practice, 6,

14-31.

Denys, P. (2010). A note on the accuracy of consumer grade GPS recievers. Survey

Quarterly, 62, 20-21.

Desender, K., Small, E., Gaublomme, E. & Verdyck, P. (2005). Rural-urban gradients

and the population genetic structure of woodland ground beetles. Conservation

Genetics, 6, 51-62.

Di Orio, A.P., Callas, R. & Schaefer, R.J. (2003). Performance of two GPS telemetry

collars under different habitat conditions. Wildlife Society Bulletin, 31, 372-379.

Dickman, C.R. (1996). Impact of exotic generalist predators on the native fauna of

Australia. Wildlife Biology, 2, 185-195.

Ditchkoff, S.S., Saalfeld, S.T. & Gibson, C.J. (2006). Animal behavior in urban

ecosystems: Modifications due to human-induced stress. Urban Ecosystems, 9,

5-12.

Dlugosch, K.M. & Parker, I.M. (2008). Founding events in species invasions: Genetic

variation, adaptive evolution, and the role of multiple introductions. Molecular

Ecology, 17, 431-449.

Donlan, C.J. & Wilcox, C. (2008). Integrating invasive mammal eradications and

biodiversity offsets for fisheries bycatch: Conservation opportunities and

challenges for seabirds and sea turtles. Biological Invasions, 10, 1053-1060.

Donnelly, M.J. & Townson, H. (2000). Evidence for extensive genetic differentiation

among populations of the malaria vector Anopheles arabiensis in Eastern

Africa. Insect Molecular Biology, 9, 357-367.

Dunedin City Council (2007). Street maps and aerial maps of Dunedin. Available from:

http://www.stats.govt.nz/Census/2006CensusHomePage.aspx.

Dunnet, G.M. (1956). A live-trapping study of the brush-tailed possum Trichosurus

vulpecula Kerr (Marsupialia). Wildlife Research, 1, 1-18.

Dunning, J.B., Danielson, B.J. & Pulliam, H.R. (1992). Ecological processes that affect

populations in complex landscapes. Oikos, 65, 169-175.

Durand, E., Jay, F., Gaggiotti, O.E. & Francois, O. (2009). Spatial inference of

admixture proportions and secondary contact zones. Molecular Biology and

Evolution, 26, 1963-1973.

References

172

Dussault, C., Courtois, R., Ouellet, J.P. & Huot, J. (1999). Evaluation of GPS telemetry

collar performance for habitat studies in the boreal forest. Wildlife Society

Bulletin, 27, 965-972.

Dussault, C., Ouellet, J.P., Courtois, R., Huot, J., Breton, L. & Jolicoeur, H. (2005).

Linking moose habitat selection to limiting factors. Ecography, 28, 619-628.

Ebenhard, T. (1988). Introduced birds and mammals and their ecological effects.

Swedish Wildlife Research, 13, 1-107.

Edenius, L. (1997). Field test of a GPS location system for moose Alces alces under

Scandinavian boreal conditions. Wildlife Biology, 3, 39-43.

Efford, M. (1991). A review of possum dispersal. DSIR Land Resources Scientific

Report 23, pp. 74. Dunedin.

Efford, M. (1998). Demographic consequences of sex-biased dispersal in a population

of brushtail possums. Journal of Animal Ecology, 67, 503-517.

Efford, M. (2000). Possum density, population structure, and dynamics. The brushtail



Ekdahl, M.O., Smith, B.L. & Money, D.F. (1970). Tuberculosis in some wild and feral

animals in New Zealand. New Zealand Veterinary Journal, 18, 44-45.

El-Rabbany, A. (2006). Introduction to GPS: The Global Positioning System, 2nd edn.

Artech House Inc., Norwood, MA.

Eldridge, M.D.B., Kinnear, J.E. & Onus, M.L. (2001). Source population of dispersing

rock-wallabies (Petrogale lateralis) identified by assignment tests on multilocus

genotypic data. Molecular Ecology, 10, 2867-2876.

Elton, C.S. (1958). The ecology of invasions by animals and plants. Methuen, London.

Erickson, W.P., McDonald, T.L., Gerow, K.G., Howlin, S. & Kern, J.W. (2001).

Statistical issues in resource selection studies with radio-marked animals. Radio

tracking and animal populations (eds J.J. Millspaugh & J.M. Marzluff), pp.

210-242. Academic Press, San Diego.

ESRI (2009). ArcGIS. Environmental Systems Resource Institute, Redlands, CA.

Etter, D.R., Hollis, K.M., Van Deelen, T.R., Ludwig, D.R., Chelsvig, J.E., Anchor,

C.L. & Warner, R.E. (2002). Survival and movements of white-tailed deer in

suburban Chicago, Illinois. Journal of Wildlife Management, 66, 500-510.

Evanno, G., Regnaut, S. & Goudet, J. (2005). Detecting the number of clusters of

individuals using the software STRUCTURE: A simulation study. Molecular

Ecology, 14, 2611-2620.

Evans, K.L., Gaston, K.J., Frantz, A.C., Simeoni, M., Sharp, S.P., McGowan, A.,

Dawson, D.A., Walasz, K., Partecke, J., Burke, T. & Hatchwell, B.J. (2009).

References

173

Independent colonization of multiple urban centres by a formerly forest

specialist bird species. Proceedings of the Royal Society B-Biological Sciences,

276, 2403-2410.

Evans, M.C. (1992). Diet of the brushtail possum Trichosurus vulpecula (Marsupialia:

Phalangeridae) in central Australia. Australian Mammalogy, 15, 25-30.

Excoffier, L. & Lischer, H.E.L. (2010). Arlequin suite ver 3.5: A new series of

programs to perform population genetics analyses under Linux and Windows.

Molecular Ecology Resources, 10, 564-567.

Eyre, T.J. (2004). Distribution and conservation status of the possums and gliders of

southern Queensland. The biology of Australian possums and gliders (eds R.L.

Goldingay & S.M. Jackson), pp. 1-25. Surrey Beatty & Sons Pty Limited,

NSW.

Faeth, S.H., Bang, C. & Saari, S. (2011). Urban biodiversity: Patterns and mechanisms.

Annals of the New York Academy of Sciences, 1223, 69-81.

Fahrig, L. (2003). Effects of habitat fragmentation on biodiversity. Annual Review of

Ecology Evolution and Systematics, 34, 487-515.

Falush, D., Stephens, M. & Pritchard, J.K. (2003). Inference of population structure

using multilocus genotype data: Linked loci and correlated allele frequencies.

Genetics, 164, 1567-1587.

Ferguson, S.H., Bergerud, A.T. & Ferguson, R. (1988). Predation risk and habitat

selection in the persistence of a remnant caribou population. Oecologia, 76,

236-245.

Fielding, A.H. & Bell, J.F. (1997). A review of methods for the assessment of

prediction errors in conservation presence/absence models. Environmental


Fitzgerald, A.E. (1976). Diet of the opossum Trichosurus vulpecula (Kerr) in the

Orongorongo Valley, Wellington, New Zealand, in relation to food-plant

availability. New Zealand Journal of Zoology, 3, 399-419.

Fitzgerald, A.E. (1984). Diet of the opossum (Trichosurus vulpecula) in three

Tasmanian forest types and its relevance to the diet of possums in New Zealand

forests. Possums and gliders (eds A.P. Smith & I.D. Hume), pp. 137-143.

Surrey Beatty in association with the Australian Mammal Society, Sydney.

Fleming, C.A. (1979). The geological history of New Zealand and its life. Auckland

University Press, Auckland.

Forman, R.T.T. & Alexander, L.E. (1998). Roads and their major ecological effects.

Annual Review of Ecology and Systematics, 29, 207-231.

Frair, J.L., Nielsen, S.E., Merrill, E.H., Lele, S.R., Boyce, M.S., Munro, R.H.M.,

Stenhouse, G.B. & Beyer, H.L. (2004). Removing GPS collar bias in habitat

selection studies. Journal of Applied Ecology, 41, 201-212.

References

174

Frair, J.L., Fieberg, J., Hebblewhite, M., Cagnacci, F., DeCesare, N.J. & Pedrotti, L.

(2010). Resolving issues of imprecise and habitat-biased locations in ecological

analyses using GPS telemetry data. Philosophical Transactions of the Royal

Society B-Biological Sciences, 365, 2187-2200.

Francois, O. & Durand, E. (2010). The state of the field: Spatially explicit Bayesian

clustering models in population genetics. Molecular Ecology Resources, 10,

773-784.

Frankham, R., Ballou, J.D. & Briscoe, D.A. (2010). Introduction to conservation

genetics, 2nd edn. Cambridge University Press, Cambridge.

Freeland, W.J. & Winter, J.W. (1975). Evolutionary consequences of eating:

Trichosurus vulpecula (marsupialia) and the genus Eucalyptus. Journal of

Chemical Ecology, 1, 439-455.

Freeman, C. & Buck, O. (2003). Development of an ecological mapping methodology

for urban areas in New Zealand. Landscape and Urban Planning, 63, 161-173.

Freeman, E.A. & Moisen, G. (2008). PresenceAbsence: An R package for presence

absence analysis. Journal of Statistical Software, 23, 1-31.

Galanti, V., Tosi, G., Rossi, R. & Foley, C. (2000). The use of GPS radio-collars to

track elephants (Loxodonta africana) in the Tarangire National Park (Tanzania).

Hystrix, 11, 27-37.

Galanti, V., Preatoni, D., Martinoti, A., Wauters, L.A. & Tosi, G. (2006). Space and

habitat use of the African elephant in the Tarangire-Manyara ecosystem,

Tanzania: Implications for conservation. Mammalian Biology, 71, 99-114.

Ganzhorn, J.U. (2002). Distribution of a folivorous lemur in relation to seasonally

varying food resources: Integrating quantitative and qualitative aspects of food

characteristics. Oecologia, 131, 427-435.

Gardner-Santana, L.C., Norris, D.E., Fornadel, C.M., Hinson, E.R., Klein, S.L. &

Glass, G.E. (2009). Commensal ecology, urban landscapes, and their influence

on the genetic characteristics of city-dwelling Norway rats (Rattus norvegicus).


Garton, E.O., Wisdom, M.J., Leban, F.A. & Johnson, B.K. (2001). Experimental design

for radiotelemetry studies. Radio tracking and animal populations (eds J.J.

Millspaugh & J.M. Marzluff), pp. 15-42. Academic Press, San Diego.

Gau, R.J., Mulders, R., Ciarniello, L.J., Heard, D.C., Chetkiewicz, C.L.B., Boyce, M.,

Munro, R., Stenhouse, G., Chruszcz, B., Gibeau, M.L., Milakovic, B. & Parker,

K.L. (2004). Uncontrolled field performance of Televilt GPS-SimplexTM

collars

on grizzly bears in western and northern Canada. Wildlife Society Bulletin, 32,

693-701.

Gehrt, S.D., Anchor, C. & White, L.A. (2009). Home range and landscape use of

coyotes in a metropolitan landscape: Conflict or coexistence? Journal of

Mammalogy, 90, 1045-1057.

References

175

Gelman, A. & Hill, J. (2007). Data analysis using regression and

multilevel/hierarchical models. Cambridge University Press, New York.

Gerlach, G. & Musolf, K. (2000). Fragmentation of landscape as a cause for genetic

subdivision in bank voles. Conservation Biology, 14, 1066-1074.

Gese, E.M., Morey, P.S. & Gehrt, S.D. (2012). Influence of the urban matrix on space

use of coyotes in the Chicago metropolitan area. Journal of Ethology, 30, 413-

425.

