Modelling Reinvasion of the Common Brushtail Possum (Trichosurus Vulpecula) Within a New Zealand Urban Centre

Predicting Possums: Modelling reinvasion of the common brushtail possum (Trichosurus vulpecula) within a New Zealand urban centre

Charlotte Ruby Patterson

A thesis submitted in partial fulfilment of the requirements for the degree of Master of Science, Ecology

University of Otago, Dunedin, New Zealand March 2020

In memory of Gary Oldcorn and Faye Patterson

For the love of learning

Abstract

Invasive mammalian pests threaten biodiversity globally across a diverse range of habitats. Recent interest in urban biodiversity enhancement has increased the need for effective urban pest control, however efforts are hampered by a lack of understanding of the unique ecological processes occurring in cities. Projects seeking to eradicate mammals from mainland urban sites face the ongoing threat of reinvasion, and as such, often represent a significant long-term investment. There is a need for fundamental research of the invasive mammals that occupy urban habitats, and for this research to be integrated into evidence-based management.

The introduced common brushtail possum (Trichosurus vulpecula) has invaded the majority of New Zealand landscapes, and is a nationally significant pest species. Decades of control and research in New Zealand have been focused on non-urban habitats, but the lens has shifted with the initiation of a national pest control initiative, which aims to eradicate T. vulpecula and several other mammalian pest species from the whole of New Zealand by 2050. New urban- based control projects have arisen in response, creating a need for urban-based research of possums. The overall aim of this thesis was to inform the management of possums in a New Zealand city, Ōtepoti/ Dunedin, by assessing the density of possums across urban habitat types, and applying this information to a spatially explicit, individual-based model of possum reinvasion.

The first aim of this study was to estimate the density of possums across three habitat types within Dunedin, highlighting the capacity for cities to harbour possums. Density was assessed at three sites, representing an urban forest fragment, and two residential areas of varying vegetation quality. Possums were live-trapped and camera-trapped over 8 days. Spatially explicit capture-recapture methods were applied at the forest fragment site to estimate density, while Minimum Number Alive estimates were calculated at the residential sites. The forest fragment was found to support possums at a density capable of inflicting harm on resident native wildlife, however the density was at the lower range of what might be predicted for its habitat type, suggesting an influence of anthropogenic disturbance. Possums were at similarly low numbers in the two residential sites, however evidence pointed to the possibility of behavioural trap avoidance, which should be pursued as a future avenue of research.

The second aim of this study was to incorporate these density estimates into the application of a spatially explicit, individual-based model of possums, to simulate trapping scenarios a

i community group can implement to manage the reinvasion of possums onto the Otago Peninsula following eradication. Patterns of possum reinvasion were examined, and the relative efficiencies of trapping efforts and layouts were assessed through simulations. Model results highlighted the importance of complete eradication of possums, targeting possum dispersal corridors, and increasing trapping effort.

The present study outlines the importance of urban areas as habitat for possums, and demonstrates the potential for a spatial modelling tool to inform urban mammalian pest management in Dunedin, and elsewhere in New Zealand.

Acknowledgements

There are many people who have played an important role in this period of my life, and who I wish to thank. First, thank you to my primary supervisor Prof. Yolanda van Heezik. Your calm wisdom, thoughtful comments, and encouragement throughout this whole process have allowed me to explore and create my own thesis, with the knowledge that I always have someone on my side to offer help and advice. Additionally, to my secondary supervisor, Prof. Phil Seddon, thank you also for always listening, and providing a new perspective each time I thought I’d considered of every possibility. Thank you, Hon. Assoc. Prof. Deb Wilson, my third supervisor, your feedback was always much appreciated, as was your expertise in many fields, which I knew I could always call on. I am also very grateful to you for connecting me with Manaaki Whenua and Predator Free Dunedin, which greatly helped in the development of this project.

A huge thank you to Audrey Lustig for all the hours you put into helping me, be it in person, on Zoom, or via email. I know my questions would have grown tiring at points, but you always managed to make it not seem like a hassle, and like I was always making great progress. Thank you also to Ceclia Arienti for your input at Manaaki Whenua.

Thank you to Rhys Millar and Predator Free Dunedin as a whole for providing funding for me to visit Manaaki Whenua in Lincoln. This was a really important time of development for me and this study, so I am very glad of this opportunity. From the OPBG I’d like to acknowledge Marcia Dale for meeting with me and discussing ideas, and also Bruce Kyle and Ursula Ellenberg, for providing helpful comments and GPS data for my modelling. Your dedication to your mission is inspiring.

Thank you Aalbert Rebergens from the DCC for providing a contractor to guide my first day of trap set-up. Thank you Murray Efford and others in the secr community for answering one too many questions about my secr analysis.

I’m so grateful to all those who came to help me out in the field. Particularly Tim, Taylor and John for carrying all those heavy traps and facing the wrath of possums – you guys are legends! I’d also like to recognise all the wonderful Dunedin residents who allowed me to skulk around in their backyards for a week in the name of ‘science’. It was a privilege to meet such a variety of folks from Dunedin.

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These past few years have been made special by the workmates and friends I’ve been surrounded by in the office. Megha, Jonah, Kim, John, Tessa, Aidan, Aaron (not to mention the glorious Blossom, Pegasus and Fleshy), it’s been a pleasure growing with you all. Thank you for all the chats, laughs, and cups of tea, you made every day better. To the Ecology / Zoology staff and students as a whole, thank you for the learning opportunities and quality experiences from undergrad until now.

To my flatmates for the duration of this degree – Ruby, Eilish, Asia, Becky, Hayley, Pearl, Taylor, Brooke, Joe, Michael, Sarah – you have brought me so much joy, made me feel at home, given me so many precious memories. Thank you.

Scott, the level of support you have given me is beyond anything I could ever have expected from one person. You have assisted me in every way. Physically, with field work, of course, but particularly, mentally, by always making me laugh, helping me to see beyond myself, teaching me to appreciate the little things, and notice all the beauty in the world.

To my family, of course, I owe the biggest thanks. Oliver, Lucy, you make me strive to be a better person every day. I’m so proud of you both, and grateful for the time we’ve gotten to spend together. Cadbury, you are the best. Mum and Dad, you never fail to raise my spirits and make me feel like I can take on anything. Your endless patience, encouraging words, and dedication to your children is truly amazing, and I am deeply grateful to you for teaching me strength and resilience. I’m also very grateful to you for all the read-throughs of my work you have done. Gary, and Grandma, thank you for all that we shared, and the inspiration you have given me to enjoy every day as it comes.

Table of Contents

Abstract ...... i

Acknowledgements ...... iii

Table of Contents ...... v

Figures ...... viii Tables ...... xiii

1. General Introduction 1 1.1 Mammalian urban invasions: A global phenomenon ...... 2 1.1.1 Invasions in a changing world ...... 2 1.1.1 Mammal invasions have significant impacts on island systems...... 2 1.1.2 Urban areas are affected by mammal invasions ...... 3 1.1.3 The importance of urban pest control ...... 4 1.2 The impact and management of invasive mammals in Aotearoa, New Zealand ...... 7 1.2.1 New Zealand mammalian pest management: From offshore to mainland ...... 7 1.2.2 Predator Free New Zealand 2050 – a regime shift ...... 8 1.3 An “unmitigated disaster” for New Zealand: The efficient invasion of the brushtail possum ...... 11 1.3.1 The ecology of New Zealand possums ...... 12 1.3.2 The impact of possums on New Zealand biota ...... 14 1.3.3 Urban possums: What do we know? ...... 16 1.4 Study background ...... 19 1.4.1 The Ōtepoti/Dunedin context ...... 19 1.4.2 Predator Free Dunedin ...... 20 1.4.3 The Otago Peninsula Biodiversity Group ...... 21 1.5 Thesis aims and structure ...... 24

2. Living in the City: Density of the common brushtail possum across urban habitat types of Dunedin, New Zealand 25 2.1 Introduction ...... 26 2.1.1 Invasive species adapt to urban environments ...... 26 2.1.2 Density of urban invaders ...... 26 2.1.3 Urban possums and their adaptive nature ...... 27

2.1.4 Possum density ...... 27 2.1.5 Urban possum density ...... 30 2.1.6 Density estimation via spatially explicit capture-recapture ...... 30 2.1.7 Study aims ...... 33 2.2 Methods ...... 34 2.2.1 Sites ...... 36 2.2.2 Trap placement ...... 40 2.2.3 Trapping protocol ...... 41 2.2.4 Camera trapping ...... 43 2.2.5 SECR analysis ...... 43 2.2.6 Camera trap analysis ...... 47 2.2.7 Maori Hill and Wakari analysis ...... 47 2.3 Results ...... 49 2.4 Discussion ...... 52 2.4.1 Forest fragment possum density ...... 52 2.4.2 Characteristics of the forest fragment population ...... 53 2.4.3 Implications for urban biodiversity ...... 54 2.4.4 Residential area possum densities ...... 55 2.4.5 Study limitations ...... 57 2.5 Conclusions ...... 58

3. Predicting reinvasion of the common brushtail possum within Dunedin, New Zealand, via spatially explicit, individual-based modelling 59 3.1 Introduction ...... 60 3.1.1 Invasive mammal management in urban areas ...... 60 3.1.2 Individual-based models for invasive species management ...... 61 3.1.3 Possums – why model?...... 62 3.1.4 History of IBMs for possum management in New Zealand ...... 63 3.1.5 The Ōtepoti/Dunedin context ...... 65 3.1.6 Study aims ...... 67 3.2 Methods ...... 68 3.2.1 Study area ...... 68 3.2.2 Otago Peninsula Biodiversity Group current and future control plans ...... 68 3.2.3 Model methods ...... 69

3.2.4 Scenario specifications ...... 78 3.2.5 Sensitivity analysis ...... 82 3.2.6 Model output visualisation ...... 83 3.3 Results ...... 84 3.3.1 Hotspot scenario ...... 84 3.3.2 Hotspot 50 scenario ...... 89 3.3.3 Trapping regimes ...... 92 3.3.4 Sensitivity analysis ...... 96 3.4 Discussion ...... 97 3.4.1 Causes of low immigration pressure ...... 98 3.4.2 Consequences of ineffective eradication ...... 99 3.4.3 Dispersal ‘corridors’ and their uses ...... 100 3.4.4 Trapping scenario findings ...... 101 3.4.5 Management summary and recommendations ...... 102 3.4.6 Future research and model improvements ...... 103 3.5 Conclusions ...... 108

4. General Discussion 109 4.1 Summary ...... 110 4.2 The necessity of urban possum parameters ...... 111 4.3 Density as an informative metric for urban possums ...... 112 4.4 Representing urban habitat in modelling ...... 113 4.5 The power of individual-based modelling for informing management ...... 114 4.6 Future applications of the model ...... 115 4.7 Concluding remarks ...... 117 4.8 Ethics ...... 118 4.9 Erratum statement ...... 118

References ...... 119

5.1 Appendix A ...... 138

5.2 Appendix B ...... 140

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Figures

1. 1 The location of three peninsula-based predator control projects around New Zealand that fall under the Predator Free New Zealand initiative: The Otago Peninsula in Dunedin, the Miramar Peninsula in Wellington, and the Māhia Peninsula in Hawke’s Bay. Each project is attempting to eradicate certain target mammalian species through a combination of private and volunteer operations...... 10

1. 2 The city of Dunedin as it is located in the South Island of New Zealand. The urban city centre is flanked by an eastern peninsula, the Otago Peninsula, which is predominantly pastureland. To the west lies the Western Harbour, which has some large patches of forest...... 20

1. 3 Geographical boundaries of the three predator control and biodiversity enhancement groups that comprise Predator Free Dunedin: The Halo Project (blue) which surrounds the predator- free fenced ecosanctuary Orokonui (pink); the Urban Linkage Project (green), and the Otago Peninsula Biodiversity Group (orange)...... 22

1. 4 a) The five operational sectors of the Otago Peninsula Biodiversity Group, within which possum control is undertaken. From North to South these are Taiaroa Head (TH), Clarks/Sheppards hill ‘pinchpoint’ (SH), Cape Saunders (CS), Portobello (PB) and the Buffer Zone (BZ). b) The Buffer Zone, a residential area that connects the city (West) to the more rural land at the start of the Peninsula (East). Within the Buffer Zone residents will be undertaking voluntary trapping to reduce reinvasion of the Peninsula...... 23

2. 1 Habitat map of the urban Dunedin area showing the home ranges of 13 female and 11 male possums (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. From Adams et al. (2014a)...... 31

2. 2 The three sites representing different urban habitat types (forest fragment, Res I, Res II; see Table 2.2 for descriptions) across Dunedin, New Zealand, where spatially explicit capture- recapture of possums was undertaken to derive habitat-specific density estimates...... 35

2. 3 The forest habitat of the Jubilee Park site, a public park and a forest fragment. The park comprises a broadleaf and fern understory, with an exotic canopy of pine and willow (Pinus spp. and Salix spp.)...... 37

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2. 4 Examples of Maori Hill gardens, which represent ‘Residential I’ habitat. These gardens tended to have a mix of mown and unmown lawn, shrubs, trees, vegetable patches, and compost heaps or bins...... 38

2. 5 Examples of ‘Residential II’ Wakari gardens. These were comprised of large areas of mown or unmown lawn, some shrubs and small trees. Houses also had gardens and compost heaps...... 39

2. 6 The placement of traps at the three sites, Jubilee, Maori Hill and Wakari, where traps were placed roughly 30m apart. Within the two residential areas trap placement depended on local conditions and resident permission...... 40

2. 7 The Grieve wire live-capture cage trap setup for the mark-recapture. Traps were baited with cinnamon-coated apple, and an icing sugar and flour visual lure extended ~30cm from the front of the trap...... 42

2. 8 Examples of the camera trap placement in relation to the live-capture cage traps...... 43

2. 9 The polygon shapefile describing the habitat types of Jubilee Park and the surrounding area. A 300m buffer around the trap locations was used to delineate a ‘habitat mask’, within which computation of density was undertaken...... 45

2. 10 The frequency distribution of distances moved in metres by possums between capture and recapture events at the Jubilee site. The average distance moved was 87.6m and the median was 51.7m...... 50

3. 1 The carrying capacity map (Kmap) that underlies the individual-based possum model of the Otago Peninsula (the Study Area), and wider Dunedin city (the Non-Study Area). Land cover information was combined with fine-scale forest classes, and the density estimates from Chapter 2, to produce carrying capacity (K) values (individual possums per ha) for different habitat across the city, which was then rasterised to a 100m resolution. Cells given a K value of -99 are those that cannot be settled on or dispersed across...... 71

3. 2 The bimodal gaussian distribution representing the seasonal monthly probability of an adult giving birth in the model. It has a major peak in autumn, and a smaller peak in spring. From Lustig et al. (2019)...... 74

3. 3 The location of traps in four of the simulated trapping scenarios, which were then converted to a density of traps per cell for input into the model. a) The current 150 traps setup in the Buffer Zone, b) with 300 traps, c) with 500 traps and d) with 300 traps but more of them

concentrated in areas of predicted higher dispersal frequency (defined from simulation results)...... 81

3. 4 The possum abundance, averaged over 40 replicates, at years 5 and 10 in the ‘Hotspot’ scenario, which simulated complete eradication of the Peninsula and no control. The population outside the Study Area (delineated in orange) was maintained at a constant 50% of its K per cell...... 85

3. 5 The possum abundance, averaged over 40 replicates, at years 20 and 40 in the ‘Hotspot’ scenario, which simulated complete eradication of the Peninsula and no control. The population outside the Study Area (delineated in orange) was maintained at a constant 50% of its K per cell...... 86

3. 6 The location of ‘hotspots’ of possum abundance on the Otago Peninsula after 40 years of reinvasion (averaged over 40 replicates)...... 88

3. 7 The accumulated dispersal map of possum movements as they reinvade the Peninsula in a scenario with no control and complete eradication (‘Hotspot’). Dispersal was quantified as the number of individual movements made across a cell on a monthly time-step...... 90

3. 8 The number of possums per year (averaged over 40 simulations) immigrating onto the Peninsula over time in two modelled scenarios: one scenario where complete eradication of the Peninsula was achieved ('Complete eradication'), and one where 50 possums remained on the Peninsula following control ('50 possums'). Possum movement was slightly higher in the first 5 years as individuals moved out from the city cells and onto the Peninsula but were not all replaced. Bars are ± standard error...... 91

3. 9 The number of juvenile possums born per year (averaged over 40 simulations) on the Peninsula over time following complete eradication of the Peninsula ('Complete eradication'), and a scenario where 50 possums remain present on the Peninsula ('50 possums'). Bars are ± standard error...... 91

3. 10 The average abundance of possums in the Peninsula Study Area per month over time in two scenarios with no control: the 'Hotspot' scenario where complete eradication occurred, and the 'Hotspot 50' scenario, where 50 possums remained on the Peninsula following eradication. Shaded bars are ± standard error and n = 40...... 92

3. 11 The total abundance of possums on the Peninsula per month (averaged over 40 replicates) over the 40 year simulations in the ‘Hotspot’ scenario where there were no traps, compared with three trapping scenarios of increasing trap density (150, 300 and 500). Shaded bars are ± standard error...... 94

3. 12 The average (n = 40) number of adult (bold line) and juvenile (dashed line) individual possums trapped on the Peninsula per year across three scenarios of increasing trap density: 150 traps, 300 traps and 500 traps...... 94

3. 13 The average (n = 40) abundance of possums on the Peninsula over the 40-year simulation period when exposed to trapping regimes of varying effort (150 traps, 300 traps, 500 traps) and layout (dotted line = habitat targeted layout, dashed line = homogenous layout)...... 95

5. 1 The derived density of possums (number per ha of effective sampling area) as a function of the buffer width of a secr habitat mask, which defines the area of integration. The dashed red line indicates the 300m buffer width chosen, at which point the density has reached an asymptote and is not further affected by buffer width...... 138

5. 2 Output from the esa.plot function in secr exploring variation in the number of individuals per effective sampling area (density) when three detection functions are applied to the null model. These detection functions are the half-normal (HN), exponential (EX) and hazard rate (HR). The exponential and half-normal predictions reach an asymptote at a buffer width of 200- 250m while the Hazard rate predictions remain sensitive to changes in buffer width...... 139

5. 3 Possum movement onto the Peninsula, averaged over 40 simulations, at year 5, 10, 20 and 40 in the homogenous K sensitivity analysis scenario where all cells were set to a K of 0.2. This analysis was conducted to explore the relative importance of the habitat type, and available area in determining the reinvasion rate of possums onto the Peninsula...... 140

5. 4 The possum abundance on the Peninsula, averaged over 40 replicates, for the sensitivity analysis where the carrying capacity of the Non-Study Area was set to be 25% lower or higher than the original model...... 142

5. 5 The possum abundance on the Peninsula, averaged over 40 replicates, for the sensitivity analysis where the maximum adult lifespan was varied by 25% above and below the average value used in the model...... 143

5. 6 The possum abundance on the Peninsula, averaged over 40 replicates, for the sensitivity analysis where the mean birth rate was varied by 15% above and below the average value used in the model...... 143

5. 7 The possum abundance on the Peninsula, averaged over 40 replicates, for the sensitivity analysis where the maximum juvenile dispersal distance was increased and decreased by 25% from the average value used in the model...... 144

5. 8 The possum abundance on the Peninsula, averaged over 40 replicates, for the sensitivity analysis where the resolution was set to 500m, instead of the 200m used in simulations. .... 144

5. 9 The possum abundance on the Peninsula, averaged over 40 replicates, for the sensitivity analysis where the juvenile trappability (Gamma1 or γ1) was varied by 25% above and below the average value used in the model...... 145

5. 10 The possum abundance on the Peninsula, averaged over 40 replicates, for the sensitivity analysis where the adult trappability (Gamma0 or γ0) was varied by 25% above and below the average value used in the model...... 145

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Tables

2. 1 A summary of the past two decades of habitat-specific possum density estimates across New Zealand, with references. Table modified from Rouco et al. (2013)...... 29

2. 2 Descriptions of the three urban habitat types in which the density of possums will be surveyed in Dunedin, New Zealand. Produced by Freeman and Buck (2003), reproduced from Adams et al. (2013)...... 34

2. 3 The total number of possums caught and recaptured (including ‘re-sightings’ via camera trap) across three sites of varying vegetation type and residential influence within the city of Dunedin, along with details of their age distribution...... 49

2. 4 Results of the AICc comparison for secr models of possum density at the Jubilee site in Dunedin. D is a derived density estimate, g0 is the one-night probability of an individual being caught if a trap is placed at the centre of its home range, and σ is a measure of home range size. AICcwt is the AICc weight. The shaded model was chosen as the top performing model, and density was estimated from this. See Methods for descriptions of the models. .... 50

2. 5 The trapping Minimum Convex Polygon (MCP) and Effective Trapping Area (ETA) sizes used to calculate the density of possums at two sites of differing residential influence within the city of Dunedin, New Zealand. The ETA was calculated to include the mean home range radius of Dunedin possums calculated by Adams et al. (2014a)...... 51

2. 6 Summarised results of density estimates for three sites of varying vegetation quality and residential influence within the city of Dunedin, New Zealand. Methods used were spatially explicit capture-recapture (SECR) or Minimum Number Alive (MNA) within an Effective Trapping Area. Density is calculated as the MNA divided by the ETA...... 51

3. 1 The cover classes included in the final habitat map of Dunedin, and the carrying capacity values associated with each cover class in the final habitat ‘Kmap’. Carrying capacity values were taken from Warburton et al. (2009), Lustig et al. (2019), and Chapter 2 of this study. Cover classes were further separated into three categories: habitats that could not be dispersed across or settled on ('No dispersal'); habitats that could be dispersed across but could not be settled on ('Dispersal only'); and habitats that could be dispersed across and settled on ('Settlement') (Lustig et al., 2019)...... 72

3. 2 The baseline possum parameters used in the model, with reference to the source of values where applicable...... 79

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3. 3 The name and description of each scenario run in the model...... 80

3. 4 Parameters that were assessed in a local sensitivity analysis, and the values that were used .. 83

3. 5 The summarised output of possum abundance from the model scenarios explored. Bold values are the total average over all replicates, while un-bolded values are the average over replicates where invasion to any non-negligible extent (> 10 individuals) occurred...... 87

5. 1 AICc values comparing secr null models which employ varying detection functions, in order to decide the most appropriate detection function for further modelling. Detection functions primarily differ in the length of the detection tail. AICcwt is the AICc weight...... 138

5. 2 Comparison of the derived density (D) values for each model with the exponential or half- normal detection function. The half-normal models are consistently slightly lower...... 139

5. 3 The abundance of possums on the Peninsula over time across the sensitivity analysis scenarios, where parameter values were varied 25% above and below the original value. Table includes the baseline Hotspot and Trap 300 scenario results for comparison...... 141

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Chapter 1 General Introduction

1.1 Mammalian urban invasions: A global phenomenon

1.1.1 Invasions in a changing world

Of all the threats to biodiversity in this time of environmental change, biological invasions, the arrival and spread of species into novel environments in which they have the potential to inflict ecological harm, are one of the most ubiquitous (Mack et al., 2000). Invasive species are now implicated in the decline and extinction of thousands of species across a range of taxa, surpassing many other natural and anthropogenic pressures as one of the main drivers of modern extinctions (Bellard et al., 2016; McGeoch et al., 2010; Thomsen et al., 2011). The effects of invasive species may manifest at the genetic, individual, population, community or ecosystem level, and include the disruption of food webs, co-evolutionary relationships and other fundamental ecological processes (Ehrenfeld, 2010; Parker et al., 1999; Simberloff et al., 2013).

Over recent decades, species have been colonising new areas at a far greater frequency. This is a consequence of human-facilitated dispersal, arising from expanding global networks (Crowl et al., 2008; Early et al., 2016; Westphal et al., 2008). Invasions are predicted to continue due to this human connectivity, and the effects of this will be felt by ecosystems alongside the impending consequences of global climate change (Diez et al., 2012; Early et al., 2016; Hulme, 2009). In order to combat these biosecurity challenges, the field of invasion biology has received considerable scientific attention, and invasive species have been the subject of a number of international agreements (Genovesi et al., 2015; E. Lowry et al., 2013; McGeoch et al., 2010). Attempts are made to control taxa at each stage of the invasion process (transport, establishment, spread), and large investments continue to be made into the improvement of detection and eradication technology, with broadly positive results (Simberloff et al., 2013a).

1.1.1 Mammal invasions have significant impacts on island systems

Mammals are particularly successful invaders, largely due to anthropogenic facilitation. Humans have transported and introduced mammals to new locations, intentionally or otherwise, since the first exploration and settlement periods began (Long, 2003). Mammalian introductions have been carried out for economic, cultural, or recreational purposes (Clout and

Russell, 2008; Long, 2003), and the magnitude and frequency of these introductions has typically enhanced their likelihood of persistence (Capellini et al., 2015; Lockwood et al., 2005). Alongside this, some mammals have life-history traits (high fecundity, short generation times) that enhance their invasion success (Capellini et al., 2015). The high trophic position of most mammals means that as invaders they often have wide-reaching impacts within novel ecosystems. Upon arrival, mammals may impact native biodiversity through direct and indirect predation, grazing, and competition pressures (Genovesi et al., 2012; Russell and Stanley, 2018). As an example, the American Mink (Neovison vison) was reported to affect 47 native species within the United Kingdom alone, including some threatened species (Genovesi et al., 2012).

Islands are particularly vulnerable to mammalian invasions, harbouring highly endemic, insular communities. These assemblages have often experienced long periods of isolation and co- evolution, thus displaying simple trophic structures and low diversification rates (Chapuis et al., 1994; Courchamp et al., 2003). Few islands have many native mammals, and this means island biota are often naïve to the threat they pose. Paired with the aforementioned factors, this naïvety has led to mammals becoming a leading cause of island extinctions (Atkinson, 2001; Blackburn et al., 2004; Jones et al., 2016a; Paulay, 1996), which is all the more concerning when it is known that islands harbour levels of biodiversity vastly disproportionate to their cumulative area (Jones et al., 2016).

