Hollow-Bearing Trees As a Habitat Resource Along an Urbanisation Gradient

Hollow-Bearing Trees as a Habitat Resource along an Urbanisation Gradient

Author Treby, Donna Louise

Published 2014

Thesis Type Thesis (PhD Doctorate)

School Griffith School of Environment

DOI https://doi.org/10.25904/1912/1674

Downloaded from http://hdl.handle.net/10072/367782

Griffith Research Online https://research-repository.griffith.edu.au

Hollow-bearing Trees as a Habitat Resource

along an Urbanisation Gradient

Donna Louise Treby MPhil (The University of Queensland)

Environmental Futures Centre. Griffith School of Environment, Griffith University, Gold Coast. A thesis submitted for the fulfilment for the requirements of the degree of Doctor of Philosophy. December 2013.

“If we all did the things we are capable of doing.

We would literally live outstanding lives.

I think; if we all lived our lives this way, we would truly create an amazing world.”

Thomas Edison.

Acknowledgements:

It would be remiss of me if I did not begin by acknowledging my principal supervisor Dr Guy Castley, for the inception, development and assistance with the completion of this study. Your generosity, open door policy and smiling face made it a pleasure to work with you. I owe you so much, but all I can give you is my respect and heartfelt thanks. Along with my associate supervisor Prof. Jean-Marc Hero their joint efforts inspired me and opened my mind to the complexities and vagaries of ecological systems and processes on such a large scale.

To my volunteers in the field, Katie Robertson who gave so much of her time and help in the early stages of my project, Agustina Barros, Ivan Gregorian, Sally Healy, Guy Castley, Katrin Lowe, Kieran Treby, Phil Treby, Erin Wallace, Nicole Glenane, Nick Clark, Mark Ballantyne, Chris Tuohy, Ryan Pearson and Nickolas Rakatopare all contributed to the collection of data for this study. A huge thanks also goes out to my research assistants Clay Simpkins, Greg Lollback and James Bone for carrying all the gear and following me around in the bush at night; we shared many laughs and I appreciated your company on some of those ‘tough’ or ‘itchy’ nights. Dr Greg Lollback, thank you for your patience and assistance with ‘R’ and statistical analysis in general. Thank you to Professor Clyde Wild and Michael Arthur for statistical support. I would also like to thank Greg Ford from Balance Environmental for helping me with my many questions about identifying bat call frequencies with Anabat. Thanks also to Kevin Wormington for supplying tree growth data and Michael Ngugi for tree mortality data.

Diana a.k.a. DD Virkki you have been a pleasure to work with, hang out with, and laugh with, thank you for being you. You also managed to help me overcome my statistical dyslexia which will stay with me forever. Special mention must go to Kat Lowe; we have shared a lot of laughs, fears and tears; but mostly laughs together, thank you for being such a true friend. To my fellow HDR comrades in Room 3.26, I thank you all for just being there as we all shared our struggles and frustrations, I wish you all the very best in your future careers. A heart-felt thank you must go to Dr Clare Morrison for providing valuable feedback on draft chapters of this thesis. Your ability to reduce my stress levels was significant (p < 0.05).

Thank you to the many people that supplied me with GIS data. Without it I would not have been able to complete my study in such fine detail. Chris Woodman at Energex, Peter Dunn

at Power Link, Annie Kelly at DERM, Blair Prince and Rodney Anderson from Gold Coast City Council. Additional thanks go to Rebecca Castley for helping me make sense of the GIS layers.

The staff from the Natural Areas Management Unit and the Land for Wildlife team within Gold Coast City Council was always extremely helpful: Jason Searle, Dr Tim Robson, Howard Taylor, Wayne Abbott, Steve Vigar, Brett Galloway, Rachael Niotakis, Darryl Larsen and Lexie Webster. To the many private land owners that allowed open access to their properties in order to undertake this research I am deeply grateful. Wal and Heather Mayr, Brit and Clay Badenock, Heather Smart, Jennie and Michael Tippet, Carolyn Reid, Steve and Karen Dalton, Julia Edwards, Peter Lyons and well as John Palmer and Sharon Kolka from Gwinganna Lifestyle Retreat, Richard Laws from Gainsborough Greens Golf Course and Steve Rowell from Jacobs Well Education Centre.

Last, but in no ways least, I cannot thank and praise my husband enough for giving me the freedom to explore and learn. Phil Treby you are one in a million, I love and admire the man that you are.

Funding for the project as well as a top up scholarship was generously supplied by Gold Coast City Council. This study was completed under the following DERM permits: Scientific Purposes Permits WITK06680910, WISP06680710 and Permit to Collect biological or geological material from Queensland State Forest, Timber Reserves and other State Lands: Permit number TWB/01/2010. Ethics approval was obtained from Griffith University Animal Ethics Committee: Ethics approval number ENV/07/09/AEC to Dr Guy Castley.

In order to complete this thesis I have had to delve, inquire and compare; spending hours, months and years of my life observing the many changing faces of nature. My work has forced me to question nature and approach her from a different aspect in order to understand her secrets; if that is at all possible. Thus, mainly through reading, this journey has led me into the company of men and women of far greater knowledge than I could ever hope to acquire.

Abstract:

Habitat fragmentation due to rapid urbanisation is occurring globally and results in small and often isolated patches of remnant bushland. These small, forest remnants contribute to the conservation of biodiversity within urban areas, as they can, and often do, contain suitable habitat structures. Habitat structures such as hollow-bearing trees are recognised as important features of forests globally. Therefore, the abundance of hollow-bearing trees within a landscape may be a controlling factor for many biota where no other habitat resources provide a feasible substitute. Therein, this thesis investigates five aspects in relation to the ecology of hollow-bearing trees as a habitat resource within a rapidly urbanising landscape.

In the first instance a review of the literature was undertaken. This presents a summary of the importance of hollow-bearing trees globally, and identifies threats associated with their conservation. Focus is centred on the importance of this habitat resource within the Australian context, concluding with a description of the study area and thesis aims. A hierarchical analysis of the current legislation that exists within Australia to protect hollow- bearing trees was then undertaken. The evaluation revealed, that despite Federal and State legislation acknowledging the importance of hollow-bearing trees to biodiversity, there are insufficient mechanisms in place at all levels of government to halt the decline of hollow- bearing trees across various landscapes. A subsequent quantitative review of local councils’ policies and websites in regards to biodiversity conservation and specifically the management of hollow-bearing trees across four eastern Australian states revealed that few (< 5%) had specific plans in place to protect this habitat resource. While existing legislation identifies the need to retain ‘forests’, ‘regional ecosystems’, ‘remnant habitat’ or vegetation communities’, it masks the fact that it is largely ineffective in delivering real on-ground conservation action for finer scale habitat features such as hollow-bearing trees, particularly those found within urban landscapes and non-protected forests.

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To gain a greater understanding of the importance of this habitat resource at the local scale the third Chapter undertook an inventory of hollow-bearing trees along an urbanisation gradient within a rapidly urbanising landscape on the Gold Coast in south-east Queensland. A total of 6048 trees from 45 remnant forest sites along the urbanisation gradient were recorded, of which there were 916 hollow bearing trees containing 2143 hollows. Twenty- four species of eucalypt were hollow-bearing, with standing dead trees containing the most hollows (15%). Hollow type was found to be dominated by small (≤ 10 cm) bayonet type hollows. The stands of remnant forest patches in this study were found to be of an even age, with 79% of all trees being within the 10-40 cm diameter at breast height (dbh) range. The urbanisation gradient did not influence hollow type, however, it was found to influence the numbers of each individual hollow type found. No relationship was found to occur between environmental variables, disturbance and the urbanisation gradient on the number of hollow- bearing tree species per site, nor did they have any influence on hollow density per site. Consistent with previous research, this study confirmed that considerable variation exists in the availability of hollow-bearing trees across the urban matrix.