Gibson, L.A., Wilson, B.A., Cahill, D.M. & Hill, J. (2004). Spatial prediction of rufous

bristlebird habitat in a coastal heathland: A GIS-based approach. Journal of

Applied Ecology, 41, 213-223.

Gillies, C.S. & St Clair, C.C. (2010). Functional responses in habitat selection by

tropical birds moving through fragmented forest. Journal of Applied Ecology,

47, 182-190.

Girard, I., Dussault, C., Ouellet, J.P., Courtois, R. & Caron, A. (2006). Balancing

number of locations with number of individuals in telemetry studies. Journal of

Wildlife Management, 70, 1249-1256.

Gleeson, D., Harman, H. & Armstrong, T. (2006). Genetics of invasive species in New

Zealand. Biological invasions in New Zealand (eds R.B. Allen & W.G. Lee), pp.

103-118. Springer-Verlag, Berlin.

Glen, A.S., Byrom, A.E., Pech, R.P., Cruz, J., Schwab, A., Sweetapple, P.J., Yockney,

I., Nugent, G., Coleman, M. & Whitford, J. (2012). Ecology of brushtail

possums in a New Zealand dryland ecosystem. New Zealand Journal of

Ecology, 36, 29-37.

Goddard, M.A., Dougill, A.J. & Benton, T.G. (2010). Scaling up from gardens:

Biodiversity conservation in urban environments. Trends in Ecology &

Evolution, 25, 90-98.

Gompper, M.E., Kays, R.W., Ray, J.C., Lapoint, S.D., Bogan, D.A. & Cryan, J.R.

(2006). A comparison of noninvasive techniques to survey carnivore

communities in northeastern North America. Wildlife Society Bulletin, 34, 1142-

1151.

Gormley, A.M., Forsyth, D.M., Griffioen, P., Lindeman, M., Ramsey, D.S.L., Scroggie,

M.P. & Woodford, L. (2011). Using presence-only and presence-absence data to

estimate the current and potential distributions of established invasive species.

Journal of Applied Ecology, 48, 25-34.

Goudet, J. (2001). FSTAT, A program to estimate and test gene diversities and fixation

indices (version 2.9.3). Available from

http://www.unil.ch/izea/softwares/fstat.html.

Goudet, J., Perrin, N. & Waser, P. (2002). Tests for sex-biased dispersal using bi-

parentally inherited genetic markers. Molecular Ecology, 11, 1103-1114.

References

176

Graves, T.A. & Waller, J.S. (2006). Understanding the causes of missed global

positioning system telemetry fixes. Journal of Wildlife Management, 70, 844-

851.

Gray, T.N.E., Phan, C. & Long, B. (2010). Modelling species distribution at multiple

spatial scales: Gibbon habitat preferences in a fragmented landscape. Animal


Green, W.Q. (1984). A review of ecological studies relevant to management of the

common brushtail possum. Possums and gliders (eds A.P. Smith & I.D. Hume),

pp. 483-499. Surrey Beatty in association with the Australian Mammal Society,

Sydney.

Green, W.Q. & Coleman, J.D. (1986). Movement of possums (Trichosurus vulpecula)

between forest and pasture in Westland, New Zealand: Implications for bovine

tuberculosis transmission. New Zealand Journal of Ecology, 9, 57-69.

Gregory, R.D. & Ballie, S.R. (1998). Large-scale habitat use of some declining British

birds. Journal of Applied Ecology, 35, 785-799.

Grimm, N.B., Faeth, S.H., Golubiewski, N.E., Redman, C.L., Wu, J., Bai, X. & Briggs,

J.M. (2008). Global change and the ecology of cities. Science, 319, 756-760.

Grinder, M.I. & Krausman, P.R. (2001). Home range, habitat use, and nocturnal

activity of coyotes in an urban environment. Journal of Wildlife Management,

65, 887-898.

Groves, C.P. (2005). Order Diprotodontia. Mammal species of the world: A taxonomic

and geographic reference (eds D.E. Wilson & D.M. Reeder), pp. 43-70. John

Hopkins University Press, Baltimore.

Grueber, C.E., Maxwell, J.M. & Jamieson, I.G. (2012). Are introduced takahe

populations on offshore islands at carrying capacity? Implications for genetic

management. New Zealand Journal of Ecology, 36, 223-227.

Gu, W.D. & Swihart, R.K. (2004). Absent or undetected? Effects of non-detection of

species occurrence on wildlife-habitat models. Biological Conservation, 116,

195-203.

Guisan, A. & Thuiller, W. (2005). Predicting species distribution: Offering more than

simple habitat models. Ecology Letters, 8, 993-1009.

Haines, A.M., Grassman, L.I., Tewes, M.E. & Janecka, J.E. (2006). First ocelot

(Leopardus pardalis) monitored with GPS telemetry. European Journal of


Hampton, J.O., Spencer, P.B.S., Alpers, D.L., Twigg, L.E., Woolnough, A.P., Doust, J.,

Higgs, T. & Pluske, J. (2004). Molecular techniques, wildlife management and

the importance of genetic population structure and dispersal: A case study with

feral pigs. Journal of Applied Ecology, 41, 735-743.

References

177

Handcock, M.S. & Stein, M.L. (1993). A Bayesian analysis of kriging. Technometrics,

35, 403-410.

Hansen, H., Hess, S.C., Cole, D. & Banko, P.C. (2007). Using population genetic tools

to develop a control strategy for feral cats (Felis catus) in Hawai ' i. Wildlife

Research, 34, 587-596.

Hansen, M.C. & Riggs, R.A. (2008). Accuracy, precision, and observation rates of

global positioning system telemetry collars. Journal of Wildlife Management,

72, 518-526.

Hardy, O.J. & Vekemans, X. (1999). Isolation by distance in a continuous population:

Reconciliation between spatial autocorrelation analysis and population genetics

models. Heredity, 83, 145-154.

Harper, M.J. (2005). Home range and den use of common brushtail possums

(Trichosurus vulpecula) in urban forest remnants. Wildlife Research, 32, 681-

687.

Harper, M.J., McCarthy, M.A. & van der Ree, R. (2008). Resources at the landscape

scale influence possum abundance. Austral Ecology, 33, 243-252.

Harris, S. (1981). The food of suburban foxes (Vulpes vulpes), with special reference to

London. Mammal Review, 11, 151-168.

Harris, S., Cresswell, W.J., Forde, P.G., Trewhella, W.J., Woollard, T. & Wray, S.

(1990). Home-range analysis using radio-tracking data - A review of problems

and techniques particularly as applied to the study of mammals. Mammal

Review, 20, 97-123.

Hauser, C.E. & McCarthy, M.A. (2009). Streamlining 'search and destroy': Cost-

effective surveillance for invasive species management. Ecology Letters, 12,

683-692.

Hazlitt, S.L., Goldizen, A.W. & Eldridge, M.D.B. (2006). Significant patterns of

population genetic structure and limited gene flow in a threatened macropodid

marsupial despite continuous habitat in southeast Queensland, Australia.

Conservation Genetics, 7, 675-689.

Hebblewhite, M., Percy, M. & Merrill, E.H. (2007). Are all global positioning system

collars created equal? Correcting habitat-induced bias using three brands in the

Central Canadian Rockies. Journal of Wildlife Management, 71, 2026-2033.

Hebblewhite, M. & Haydon, D.T. (2010). Distinguishing technology from biology: A

critical review of the use of GPS telemetry data in ecology. Philosophical

Transactions of the Royal Society B, 365, 2303-2312.

Hemson, G., Johnson, P., South, A., Kenward, R., Ripley, R. & MacDonald, D. (2005).

Are kernels the mustard? Data from global positioning system (GPS) collars

suggests problems for kernel home-range analyses with least-squares cross-

validation. Journal of Animal Ecology, 74, 455-463.

References

178

Higuchi, H., Ozaki, K., Fijita, G., Minton, J., Ueta, M., Soma, M. & Mita, N. (1996).

Satellite tracking of White-naped crane migration and the importance of the

Korean demilitarized zone. Conservation Biology, 10, 806-812.

Hill, N.J., Deane, E.M. & Power, M.L. (2008). Prevalence and genetic characterization

of Cryptosporidium isolates from common brushtail possums (Trichosurus

vulpecula) adapted to urban settings. Applied and Environmental Microbiology,

74, 5549-5555.

Hines, J.E. (2006). PRESENCE2: Software to estimate patch occupancy and related

parameters. USGS-PWRC,

http://www.mbr-pwrc.usgs.gov/software/presence.html.

Hines, J.E. (2011). PRESENCE analysis help forum.

http://www.phidot.org/forum/viewforum.php?f=11. Accessed 20.06.11.

Hirzel, A.H., Le Lay, G., Helfer, V., Randin, C. & Guisan, A. (2006). Evaluating the

ability of habitat suitability models to predict species presences. Ecological

Modelling, 199, 142-152.

Hobbs, R.J., Higgs, E. & Harris, J.A. (2009). Novel ecosystems: Implications for

conservation and restoration. Trends in Ecology & Evolution, 24, 599-605.

Hocking, G.J. (1981). The population ecology of the brush-tailed possum, Trichosurus

vulpecula (Kerr), in Tasmania. MSc thesis. University of Tasmania.

Hoffman, J.I. & Amos, W. (2005). Microsatellite genotyping errors: Detection

approaches, common sources and consequences for paternal exclusion.


Hofmann-Wellenhof, B., Lichtenegger, H. & Collins, J. (2001). Global positioning

system: Theory and practice, 5th edn. Springer-Velag, Wien, Germany.

Holt, R.D. (2003). On the evolutionary ecology of species' ranges. Evolutionary

Ecology Research, 5, 159-178.

Hope, D., Gries, C., Zhu, W.X., Fagan, W.F., Redman, C.L., Grimm, N.B., Nelson,

A.L., Martin, C. & Kinzig, A. (2003). Socioeconomics drive urban plant

diversity. Proceedings of the National Academy of Sciences of the United States

of America, 100, 8788-8792.

Horn, J.A., Mateus-Pinilla, N., Warner, R.E. & Heske, E.J. (2011). Home range, habitat

use, and activity patterns of free-roaming domestic cats. Journal of Wildlife

Management, 75, 1177-1185.

Horne, J.S., Garton, E.O., Krone, S.M. & Lewis, J.S. (2007). Analyzing animal

movements using Brownian bridges. Ecology, 88, 2354-2363.

Hosmer, D.W. & Lemeshow, S. (2000). Applied logistic regression. Wiley, New York.

References

179

How, R.A. (1981). Population parameters of two congeneric possums, Trichosurus

spp., in north eastern New South Wales. Australian Journal of Zoology, 29,

205-215.

Howald, G., Donlan, C.J., Galvan, J.P., Russell, J.C., Parkes, J., Samaniego, A., Wang,

Y., Veitch, D., Genovesi, P., Pascal, M., Saunders, A. & Tershy, B. (2007).

Invasive rodent eradication on islands. Conservation Biology, 21, 1258-1268.

Howald, G., Donlan, C.J., Faulkner, K.R., Ortega, S., Gellerman, H., Croll, D.A. &

Tershy, B.R. (2009). Eradication of black rats Rattus rattus from Anacapa

Island. Oryx, 44, 30-40.

Howard, W.E. (1967). Biological control of vertebrate pests. Proceedings of the 3rd

Vertebrate Pest Conference (ed. M.W. Cummings), pp. 137-157. San Francisco,

California.

Howlin, S., Erickson, W.P. & Nielsen, R.M. (2003). A validation technique for

assessing predictive abilities of resource selection functions Proceedings of the

symposium on resource selection methods and applications (ed. S. Huzurbazar),

pp. 40-51. Omnipress, Madison, Wisconsin, USA.