1.1.2 Urban areas are affected by mammal invasions

Though traditionally not the focus of invasion biology research, urban environments suffer from the impacts of biological invasions (Russell and Stanley, 2018). Cities are heterogeneous habitats, with patchy resource distribution and strong disturbance effects that favour species with invasive characteristics (Adams et al., 2014a; E. Lowry et al., 2013; H. Lowry et al., 2013; McKinney, 2006, 2002). Successful invasive species may be more abundant in urban environments than anywhere else in their exotic range because of these exploitable conditions, and anthropogenic subsidies such as artificial food sources and shelter may further allow species to exist at high densities (Adams et al., 2014a; Newsome et al., 2015).

Mammals are among the most successful urban invaders, often living in close proximity to human-modified structures. Rats (Rattus spp.), for example, are one of the most pervasive and

3 destructive invasive vertebrates in the world (Jones et al., 2016), and they are heavily associated with human-occupied urban areas (Combs et al., 2018). These generalists have rapidly adapted to exploit human resources, and are now found in cities worldwide, causing huge public health concerns over the transmission of zoonotic pathogens, extensive economic harm through structural damage and the spoiling of foodstuffs, as well as having significant negative impacts on native ecosystems through both competition and predation (Aplin et al., 2003; Combs et al., 2018; Feng and Himsworth, 2014). The high capacity for behavioural change in some mammals may greatly contribute to their ability to succeed in urban environments, either through exploitation or avoidance of human activity (H. Lowry et al., 2013; Sol et al., 2013). Rodents and other mammals of a small to medium size tend to represent most urban invasive mammals (Riem et al., 2012; White et al., 2012), although larger mammals can also occupy cities (Bateman and Fleming, 2012; Lyons, 2005). As with Rattus spp., other urban mammal invasions produce a triad of harmful impacts, threatening urban ecosystems as well as the economy and health of nearby humans (García et al., 2012; Kauhala and Kowalczyk, 2011).

1.1.3 The importance of urban pest control

Biodiversity enhancement is now being encouraged in urban areas, including within private residential gardens and public amenity or business spaces. A variety of methods are employed, including the provision of artificial habitats or supplementary feeders for invertebrates, reptiles and birds; structural enhancement and restoration of regeneration processes through the planting of native flora; and adaptation and innovation within urban development that accommodates natural processes (Collins et al., 2017; Gaston et al., 2005; Glen et al., 2013; Whitmore et al., 2002). Organisms, like rats, that threaten human health, property, or economy have been the priority of urban pest management initiatives until recently, with obvious incentives. These control efforts are typically reactive, instigated when damage thresholds are exceeded (Russell and Stanley, 2018). However, efforts to control urban invasive species for a wider range of more holistic goals – as outlined below – are becoming more commonplace alongside this restoration and biodiversity enhancement work (Doherty and Ritchie, 2017; Russell and Stanley, 2018).

Urban habitats are characterised by high levels of disturbance, habitat fragmentation and patchy resource distribution (Harper, 2005; H. Lowry et al., 2013), generally tending to support less

4 biodiversity and at lower densities than other habitats (Aronson et al., 2014; Carthew et al., 2015; Chace and Walsh, 2006). Nevertheless, urban areas sustain native biodiversity, including threatened species (Angold et al., 2006; Aronson et al., 2014; Gallo et al., 2017). Cities provide novel ecological niches (Carthew et al., 2015), wildlife movement corridors (Bolger et al., 2001; Munshi‐South, 2012; Vergnes et al., 2013), and refuge or surrogate habitat for species (Chace and Walsh, 2006; Hall et al., 2017; Merola‐Zwartjes and DeLong, 2005; Soga et al., 2014). Moreover, these habitats could be focal sites for future reintroductions (van Heezik and Seddon, 2018). Links have been found between invasive species and declines in urban biodiversity and abundance, particularly through predation and competition pressure (Monceau et al., 2014; Newsome et al., 2015; Rodewald and Kearns, 2011; Shochat et al., 2010). These effects can be compounded by the relative influences of other urban threats, such as habitat fragmentation (Bar-Massada et al., 2014; Dures and Cumming, 2010; Shochat et al., 2010). Efforts to control urban invaders are therefore crucial for restoring and protecting important urban wildlife.

The threats to urban biodiversity posed by invasive species also have implications for the wellbeing of humans, and hence provide a rationale for control. Biotic homogenisation of urban habitat by invasive adapters can create biologically depauperate environments for humans to live in that are dominated by a small number of non-native species (McKinney, 2006). The world’s population is growing at a rate of around 1% per year, with predictions that it will reach 9.8 billion by 2050 (UN, 2017). At present, just over half of all people live in urban areas globally, and this is predicted to increase to 68.4% by 2050 (UN, 2018). In developed countries, like New Zealand, this value is already much higher (85.6% in 2013) (Stats NZ, 2013a). This demographic shift towards cities has important implications for both biodiversity conservation and human wellbeing. Research suggests that urbanization is leading people to become increasingly isolated and disconnected from any form of natural environment, sometimes termed the “extinction of experience” (Miller, 2005). This has been one factor implicated in the ‘epidemic’ of stress, anxiety and mood disorders (e.g. depression) seen in urban-dwellers (Cox et al., 2017; Soga and Gaston, 2016). At a time when human-nature interactions are at their lowest (Soga and Gaston, 2016), urban biodiversity provides opportunities for people to experience and connect with nature (Dearborn and Kark, 2010). Future conservation and restoration will depend on the engagement of people in cities (Dunn et al., 2006; Norton et al., 2016), and evidence for the wider benefits of increased urban biodiversity and green spaces,

5 particularly in regards to public health, is diverse and generally undisputed (Frumkin et al., 2017; Shanahan et al., 2015; Wolch et al., 2014).

Finally, little is known about the biology of invasive species in urban environments, but many characteristics are likely to differ in fundamental ways from those in other habitats (Russell and Stanley, 2018). The availability of artificial food sources in urban areas might enable species to exist at greater densities than elsewhere (Adams et al., 2014a; Newsome et al., 2015), and behavioural flexibility might render control techniques used in other habitats less efficient or even ineffective (Adams et al., 2014a). Furthermore, without adequate control, urban areas could act as sources of reinvasion into non-urban areas (Adams et al., 2014a, 2013). Research is needed to understand the unique ecological processes occurring in cities, and to effectively manage invasive species that are present there (Doherty and Ritchie, 2017; Gallo et al., 2017; Russell and Stanley, 2018).

1.2 The impact and management of invasive mammals in Aotearoa, New Zealand

New Zealand provides a striking example of island vulnerability to introduced species invasion (Carthew et al., 2015). Mammalian pest species, introduced by both Tangata Whenua (the indigenous Māori) and European settlers, have caused considerable damage to New Zealand’s highly endemic ecosystems. Of the 32 successfully established mammal species, some were introduced unintentionally (e.g. Rattus spp.), some for recreational purposes (e.g. red deer, Cervus elaphus; brown trout, Salmo trutta), while others were brought over to feed and support the human settlements (sheep, Ovis aries; cattle, Bos taurus), resulting in a new assemblage of herbivores, omnivores and predators (Veitch and Clout, 2001). Having evolved within an insular, mammalian-predator-free environment, New Zealand’s native flora and fauna have been ill-equipped to defend themselves against the predation, grazing and competition pressures of these introduced species (Atkinson, 2006; Carthew et al., 2015; Duncan and Blackburn, 2004; Goldson et al., 2015). As a result, many endemic bird, reptile, invertebrate, and plant species have suffered dramatic declines in abundance and distribution over the past 700 years since the first human arrivals (Clout, 2001; Duncan and Blackburn, 2004; Towns and Daugherty, 1994). To highlight the scale of this devastation, one quarter of native bird species have become extinct in this time (Russell and Stanley, 2018), and, despite considerable invasive species control efforts over the past 50 years, more than half of those extant are still threatened (Norton et al., 2016). The ongoing effects of invasive mammals have broken down natural processes underpinning ecosystem resilience, including pollination and dispersal mutualisms, which suffer from a lack of native animal vectors (Kelly et al., 2010; Norton et al., 2016). This has been exacerbated by the effects of historic exploitative land-use (Norton et al., 2016). Invasive mammals are also estimated to have severe impacts on New Zealand’s economy, particularly within the dominant agriculture, horticulture and forestry sectors (Giera et al., 2009; Russell et al., 2015).

1.2.1 New Zealand mammalian pest management: From offshore to mainland

As a matter of necessity, New Zealand has become a world-leader in mammalian pest management (Courchamp et al., 2003; Jones et al., 2016). Historically most control has

7 occurred on offshore islands, with over 100 such islands having been successfully made mammalian pest-free (Towns et al., 2013). This has led to ecosystem restoration, and translocation of native species (Clout and Russell, 2006). Several of New Zealand’s most threatened endemic species (kākāpō, Strigops habroptilus; takahē, Porphyrio hochstetteri; tuatara, Sphenodon punctatus) have their largest, or only remaining, populations on these offshore islands (Clout and Russell, 2006; Saunders and Norton, 2001). Much of this offshore eradication was initially carried out through traditional trapping and shooting, which gradually shifted to include aerial and ground broadcasted anticoagulant baits (Clout and Russell, 2006).

Mammalian pest control and resulting biodiversity enhancement has taken longer to develop on the mainland (the North and South Islands), primarily due to the lack of resources and technology to effectively apply multi-species control over larger areas. The first species to be subjected to broad-scale mainland control were browsing mammals (deer, Cervus spp., Dama dama and Odocoileus virginianus; goats, Capra hircus; possums, Trichosurus vulpecula). Intensive mainland management began in earnest between 1995 and 1996, when the Department of Conservation began to target high priority conservation areas by initiating six projects across the country ranging in size from 117 ha to over 6000 ha (Parkes, 1996; Saunders and Norton, 2001). At the same time, research was proving that species recovery was achievable on the mainland, albeit with the requirement that predators be maintained at very low densities for several consecutive years (Innes et al., 1999). Mainland control also occurred in areas of agricultural value, mainly to manage the spread of vector-borne pathogens that threatened livestock (Parkes and Murphy, 2003).

Since those first operations, mammalian pest control in New Zealand has gradually been scaled up to the point where in 2014, approximately 45% of New Zealand’s mainland area, or 11.8 million ha, received some form of control (Russell et al., 2015). Small sites have been cleared of all pests (except for mice, Mus musculus) and enclosed in predator-proof fencing. These ‘ecosanctuaries’ were one of the first means by which eradication was achieved and maintained on mainland New Zealand, and following their first successes, many have been established across the country (Russell et al., 2015).

1.2.2 Predator Free New Zealand 2050 – a regime shift

In 2016, the New Zealand Government announced the ambitious goal of eradicating three introduced mammals from New Zealand by 2050 (rats: Rattus rattus, R. norvegicus, R. exulans;

8 stoats: Mustela erminea; and the common brushtail possum: Trichosurus vulpecula) (Linklater and Steer, 2018). This goal represents a shift towards higher intensity mainland-based management, and nation-wide coordination of the many regional and local pest control initiatives already operating, run by various governing bodies, as well as private and volunteer groups (Russell et al., 2015). The ‘Mainland Island’ approach, where specific areas are targeted with intensive pest control to create ‘islands’ of zero or low pest density, is central to achieving this vision of a Predator free New Zealand (Courchamp et al., 2003; Russell et al., 2015; Saunders and Norton, 2001). Mainland operations are being upscaled across the country, aided by the rapid development of novel technology. Ecosanctuaries are an important component of the Predator Free NZ 2050 (PFNZ) strategy, providing areas of zero mammalian pests that form the centre of ever-widening ‘halos’ of pest control and biodiversity spill-over (Innes et al., 2019). However, areas with this level of intensive management, described as the “lynch pin” of PFNZ (Russell et al., 2015), represent less than 1% of New Zealand’s land area and are not able to be replicated across scales greater than a few thousand hectares (Russell et al., 2015). Instead, PFNZ will also require the use of natural landscape features, such as peninsulas, to enable eradication attempts over much larger mainland areas, while still limiting the potential for reinvasion. Multiple peninsula-based projects are now underway, including on the Māhia Peninsula in Hawke’s Bay, the Miramar Peninsula of Wellington, and the Otago Peninsula of Dunedin (Fig. 1.1). These large-scale peninsula operations, along with other PFNZ projects, are based in urban or peri-urban, highly populated areas.

The PFNZ strategy places high importance on the engagement and participation of New Zealand residents. There are already over a thousand community conservation groups across New Zealand controlling for mammalian pests (Russell and Stanley, 2018), and PFNZ will provide a level of coordination and support for these groups not previously seen, that seeks to tap into the potential of the volunteer knowledge-base and their work-force. Similarly, new projects encouraging backyard and amenity area trapping in cities will emerge from this, providing further opportunities for urban residents to engage with local conservation initiatives. This means that PFNZ is in large part a social movement, as well as a conservation and pest control one (Glen et al., 2013; Russell et al., 2015), representing a unique combination of “top- down” national control and leadership from the Government’s Predator Free 2050 Ltd., the National Science Challenge for Biological Heritage, and Territorial Local Authorities, as well as a groundswell of “bottom-up” community conservation initiatives (Russell and Stanley, 2018; van Heezik and Seddon, 2018).

Figure 1. 1 The location of three peninsula-based predator control projects around New Zealand that fall under the Predator Free New Zealand initiative: The Otago Peninsula in Dunedin, the Miramar Peninsula in Wellington, and the Māhia Peninsula in Hawke’s Bay. Each project is attempting to eradicate certain target mammalian species through a combination of private and volunteer operations.

1.3 An “unmitigated disaster” for New Zealand1: The efficient invasion of the brushtail possum

The common brushtail possum (Trichosurus vulpecula) is a marsupial of the family Phalangeridae, which consists of 28 tree-dwelling marsupials native to Australasia (Jackson et al., 2015; Kerle, 1984). T. vulpecula (herein referred to as ‘possum’) is endemic to mainland Australia, Tasmania, and some offshore islands (Strahan, 1983) where it naturally occupies native open forests and woodland (primarily Eucalyptus spp.) (Kerle, 1984). Possums were first introduced to New Zealand in 1858 to establish a fur and skin trade industry (Pracy, 1974; Clout and Ericksen, 2000). Further introductions initiated by local acclimatisation societies occurred across New Zealand, particularly between 1890 and 1900, which greatly accelerated natural rates of spread (Cowan, 2005; Clout and Ericksen, 2000). Subsequently, possums became very successful and now cover > 90% of New Zealand, inhabiting all environments except permanent wetlands, alpine zones above ~2000m and some offshore islands (Shepherd et al., 2018). Total possum numbers in New Zealand in 2009 were estimated to be around 30 million (Warburton et al., 2009), with average densities of 2 – 10 possums per hectare across most habitat types (Coleman et al., 1980; Nugent et al., 2016). In contrast, possums exist at lower densities of ~1 ha-1 in most areas of Australia, reaching up to 4 ha-1 in forests (Cowan, 2005; Kerle, 2004; Kerle et al., 1992; Paull and Kerle, 2004). At their most abundant in New Zealand, possums can reach densities 20x higher than those seen in Australia (Coleman et al., 1980; Efford, 2000; Rouco et al., 2013). The notable success of possums in New Zealand compared to their native range has been attributed to fewer predators, parasites, diseases, and competitors (Cowan, 2005; Cowan, 2001), along with their ability to occupy a wide range of habitats (Clout and Ericksen, 2000), and the higher productivity of New Zealand’s forests compared to the arid woodlands of Australia (Clout and Ericksen, 2000).

The proliferation of possums in New Zealand has had serious ramifications. As well as being a significant ecological threat (see description of impacts below), their consumption of an estimated 7.67 million tonnes of vegetation annually includes economically important species like pine (Pinus spp.), eucalyptus (Eucalyptus spp.), and clover (Trifolium spp.) (Russell et al., 2015). As wildlife reservoirs and vectors of bovine tuberculosis (Tb), they also threaten the dominant agricultural systems of the New Zealand economy, particularly dairy (valued at

1 (Clout, 1999)

NZ$12.2 billion in export earnings in 2013) (Russell et al., 2015; Warburton and Livingstone, 2015).

1.3.1 The ecology of New Zealand possums

Females are polygamous and polyoestrous, usually breeding for the first time at 1 – 2 years old (Cowan, 2014). There is high breeding seasonality across populations, which may be influenced by a number of factors including climate, photoperiod, density, and food quantity or quality (Cowan, 2014). There tends to be a peak of births in autumn, and often a second birth pulse in spring depending on the condition of the mother (Kerle, 1984; Lowry et al., 2000). Year-round breeding may occur in situations where food is less limiting and of better quality (Statham and Statham, 1997). Long-term data suggest a high percentage (>80%) of adult females breed in most years (Ramsey et al., 2002), with reproductive success declining for females at around 6-8 years of age (Cowan, 2014). Females are pregnant for ~18 days and typically give birth to one joey which will remain in the pouch developing for ~120 days before beginning to emerge as a backrider (Cowan, 2005). At 170 – 240 days old the joey permanently leaves the pouch, remaining in the vicinity of its mother until it reaches its first birthday, at which point it undergoes dispersal (Cowan, 2005). Males will mature slightly later than females, at 1 – 2 years of age (Cowan, 2005).

Possums are generalist folivores, although they are opportunistically omnivorous. Foliage tends to comprise 50 – 90% of an individual’s diet (Nugent et al., 2000). In Australia this means their diet primarily consists of Eucalyptus spp., while in New Zealand they feed on 80+ native forest canopy tree species (Payton, 2000). Although possums feed on this wide range of plant species, individual possums tend to selectively browse certain species (Payton, 2000; Sweetapple, 2003; Sweetapple et al., 2002). This selectivity may be context dependent, potentially being influenced by the nutritional quality of foliage – which differs within and between plant species across the landscape, the availability of local food types, tree size, or secondary metabolite content (Cowan, 2005; Holland et al., 2016; Windley et al., 2016; Windley and Foley, 2015). Diet also varies with sex, season and altitude (Coleman et al., 1985; Fitzgerald, 1976). A small gut capacity limits the ability of possums to digest fibrous plant tissue, leading individuals to opportunistically supplement their folivorous diet with high

12 energy food items including flowers, fruits, human food scraps, birds, bird eggs and invertebrates (Adams et al., 2013; Harper, 2005; Nugent et al., 2000; Sweetapple, 2003).

Possums are generally solitary, with social interaction increasing in frequency in breeding seasons (Cowan, 2005). Possums are nocturnal and arboreal, typically moving above ground, but they will also move and forage along the ground when necessary (Cowan and Clout, 2000). Possums can swim across small bodies of water (e.g. streams) but avoid water when possible (Cowan, 2005; Cowan et al., 2007). Adult possums occupy a home range within which they will have multiple den sites that they frequently visit. Overlap often occurs between possum home ranges (Crawley, 1973; Green, 1984; Paterson et al., 1995), and does so with increasing frequency when possum density is high. Arboreal den sites are utilised in Australia, while in New Zealand possums use both arboreal and ground-based dens situated in natural tree hollows, epiphytes, under logs or in root systems and rocky outcrops, along with dens in anthropogenic structures such as house roofs (Glen et al., 2012; Ji et al., 2005; Statham and Statham, 1997). Individuals generally use between 5 and 10 den sites in forest habitats, with males tending to use more, and switch more often than females (Cowan, 2005; Whyte et al., 2013). Some den-sharing will occur, particularly by a mother and her young, or by possums in areas of higher population density (Caley et al., 1998; Day et al., 2000; Fairweather et al., 1987)

Most juvenile possums will disperse from their maternal home range at the age of 9 – 12 months, as they approach sexual maturity (Cowan et al., 1996; Efford, 1998). This results in peaks in movement around late summer and early autumn (Cowan and Clout, 2000). Dispersing possums can travel long distances (up to 3km) each night (Cowan and Rhodes, 1993; Cowan et al., 1996, 1997), generally settling within a month (Blackie, 2010). Average dispersal distances are around 4km (Cowan and Clout, 2000), with roughly 20-25% of juveniles undergoing ‘long-distance’ dispersal (> 2km) (Clout and Efford, 1984; Cowan and Rhodes, 1993; Cowan et al., 1996; Efford, 1998; Cowan, 2000). This long-distance dispersal is male- biased (around 4:1), reflecting the propensity females have for settling close to their maternal home range (Blackie et al., 2011; Cowan, 2000; Efford, 1991, 1998). However, when females do disperse, they tend to do so over further distances, with the maximum recorded distances of two females dispersing being 32km and 41km respectively, in rural habitat (Efford, 1991; Cowan and Clout, 2000).

Once established, a possum will maintain a relatively stable home range over its lifetime, with observations of seasonal changes and permanent shifts of more than a few hundred metres

13 being uncommon (Cowan and Clout, 2000; Pech et al., 2010; Yockney et al., 2015). Male home ranges (mean 1.9 ha; range 0.7 – 3.4 ha) are generally larger than those of female (mean 1.3 ha; range 0.6 – 2.7 ha), although home range size in both sexes is heavily influenced by habitat- specific resource availability, and local possum density (Cowan and Clout, 2000; Richardson et al., 2017; Whyte et al., 2013). This relationship is highly complex and non-linear, with low possum density sites sometimes producing larger or smaller home ranges than high possum density sites, depending on resource availability (Richardson et al., 2017; Whyte et al., 2013). Within-site home range size heterogeneity is not uncommon either, particularly in heterogeneous or patchy landscapes. Possums in these landscapes may display a ‘bimodal’ home range pattern, where they den at one site and then move, sometimes across large distances, to patches to feed (Brockie et al., 1997; Cowan and Clout, 2000; Efford et al., 1994), or, they may remain concentrated around a small area of energy-rich resources, and therefore have consistently smaller home ranges (Adams et al., 2014a). The largest home ranges (up to 100 ha) have been recorded in New Zealand high-country grasslands, which are relatively resource-poor, low density habitats (Glen et al., 2012; Rouco et al., 2017; Yockney et al., 2015).

1.3.2 The impact of possums on New Zealand biota

The impact of possums permeates across multiple levels of New Zealand’s ecosystems (Courchamp et al., 2003). Primarily, they are highly destructive folivores that exert vast and complex pressures on all New Zealand forest types. Their diet is usually dominated by certain favoured species which they may selectively browse. These are, broadly, canopy dominants in more mature forests like NZ southern rātā (Metrosideros umbellata), kāmahi (Weinmannia racemosa), kohekohe (Dysoxylum spectabile and māhoe (Melicytus ramiflorus), along with shrubs and small trees characteristic of regenerating vegetation, such as kōtukutuku (Fuchsia excorticata), and patē (Schefflera digitata) (Payton, 2000). While New Zealand forest canopies would naturally have experienced some level of localized browsing pressure from birds such as kererū (Hemiphaga novaeseelandiae) and kokako (Callaeas spp.), possums occupy a niche that was essentially open before their arrival (Payton, 2000). Selective browsing can considerably change the canopy composition of a forest (Payton, 2000). Browsing preference may be related to tree size, with larger trees often being browsed at rates disproportionate to their prevalence (Duncan et al., 2011; Gormley et al., 2012; Holland et al., 2016). Palatability also plays an important role in determining the browsing choices, and hence, impact, of

14 possums. Windley and Foley (2015) found that the nutritional quality of foliage differs within and between species, across the landscape and seasonally, which in turn was found to influence the relative browsing pressure of possums (Windley et al., 2016).

Possum damage has been linked to extensive areas of canopy die-back across New Zealand over the last 60 years, however the degree of possum impact in relation to other causes has been a contentious topic (Byrom et al., 2016; Payton, 2000). Canopy damage and die-back is known to differ within and between populations, communities and ecosystems (Duncan et al., 2011). For example, fuchsia, a popular possum target, shows regional differences in population-level browsing damage and mortality (Pekelharing et al., 1998; Sweetapple and Nugent, 1999), and similar patterns have been observed in loranthaceous mistletoes (de Lange and Norton, 1997). This heterogeneity highlights the complex factors that must be at play to stimulate die-back events. At present, it is accepted that canopy die-back may be directly caused by possums, particularly when they reach a site-specific threshold of damage, beyond which many individual trees are less able to recover (Holland et al., 2013). Die-back initiation may also depend on the population stage of the possums; if they are in the irruptive phase (10 – 15 years following invasion), they may cause the most discernible and rapid damage, whereas damage may be more gradual beyond this point (Payton et al., 1997; Thomas et al., 1993). Other abiotic and biotic factors such as snow, wind, flooding, drought, and other herbivore browsing may exacerbate or modulate the risk of canopy die-back in response to possum browsing (Cowan et al., 1997; Macinnis-Ng and Schwendenmann, 2015; Payton, 2000)

The effects of possum browsing may take many years to develop and be very long-lasting, meaning it can be difficult to assign causation to damage that is the result of historic possum invasion (Efford, 2000). For example, Ogden and Buddenhagen (1995) observed that kohekohe experienced a 50% loss in basal area over 10 years which was gradually resulting in die-back that they predicted would remove the species as a dominant canopy tree in 10 – 15 years. This means that possum control may also not result in immediate recovery of targeted species, as long-term detrimental legacy effects may persist. In a recent study, Sweetapple et al. (2016) measured variable rates of crown foliage recovery from 10 – 20+ years for different species following possum removal, although overall 20-year mortality rate was similar across browsed and un-browsed species.

Secondary effects of possums on ecosystems arise from their tendency to opportunistically supplement their foliage diet with energy-rich local food sources. These may be flowers and

15 fruits, potentially placing possums in direct competition with native bird species such as tui (Prosthemadera novaseelandiae) and kererū (Cowan, 1990; Cowan and Waddington, 1990). Evidence for direct competition is lacking, although it is known that some species such as kererū breed more frequently and successfully when there is a large fruit crop and may not breed at all without it (Clout et al., 1995; Powlesland et al., 2003). Possum browsing may reduce forest regeneration capacity as well through the suppression of fruit (Cowan, 1991; Cowan and Waddington, 1990). Possum predation on chicks and eggs, presumably for greater energy-intake, is also now known to be widespread (Brown et al., 1993, Innes et al., 1994; van Heezik et al., 2008). This includes threatened species such as the kōkako (Callaeas cinereus wilsoni), kiwi (Apteryx spp.), tīeke/North Island saddleback (Philesturnus carunculatus rufusater), pīwakawaka/fantail (Rhipidura fuliginosa), and kereru (Brown et al., 1993; McLennan et al., 1996; Sadlier, 2000). The impact of this competition and predation on native birds is illustrated by documented significant increases in bird population abundance and reproductive success following intensive possum control in a variety of habitats (see Byrom et al., 2016 for summary). Invertebrates are also affected by possum predation, including endemic organisms such as wētā (Anostostomatidae & Rhaphidophoridae) and Powelliphanta spp. snails (Clout, 2006; Nugent et al., 2000).