The functional ecology of habitat remnants along the urbanisation gradient was then investigated in Chapter 4. This chapter quantified the impacts of urbanisation, stand density, landscape variables such as fire and logging history as well as environmental variables on hollow-bearing tree density within urban remnant forests. Logging history was found to be the driving factor influencing hollow-bearing tree density along the urbanisation gradient on the Gold Coast. Historical land-use practices and their effects leave long lasting impacts on the environment driving habitat structure and function over time. These findings highlight the significance of incorporating historical land use practices into current and future urban planning, as these impact on remaining biodiversity values in rapidly changing landscapes such as those found along urbanisation gradients. These findings, will therefore be of benefit to managers and planners when making decisions about where, and how to best to manage for hollow-bearing trees in an urban matrix.

Once the factors influencing the density of hollow-bearing trees in urban remnants had been identified in Chapter 4 it was then possible to quantify the impact of urbanisation on the persistence and availability of hollow-bearing trees. Chapter 5 therefore addressed the persistence of the hollow-bearing tree resources by modelling the influences of certain tree, disturbance and environmental variables on hollow formation, as there have been very few

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studies investigating these relationships within urban remnant forest patches. The relationship between dbh and hollow type was also investigated and demonstrated that the probabilities of trees having hollows increased with dbh and that this pattern was consistent across all hollow types. These results therefore confirm previous studies highlighting the value of large trees within the landscape as habitat refugia. Modelling the formation and persistence of hollow-bearing trees from the recruitment cohort of trees on the Gold Coast over the long-term (150 years) revealed that ongoing land clearing would have substantial impact on the hollow-bearing tree resource along the gradient. These findings provide the foundations for understanding how this resource is likely to persist across the landscape and supports suggestions for an adaptive management paradigm to be put in place to ameliorate the expected loss of hollow-bearing trees due to the urbanisation process.

Finally, the richness and relative occupancy of hollow-using vertebrate fauna found within remnant forest patches along an urban gradient was quantified by using a number of survey methods. Forty-two species were recorded, representing approximately 33.6% of hollow- using species that are known to occur within south-east Queensland. The majority of species observed were classified as common, falling within the ‘urban exploiter’ or ‘urban adapter’ categories. Spotlighting surveys recorded the most species, with birds and mammals being the most abundant species found along the urbanisation gradient. There was no difference in the relative occupancy of birds and mammals among sites. There was however, a difference in relative occupancy of both birds and mammals along the urbanisation gradient, where control sites had lower occupancy estimates than all other gradient categories. Hollows with a diameter ≤10 cm were those with the greatest wildlife utilisation and occurred at a height between 0–5 m. For all taxonomic groups combined site area and tree species diversity were the most important variables influencing the distribution of hollow-using fauna along the urbanisation gradient. Common species such as the Australian Owlet Nightjar, squirrel glider, common brushtail possum and Gould’s wattled bat accounted for 26.9% of the variation in the relative abundance of fauna among sites. Microbats appeared to be influenced more by the urbanisation gradient than other fauna as they were more prevalent in control and peri- urban sites than rural or urban sites. Furthermore, the urbanisation gradient, past logging and the density of hollow-bearing trees were identified as important drivers of microbat richness in remnant patches.

To quantify and manage habitat quality for wildlife in modified landscapes it is necessary to consider the often differing matrix elements found at patch level within the entire landscape. The preceding findings provide valuable information about the distribution and abundance of hollow-bearing trees along an urbanisation gradient. Factors that affect this availability are also highlighted and the potential implications for hollow-using fauna are identified. This study is the first to quantify variation in hollow-bearing trees along an urbanisation gradient and has thus improved our understanding of this dynamic system. This thesis has demonstrated the value of undertaking an inventory of hollow-bearing trees along an urbanisation gradient in order to understand remnant forest condition and the subsequent associated hollow-using vertebrate fauna occupying forest remnants. The findings have important implications for the future conservation of biodiversity in urban landscapes. Urban planning efforts need to acknowledge all the relevant biodiversity elements (e.g. habitat features) within the urban landscape. Furthermore, conservation managers and planners need to recognise that the persistence and functional value of these elements may require future planning consideration to be in the order of hundreds of years.

Table of Contents:

Table of Contents: ...... Statement of Access: ...... i Statement of Contribution by Others: ...... ii Statement of Contribution by Others: ...... iii Acknowledgements: ...... iv Statement of Originality: ...... vi Abstract: ...... vii List of Figures: ...... xiv List of Tables: ...... xvii Chapter 1: An overview of hollow-bearing trees and their conservation and management in urban landscapes...... 1 Introduction ...... 1 Urban forest patches ...... 2 Hollow-bearing trees ...... 4 Tree hollow formation ...... 4 Threats to hollow-bearing trees ...... 5 Timber harvesting ...... 5 Agriculture ...... 5 Firewood collection...... 6 Altered fire regimes ...... 6 Urbanisation...... 6 Conservation of hollow-bearing trees ...... 7 Wildlife use of tree hollows in Australia ...... 8 Tree-hollow selection by wildlife ...... 8 The impacts of urbanisation on biodiversity ...... 12 A brief history of human settlement and timber harvesting in south-east Queensland13 Study area ...... 14 Thesis aims and structure ...... 17

Chapter 2: Forest conservation policy implementation gaps: Consequences for the management of hollow-bearing trees in Australia. Conservation and Society 12, 1. 2014...... 20 Abstract ...... 20 Introduction ...... 21 Methods ...... 23 Results ...... 25 Legislation and the conservation of Australian forests and hollow-bearing trees .... 25 Local level ranking of the importance of biodiversity assets, in particular hollow-bearing trees ...... 30 Discussion ...... 35 Conservation challenges and the way forward ...... 37 Conclusion ...... 39 Chapter 3: Hollow-bearing trees as a habitat resource within an urbanising landscape...... 41 Introduction ...... 41 Methods ...... 43 Study sites ...... 43 Plot sampling protocol ...... 44 Landscape variables ...... 49 Urbanisation gradient ...... 49 Data analysis ...... 52 Results ...... 53 Hollow bearing trees ...... 53 The impact of environmental variables, disturbance and the urbanisation gradient on hollow-bearing tree species abundance and hollow type ...... 62 Discussion ...... 64 Chapter 4: The impacts of landscape variables on hollow-bearing trees along an urbanisation gradient...... 68 Introduction ...... 68 Methods ...... 70 Data analysis ...... 75 Results ...... 76 Discussion ...... 78 Chapter 5: The retention of recruitment trees and time to hollow formation ...... 81

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Introduction ...... 81 Research aims ...... 83 Methods ...... 83 Plot sampling protocol ...... 83 Growth rates for eucalypt species ...... 84 Growth rates in the eucalypt communities in urban fragments ...... 85 Mortality rates for eucalypt trees ...... 86 Recruitment trees and hollow development ...... 87 Loss of hollow-bearing trees due to habitat loss from land clearing ...... 88 Diameter at breast height and bark type as a predictor of hollow type ...... 89 Data analysis ...... 90 Univariate determinants of hollow presence by dbh ...... 90 Estimating hollow type probability ...... 90 Tree, disturbance and environmental variables as predictors of a tree containing a hollow ...... 90 Results ...... 91 Estimating hollow and hollow type probability ...... 93 Predictors of trees containing hollows ...... 95 Recruitment trees, hollow development and the influence of land clearing ...... 96 Discussion ...... 98 Factors influencing hollow formation ...... 98 Hollow-bearing tree recruitment ...... 100 Management implications of hollow-bearing tree recruitment ...... 100 Chapter 6: Forest patch occupancy and use of hollow-bearing trees by vertebrates along the urbanisation gradient...... 102 Introduction ...... 102 Methods ...... 105 Diurnal surveys ...... 109 Spotlighting ...... 109 Stagwatching ...... 110 Anabat surveys ...... 112 Bat call identification ...... 112 Pole camera...... 113 Species occupancy ...... 113