Hrbek, T., Farias, I.P., Crossa, M., Sampaio, I., Porto, J.I.R. & Meyer, A. (2005).

Population genetic analysis of Arapaima gigas, one of the largest freshwater

fishes of the Amazon basin: Implications for its conservation. Animal


Huck, M., Frantz, A.C., Dawson, D.A., Burke, T. & Roper, T.J. (2008). Low genetic

variability, female-biased dispersal and high movement rates in an urban

population of Eurasian badgers Meles meles. Journal of Animal Ecology, 77,

905-915.

Hughes, J.J., Ward, D. & Perrin, M.R. (1994). Predation risk and competition affect

habitat selection and activity of namib desert gerbils. Ecology, 75, 1397-1405.

Hulbert, I.A.R. & French, J. (2001). The accuracy of GPS for wildlife telemetry and

habitat mapping. Journal of Applied Ecology, 38, 869-878.

Hume, I.D. (1999). Marsupial nutrition. Cambridge University Press, Cambridge.

Hurford, A. (2009). GPS measurement error gives rise to spurious 180 degrees turning

angles and strong directional biases in animal movement data. Plos One, 4,

e5632. doi:5610.1371/journal.pone.0005632.

Innes, J., Crook, B. & Jansen, B.D. (1994). A time-lapse video camera system for

detecting predators at nests of forest birds: A trial with North Island Kokako.

Proceedings of the Resource Technology '94 Conference (ed. I. Bishop), pp.

439-448. University of Melbourne, Melbourne.

Iverson, J.B. (1978). The impact of feral cats and dogs on populations of the West

Indian rock iguana, Cyclura carinata. Biological Conservation, 14, 63-73.

References

180

Jakobsson, M. & Rosenberg, N.A. (2007). CLUMPP: A cluster matching and

permutation program for dealing with label switching and multimodality in

analysis of population structure. Bioinformatics, 23, 1801-1806.

Jamieson, I.G. (2011). Founder effects, inbreeding, and loss of genetic diversity in four

avian reintroduction programs. Conservation Biology, 25, 115-123.

Jeschke, J.M. (2008). Across islands and continents, mammals are more successful

invaders than birds. Diversity and Distributions, 14, 913-916.

Ji, W., Sarre, S.D., Aitken, N., Hankin, R.K.S. & Clout, M.N. (2001). Sex-biased

dispersal and a density-independent mating system in the Australian brushtail

possum, as revealed by minisatellite DNA profiling. Molecular Ecology, 10,

1527-1537.

Jiang, Z., Sugita, M., Kitahara, M., Takatsuki, S., Goto, T. & Yoshida, Y. (2008).

Effects of habitat feature, antenna position, movement, and fix interval on GPS

radio collar performance in Mount Fuji, central Japan. Ecological Research, 23,

581-588.

Jimenez-Valverde, A. & Lobo, J.M. (2007). Threshold criteria for conversion of

probability of species presence to either-or presence-absence. Acta Oecologica-

International Journal of Ecology, 31, 361-369.

Johansson, A., Karlsson, P. & Gyllensten, U. (2003). A novel method for automatic

genotyping of microsatellite markers based on parametric pattern recognition.

Human Genetics, 113, 316-324.

Johnson, B.K., Kern, J.W., Wisdom, M.J., Findholt, S.L. & Kie, J.G. (2000). Resource

selection and spatial separation of mule deer and elk during spring. Journal of


Johnson, D.H. (1980). The comparison of usage and availability measurements for

evaluating resource preference. Ecology, 61, 65-71.

Jolly, J.N. (1976). Habitat use and movements of the opossum (Trichosurus vulpecula)

in a pastoral habitat on Banks Peninsula. Proceedings of the New Zealand

Ecological Society, 23, 70-78.

Jones, H.P., Williamhenry, R., Howald, G.R., Tershy, B.R. & Croll, D.A. (2005).

Predation of artificial Xantus's murrelet (Synthliboramphus hypoleucus scrippsi)

nests before and after black rat (Rattus rattus) eradication. Environmental


Jones, M.E., Paetkau, D., Geffen, E. & Moritz, C. (2004). Genetic diversity and

population structure of Tasmanian devils, the largest marsupial carnivore.


Kaneko, Y., Maruyama, N. & Macdonald, D.W. (2006). Food habits and habitat

selection of suburban badgers (Meles meles) in Japan. Journal of Zoology, 270,

78-89.

References

181

Kenward, R.E., Walls, S.S., South, A.B. & Casey, N.M. (2008). Ranges8: For the

analysis of tracking and location data. Anatrack Ltd, Wareham, UK.

Kerle, A., Foulkes, J.N., Kimber, R.G. & Papenfus, D. (1992). The decline of the

brushtail possum, Trichosurus vulpecula (Kerr 1798), in arid Australia.

Rangeland Journal, 14, 107-127.

Kerle, A. (2001). Possums: The brushtails, ringtails and greater glider. University of

New South Wales Press Ltd, Sydney, Australia.

Kerle, A. (2004). A cautionary tale: Decline of the common brushtail possum

Trichosurus vulpecula and common ringtail possum Pseudocheirus peregrinus

in the woodlands of the western slopes and plains of New South Wales. The

biology of Australian possums and gliders (eds R.L. Goldingay & S.M.

Jackson), pp. 71-84. Surrey Beatty & Sons Pty Limited, Sydney.

Kerle, J.A. (1984). Variation in the ecology of Trichosurus: Its adaptive significance.

Possums and gliders (eds A.P. Smith & I.D. Hume), pp. 115-128. Surrey Beatty

in association with the Australian Mammal Society, Sydney.

Kernohan, B.J., Gitzen, R.A. & Millspaugh, J. (2001). Analysis of animal space use and

movements. Radio tracking and animal populations (eds J.J. Millspaugh & J.M.

Marzluff), pp. 127-166. Academic Press, San Diego.

Kertson, B.N. & Marzluff, J.M. (2011). Improving studies of resource selection by

understanding resource use. Environmental Conservation, 38, 18-27.

Key, G.E. & Woods, R.D. (1996). Spool-and-line studies on the behavioural ecology of

rats (Rattus spp.) in the Galapagos Islands. Canadian Journal of Zoology, 74,

733-737.

King, C.M. (2005). The handbook of New Zealand mammals, 2nd edn. Oxford

University Press, Melbourne.

King, C.M., Innes, J.G., Gleeson, D., Fitzgerald, N., Winstanley, T., O'Brien, B.,

Bridgman, L. & Cox, N. (2011). Reinvasion by ship rats (Rattus rattus) of forest

fragments after eradication. Biological Invasions, 13, 2391-2408.

King, T.M., Williams, M. & Lambert, D.M. (2000). Dams, ducks and DNA: Identifying

the effects of a hydro-electric scheme on New Zealand’s endangered blue duck.


Koehler, G.M. & Hornocker, M.G. (1991). Seasonal resource use among mountain

lions, bobcats, and coyotes. Journal of Mammalogy, 72, 391-396.

Kolbe, J.J., Glor, R.E., Schettino, L.R.G., Lara, A.C., Larson, A. & Losos, J.B. (2004).

Genetic variation increases during biological invasion by a Cuban lizard.

Nature, 431, 177-181.

Krajick, K. (2005). Winning the war against island invaders. Science, 310, 1410-1413.

References

182

Kranstauber, B., Kays, R., LaPoint, S.D., Wikelski, M. & Safi, K. (2012). A dynamic

Brownian bridge movement model to estimate utilization distributions for

heterogeneous animal movement. Journal of Animal Ecology, 81, 738-746.

Krebs, J.R. & Davies, N.B. (1997). Behavioural ecology: An evolutionary approach.

Blackwell Scientific Publications, Oxford.

Krummel, J.R., Gardner, R.H., Sugihara, G., Oneill, R.V. & Coleman, P.R. (1987).

Landscape patterns in a disturbed environment. Oikos, 48, 321-324.

Lam, M.K.-P., Hickson, R.E., Cowan, P.E. & Cooper, D.W. (2000). A major

histocompatibility (MHC) microsatellite locus in brushtail possums

(Trichosurus vulpecula). Online Journal of Veterinary Research, 4, 139-141.

Land, E.D., Shindle, D.B., Kawula, R.J., Benson, J.F., Lotz, M.A. & Onorato, D.P.

(2008). Florida panther habitat selection analysis of concurrent GPS and VHF

telemetry data. Journal of Wildlife Management, 72, 633-639.

Latch, E.K., Dharmarajan, G., Glaubitz, J.C. & Rhodes, O.E. (2006). Relative

performance of Bayesian clustering software for inferring population

substructure and individual assignment at low levels of population

differentiation. Conservation Genetics, 7, 295-302.

Laver, P.N. & Kelly, M.J. (2008). A critical review of home range studies. Journal of


Lawson, E.J. & Rodgers, A.R. (1997). Differences in home-range size computed in

commonly used software programs. Wildlife Society Bulletin, 25, 721-729.

le Mar, K. (2002). Spatial organisation and habitat selection patterns of three

marsupial herbivores within a patchy forest environment. PhD Thesis,

University of Tasmania.

Lecis, R., Pierpaoli, M., Biro, Z.S., Szemethy, L., Ragni, B., Vercillo, F. & Randi, E.

(2006). Bayesian analyses of admixture in wild and domestic cats (Felis

silvestris) using linked microsatellite loci. Molecular Ecology, 15, 119-131.

Lemmon, P.E. (1956). A spherical densiometer for estimating forest overstory density.

Forest Science, 2, 314-320.

Lepczyk, C.A., Mertig, A.G. & Liu, J.G. (2004). Landowners and cat predation across

rural-to-urban landscapes. Biological Conservation, 115, 191-201.

Leutert, A. (1988). Mortality, foliage loss, and possum browsing in southern rata

(Metrosideros umbellata) in Westland, New Zealand. New Zealand Journal of

Botany, 26, 7-20.

Lewis, J.S., Rachlow, J.L., Garton, E.O. & Vierling, L.A. (2007). Effects of habitat on

GPS collar performance: Using data screening to reduce location error. Journal

of Applied Ecology, 44, 663-671.

References

183

Lima, S.L. & Dill, L.M. (1990). Behavioral decisions made under the risk of predation:

A review and prospectus. Canadian Journal of Zoology, 68, 619-640.

Litt, M., Hauge, X. & Sharma, V. (1993). Shadow bands seen when typing

polymorphic dinucleotide repeats: Some causes and cures. Biotechniques, 15,

280-284.

Lizcano, D.J. & Cavelier, J. (2004). Using GPS collars to study mountain tapirs

(Tapirus pinchaque) in the central Andes of Colombia. Tapir Conservation, 13,

18-23.

Long, R.A., Muir, J.D., Rachlow, J.L. & Kie, J.G. (2009). A comparison of two

modeling approaches for evaluating wildlife-habitat relationships. Journal of


Long, R.A., Donovan, T.M., MacKay, P., Zielinski, W.J. & Buzas, J.S. (2011).

Predicting carnivore occurence with noninvasive surveys and occupancy

modeling. Landscape Ecology, 26, 327-340.

Loram, A., Tratalos, J., Warren, P.H. & Gaston, K.J. (2007). Urban domestic gardens

(X): The extent & structure of the resource in five major cities. Landscape

Ecology, 22, 601-615.

Lowe, A., Harris, S. & Ashton, P. (2004). Ecological genetics: Design, analysis, and

application. Blackwell Science Ltd, Oxford.

Luck, G.W. (2002). The habitat requirements of the rufous treecreeper (Climacteris

rufa). 2. Validating predictive habitat models. Biological Conservation, 105,

395-403.

Luniak, M. (2004). Synurbization - adaptation of animal wildlife to urban development.