1.3.3 Urban possums: What do we know?

Before European arrival in Australia, possums were widespread and abundant (Kerle, 2001). However, a combination of factors including habitat destruction, predation and disease have caused rapid declines in the range of possums, and they are now found across less than half of their previous range, and at lower densities (Carthew et al., 2015; Matthews et al., 2004; Stobo- Wilson et al., 2019). Possums are now at their most abundant in cities, with densities averaging 5 – 7 ha-1, but reaching up to 12 ha-1 (Hill et al., 2007). Possums frequent cities in New Zealand also, although their density remains unknown (Adams et al., 2013).

Within cities in Australia and New Zealand, possums may occupy forest fragments and parks but are also found within residential properties (Adams et al., 2014a, 2013; Russell et al., 2011). Their regular occupation of residential housing in Australia has caused some human-wildlife conflict, as they can be noisy and destructive (Hill et al., 2007; Russell et al., 2011). Similarly, there is concern for their potential to harbour anthropozoonotic diseases and parasites (Hillman

16 et al., 2018; Waters et al., 2019). Due to their status as a protected native species, non-lethal management approaches have been researched and applied across Australian cities, including relocations, structural prevention, and fertility control agents (Eymann et al., 2013; Hill et al., 2007; Russell et al., 2011). Urban management of possums in New Zealand, when undertaken, is instead done by lethal means.

There is a general paucity of knowledge concerning the biology of possums in urban areas in both their native and introduced range, despite the fact that possum ecology tends to differ across habitats (Nugent et al., 2016), and that in both Australia and New Zealand urban possums are of management interest (Eymann et al., 2013; Statham and Statham, 1997). Gradually, studies are beginning to remediate this gap, however. Research assessing the denning and home range behaviours of urban possums in Australia and New Zealand has found that individuals tend to den in tree hollows when in more natural urban sites, but will preferentially choose to den in artificial structures such as house roofs, under decks, in chimneys etc. when they are available (Adams, 2013; Carthew et al., 2015). Possums select both native and exotic tree species as den sites, with preference determined more by tree physical characteristics (Carthew et al., 2015; Statham and Statham, 1997).

Urban home range estimates from one Australian study indicated a male bias in size, with males occupying an average home range of 8.61 ha and females, 2.03 ha (Statham and Statham, 1997). The male estimate was highly variable however (range 0.34 ha – 42.07 ha), and less so for females (range 0.39 – 7.25 ha), which is reminiscent of the large ‘bi-modal’ ranges seen in grassland habitat of New Zealand (Glen et al., 2012; Rouco et al., 2017; Yockney et al., 2015), possibly reflecting the patchy distribution of urban resources. A different study in Melbourne estimated smaller home range sizes than Statham and Statham (1997): 1.02 mean, range 0.35 – 3.80 ha for females, 1.19 ha mean, range 0.53 – 1.58 ha for males (Harper, 2005). In New Zealand home range appears similarly small, at 2.42 ha mean, range 1.21 – 5.04 ha for females and 4.86 ha mean, range 1.51 – 8.99 ha for males (Adams et al., 2014a).

Urban dispersal behaviour has been studied in Australia through genetic analysis by Stow et al. (2006). They found higher genetic relatedness between females sampled at the same site, and this declined rapidly with distance, whereas the opposite pattern was apparent for males, suggesting male-biased dispersal. However, male relatedness declined to low levels by 900m, further suggesting a high level of philopatry in both males and females (Stow et al., 2006). This is supported by observations that relocated possums can be known to return over substantial

17 distances to their original home range site (Eymann et al., 2013). In New Zealand, genetic analysis of several loci within an urban population showed elevated genetic relatedness amongst possums, suggestive of philopatry across both sexes as well (Adams et al., 2014b).

The reproductive biology of urban possums has also received some attention in Australia. In two studies, possums were found to follow the bi-modal breeding pattern common across most habitat types, with a large peak in autumn and smaller one in spring (Eymann et al., 2013; Statham and Statham, 1997). Year-round breeding at a lower rate was observed in one population by Statham and Statham (1997). Similarly, some females produced more than one young within a year, suggestive of high reproductive output, although they could not rule out it instead being a response to the death of the first offspring (Eymann et al., 2013; Statham and Statham, 1997).

1.4 Study background

1.4.1 The Ōtepoti/Dunedin context

The city of Ōtepoti/ Dunedin (~130,000 residents) (StatsNZ, 2018) is located on the South East coast of New Zealand (Fig. 1.2) (Lat 45°52’S Long 170°30’E). The centre of the city sits at the head of the Otago Harbour, and residential areas extend from this centre, gradually transitioning to rural land beyond the immediate city limits. A town belt of exotic and regenerating native forest stretches 5km across the North East end of the city centre. A large 20km (~9,000ha) peninsula forms the eastern edge of the harbour and connects with the inner city at a narrow neck (Fig. 1.2).

Described as the ‘wildlife flagship’ for Dunedin, the Otago Peninsula is an area of significant biodiversity value (Biodiversity Strategy for Dunedin City, 2007). The coastline and headland provide important breeding habitat for Nationally Important and Endangered species, including being the only mainland breeding site for the Toroa/Northern Royal Albatross (Diomedea sanfordi), and habitat for the Hoiho/yellow-eyed penguin (Megadyptes antipodes), rāpoka/whakahao/New Zealand sea lion (Phocarctos hookeri), kororā/little penguin, kekeno/New Zealand fur seal (Arctocephalus forsteri), and a variety of other native seabirds (Biodiversity Strategy for Dunedin City, 2007). The Peninsula’s forest fragments are home to a variety of native bird species including tūi, korimako/bellbird (Anthornis melanura), tauhou/silvereye (Zosterops lateralis), riroriro/grey warbler (Gerygone igata), miromiro/tomtit (Petroica macrocephala), tītipounamu/rifleman (Acanthisitta chloris), pīpipi/brown creeper (Mohoua novaeseelandiae) and the kārearea/NZ falcon (Falco novaeseelandiae) (OPBG Bird Monitoring Report, 2017), while endemic reptiles including the Southern grass skink (Oligosoma polychroma, Clade 5), cryptic skink (Oligosoma inconspicuum), korero gecko (Woodworthia sp. ‘Otago-large’), and mokokākāriki/jeweled geckos (Naultinus gemmeus) occupy grasslands and low-shrubs (OPBG Lizard Monitoring Report, 2016). Tidal inlets serve as feeding grounds for a variety of wading birds (Biodiversity Strategy for Dunedin City, 2007).

Figure 1. 2 The city of Dunedin as it is located in the South Island of New Zealand. The urban city centre is flanked by an eastern peninsula, the Otago Peninsula, which is predominantly pastureland. To the west lies the Western Harbour, which has some large patches of forest.

1.4.2 Predator Free Dunedin

In 2018, various Dunedin interest groups, including local and regional council, government agencies, local Rūnaka (Māori governing bodies), and community groups came together to sign a Memorandum of Understanding that marked the formation of the Predator Free Dunedin Charitable Trust (PFD). Like other PFNZ city projects, PFD will receive financial support from Predator Free NZ Ltd. for the first few years of operations. The operational core of PFD is comprised of three on-the-ground groups managing different sections of the city’s 31,000ha (Fig. 1.3). These are the Landscape Connections Trust or the ‘Halo Project’, a community group which has been in operation since 2014 and controls predators in a halo around Dunedin’s fenced ecosanctuary, Orokonui; the Urban Linkage Project, initiated in late 2019 to

20 manage control in the urban centre; and the Otago Peninsula Biodiversity Group, which since 2010 has been managing the Otago Peninsula. The preliminary goals of PFD are complete eradication of possums from the Otago Peninsula, and suppression of possums and mustelids across the western harbour, using State Highway 1 as a natural boundary (www.predatorfreenz/Dunedin).

1.4.3 The Otago Peninsula Biodiversity Group

In 2010, a community-led pest-control initiative began on the Otago Peninsula of Dunedin, through the formation of the Otago Peninsula Biodiversity Trust Incorporated, colloquially known as the Otago Peninsula Biodiversity Group. This trust (herein referred to as the OPBG) was comprised of landowners, farmers, iwi partners and other stakeholders, who came together with the collective goal of improving and restoring the ecology of the Peninsula, as summarised by their mission statement:

“Working with our community to create a pest-free Peninsula by 2050, in order to enhance our biodiversity.”2

It is important to the OPBG is that these natural enhancement efforts be deeply entrenched within the community, and that the benefits of their work are felt by both the indigenous wildlife and the people of the Peninsula. As a flagship project, an initial target of possum eradication across the Peninsula by 2018 was set. Possum control operations were divided into 5 ‘sectors’, beginning at the northern tip of the Peninsula in the ‘Taiaroa Head’ sector, all the way to the point at which the Peninsula connects with the city in the ‘Buffer Zone’ sector (Fig. 1.4a). Regular public and private consultation, along with community engagement and volunteering, has formed a large part of the management procedure. Operations have taken place in regular seasonal ‘knockdown’ phases within different sectors, consisting of baiting, trapping and poisoning, along with ‘mop up’ operations carried out with a variety of techniques. As of August 2019, The OPBG has reached the final stages of its possum repression operation and is beginning a four-year final eradication plan ending in 2023 (Thorsen et al., 2019). From there, the focus will be on defending the Peninsula from reinvasion, which is expected to only occur via the Buffer Zone (Adams et al., 2014b).

2 OPBT Summarised Strategic Plan, 2013

Figure 1. 3 Geographical boundaries of the three predator control and biodiversity enhancement groups that comprise Predator Free Dunedin: The Halo Project (blue) which surrounds the predator-free fenced ecosanctuary Orokonui (pink); the Urban Linkage Project (green), and the Otago Peninsula Biodiversity Group (orange).

Figure 1. 4 (a) The five operational sectors of the Otago Peninsula Biodiversity Group, within which possum control is undertaken. From North to South these are Taiaroa Head (TH), Clarks/Sheppards hill ‘pinchpoint’ (SH), Cape Saunders (CS), Portobello (PB) and the Buffer Zone (BZ). (b) The Buffer Zone, a residential area that connects the city (West) to the more rural land at the start of the Peninsula (East). Within the Buffer Zone residents will be undertaking voluntary trapping to reduce reinvasion of the Peninsula.

1.5 Thesis aims and structure

This is a study of two parts. In the first, I aim to provide the first estimates of urban possum density in New Zealand, and in the second, incorporate this information into a spatially explicit, individual-based model to simulate possum reinvasion of the Otago Peninsula.

Chapter 2 is a mark-recapture study of possums across three Dunedin sites. Each site represents a typical urban New Zealand habitat type, including a forest fragment and two residential areas of decreasing vegetation quality. Through spatially explicit mark-recapture analyses I aim to derive a density estimate of possum populations at each of the three sites, representing three habitat-specific density values. Density is a robust and transferable metric, relevant to many aspects of possum management and research. By presenting these initial density values, I hope to highlight the capacity for cities to harbour possum populations, and aim to provide information that can be incorporated into existing and future modelling and management tools.

In Chapter 3, I apply a spatially explicit, individual-based model to an urban environment, a unique application that modifies a newly-developed spatial tool created by Manaaki Whenua – Landcare Research, a New Zealand Crown Research Institute. The model simulates possums in a spatially explicit landscape, with juvenile dispersal as an individual-based process. I will first incorporate findings from Chapter 2 within the model, and then apply it to examine the nature of possum reinvasion onto the Otago Peninsula, explicitly modelling future management scenarios to assess their potential for minimising possum reinvasion. Furthermore, I will analyse how factors such as initial peninsula population density, control efficacy, and source population density influence the outcomes of these scenarios, thereby highlighting which risks need to be managed, and what information is currently lacking. With this work I aim to inform future management decisions regarding the best-practice method (scale and intensity of control) for reinvasion-prevention of possums onto the Peninsula. I also aim to highlight the efficacy and scope of this modelling tool for guiding future urban management projects elsewhere in New Zealand.

Chapter 2 Living in the City: Density of the common brushtail possum across urban habitat types of Dunedin, New Zealand

2.1 Introduction

2.1.1 Invasive species adapt to urban environments

The unique and challenging conditions of urban areas can favour certain species, termed ‘urban adapters’, over others (McKinney, 2006). These species are often non-native generalists with high behavioural flexibility, allowing them to navigate and exploit novel environments (Adams et al., 2014a; E. Lowry et al., 2013; H. Lowry et al., 2013; McKinney, 2006, 2002). Urban adapters make behavioural changes in response to novel challenges, including adjusting the timing of their activity (Ditchkoff et al., 2006; Riley et al., 2003; Schoeman, 2016), and using novel food resources and den sites (Beckmann and Berger, 2003; Kanda, 2005; Podgórski et al., 2013; Wright et al., 2012). Consequently, urban-adapter populations have been shown to act differently to their rural counterparts (Sol et al., 2013; Traut and Hostetler, 2003), sometimes even displaying completely novel behaviours that constitute a unique ‘urban ecology’ (Ditchkoff et al., 2006; H. Lowry et al., 2013; Russell and Stanley, 2018). We know relatively little of the biology of organisms occupying urban areas, many of which are invasive species (Russell and Stanley, 2018), and unpredictable ecological relationships might further complicate our understanding of invasive species’ impacts in urban spaces (Dukes et al., 2009). Research is needed to understand the unique ecological processes in cities, so as to effectively manage the invasive species that are present there (Doherty and Ritchie, 2017; Gallo et al., 2017; Russell and Stanley, 2018).

2.1.2 Density of urban invaders

Density, the number of individuals per unit area, is of fundamental interest when studying the population biology of well-established invasive species (Russell and Stanley, 2018; Simberloff, 2003). It is common to use population summary metrics such as density to make inferences regarding the impact of invasive species (Efford, 2000; Gormley et al., 2012; Ricciardi et al., 2013; Ruscoe et al., 2013). As opposed to indices of relative abundance, density provides a rigorous and transferable estimate of population size in a given area, which is then relevant for all aspects of management and research, including informing monitoring and predictive modelling, measuring control efficacy, and understanding space use (Efford, 2004; Glen et al.,

2012; Obbard et al., 2010; Parmenter et al., 2003). Despite the importance of this metric, and observations that urban adapters can exist at surprisingly high densities within cities (Baker and Harris, 2007; Lowry et al., 2011; Newsome et al., 2015; Weaving et al., 2011), reliable estimates of the density of invasive species in urban areas are sorely lacking (Russell and Stanley, 2018). As the extent of urban areas grow globally, and invasive species continue to proliferate, knowledge of this fundamental aspect of species’ ecology will become increasingly important.

2.1.3 Urban possums and their adaptive nature

Possums inhabit almost all New Zealand environments (Shepherd et al., 2018), and have the capacity to live successfully within urban areas in both Australia and New Zealand (Adams et al., 2014a; Eymann et al., 2013; Matthews et al., 2004). In Australia, possums can be found at their highest densities in cities – comparable to densities in some New Zealand forests – leading some to term urban areas their ‘last stronghold’ in Australia (Carthew et al., 2015; Eymann et al., 2013; Matthews et al., 2004; Stow et al., 2006). Possum ‘urban adapter’ characteristics allowing this success include behavioural flexibility, which allows possums to adapt to and exploit novel resources, as well as a high tolerance of disturbance (Adams et al., 2014a; Carthew et al., 2015). The use of novel den sites (roofs, buildings), and exploitation of human- provided supplementary food resources (compost heaps, fruit trees), are examples of this adaptive behaviour, and show the breadth of novel resources available to possums in these habitats (Carthew et al., 2015; Harper, 2005; Statham and Statham, 1997).

2.1.4 Possum density

Density is appreciated to be one of the most important metrics of possum impact (Efford, 2000). Interactions with native and non-native fauna, including nest predation and competition, are expected to increase with density (Efford, 2000), and possum foliage consumption – a major contributor to the destruction of many palatable forest species – can be greater at higher density, although the pattern of this relationship is variable spatially and temporally (Duncan et al., 2011; Efford, 2000; Holland et al., 2013). Possum home range size, number of offspring, dispersal distance, and other important population characteristics are also expected to change

27 with density (Isaac and Johnson, 2003; Richardson et al., 2017; Whyte et al., 2013). Possum densities across New Zealand tend to be habitat-specific, generally ranging from between 2 – 10 individuals ha-1, but sometimes exceeding 20 ha-1 (Coleman et al., 1985; Nugent et al., 2016). This habitat specificity is linked to the importance of habitat for providing key density- defining resources, including, most importantly, food, but also den sites and tree cover for movement. The relative availability and quality of these resources will affect the reproductive output of female possums, thereby directly influencing population size and growth in an area (Nugent et al., 2000). Resource availability can also mediate social interactions such as intraspecific competition, which can influence the number of individuals able to coexist within a given area (Caley et al., 1998).

A long history of possum research in New Zealand has resulted in estimates of possum density across most New Zealand habitat types (Table 2.1). Methods for these previous density estimates include conversion of Trap Catch Index estimates (Pech et al., 2010; Sweetapple, 2008), total removal trapping (Efford et al., 2000), and live-capture using the Jolly-Seber method (Ji et al., 2005), or other capture-recapture methods, including spatially explicit capture-recapture (SECR) (Arthur et al., 2004; Richardson et al., 2017; Rouco et al., 2013). Density has been estimated in order to assess habitat carrying capacities (Glen et al., 2012; Rouco et al., 2013), and to explore how density influences population characteristics such as relative foliage browsing pressure (Nugent et al., 2010; Sweetapple, 2008), contact rates (Ji et al., 2005), home range size (Efford et al., 2000), and population recovery rates (Pech et al., 2010). It has also been calculated indirectly when exploring other research questions (Blackie et al., 2011; Efford et al., 2005; Forsyth et al., 2005). However, the carrying capacity of different habitat types - often estimated as long-term average possum density without control - is poorly known (Efford, 2000), as most of these prior density estimates represent only snapshots in time. One of the few long-term population studies, in the Orongorongo Valley, lower North Island, has shown what appears to be a “habitat-determined equilibrium density”: a population density that fluctuates around a mean, with peaks and troughs during extreme weather events that are rapidly corrected via some form of population regulation (Efford, 1998; Efford, 2000). Therefore, it might be that the previous density estimates of Table 2.1, when averaged, can give a good indication of the approximate habitat equilibrium carrying capacity.

Table 2. 1 A summary of the past two decades of habitat-specific possum density estimates across New Zealand, with references. Table modified from Rouco et al. (2013).

Habitat Location Density Method Reference (possum ha-1)

Podocarp- Auckland Region 3.3 (winter) LT* Ji et al. 2005 broadleaved 4.1 (summer)

Podocarp- Waikato Region 14 RT** Blackie et al. 2011 broadleaved

Podocarp- Orongorongo 9 – 12 LT Arthur et al. 2004 broadleaved Valley

Podocarp- Orongorongo 4.1 – 6.9 LT Richardson et al. broadleaved Valley 2017

Podocarp- Dunedin Region 16 LT Efford et al. 2000 broadleaved

Mixed podocarp Maungatautari 5.2TCI^ RT Forsyth et al. 2005

Mixed broadleaved Wanganui area 10.8 LT Nugent et al. 2010

Nothofagus spp. Kaimanawa Range 5.6TCI RT Pech et al. 2010

Nothofagus spp. Craigieburn Range 2TCI RT Sweetapple 2008

Pinus radiata Waitarere 1.7 – 2.5 LT Efford et al. 2005

Pinus radiata East Canterbury 1 or 2 LT Whyte et al. 2013 (Hororata / Whitecliffs)

Exotic: Oak East Canterbury 7 LT Whyte et al. 2013 (Quercus robur) (Hororata / and sycamore (Acer Whitecliffs) pseudoplatanus)

Farmland Miranda 4.4TCI RT Forsyth et al. 2005

Grassland/shrubland Molesworth 1.7TCI RT Glen et al. 2012 Station

Grassland/shrubland Central Otago 0.4 – 0.7 LT Rouco et al. 2013

*LT = live trapping ** RT = removal trapping ^TCI = trap catch index

2.1.5 Urban possum density

Urban possum densities are yet to be formally estimated in New Zealand. The urban environment can be divided into distinct habitat types, often differing vastly in terms of vegetation structure, proximity to human residential areas, levels of anthropogenic disturbance, and other characteristics affecting possum density (Freeman and Buck, 2003). What little research has been done on urban possums shows that their distribution in New Zealand and Australian urban environments appears to be influenced by food and den site availability (Adams et al., 2013; Harper et al., 2008), leading most home ranges to intersect with forest fragments (Adams et al., 2014a; Harper, 2005; Harper et al., 2008). However, recent New Zealand research suggests possums are also capable of living within urban residential areas entirely independent of urban forest fragments (Fig 2.1) (Adams et al., 2013, 2014a).

Residential areas and urban forest fragments therefore both represent potential habitat for possums. Typically, possums will opportunistically supplement their low-nutrient foliage diet with high-energy food sources such as flowers, fruits, invertebrates, and eggs (Bolton and Ahokas, 1997; Harper, 2005). Residential areas may provide similarly high-energy food sources from fruit trees, compost heaps and vegetable gardens, the presence of which can influence the probability of residential sites being occupied by possums (Adams et al., 2013). These resources, along with vegetation structure and type, may be the main determinant of density across urban habitat types (Adams et al., 2013, 2014a, 2014b).

2.1.6 Density estimation via spatially explicit capture-recapture

Calculations of population density are often derived by dividing the number of detected individuals by the area sampled. However, a key issue when estimating the density of free- ranging animals is how to represent the true area being sampled, or the “Effective Trapping Area” (ETA). It is too simplistic to consider only the area covered by ‘detectors’ (e.g. traps, nets), as this has been shown to generally result in biased over-estimates of the true population density (Borchers and Efford, 2008). Dice (1938) suggested overcoming this by adding a ‘buffer’ width (W) to the area of the detector layout, equal to the home range radius of the species, but this depends in turn on accurate home range estimates, and biases are still unavoidable (Efford, 2004). Nevertheless, this method has been widely used (Brown et al., 1996; Hawkins and Racey, 2005; Sharma et al., 2010; Witt, 1992). Alongside the method of

Figure 2. 1 Habitat map of the urban Dunedin area showing the home ranges of 13 female and 11 male possums (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. From Adams et al. (2014a).

Dice (1938), many other methods have been proposed to estimate W (Tioli et al., 2009). The spatially explicit capture-recapture (SECR) method first outlined by Efford (2004) attempts to overcome the issue of defining the ETA by considering the variation in capture probability of individuals across space (Borchers, 2012). This is done by modelling every detector in space individually and by simulating the distribution of animal home range centres across the landscape as a spatial point process – also called the ‘state model’. An ‘observation model’ then simulates the probability of an individual being detected given the distance of a detector from its home range centre. By not including movement within the state model, this approach eliminates the requirement of associating a known region with the traps (Borchers, 2012).

To carry out density estimation using SECR (R program secr) (Efford, 2019), data on the location of detectors, along with the identity and location of captured and recaptured individuals, are required. Program secr estimates density (D) by fitting the state and observation models to the detection data. Within the latter model, two important parameters together define the detection probability: a measure of home-range size (σ), and the one-night probability of capture of an individual for a detector placed within the centre of the home range (g0). These two parameters are estimated during the model fitting process, which gives rise to the final density (D) estimate. Three methods are available to fit this type of SECR model to data; inverse prediction (Efford, 2004), maximum likelihood (Borchers and Efford, 2008) and Bayesian (Royle et al., 2009).

2.1.7 Study aims

In this study I explored the density of possums within the city of Ōtepoti/Dunedin, New Zealand via spatially explicit mark-recapture methods, with the aim of producing the first density estimates of possums in urban New Zealand. I aimed to derive density estimates of possums for three distinct urban habitat types of varying vegetation quality, quantity and residential influence, reflecting typical patterns of resource heterogeneity within urban areas. These were a forest fragment, and two residential areas with high to moderate vegetation cover and structural complexity. I hypothesised that possums would be present in each habitat type, but at densities lower than in podocarp-broadleaved forests (Arthur et al., 2004; Blackie et al., 2011; Efford et al., 2000; Ji et al., 2005) due to higher rates of disturbance and more heterogeneous distribution of optimal habitat and food choices. I further hypothesised that possum density would reflect the availability of their primary food, plant material, and as such would be highest in forest (Adams et al., 2013), similar to patterns in Australia which show that possums often preferentially select for Eucalyptus forest remnant sites within urban areas (Harper, 2005). I predicted that areas with well vegetated gardens (see Res I methods) would harbour lower densities than urban forest fragments, and that densities would be the lowest in areas with the least vegetation and lowest supplementary food availability (see Res II methods) (Adams et al., 2014a, 2013; Villaseñor et al., 2014).

Establishing baseline data of urban possum densities in New Zealand is important in order to effectively plan and achieve future possum control and biodiversity outcomes (Carthew et al., 2015; Eymann et al., 2013), including through the use of alternative approaches such as the predictive modelling used in Chapter 3 (Warburton et al., 2009). This research was conducted with the aim of gathering this baseline possum density data, representative of a typical New Zealand urban residential area, to contribute to conservation efforts in Ōtepoti/Dunedin and other urban centres, and to stimulate further discourse around urban possum management.

2.2 Methods

Capture-recapture trapping was undertaken across three sites within the city of Dunedin, New Zealand, representing three habitat types, as defined by the Dunedin Habitat Map (Freeman and Buck, 2003). These habitat types were (1) Forest fragment, a pocket of continuous tree and shrub vegetation surrounded by urban boundaries; (2) Residential I, well-vegetated and structurally complex backyards; and (3) Residential II, semi-vegetated backyard spaces dominated by shrubs and lawn (see Table 2.2 for full descriptions). Each habitat type was represented by a site around Dunedin (Fig. 2.2) where the mark-recapture took place from February to April 2019, encompassing the late summer period when possum activity is expected to be at its highest (Cowan and Clout, 2000).

Table 2. 2 Descriptions of the three urban habitat types in which the density of possums will be surveyed in Dunedin, New Zealand. Produced by Freeman and Buck (2003), reproduced from Adams et al. (2013).

Forest fragment Structure-rich tree stands, composed of both exotic and native tree species, forming closed Site name: ‘Jubilee’ canopies ranging from one hectare to 24 hectares in area (mean = 4ha), which are surrounded by modified residential landscapes.