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Statistical analysis ...... 114 Determinants of species richness and relative occupancy of hollow-using vertebrate fauna along an urbanisation gradient ...... 114 Results ...... 116 Diurnal surveys ...... 119 Spotlighting ...... 119 Stagwatching ...... 119 Anabat survey ...... 122 Pole camera...... 122 Tree species, hollow type and use ...... 123 Drivers of hollow-using vertebrate species richness along an urbanisation gradient126 Vertebrate assemblages and relative occupancy of fauna along the urbanisation gradient...... 129 Discussion ...... 131 General summary ...... 131 Tree species, hollow type and use ...... 132 The impacts of the urbanisation gradient on species richness ...... 133 The effects of the urbanisation gradient, landscape and environmental variables on fauna ...... 134 Chapter 7: General Discussion ...... 135 Policy-implementation gaps ...... 137 Contemporary urban planning ...... 138 Natural resource management: gradients, biotic responses and temporal scales ...... 140 Recommendations and further research ...... 142 List of References: ...... 144 List of Appendices: ...... 183 Appendix 1: Hollow using fauna of Australia ...... 184 Appendix 2: Regional ecosystems containing wet and dry sclerophyll communities and their status found on the Gold Coast (Queensland Government 2009)...... 192 Appendix 3: Site and plot number with GPS co-ordinates...... 195 Appendix 4: Cadastre, Infrastructure and Regional Ecosystem data used to calculate urbanisation gradient...... 195 Appendix 5: Model selection ...... 196 Appendix 6: Tree growth rates ...... 200

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List of Figures:

Figure 1.1: Map showing the 45 research sites studied within the Gold Coast, Brisbane, Logan and Redland Shires, south-east Queensland, Australia………………………………15

Figure 2.1: The relative importance placed on the environment by local councils based on the ranking of ‘environment’ tabs in relation to all other tabs on primary council web pages…………………………………...…………………………………………..……….32

Figure 3.1: Tree form characteristics and senescence adapted from Whitford (2002). S1. Mature living tree S2 Mature, living tree with a dead or broken top S3. Living tree with most branches still intact S4. Living tree with the top 25% broken off S5. Living tree with the top 25-50% broken away S6. Living tree with the top 50-75% broken away S7. A solid dead tree with 75% of the top broken away S8. Hollow stump……………………………..….……...47

Figure 3.2: Tree hollow types (Lindenmayer et al. 2000)…………………………………48

Figure 3.3: The 250 m buffer placed around sites to capture housing, cadastre and regional ecosystem data………………………………………………………………………….…...51

Figure 3.4: The number of hollows per hollow size category……………………………..59

Figure 3.5: The relationship between hollow type and size (mean ±S.E). The number of hollows for each hollow type are; Butt 269, Branch-end 735, Bayonet 914, Fissure 48, Trunk- main 98, Trunk-top 54, Branch-main 25……………………………………………………60

Figure 3.6: Number of trees surveyed in each dbh size category. All trees represent the combination of hollow-bearing and non-hollow bearing trees. Results for hollow-bearing trees are then presented separately………………………………………………………….61

Figure 3.7: The relationship between hollow type and the urbanisation gradient (mean ±S.E.)………………………………………………………………………..…………...... 62

Figure 3.8: Spatial arrangement of sites in the multidimensional space based on the density of hollow-bearing trees. The urbanisation gradient and site area account for 45.5% (PCO1) and 33.1% (PCO2) of the variation respectively. Legend: u = urban, r = rural, c = control, p = peri-urban………………………………………………………………………..….…….63

Figure 5.1: The dbh range of recruitment trees compared to all trees surveyed.….……...91

Figure 5.2: Probability of a tree having a hollow for (a) all hollow types; (b) bayonet; (c) butt; (d) branch end; (e) fissure; (f) trunk main; (g) trunk top and (h) branch main hollows. Values represent the number of hollow-bearing trees in each dbh size class category.…...………………………………………………………………………….…...94

Figure 5.3: The predicted number of trees by dbh category over 10, 50, 100 and 150 years showing the progression through time of the current cohort of recruitment trees into hollow- bearing………………………………………………………………………..…………….96

Figure 5.4: Cumulative loss (mean ± SD) of hollow bearing trees from the recruitment cohort of trees within urban forest patches due to loss of habitat due to land clearing for urban development and infrastructure over a 150 year time frame…………………….…...……..97

Figure 6.1: The number of bird and mammal species found to occupy all sites as a function of cumulative survey effort during spotlighting surveys……………………………..……117

Figure 6.2: Trichosurus vulpecula found occupying a Eucalyptus siderophloia hollow……………………………………………………………..…………..…………..120

Figure 6.3: The eggs of Ninox novaeseelandiae found in a large E. tereticornis hollow…………………………………………………………………………..…………121

Figure 6.4: Litoria caerulea in water filled hollow…………………………..……….…121

Figure 6.5: The relationship between tree species diversity and hollow-using vertebrate species richness across all sites……………………………………………………………126

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Figure 6.6: The relationship between tree species diversity and hollow using vertebrate species rates of occupancy across all sites………………………………………….………126

Figure 6.7: The amount of variation in species relative abundance along the urbanisation gradient. The Australian Owlet Nightjar and squirrel glider account for 14.1% (PCO1) while the common brushtail possum and Gould’s wattled bat accounted for 12.8% (PCO2)……………………………………………………………………………………..128

Figure 6.8: Occupancy rates of birds and mammals along the urbanisation gradient……..………………………………………………………………………………129

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List of Tables:

Table 1.1: Studies undertaken across Australia on the minimum diameter at breast height (dbh) of trees found to be suitable for hollow development and occupation by arboreal marsupials………………………………………………………………………….………..10

Table 1.2: A summary of the characteristics of trees used by a range of hollow dependent taxa in Australia. Modified from Gibbons and Lindenmayer (2002)………………………..11

Table 2.1: Data from the web pages of local councils in relation to how they place themselves environmentally in a social context. The number of environmental strategies for each council are those listed within each primary ‘Environment’ tab on council web pages…………………………………………….…………………...……………………...31

Table 2.2: Councils contacted to request information on strategies and policies targeted to hollow-bearing trees at the local level, and number of respondents by state…………………………………………………………………………………………..33

Table 2.3: Specific measures implemented by two urban local councils to protect and preserve hollow-bearing trees on public and private land in Queensland and New South Wales………………………………………………………………………………………...34

Table 3.1: Summary of tree, tree-hollow, environmental and disturbance variables and the urbanisation gradient quantified at each plot………………….…………………………..…46

Table 3.2: A summary of species dominance and hollow-bearing tree prevalence within eucalypt communities recorded from 45 forest remnants along an urbanisation gradient. HBT’s = hollow-bearing trees………………………………………………………………54

Table 3.3: A description of hollow-bearing (HBT) tree attributes found across all sites sorted by gradient then area. Gradient: R= rural, U=urban, P=peri-urban, C=control………………………………………………………..………………………….56

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Table 3.4: Summary of trees species and hollow type and total number of hollows found across all sites………………………………………………..……………………………..58

Table 4.1: The urbanisation index, hollow-bearing tree, landscape and environmental variables quantified at each site………………………………………..…………………...71

Table 4.2: Landscape variables examined in association with hollow-bearing tree density from 45 urban remnant sites. Values in brackets represent where a site is located along the urbanisation gradient. Standard errors are supplied for sites with multiple plots. Urbanisation index: urban = high value, rural = low value…………………………………………..……72

Table 4.3: Environmental variables examined for associations with hollow-bearing trees in each of the 45 remnant sites. Standard errors are supplied for sites with multiple plots………………………………………………………………………..………………...74

Table 4.4: Best approximating model comparisons of explanatory landscape and environmental variables associated with the density of hollow-bearing trees. Akaike models with ∆i values < 4 are presented. The next model (AICc > 4) demonstrates the distance between the two best models and the third……………………………….…………………77