Proceedings of the 4th International Symposium on Urban Wildlife

Conservation (eds W.W. Shaw, L.K. Harris & L. Vandruff), pp. 50-55.

University of Arizona, Tucson, Arizona.

MacArthur, R.H. (1972). Geographical ecology: Patterns in the distribution of species.

Harper & Row, New York.

Mack, R.N., Simberloff, D., Lonsdale, W.M., Evans, H., Clout, M. & Bazzaz, F.A.

(2000). Biotic invasions: Causes, epidemiology, global consequences, and

control. Ecological Applications, 10, 689-710.

MacKay, J.W.B., Russell, J.C. & Murphy, E.C. (2007). Eradicating house mice from

islands: Successes, failures and the way forward. Managing vertebrate invasive

species: Proceedings of an international symposium (eds G.W. Witmer, W.C.

Pitt & K.A. Fagerstone), pp. 294-304. United States Department of Agriculture,

Fort Collins.

MacKenzie, D.I., Nichols, J.D., Lachman, G.B., Droege, S., Royle, J.A. & Langtimm,

C.A. (2002). Estimating site occupancy rates when detection probabilities are

less than one. Ecology, 83, 2248-2255.

References

184

MacKenzie, D.I., Nichols, J.D., Hines, J.E., Knutson, M.G. & Franklin, A.B. (2003).

Estimating site occupancy, colonization, and local extinction when a species is

detected imperfectly. Ecology, 84, 2200-2207.

MacKenzie, D.I. & Bailey, L.L. (2004). Assessing the fit of site-occupancy models.

Journal of Agricultural Biological and Environmental Statistics, 9, 300-318.

MacKenzie, D.I., Nichols, J.D., Royle, J.A., Pollock, K.H., Bailey, L.L. & Hines, J.E.

(2006). Occupancy estimation and modeling: Inferring patterns and dynamics

of species occurrence. Elsevier Academic Press, San Diego, California.

Mackey, B.G. & Lindenmayer, D.B. (2001). Towards a hierarchical framework for

modelling the spatial distribution of animals. Journal of Biogeography, 28,

1147-1166.

MacLennan, D.G. (1984). The feeding behaviour and activity patterns of the brushtail

possum, Trichosurus vulpecula, in an open eucalypt woodland in southeast

Queensland. Possums and gliders (eds A.P. Smith & I.D. Hume), pp. 155-161.

Surrey Beatty in association with the Australian Mammal Society, Sydney.

Magle, S.B., Hunt, V.M., Vernon, M. & Crooks, K.R. (2012). Urban wildlife research:

Past, present, and future. Biological Conservation, 155, 23-32.

Manel, S., Williams, H.C. & Ormerod, S.J. (2001). Evaluating presence-absence

models in ecology: The need to account for prevalence. Journal of Applied

Ecology, 38, 921-931.

Manel, S., Schwartz, M.K., Luikart, G. & Taberlet, P. (2003). Landscape genetics:

Combining landscape ecology and population genetics. Trends in Ecology &

Evolution, 18, 189-197.

Manly, B.F.J., McDonald, L.L., Thomas, D.L., McDonald, T.L. & Erickson, W.P.

(2002). Resource selection by animals: Statistical design and analysis for field

studies, 2nd edn. Kluwer Academic Publishers, Dordrecht, The Netherlands.

Mantel, N. (1967). The detection of disease clustering and a generalized regression

approach. Cancer Research, 27, 209-220.

Marks, C.A. & Bloomfield, T.E. (1999). Distribution and density estimates for urban

foxes (Vulpes vulpes) in Melbourne: Implications for rabies control. Wildlife

Research, 26, 763-775.

Marks, C.A. & Bloomfield, T.E. (2006). Home-range size and selection of natal den

and diurnal shelter sites by urban red foxes (Vulpes vulpes) in Melbourne.


Marzluff, J.M., Vekasy, M.S. & Coody, C. (1994). Comparative accuracy of aerial and

ground telemetry locations of foraging raptors. Condor, 96, 447-454.

Marzluff, J.M., Knick, S.T. & Millspaugh, J. (2001). High-tech behavioural ecology:

Modeling the distribution of animal activities to better understand wildlife space

References

185

use and resource selection. Radio tracking and animal populations (eds J.J.

Millspaugh & J.M. Marzluff), pp. 309-326. Academic Press, San Diego.

Marzluff, J.M., Millspaugh, J.J., Hurvitz, P. & Handcock, M.S. (2004). Relating

resources to a probabilistic measure of space use: Forest fragments and Steller's

Jays. Ecology, 85, 1411-1427.

Mason, C.F. (2000). Thrushes now largely restricted to the built environment in eastern

England. Diversity and Distributions, 6, 189-194.

Mathieu, R., Freeman, C. & Aryal, J. (2007). Mapping private gardens in urban areas

using object-oriented techniques and very high-resolution satellite imagery.

Landscape and Urban Planning, 81, 179-192.

Matthews, A., Lunney, D., Waples, K. & Hardy, J. (2004). Brushtail possums:

"Champion of the suburbs" or "Our tormentors"? Urban wildlife: More than

meets the eye (eds D. Lunney & S. Burgin), pp. 159-168. Royal Zoological

Society of New South Wales, Sydney.

McClennen, N., Wigglesworth, R.R. & Anderson, S.H. (2001). The effect of suburban

and agricultural development on the activity patterns of coyotes (Canis latrans).

American Midland Naturalist, 146, 27-36.

McDonald-Madden, E., Akers, L.K., Brenner, D.J., Howell, S., Patullo, B.W. & Elgar,

M.A. (2000). Possums in the park: Efficient foraging under the risk of predation

or of competition? Australian Journal of Zoology, 48, 155-160.

McDowall, R. (1994). Gamekeepers for the nation: The story of New Zealand's

acclimatisation societies, 1861-1990. Canterbury University Press,

Christchurch.

McKay, G.M. & Winter, J.W. (1989). Phalangeridae. Fauna of Australia Volume 1B

mammalia (eds D.W. Walton & B.J. Richardson), pp. 636-651. AGPS

Canberra, Canberra.

McKelvey, K.S., Von Kienast, J., Aubry, K.B., Koehler, G.M., Maletzke, B.T., Squires,

J.R., Lindquist, E.L., Loch, S. & Schwartz, M.K. (2006). DNA analysis of hair

and scat collected along snow tracks to document the presence of Canada lynx.

Wildlife Society Bulletin, 34, 451-455.

McKinney, M.L. (2002). Urbanization, biodiversity, and conservation. Bioscience, 52,

883-890.

McKinney, M.L. (2006). Urbanization as a major cause of biotic homogenization.


McLennan, J.A., Potter, M.A., Robertson, H.A., Wake, G.C., Colbourne, R., Dew, L.,

Joyce, L., McCann, A.J., Miles, J., Miller, P.J. & Reid, J. (1996). Role of

predation in the decline of kiwi, Apteryx spp, in New Zealand. New Zealand


References

186

McNab, B.K. (1963). Bioenergetics and the determination of home range size.

American Naturalist, 97, 133-140.

Meads, M.J. (1976). Effects of opossum browsing on northern rata trees in the

Orongorongo Valley, Wellington, New Zealand. New Zealand Journal of

Zoology, 3, 127-139.

Meenken, D. (2005). Contagion and WaxTag possum monitoring. Available from

http://www.pestcontrolresearch.co.nz/docs-

monitoring/contagionandwaxtagmonitoring.pdf, Christchurch.

Merila, J., Bjorklund, M. & Baker, A.J. (1996). The successful founder: Genetics of

introduced Carduelis chloris (greenfinch) populations in New Zealand.

Heredity, 77, 410-422.

Merrill, S.B., Adams, L.G., Nelson, M.E. & Mech, L.D. (1998). Testing releasable GPS

radiocollars on wolves and white-tailed deer. Wildlife Society Bulletin, 26, 830-

835.

Metz, C.E. (1978). Basic principles of ROC analysis. Seminars in Nuclear Medicine, 8,

283-298.

Miller, J.R. (2005). Biodiversity conservation and the extinction of experience. Trends

in Ecology & Evolution, 20, 430-434.

Millspaugh, J.J., Nielson, R.M., McDonald, L., Marzluff, J.M., Gitzen, R.A.,

Rittenhouse, C.D., Hubbard, M.W. & Sheriff, S.L. (2006). Analysis of resource

selection using utilization distributions. Journal of Wildlife Management, 70,

384-395.

Mitrovski, P., Heinze, D.A., Broome, L., Hoffmann, A.A. & Weeks, A.R. (2007). High

levels of variation despite genetic fragmentation in populations of the

endangered mountain pygmy-possum, Burramys parvus, in alpine Australia.


Moen, R., Pastor, J., Cohen, Y. & Schwartz, C.C. (1996). Effects of moose movement

and habitat use on GPS collar performance. Journal of Wildlife Management,

60, 659-668.

Moen, R., Pastor, J. & Cohen, Y. (1997). Accuracy of GPS telemetry collar locations

with differential correction. Journal of Wildlife Management, 61, 530-539.

Moen, R., Pastor, J. & Cohen, Y. (2001). Effects of animal activity on GPS telemetry

location attempts. Alces, 37, 207-216.

Mohr, C.O. & Stumpf, W.A. (1966). Comparison of methods for calculating areas of

animal activity. Journal of Wildlife Management, 30, 293-&.

Moller, A.P., Diaz, M., Flensted-Jensen, E., Grim, T., Ibanez-Alamo, J.D., Jokimaki, J.,

Mand, R., Marko, G. & Tryjanowski, P. (2012). High urban population density

of birds reflects their timing of urbanization. Oecologia, 170, 867-875.

References

187

Moorcroft, P.R., Lewis, M.A. & Crabtree, R.L. (1999). Home range analysis using a

mechanistic home range model. Ecology, 80, 1656-1665.

Moorcroft, P.R., Lewis, M.A. & Crabtree, R.L. (2006). Mechanistic home range

models capture spatial patterns and dynamics of coyote territories in

Yellowstone. Proceedings of the Royal Society B-Biological Sciences, 273,

1651-1659.

Moorcroft, P.R. & Barnett, A. (2008). Mechanistic home range models and resource

selection analysis: A reconciliation and unification. Ecology, 89, 1112-1119.

Mortberg, U. & Wallentinus, H.G. (2000). Red-listed forest bird species in an urban

environment: Assessment of green space corridors. Landscape and Urban

Planning, 50, 215-226.

Murcia, C. (1995). Edge effects in fragmented forests: Implications for conservation.

Trends in Ecology & Evolution, 10, 58-62.

Myers, J.H., Savoie, A. & van Randen, E. (1998). Eradication and pest management.

Annual Review of Entomology, 43, 471-491.

Myers, J.H., Simberloff, D., Kuris, A.M. & Carey, J.R. (2000). Eradication revisited:

Dealing with exotic species. Trends in Ecology & Evolution, 15, 316-320.

Nathan, R., Getz, W.M., Revilla, E., Holyoak, M., Kadmon, R., Saltz, D. & Smouse,

P.E. (2008). A movement ecology paradigm for unifying organismal movement

research. Proceedings of the National Academy of Sciences, 105, 52-59.

National Possum Control Agencies (2010). Possum population monitoring using the

WaxTag method. National Possum Control Agencies, Wellington.

Nei, M., Maruyama, T. & Chakraborty, R. (1975). The bottleneck effect and genetic

variability in populations. Evolution, 29, 1-10.

Neigel, J.E. (1997). A comparison of alternative strategies for estimating gene flow

from genetic markers. Annual Review of Ecology and Systematics, 28, 105-128.