Residential I (Res I) Residential areas with greater than one third of the property size comprised of mature, Site name: ‘Maori Hill’ 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-1.

Residential II (Res II) Residential areas with greater than one third of the property size comprised of structurally- Site name: ‘Wakari’ less complex gardens dominated by lawns. Green cover ranges between 42 – 50% with a mean housing density of 12.52 ha-1.

Figure 2. 2 The three sites representing different urban habitat types (forest fragment, Res I, Res II; see Table 2.2 for descriptions) across Dunedin, New Zealand, where spatially explicit capture-recapture of possums was undertaken to derive habitat-specific density estimates.

2.2.1 Sites

Forest Fragment – ‘Jubilee Park’

Chosen to represent a Dunedin urban forest fragment, Jubilee Park is a 23 ha amenity area managed by the Dunedin City Council, situated near the centre of the city (Fig. 2.2). The park is bordered to the east and west by roads and is part of the Dunedin Town Belt. The vegetation of Jubilee Park is a mix of regenerating native broadleaf and fern species (Fig. 2.3), particularly Pittosporum spp. and Griselinia littoralis which comprise the mid-lower layers of undergrowth, along with dominant exotic tree species (Pinus spp. and Salix spp.). Jubilee Park was deemed representative of a Dunedin urban forest fragment due to its small size and mix of exotic and regenerating native forest, along with its proximity to residential areas and the urban centre. No possum control had been implemented by the Dunedin City Council for the preceding 8 months, presenting an opportunity to study a semi-recovered possum population. Jubilee park has a history of possum control (A. Rebergen, Dunedin City Council, pers. comm.), which is representative of the typical urban environment in New Zealand at present, as urban possum control takes place in most of the main centres (GWRC, 2019, http://www.gw.govt.nz; AC, 2019, www.aucklandcouncil.govt.nz; CCC, 2019, www.ccc.govt.nz; HCC, 2018, www.hamilton.govt.nz). Hence, density estimates will represent a typical urban habitat that might have a history of intermittent possum control. Trapping at this site commenced at the start of February, 2019.

Figure 2. 3 The forest habitat of the Jubilee Park site, a public park and a forest fragment. The park comprises a broadleaf and fern understory, with an exotic canopy of pine and willow (Pinus spp. and Salix spp.).

Residential I – ‘Maori Hill’

Maori Hill, a suburb of around 1,900 residents (Stats NZ, 2013b), is situated above the northern end of the Dunedin Town Belt (Fig. 2.2). Housing density is low, the average garden size is moderate to large (700m2 – 1000m2), and backyard vegetation tends to be a structurally complex combination of mown and unmown grasses, small shrubs, herbs and trees (Fig 2.4). A high proportion of houses had fruiting trees and open or closed compost heaps. The average distance of traps to the nearby Town Belt forest strip, calculated on QGIS (v 3.6.2), was 231m (SD = 75m). The Maori Hill suburb is relatively close to a forest fragment and this site was chosen with the knowledge that proximity to forest fragment is

37 likely to increase the density of possums (Adams et al., 2014a, 2013). Maori Hill was meant to represent a typical ‘Residential I’ habitat, and these are often near forest fragments. Some backyards at the site had domestic animals, including cats, rabbits, dogs and chickens. Trapping in Maori Hill commenced at the start of April, 2019.

Figure 2. 4 Examples of Maori Hill gardens, which represent “Residential I” habitat. These gardens tended to have a mix of mown and unmown lawn, shrubs, trees, vegetable patches, and compost heaps or bins.

Residential II – ‘Wakari’

Around 3,000 residents live within the suburb of Wakari (Stats NZ, 2013c), which is located to the west of Maori Hill, 3 km from the city centre (Fig. 2.2). In general, gardens in this suburb are of moderate size (average ~800m2) and are dominated by open areas of mown and unmown grass and low shrubs (Fig. 2.5). Tree cover is relatively sparse, leading to low structural complexity across all gardens. Within this category there was some diversity however, as a few sites were well-vegetated. Some backyards had compost heaps, vegetable gardens and fruit trees. The average distance of traps to the Town Belt was 491m (SD = 60m). Trapping in the Wakari site commenced at the end of March, 2019.

Figure 2. 5 Examples of Residential II Wakari gardens. These were comprised of large areas of mown or unmown lawn, some shrubs and small trees. Houses also had gardens and compost heaps.

2.2.2 Trap placement

Grieve wire live-capture cage traps were used (Montague and Warburton, 2000). In total 70 traps were placed at Jubilee Park (average trap spacing = 30m), 64 at Maori Hill (25m spacing) and 65 at Wakari (21m spacing) (Fig. 2.6). The target 30m trap spacing was chosen to account for the lowest estimated home-range sizes of the urban possums in Dunedin (Adams et al., 2014a), while minimising the chances of the same individual always being caught in the same trap (Efford, 2004; Efford and Cowan, 2004; Nugent et al., 2011).

Figure 2. 6 The placement of traps at the three sites, Jubilee, Maori Hill and Wakari, where traps were placed roughly 30m apart. Within the two residential areas trap placement depended on local conditions and resident permission.

Jubilee Park

The first trap-line was orientated via a compass bearing, and 10 traps were placed at roughly 30m spacing. All other trap locations were chosen to construct a grid relative to this first line, with placements decided using a Garmin GPS unit. As Jubilee Park is a public recreation space, trap placement was somewhat dependent on the location of paths that crossed through the grid and attempts were made to minimise the public’s view of each trap.

Maori Hill and Wakari

For the two residential sites, areas of continuous habitat were chosen based on the Dunedin Habitat Map classification of habitat types (Freeman and Buck, 2003). A month prior to trapping, participation invitations were distributed to ~150 houses within the chosen area, and the site was narrowed down further as a result of the responses. The final trapping area was chosen to maximise the continuity of the grid and uniformity of the habitat, while keeping the distance to the nearest forest fragment even across the site. Traps were located within private backyards. Trap placement was initially determined by constructing a 30m x 30m grid within Google Earth Pro (https://www.google.com/earth/) which was shifted to avoid roads or buildings. From these points street addresses were retrieved from the Dunedin City Council rates database (https://www.dunedin.govt.nz/do-it-online/search/rates), and permission obtained to access properties. Final trap placement was in keeping with the allocated grid point, where possible, but other factors made placement uneven at times, including refusal by home- owners for a property to be used in the study, the type of available placement surface, visibility from paths and roads, and keeping traps away from the house to avoid disturbing residents. Most traps were placed in the back corners of gardens, under shrub or tree cover, although there was some diversity in trap placement conditions.

2.2.3 Trapping protocol

At each site, trapping was done over eight consecutive nights during a stable weather window. Possums are likely to occupy all parts of their home-range over a period of this length, increasing the likelihood of detection (Adams et al., 2013). Traps were set with a cinnamon-

41 coated apple bait, and a 50:50 flour and icing sugar blaze lure extending 30cm along the ground in front of the trap (Fig 2.7) (Rouco C., pers. comm). Traps were left overnight, checked each morning for possums and re-baited when the bait had been removed by possums or other animals. At regular intervals all traps were re-baited. Captured possums were placed in cloth bags and transported by foot a short distance to be anaesthetised with a SHOOF Portable Anaesthetic Machine (SHOOF International). Possums were subjected to a 5% isoflurane gas and 1.5% oxygen combination within a 40L chamber for approximately 10 minutes, or until no signs of alertness (open eyes, response to gentle tipping) were observed. When unconscious, the possum was transferred to a nose cone and a lower 2.5% isoflurane dose was continually administered. Possums were sexed, when possible, and their age was determined by approximate body weight (adult >2.5kg) (Cowan and White, 1989; Winter, 1980). Any joeys were recorded. Adults were given two ear tags with unique identifier codes (National Band and Tag Co., Kentucky, USA, size 1005-3, style 893) before being returned to within 10m of their capture location, allowed to wake up in the cloth bag, and released. On two occasions in Jubilee Park possums escaped untagged, but photo identification was deemed suitable as a recapture method for these individuals. Recaptured animals had occasionally lost both ear tags, but could be identified by unique traits (ear shape, muzzle cuts, colouration pattern or tail characteristics). Recaptured animals had their trap location recorded before being released near the trap.

Figure 2. 7 The Grieve wire live-capture cage trap setup for the mark-recapture. Traps were baited with cinnamon-coated apple, and an icing sugar and flour visual lure extended ~30cm from the front of the trap.

2.2.4 Camera trapping

Due to low recapture rates at the Maori Hill and Wakari sites, camera traps were installed for the last three nights at Wakari, and the last four nights at Maori Hill to observe any possums that approached but did not enter traps. Twelve cameras (Bushnell Trophy Camera model 119836, brown) were placed semi-systematically, beginning at a random trap number and then at every 5th trap along the street. Lack of attachment surfaces meant some cameras had to be moved to the next trap along. Camera traps were set 30 – 60cm from the ground, at distances of 2 – 7m from the trap location, and were attached to trees, fences or other suitable surfaces (Fig. 2.8). Cameras were set to video 30s of video when motion sensors were activated, with 10s intervals between videos. Cameras were set at 6pm each day and left overnight until the corresponding trap was checked in the morning. Two cameras at the Maori Hill site were not positioned correctly on one night, and one camera at Wakari was not turned on one night.

Figure 2. 8 Examples of the camera trap placement in relation to the live-capture cage traps.

2.2.5 SECR analysis

The density of possums at the Jubilee site was estimated via likelihood-based spatially explicit capture-recapture in R (v. 3.5.2) (R Core Team, 2018) using the secr package v. 3.2.1 (Borchers and Efford, 2008; Efford, 2019a). The population was assumed to be closed (no births, deaths, immigration or emigration) (Otis et al., 1978) during the short trapping period.

SECR models require a definition of the ‘area of integration’ or ‘state space’: the region across which the activity of target animals can be centred, and across which the maximum likelihood is integrated by a process of weighted summation (Efford, 2019b). This allows for the exclusion of non-habitat (e.g. water bodies) in fragmented landscapes, where no home-range activity centres will be placed, minimising the potential for underestimation of habitat-specific densities (Borchers and Efford, 2008; Efford, 2019b; Hooker et al., 2015). The main caveat for the creation of this ‘Habitat Mask’ area is that it must be large enough to encompass all individuals with non-negligible detection probabilities (Borchers and Efford, 2008; Efford, 2004; Royle and Young, 2008). Because the Jubilee site was located within a heterogeneous urban landscape, it was necessary to specify areas of habitat and non-habitat through the creation of a ‘mask’ object (Braczkowski et al., 2016; Hooker et al., 2015; Lamb et al., 2018). Trapping was conducted only within the forest fragment and hence, forested areas were the target of the density estimate. A habitat mask was created in QGIS using the National Landcover Database v. 4.1 shapefile, accessed through the Land Resource Information System (LRIS) portal (https://lris.scinfo.org.nz/layer/48423-lcdb-v41-land-cover-database-version- 41-mainland-new-zealand/). All forest-type habitat surrounding Jubilee Park was classified as habitat, which included both indigenous and exotic forest landcover. Next, a polygon shapefile of the forest habitat was created, which excluded areas of ‘non-habitat’, i.e. urban parkland/open space, and residential housing areas (Fig. 2.9). This shapefile could be loaded into secr to be transformed into a mask object following selection of an appropriate buffer width.

The choice of the buffer width surrounding the traps is the second important decision when defining the area of integration, and can be informed by several pre and post model-fitting checks. A buffer width of 300m was chosen based on an initial estimate of σ (the parameter defining the rate of decline in detection probability away from a trap), and retrospective secr mask-checking functions that indicate the buffer size at which density estimates tend to asymptote and become insensitive to further increases (Appendix A, Fig. 5.1) (Efford, 2019b). When applied to the forest habitat polygon, this resulted in a total mask area of 27.5ha (Fig. 2.9).

Figure 2. 9 The polygon shapefile describing the habitat types of Jubilee Park and the surrounding area. A 300m buffer around the trap locations was used to delineate a ‘habitat mask’, within which computation of density was undertaken.

Detection function for model fitting

Alternative options for the detection function - which describes the probability of capture of an individual in relation to the distance of a trap from its home range centre - were explored. Three null models with varying detection functions were fitted and compared via Akaike’s Information Criterion corrected for small sample size (AICc) (Efford, 2004; Hurvich and Tsai, 1989). These were the half-normal, exponential and hazard rate, which primarily differ in the length of the detection tail, and hence, the allowance for detections at far distances. Although the hazard rate detection function had a considerably lower AICc score than both the exponential and half-normal functions (Appendix A, Table 5.1), estimates were highly sensitive to buffer width (Appendix A, Fig. 5.2). The exponential detection function was used instead, with the second lowest AICc score, and a density estimate that reached an asymptote at a buffer width of around 250m (Appendix A, Fig. 5.2). As the half-normal is the most common detection function used, final density values using the exponential detection function

45 were compared with those derived using the half-normal detection function. Derived density values from the half-normal tended to be slightly lower than those from the exponential, but otherwise relatively similar (Appendix A, Table 5.2).

Model selection

Several models with varying spatial detection parameter values were considered of interest and were fitted to the detection data. These models were chosen to represent potential trapping biases and confounding effects (Borchers and Efford, 2008; Rouco et al., 2013). All model covariates were applied to 𝜎 and 𝑔0. These were a null model, where 𝜎 and 𝑔0 were set to 1; model b which simulated a permanent step-change behavioural response to capture in which animals that had been captured had an altered detection probability (Borchers and Efford, 2008; Otis et al., 1978); model bk, a permanent behavioural response to capture that was specific to trap location; model B, a transient behavioural response to capture that lasted until the next trapping occasion; model Bk, a location-specific transient behavioural response to capture and finally, an age model which included a 2-level age covariate (adult, juvenile).

Model fitting was carried out by maximising the conditional likelihood, assuming a multi-catch estimator, which has been shown to be relatively unbiased for representing single-catch trap data when trap saturation is low (Efford et al., 2009). Model fits were compared via AICc (Borchers and Efford, 2008). Models within ~2 AICc of the top-ranking model were considered to have substantial support (Burnham and Anderson, 2002). However, these models were examined to see if they included additional parameters to the top-ranking model, and if an additional parameter caused a < 2 increase in the AICc score, the parameter was considered uninformative, as the observed difference in AICc score may have been coming from the AICc additional parameter penalty (Arnold, 2010; Odell et al., 2008; Wilson et al., 2017). Models with uninformative parameters were not considered further. Density was then derived from the top-ranking model as:

푛 −1 퐷̂ = ∑ 푎푖(휃̂) 푖=1 where 푎푖(휃̂) is an estimate of the effective sampling area for animal 푖 with detection parameter vector 휃 (Efford, 2019c).

2.2.6 Camera trap analysis

Camera trap videos were analysed for possum presence and identity. If a possum had a visible ear tag, it was marked as a recapture. The individual could then be identified because on the occasions when this happened, only one possum in the grid had been caught and given an ear tag at that point in time. When footage allowed, individuals and camera ‘recaptures’ were identified based on distinctive features; in particular, ear shape and rips, face shape, colouration patterns, body size or tail shape.

2.2.7 Maori Hill and Wakari analysis

Due to a low number of recaptures, the data at the Maori Hill and Wakari sites could not be analysed via SECR methods. Instead, the camera trap information was combined with the live- capture information to calculate a ‘Minimum Number Alive’ (MNA) (Krebs, 1966) estimate for possums at each site (Pickett et al., 2005; Wilson et al., 2007). Next, a minimum convex polygon around the trap locations was calculated in QGIS to extract the area covered by the trapping grid (Burgman and Fox, 2003). There are a number of potential definitions of W, the buffer width used to determine the Effective Trapping Area (ETA). Mark-recapture studies can use capture data to estimate the average movement of individuals within their home range, either by estimating the mean distance moved by individuals between capture events (Brant, 1962), by estimating 𝜎 (Efford et al., 2005), or by calculating the mean maximum distance moved by individuals recaptured at least twice (Wilson and Anderson, 1985). However, the low number of recaptures at the Wakari and Maori Hill sites compromises the robustness of this method, because true movement patterns are unlikely to be revealed by these few individuals (Foster and Harmsen, 2012; Parmenter et al., 2003).

As an alternative, information extracted from radio-telemetry or GPS movement studies of home-range size may be used to inform buffer width (Harper and Rutherford, 2016; Hickson et al., 1986; Tioli et al., 2009). Radio tracking tends to result in larger ETAs, which are less likely to overestimate density than trap-revealed movement information (Tioli et al., 2009). When using this type of movement data, it is important to use estimates that are relevant to the study population, as estimates will still be dependent on the conditions of the initial sampling (Tioli et al., 2009; Wilson et al., 2007). The most relevant home range size values for this study

47 come from Adams et al (2014a), who GPS tracked 11 male and 13 female possums in residential Dunedin gardens between 2010 – 2011 (Fig. 2.1). They calculated home range using two techniques: the 100% minimum convex polygon (MCP) method and the Brownian bridges method. The latter method, which uses a Brownian bridge movement model (BBMM) to create a 95% utilisation distribution, produces a home range estimate that excludes areas of non-use, and thus is expected to improve the accuracy of home range estimates relative to the MCP method (Horne et al., 2007). The Brownian bridge estimates for mean home range size in Dunedin possums were 2.42 ha (± 0.09) for females and 4.86 ha (± 0.75) for males, with an overall population average of 3.54 ha (± 0.45) (Adams et al., 2014a). Because of the low sample size and discrepancies between male and female estimates, the population average value was used in this study. Assuming a circular home range area, the buffer width of the average home range radius was added to the trapping area to create the final ETA for each site (Foster and Harmsen, 2012; Harper and Rutherford, 2016; Hickson et al., 1986). ‘Minimum density’ was then calculated as the MNA divided by the ETA.

2.3 Results

In total 52 individuals were captured at the Jubilee site, and the proportion of juveniles and adults was roughly equal (Table 2.3). A total of 42 recaptures were recorded, with most individuals being re-captured one or two times. Five females had pouch joeys (range 0 – 120 days old; Cooke and Alley, 2002) at varying stages of development, although smaller joeys within the pouch may have been missed. The average distance moved by recaptured individuals was 87.6m, but this varied between 0m and 400m with a median of 51.7m (Fig. 2.10).

Table 2. 3 The total number of possums caught and recaptured (including ‘re-sightings’ via camera trap) across three sites of varying vegetation type and residential influence within the city of Dunedin, along with details of their age distribution.

Site Total number of Total number of Age distribution individuals recaptures

Jubilee 52 42 Juveniles (20) Adults (32)

Maori Hill 2 3 Adult (2)

Wakari 5 2 Adult (5)

The null model was the top-ranking model based on AICc scores, while three other models (model b, the permanent behaviour step-change; model bk, the location-specific permanent behavioural response; and the age model ) were within ~2 AICc units of the null model (Table 2.4). Although this, along with their moderate AICc weights, means that the b, bk and age model all fit the criteria of being well-supported, each had two parameters more than the null model while remaining under 4 AICc units from the null model, leading them to be classified as models containing uninformative parameters (Arnold 2010). This left no other models with a significant level of support, so a final density estimate was derived from the null model (Table 2.4). The mean possum density (± SE) at the Jubilee site was 3.7 ha-1 (± 0.6).

Figure 2. 10 The frequency distribution of distances moved in metres by possums between capture and recapture events at the Jubilee site. The average distance moved was 87.6m and the median was 51.7m.

Table 2. 4 Results of the AICc comparison for secr models of possum density at the Jubilee site in Dunedin. 퐷̂ is a derived density estimate, 𝑔̂0 is the one-night probability of an individual being caught if a trap is placed at the centre of its home range, and 𝜎̂ is a measure of home range size. AICcwt is the AICc weight. The shaded model was chosen as the top performing model, and density was estimated from this. See Methods for descriptions of the models.

Model Parameters 푫̂ 품̂ퟎ 흈̂ Log AICc ΔAICc AICcwt likelihood

null 𝑔0~1 𝜎~1 3.68 0.06 40.94 -422.16 848.56 0.00 0.36 b 𝑔0~b 𝜎~b 2.99 0.07 49.96 -420.33 849.51 0.95 0.23 bk 𝑔0~bk 𝜎~bk 3.68 0.07 39.16 -420.58 850.02 1.46 0.18 age 𝑔0~age 𝜎~age 3.79 0.06 36.43 -420.93 850.72 2.16 0.12 B 𝑔0~B 𝜎~B 3.77 0.07 36.94 -421.48 851.82 3.25 0.07 Bk 𝑔0~Bk 𝜎~Bk 3.69 0.06 40.55 -422.05 852.95 4.39 0.04

Across the trapping period two individuals were identified at Maori Hill, and five at Wakari. They were all adult, and one female at Wakari had a back-riding juvenile (age ~120 – 240 days old; Cooke and Alley, 2002) (Table 2.3). One individual at Maori Hill was re-sighted by camera trap on two separate nights (day 5 and day 8), and at two different traps on one night. At Wakari, one individual was re-sighted on two consecutive nights, while another was seen at two different traps on the same night. Due to the final trap spacing, the ETA was smaller at Maori Hill (19.4 ha) than at Wakari (23.4 ha) (Table 2.5). Estimated minimum possum density was below 1 individual per ha at both sites, and Maori Hill had a slightly lower density of 0.1 individuals ha-1 compared to the 0.2 individuals ha-1 estimated for Wakari (Table 2.6).

Table 2. 5 The trapping Minimum Convex Polygon (MCP) and Effective Trapping Area (ETA) sizes used to calculate the density of possums at two sites of differing residential influence within the city of Dunedin, New Zealand. The ETA was calculated to include the mean home range radius of Dunedin possums calculated by Adams et al. (2014a).

Site Trapping grid MCP (ha) ETA (ha)

Maori Hill 6.385 19.423 Wakari 8.744 23.410

Table 2. 6 Summarised results of density estimates for three sites of varying vegetation quality and residential influence within the city of Dunedin, New Zealand. Methods used were spatially explicit capture-recapture (SECR) or Minimum Number Alive (MNA) within an Effective Trapping Area. Density is calculated as the MNA divided by the ETA.

Site Method Density (no. ha-1) (± SE)

Jubilee SECR 3.7 (3.1 – 4.3) Maori Hill MNA 0.1 Wakari MNA 0.2

2.4 Discussion

This study aimed to assess the density of possums across Dunedin urban habitat types via spatially explicit capture-recapture. The density of possums varied substantially across the three study sites, highlighting the importance of fine-scale habitat differentiation in determining the relative densities of geographically-close urban possum populations. As expected, the forest fragment supported the highest density of possums. The two residential sites had similarly low capture rates that did not appear to reflect relative vegetation availability. Although I will compare the densities across the three study sites, it must be noted that they were derived from different techniques and are therefore not strictly comparable.

2.4.1 Forest fragment possum density

Density of possums at Jubilee Park appeared to be as predicted: lower than might be expected within that forest type in other New Zealand ecosystems. Forest that is a mix of native broadleaved and exotic species in New Zealand has been estimated to support possum densities that range between 7 and 10 individuals ha-1 (Nugent et al., 2010; Whyte et al., 2013), while native podocarp-broadleaved forests have an estimated 3 to 16 individuals ha-1 (Arthur et al., 2004; Blackie et al., 2011; Efford et al., 2000; Ji et al., 2005). The 3.7 ha-1 estimate of this study is at the lower end of the podocarp-broadleaved range, and is instead most similar to studies from native southern beech forest (Nothofagus spp.) (Pech et al., 2010; Sweetapple, 2008) and radiata pine plantations (Pinus radiata) (Efford et al., 2005; Whyte et al., 2013). These are habitats where the vegetation tends to be unpalatable to possums, and the diversity of food choices is somewhat limited, which might restrict possum numbers (Rouco et al., 2013).

Urban forest fragments are often characterised by unique conditions that are likely to affect the organisms occupying them. These include high levels of disturbance from the presence of humans and their domesticated animals (Baker and Harris, 2007; Godefroid and Koedam, 2003), as well as noise and light pollution (Mörtberg, 2001), and pervasive edge effects that can extend from the corresponding urban matrix (Caryl et al., 2013; Mörtberg, 2001). Urbanization and its corresponding disturbances have been associated with a reduction in the diversity of arboreal marsupial assemblages (Isaac et al., 2014), while Brearley et al (2012) showed that an Australian marsupial, the squirrel glider (Petaurus norfolcensis), occupying

52 forest fragments adjacent to an urban edge tended to display higher hair cortisol levels (a measure of stress) than individuals adjacent to less urbanised areas. Furthermore, Jubilee Park is surrounded by roads which can be barriers to movement and a mortality risk for mobile urban organisms (Baker and Harris, 2007; Ramp and Ben‐Ami, 2006). In Australia, roads are often avoided by possums (Brockie et al., 2009; Forman and Alexander, 1998; Statham and Statham, 1997).

It might be expected that with all these disturbance pressures, small forest fragments in cities would host lower possum densities than other areas of a similar forest type. I suggest that the density estimated at Jubilee Park reinforces the importance of forest fragments for harbouring urban possum populations, but also supports the notion that forest fragments are a unique environment, where more factors are at play to affect the density of possums than can be encompassed just by considering the forest type. This concept, that possum density is a feature of the overall environmental context of a habitat, is not new. Forest edges connected to pastureland have been found to typically support higher possum densities than expected for the forest type, which is thought to be linked to the opportunities present in this transitional context (Coleman et al., 1985; Efford et al., 2000). As such, urban forest fragments should be considered their own habitat type, and further estimates of density in this habitat should be made to inform models that are based on habitat classifications.

2.4.2 Characteristics of the forest fragment population

The age structure of the Jubilee population was a relatively even mix of juveniles and adults. Adult possums do not tend to shift their home range once it has been established, meaning most were probably resident before control was done (more than 8 months ago), or were based nearby outside of the forest fragment, and have made small adjustments to their movements (Cowan, 1993; Efford et al., 2000). All juvenile possums go through a dispersal phase, where distance is generally male-biased and females remain comparatively close to their maternal home range (Clout and Efford, 1984). As such, I would have expected a higher representation of juveniles in the population than adults due to the relatively recent control 8 months prior, but it is likely that some of the adults were not trapped during this control. Nevertheless, the presence of adults and juveniles suggests that the population is a combination of long-term residents and immigrants.