Table 4.5: Parameter estimates for the landscape and environmental variables associated with the density of hollow-bearing trees per hectare across all sites. Variables for each explanatory analysis are ordered by their relative importance…………………………….....77

Table 5.1: Annual dbh growth rates (cm/yr) in E. pilularis, E. microcorys and E. racemosa (Wormington and Lamb 1999) and Corymbia citriodora (Ross 1999) in dry sclerophyll forests of south-east Queensland………………………………………………………..…..84

Table 5.2: Annual incremental dbh growth rates calculated by bark type. Growth rates for two common species are also provided…………………………………………….……….86

Table 5.3: The probability of a recruitment tree having a hollow as a function of dbh…………………………………………………………………..……………………...88

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Table 5.4: Risk assessment of sites due to land clearing for urban development and infrastructure. Risk increases down and across (left–right) the table………..…..………….89

Table 5.5: The classification of the seven available hollow types as used in this study……………………………………………………………………….……………….89

Table 5.6: The 33 species of recruitment trees sorted by bark type and absolute abundance in all sites. Bold numbers represent the cumulative total for each bark type………………...………………………………………..…………….……….……….92

Table 5.7: Model comparisons for explanatory tree, disturbance and environmental variables for assessing probability of a tree having a hollow. Only models with a ∆i < 2 are presented………………………………………………………………………..……….…93

Table 5.8: Parameter estimates for explanatory tree, disturbance and environmental variables used to model the probability of a tree having a hollow. Variables are ordered by their relative importance………………………………………………………..…….…….95

Table 5.9: Summary of the current cohort of recruitment trees transitioning to hollow- bearing trees over 150 years from the 45 sites within the study area. The loss of hollow- bearing trees due to natural mortality are presented as well as the loss of remnant habitat over time……….…………………………………..……………………………………..……….96

Table 6.1: Area and hollow-bearing tree density of sites used for fauna surveys. Standard errors are supplied for sites with multiple plots. c = control, p = peri-urban, r = rural, u = urban. ………………………………………………………...... …….…….106

Table 6.2: Landscape, environment and tree variables quantified at each plot…………...107

Table 6.3: Features that can be used to distinguish the different species of gliders and possums (Menkhorst and Knight 2001; Lindenmayer 2002)…………………………..…..109

Table 6.4: The number of sites where vertebrate species were observed using each survey method. The total number of sites where a species was found as well as the total number of individuals recorded (in brackets) is also presented. Relative species occupancy across all sites is also estimated. Roosting sites or large numbers of individuals recorded at night are estimates and have a ~ next to their values….………………………………………….…..115

Table 6.5: The number of species recorded during surveys at each site; sorted by site with the highest number of species found. Some species were found by more than one method. Therefore, in some instances, taxon totals will be different from method totals. Species tallies during spotlighting include those observed transit to and from plots……………..…118

Table 6.6: Tree species, hollow type and size that hollow-using fauna were found in, from all surveys. Highlighted rows represent the highest number of hollow-using species found in each category. Numbers are a representation of the number of each species found in each hollow...…………………………………………………………………………………....122

Table 6.7: Best approximating model comparisons of landscape and the urbanisation gradient variables associated with the number of hollow-using vertebrate species found

across all sites. Only Akaike models with ∆i values ≤ 4 are presented……………....…….124

Table 6.8: Parameter estimates of the fragment, landscape and urbanisation gradient variables associated with the number of species found at each site. Variables for each analysis are ordered by their relative importance………….……………………………….125

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Chapter 1: An overview of hollow-bearing trees and their conservation and management in urban landscapes.

Introduction Habitat is the suite of resources and environmental conditions that determine the presence, survival and reproduction of a population (Caughley and Sinclair 1994). Habitat structures such as hollow-bearing trees are globally recognised as important features of forests for the conservation of wildlife (Spies et al. 1988; Newton 1994; Lindenmayer et al. 1997), but also provide important structural heterogeneity in natural and recently modified landscapes (Mawson and Long 1994; Wormington et al. 2002; Aitken and Martin 2004). The low abundance of hollow-bearing trees within a landscape may be a limiting/controlling factor for many biota as no other habitat resource represents a feasible substitute (Gibbons and Lindenmayer 2002). Arthropods, reptiles, birds and mammals all utilise hollow-bearing trees with their reliance inextricably linked to these trees for food, shelter and nesting. Consequently, species diversity in forests is often strongly linked to this resource (Saunders et al. 1982; Lindenmayer et al. 1997; Fitzgerald et al. 2003; Tews et al. 2004; Smith et al. 2007).

Australia has approximately 300 vertebrate species that are recognised hollow-users (Gibbons and Lindenmayer 2002) with the use of hollow-bearing trees by fauna well studied within the ‘natural’ areas of the Australian bush (Fleay 1947; Kitching and Callaghan 1982; Kehl and Borsboom 1984; Belcher 1995; Comport et al. 1996; Krebs 1998; Lamb et al. 1998; Lindenmayer et al. 2000a; Gibbons and Lindenmayer 2002; Gibbons et al. 2002; Luck 2002; Lumsden et al. 2002; Rowston et al. 2002; Fitzgerald et al. 2003; Eyre 2005; Wormington et al. 2005; Cameron 2006; van der Ree et al. 2006; Smith et al. 2007). Fewer studies, have investigated these relationships in urban landscapes. Those that have, demonstrate that urbanisation has a significant impact on the distribution and abundance of hollow-bearing

trees (Harper et al. 2005a; van der Ree and McCarthy 2005; Harper et al. 2008) and that in some areas, the loss of hollow-bearing trees from the urban landscape threatens the persistence of biodiversity in forest patches (Harper et al. 2005a). Previous work on hollow- bearing trees in Australian urban environments is confined to regions within the dry sclerophyll forests of Victoria (Harper et al. 2005a; van der Ree and McCarthy 2005; Harper et al. 2008) and New South Wales (Davis et al. 2013). It is uncertain whether the patterns observed in these regions apply in sub-tropical urban environments which prompted this investigation. To gain an understanding of the context underpinning this study this chapter provides a review of urban forest fragments and their role in protecting biodiversity in urban areas. It focuses on the role of hollow-bearing trees within these environments and threats posed to this habitat resource focusing on the impacts of the urbanisation gradient.

Urban forest patches Urban forest patches are described as either all vegetation in a city or town or, if it has been altered, as a representation of the fauna and structure and floristics of the natural vegetation that once dominated a site (Webb and Foley 1996). These forest patches can be found in the vegetation along urban streets, in urban parks, remnant forest patches, abandoned sites and residential areas. Given the current rate of urbanisation globally, these urban forest areas will continue to grow in value (De Wet et al. 1998; Alvey 2006). In the past, the arrangement of urban habitat remnants has not been related to urban wildlife management, but simply happens to be what remains after the urbanisation process (Lunney and Burgin 2004a). However, these days urban development will in some cases have strategic plans in place for the retention of biodiversity (Niemelä 1999; Snep and Opdam 2010). Traditionally, urban habitats have been considered depauperate in biodiversity, containing high numbers of introduced pest species and/or generalist species of both flora and fauna. Evidence is now revealing that urban and suburban forest patch habitats can and often do contain relatively high levels of biodiversity (McKinney 2002; Cornelius and Hermy 2004; Williams et al. 2005a; Williams et al. 2005b; Alvey 2006; Zhao et al. 2006; Harper et al. 2008; McKinney 2008). Not only can urban habitats support an abundance of native species, they sometimes have a greater diversity than those found in the surrounding landscape (Jim and Liu 2001; Cornelius and Hermy 2004; Stewart et al. 2004) and can include endangered species (Alvey 2006).