Nielsen, R.M., Sawyer, H. & McDonald, T.L. (2011). BBMM: Brownian bridge

movement model for estimating the movement path of an animal using discrete

location data. R Foundation for Statistical Computing, Vienna, Austria.

http://CRAN.Rproject.org/package=BBMM.

Nielson, S.E., Boyce, M.S., Stenhouse, G.B. & Munro, R.H.M. (2002). Modeling

grizzly bear habitats in the Yellowhead Ecosystem of Alberta: Taking

autocorrelation seriously. Ursus, 13, 45-56.

Noel, S., Ouellet, M., Galois, P. & Lapointe, F.J. (2007). Impact of urban fragmentation

on the genetic structure of the eastern red-backed salamander. Conservation

Genetics, 8, 599-606.

References

188

Nogales, M., Martin, A., Tershy, B.R., Donlan, C.J., Witch, D., Puerta, N., Wood, B. &

Alonso, J. (2004). A review of feral cat eradication on islands. Conservation

Biology, 18, 310-319.

Norbury, G.L., Norbury, D.C. & Heyward, R.P. (1998). Space use and denning

behaviour of wild ferrets (Mustela furo) and cats (Felis catus). New Zealand


North, S.G., Bullock, D.J. & Dulloo, M.E. (1994). Changes in the vegetation and reptile

populations on Round Island, Mauritius, following eradication of rabbits.


Nsubuga, A.M., Holzman, J., Chemnick, L.G. & Ryder, O.A. (2010). The cryptic

genetic structure of the North American captive gorilla population.


Nugent, G., Fraser, K.W. & Sweetapple, P.J. (1997). Comparison of red deer and

possum diets and impacts in podocarp-hardwood forest, Waihaha Catchment,

Pureora Conservation Park. Science for Conservation, Wellington.

Nugent, G., Sweetapple, P., Coleman, J. & Suisted, P. (2000). Possum feeding patterns:

Dietary tactics of a reluctant folivore. The brushtail possum: Biology, impact

and management of an introduced marsupial (ed. T.L. Montague), pp. 10-23.

Manaaki Whenua Press, Lincoln, New Zealand.

Nugent, G., Fraser, W. & Sweetapple, P. (2001). Top down or bottom up? Comparing

the impacts of introduced arboreal possums and 'terrestrial' ruminants on native

forests in New Zealand. Biological Conservation, 99, 65-79.

O'Neil, B.D. & Pharo, H.J. (1995). The control of bovine tuberculosis in New Zealand.

New Zealand Veterinary Journal, 43, 249-255.

Ogilvie, S.C., Paterson, A.M., Ross, J.G. & Thomas, M.D. (2006). Improving

techniques for the WaxTag possum (Trichosurus vulpecula) monitoring index.

New Zealand Plant Protection, 59, 28-33.

Oppel, S., Beaven, B.M., Bolton, M., Vickery, J. & Bodey, T.W. (2010). Eradication of

invasive mammals on islands inhabited by humans and domesticated animals.

Conservation Biology, 25, 232-240.

Osborne, P.E., Alonso, J.C. & Bryant, R.G. (2001). Modelling landscape-scale habitat

use using GIS and remote sensing: A case study with great bustards. Journal of

Applied Ecology, 38, 458-471.

Ostfeld, R.S. & Keesing, F. (2000). Pulsed resources and community dynamics of

consumers in terrestrial ecosystems. Trends in Ecology & Evolution, 15, 232-

237.

Otis, D.L. & White, G.C. (1999). Autocorrelation of location estimates and the analysis

of radiotracking data. Journal of Wildlife Management, 63, 1039-1044.

References

189

Owen, H.J. & Norton, D.A. (1995). The diet of introduced brushtail possums

Trichosurus vulpecula in a low diversity New Zealand Nothofagus forest and

possible implications for conservation management. Biological Conservation,

71, 339-345.

Paetkau, D., Waits, L.P., Clarkson, P.L., Craighead, L. & Strobeck, C. (1997). An

empirical evaluation of genetic distance statistics using microsatellite data from

bear (Ursidae) populations. Genetics, 147, 1943-1957.

Paetkau, D., Slade, R., Burden, M. & Estoup, A. (2004). Genetic assignment methods

for the direct, real-time estimation of migration rate: A simulation-based

exploration of accuracy and power. Molecular Ecology, 13, 55-65.

Palsson, S. (2000). Microsatellite variation in Daphnia pulex from both sides of the

Baltic Sea. Molecular Ecology, 9, 1075-1088.

Parker, I.M., Simberloff, D., Lonsdale, W.M., Goodell, K., Wonham, M., Kareiva,

P.M., Williamson, M.H., Von Holle, B., Moyle, P.B., Byers, J.E. & Byers, L.G.

(1999). Impact: Toward a framework for understanding the ecological effects of

invaders. Biological Invasions, 1, 3-19.

Parkes, J. & Murphy, E. (2003). Management of introduced mammals in New Zealand.

New Zealand Journal of Zoology, 30, 335-359.

Partecke, J., Gwinner, E. & Bensch, S. (2006). Is urbanisation of European blackbirds

(Turdus merula) associated with genetic differentiation? Journal of

Ornithology, 147, 549-552.

Pass, G.J., McLean, S. & Stupans, I. (1999). Induction of xenobiotic metabolising

enzymes in the common brushtail possum, Trichosurus vulpecula, by

Eucalyptus terpenes. Comparative Biochemistry and Physiology C-

Pharmacology Toxicology & Endocrinology, 124, 239-246.

Paterson, B.M., Morris, R.S., Weston, J. & Cowan, P.E. (1995). Foraging and denning

patterns of brushtail possums, and their possible relationship to contact with

cattle and the transmission of bovine tuberculosis. New Zealand Veterinary

Journal, 43, 281-288.

Patterson, T.A., Thomas, L., Wilcox, C., Ovaskainen, O. & Matthiopoulos, J. (2008).

State-space models of individual animal movement. Trends in Ecology &

Evolution, 23, 87-94.

Paull, D. & Kerle, A. (2004). Recent decline of common brushtail and common ringtail

possums in the Pilliga forest, New South Wales? The biology of Australian

possums and gliders (eds R.L. Goldingay & S.M. Jackson), pp. 85-90. Surrey

Beatty & Sons Pty Limited, Sydney.

Payton, I. (2000). Damage to native forests. The brushtail possum: Biology, impact and

management of an introduced marsupial (ed. T.L. Montague), pp. 111-125.

Manaaki Whenua Press, Lincoln, New Zealand.

References

190

Peakall, R. & Smouse, P.E. (2006). GENALEX 6: Genetic analysis in Excel.

Population genetic software for teaching and research. Molecular Ecology

Notes, 6, 288-295.

Pearce, J. & Ferrier, S. (2000). Evaluating the predictive performance of habitat models

developed using logistic regression. Ecological Modelling, 133, 225-245.

Pebsworth, P.A., Morgan, H.R. & Huffman, M.A. (2012). Evaluating home range

techniques: Use of Global Positioning System (GPS) collar data from chacma

baboons. Primates, 53, 345-355.

Pech, R., Byrom, A., Anderson, D., Thomson, C. & Coleman, M. (2010). The effect of

poisoned and notional vaccinated buffers on possum (Trichosurus vulpecula)

movements: Minimising the risk of bovine tuberculosis spread from forest to

farmland. Wildlife Research, 37, 282-292.

Petren, K. & Case, T.J. (1996). An experimental demonstration of exploitation

competition in an ongoing invasion. Ecology, 77, 118-132.

Pickett, K.N., Hik, D.S., Newsome, A.E. & Pech, R.P. (2005). The influence of

predation risk on foraging behaviour of brushtail possums in Australian

woodlands. Wildlife Research, 32, 121-130.

Pickett, S.T.A., Cadenasso, M.L., Grove, J.M., Groffman, P.M., Band, L.E., Boone,

C.G., Burch, W.R., Grimmond, C.S.B., Hom, J., Jenkins, J.C., Law, N.L.,

Nilon, C.H., Pouyat, R.V., Szlavecz, K., Warren, P.S. & Wilson, M.A. (2008).

Beyond urban legends: An emerging framework of urban ecology, as illustrated

by the Baltimore Ecosystem Study. Bioscience, 58, 139-150.

Piertney, S.B., MacColl, A.D.C., Bacon, P.J. & Dallas, J.F. (1998). Local genetic

structure in red grouse (Lagopus lagopus scoticus): Evidence from

microsatellite DNA markers. Molecular Ecology, 7, 1645-1654.

Pilot, M., Jedrzejewski, W., Branicki, W., Sidorovich, V.E., Jedrzejewska, B., Stachura,

K. & Funk, S.M. (2006). Ecological factors influence population genetic

structure of European grey wolves. Molecular Ecology, 15, 4533-4553.

Pimentel, D., Zuniga, R. & Morrison, D. (2005). Update on the environmental and

economic costs associated with alien-invasive species in the United States.

Ecological Economics, 52, 273-288.

Pinceel, J., Jordaens, K., Van Houtte, N., Bernon, G. & Backeljau, T. (2005).

Population genetics and identity of an introduced terrestrial slug: Arion

subfuscus s.l. in the North-East USA (Gastropoda, Pulmonata, Arionidae).

Genetica, 125, 155-171.

Piry, S., Alapetite, A., Cornuet, J.M., Paetkau, D., Baudouin, L. & Estoup, A. (2004).

GENECLASS2: A software for genetic assignment and first-generation migrant

detection. Journal of Heredity, 95, 536-539.

Podgorski, T., Bas, G., Jedrzejewska, B., Sonnichsen, L., Sniezko, S., Jedrzejewski, W.

& Okarma, H. (2013). Spatiotemporal behavioral plasticity of wild boar (Sus

References

191

scrofa) under contrasting conditions of human pressure: Primeval forest and

metropolitan area. Journal of Mammalogy, 94, 109-119.

Pope, L.C., Sharp, A. & Moritz, C. (1996). Population structure of the yellow-footed

rock-wallaby Petrogale xanthopus (Gray, 1854) inferred from mtDNA

sequences and microsatellite loci. Molecular Ecology, 5, 629-640.

Powell, R.A. (2000). Animal home ranges and territories and home range estimators.

Research techniques in animal ecology: Controversies and consequences (eds

L. Boitoni & T.K. Fuller), pp. 65-110. Columbia University Press, New York.

Powell, R.A. & Mitchell, M.S. (2012). What is a home range? Journal of Mammalogy,

93, 948-958.

Pracy, L.T. (1974). Introduction and liberation of the opossum (Trichosurus vulpecula)

into New Zealand., pp. 28. New Zealand Forest Service, Wellington.

Prange, S., Gehrt, S.D. & Wiggers, E.P. (2004). Influences of anthropogenic resources

on raccoon (Procyon lotor) movements and spatial distribution. Journal of

Mammalogy, 85, 483-490.

Pritchard, J.K., Stephens, M. & Donnelly, P. (2000). Inference of population structure

using multilocus genotype data. Genetics, 155, 945-959.

Queller, D.C. & Goodnight, K.F. (1989). Estimating relatedness using genetic markers.

Evolution, 43, 258-275.

R Development Core Team (2011). R: A language and environment for statistical

computing (version 14.1). R Foundation for Statistical Computing Vienna,

Austria. http://www.R-project.org.

Rambaut, A. & Drummond, A.J. (2009). Tracer v1.5. Available from

http://tree.bio.ed.ac.uk/software/tracer/.

Ramsey, D., Efford, M., Cowan, P. & Coleman, J. (2002). Factors influencing annual

variation in breeding by common brushtail possums (Trichosurus vulpecula) in

New Zealand. Wildlife Research, 29, 39-50.