The observation of pouch young and back-riders (Cooke and Alley, 2002) at various stages of development suggests an actively breeding population. Sampling was done at the end of summer at this site, which means reproduction in the spring is likely to be occurring. Typically, a bi-modal birth distribution (including the second spring birth peak along with the usual autumn peak) is interpreted as a sign of ample food resources (Kerle and Howe, 1992), as mothers must be in good condition to breed more frequently than once per year (Kerle, 1984). The resources within and around this forest fragment may therefore be sufficient to sustain a functioning population of high reproductive capacity, although it is also possible that, due to periodic artificial population reduction, the possum population is perpetually in a growth phase where food/den availability is not limiting, allowing for greater female reproductive success (Ramsey et al., 2002).

2.4.3 Implications for urban biodiversity

The density of possums found in the Jubilee forest fragment indicates a potential for significant impacts on resident native biodiversity. Adams et al. (2014a, 2013) found evidence that possums in Dunedin were more likely to frequent urban habitats that also play a role as refugia for urban biodiversity such as native birds (van Heezik et al., 2010, 2008), and the findings of this study support that conclusion. Hence, possums may be exerting high predation and competition pressure on native forest bird species that also use this forest fragment habitat.

Furthermore, higher possum densities are likely to result in greater foliage browsing pressure (Duncan et al., 2011), and their estimated density at this site may be enough to trigger die-back of regenerating native vegetation. Holland et al. (2013) demonstrated that, depending on the vegetation type, possum density can reach a point at which they cause an irreversible ‘damage threshold’ to be crossed, which is a level of browsing damage that a plant cannot recover from, generally resulting in the death of the tree. At some sites this threshold appeared to be reached at densities of 3 – 4 individuals ha-1 (Holland et al., 2013). It is important to note that while this area is regularly controlled by the Dunedin City Council, having possums at this density, even for a short time, might cause this damage threshold to be crossed, leading to die-back even after control has happened (Holland et al., 2013; Rose et al., 1992).

2.4.4 Residential area possum densities

The estimated minimum possum densities at the Maori Hill and Wakari sites are lower than any recorded densities in New Zealand, but are most similar to the grassland/shrubland estimates of Rouco et al. (2013) and Glen et al. (2012) (0.4 – 1.7 ha-1), as well as some of the lower estimates made in Nothofagus spp. southern beech forest (0.5 ha-1) (Clout, 1977, Clout and Gaze, 1984). In Australia, comparatively low densities have been recorded in a variety of locations primarily comprised of Eucalyptus spp., including sites around Canberra (0.49 ha-1; Dunnet, 1964), Tasmania (0.31 ha-1; Hocking, 1981; 0.04 ha-1; le Mar and McArthur, 2005), New South Wales (0.44 ha-1) (How, 1972) and South-Western Australia (0.28 – 2.84 ha-1) (How and Hillcox, 2000). Some of these low densities can be attributed to anthropogenic habitat degradation and disturbance, while others simply reflect poor habitat quality for possums (le Mar and McArthur, 2005). Research has shown that in New Zealand urban settings, possums preferentially select for forest fragments, but have the capacity to move through, and have home ranges in, residential areas (Adams et al., 2014a, 2014b). However, these residential areas are arguably more challenging environments for possums to reside in due to the high heterogeneity of resources, lack of continuous vegetation, and threats from anthropogenic disturbance. Hence, it might be concluded that the habitats represented by Maori Hill and Wakari are unsuitable for possum occupation in general, except for those few individuals that may be particularly behaviourally flexible and opportunistic.

Urban adapters can display distinct behavioural differences at the individual level, which may influence their capacity to negotiate highly urbanised environments (H. Lowry et al., 2013; Réale et al., 2007). In male song sparrows (Melospiza melodia) urban individuals displayed more bold and territorial behaviour compared to their rural counterparts (Evans et al., 2010), while urban noisy miners (Manorina melanocephela) were similarly bold in temperament (Lowry et al., 2011), and eastern chipmunks (Marmota flaviventris) that were more explorative and docile occupied more human-disturbed sites (Martin and Réale, 2008). It has been suggested that possums occupying residential sites may be particularly well-adapted individuals (Adams et al., 2014a). If so, these observed individuals might be anomalies that have strayed from the forest fragments, where their conspecifics are based. With this conclusion comes the implication that residential habitat does not routinely harbour possums at any high density, and that a permanent population of possums primarily foraging, denning and reproducing in residential areas is unlikely to be common.

Alternatively, there could be more possums at these sites than trapping suggests due to behavioural avoidance of trapping devices. I tend to think that this is more likely than the explanation above. My conjecture is based on multiple prior observations that urban possums were difficult to recapture (< 5% success rate), and were trap-shy (Adams et al., 2014a; Eymann et al., 2013; Statham and Statham, 1997), along with anecdotal evidence from many households that they routinely had a possum in their backyard, often denning in their house roof, and evidence of recent possum presence (scat, browsing damage) at sites where no possums were caught. Furthermore, camera traps picked up a number of individuals not caught in cage traps, and behaviours that could be interpreted as ‘wary’ were observed on the camera videos, including one individual investigating and putting its head into a trap before backing away and not taking the bait. Is this type of behaviour seen in non-urban animals? Perhaps, but further research into the behavioural distinctions of urban populations could be an important avenue of research, particularly if there are changes in the trappability of urban possums, which would have implications for management.

Contrary to predictions, the more vegetated Maori Hill site had a very similar and slightly lower estimated minimum possum density than Wakari. This is unexpected when considering the relative vegetation levels of the two sites and their proximity to the Town Belt. It is possible that possums with access to forest fragments will preferentially choose to spend time in there, as was suggested by the findings of Adams et al (2014a), who noted that possums infrequently selected for Residential I habitat in their home-range movements, more often occupying forest fragment or Residential II habitat. The closest forest fragment at the Wakari site was around 350m away, a distance that possums could move across over a multi-day period. Thus, it would also be possible for a possum to fit both a forest fragment and the trapping site at Wakari within a home range of < 4 ha-1. Further study examining density at sites even more independent of forest fragments might be useful to understand more about how many possums a residential site can support. It is still, however, interesting to note that possums are frequenting these areas of high residential influence, and for multiple consecutive and non-consecutive nights in the case of two of the identified individuals, suggesting these areas form an important part of their home range. Similarly, these sites obviously do provide habitat for possums, which contradicts the default decision to represent the carrying capacity of these habitat types as zero when conducting spatial modelling of possums (Warburton et al., 2009).

2.4.5 Study limitations

This study presents only a snapshot density estimate of single populations at three sites during one time of year (late summer – early autumn). Possum density is known to be non-static, fluctuating seasonally with the availability of food resources (Warburton et al., 2009), and timing of breeding periods. For example, density might be higher in the late spring to summer period when juveniles are dispersing (Rouco et al., 2013). As an example of this, a long-term mark-recapture study in a podocarp-hardwood forest in the Orongorongo Valley saw one undisturbed population vary over a 35 year period from 6.5 individuals ha-1 to 13.7 individuals ha-1, with an average of 9.8 ha-1, representing a difference of ±35% (Efford, 2000; Efford and Cowan, 2004). Comparison of density estimates reveals that there is variation between estimates from the same habitat, as shown in Table 2.1, depending on site-specific characteristics (Efford, 2000). These prior research findings show that extrapolation of my results to other similar habitat types should be done with caution, although urban areas might be buffered from some seasonal fluctuations due to year-round food supplies (H. Lowry et al., 2013). Ideally, long-term density estimates should be made for urban habitats across a range of sites, as the findings of this study suggest the potential for important differences between urban habitats and other ecosystems. Camera traps present a viable alternative for studying possums in heterogeneous urban habitats, and residential areas in particular.

2.5 Conclusions

The density of possums varied considerably across the three urban habitat types studied. The forest fragment density fell within the lower range of previous estimates for its forest type, potentially reflecting the unique and restrictive conditions of a forest fragment environment. This suggests that urban forest fragments should be further explored as their own habitat type for possums through spatially and temporally replicated studies of density, and other population biology information. Low densities at the Wakari and Maori Hill sites did not seem to depend on relative vegetation cover, as predicted, and robust density estimates were hindered by low recapture rates. Nonetheless, these sites obviously do provide habitat for possums, which contradicts the default decision to represent the carrying capacity of these habitat types as zero (Warburton et al., 2009). Further research would be useful to explore the potential for urban possums to behave in different ways to possums in other habitats, potentially explaining this lack of recapture success.

Chapter 3 Predicting reinvasion of the common brushtail possum within Dunedin, New Zealand, via spatially explicit, individual- based modelling

3.1 Introduction

3.1.1 Invasive mammal management in urban areas

Management of invasive mammalian pest species has become imperative to avoid some of the worst effects of invasions, and the relative merits of different management techniques have come under scrutiny as a consequence. Interventions for invasive species control can include prevention of invasion stages (arrival, survival and spread), mitigation of effects when a population is established via ongoing suppression, or total eradication (Marbuah et al., 2014; Simberloff et al., 2013b). Historically, the majority of intense control efforts have been aimed at the first two interventions, however investments in technology and an increasing knowledge base are making eradications more feasible (Crowley et al., 2017; Genovesi and Carnevali, 2011; Simberloff et al., 2013), and there are compelling economic and ethical incentives for eradication over ongoing suppression.

Pest eradications have mostly been attempted on small, uninhabited islands due to the lower risk of reinvasion (Genovesi and Carnevali, 2011), and considerable conservation gains have been made as a result of these efforts (Courchamp et al., 2003; Jones et al., 2016). However, more ambitious large-scale eradication programmes at mainland sites have recently been documented in the international literature (Bryce et al., 2011; Robertson et al., 2017; Robertson et al., 2019). This includes urban areas, where, as discussed in Chapters 1 and 2, there is an increasing need for effective pest control (Doherty and Ritchie, 2017). These mainland mammalian pest control initiatives are often fraught with uncertainty; gaps in the knowledge of the target species’ ecology in the introduced range, uncertainty around the biological effects of the invasive species, and importantly, uncertainty around the effectiveness of alternative management strategies (Marbuah et al., 2014; McLane et al., 2011). These uncertainties, paired with the large economic burden of control regimes (Doherty and Ritchie, 2017), render it imperative that managers make decisions that are evidence-based, well-planned, and demonstrably economically efficient (Byrom et al., 2016; Courchamp et al., 2003b; Rouco et al., 2017; Russell and Stanley, 2018; Sweetapple et al., 2014). In this lies the potential of predictive simulation models (Anderson et al., 2016).

3.1.2 Individual-based models for invasive species management

In recent decades, simulation models have become an important tool for wildlife management (Barlow, 2000; McLane et al., 2011). One type, the Individual-Based Model (IBM), has become particularly popular for addressing management and research questions. IBMs take a ‘bottom-up’ approach when representing a system, simulating the individual interacting units of the system, or ‘agents’, which are usually individual organisms (Tang and Bennett, 2010). These agents move through a simulated environment, representing a landscape with habitat features, and are programmed to follow simple rules that influence how they interact with each other and their environment. By modelling the behaviour and interactions of many single individuals in an environment with internal processes of a researcher’s creation, IBMs allow scientists to explore the complex patterns, or emergent properties, that can arise in a system as a whole from these initial individuals (McLane et al., 2011; Tang and Bennett, 2010).

Although focus has been particularly on the use of IBMs for conservation purposes (e.g. Goss‐ Custard and Stillman, 2008; Grosman et al., 2009; Kramer‐Schadt et al., 2004), they can also be an important tool for the management of invasive species. IBMs have been used to predict the spread of invasive species into new areas, and to explore the factors affecting patterns of invasion, potentially guiding proactive measures to prevent further harm (Bertolino et al., 2008; Higgins et al., 1996; Pitt et al., 2009; Scanlan and Vanderwoude, 2006). Alongside this, IBMs have been employed to compare the effectiveness of alternative control strategies for pre- existing established populations (Bonesi et al., 2007; Buckley et al., 2003; Keith and Spring, 2013). This power to simulate future scenarios that incorporate the spatial relationship between an organism’s environment and its behaviour helps to reduce the inherent uncertainty faced by decision-makers, creating more effective management regimes (Bonesi et al., 2007; McLane et al., 2011; Peterson et al., 2003). Importantly, IBMs can also provide a long-term, ecosystem- wide view of the study system, where the true complexity of interacting processes can be accounted for (Anderson et al., 2016), which might increase the long-term success of the pest management (Mack et al., 2000; Zavaleta et al., 2001). Finally, IBMs might also highlight data deficiencies, allowing researchers to pinpoint information gaps in a target species’ ecology, or the environment they exist within (Barlow, 2000; Travis and Park, 2004).

3.1.3 Possums – why model?

There are several compelling reasons why modelling, particularly individual-based spatial modelling, represents an important tool in the ‘management toolbox’ for possums in New Zealand. Firstly, as discussed in Chapter 1 and 2, the need for effective possum control is high due to their detrimental impacts on native New Zealand biota. Decades of research has led to many successful attempts to reduce and maintain possums at low densities across New Zealand, however, there are still challenges in the selection and implementation of appropriate and effective control for possums (e.g. bait type, trap placement) (Brown et al., 2015; Byrom et al., 2016). New Zealand is beginning to attempt eradications over larger areas on the mainland, and investment in technological improvements is now making these localised eradications more of a possibility (Etherington et al., 2014; Genovesi and Carnevali, 2011; Simberloff et al., 2013). However, mainland eradication efforts are hampered by the ongoing threat of reinvasion from surrounding areas (Carter et al., 2016; Doherty and Ritchie, 2017; Newsome et al., 2015; Rouco et al., 2017; Saunders and Norton, 2001), instead remaining in a state of “perpetual eradication”, whereby new invaders are constantly removed from the control area (Carter et al., 2016). As such, efforts to eradicate mainland possums represent a significant ongoing commitment and long-term investment of both time and money (Doherty and Ritchie, 2017). New approaches are needed to supplement current practice in order to increase the efficiency and cost-effectiveness of mainland eradication.

The second argument for the use of possum IBMs is the spatial and individual nature of possum ecology. The distribution and abundance of possums at landscape and local scales is highly linked to habitat type, making them a suitable candidate for spatial habitat modelling. As outlined in Chapter 2, possum density in particular appears to be highly habitat-dependent, and therefore, somewhat predictable across space. Further, most other important aspects of their ecology are linked to habitat type, including reproductive output, dispersal distance, home range size, energy intake and intraspecific interactions (Glen et al., 2012; Glen and Byrom, 2014). Thus, spatially explicit modelling is appropriate to represent important, habitat-specific, processes that determine possum population dynamics. IBMs will be particularly useful in situations where the habitat of interest is highly heterogeneous (e.g. urban areas) (Adams, 2013), and this complexity can be represented in the model. An IBM is also suitable for possums because individual decision-making by possums and local interactions are important for determining population spread (Ramsey and Efford, 2010).

Finally, IBMs present a viable tool to bolster support for management decisions in human- dominated landscapes. Within the strategy for New Zealand to reach the PFNZ goal by 2050, emphasis has been placed on the importance of encouraging pest control in major New Zealand urban centres. In this way, the PFNZ initiative is centred on the active encouragement and participation of local governing bodies, and the community groups and residents under their jurisdiction (Russell et al., 2015). Pest control in human-dominated landscapes has its own unique set of challenges, which managers must face as they begin to implement city-wide mammalian pest control plans (Harris et al., 2012; Ogden and Gilbert, 2009; Oppel et al., 2011). For one, large-scale aerial application of toxic bait is unacceptable to most members of the public (Glen et al., 2013), so control must take the form of more time-intensive, ground-based methods such as trapping and bait stations, which are less efficient and can be limited by property access issues (Glen et al., 2013). Residential housing and food supplies can support greater densities of pest species, as well as rendering some baits less effective (Oppel et al., 2011). Any decisions made rely on the support of the community (Crowley et al., 2017; Glen et al., 2013), so any tool which can justify management decisions, while allowing groups to maximise efficiency within their constraints, will be valuable for urban mammalian pest control efforts.

3.1.4 History of IBMs for possum management in New Zealand

There has been interest in developing various modelling tools for possum management in New Zealand over recent decades. These have mostly been fuelled by the need to predict and manage the spread of bovine Tuberculosis (Tb), which threatens the New Zealand agricultural industry, and of which possums are an important vector and reservoir (Warburton and Livingstone, 2015).

Tb models

The first Tb models were rooted in epidemiological theory. Generally simple in structure (i.e. deterministic and non-spatial), they consisted of a population designated into various classes depending on infection status, within which various population-level processes, such as reproduction and density-dependent mortality, would occur (Barlow, 1991a, 1991b). The first

63 model was created to closely match the observed epidemiology of Tb within populations. Later, basic spatial elements were incorporated by implementing the Tb model across a grid of 1km cells, allowing for sex and age-structured dispersal and transmission of Tb across cells, along with more realistic representations of the patchiness of the disease (Barlow, 1993; Barlow and Kean, 1995). These first models were used to assess Tb management questions, and they revealed some important insights into the nature of Tb within possum populations (Barlow, 2000). Importantly, from these first models the ‘threshold concept’ was formed, where reducing and maintaining possum populations at a size below around 50% of their natural density will eliminate Tb from the population within several years (Barlow, 1991b). Today, managers still incorporate this into their decision-making, and there is some field-based support for this concept (Barlow, 2000; Caley et al., 1999). Finally, Tb models were also used to address specific scenarios, including exploring the relative success of culling, vaccination and immunocontraception as control methods (Barlow, 1996).

Further developments in Tb modelling came as more complex models were attempted. The models of Pfieffer (1994), Pfieffer et al. (1995) and Efford (1996) represented the first foray into stochastic individual-based models for possum modelling. Pfieffer et al. (1995) and Pfieffer et al. (1994) created a model – ‘PossPop’ – with highly detailed individual behaviour and Tb dynamics, while Efford (1996) produced a model that simulated possums that would give birth, die and disperse across a habitat map represented by raster GIS files. From these first attempts, Ramsey and Efford (2010) eventually developed a spatially explicit, IBM for possums. They aimed to directly simulate individual behavioural interactions and disease transmission, thus simulating the local-scale dynamics responsible for patterns of Tb transmission and proliferation. Their model is grid-based, beginning with a raster GIS landscape of carrying capacities which is populated with possums, with each individual being assigned a sex and home range centre within a cell. The model simulates birth, death, home range movement, and sex and age-specific dispersal, as well as the local transfer of disease, all while incorporating demographic stochasticity (Ramsey and Efford, 2010). When compared with the model of Barlow (2000), the model of Ramsey and Efford (2010) predicted some novel Tb dynamics, including a lower propensity for Tb to establish rapidly within populations.

Beyond Tb models

Although possums were recognised as an ecological pest during the development period of these models, attention was not given to their control for conservation purposes. As the main focus of possum has extended beyond Tb management, the IBM of Ramsey and Efford (2010) has been adapted – by removing the Tb epidemiological elements – and applied to conservation management questions. Shepherd et al. (2018) made minor changes to the model, including increasing the computational capacity, and reintroduced it as a management tool for simulating possum population dynamics across large areas of New Zealand that are primarily controlled for conservation purposes. With this model, they explored reinvasion scenarios for island and mainland areas, and made management recommendations (Shepherd et al., 2018). Lustig et al. (2019) took elements from the Ramsey and Efford (2010) model, but made some revisions. Instead of every individual being modelled across all processes (birth, death etc.), population- level processes were modelled within cells based on probabilities. This lowered the computational expectations of the model, allowing finer detail in other aspects of the population. As a ‘spread’ model, it incorporated individual-based juvenile dispersal, an important distinction from previous models. By considering individual dispersal that is both habitat and density dependent, this model was particularly useful for exploring reinvasion scenarios (Lustig et al., 2019).

3.1.5 The Ōtepoti/Dunedin context

In 2010, a community-led pest-control initiative began in the city of Ōtepoti/Dunedin, New Zealand, managed by the Otago Peninsula Biodiversity Group (OPBG). Their goal: to eradicate all mammalian pests from the Otago Peninsula, starting with possums (OPBG operational report, 2010). As of August 2019, the OPBG has reached the final stages of its possum repression operation and is beginning a four-year final eradication plan, ending in 2023 (Thorsen et al., 2019). The next challenge for the OPBG will be to prevent reinvasion of the Peninsula. Adams et al. (2014b) examined 11 microsatellite loci in possums sampled from 7 geographically distinct Dunedin populations and found evidence to support an ‘isolation by distance’ pattern, with possums that were geographically close being more related than those further separated. The differences between the populations on either side of the head of the harbour (Aramoana and Tairoa Head, see Fig. 1.4a) suggest little to no gene flow occurs,

65 essentially ruling out the harbour waters as a reinvasion route for possums onto the Peninsula. Instead, they identify the City – Buffer Zone as the main potential reinvasion pathway onto the Peninsula (Fig. 1.4a) (Adams et al., 2014b).

The OPBG aims to prevent reinvasion of possums travelling through the residential suburbs at the base of the Peninsula, which are connected to the rest of the Dunedin city (referred to as the “Buffer Zone”) (see Fig. 1.4b). To achieve this, the OPBG has established a group of residents, called the “Guardians of the Otago Peninsula”, who will carry out regular trapping within amenity areas, and their own backyards, within the Buffer Zone. The control there will be ongoing into the foreseeable future to ensure the Peninsula remains possum-free. This transition period from eradication to reinvasion prevention presents an opportunity to explore potential future possum reinvasion scenarios, and to investigate optimal trapping effort and layout to minimise reinvasion.

3.1.6 Study aims

By simulating the possum population of Dunedin under conditions of no control, as well as various trapping arrangements, this study adapted a new spatial individual-based model of possums, developed by Lustig et al. (2019), to ask specific management questions regarding the reinvasion prevention of possums from an urban area onto the Otago Peninsula. Through these simulations the study aimed to:

(1) Determine possum population trajectories and potential reinvasion pathways onto the Otago Peninsula; (2) Assess the efficacy of alternative trapping layouts and effort in preventing reinvasion of the Otago Peninsula and; (3) Through application, demonstrate the potential for this spatial modelling tool to inform mainland mammalian pest management in urban Dunedin, and elsewhere in New Zealand.

3.2 Methods

3.2.1 Study area

This study took place in Ōtepoti/ Dunedin where the Otago Peninsula is a prominent feature of the Dunedin landscape (see Fig 1.2). The landscape comprises low hilly farmland, beaches, cliffs and pockets of peri-urban development where the Peninsula’s permanent residents live. Most housing is concentrated towards the end of the Peninsula closest to the city and along the edge of the Peninsula facing the harbour. Large areas of the Peninsula were covered in podocarp-broadleaved forest prior to European settlement in the 1840s, most of which was converted to exotic pasture which remains the dominant landcover of the Peninsula (Johnson, 2004). Remnant and regenerating forest fragments remain however, comprising some podocarps – primarily Hall’s tōtara (Podocarpus laetus) and matai (Prumnopitys taxifolia) – along with other tree species including kapuka/broadleaf (Griselinia littoralis), ngaio (Myoporum laetum), kohuhu (Pittosporum tenuifolium), tarata/lemonwood (Pittosporum eugenioides), houhere/narrow-leaved lacebark (Hoheria angustifolia), manatu/lowland ribbonwood (Plagianthus regius), kanuka (Kunzea ericoides), māhoe/whitey-wood (Melicytus ramiflorus), kōtukutuku/tree fuchsia (Fuchsia excorticata) and kōwhai (Sophora microphylla) (Johnson, 2004). Non-native plantation forest of pine (Pinus spp.), macrocarpa (Cupressus macrocarpa), eucalyptus (Eucalyptus spp.) and sycamore (Acer pseudoplatanus) are also present.

3.2.2 Otago Peninsula Biodiversity Group control plans

As of August 2019, The Otago Peninsula Biodiversity Group (OPBG) had reached the final stages of its possum repression operation and began a four-year final eradication phase ending in 2023 (Thorsen et al., 2019). Reported possum abundance on the Peninsula is low, however, ‘hotspots’ of possum activity remain, particularly in high-quality habitat patches and areas where access is restricted by private landowners or landscape topography (Kyle B. pers. comm.). The Guardians of the Buffer Zone are using kill-traps (Trapinators and Timms traps) in their backyards, while several trapping lines operated by OPBG volunteers span areas of public land (i.e. bush reserves). At present there are a total of 111 traps in this ‘buffer’ area at the base of the Peninsula, making a trapping effort of ~4.4 traps ha-1.

In the future, the OPBG plans to complete a ring of traps around the Buffer Zone and carry out a systematic sweep inside the ring with cage traps to rid properties within the Buffer Zone of possums. My study focuses beyond this time, to when the Buffer Zone is free of possums and the eradication goal of “no possum detections for 2 years” (Thorsen et al., 2019) has been achieved on the Otago Peninsula. At this point, in situ breeding of possums should not contribute to the recovery of the Peninsula population. Instead, the only source of possums will be through reinvasion from the city via the Buffer Zone. The OPBG Guardians will by that stage finalise their permanent trapping setup in the Buffer Zone, with traps being deployed across the suburbs, to be set and checked regularly on a permanent basis.

3.2.3 Model methods

The model was originally developed by Lustig et al. (2019) of Manaaki Whenua - Landcare Research. Modifications, outlined below, were made to develop and apply the model within my study. The model structure is comprised of three ‘levels’: a habitat layer that divides the landscape into distinct landcover types that are assigned possum carrying capacity values, a possum population layer of adults that occupy stable home ranges and juveniles that undergo one natal dispersal event, and a trapping layer for simulating ground-based trapping layout and effort.

3.2.3.1 Habitat mapping: Study area and carrying capacity

Shapefile layers were created in GRASS GIS v.7.6.1 (GRASS Development Team, 2019) and QGIS v.3.6.2 (QGIS Development Team, 2019). The Dunedin Primary Parcel cadastral shapefile, acquired from the Land Information New Zealand (LINZ) database (https://data.linz.govt.nz/layer/50772-nz-primary-parcels/), was used to delineate a ‘Study Area’ encompassing the entire peninsula to the base of the Buffer Zone (Fig 3.1) within which trapping occurred and the population trajectory was monitored. The ‘Non-Study Area’ population constituted the population of potential immigrants onto the Peninsula from the wider city area. The computational extent of the model was limited to a 12km wide buffer around the Study Area, equivalent to the maximum recorded daily dispersal distance of a juvenile possum (Byrom et al., 2015).