Therein, a number of studies have found a positive correlation between human population density and plant, mammal, reptile and amphibian species richness (Balmford et al. 2001; Araújo 2003) including widespread, narrow range endemics and threatened species (Balmford et al. 2001). These relationships exist because the ecological factors contributing to the suitability of an area for human habitation may be similar to the factors which attract other species to the area (Araújo 2003). Alternatively, direct and indirect human action could increase overall species richness through the introduction of native and non-native species and increased landscape heterogeneity (Araújo 2003; Williams et al. 2006). Whether this is acceptable in relation to biodiversity and conservation is debatable. Constant pressure from human populations will continue to influence the current and future persistence of indigenous urban flora and fauna (Lunney and Burgin 2004a) highlighting the need for the retention and ongoing management of urban forest patches.

Land managers and biologists generally focus on the retention of large forest patches as valuable biodiversity refugia. Much of this is driven by early landscape ecology theory, particularly the single large reserves over several small reserves (SLOSS) debate (Tjørve 2010). While the conservation benefits of larger areas is generally accepted, the best design is possibly a combination of both small and large reserves, as neither will be the better strategy alone (Tjørve 2010). As highlighted by Daily (1995), Lindenmayer and Fischer (2006) and Taylor and Goldingay (2009), smaller fragments of natural habitat, such as urban forest patches were generally dismissed, as they were not considered to contain sufficient resources to meet the needs of many individual species. Today they are no longer overlooked, as they can contribute to the conservation of biodiversity within urban areas (Cowling and Bond 1991; Wood 1993; Godefroid and Koedam 2003; Catterall 2004). The value of remnant forest patches in urban landscapes depends on their ability to provide resources for indigenous flora and fauna (Matlack 1993; Godefroid and Koedam 2003; Harper et al. 2005a; Alvey 2006; Smith et al. 2006). The availability of one such resource, tree hollows, will limit the distribution and abundance of obligate hollow-bearing tree users and species richness in general, particularly in Australia (Gibbons and Lindenmayer 2002). Therefore, the conservation of this resource is paramount to the maintenance of ecosystem integrity in urban environments (Harper et al. 2005a). The following sections present an overview of hollow-bearing trees as an important habitat resource, the threats they face and their importance for wildlife within an Australian context.

Hollow-bearing trees

Tree hollow formation Within Australia, tree hollow formation can develop in eucalypts at any stage of growth (Gibbons et al., 2000; Adkins, 2006) but hollows suitable for occupation by vertebrate fauna in Queensland usually require a tree to be approximately 150 years of age for this to occur (Ross 1998). Therein, the presence of tree hollows is related to tree age, with the older trees that are hundreds of years old generally having a greater prevalence of hollows (Saunders et al. 1982; Whitford 2002; Goldingay 2009; Loyn and Kennedy 2009; Ranius et al. 2009; Webala et al. 2011). However, individual eucalypt species have differing rates of hollow formation. For example, Mackowski (1984) suggested that in blackbutt (Eucalyptus pilularis) hollow formation commenced at approximately 150 years of age, while between 170 and 200 years are required for species such as tallowwood (Eucalyptus microcorys) and scribbly gum (Eucalyptus racemosa) (Ross 1998). Hollow-bearing trees can also be long lived, and the Long-Billed Corella, Cacatua tenuirostris have been observed utilising hollows in Jarrah, Eucalyptus marginata communities of south Western Australia in trees ranging between 680 and 1333 years (Mawson and Long 1994). Apart from tree age and species, the formation of hollows is also influenced by diameter at breast height (dbh), tree health, growth habits, stand features, site productivity, fire and management history (Gibbons and Lindenmayer 2002; Adkins 2006).

Australia lacks vertebrate species that excavate tree hollows (Gibbons and Lindenmayer 2002; Blakely et al. 2008), many species of parrots (Higgins 1999) and arboreal marsupials (Gibbons and Lindenmayer 2002) will enlarge existing hollow openings and carry out internal modifications while nesting. Therefore, for eucalypt species, there are also three essential preconditions for hollow formation to occur; (i) a tree must be under some type of physiological stress or subjected to injury, (ii) heartwood decay must be present and (iii) trees must be of sufficient size to endure once decayed. Physiological weakness will also increase in a tree with age which will also promote hollow formation (Gibbons and Lindenmayer 2002). Within eucalypts the heartwood is more vulnerable to decay by living organisms that gain access after the tree has been subjected to injury e.g. fire-scars or limb breakage (Harper et al. 2005a) allowing fungi followed by invertebrates, usually termites to excavate the heartwood leaving the living sapwood as the walls of the hollow (Gibbons and Lindenmayer 2002). Fire can also play an integral role in hollow formation by reducing the time taken for

hollow development by approximately 100 years (Inions et al. 1989b). It has been suggested that fire disturbance alone will influence the number of tree hollows found in a forest and that this will vary between wet and dry sclerophyll forests (Gibbons and Lindenmayer 2002).

Threats to hollow-bearing trees The main threats to the retention and longevity of hollow-bearing trees comes from landscape modifications by humans including forestry, agriculture, firewood collection, urban development, fragmentation and altered fire regimes (Lamb et al. 1998; Gibbons and Lindenmayer 2002). Within Australia, the clearing and disturbance of natural vegetation and subsequent habitat fragmentation is recognised as one of the principle drivers of biodiversity declines (Williams et al. 2001). Protection for hollow-bearing trees and the species that are reliant upon them in Australia is supposedly provided for by legislation, policy and strategic management plans. However, disagreement surrounding forest management has resulted in the degeneration of national plans as interpreted by states and territories, as well as individual stakeholders (Musselwhite and Herath 2005).

Timber harvesting In sites managed for wood production, there has been a global recognition of the reduction in tree hollows, which in turn leads to reduced biodiversity (Shukla et al. 1990; Poonswad et al. 2005; Holloway et al. 2007; Politi and Hunter 2009; Shearman et al. 2009: Law et al. 2013). Within Australia, hollow-bearing trees are often considered to be of low commercial value and are generally believed to suppress forest regeneration (Gibbons and Lindenmayer 2002). In Queensland, if they are not harvested as saw logs they are removed for firewood or fence posts (Lamb et al. 1998). The harvesting of timber reduces the availability of tree hollows by reducing the diversity of hollow types, hollow abundance, altering the spatial arrangement of tree hollows and also increases the rate of destruction of retained trees (Catterall and Kingston 1993; Lindenmayer et al. 1997; Smith et al. 2009: Law et al. 2013).

Agriculture Agriculture is the world’s most extensive form of land use occupying approximately 40% of the global land surface (Foley et al. 2005) and contributing significantly to deforestation (Bowen et al. 2007). Deforestation as a result of expanding subsistence agriculture threatens

many natural habitats in developing nations (Politi and Hunter 2009; Shearman et al. 2009), while across Australia it is estimated that there has been a 80-90% reduction in tree hollow resources due to commercial agriculture alone (Gibbons and Lindenmayer 2002).

Firewood collection Firewood collection impacts negatively on forests and biodiversity in general (du Plessis 1995; Williams and Shackelton 2002; Berg and Dunkerley 2004). There has been a considerable amount of research focusing on the widespread use of firewood throughout countries on the African and Asian subcontinents (Fox 1984; Shackleton 1993; Vermeulen et al. 1996; Tabuti et al. 2003). The problems associated with firewood collection, are not restricted to these developing nations. For example, the Murray Darling Basin of Australia, 2 an agricultural area of 1 million km , contains approximately 29% native forest and produces one third of the 607 million oven dry tonne of firewood burnt annually from its forests (West et al. 2008).

Altered fire regimes The influence of natural and management fires on forests and woodlands can impact biodiversity at the landscape scale. Fire is a frequent and widespread phenomenon within Australian environments and many vegetation communities have become adapted to fire (Wardell-Johnson 2000). While fire is an important factor in the hollow-formation process in eucalypt communities (Adkins 2006), research has found that there is a notable depletion of hollow-bearing trees in areas that are being burnt under more frequent fire regimes. This is primarily because trees that were dead before a fire, are destroyed at a higher rate than living trees (Inions et al. 1989b; Gibbons and Lindenmayer 2002). Dead trees or stags represent a large proportion of hollow-bearing trees in some forests types (44%) (Eyre and Smith 1997; Ross 1999), making them susceptible to inappropriate fire regimes.