Rannala, B. & Mountain, J.L. (1997). Detecting immigration by using multilocus

genotypes. Proceedings of the National Academy of Sciences of the United

States of America, 94, 9197-9201.

Rauzon, M.J. (2007). Island restoration: Exploring the past, anticipating the future.

Marine Ornithology, 35, 97-107.

Raymond, M. & Rousset, F. (1995). GENEPOP (Version 1.2): Population genetics

software for exact tests and ecumenicism. Journal of Heredity, 86, 248-249.

Recio, M.R., Mathieu, R., Maloney, R. & Seddon, P.J. (2010). First results of feral cats

(Felis catus) monitored with GPS collars in New Zealand. New Zealand Journal

of Ecology, 34, 288-296.

References

192

Recio, M.R., Mathieu, R., Denys, P., Sirguey, P. & Seddon, P.J. (2011). Lightweight

GPS-tags, one giant leap for wildlife tracking? An assessment approach. Plos

One, 6, e28225. doi:28210.21371/journal.pone.0028225.

Rempel, R.S., Rodgers, A.R. & Abraham, K.F. (1995). Performance of a GPS animal

location system under boreal forest canopy. Journal of Wildlife Management,

59, 543-551.

Rempel, R.S. & Rodgers, A.R. (1997). Effects of differential correction on accuracy of

a GPS animal location system. Journal of Wildlife Management, 61, 525-530.

Rettie, W.J. & Messier, F. (2000). Hierarchical habitat selection by woodland caribou:

Its relationship to limiting factors. Ecography, 23, 466-478.

Reunanen, P., Nikula, A., Monkkonen, M., Hurme, E. & Nivala, V. (2002). Predicting

occupancy for the Siberian flying squirrel in old-growth forest patches.


Riley, S.P.D., Sauvajot, R.M., Fuller, T.K., York, E.C., Kamradt, D.A., Bromley, C. &

Wayne, R.K. (2003). Effects of urbanization and habitat fragmentation on

bobcats and coyotes in southern California. Conservation Biology, 17, 566-576.

Riley, S.P.D. (2006). Spatial ecology of bobcats and gray foxes in urban and rural

zones of a national park. The Journal of Wildlife Management, 70, 1425-1435.

Rittenhouse, C.D., Millspaugh, J.J., Hubbard, M.W., Sheriff, S.L. & Dijak, W.D.

(2008). Resource selection by translocated three-toed box turtles in Missouri.


Robertson, B.C. & Gemmell, N.J. (2004). Defining eradication units to control invasive

pests. Journal of Applied Ecology, 41, 1042-1048.

Robertson, B.C. (2006). The role of genetics in kakapo recovery. Notornis, 53, 173-

183.

Robertson, B.C., Steeves, T.E., McBride, K.P., Goldstien, S.J., Williams, M. &

Gemmell, N.J. (2007). Phylogeography of the New Zealand blue duck

(Hymenolaimus malacorhynchos): Implications for translocation and species

recovery. Conservation Genetics, 8, 1431-1440.

Robinson, S.K. & Holmes, R.T. (1982). Forgaing behavior of forest birds: The

relationship among search tactics, diet, and habitat structure. Ecology, 63, 1918-

1931.

Rodgers, A.R., Rempel, R.S. & Abraham, K.F. (1996). A GPS-based telemetry system.

Wildlife Society Bulletin, 24, 559-566.

Rodgers, A.R. (2001). Tracking animals with GPS: The first 10 years. Tracking

animals with GPS (eds A.M. Sibbald & I.J. Gordon), pp. 1-10. The Macaulay

Land Use Research Institute, Aberdeen, Scotland.

References

193

Rodriguez, C., Torres, R. & Drummond, H. (2006). Eradicating introduced mammals

from a forested tropical island. Biological Conservation, 130, 98-105.

Rogers, G.M. & Leathwick, J.R. (1997). Factors predisposing forests to canopy

collapse in the southern Ruahine Range, New Zealand. Biological


Roitberg, B.D. & Mangel, M. (1997). Individuals on the landscape: Behavior can

mitigate landscape differences among habitats. Oikos, 80, 234-240.

Rollins, L.A., Woolnough, A.P. & Sherwin, W.B. (2006). Population genetic tools for

pest management: A review. Wildlife Research, 33, 251-261.

Rosatte, R., Kelly, P. & Power, M. (2011). Home range, movements, and habitat

utilization of striped skunk (Mephitis mephitis) in Scarborough, Ontario,

Canada: Disease management implications. The Canadian Field Naturalist,

125, 27-33.

Rose, A.B., Pekelharing, C.J. & Platt, K.H. (1992). Magnitude of canopy dieback and

implications for conservation of Southern rata-Kamahi (Metrosideris umbellata-

Weinmannia racemosa) forests, Central Westland, New Zealand. New Zealand


Rose, E., Nagel, P. & Haag-Wackernagel, D. (2005). Suitability of using the global

positioning system (GPS) for studying feral pigeons Columba livia in the urban

habitat. Bird Study, 52, 145-152.

Rosenberg, N.A. (2004). DISTRUCT: A program for the graphical display of

population structure. Molecular Ecology Notes, 4, 137-138.

Rosenzweig, M.L. (1991). Habitat selection and population interactions: The search for

mechanism. American Naturalist, 137, S5-S28.

Roshier, D.A., Doerr, V.A.J. & Doerr, E.D. (2008). Animal movement in dynamic

landscapes: Interaction between behavioural strategies and resource

distributions. Oecologia, 156, 465-477.

Rouco, C., Norbury, G.L., Smith, J., Bryom, A.E. & Pech, R.P. (2013). Population

density estimates of brushtail possums (Trichosurus vulpecula) in dry grassland

in New Zealand. New Zealand Journal of Ecology, 37, 12-17.

Rumble, M.A. & Lindzey, F. (1997). Effects of forest vegetation and topography on

global positioning system collars for elk. Resource Technology Institute

Symposium, 4, 492-501.

Rumble, M.A., Benkobi, L., Lindzey, F. & Gamo, R.S. (2001). Evaluating elk habitat

interactions with GPS collars. Tracking animals with GPS (eds A.M. Sibbald &

I.J. Gordon), pp. 11-17. The Macaulay Land Use Research Institute, Aberdeen,

Scotland.

References

194

Rutkowski, R., Rejt, L. & Szczuka, A. (2006). Analysis of microsatellite polymorphism

and genetic differentiation in urban and rural kestrels Falco tinnunculus (L.).

Polish Journal of Ecology, 54, 473-480.

Sadlier, R. (2000). Evidence of possums as predators of native animals. The brushtail



Sager-Fradkin, K.A., Jenkins, K.J., Hoffman, R.A., Happe, P.J., Beecham, J.J. &

Wright, R.G. (2007). Fix success and accuracy of global positioning system

collars in old-growth temperate coniferous forests. Journal of Wildlife

Management, 71, 1298-1308.

Samama, N. (2008). Global positioning: Technologies and performance. Wiley-

Interscience, Hoboken, New Jersey.

Samuel, M.D., Pierce, D.J. & Garton, E.O. (1985). Identifying areas of concentrated

use within the home range. Journal of Animal Ecology, 54, 711-719.

Samuel, M.D. & Fuller, M.R. (1996). Wildlife telemetry. Research and management

techniques for wildlife and habitats (ed. T.A. Bookhout), pp. 370-418. The

Wildlife Society Inc, Maryland.

San Jose, C. & Lovari, S. (1998). Ranging movements of female roe deer: Do home-

loving does roam to mate? Ethology, 104, 721-728.

Sanderson, G.C. (1966). The study of mammal movements: A review. Journal of


Sarre, S.D., Aitken, N., Clout, M.N., Ji, W., Robins, J. & Lambert, D.M. (2000).

Molecular ecology and biological control: The mating system of a marsupial

pest. Molecular Ecology, 9, 723-733.

Saunders, A. & Norton, D.A. (2001). Ecological restoration at mainland islands in New

Zealand. Biological Conservation, 99, 109-119.

Saunders, D.A., Hobbs, R.J. & Margules, C.R. (1991). Biological consequences of

ecosystem fragmentation: A review. Conservation Biology, 5, 18-32.

Saunders, G. & Bryant, H. (1988). The evaluation of a feral pig eradication program

during a simulated exotic disease outbreak. Australian Wildlife Research, 15,

73-81.

Sawyer, H., Nielson, R.M., Lindzey, F.G., Keith, L., Powell, J.H. & Abraham, A.A.

(2007). Habitat selection of Rocky Mountain elk in a nonforested environment.


Sawyer, H., Kauffman, M.J., Nielson, R.M. & Horne, J.S. (2009). Identifying and

prioritizing ungulate migration routes for landscape-level conservation.


References

195

Schmuki, C., Vorburger, C., Runciman, D., Maceachern, S. & Sunnucks, P. (2006).

When log-dwellers meet loggers: Impacts of forest fragmentation on two

endemic log-dwelling beetles in southeastern Australia. Molecular Ecology, 15,

1481-1492.

Schuelke, M. (2000). An economic method for the fluorescent labeling of PCR

fragments. Nature Biotechnology, 18, 233-234.

Seaman, D.E., Millspaugh, J.J., Kernohan, B.J., Brundige, G.C., Raedeke, K.J. &

Gitzen, R.A. (1999). Effects of sample size on kernel home range estimates.


Seegar, W.S., Cutchis, P.N., Fuller, M.R., Suter, J.J., Bhatnagar, V. & Wall, J.G.

(1996). Fifteen years of satellite tracking development and application to

wildlife research and conservation. Johns Hopkins APL Technical Digest, 17,

401-411.

Shea, K. & Chesson, P. (2002). Community ecology theory as a framework for

biological invasions. Trends in Ecology & Evolution, 17, 170-176.

Shigesada, N. & Kawasaki, K. (1997). Biological invasions: Theory and practice.

Oxford University Press, Oxford.

Silverman, B.W. (1986). Density estimation for statistics and data analysis. Chapman

and Hall, London.

Sinclair, E.A. (2001). Phylogeographic variation in the quokka, Setonix brachyurus

(Marsupialia : Macropodidae): Implications for conservation. Animal


Skrondal, A. & Rabe-Hesketh, S. (2004). Generalized latent variable modeling:

Multilevel, longitudinal, and structural equation models. Chapman and Hall,

New York.

Smith, A.P. & Lee, A.K. (1984). The evolution of strategies for survival and

reproduction in possums and gliders. Possums and gliders (eds A.P. Smith &

I.D. Hume), pp. 17-33. Surrey Beatty in association with the Australian

Mammal Society, Sydney.

Smith, D.G., Shiinoki, E.K. & VanderWerf, E.A. (2006). Recovery of native species

following rat eradication on Mokoli'i Island, O'ahu, Hawai'i. Pacific Science,

60, 299-303.

Smith, H.T. & Engeman, R.M. (2002). An extraordinary raccoon, Procyon lotor,

density at an urban park. Canadian Field-Naturalist, 116, 636-639.

Sol, D., Bartomeus, I. & Griffin, A.S. (2012). The paradox of invasion in birds:

Competitive superiority or ecological opportunism? Oecologia, 169, 553-564.

Spong, G., Johansson, M. & Bjorklund, M. (2000). High genetic variation in leopards

indicates large and long-term stable effective population size. Molecular

Ecology, 9, 1773-1782.

References

196

SPSS Inc (2006). SPSS for Windows, Version 15.0. SPSS Inc, Chicago.

Statham, M. & Statham, H.L. (1997). Movements and habits of brushtail possums

(Trichosurus vulpecula Kerr) in an urban area. Wildlife Research, 24, 715-726.