The carrying capacity layer (Kmap) of the model was modified from the National Landcover Database v.4.1 shapefile (LCDB), accessed through the Land Resource Information System (LRIS) portal (https://lris.scinfo.org.nz/layer/48423-lcdb-v41-land-cover-database-version- 41-mainland-new-zealand/). This vector shapefile partitions the environment into land cover and land use classes based on satellite information. Several other polygon classes were added to this base map to include finer-scale habitat information, where available, and to allow for the inclusion of possum density estimates calculated in Chapter 2 for urban forest fragments. Within the LCDB map a new ‘Forest Fragment’ class was created by selecting polygons of the habitat classes ‘Broadleaved Indigenous Hardwoods’, ‘Exotic Forest’, ‘Podocarp-Broadleaved Forest’ and ‘Unspecified Indigenous Forest’ that met the criteria of being small (< 30ha) and having at least 75% of their perimeter surrounded by urban habitat classes (residential or industrial). To further differentiate urban residential habitat and to later assign carrying capacity values from densities calculated in Chapter 2, this updated LCDB map was then combined with elements of the Dunedin Habitat Map (Freeman and Buck, 2003). These were the ‘Residential II’ and ‘Residential I’ polygons (see Table 2.2), replacing the LCDB’s single classification for all urban habitat as ‘Built-Up Settlement’. Finally, fine-scale forest information from the EcoSat South Island Forest Layer (https://lris.scinfo.org.nz/layer/48416- ecosat-forest-south-island/) was patched to the updated LCDB map to create the final Kmap vector shapefile.

Possum carrying capacity (K) values were assigned to each habitat class based on prior research, expert opinion, and my estimates of density calculated in Chapter 2 (Table 3.1) (Efford, 2000; Lustig et al., 2019; Warburton et al., 2009). Single values were used, despite the knowledge that density tends to fluctuate in response to micro-habitat characteristics such as den and food availability (Efford, 2000). Including the estimates for Residential I and Residential II habitat calculated in Chapter 2 as K values, despite these being minimum number known alive (MNA) estimates, allowed for the conservative representation of these habitat types as potential movement corridors and habitat for possums. This updates previous spatial possum modelling which has represented all urban areas as being homogenously unsuitable for settlement, with carrying capacities of zero (Lustig et al., 2019; Ramsey and Efford, 2010; Warburton et al. 2009). At this stage the habitat classes were also divided into three categories: areas that individuals can move into and settle in, areas that can be dispersed across but not settled in (e.g. rivers), and areas that cannot be entered (large water bodies) (Table 3.1). The map was rasterized to a resolution of 100m to create the final Kmap for model input (Fig. 3.1).

Figure 3. 1 The carrying capacity map (Kmap) that underlies the individual-based possum model of the Otago Peninsula (the Study Area), and wider Dunedin city (the Non-Study Area). Land cover information was combined with fine-scale forest classes, and the density estimates from Chapter 2, to produce carrying capacity (K) values (individual possums per ha) for different habitat across the city, which was then rasterised to a 100m resolution. Cells given a K value of -99 are those that cannot be settled on or dispersed across.

Table 3. 1 The cover classes included in the final habitat map of Dunedin, and the carrying capacity values associated with each cover class in the final habitat ‘Kmap’. Carrying capacity values were taken from Warburton et al. (2009), Lustig et al. (2019), and Chapter 2 of this study. Cover classes were further separated into three categories: habitats that could not be dispersed across or settled on (‘No dispersal’); habitats that could be dispersed across but could not be settled on (‘Dispersal only’); and habitats that could be dispersed across and settled on (‘Settlement’) (Lustig et al., 2019).

Cover class Carrying capacity Settlement, dispersal or no (no. ha-1) dispersal

Built-up area (settlement) 0 Dispersal only Urban parkland / open space 0.2 Settlement Surface mine or dump 0 Dispersal only Transport infrastructure 0 Dispersal only Sand or gravel 0 Dispersal only Gravel or rock 0 Dispersal only Landslide 0 Dispersal only Lake or pond 0 Dispersal only Estuarine open water 0 No dispersal Short-rotation cropland 0.2 Settlement High producing exotic grassland 0.2 Settlement Low producing grassland 0.2 Settlement Tall tussock grassland 0.2 Settlement Herbaceous freshwater vegetation 0 Dispersal only Herbaceous saline vegetation 0 Dispersal only Flaxland 3 Settlement Gorse and/or broom 3 Settlement Mānuka and/or kānuka 3 Settlement Matagouri or grey scrub 3 Settlement Broadleaved indigenous hardwoods 3 Settlement Mixed exotic shrubland 3 Settlement Forest harvested 0.2 Settlement Exotic forest 2 Settlement Deciduous hardwoods 2 Settlement Indigenous forest 5.5 Settlement Podocarp-broadleaved forest 9 Settlement Residential I 0.11 Settlement Residential II 0.23 Settlement Forest Fragment 3.67 Settlement

3.2.3.2 Possum population layer

Initialization, reproduction and natural mortality

Only females, as the young-carrying individuals, are represented in the model (Lustig et al., 2019). Population processes were modelled at the cell level on a monthly time-step rather than explicitly modelling individual home ranges. Cell resolution was set to 200m, smaller than the 500m resolution Lustig et al. (2019) originally applied. This smaller resolution was chosen for two reasons. Firstly, to allow for finer-scale trap placement during control modelling while still taking account of possum minimum home range sizes and average daily dispersal distances (Byrom et al., 2015; Glen et al., 2017). Secondly, to represent the landscape at a finer resolution to explore the effect of habitat on possum distribution, because the total landscape area modelled was smaller than in previous studies. Model dependence on this resolution change was assessed in sensitivity analyses (Appendix B, Table 5.3, Fig. 5.8).

At the beginning of each simulation an adult possum population of individuals was initiated within the Peninsula Study Area by randomly adding individuals to cells until a user-defined total population size was reached. The population in the Non-Study Area was initiated at a user-defined percentage of the carrying capacity per cell, with cells of a carrying capacity < 1 possum ha-1 assigned one individual at a probability of 0.1. This resulted in cells of varying adult density (no. individuals per cell) across the city.

Adult reproduction followed the breeding seasonality of possums, with the monthly probability of an adult reproducing being modelled as a bimodal Gaussian process with a major peak in autumn and lesser one in spring (Fig. 3.2) (Tyndale-Biscoe, 1955). The number of offspring produced per year was determined by a beta-PERT probability parameterized by the available field-based knowledge of annual female reproductive rates (Glen et al., 2017; Lustig et al., 2019). There was no consideration of density-dependent Allee effects; even at low population sizes individuals had a probability of reproducing.

Figure 3. 2 The bimodal gaussian distribution representing the seasonal monthly probability of an adult giving birth in the model. It has a major peak in autumn, and a smaller peak in spring. From Lustig et al. (2019).

Adult natural mortality was modelled as a constant monthly probability (푃푚), not considering age or density effects. Field-based estimates of the average possum lifespan (13 or 12yr); (Cowan, 2001; Efford, 1998) were transformed into this probability via the equation:

1 푃 = 푚 푙 + 1⁄푑푡 where 푙 is lifespan and 푑푡 is the monthly time step. Individuals were randomly removed from the population to fulfil the monthly population mortality requirements. Juveniles that had not yet dispersed were assumed to die if their mother died. When control was applied, natural mortality represented only a small fraction of the total adult mortality. This was deemed representative of the dynamics of controlled possum populations.

Individual-based juvenile dispersal

Juvenile dispersal was modelled as an individual-based ‘biased random walk’ process on a daily time-step. Dispersal was initiated 12 months after an individual was born to match the recorded onset of sexual activity and movement in juvenile possums (Blackie et al., 2011;

Cowan et al., 1997; Efford, 1998). Leaving their birthplace, juveniles travelled across the landscape cell-by-cell, investigating the neighbouring four cells at each time-step to determine their trajectory. The probability of entering a cell was dependent on the ‘quality’ of the four surrounding cells. Movement was biased towards cells of higher overall quality. The weighted cell ‘quality’ at location (푥, 푦) was determined by the equation:

ℎ(푥,푦) 푄 = (푥,푦) 1 × (푎푑푗푄) where ℎ(푥,푦) is the habitat quality of the cell at location 푥, 푦 and 푎푑푗푄 is the summed qualities of the four adjacent cells. Habitat quality for each cell was set to its carrying capacity, except for those cells, as mentioned above, that could not be dispersed across (ℎ(푥,푦) = 0) and those that could facilitate dispersal but not settlement (ℎ(푥,푦) = 0.2). If cells were equally attractive, the probability of each movement direction was equal. Juveniles could not move backwards, except if they reached non-dispersal habitat, at which point they could return the way they had come. Juveniles were able to disperse beyond the model’s simulation region but could not arrive from outside of this area.

The probability of a juvenile settling in a cell of location (푥, 푦) at time 푡 was determined by the cell’s habitat quality and the population density relative to its carrying capacity. Dependencies on habitat and density were modelled by the following equations:

푅⁄푅푚푎푥 푃푠푒푡푡푙푒푡,(푥,푦) = 1 − (1 − 푃푟(푁푡,(푥,푦))푃푟(퐾(푥,푦))) where 푃푟(푁푡,(푥,푦)) represents the density dependence and 푃푟(퐾(푥,푦)) the habitat dependence, while 푅 and 푅푚푎푥 represent the chosen model resolution and maximum resolution (푅푚푎푥 = 2000) in order to maintain the probability across different input spatial scales. Density dependence is given by:

푁푡,(푥,푦) 푃푟(푁푡,(푥,푦)) = 1 − 퐾(푥,푦) where 푁푡,(푥,푦) is the total number of adults and juveniles in the target cell at time 푡 and 퐾(푥,푦) is the carrying capacity of that cell. Habitat dependence is given by:

1 푃푟(퐾(푥,푦)) = 1 − 1 + 푒퐾(푥,푦)⁄퐾푚푒푎푛−1 where 퐾푚푒푎푛 is the average of carrying capacity values across the entire Study Area and Non- Study Area.

There is one exception that required an alternative definition of 푃푠푒푡푡푙푒푡,(푥,푦). This is when the juvenile has moved into a landscape which can only be dispersed through. In this instance

푃푠푒푡푡푙푒푡,(푥,푦) is set to zero. The final probability of settlement is thus represented as:

푅⁄푅푚푎푥 (1 − 푃푟(푁푡,(푥,푦))푃푟(퐾(푥,푦))) if 퐾 > 0 푃푠푒푡푡푙푒푡,(푥,푦) = { 0 if 퐾 = 0

Upon settlement, the juvenile is assumed to have been recruited into the sub-population of the cell and will remain there until death. Field studies have shown that juvenile possum dispersal distances are highly variable, with individuals moving up to 2 – 3km per night (Cowan, 2001; Cowan and Rhodes, 1993), and dispersing as far as 40km in one instance (Byrom et al., 2015).

A maximum dispersal Euclidian distance (푑푚푎푥) of 12km was taken from the average of recent field data (Byrom et al., 2015; Cowan et al., 1996). If a juvenile had not settled at this distance it was assumed to die and was removed from the population. In this way the juvenile dispersal phase included a density-dependent natural mortality.

3.2.3.3 Control Layer

Population control in Non-Study Area habitat

Natural birth and death processes were modelled across the Study Area and Non-Study Area. In order to maintain the Non-Study Area population at a realistic density without adding to the computational load of the model, it was decided that the Non-Study Area population would be maintained at a user-defined percentage of the cell carrying capacity. At the start of each year, individuals were randomly removed from the Non-Study Area population to simulate maintenance control efforts across central Dunedin and the Western Harbour. Variations to the chosen percentage were explored in sensitivity analyses (Appendix B, Table 5.3, Fig. 5.4).

Adult trapping

Control of adult possums was modelled on a monthly time-step, occurring only in user- specified months of the year. Instead of modelling the trapping probability of individual adult possums, as was done in the Spatial Possum Model of Ramsey and Efford (2010), here trapping

76 probability was transformed to be a function of trap density per cell (Lustig et al., 2019). Only those possums present in a cell were at risk of being captured by a trap within that cell.

The model assumed that all adults occupy circular home ranges, and the probability of an adult being captured was a function of home range size (Ball et al., 2005). Model inputs 𝑔0 and 𝜎 jointly determine this function, being, respectively, the probability of capture of an individual when a trap is placed at the centre of its home-range, and the spatial decay parameter of the half-normal home range kernel. The function describing the probability of capture of an individual with a home-range centre at location 푖 by trap 푗 at time 푡 is given as:

2 −푟 푖푗 ( 2 ) 푃푐(푟푖푗) = 𝑔0푒 2휎 where 푟푖푗 is the distance between trap 푗 and the home-range centre location 푖. Essentially, the function depicts the decrease in probability of capture with distance from the home range centre (Glen et al., 2017; Warburton and Gormley, 2015). Traps are assumed to be distributed in a random uniform manner within the cell as a spatial Poisson process. Calculations result in a final probability of an individual at location x,y being trapped over k nights expressed as:

2 −2휋훾0휎 푘휌 푃푎푑푢푙푡푠 = 1 − 푒 where ρ is the density of traps (traps ha-1) in the grid cell (see Lustig et al., 2019 for calculations to reach this equation).

Juvenile trapping

Juvenile trapping was modelled individually on a daily time-step during the dispersal phase each year. Juveniles could encounter traps that were a distance 푊 from their location, resulting in a potential trapping area 퐴 around the individual as it moved across the landscape. 푊 was set to 2m as a representation of juvenile perception (Lustig et al., 2019). The probability of capture 𝑔1 within this area was constant (no spatial decay). 퐴 is represented by the equation:

퐴 = 푉푊훿푡 where 푉 is the mean velocity of dispersing juveniles and 훿푡 is the time an individual spends in a grid cell, which is separately defined as:

푅푡 훿푡 = 푚푎푥 푑푚푎푥 where 푅 is the spatial resolution and 푡푚푎푥 is the maximum dispersal time, while 푑푚푎푥 is the maximum dispersal distance. Mean velocity was estimated to be 푉 = 0.0347 푚⁄푠푒푐 by Lustig et al. (2019) from previous dispersal distance estimates (Byrom et al., 2015; Cowan, 2001).

Due to a lack of juvenile-specific field research, the juvenile capture probability 𝑔1 was assumed to be equal to the adult 𝑔0 parameter value, however the effects of this assumption were explored in sensitivity analyses (Appendix B, Table 5.3, Fig. 5.9, Fig. 5.10). At each grid cell a juvenile passed through during dispersal, the probability of capture was defined as:

−퐴푔1휌 푃juveniles = 1 − 푒 Where 𝜌 is trap density in each cell.

Capture probability for adults and juveniles was modelled as independent of habitat or other characteristics, and traps were assumed to be multi-catch with no trap-saturation.

3.2.4 Scenario specifications

Unless otherwise stated, all simulations used the average model parameter values outlined in Table 3.2, were run for 40 years over 40 iterations, and assumed complete eradication of possums on the Peninsula.

Scenario One: No control scenario – ‘Hotspot’

A baseline simulation was conducted with no possum control (no traps operating in the Buffer Zone) to quantify natural possum reinvasion onto the Peninsula, and to identify the location of potential ‘hotspots’ of reinvasion habitat on the Peninsula (Table 3.3).

Scenario Two: No control without complete eradication – ‘Hotspot 50’

In order to explore the implications of the OPBG not entirely succeeding in their eradication attempt, a scenario with some remaining possums on the Peninsula was simulated. The number

78 of initial possums on the 9,800ha peninsula was set to 50, equalling approximately 0.005 individuals ha-1. This is a low enough density of possums to realistically remain on the Peninsula undetected following the OPBG’s eradication target of “two years with no possum indications within the eradication area” (Thorsen et al., 2019), considering that a residual trap catch index (RTCI) value of 1% (usually a target control value) corresponds with possums at a density of 0.01 ha-1 (Ramsey et al., 2005). No control was applied in this scenario.

Table 3. 2 The baseline possum parameters used in the model, with reference to the source of values where applicable.

Parameter Value Source

Maximum dispersal distance 12km Glen and Byrom (2014)

Minimum reproductive rate per 0.5 Hickling and Pekelharing (1989); female Hone et al. (2010)

Mean reproductive rate per female 0.7 Hickling and Pekelharing (1989); Hone et al. (2010)

Maximum reproductive rate per Hickling and Pekelharing (1989); 1.02 female Hone et al. (2010)

Life expectancy 12 years Cowan (2001)

𝑔0 (푎푑푢푙푡) 0.05 Glen and Byrom (2014)

𝑔1 (푗푢푣푒푛푖푙푒) 0.05 Glen and Byrom (2014)

𝜎 63 Glen and Byrom (2014) Number of nights trapping per month 25

Number of months trapping per year 12

K of Non-Study Area 50%

Resolution 200m

Table 3. 3 The name and description of each scenario run in the model.

Scenario name Description

Hotspot Complete eradication, no possums remaining on the Peninsula. No control.

Hotspot 50 Partial eradication with 50 possums remaining on the Peninsula. No control.

Trap 150 Complete eradication, no possums remaining on the Peninsula. 150 traps in current OPBG locations.

Trap 300 Complete eradication, no possums remaining on the Peninsula. 300 traps in the Buffer Zone.

Trap 500 Complete eradication, no possums remaining on the Peninsula. 500 traps in the Buffer Zone.

Homogenous Traps (x3) Complete eradication, no possums remaining on the Peninsula. 150, 300 or 500 traps located evenly across the Buffer Zone. Corridor Targeted (x3) Complete eradication, no possums remaining on the Peninsula. 150, 300 or 500 traps located in areas of high predicted dispersal (from results).

Scenario Three: Current plans of the OPBG – ‘Trap 150’

A representation of the planned future control of the OPBG was simulated to explore the effectiveness of this regime. GPS locations of current traps in residential and public spaces were obtained from the OPBG, along with areas where future trapping is planned. Present trap locations were plotted in QGIS, and other traps were randomly placed within the planned target areas to reach a total trapping effort of 150 traps (Fig. 3.3a). Trapping locations were then converted to a raster map of relative trap density within GrassGIS by dividing the trap number by the cell area (푅 = 100m). Currently, the OPBG has in place a partially-complete exclusion fence which stretches across the Peninsula edge of the Buffer Zone. It was decided not to simulate this exclusion fence because it is not yet known how effective this fence will be at excluding possums. The fence is also situated in habitat types that possums already have a lower probability of moving over.

Figure 3. 3 The location of traps in four of the simulated trapping scenarios, which were then converted to a density of traps per cell for input into the model. a) The current 150 traps setup in the Buffer Zone, b) with 300 traps, c) with 500 traps and d) with 300 traps but more of them concentrated in areas of predicted higher dispersal frequency (defined from simulation results).

Scenario Four and Five: Higher trap density – ‘Trap 300’ and ‘Trap 500’

The most likely adjustment the OPBG can make will be to increase the number of traps in the area and hence, the total trap density. Two scenarios were made in QGIS where 150 or 350 traps were randomly added within the Buffer Zone, so that, when combined with the location

81 of the previous 150 scenario, they made scenarios of 300 (Fig. 3.3b) and 500 (Fig. 3.3c) traps respectively.

Scenario Six and Seven: Altering trap layout – ‘Homogenous’ and ‘Corridor Targeted’

Based on the findings of the trapping and no control scenarios, a set of trapping scenarios were explored to assess the relative effectiveness of varying the spatial layout of traps versus increasing the trap density. The ‘Homogenous’ scenarios utilised 150, 300 or 500 traps and looked at whether a more homogenous distribution would be more effective than current trap locations, while the ‘Corridor Targeted’ scenarios employed traps more directly in the areas identified as potential reinvasion corridors across the different trapping efforts (Fig. 3.3d).

3.2.5 Sensitivity analysis

The sensitivity of the model to the parameter values was assessed via a local sensitivity analysis (Xu et al., 2004). Parameters were varied to be 25% above and below their average value, except when this did not fit within recorded ranges (Table 3.4) (Lustig et al., 2019). Sensitivity analyses that were exceptions to the above description are outlined below.

Homogenous K sensitivity analysis

To assess the relative importance of habitat type and the habitat geography on possum reinvasion, a simulation was run with a homogenous carrying capacity (K = 2) across all cells.

Table 3. 4 Parameters that were assessed in a local sensitivity analysis, and the values that were used

Parameter Value % change relative to average

K of Non-Study Area 25% +25% 75% -25%

Life expectancy 9 +25% 15 -25%

Maximum dispersal distance 9km +25% 15km -25%

Mean reproductive rate per 0.6 +15% female 0.8 -15%

Juvenile trappability (𝑔1) 0.0325 +25% 0.0675 -25%

Adult trappability (𝑔0) 0.0325 +25% 0.0675 -25% Homogenous K K = 2

Resolution 500m

3.2.6 Model output visualisation

Output graphs were created in R v.3.5.2 (R Core Team, 2018) using the package ggplot2 (Wickham, 2016), and in QGIS. The resolution of output maps was adjusted to 50m in QGIS using the ‘Multi-Level B-Spline’ tool for visual purposes. Possum number per cell each month was averaged over all replicates to produce maps of abundance over time. The model recorded each time a possum moved through a cell and these movements were totalled over all years and averaged across replicates to map the areas of greatest movement. The number of individuals immigrating onto the Peninsula each month was tracked, as was the number of individuals born on the Peninsula each month. The number of trapped juveniles and adults was also recorded.

3.3 Results

3.3.1 Hotspot scenario

Abundance

In the absence of control and with the assumption of complete eradication, possum reinvasion onto the Peninsula was gradual over the 40 simulated years (Fig. 3.4, Fig. 3.5). For the first 10 years, the population showed little sign of increase beyond a few individuals (Table 3.5). The gradient of abundance increased steadily from year 10 onwards, with total population accumulation reaching rates far higher between years 20 and 40 than during the first 10 years (Table 3.5). Population spread across the Peninsula occurred in a ‘rolling front’, with new possums covering all available adjacent habitat in an even pattern of dispersion (Fig. 3.4, Fig. 3.5). At the end of the 40 years, the eastern end of the Peninsula remained uninvaded (Fig. 3.5). From a slow accumulation at the beginning, the population reached an average abundance of over 1400 individuals (Table 3.5). There was a relatively high consistency of this pattern across replicates (Std. error = 95.76), however, 12.5% (n = 5) of simulations saw no reinvasion of possums to any substantial extent or number over the 40 years (Table 3.5).

Hotspot areas

At the end of the simulated time-period, areas of high-density possum occupation could be distinguished (Fig. 3.6). These ‘hotspots’ of settlement hosted possums at densities of 4 – 5 individuals ha-1, and corresponded closely with areas of higher quality forest fragment habitat. In particular, the ‘Sandymount’ area, and the forest above the Portobello township, as well as the grounds of a local tourist attraction, Larnach Castle, had the largest continuous hotspot habitat (Fig. 3.6). At the western end of the Peninsula, the South and North coast has forest habitat that possums accumulated in, and the forest corridors of the Buffer Zone also saw a high possum density at the end of the 40 years (Fig. 3.6).

Figure 3. 4 The possum abundance, averaged over 40 replicates, at years 5 and 10 in the ‘Hotspot’ scenario, which simulated complete eradication of the Peninsula and no control. The population outside the Study Area (delineated in orange) was maintained at a constant 50% of its K per cell.

Figure 3. 5 The possum abundance, averaged over 40 replicates, at years 20 and 40 in the ‘Hotspot’ scenario, which simulated complete eradication of the Peninsula and no control. The population outside the Study Area (delineated in orange) was maintained at a constant 50% of its K per cell.

Table 3. 5 The summarised output of possum abundance from the model scenarios explored. Bold values are the total average over all replicates, while un-bolded values are the average over replicates where invasion to any non-negligible extent (> 10 individuals) occurred.

Simulation Proportion 5 year 10 year 20 year 40 year invaded* abundance abundance abundance abundance

Hotspot 0.875 5.9 (± 0.9) 37 (± 5.3) 323.9 (± 27.2) 1433.4 (± 95.8) 6.7 (± 0.9) 42.3 (± 5.5) 370.2 (± 21.9) 1638.2 (± 48.9)

Hotspot 50 1 507.6 (± 10.5) 2459.7 (± 23.9) 3352.8 (± 3.5) 3374 (± 2.9)

Trap 150 0.625 3.5 (± 0.7) 12.3 (± 2.4) 73.2 (± 13.8) 630.1 (± 96.3) 5.5 (± 0.9) 19.6 (± 3.0) 117.2 (± 16.7) 1008.2 (± 92.2)

Trap 300 0.3 0.8 (± 0.2) 0.9 (± 0.3) 19.4 (± 11.1) 166.0 (± 59.8) 2.5 (± 0.48) 11.8 (± 3.7) 64.5 (± 33.6) 553.1 (± 147.9)

Trap 500 0.175 0.7 (± 0.2) 0.85 (± 0.3) 3.9 (± 2.3) 63.4 (± 33.2) 2.4 (± 0.9) 4.1 (± 1.2) 5.7 (± 11.3) 360 (± 144.2)

Homogenous 0.55 1.4 (± 0.3) 3.6 (± 0.7) 28.1 (± 8.0) 402.6 (± 73.9) Trap 150 2.5 (± 0.5) 6.6 (± 1.0) 51.0 (± 12.5) 732.0 (± 84.1)

Homogenous 0.25 0.7 (± 0.2) 1.1 (± 0.3) 4.3 (± 1.8) 94.4 (± 35.4) Trap 300 1.7 (± 0.3) 3.6 (± 0.9) 16.9 (± 5.7) 377.7 (± 96.5)

Homogenous 0.1 0.5 (± 0.1) 0.7 (± 0.2) 4.3 (± 2.6) 74.8 (± 44.8) Trap 500 1.5 (± 0.4) 3.3 (± 0.4) 40 (± 18.5) 745.5 (± 275.3)

Corridor 0.425 1.5 (± 0.3) 2.8 (± 0.7) 15.6 (± 4.8) 256.6 (± 62.7) Targeted 150 3 (± 0.6) 6.2 (± 1.2) 36.2 (± 9.2) 603 (± 97.5)

Corridor 0.075 0.7 (± 0.2) 0.9 (± 0.3) 0.0 (± 0.0) 9.5 (± 6.0) Targeted 300 2.3 (± 0.7) 4 (± 0.9) 0.0 (± 0.0) 126.3 (± 47.4)

Corridor 0 0.28 (± 0.1) 0.2 (± 0.1) 0.1 (± 0.0) 0.0 (± 0.0) Targeted 500 0.0 (± 0.0) 0.0 (± 0.0) 0.0 (± 0.0) 0.0 (± 0.0)

*The proportion of replicates that saw any non-negligible number of possums move onto the Peninsula (>10)

Figure 3. 6 The location of ‘hotspots’ of possum abundance on the Otago Peninsula after 40 years of reinvasion (averaged over 40 replicates). 88

Dispersal movements

A map of accumulated dispersal movements displays routes that were highly frequented by simulated juveniles moving onto the Peninsula (Fig. 3.7). These areas saw, on average, more than 800 total possum movements per cell by the 40th year. The most frequented cells saw over 1500 movements across them (Fig. 3.7). Highest movement was seen in some distinct locations, closely mirroring the ‘hotspot’ areas of high final possum density. From the city into the Buffer Zone, the suburbs of Musselburgh and Andersons Bay were the most frequented. The southern coast, including the beaches and associated vegetation of St Kilda, Tomahawk and Smails saw a high number of dispersal movements. Most other dispersal corridors coincided with forest fragments. Areas of low dispersal tended to be pasture or residential land, and were less frequented particularly when available forest fragment habitat was nearby (Fig. 3.7).