Urbanisation Urbanisation dramatically alters the biota of areas that become cities and towns (McKinney 2002; Williams et al. 2006), from the decline and destruction of native grasslands (Williams et al. 2004), to the loss of forests (Bowen et al. 2007) and a reduction in faunal assemblages (van der Ree and McCarthy 2005). In addition to the ongoing risk of further habitat loss for

development and the alteration of natural ecological processes, habitat remnants and their resources also face threats unique to the urban landscape (Zhao et al. 2006; Harper et al. 2008; McKinney 2008). For example, regulatory controls in urban areas may classify a tree as hazardous if there is potential for harm to people and property and are therefore removed (Terho 2009). As one of the key focal areas of this research the impacts of urbanisation are discussed in more detail later in this chapter.

Conservation of hollow-bearing trees Hollow-bearing trees provide habitat for diverse taxonomic groups and as such has prompted the recognition of the importance of hollow-bearing trees globally. Conservation efforts take place within production landscapes (e.g. forestry) (Mazurek and Zielinski 2004; Holloway et al. 2007; Smith et al. 2009) but also in areas designated as reserves (Simberloff et al. 1992; Pirnat 2000; Rhodes et al. 2006; Munks 2009). Within Australia, forest managers have long recognised the need to retain hollow-bearing trees in eucalypt forests. There is, however, considerable debate surrounding the spatial arrangement of hollow-bearing trees within production forests (Lindenmayer et al. 1990; Gibbons and Lindenmayer 1996). Early forestry practices subscribed to the theory that the clumping of trees would be of greater benefit to wildlife (Queensland Government 1996), while others argued that an even distribution of trees across the landscape was preferable (Lindenmayer et al. 1990; Gibbons and Lindenmayer 1996). However, these recommendations may not apply outside of production forests, highlighting the need for local government bodies to implement programs aimed at the protection and retention of hollow-bearing trees on commercial, public and private lands.

It has been suggested that live hollow-bearing trees are of greater importance to wildlife in Australia, since they are longer lived than standing dead trees (Moloney et al. 2002). Stags (standing dead trees with hollows) comprise a significant component of the hollow-bearing tree resource within a landscape, in some cases making up almost 50% of hollow-bearing tree availability (Moloney et al. 2002). In general, standing dead trees are more likely to contain hollows than dead trees (Eyre 2005; Harper et al. 2005a). Stags therefore provide a valuable additional resource, with certain fauna displaying a preference for stags as denning and nest sites (Lindenmayer et al. 1991a; Eyre 2004; Cameron 2006).

Wildlife use of tree hollows in Australia The availability of tree hollows in living or dead trees is fundamental to the survival of a significant number of Australian species across various taxa (Lindenmayer 2002; Rowston et al. 2002). In Australia 303 native vertebrate species and an additional 10 introduced species are hollow users. Excluding introduced species, this figure represents 27 terrestrial amphibian species (13%), 79 reptiles (10%), 114 birds (15%) and 83 mammals (31%) (Gibbons and Lindenmayer 2002) (Appendix1).

Tree hollows perform important functional roles within the landscape by providing fauna with microclimates suitable for occupancy (Gibbons and Lindenmayer 2002). Hollows ameliorate ambient temperatures (Lumsden et al. 1994), collect water (Kitching and Callaghan 1982), provide nest sites (Goldingay 2009) and forage resources (Mansergh and Husley 1985) for a wide range of taxa. Despite the high prevalence of hollow dependence among Australian fauna, quantitative data reporting on hollow use by vertebrate fauna are scarce. It is difficult to characterise species as obligate hollow-users or those that exploit this resource opportunistically, as the relationship some species have with tree hollows can change throughout their distribution depending on climatic conditions (Gibbons and Lindenmayer 2002). For example, the sugar glider Petaurus breviceps has been found to nest in piles of timber on the ground (Fleay 1947), under tin sheeting and in thickets of black- berry (Morrison 1978) as well as hollow-bearing trees (Traill and Lill 1997). This suggests a lack of available tree hollows in the landscape as well as a certain degree of opportunism by vertebrate hollow users. The common ringtail possum Pseudocheirus peregrinus regularly uses tree hollows but also constructs nests or dreys, and is one of the few arboreal marsupials to do so (Gibbons and Lindenmayer 2002).

Tree-hollow selection by wildlife Hollow selection by fauna is non-random, and most choose hollows based on site and hollow attributes (Gibbons et al. 2002; Eyre 2005; van der Ree et al. 2006). Some species only occupy tree hollows in certain parts of the landscape, while others determine hollow suitability based on the proximity of predators and conspecifics (Gibbons and Lindenmayer 2002). Furthermore, occupancy of tree hollows is positively correlated with diameter at breast height (dbh) (Gibbons et al. 2002). Within Australia, there is a positive relationship between tree diameter and the number of hollows present, with different eucalypt species

having higher or lower rates of tree hollow development (Pausas et al. 1995; Lindenmayer et al. 2000a). While trees with larger dbh are likely to contain more occupied hollows, they are less likely to contain a high number of hollows compared with trees from within the mid diameter class (Gibbons and Lindenmayer 2002). This highlights the variability in dbh considered suitable for tree hollow formation and occupation by arboreal marsupials, throughout Australia (Table 1.1). Other factors such as hollow diameter, tree form or degree of senescence (Table 1.2) have also been shown to influence hollow use (Gibbons et al. 2002). While some species may prefer tall trees in early successional stages, others prefer trees in the late stages of decay (Lindenmayer et al. 1991a), or those that have already died (stags) (Eyre 2004; Cameron 2006).

It is important to note, that not all hollows and hollow-bearing trees are used by fauna. Studies within Australia have found that roughly 50% of all hollows and all hollow-bearing trees contained evidence of prior hollow occupancy (Saunders et al. 1982; Gibbons and Lindenmayer 2002). In highly developed regions occupancy levels may be even further depressed, with less than 10% occupancy being recorded in some urban forests (Ogden 2009). Some species also display site-specific fidelity (Frith 1976; Murphy et al. 2003). For example, small mammals such as the agile antechinus Antechinus agilis use particular hollows for mating or nest sites by successive generations (Lazengy-Cohen and Cockburn 1991). Despite the dependence on hollows by Australian fauna, occupancy rates and tree hollow use by individuals are rarely quantified in the literature.

Table 1.1: Studies undertaken across Australia on the minimum diameter at breast height (dbh) of trees found to be suitable for hollow development and occupation by arboreal marsupials.

Minimum Study Location Species dbh required Mackowski 1984 New South Wales possums/gliders 100 cm Inions et al. 1989 Western Australia brush-tailed possum 40-50 cm Smith and Lindenmayer 1992 Victoria Leadbeater’s possum >50 cm Gibbons and Lindenmayer 2002 Victoria not specified 30 cm Harper et al. 2005b Victoria not specified >40 cm Wormington et al. 2003; Queensland not specified >20 cm Wormington et al. 2005 Eyre 2006 Queensland greater glider >20 cm Smith et al. 2007 Queensland greater glider >50 cm Koch et al. 2008c Tasmania not specified >56 cm

Table 1.2: A summary of the characteristics of trees used by a range of hollow dependent taxa in Australia. Modified from Gibbons and Lindenmayer (2002).