Statistics New Zealand (2006). 2006 Census data: Statistics New Zealand. Available

from: http://www.stats.govt.nz/Census/2006CensusHomePage.aspx.

Stokely, J.M. (2005). The feasibility of utilizing the cellular infrastructure for urban

wildlife telemetry. Virginia Polytechnic Institute and State University, Virgina.

Storm, D.J., Nielsen, C.K., Schauber, E.M. & Woolf, A. (2007). Space use and survival

of white-tailed deer in an exurban landscape. Journal of Wildlife Management,

71, 1170-1176.

Stow, A.J., Minarovic, N., Eymann, J., Cooper, D.W. & Webley, L.S. (2006). Genetic

structure infers generally high philopatry and male-biased dispersal of brushtail

possums (Trichosurus vulpecula) in urban Australia. Wildlife Research, 33,

409-415.

Strohbach, M.W., Haase, D. & Kabisch, N. (2009). Birds and the city: Urban

biodiversity, land use, and socioeconomics. Ecology and Society, 14.

Sulkava, R. (2007). Snow tracking: A relevant method for estimating otter Lutra lutra

populations. Wildlife Biology, 13, 208-218.

Takami, Y., Koshio, C., Ishii, M., Fujii, H., Hidaka, T. & Shimizu, I. (2004). Genetic

diversity and structure of urban populations of Pieris butterflies assessed using

amplified fragment length polymorphism. Molecular Ecology, 13, 245-258.

Taylor, A.C. & Cooper, D.W. (1998). Microsatellite markers for the Phalangerid

marsupial, the common brushtail possum (Trichosurus vulpecula). Molecular

Ecology, 7, 1780-1782.

Taylor, A.C., Cowan, P.E., Lam, M.K., Harrison, G.A. & Cooper, D.W. (1999).

Genetic marker studies in New Zealand brushtail possum populations

(Trichosurus vulpecula). Advances in the biological control of possums (ed. G.

Sutherland), pp. 77-79. The Royal Society of New Zealand Miscellaneous

Series 56, Wellington.

Taylor, A.C., Cowan, P.E., Fricke, B.L. & Cooper, D.W. (2000). Genetic analysis of

the mating system of the common brushtail possum (Trichosurus vulpecula) in

New Zealand farmland. Molecular Ecology, 9, 869-879.

Taylor, A.C., Cowan, P.E., Fricke, B.L., Geddes, S., Hansen, B.D., Lam, M. & Cooper,

D.W. (2004). High microsatellite diversity and differential structuring among

populations of the introduced common brushtail possum, Trichosurus

vulpecula, in New Zealand. Genetical Research, 83, 101-111.

Taylor, P.D., Fahrig, L., Henein, K. & Merriam, G. (1993). Connectivity is a vital

element of landscape structure. Oikos, 68, 571-573.

References

197

Taylor, R.H. (1979). Predation on sooty terns at Raoul Island by rats and cats. Notornis,

26, 199-202.

Tchamba, M.N., Bauer, H. & DeIongh, H.H. (1995). Application of VHF-radio and

satellite telemetry techniques on elephants in northern Cameroon. African


Tew, T.E. & Macdonald, D.W. (1994). Dynamics of space use and male vigour

amongst wood mice, Apodemus sylvaticus, in the cereal ecosystem. Behavioral

Ecology and Sociobiology, 34, 337-345.

Thomas, D.L. & Taylor, E.J. (2006). Study designs and tests for comparing resource

use and availability II. Journal of Wildlife Management, 70, 324-336.

Thomas, M. (2011). Some notes on the identification of bite-marks on WaxTags. pp. 6.

Pest Control Research, Christchurch, New Zealand.

Thomas, M.D., Warburton, B. & Coleman, J.D. (1984). Brush-tailed possum

(Trichosurus vulpecula) movements about an erosion-control planting of

poplars. New Zealand Journal of Zoology, 11, 429-436.

Thomas, M.D., Morgan, D.R. & Maddigan, F. (2007). Accuracy of possum monitoring

using WaxTags. Pest Control Research Contract Report 2006/7.

Thomsen, M.S., Olden, J.D., Wernberg, T., Griffin, J.N. & Silliman, B.R. (2011). A

broad framework to organize and compare ecological invasion impacts.

Environmental Research, 111, 899-908.

Tiedemann, R., Hardy, O., Vekemans, X. & Milinkovitch, M.C. (2000). Higher impact

of female than male migration on population structure in large mammals.


Tigas, L.A., Van Vuren, D.H. & Sauvajot, R.M. (2002). Behavioral responses of

bobcats and coyotes to habitat fragmentation and corridors in an urban

environment. Biological Conservation, 108, 299-306.

Towns, D.R. & Daugherty, C.H. (1994). Patterns of range contractions and extinctions

in the New Zealand herpetofauna following human colonization. New Zealand

Journal of Zoology, 21, 325-339.

Towns, D.R., Daugherty, C.H. & Cree, A. (2001). Raising the prospects for a forgotten

fauna: A review of 10 years of conservation effort for New Zealand reptiles.


Towns, D.R. & Broome, K.G. (2003). From small Maria to massive Campbell: Forty

years of rat eradications from New Zealand islands. New Zealand Journal of

Zoology, 30, 377-398.

Towns, D.R., West, C.J. & Broome, K.G. (2013). Purposes, outcomes and challenges

of eradicating invasive mammals from New Zealand islands: An historical

perspective. Wildlife Research, 40, 94-107.

References

198

Triggs, S.J. (1982). Comparative ecology of the possum, Trichosurus vulpecula in three

pastoral habitats. MSc thesis. University of Auckland.

Triggs, S.J. & Green, W.Q. (1989). Geographic patterns of genetic variation in brushtail

possums Trichosurus vulpecula and implications for pest control. New Zealand


Turchin, P. (1991). Translating foraging movements in heterogeneous environments

into the spatial distribution of foragers. Ecology, 72, 1253-1266.

Turchin, P. (1998). Quantitative analysis of movement: Measuring and modeling

population redistribution in animals and plants. Sinauer Associate, Sunderland.

Turner, M.G. (1989). Landscape ecology: The effect of pattern on process. Annual

Review of Ecology and Systematics, 20, 171-197.

Turner, W.R., Nakamura, T. & Dinetti, M. (2004). Global urbanization and the

separation of humans from nature. Bioscience, 54, 585-590.

Tyndale-Biscoe, C.H. & Renfree, M. (1987). Reproductive physiology of marsupials.

Cambridge University Press, Cambridge.

Tyre, A.J., Tenhumberg, B., Field, S.A., Niejalke, D., Parris, K. & Possingham, H.P.

(2003). Improving precision and reducing bias in biological surveys: Estimating

false-negative error rates. Ecological Applications, 13, 1790-1801.

United Nations (2011). World urbanization prospects: The 2011 revision. New York.

Available from http://www.un.org/esa/population/.

Valeix, M., Loveridge, A.J., Chamaille-Jammes, S., Davidson, Z., Murindagomo, F.,

Fritz, H. & Macdonald, D.W. (2009). Behavioral adjustments of African

herbivores to predation risk by lions: Spatiotemporal variations influence habitat

use. Ecology, 90, 23-30.

van Heezik, Y., Smyth, A. & Mathieu, R. (2008a). Diversity of native and exotic birds

across an urban gradient in a New Zealand city. Landscape and Urban

Planning, 87, 223-232.

van Heezik, Y., Ludwig, K., Whitwell, S. & McLean, I.G. (2008b). Nest survival of

birds in an urban environment in New Zealand. New Zealand Journal of

Ecology, 32, 155-165.

van Heezik, Y., Smyth, A., Adams, A. & Gordon, J. (2010). Do domestic cats impose

an unsustainable harvest on urban bird populations? Biological Conservation,

143, 121-130.

Van Winkle, W. (1975). Comparison of several probabilistic home-range models.


Veblen, T.T. & Stewart, G.H. (1982). The effects of introduced wild animals on New

Zealand forests. Annals of the Association of American Geographers, 72, 372-

397.

References

199

Veitch, C.R. & Bell, B., D. (1990). Eradication of introduced animals from the islands

of New Zealand. Ecological restoration of New Zealand islands, Conservation

Sciences Publication No 2 (eds D.R. Towns, I.A.E. Atkinson & C.H.

Daugherty), pp. 137-146. Department of Conservation, Wellington.

Veitch, C.R. & Clout, M.N. (2001). Human dimensions in the management of invasive

species in New Zealand. The great reshuffling: Human dimensions of invasive

alien species (ed. J.A. McNeely), pp. 63-71. IUCN, Gland, Switzerland and

Cambridge, UK.

Vila, M., Basnou, C., Pysek, P., Josefsson, M., Genovesi, P., Gollasch, S., Nentwig,

W., Olenin, S., Roques, A., Roy, D., Hulme, P.E. & DAISIE partners (2010).

How well do we understand the impacts of alien species on ecosystem services?

A pan-European, cross-taxa assessment. Frontiers in Ecology and the

Environment, 8, 135-144.

Villard, M.A., Trzcinski, M.K. & Merriam, G. (1999). Fragmentation effects on forest

birds: Relative influence of woodland cover and configuration on landscape

occupancy. Conservation Biology, 13, 774-783.

Villepique, J.T., Bleich, V.C., Pierce, B.M., Stephenson, T.R., Botta, R.A. & Bowyer,

R.T. (2008). Evaluating GPS collar error: A critical evaluation of Televilt

POSREC-ScienceTM

; collars and a method for screening location data.

California Fish and Game, 94, 155-168.

Vitousek, P.M., D'Antonio, C.M., Loope, L.L., Rejmanek, M. & Westbrooks, R.

(1997). Introduced species: A significant component of human-caused global

change. New Zealand Journal of Ecology, 21, 1-16.

Walker, F.M., Sunnucks, P. & Taylor, A.C. (2008). Evidence for habitat fragmentation

altering within-population processes in wombats. Molecular Ecology, 17, 1674-

1684.

Walsh, P.S., Metzger, D.A. & Higuchi, R. (1991). Chelex 100 as a medium for simple

extraction of DNA for PCR-based typing from forensic material. Biotechniques,

10, 506-513.

Wandeler, P., Funk, S.M., Largiader, C.R., Gloor, S. & Breitenmoser, U. (2003). The

city-fox phenomenon: Genetic consequences of a recent colonization of urban

habitat. Molecular Ecology, 12, 647-656.

Waples, R.S. & Gaggiotti, O. (2006). What is a population? An empirical evaluation of

some genetic methods for identifying the number of gene pools and their degree

of connectivity. Molecular Ecology, 15, 1419-1439.

Warburton, B. (1977). Ecology of the Australian brush-tailed possum (Trichosurus

vulpecula Kerr) in an exotic forest. MSc Thesis, University of Canterbury.

Warburton, B. (1978). Foods of the Australian brushtailed opossum (Trichosurus

vulpecula) in an exotic forest. New Zealand Journal of Ecology, 1, 126-131.

References

200

Ward, G.D. (1978). Habitat use and home range of radio tagged opossums Trichosurus

vulpecula (Kerr) in New Zealand lowland forest. The ecology of arboreal

folivores. Symposia of the National Zoological Park (ed. G.G. Montgomery),

pp. 267-287. Smithsonian Institution Press, Washington DC.

Ward, G.D. (1984). Comparison of trap- and radio-revealed home ranges of the brush-

tailed possum (Trichosurus vulpecula Kerr) in New Zealand lowland forest.

New Zealand Journal of Zoology, 11, 85-92.

Ward, G.D. (1985). The fate of young radiotagged common brushtail possums,

Trichosurus vulpecula, in New Zealand lowland forest. Australian Wildlife

Research, 12, 145-150.