Immigration vs. births

The relative contribution of possum immigration and in situ reproduction to population growth on the Peninsula changed over the 40-year period in the Hotspot scenario. While immigration remained very low over time (on average < 1 individual per year) (Fig. 3.8), the number of possums born on the Peninsula steadily increased to over 900 young per year (Fig. 3.9). The shape of the birth curve matched the shape of the population growth curve closely.

3.3.2 Hotspot 50 scenario

When there were 50 individuals remaining on the Peninsula at the beginning of simulations, the rate of increase in Peninsula possum abundance was far greater than in the Hotspot scenario (Fig. 3.10). Within the first 5 years the average population had reached ~500 individuals (Table 3.5), and the gradient of change became exponential between the 5th and 10th years (Fig. 3.10). The population reached carrying capacity (~3300 individuals) at year 15 and remained in an equilibrium state beyond this point (Table 3.5, Fig. 3.10). The average variance around the curve was low, representing a high continuity of effect across replicates (Fig. 3.10). As in the Hotspot scenario, immigration contributed very little to the total population growth (Fig. 3.8).

Figure 3. 7 The accumulated dispersal map of possum movements as they reinvade the Peninsula in a scenario with no control and complete eradication (‘Hotspot’). Dispersal was quantified as the number of individual movements made across a cell on a monthly time-step.

Figure 3. 8 The number of possums per year (averaged over 40 simulations) immigrating onto the Peninsula over time in two modelled scenarios: one scenario where complete eradication of the Peninsula was achieved (‘Complete eradication’), and one where 50 possums remained on the Peninsula following control (‘50 possums’). Possum movement was slightly higher in the first 5 years as individuals moved out from the city cells and onto the Peninsula but were not all replaced. Bars are ± standard error.

Figure 3. 9 The number of juvenile possums born per year (averaged over 40 simulations) on the Peninsula over time following complete eradication of the Peninsula (‘Complete eradication’), and a scenario where 50 possums remain present on the Peninsula (‘50 possums’). Bars are ± standard error.

Figure 3. 10 The average abundance of possums in the Peninsula Study Area per month over time in two scenarios with no control: the ‘Hotspot’ scenario where complete eradication occurred, and the ‘Hotspot 50’scenario, where 50 possums remained on the Peninsula following eradication. Shaded bars are ± standard error and 푛 = 40.

Instead, it was again in situ reproduction that added individuals to the population, and this occurred at a far greater rate than in the Hotspot scenario (Fig. 3.8). By the time population equilibrium was reached, over 2200 individuals were being born each year (Fig. 3.8).

3.3.3 Trapping regimes

All trapping regimes substantially reduced possum final abundance compared to the Hotspot scenario (Fig. 3.11). Overall, the annual number of juveniles trapped was low across trapping scenarios, while adult trap rates tended to increase gradually if the population increased (Fig. 3.12).

Trap 150 scenario

The current trapping effort and layout did not, on average, prevent reinvasion of the Peninsula. The first substantial population growth on the Peninsula occurred after ~15 years, and total population growth was slow, reaching an average abundance of 630 individuals after 40 years

(Fig. 3.11, Table 3.5). However, approximately one third of replicates had negligible levels of reinvasion (< 10 individuals over 40 years), lowering the total average (Table 3.5). Of those replicates where non-negligible reinvasion occurred, the average population size at 40 years was higher (~1000 individuals) and increased rapidly from year 20 (average 117 individuals) to 40 (Table 3.5).

Trap 300 scenario

The scenario where 300 traps were applied across the buffer zone did not result in total reinvasion prevention, however it did cause a significant reduction in the average final total abundance of individuals on the Peninsula compared with the 150 trap scenario (Fig. 3.11, Table 3.5). Total abundance only reached half the number of individuals seen in the Trap 150 scenario (mean = 165 individuals) (Table 3.5). Alongside this, only 30% of the replicates saw any significant reinvasion onto the Peninsula at all (Table 3.5). Even when the replicates where no reinvasion occurred were factored out, the overall abundance (mean = 553 individuals) was half that of the Trap 150 scenario (Table 3.5).

Trap 500 scenario

The 500 trap scenario displayed a similar pattern to the 300 trap scenario, but with further increases in reinvasion time, and reductions in the average total abundance of individuals after 40 years (mean = 63) (Fig. 3.11, Table 3.5). The proportion of replicates that showed reinvasion was low, at only 0.1, or 10% of all replicates, indicating that in most cases reinvasion was prevented (Table 3.5). When invasion did occur and was sustained (initial individuals were not trapped), population expansion was rapid and resulted in final abundance totals of 150 – 1000+ individuals (mean = 360 individuals) (Table 3.5).

Figure 3. 11 The total abundance of possums on the Peninsula per month (averaged over 40 replicates) over the 40 year simulations in the ‘Hotspot’ scenario where there were no traps, compared with three trapping scenarios of increasing trap density (150, 300 and 500). Shaded bars are ± standard error.

Figure 3. 12 The average (n = 40) number of adult (bold line) and juvenile (dashed line) individual possums trapped on the Peninsula per year across three scenarios of increasing trap density: 150 traps, 300 traps and 500 traps.

Trap spatial layout

Altering the trap layout while maintaining the same trapping effort affected possum reinvasion rates and final possum abundance on the Peninsula (Fig. 3.13, Table 3.5). Spreading traps homogeneously reduced the reinvasion of possums substantially in the 150 trap and 300 trap scenarios (from a 40-year average abundance of 630 to 402 individuals, and 166 to 94 individuals respectively), while it had less effect on the already low reinvasion rate of the 500 trap scenario (Fig. 3.13, Table 3.5). Targeting dispersal corridors identified by the model significantly reduced possum reinvasion onto the Peninsula in all three trapping scenarios, but particularly for the Trap 300 and Trap 500 scenarios where reinvasion became almost negligible (Fig. 3.13, Table 3.5). In the 300 trap scenario, only 7.5% of replicates saw any reinvasion above 10 individuals, and the final abundance for those replicates that did have reinvasion was still on average much lower (mean = 126) than the Trap 300 average at 40 years (mean = 553.1) (Table 3.5). Meanwhile, the Corridor Targeted 500 scenario saw no reinvasion of 10 individuals or more at the end of 40 years (Table 3.5).

Figure 3. 13 The average (n = 40) abundance of possums on the Peninsula over the 40-year simulation period when exposed to trapping regimes of varying effort (150 traps, 300 traps, 500 traps) and layout (dotted line = habitat targeted layout, dashed line = homogenous layout).

3.3.4 Sensitivity analysis

Homogenous scenario

The results of the simulation where the environment had a homogenous K were consistent with the findings of the Hotspot analysis, that reinvasion, even without the influence of habitat type, was slow onto the Peninsula (Appendix B, Fig. 5.3, Table 5.3). Spread onto the Peninsula was more rapid than in the Hotspot analysis and population growth was steady, but after 40 years, possums had still not reinvaded all of the Peninsula.

Model sensitivity

The model was most sensitive to changes in the carrying capacity of the Non-Study Area and the maximum dispersal distance of juveniles (Appendix B, Fig. 5.4, Fig. 5.7, Table 5.3). In particular, setting the K to be 25% for the Non-Study Area – or the Dunedin city centre – reduced the possum population substantially (from a 40-year average abundance of 1433 individuals in the Hotspot scenario to 750 individuals in the 25% lower K), as did lowering the maximum dispersal distance to 9km (from 1433 to 594 individuals) (Appendix B, Table 5.3). The 25% increase of these same parameters however produced only a small difference from the average model. All other parameter changes had small effects on the population trajectory (Appendix B, Table 5.3, Fig. 5.5 – 5.6, Fig. 5.8 – 5.10).

3.4 Discussion

Utilising a new spatially explicit, individual-based possum model, this study aimed to predict the reinvasion dynamics of possums onto the Otago Peninsula, and to explore the relative effectiveness of various trapping efforts and layouts. Results predicted the distribution and abundance of possums as they reinvaded the Peninsula, which was uneven across time and space, and the means by which the OPBG may limit reinvasion onto the Peninsula through efficient trapping.

In the scenario where there was no control, complete reinvasion of the Peninsula was not reached over the 40-year study period and rates of spread were slow. Prior simulations of possum populations, and some field data, have observed comparatively gradual rates of population recovery following significant control or eradication. Veltman and Pinder (2001) used data from DOC 1080 operations to simulate recovery of possums over moderate to large areas (mean = 5,255 ha), and estimated that it would take ~15 years for a population to recover following a 90% population reduction. Hickling and Pekelharing (1989) observed a gradual population recovery in a section of rātā/kāmahi forest after a poison operation with an 85% kill rate, where the population only reached around 60% of its original estimated carrying capacity 10 years post-control (Hickling and Pekelharing, 1989). This slow recovery rate was despite the high-quality habitat in the area. Importantly, these studies were across large areas where the intrinsic population growth rate (in situ reproduction) constituted the majority of population recovery, and immigration contributed comparatively little.

Faster rates of population recovery have been measured in some field studies with sites of varying area, potentially due to the higher contribution of immigration to population recovery rather than in situ reproduction. Ji et al. (2005) recorded rapid initial population recovery within two 6 ha forest remnant sites situated within a mosaic of forest patches and farmland. Within two months, the patches had recovered over one-third of their population, and after two years one patch had recovered to 56% of its previous population (Ji et al., 2005). Similarly, Green and Coleman (1984) reported a substantial recovery of possums 3 years following comprehensive control in a podocarp/mixed hardwood forest. They attributed most of this rapid recovery to immigration from dispersing juveniles (Green and Coleman, 1984). The inclusion of immigration also accelerated population recovery to under 5 years in simulations by Ramsey and Efford (2010) over a large area (17,000ha).

However, even when immigration is the more important factor at play, the available habitat type can slow rates of reinvasion. For instance, Cowan et al. (1997), studying a controlled area surrounded by a possum-occupied habitat of potential immigrants, observed a slow 50% population recovery after 5 years, which they attributed to the lower quality farmland habitat within and surrounding the control area. Similarly, a possum population expansion simulated across Banks Peninsula (115,000 ha) following possum eradication was initially rapid due to a continuous supply of immigrants from the adjacent uncontrolled habitat, but this expansion slowed over time in response to the proportion of exotic low-quality habitat in the area (Shepherd et al., 2018). In comparison, a parallel simulation of a ‘possum-free’ Stewart Island (174,600 ha) following the arrival of a small (20 individuals) founder population was initially far slower than in the Banks Peninsula simulation, despite the high quality habitat available at Stewart Island (Shepherd et al., 2018). The low ‘immigration pressure’ of these few founders, and the slow intrinsic rate of reproduction for the possum population, slowed this initial recovery. Thus, it appears that a combination of immigration pressure and habitat type is likely to shape patterns of possum reinvasion. On the Peninsula, the low number of immigrants resulted in a slow development of in situ reproduction, which became the dominant driver of population growth. This slow initial immigration, alongside the low-quality grassland habitat across much of the Peninsula, seems to have combined to produce a remarkably slow spread of possums back onto the Peninsula.

3.4.1 Causes of low immigration pressure

Adams et al. (2014b) found significant genetic differentiation between possums occupying the centre city of Dunedin and those occupying the Buffer Zone, despite their proximity, which they speculated was due to the highly urbanised Buffer Zone-to-city suburbs limiting movement. This habitat in the Buffer Zone and the city adjacent to the Peninsula is predominantly low-quality urban habitat with little tree cover for possums to shelter in and move through. Occupancy of possums is low in this area (Adams et al., 2013), presumably due to the lower availability of habitat and the risk of disturbance from pets, humans and other anthropogenic causes (see Chapter 2). Movement activity of urban possums in this area has also been observed to be relatively low (2.87 ha average home range size and average nightly movements of 550m) (Adams et al. 2014a), which might correspond with reduced dispersal distances, considering that juveniles typically disperse a maximum of two or three home range

98 sizes away from their birthplace (Cowan and Clout, 2000). Reduced movement is not unexpected given the complex factors at play in urban areas that might limit movement, including the risk of cars, and lack of continuous habitat. In other species, an urban matrix of unsuitable habitat surrounding a suitable habitat patch can limit dispersal distance (Etter et al., 2002; Murcia, 1995; Stow et al., 2001), and habitat fragmentation is known to limit the movement of other Australian marsupials (Caryl et al., 2013; Jones et al., 2004; Walker et al., 2008).

When the habitat was set to be homogenous in the sensitivity analysis however, possum dispersal was still gradual onto the Peninsula. This implies that although habitat type and subsequent dispersal limitation might play a role in reducing possum reinvasion, the actual area available for dispersal might be the more important factor limiting reinvasion. Because the Buffer Zone is such a narrow strip of land, the area available for possums moving onto the Peninsula to disperse across is low. The current phase of the Predator Free NZ 2050 programme relies heavily on the idea that physical features of the landscape can be used as natural barriers to reinvasion (Bell et al., 2019; Norton et al., 2016; Russell et al., 2015). Thus, it is heartening to see that this concept is also supported by my study’s model. The habitat type adjacent to the target control area may become a more important factor in reinvasion across larger areas, as was the case in the simulated reinvasion of Banks Peninsula by Shepherd et al. (2018).

3.4.2 Consequences of ineffective eradication

The fact that in situ reproduction was the main driver of population growth on the Peninsula, and not immigration, was emphasised in the simulation that began with 50 possums remaining on the Peninsula following ‘eradication’. This scenario saw a far higher rate of population recovery than the scenario with complete eradication and the population reached equilibrium by ~15 years. In essence, this simulation represented a scenario where the barrier of the Buffer Zone was redundant and, freed from the necessity of overcoming this dispersal barrier in order to begin to expand on the Peninsula, the possum population was able to proliferate according to its intrinsic rate of increase. Similarly, in most other scenarios with no individuals left on the Peninsula initially, once there was some level of reinvasion onto the Peninsula the population expansion from in situ reproduction became rapid.

It is possible that the model over-represented population growth rates at low densities due to a lack of density dependent reproductive limitation (Allee effect), however, the stark contrast between the outcomes of the Hotspot and Hotspot 50 scenarios, even with such a small ‘founder’ population remaining on the Peninsula, suggests that the most imperative priority for the OPBG will be to remove all individuals when they complete eradication, and to ensure that an effective method of possum detection is established in the Buffer Zone and across the entire Peninsula post-eradication. Preventing reinvasion via trapping will also be important for limiting population spread, as discussed below. The ‘remove and protect’ model of pest control, developed by Zero Invasive Predators Ltd., captures the essence of this (Bell et al., 2019). Their work is focussing on eradicating pests from large mainland sites and then ‘defending’ the area from reinvasion through a network of detection devices. In this way, the issue of ‘perpetual eradication’ is avoided by preventing establishment through the early detection of mammalian pest incursions (Bell et al., 2019). Detecting pests when at low densities like this is a considerable challenge in and of itself (Parkes and Murphy, 2003), and currently an active area of research (Glen et al., 2016; Gsell et al., 2010; Nathan et al., 2013; Ruffell et al., 2015).

3.4.3 Dispersal ‘corridors’ and their uses

Despite possum dispersal onto the Peninsula being infrequent, identifying areas where possum movement is highest is still useful as a means of designing targeted and efficient trapping. Mapping the average total dispersal movements of possums identified the key areas where dispersal activity was highest. In general, these coincided with high-quality forest fragment habitat forming ‘reinvasion corridors’ that acted as a conduit for possums back onto the Peninsula. The forest type of Larnach Castle, for example, is mostly indigenous forest, which can support possum populations of 5.5 individuals ha-1 (Warburton et al., 2009). Sandymount is the one of the largest areas of continuous forest habitat on the Peninsula, a mix of exotic shrubland, broadleaved indigenous hardwoods and mānuka/kānuka, all with a carrying capacity of 3 individuals ha-1 (Warburton et al., 2009). The forest above Portobello is similarly high quality in places (K = 5.5 individuals ha-1) (Warburton et al., 2009), and it is also a very narrow area that possums would have to cross to encroach onto more of the Peninsula. Possums are known to disperse across a range of habitats, but typically move arboreally via vegetation (Cowan and Clout, 2000). Dispersing juveniles will sometimes stop multiple times for a few days (Cowan and Rhodes, 1993), presumably to forage and den along their route (Cowan,

100

2016), This also makes it more likely that they will include forested areas – where food and shelter are available – in their path (Cowan et al., 1997, 1996; Etherington et al., 2014).

In particular, it is useful to highlight that the southern coast is a predicted corridor, possibly made more attractive by the lack of other options if a possum is to avoid the most heavily urbanised suburbs of the Buffer Zone. As such, results suggest the OPBG might gain more efficiency from placing more traps in this location, and within other corridors.

3.4.4 Trapping scenario findings

Possum populations under all trapping regimes showed a similarly slow population increase initially, probably due to the landscape barrier discussed above. However, rates of recovery beyond this point differed between the trap layout and efforts explored. After ~15 years disparities began to arise in the population growth across the trapping regimes of varying effort (150, 300 and 500 traps), and ultimately the trapping scenario with 150 traps was substantially less effective than the 300 or 500 trap scenarios. Essentially, the different trapping regimes are determining the permeability of the trapping ‘barrier’, which in turn determines the ‘founder population’ onto the Peninsula. This is reminiscent of simulations by Shepherd et al. (2018), who showed that predicted population expansion on Great Barrier Island (~28,000ha) was gradual but persistent even with a small founder population of 10 individuals that received no more immigrants. The simulation results suggest that although immigration pressure is very low, reinvasion is not likely to be prevented by the current trap layout. Preventing those few individuals from initially crossing onto the Peninsula will be crucial for preventing re- population of the Peninsula, and management decisions by the OPBG must therefore take this risk into account.

Trapping effort appears to be the most important factor distinguishing the success of scenarios, with the largest improvement in reinvasion prevention seen between the 150 and 300 trap scenarios. However, having a more targeted approach, where traps were placed in strategic locations intersecting ‘corridors’ of movement (as identified by the model), when combined with an increase in traps, reduced the population growth to almost zero. This suggests that targeting traps toward predicted movement corridors may yield more possum kills, as individuals are taken on their way through areas of high use, and are stopped from settling in these areas. Admittedly, the results of the targeted trapping are likely to reflect the habitat

101 selection of possums in the model. Nevertheless, previous research has highlighted the potential usefulness of identifying pest movement corridors to perform more targeted and efficient control (Fraser et al., 2013; Fresia et al., 2014; Recio et al., 2015). Most importantly, the findings from the targeted trapping scenario highlight the potential for a more efficient trapping regime, using less traps. The OPBG should focus effort on determining reinvasion corridors to inform this targeted trapping, as from the outcome of the models it appears that applying 300 traps in targeted positions may be the most effective and efficient means of preventing reinvasion.

3.4.5 Management summary and recommendations

Reinvasion of possums onto the Peninsula was gradual in the absence of control, with immigration somewhat limited by the geography of the Buffer Zone. However, reinvasion became more rapid over time as in situ reproduction contributed new individuals to the Peninsula. Certain high-quality habitat became ‘hotspots’ of reinvasion and dispersal movement, and when a population of possums remained on the Peninsula at the start of simulations, re-population was far more rapid due to in situ reproduction. In this scenario, the Buffer Zone landscape barrier became essentially redundant in preventing population growth. The targeted trapping method was the most effective for minimising reinvasion, while the 300 and 500 trap scenarios were similarly successful, and more effective than the current 150 trap arrangement.

The results of the model highlight that the Peninsula will receive some protection from significant reinvasion by the natural landscape barrier of the Buffer Zone. However, there are key issues that should be the focus of future management decisions. In order to minimise reinvasion risk based on the predictions of the model, I recommend that the OPBG should:

1. Prioritise rigorous eradication to ensure no possums remain on the Peninsula at the end of their final eradication phase.

a. This includes being aware that even when the OPBG’s eradication target of “no possum detections for 2 years” is reached, there may be enough of a residual, undetected possum population to still expand.

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b. This may involve adopting novel detection techniques to identify the final few remaining individuals as research continues in this field over the next 5 years. Maintaining detection equipment throughout the Peninsula will be important in order to detect any newly dispersing juveniles.

2. Aim for 300-500 traps in the Buffer Zone, and target them at known habitat corridors, including the coastal reinvasion route.

a. Monitor trap catch across the Buffer Zone to identify corridors and adjust trapping accordingly.

3. Maintain an ongoing long-term commitment to trapping in the Buffer Zone. This will be needed to prevent reinvasion, as possum expansion was delayed in most scenarios by almost 15 years due to the low amount of immigration occurring

3.4.6 Future research and model improvements

Alongside predicting the future outcomes of management strategies for a target organism, one of the most important uses of individual-based simulation models is to identify knowledge gaps in the study system. Through simulating the population of possums reinvading the Peninsula, this study has identified some key information about urban possum ecology that needs to be the subject of further research. Of most importance, particularly for future modelling work of this sort, is the characterisation of fine-scale habitat selection and decision-making in dispersing juveniles, and the more accurate classification of the carrying capacity of a greater variety of habitats.

Juvenile dispersal

At present, juveniles always disperse in the model, choosing where to move based on a probability that is determined by the relative habitat quality (represented by K) of the surrounding cells. The decision to settle is also based on the habitat quality, and on the density of possums already in the cell, meaning it is both habitat and density-dependent. The reinvasion process is entirely determined by this model of juvenile dispersal. This makes the accuracy of

103 this depiction of dispersal an important determinant of the accuracy of the predictions of the model. For example, the ‘reinvasion corridors’ identified in this study are based entirely on the assumptions of juvenile movement and decision-making.

Dispersal can be divided into three phases: leaving, moving and settling, each with their own costs and trade-offs (Bonte et al., 2012; Travis et al., 2013). As has been identified previously, information on two dispersal phases in particular is needed: movement (how do juvenile possums typically move across landscapes, and why do they move this way?), and settlement (what causes juveniles to choose to settle and establish a home range?) (Cowan, 2016; Russell et al., 2015; Travis et al., 2013). Some characteristics of juvenile possum movement and settlement have been studied. Juveniles are known to make stops in multiple locations as they disperse, before settling (Cowan and Rhodes, 1993), which has been suggested to be a sign of them ‘sampling’ the habitat to assess its suitability (Cowan, 2016). Barriers to dispersal are known to include some rivers, larger bodies of water and busy roads (Brockie et al., 2009; Etherington et al., 2014), while vegetation cover may act as a conduit for movement (Etherington et al., 2014). We also have information on the distribution of dispersal distances for males and females, and the maximum distances travelled (Cowan et al., 1996; Cowan, 2000; Cowan and Clout, 2000; Cowan and Rhodes, 1993; Efford, 1998). Based on studies of other organisms, we know that factors influencing the decision to settle in an area might include local possum density, habitat quality, resource availability, inbreeding avoidance and mating opportunities (Banks and Lindenmayer, 2014; Shaw and Kokko, 2014; Sweetapple and Nugent, 2009), while individual variables such as sex and body condition might also be important (Banks and Lindenmayer, 2014; Stamps, 2006).

We particularly need dispersal information that is habitat-specific. Most aspects of possum ecology are linked to the habitat context, and the same appears to apply for dispersal. Dispersal distance, for example, has been shown to differ substantially across habitats (Glen and Byrom, 2014). Very little research on urban possum dispersal has been done in Australia (Stow et al., 2006), and none in New Zealand. Habitat fragmentation and anthropogenic influences are likely to influence dispersal decision-making in animals by altering the distribution of habitat and providing novel stimuli and/or deterrents (Banks and Lindenmayer, 2014). The suitability of disjointed habitat patches in cities is also likely to be highly influenced by the landscape context, or matrix, surrounding them (Banks and Lindenmayer, 2014), potentially creating novel habitat types (e.g. forest fragments – see Chapter 2). The one Australian study of urban possum dispersal used genetic markers, finding a male-biased dispersal ratio in the possums

104 studied. This is characteristic of possum dispersal patterns in the non-urban habitats of Australia and New Zealand (Stow et al., 2006). Stow et al. (2006) also found high levels of philopatry and localised genotypic similarities that suggest even males will not often disperse beyond 900m, which is lower than the typical average maximum dispersal distance in non- urban habitats (Glen and Byrom, 2014; Stow et al., 2006).

Most dispersal information for possums has come via mark-recapture, radio telemetry and molecular techniques (Stow et al., 2006), however, fine-scale GPS movement data would be the next step to gather habitat-selection information (e.g. Braaker et al., 2014; Cowan, 2016). A study like that of Banks and Lindenmayer (2014), who used genetic analyses to determine the demographic, environmental and genetic factors affecting the probability of settlement for dispersing male agile antechinus (Antechinus agilis), might also provide useful information on the probability of possums settling in different areas. Similarly, a more detailed ‘cost-surface’, which represents the landscape as a variety of weighted variables (e.g. road size, tree cover), might provide useful predictions for dispersal pathways that could then be validated with fine- scale GPS data (Etherington et al., 2014). Finally, analysis of the distance at which juvenile possums can perceive stimuli (auditory, visual, olfactory) as they disperse, and which stimuli are most important for determining dispersal choices would be helpful to more accurately depict their environmental sensing and decision-making.