Species Variables influencing occupancy Source

Arboreal marsupials Tree shape, number of hollows Lindenmayer et al. 1991a Leadbeater’s possum Tree size, tree shape, number of Lindenmayer et al. 1991a hollows, surrounding vegetation Mountain brushtail Number of hollows, surrounding Lindenmayer et al. 1996 possum vegetation type Common brushtail possum Tree diameter, number of hollows Wood and Wallis 1998 Sugar glider Number of fissures Lindenmayer et al. 1991a Squirrel glider Tree species, tree health, tree Rowston 1998; diameter, hollow entrance size and dead trees Beyer et al. 2008 Greater glider Tree size Lindenmayer et al. 1991a Brush-tailed phascogale Tree diameter, tree health Rhind 1996

Brown antechinus Tree shape Lindenmayer et al. 1991a Brown antechinus Tree diameter, tree health Cockburn and Lazenby- Cohen 1992 Australian Owlet-nightjar Height of entrance above ground, Bringham et al. 1998 number of hollows, proximity to other trees with hollows. Barking Owl Tree diameter, diameter of entrance Taylor and Kirstin 1999 to hollow Gouldian Finch Tree diameter, hollow entrance Tideman et al. 1992 width, steep slopes, single stemmed trees Striated Pardalote Number of hollows, hollow depth, Taylor and Haseler 1993 fire scarring, tree species Parrot species generally Dead trees Goldingay 2009 Broad-headed snake Tree health, tree diameter, number Webb and Shine 1997 of hollows Vertebrate fauna generally number of hollows, tree health, tree Gibbons et al. 2002 diameter

The impacts of urbanisation on biodiversity Natural systems play an important ecological, social and economic role within suburban landscapes, providing habitat for wildlife as well as being places for community recreation and stormwater management (McWilliam et al. 2010). The maintenance of such functional connectivity across the landscape is central to managing biodiversity within the urban matrix (Catterall and Kingston 1993; Goldingay and Sharpe 2004a; Damschen et al. 2006). The urban landscape however, is characterised by a complex mosaic of land uses; with the end result a combination of developed land and remnant patches of vegetation (Kendle and Forbes 1997; Taylor and Goldingay 2009; McWilliam et al. 2010). Until recently urban ecosystems have been largely ignored in terms of rigorous scientific research (McDonnell et al. 1993; Catterall 2009). However, as urban areas often contain novel combinations of environmental variables, they will provide a whole suite of new ecological processes affecting biodiversity (Catterall 2009). Urban regions settled hundreds of years ago support small proportions of the original fauna and flora with low probabilities of persistence; subsequently, the urbanisation process is pivotal to the retention of urban biodiversity (McKinney 2002; van der Ree and McCarthy 2005). It has recently been proposed, that the urbanisation gradient is more than a scientific model, but is a valid research methodology when studying urban ecosystems (Qureshi et al. 2013), as urbanisation acts as a powerful filter by influencing species composition (Brady et al. 2009), highlighting the relationship between habitat loss and the urbanisation gradient (McKinney 2002).

Faunal species that are heavily affected by urbanisation are those that are sensitive to human persecution and habitat disturbance and are usually large mammals, avifauna and species that rely on stands of old forest for food and shelter (Gibbons and Lindenmayer 2002; McKinney 2002). Urban adapters are often referred to as edge species, able to exploit the surrounding anthropogenic habitat and often attain a biomass greater than those found in natural surroundings (McKinney 2002; Low 2003). These urban avoiders are often habitat specialists (McKinney 2002; McKinney 2006). Conversely some species are urban exploiters and are often totally dependent on human resources for survival and are composed of early successional species from nearby ecosystems that are well adapted to intensely modified urban environments (McKinney 2002; Loss et al. 2009). This implies that some species do not perceive a modified landscape as isolated, but simply another environment in which to obtain resources (Low 2003; Harper et al. 2008) and have learnt to adapt to, or exploit the urban environment.

Decisions made in regards to development and management of urban forest patches are often made without an adequate understanding of the working of natural ecosystems. Invariably this means that irreversible environmental damage has and will continue to occur (Webb and Foley 1996). Therefore, in modified urban landscapes where resource availability differs across the various landscape elements e.g. natural, residential and industrial; human intervention may be required to ensure that ecological processes continue to operate (Harper et al. 2008), particularly in regards to timber harvesting and the availability of hollow-bearing trees.

A brief history of human settlement and timber harvesting in south-east Queensland Since 1824 south-east Queensland has been appreciated for its eucalypt forests, stands of hoop pine (Araucaria cunninghamii) and red cedar (Toona ciliate) (Catterall and Kingston 1993; Kowald 1996; Powell 1998a). The discovery of red cedar in the region marked the beginning of the timber industry and settlement in Queensland (Kowald 1996). By 1839 timber was required to meet the immediate needs for housing and firewood in the greater Brisbane area and surrounding region (Powell 1998b). Timber soon became an important export commodity resulting in increased settlement by timber getters particularly from the Tweed region in New South Wales (Longhurst 1996). In addition to the timber getters, pastoralists settled on the land and cleared it for grazing.

Due to the rapid settlement and associated deforestation of south-east Queensland, regulation was soon established in order to better manage and control land clearing practices (Kowald 1996; Powell 1998a). While some regulations for the protection of forest resources were heeded, it soon became apparent that licensing could not be enforced due to the lack of manpower. Therefore, by 1879 pressures for the regulation of forests were ignored by the immediate need for land (Kowald 1996; Powell 1998a). The demand for timber continued for both local and export markets, with the use of timber for the local construction of homes increasing after both world wars (Kowald 1996; Ross 1998). By the 1980’s timber production shifted to plantation pine and the 1990’s saw the attitude towards native forests shift to one of conservation (Powell 1998a).

Despite the downward trend in hardwood production timbers, large scale clearing continued throughout Queensland, between 1990 and 1995 the rate of clearing increased by 700%,

driven by the need to clear land for agriculture (Powell 1998a). Within south-east Queensland, this era also saw greater public concern over the deterioration of the environment with public disputes arising over the conservation of remnant bushland (Catterall and Kingston 1993). Today remnant forests in south-east Queensland are protected in a number of National Parks and World Heritage sites. However, the conservation reserve system, like most others in the world (Pressey 1994), does not effectively represent or protect the biodiversity within the region (McAlpine et al. 2007). Many of these national parks and reserves protect the rainforests of the region with less attention being given to the protection of hardwood eucalypt forests (McAlpine et al. 2007), particularly in rapidly developing urban areas (Catterall and Kingston 1993).

Study area This study was primarily conducted on the Gold Coast, south-east Queensland, Australia. The Gold Coast is located approximately 70 km south of Brisbane occupying an area of 1451 km2 stretching from the New South Wales border in the south to the Logan River in the north and from the coastline to the McPherson and Darlington Ranges in the west (Figure 1.1).

The Gold Coast is the seventh largest city in Australia with a rapidly increasing population, currently estimated at around 500,000 (Pickering et al. 2010). Upon the development of the first regional plan for south-east Queensland in 2005, the State Government recognised that urban growth was proceeding in an extraordinarily haphazard manner (Spearbritt 2009). While the Gold Coast retains more than 45% of its natural habitat (Pickering et al. 2010), land clearing due to the urbanisation process has resulted in a heterogeneous landscape of natural habitat remnants, some of very high conservation value, within a variable urbanisation matrix (Pickering et al. 2010). The Gold Coast shire contains a higher level of remnant bushland than any other shire in south-east Queensland and has a large number of vegetation communities represented in the remaining fragments (Catterall and Kingston 1993). Despite 45% of the Gold Coast persisting as native vegetation, only 19% is protected on public land managed by either Gold Coast City Council or State Government. Private land that is managed under voluntary conservation agreements accounts for an additional 3% (Gold Coast City Council 2009).

Figure 1.1: Map showing the 45 research sites studied within the Gold Coast, Brisbane, Logan and Redland Shires, south-east Queensland, Australia.

This cumulative figure of 22% does not meet with the recommended guidelines for 30% preservation of all native vegetation to retain an adequate retention of biodiversity within the shire (Gold Coast City Council 2009). Furthermore, approximately 14% of remaining native vegetation on the Gold Coast occurs within the urban footprint, with approximately 70% of this vegetation being unprotected, or on private land (Gold Coast City Council 2009) and prone to development pressures. The loss of some threatened regional ecosystems, such as Blackbutt forests, has been particularly severe where there has been more than a 90% reduction in habitat (Pickering et al. 2010). Projected land use commitments across the city for future urban, industrial and infrastructure requirements, amount to 44000 ha of native vegetation that will be cleared between 2009-2019 (Gold Coast City Council 2009). This will result in a significant reduction in the extent and connectivity of the remaining habitats, which are important for the retention of urban biodiversity (Koh and Sodhi 2004; Tratalos et al. 2007; Marzluff and Rodewald 2008).