Warren, P.S., Katti, M., Ermann, M. & Brazel, A. (2006). Urban bioacoustics: It's not

just noise. Animal Behaviour, 71, 491-502.

White, C.G., Schweitzer, S.H., Moore, C.T., Parnell, I.B. & Lewis-Weis, L.A. (2005).

Evaluation of the landscape surrounding northern bobwhite nest sites: A

multiscale analysis. Journal of Wildlife Management, 69, 1528-1537.

White, G.C. & Garrott, R.A. (1990). Analysis of wildlife radio-tracking data. Academic

Press, San Diego.

White, P.C.L., Saunders, G. & Harris, S. (1996). Spatio-temporal patterns of home

range use by foxes (Vulpes vulpes) in urban environments. Journal of Animal

Ecology, 65, 121-125.

Whitlock, M.C., Ingvarsson, P.K. & Hatfield, T. (2000). Local drift load and the

heterosis of interconnected populations. Heredity, 84, 452-457.

Wilkinson, J. & Dickman, C.R. (2006). Cuscuses and brushtail possums. The

encyclopedia of mammals (ed. D.W. MacDonald), pp. 36-41. Oxford University

Press, New York.

Williamson, M. (1996). Biological invasions. Chapman and Hall, London.

Wilson, G.A. & Rannala, B. (2003). Bayesian inference of recent migration rates using

multilocus genotypes. Genetics, 163, 1177-1191.

Winter, J.W. (1963). Observations on a population of the brush-tailed opossum

(Trichosurus vulpecula Kerr). MSc Thesis, University of Otago.

Winter, J.W. (1976). The behaviour and social organisation of the brush-tail possum

(Trichosurus vulpecula Kerr). PhD Thesis, University of Queensland.

Winter, J.W. (1980). Tooth wear as an age index in a population of the brushtailed

possum, Trichosurus vulpecula (Kerr). Australian Wildlife Research, 7, 359-

363.

Wintle, B.A., Elith, J. & Potts, J.M. (2005). Fauna habitat modelling and mapping: A

review and case study in the Lower Hunter Central Coast region of NSW.

Austral Ecology, 30, 719-738.

References

201

Withey, J.C., Bloxton, T.D. & Marzluff, J.M. (2001). Effects of tagging and location

error in wildlife radiotelemetry studies. Radio tracking and animal populations

(eds J.J. Millspaugh & J.M. Marzluff), pp. 43-69. Academic, San Diego,

California.

Wood, B.C. & Pullin, A.S. (2002). Persistence of species in a fragmented urban

landscape: The importance of dispersal ability and habitat availability for

grassland butterflies. Biodiversity and Conservation, 11, 1451-1468.

Worton, B.J. (1989). Kernel methods for estimating the utilization distribution in home-

range studies. Ecology, 70, 164-168.

Wright, J.D., Burt, M.S. & Jackson, V.L. (2012). Influences of an urban environment

on home range and body mass of Virginia opossums (Didelphis virginiana).

Northeastern Naturalist, 19, 77-86.

Wright, S. (1943). Isolation by distance. Genetics, 28, 114-138.

Wright, S. (1946). Isolation by distance under diverse systems of mating. Genetics, 31,

39-59.

Yom-Tov, Y., Green, W.O. & Coleman, J.D. (1986). Morphological trends in the

common brushtail possum, Trichosurus vulpecula, in New Zealand. Journal of

Zoology, 208, 583-593.

Zar, J.H. (1984). Biostatistical analysis, 2nd edn. Prentice-Hall, Englewood Cliffs, New

Jeresy.

Zenger, K.R., Richardson, B.J. & Vachot-Griffin, A.M. (2003). A rapid population

expansion retains genetic diversity within European rabbits in Australia.


Zimmerman, J.W. & Powell, R.A. (1995). Radiotelemetry error: Location error method

compared with error polygons and confidence ellipses. Canadian Journal of

Zoology, 73, 1123-1133.

Zweifel-Schielly, B. & Suter, W. (2007). Performance of GPS telemetry collars for red

deer Cervus elaphus in rugged Alpine terrain under controlled and free-living

conditions. Wildlife Biology, 13, 299-312.

Appendices

202

Appendices

An urban GPS collared common brushtail possum (Trichosurus vulpecula).

Appendices

203

Appendix 1: List of species

Common name Latin name Authority

American black bear Ursus americanus Pallas 1780

Arctic fox Alopex lagopus Linnaeus 1758

Black rat Rattus rattus Linnaeus 1758

Brush-tailed rock wallaby Petrogale penicillata Gray 1827

Carpet python Morelia spilota Lacépède 1804

Cat Felis catus Linnaeus 1758

Chacma baboon Papio cynocephalus ursinus Kerr 1792

Common brushtail possum Trichosurus vulpecula Kerr 1792

Common house gecko Hemidactylus frenatus Schlegel 1836

Coyote Canis latrans Say 1823

Bobcat Lynx rufus Schreber 1977

Brown anole Anolis sagrei Duméril & Bibron

1837

Dingo Canis familiaris Linnaeus 1758

Dog Canis lupus familiaris Linnaeus 1758

Elephant Loxodonta africana Blumenbach 1797

Eastern grey squirrel Sciurus carolinensis Gmelin 1788

Eurasian badgers Meles meles Linnaeus 1758

Eurasian red squirrel Sciurus vulgaris Linnaeus 1758

European blackbird Turdus merula Linnaeus 1758

European rabbit Oryctolagus cuniculus Linnaeus 1758

European starling Sturnus vulgaris Linnaeus 1758

Fantail Rhipidura fuliginosa Sparrman 1787

Feral pigeon Columba livia Gmelin 1789

Ferret Mustela furo Linnaeus 1758

Fisher Martes pennanti Erxleben 1777

Goat Capra hircus Linnaeus 1758

Greenfinch Carduelis chloris Linnaeus 1758

Grizzly bear Ursus arctos Linnaeus 1758

Haast’s eagle Harpagornis moorei Haast 1872

Appendices

204

Hedgehog Erinaceus europaeus Linnaeus 1758

Himalayan thar Hemitragus jemlahicus Smith 1826

House mouse Mus musculus Linnaeus 1758

Kererū Hemiphaga novaeseelandiae Gmelin 1789

Kōkako Callaeas cinereus wilsoni Bonaparte 1850

Lace monitor Varanus varius White 1790

Lion Panthera leo Linnaeus 1758

Moose Alces alces Linnaeus 1758

Mountain pygmy possum Burramys parvus Broom 1896

Mourning gecko Lepidodactylus lugubris Duméril & Bibron

1836

North Island saddleback Philesturnus carunculatus rufusater Lesson 1828

Northern raccoon Procyon lotor Linnaeus 1758

Norway rat Rattus norvegicus Berkenhout 1769

Ocelot Leopardus pardalis Linnaeus 1758

Pig Sus scrofa Linnaeus 1758

Polynesian rat Rattus exulans Peale 1848

Powerful owl Ninox strenua Gould 1838

Quokka Setonix brachyurus Quoy & Gaimard

1830

Red fox Vulpes vulpes Linnaeus 1758

Rufous bristlebird Dasyornis broadbenti Milligan 1902

Sambar deer Rusa unicolor Kerr 1792

Stoat Mustela erminea Linnaeus 1758

Stone marten Martes foina Erxleben 1777

Striped skunk Mephitis mephitis Schreber 1776

Tasmanian devil Sarcophilus harrisii Boitard 1841

Weasel Mustela nivalis Linnaeus 1766

Wedge-tailed eagle Aquila audax Latham 1801

Wolf Canis lupus Linnaeus 1758

Yellow-cheeked Nomascus gabriellae Thomas 1909

crested gibbon

Appendices

205

Appendix 2: OPBG possum eradication

information The following information has been sourced from pamphlets

2 the OPBG has distributed

containing the management plan for their campaign to eradicate possums from the

Otago Peninsula, with an aim of “towards a pest-free Peninsula”.

2 For pamphlets and more information, see the OPBG’s website at http://www.pestfreepeninsula.org.nz

Appendices

206

Appendices

207

Appendix 3: Incremental graphs

The presence of an asymptote in incremental analyses indicates that a home range has

been fully revealed for an individual, and indicates the number of poisitional fixes

required before home ranges are revealed. The following incremental graphs are for the

24 possums tracked within this study.

Males:

0 36 72 108 144 180

00

2020

4040

6060

8080

100100

Num. locations

Area(%) Max. range area 3.486652 ha

0 17 34 51 68 85

00

2020

4040

6060

8080

100100

Num. locations


0 41 82 123 164 205

00

2020

4040

6060

8080

100100

Num. locations


0 57 114 171 228 285

00

2020

4040

6060

8080

100100

Num. locations


0 58 116 174 232 290

00

2020

4040

6060

8080

100100

Num. locations


0 11 22 33 44 55

00

2020

4040

6060

8080

100100

Num. locations


Appendices

208

0 28 56 84 112 140

00

2020

4040

6060

8080

100100

Num. locations


0 29 58 87 116 145

00

2020

4040

6060

8080

100100

Num. locations


0 60 120 180 240 300

00

2020

4040

6060

8080

100100

Num. locations


0 25 50 75 100 125

00

2020

4040

6060

8080

100100

Num. locations


0 41 82 123 164 205

00

2020

4040

6060

8080

100100

Num. locations


Appendices

209

Females:

0 44 88 132 176 220

00

2020

4040

6060

8080

100100

Num. locations


0 38 76 114 152 190

00

2020

4040

6060

8080

100100

Num. locations


0 36 72 108 144 180

00

2020

4040

6060

8080

100100

Num. locations


0 43 86 129 172 215

00

2020

4040

6060

8080

100100

Num. locations


0 34 68 102 136 170

00

2020

4040

6060

8080

100100

Num. locations


0 27 54 81 108 135

00

2020

4040

6060

8080

100100

Num. locations


0 52 104 156 208 260

00

2020

4040

6060

8080

100100

Num. locations


0 15 30 45 60 75

00

2020

4040

6060

8080

100100

Num. locations


Appendices

210

0 42 84 126 168 210

00

2020

4040

6060

8080

100100

Num. locations


0 31 62 93 124 155

00

2020

4040

6060

8080

100100

Num. locations


0 12 24 36 48 60

00

2020

4040

6060

8080

100100

Num. locations


0 72 144 216 288 360

00

2020

4040

6060

8080

100100

Num. locations


0 35 70 105 140 175

00

2020

4040

6060

8080

100100

Num. locations


Appendices

211

Appendix 4: Resource utilisation function

equations

The following three equations were sourced from Marzluff et al. (2004) to examine the

coefficients of resource variables and their variances in resource utilisation functions

(RUFs). Unstandardised coefficients generated by the ‘ruf’’ package in R enables the

prediction of a species expected use of examined resources within a study area, while

standardised coefficients can be used to rank the use of resources by a species.

Equation 1: Estimating the standardised partial regression coefficients ( β̂ ) for each

resource variable (j):

RUF

xj

s

sjj β̂ β̂

where jβ̂ is the maximum likelihood estimate of jβ̂ , the unstandardised

partial regression coefficient from the multiple regression equation; sxj is

the standard deviation for each resource variable examined; and sRUF is

the standard deviation of the 95% UD values.

Equation 2: Calculating the arithmetic means ( βˆ

) of coefficients for each resource

variable to produce an average RUF with the variance:

ijjn

inβ̂SE )β

ˆVar( 2

21

1

for i, .... n individuals.

*

* *

* *

Appendices

212

Equation 3: Conservative variance estimates which include both intra- and inter-

animal variation can be calculated for coefficients of each resource

variable in an average RUF by:

2βˆ

β̂( )βˆ

Var(1 1-

1j)-ijj

n

in

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