Habitat carrying capacity values

The K values of the habitat underpin every aspect of the model, and research should aim to more accurately characterise these values for all habitat types. As shown in the Sensitivity Analysis, varying the number of possums spread across the Non-Study Area landscape significantly altered the reinvasion onto the Peninsula. The K values are also linked to the representation of juvenile dispersal, which, as discussed above, heavily influences the outcome of the model. The K values have been relied on in many models already, including that of Ramsey and Efford (2010), Shepherd et al. (2018), and Lustig et al. (2019). Further, even the most current estimate of total possum numbers in New Zealand is based on these K values (Warburton et al., 2009).

By updating the urban habitat K values with estimates from Chapter 2 of this thesis, the model in this study more accurately represented the capacity of these habitats to support possum

105 movement and settlement, which was important in this highly urban context. Leaving these finer-scale differences out and defining a K of zero for all urban areas, as other studies have done, constitutes a mis-representation of an important available habitat for possums. I therefore argue that further classification of possum carrying capacity values should be carried out, with replication, for all major habitat types in New Zealand if modelling of this type is going to continue to be used. However, as demonstrated in this study, at times it may be more feasible to identify key habitat types that will be within the study area of a potential model, and conduct research or apply relevant expert opinion to supply more accurate K values for these habitats. Extending this, local landscape and topographic features (e.g. rivers, roads, elevation) could be incorporated into the base habitat map, as was done in the analysis of Etherington et al. (2014).

Model validation

Lustig et al. (2019) made efforts to validate the predictions of their model by comparing predicted possum density with recorded pest detections from chew-cards (Brown et al., 2016) across their study area. They found the model’s results to be generally consistent with the recorded pest detections, with little evidence of over or under-prediction of the model. No validation of the model’s predictions could be undertaken in this study however, and if it is to continue to be used as a predictive tool in the future, this should be remedied. Model validation for this study might take the form of comparing predicted possum relative abundance in key locations around the Peninsula with observed relative abundance calculated from trapping or detection data.

The benefits of IBMs for urban pest management

The simulations of this study have highlighted some of the potential benefits of using individual-based, spatially explicit modelling, and the scope for it to inform future New Zealand pest control projects. By incorporating the effect of individual decision-making, the model was able to highlight the importance of individuals in determining overall population trajectories. For example, in many cases, the invasion of only a small number of individuals was sufficient to initiate population growth on the Peninsula, and when there were some

106 individuals remaining on the Peninsula at the end of the ‘eradication’, population expansion was significantly higher.

Simulations spanned a 40 year period and represented not only the possums immediately adjacent to the Peninsula, but also across the wider Dunedin city area. In this way, the model allowed for a long-term, wide-scale view of the study system, which is a distinct advantage of spatial IBMs. The long-term view showed that managers will have to be vigilant in their control at the Buffer Zone over many years, because in most cases reinvasion occurred only very gradually over the first 5-10 years. The city-wide view of the model allowed for inspection of the interaction between central city densities and subsequent reinvasion rates onto the Peninsula, which were enhanced or reduced by a higher or lower city K respectively. This emphasised the connectivity of landscapes. Finally, the spatial element of the model was particularly advantageous in the urban context because it allowed for very fine-scale representation of habitat heterogeneity and its influences on possum distributions.

Through the provision of realistic predictions of future outcomes, the OPBG can use information from the model to plan their further trapping layout in the Buffer Zone, encourage more residents in the Buffer Zone to join the Guardians, prioritise rigorous eradication efforts in the near-term and justify expenditure to achieve complete eradication, and highlight the importance of the Guardians maintaining an ongoing commitment to trapping in the Buffer Zone. Further, if the OPBG is able to measure the rate and location of detections in the Buffer Zone and elsewhere on the Peninsula, they may apply the model again in an ‘adaptive management’ approach (Lustig et al., 2019), re-classifying some parameters such as trap location, city density and habitat carrying capacity.

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3.5 Conclusions

The findings of this study have demonstrated the way a new spatial modelling tool developed by Lustig et al. (2019) can inform an urban pest management initiative. The model was able to simulate the patterns of future possum abundance on the Peninsula, highlighting areas of congregation and possible reinvasion routes that should be targeted with control. It also demonstrated the importance of total eradication for minimising re-population via in situ births. Comparison of various trapping arrangements predicted the most efficient trapping layout to minimise reinvasion. Finally, the modelling highlighted key avenues of further research for possums occupying urban habitats in New Zealand.

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Chapter 4 General Discussion

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4.1 Summary

The aim of this research was to provide the first density estimates of possums in urban New Zealand, and to incorporate this information into a spatially explicit, individual-based model to simulate possum reinvasion of the Otago Peninsula. This approach combined fundamental population research for urban possums, and the application of this knowledge to a management-oriented spatial model, exemplifying the importance of case-specific studies to inform the management of possums in New Zealand.

Despite possums being known to occupy urban habitats, and the growing importance of urban biodiversity enhancement, which they may hinder, little population information has been gathered on urban possums in their native range (Australia), or New Zealand. As was hypothesised, this research found possums at a substantial density in an urban forest fragment, and lower densities in residential areas. With this research I have shown that urban areas have the potential to harbour significant possum populations, but that this density differs between forested and residential areas. As discussed in Chapter 2, these differences are likely to be due to the availability of habitat and food resources, and the presence of disturbances that may discourage possum occupation.

Taking this knowledge and incorporating it into a spatial simulation model, I have shown the importance of fundamental population research to inform novel management tools. The spatial model was used to explore the predicted abundance of possums, their dispersal routes, where they might congregate and the relative effectiveness of trapping regimes. In Chapter 3, I discussed how this allows managers to consider their priorities and risks, as well as highlighting future research in the study system.

The findings of this study have increased our knowledge of urban New Zealand possum ecology, as well as introducing and demonstrating the use of novel individual-based spatial modelling for urban pest management.

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4.2 The necessity of urban possum parameters

As humans have migrated towards cities, researchers have gradually become aware of the biodiversity that has been lost via urbanisation (McDonald et al., 2013; McKinney, 2002), and with that, the link between humans and the natural environment (Miller, 2005; Soga and Gaston, 2016). Ecologists and health scientists, operating under their own research agendas, have come to the same conclusion that the ‘greening’ of urban spaces is the crucial next step in urban development (Cox et al., 2017; Frumkin et al., 2017; Wolch et al., 2014). In response, there has been an increase in activity, research and discussion around ways to bolster the biodiversity in a city, while making humans more engaged in this process (Beumer and Martens, 2015; Collins et al., 2017; Gaston et al., 2005; Gill et al., 2009; Glen et al., 2013; Whitmore et al., 2002).

With the heightened attention on urban biodiversity and its associated benefits must come a concurrent surge of effort to understand the unique ecology of these environments. Urbanisation has not been negative for all species, and some ‘urban adapters’ are exploiting the novel opportunities of cities (McKinney, 2006). Urban conditions often favour traits that are typical of invasive species, and hence, a disproportionate number of these ‘urban adapters’ are invasive species, potentially creating a new threat for returning or resident native biodiversity (Adams et al., 2014a; H. Lowry et al., 2013a; McKinney, 2006). Because of this, urban biodiversity enhancement initiatives often involve substantial pest control. Yet, to not gather baseline population data on these invasive species that live in urban areas, is to not acknowledge that in order to persist in the urban environment, a species is likely to be behaving differently to how it does in its ‘natural’ preferred ecosystem. Without baseline demographic data for urban invasive species, we are unable to truly assess the potential impact of these species on urban biodiversity, and this is hindering our urban biodiversity goals.

As an example of this, by not assessing the density of possums in the city of Dunedin, managers either do not know their densities at all, and rely on regular control to keep populations low, assessing relative abundance from detection indices that might be less effective because of lower urban trappability, or assume their densities based on information from different non- urban habitats. This oversight means that possums, even with control, may at times be existing at densities that are high enough to trigger die-back events, as seen in Chapter 2, jeopardising the work done to regenerate the forest of the Dunedin Town Belt and other forested areas around the city.

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4.3 Density as an informative metric for urban possums

When approaching the study of an organism, there are a large number of population parameters that might be useful to investigate. However, to maximise efficiency in a typically resource- limited discipline, ecologists will often choose to explore those parameters that are key to understanding the population dynamics of the organism. In the study of possums in New Zealand, density is a parameter that illuminates many aspects of their ecology, including indicating their potential to impact native biodiversity (Duncan et al., 2011; Efford, 2000; Holland et al., 2013), suggesting the quality of the habitat and the key resources it holds (e.g. food, den sites and movement cover), and being linked to demographic parameters such as home range size and number of offspring (Isaac and Johnson, 2003; Richardson et al., 2017; Whyte et al., 2013).

The findings of Chapter 2 show that urban habitats of differing vegetation and residential influence do harbour possums, but at varying densities. As in other habitats, vegetation cover appears to be the most important determining factor, with a higher population density in the urban forest fragment compared to the residential sites. However, the slightly lower-than- predicted density for the indigenous-exotic habitat type of Jubilee Park suggests other factors are also important in determining urban possum success. Instead of being wholly dependent on the forest type, it is plausible that density in urban forest fragments is influenced by the stressors and edge effects of roads and human activity (Baker and Harris, 2007; Caryl et al., 2013; Godefroid and Koedam, 2003; Mörtberg, 2001).

When considering the potential impact of urban possums on native biodiversity, the calculated density can provide some insight. At present, the density of the forest fragment indicates that possums are at a number capable of causing harm to native biodiversity. It is not known how possums’ activity as predators of adult birds, chicks and eggs relates to density, however, their impact as browsers has been found to change with density (Duncan et al., 2011). If the urban populations are semi-regularly controlled, but not reduced to a low density, they may be in a constant population growth phase, which can correspond with greater damage to forest species (Payton et al., 1997; Thomas et al., 1993). The flora of urban forest fragments may already be experiencing stress from anthropogenic disturbance, edge effects, and competition with invasive plants (O’Brien et al., 2012; Ohlemüller et al., 2006; Ranta et al., 2013), while fauna may be subject to predation from other invasive mammals such as rats, hedgehogs, mice and

112 feral or domestic cats, all of which can reach high abundances in New Zealand cities (Gillies and Clout, 2003; Glen et al., 2012; Morgan et al., 2009; van Heezik et al., 2010).

Possum impacts on biodiversity do not seem as likely in the low density residential areas, however there is some previous literature to suggest that possums may be more trap-shy in these areas (Adams et al., 2014a; Eymann et al., 2013; Statham and Statham, 1997), meaning they may be at a higher density than estimated. There was some evidence to suggest this in the study of Chapter 2 as well. This presents an avenue of research assessing whether possums show any behavioural changes that alter their trappability in urban areas. Behavioural adjustments to the conditions of a city are regularly observed in other animals, including increased wariness to novel stimuli (‘neophobia’) (Cowan, 1977; Echeverría and Vassallo, 2008). Lower trappability would have direct consequences for the management of urban possums, as well as for representing them via modelling, as was done in Chapter 3.

Thus, as a metric, the density estimates of Chapter 2 provide information on the potential impacts of possums on urban Dunedin biodiversity. They also suggest the resource availability and conditions occurring in three urban habitats, emphasising the importance of spatial habitat heterogeneity in determining the distribution of possums in urban areas. Finally, the density estimates highlight a potential behavioural difference in urban possums that requires further investigation.

4.4 Representing urban habitat in modelling

The findings of Chapter 2 emphasise another important point. While urban areas constitute a comparatively small amount of New Zealand land area (< 2% in 2008), they are expanding (Ministry for the Environment, 2010), and are significant in terms of PFNZ efforts. Globally, urban land cover is expected to nearly triple between 2000 and 2030 (Seto et al., 2012). To not include urban areas as habitat for possums or other invasive mammalian pests in modelling is to disregard an important land cover type, with consequences for the accuracy of predictions. Previous studies have represented urban areas as inhospitable for possums (Lustig et al., 2019; Shepherd et al., 2018; Warburton et al., 2009), while some habitat occupancy modelling of other mammals has represented cities as homogenous habitat capable of sustaining some individuals (Chupp et al., 2013; Saito and Koike, 2013). Few have gone so far as to represent

113 the within-habitat heterogeneity and patchiness of resources in cities (Morgan et al., 2009; Traweger and Slotta-Bachmayr, 2005).

Including heterogeneous urban areas in predictive spatial modelling is important not only to accurately represent the capacity of urban areas to harbour organisms, but also to accurately represent their role in the wider context of the landscape. In some instances, urban areas may act as a link between areas of higher quality habitat for animal movement. Dunedin is a good case-study of this, as the forested regions of the outer Dunedin city and the forested areas of the Peninsula are connected via the highly urbanised centre city and Buffer Zone suburbs. It was apparent from simulations that disregarding this urban zone as habitat for possums with its own carrying capacity was to mis-represent the connectivity of the landscape, and thus, the metric of interest – reinvasion rates of possums onto the Peninsula.

4.5 The power of individual-based modelling for informing management

Invasive species management is going to continue to be an imperative and expensive undertaking in New Zealand and elsewhere in the world. Eradication is becoming the goal for international projects, and researchers are looking to New Zealand as world-leaders in large- scale mammalian eradication (Blackie et al., 2014; Russell et al., 2015; Russell and Broome, 2016). It has been acknowledged that in order for ambitious national projects to succeed, New Zealand will need to employ many different tools simultaneously, adopting novel techniques where appropriate (Blackie et al., 2014; Russell et al., 2015). Spatial modelling has its place within this narrative as one tool which can support the decision-making and management of invasive New Zealand pests.

As discussed in Chapter 3, individual-based spatial modelling enables the simulation of individuals that move and interact with their environment and with each other. They do so at a user-defined scale that can encompass many years and entire landscapes. The use of IBMs in urban invasive species management has been minimal until now, but urban areas might represent one of the best study systems for IBM application. This is because cities are highly heterogeneous in space (Cadenasso et al., 2007), with many patches of adjacent habitat that are vastly disparate (LaPoint et al., 2015). Being able to represent the habitat at a fine scale, is to

114 accurately represent the diverse conditions experienced by an organism living in or moving through a city (Braaker et al., 2014).

For possums specifically, IBMs at a fine resolution can highlight the influence that habitat has on their overall distribution, and how the connection of some habitats can act as a conduit or barrier to overall possum movement. As we move towards more urban and peri-urban-based control for Predator Free NZ 2050, this ability to predict fine-scale mammal distributions, and represent fine-scale control measures will become crucial for accurately depicting these complex habitats.

4.6 Future applications of the model

With different population parameters, there is potential for the model to be applied to other mammalian pest species across different landscape types, including those mammalian pests that are the target of PFNZ (stoats and rats), as well as others such as hedgehogs (Erinaceus europaeus), feral cats (Felis catus), mice, and ferrets (Mustela furo). A substantial amount of data has been collected for these key vertebrate pests in New Zealand, including habitat- specific home range sizes, maximum, average and sex-specific dispersal distances, birth and death rates, and the spatial detection parameters 𝜎 and 𝑔0 (Cowan, 2005; Glen and Byrom, 2014, Hamlin, 2018). Furthermore, some habitat selection behaviours have been characterised in the literature, potentially forming the basis for modelling of the different phases of dispersal (Glen and Byrom, 2014). This means that many of the essential parameters for modelling these pest animals in New Zealand are already available for various habitat types. Some spatial modelling is already utilising this data (Anderson et al., 2016; Etherington et al., 2014; Lustig et al., 2019; Recio et al., 2015; Shepherd et al., 2018), along with non-spatial population modelling (e.g. Leo et al., 2018; Tompkins and Veltman, 2006).

The spatially explicit nature of the model means that conditions can be tailored to suit the study system, as was done in the study of Chapter 3. More fine-scale habitat maps can be used, where available, and other relevant landscape features can be included in the base map of habitat carrying capacity (Etherington et al., 2014). The history of pest control in an area may inform the preliminary density outside of the study area, and unique control methods, arrangement and time-span of control can readily be represented through the trapping layer of the model. This includes representing control via methods other than trapping (i.e. bait stations), dependent on

115 the availability of relevant parameters. As noted by Lustig et al. (2019), the model could also be extended to include multiple interacting species.

A variety of questions might be explored through use of the model. As the study of Chapter 2 demonstrated, the success of different spatial and temporal control layouts in preventing reinvasion, and their relative efficiencies, could be explored. As shown in the work of Lustig et al. (2019), the model can also be applied to questions pertaining to the eradication of pests from an area. This includes the relative success of various control options, and a cost/benefit analysis of each to find the most efficient option. Alternatively, Shepherd et al. (2018) demonstrated that models of this type could be used to simulate the effects of individual incursion events on re-population of an area.

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4.7 Concluding remarks

Urban environments are a unique habitat for possums, and should be treated as such. The approach I have shown is one where close consideration of the habitat of the study system, and tailoring my analysis to this system, has ultimately produced more defensibly realistic and relevant results. Urban density of possums was found to differ substantially across the three studied habitat types, reflecting the unique conditions present within the heterogeneous urban environment. The forest fragment habitat supported a density of possums capable of causing harm to native biodiversity, but low enough to suggest there were some negative impacts arising from the proximity of the urban matrix. The two residential areas displayed low possum densities, but the density of possums in this habitat warrants further investigation due to the potential for possum behavioural avoidance of trapping devices. Using an IBM answered key questions related to the management of possums reinvading the Otago Peninsula. In particular, the IBM highlighted movement corridors and areas where possums might congregate, and indicated the most efficient trapping regime for the OPBG to pursue. Further questions have arisen from this modelling, including the nature of juvenile dispersal. In combination, this density and modelling information progresses the research of urban possums and their management in New Zealand, at a time when this subject is only growing in relevance.

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4.8 Ethics

This study was conducted with approval from the University of Otago Animal Ethics Committee under the AUP-18-201.

4.9 Erratum statement

An error in the Kmap of the model was found late in the development of this thesis. Two land cover types (‘Res III’ or and ‘Res I’ were given the opposite K value of 0.11 and 0.21 respectively). The effect of this change is expected to be minimal, just altering the probability of juvenile individuals moving through these two habitats. From preliminary re-running of the model it appears the error slightly reduces the final possum abundance on the Peninsula in the ‘Hotspot’ scenario, but does not alter the pattern of reinvasion or predicted possum distribution. This error will be addressed in revision for stakeholders and publication.

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5.1 Appendix A

Figure 5. 1 The derived density of possums (number per ha of effective sampling area) as a function of the buffer width of a secr habitat mask, which defines the area of integration. The dashed red line indicates the 300m buffer width chosen, at which point the density has reached an asymptote and is not further affected by buffer width.

Table 5. 1 AICc values comparing secr null models which employ varying detection functions, in order to decide the most appropriate detection function for further modelling. Detection functions primarily differ in the length of the detection tail. AICcwt is the AICc weight.

Model Detection Log AICc ∆AICc AICcwt Function Likelihood

g0~1 σ~1 Hazard Rate -414.8529 836.206 0.000 1 g0~1 σ~1 Exponential -422.1584 848.562 12.356 0 g0~1 σ~1 Half Normal -431.6111 867.467 31.261 0

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Figure 5. 2 Output from the esa.plot function in secr exploring variation in the number of individuals per effective sampling area (density) when three detection functions are applied to the null model. These detection functions are the half-normal (HN), exponential (EX) and hazard rate (HR). The exponential and half-normal predictions reach an asymptote at a buffer width of 200-250m while the Hazard rate predictions remain sensitive to changes in buffer width.

Table 5. 2 Comparison of the derived density (퐷̂) values for each model with the exponential or half-normal detection function. The half-normal models are consistently slightly lower.

Model Exponential Half-normal 푫̂

null 3.68 3.35 b 2.99 2.78 bk 3.68 3.38 age 3.79 3.57 B 3.77 3.45 Bk 3.69 3.37

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5.2 Appendix B

Figure 5. 3 Possum movement onto the Peninsula, averaged over 40 simulations, at year 5, 10, 20 and 40 in the homogenous K sensitivity analysis scenario where all cells were set to a K of 0.2. This analysis was conducted to explore the relative importance of the habitat type, and available area in determining the reinvasion rate of possums onto the Peninsula.

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Table 5. 3 The abundance of possums on the Peninsula over time across the sensitivity analysis scenarios, where parameter values were varied 25% above and below the original value. Table includes the baseline Hotspot and Trap 300 scenario results for comparison. Bold values are the total average over all replicates, while un-bolded values are the average over replicates where invasion to any non-negligible extent (> 10 individuals) occurred.

Simulation Proportion 5 year 10 year 20 year 40 year invaded* abundance abundance abundance abundance

Hotspot 0.875 5.9 (± 0.9) 37 (± 5.3) 323.9 (± 27.2) 1433.4 (± 95.8) 6.7 (± 0.9) 42.3 (± 5.5) 370.2 (± 21.9) 1638.2 (± 48.9)

Homogenous K 1 210.4 (± 3.9) 798.6 (± 11.0) 2341.1 (± 21.1) 6442.9 (± 47.6)

25% K 0.53 2.5 (± 0.6) 14.4 (± 4.0) 145.5 (± 27.2) 750.3 (± 120.7) 4.7 (± 1.0) 27.4 (± 6.4) 277.1 (± 36.8) 1429.0 (± 81.9)

75% K 1 12.3 (± 1.4) 66.1 (± 6.9) 415.0 (± 18.5) 1670.3 (± 44.5)

Lifespan 9 0.6 4.9 (± 1.0) 31.4 (± 6.3) 223.0 (± 31.8) 948.1 (± 127.2) 8.1 (± 1.4) 52.3 (± 8.0) 371.6 (± 22.4) 1580.1 (± 58.0) Lifespan 15 0.73 5.3 (± 1.0) 34.8 (± 5.9) 283.6 (± 32.9) 1241.0 (± 127.0) 7.2 (± 1.2) 48 (± 6.6) 391.1 (± 24.8) 1711.7 (± 54.1)

Mean birth 0.6 0.78 3.9 (± 0.7) 20.4 (± 3.4) 200.5 (± 25.1) 941.8 (± 93.0) 5.0 (± 0.8) 26.4 (± 3.8) 258.7 (± 23.7) 1215.3 (± 60.8) Mean birth 0.8 0.7 6.7 (± 1.2) 44.6 (± 7.6) 320.1 (± 35.9) 1297.8 (± 135.4) 9.5 (± 1.4) 63.7 (± 8.7) 457.3 (± 19.6) 1854 (± 24.3) Max. dispersal 0.53 4.5 (± 1.0) 20.4 (± 4.5) 146.8 (± 23.7) 594.0 (± 89.8) 9km 8.6 (± 1.5) 38.8 (± 6.4) 279.5 (± 20.6) 1131.5 (± 54.0)

Max. dispersal 0.63 4.3 (± 0.9) 30.8 (± 5.8) 275.7 (± 37.9) 1159.4 (± 145.1) 15km 6.9 (± 1.1) 49.3 (± 7.0) 441.2 (± 27.4) 1855 (± 48.1)

Trap 300 0.3 0.8 (± 0.2) 0.9 (± 0.3) 19.4 (± 11.1) 166.0 (± 59.8) 2.5 (± 0.48) 11.8 (± 3.7) 64.5 (± 33.6) 553.1 (± 147.9)

Juvenile trapping 0.35 1.8 (± 0.4) 4.8 (± 1.5) 35.2 (± 14.7) 292.7 (± 83.3) 0.0375 4.7 (± 0.7) 13.4 (± 3.3) 100.2 (± 36.1) 835.9 (± 155.4) Juvenile trapping 0.38 1.1 (± 0.3) 3.4 (± 1.2) 19.8 (± 8.9) 280.3 (± 70.6) 0.0625 2.9 (± 0.5) 9.5 (± 3.0) 56.6 (± 21.4) 747.4 (± 110.5)

* The proportion of replicates that saw any non-negligible number of reinvading possums (> 10).

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Table 5. 4 contd. The abundance of possums on the Peninsula over time across the sensitivity analysis scenarios, where parameter values were varied 25% above and below the original value. Table includes the baseline Hotspot and Trap 300 scenario results for comparison. Bold values are the total average over all replicates, while un-bolded values are the average over replicates where invasion to any non-negligible extent (> 10 individuals) occurred.

Simulation Proportion 5 year 10 year 20 year 40 year invaded* abundance abundance abundance abundance

Adult trapping 0.35 1.1 (± 0.3) 2.3 (± 0.7) 16.4 (± 6.3) 212.5 (± 71.9) 0.0375 2.9 (± 0.6) 6.4 (± 1.5) 46.9 (± 14.9) 607 (± 158.6)

Adult trapping 0.38 1.2 (± 0.3) 2.7 (± 0.9) 20.3 (± 8.5) 231.0 (± 73.8) 0.0625 3.1 (± 0.5) 7.1 (± 2.0) 54.1 (± 19.9) 615.9 (± 151.5)

* The proportion of replicates that saw any non-negligible number of reinvading possums (> 10).

Figure 5. 4 The possum abundance on the Peninsula, averaged over 40 replicates, for the sensitivity analysis where the carrying capacity of the Non-Study Area was set to be 25% lower or higher than the original model.

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Figure 5. 5 The possum abundance on the Peninsula, averaged over 40 replicates, for the sensitivity analysis where the maximum adult lifespan was varied by 25% above and below the average value used in the model.

Figure 5. 6 The possum abundance on the Peninsula, averaged over 40 replicates, for the sensitivity analysis where the mean birth rate was varied by 15% above and below the average value used in the model.

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Figure 5. 7 The possum abundance on the Peninsula, averaged over 40 replicates, for the sensitivity analysis where the maximum juvenile dispersal distance was increased and decreased by 25% from the average value used in the model.

Figure 5. 8 The possum abundance on the Peninsula, averaged over 40 replicates, for the sensitivity analysis where the resolution was set to 500m, instead of the 200m used in simulations.

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Figure 5. 9 The possum abundance on the Peninsula, averaged over 40 replicates, for the sensitivity analysis where the juvenile trappability (Gamma1 or 훾1) was varied by 25% above and below the average value used in the model. Trapping was set to the ‘Trap 300’ scenario for the comparison.

Figure 5. 10 The possum abundance on the Peninsula, averaged over 40 replicates, for the sensitivity analysis where the adult trappability (Gamma0 or 훾0) was varied by 25% above and below the average value used in the model. Trapping was set to the ‘Trap 300’ scenario for the comparison.

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