The Gold Coast comprises a myriad of landscapes including a coastal plain that includes beaches, dunes, river deltas, bays, estuaries and wetlands; rolling foothills and low mountain ranges supporting heath-land, melaleuca swamps; wet and dry sclerophyll forest and rainforest vegetation communities. The topography rises from sea level up to 1010 m on the mountain escarpments of the hinterland (Gold Coast City Council 2007). The main geological unit is the Neranleigh-Fernvale beds of Tertiary origin which make up 62.3% of the shire (Whitlow 2000). The most common soils are the red and yellow Podzolics found on the hills. These are duplex soils which have a distinct loamy surface-soil and clay sub-soil which are quite acidic (pH 4.5) and therefore makes them less fertile. The more urbanised areas of the City would be; metamorphic hills, fine textured alluvia, or dune sands depending on the elevation, topography and proximity to the ocean. This range of altitudes and soil types combine to produce a unique set of environmental habitats resulting in the Gold Coast being a regional biodiversity hotspot (Catterall and Kingston 1993). The average annual rainfall on the Gold Coast varies from 1500 mm on the coast to 3000 mm in the hinterland ranges (Gold Coast City Council 2007) with a temperature range between 0°C- 35°C (Bureau of Meteorology 2009).

The region supports over 1550 species of native plants, approximately 323 bird species, 105 species of reptiles and amphibians and over 70 species of mammals (Gold Coast City Council 2007), some of which are listed as threatened or endangered (Queensland Government 1992).

Thesis aims and structure Due to the extent of land clearing for urbanisation and forestry, the resulting depletion of hollow-bearing trees will lead to a shortage of this resource for many years to come. This study of the distribution, abundance and use of the hollow-bearing tree resources in remnant forest patches of urban bushland will improve our understanding of the importance of this resource along an urban gradient. This could potentially lead to a re-evaluation of forest patches not just as places of refuge, but more importantly as potential functioning ecosystems, which until now have not been adequately investigated. Consequently, this study will increase our knowledge and lead to a heightened understanding of the resources and biodiversity found within these sites and subsequently to a greater ability to manage them more effectively.

Biotic homogenisation along the urban-rural gradient is one of the key issues affecting urban biodiversity (Alvey 2006; McKinney 2006). Here the loss of species richness is attributed to the increase in generalist species at the expense of those with more specialised habitat requirements, such a dependence on hollow-bearing trees (Smith et al. 2007; Harper et al. 2008). Within the urban landscape the effects of anthropogenic activities are ever present and ongoing. Therein, the current study will assess the dynamics of hollow-bearing trees as a habitat resource along an urbanisation gradient in south-east Queensland, Australia. No similar studies have been carried out in south-east Queensland, an area of rapid expansion and urban growth (Graymore et al. 2008; Gurran 2008).

The overall aim of this study was to identify the biotic and abiotic factors influencing hollow- bearing tree availability, density and use along an urbanisation gradient at the landscape scale. Hypotheses were derived from the current literature where the urban gradient was identified as a powerful filter influencing biodiversity in general. Tree hollows provide essential habitat for wildlife; yet despite the acknowledged importance of this resource, within the current literature there exists a paucity of information about the use and

availability of tree hollows found within remnant urban forest patches (Crooks et al. 2004; Goldingay and Sharpe 2004a; Beyer et al. 2008; Brearley et al. 2010).

The following chapters describe the relationship between the urbanisation gradient, hollow- bearing trees and their use. However, the first objective was to undertake a review and some primary data analysis of the current legislation in place within Australia to protect hollow- bearing trees, with a particular focus on urban centres. This was undertaken to understand what measures are in place to protect hollow-bearing trees from the landscape scale down to individual tree level (Chapter 2).

Before any hollow-bearing tree management options can be considered, information is required on the availability of hollow-bearing trees as well as hollow number, type and size. An inventory of hollow-bearing trees along an urbanisation gradient was undertaken to quantify the current stock of hollow-bearing trees (Chapter 3). The distribution and abundance of hollow-bearing trees was hypothesised to differ along the urban gradient in response to the rate of urbanisation and historical logging such that the expectation was that heavily urbanised patches would have fewer hollow-bearing trees than those in more rural areas.

Chapter 4 investigated if there was a relationship between landscape dynamics and hollow- bearing trees and tested the hypothesis that the urbanisation gradient was having an effect on hollow-bearing trees. This was done to understand what the impacts of the urbanisation gradient, individual tree disturbances and past anthropogenic activities such as timber harvesting and land clearing are having on hollow-bearing trees.

Chapter 5 assesses the dynamics of the hollow-bearing tree resource itself and then asks the following questions; (i) How long until hollow formation occurs in the current cohort of recruitment trees (i.e. non-hollow-bearing trees of those species that have the potential to form hollows in the future, see Chapter 5 for further definition)? And (ii) Does dbh influence hollow type? The loss of hollow-bearing trees along the gradient due to projected land clearing rates for the urbanisation process over the next 10, 50, 100 and 150 years are predicted. This information is vital for managers and planners in order to gain an understanding of the long term management of hollow-bearing trees.

Finally, to better understand the functional role of urban forest patches, the relationships between hollow-using vertebrate fauna found within remnant forest patches and a combination of landscape, hollow-bearing tree variables and disturbance parameters along the urban gradient were quantified in Chapter 6. It is hypothesized that the species richness and occupancy of hollow-using vertebrate fauna differs along the urban gradient.

Chapter 7 provides a synopsis of this study and summarises the implications of Chapters 2–6 for hollow-bearing trees along an urbanisation gradient and offers suggestions for future research.

Chapter 2: Forest conservation policy implementation gaps: Consequences for the management of hollow-bearing trees in Australia. Conservation and Society 12, 2014.

Abstract Hollow-bearing trees in native forests and woodlands are significant habitat resources for many Australian fauna. However, habitat removal, from commercial harvesting and urban development continue to threaten these ecosystems. Protection for these habitats and their species is purportedly provided for in legislation, policy and strategic management plans. Yet public debate and disagreement surrounding forest management has resulted in the disintegration of national plans as interpreted by states and territories as well as individual stakeholders. The implications of this on managing forests for biodiversity conservation have in some cases been extreme. This Chapter presents a hierarchical review of the current legislation and policy mechanisms underpinning forest conservation in Australia paying specific attention to important habitat features such as hollow-bearing trees. Apart from Federal and State legislation acknowledging the importance of hollow-bearing trees to biodiversity, sufficient mechanisms to halt the ongoing loss of this resource from Australian landscapes at the local level seem to be lacking. Hollow-bearing tree conservation strategies from 46 local council areas in Victoria, New South Wales, Tasmania and Queensland were reviewed. Few (<5%) respondents indicated that they have specific plans for the conservation and management of hollow-bearing trees, highlighting a policy implementation gap at the local level. Furthermore, apparent environmental management strategies and actions rank relatively low on local council priorities. Therefore, a stronger focus on conservation actions geared towards management of critical habitat features across the landscape supported by robust local, national and international policy is required.

Introduction Sound environmental policy is the cornerstone of global conservation efforts, directing action at local, regional, national and international levels (Thomas 2007). However, over the past five decades environmental policy decisions in Australia have caused considerable conflict within all levels of government. Consequently national plans as interpreted by states and territories as well as individual stakeholders have collapsed, resulting in far reaching policy- implementation gaps, particularly for forest management (Musselwhite and Herath 2007). The inability to resolve conflicts nationally has led to a number of environmental assets important for the retention of biodiversity being at risk. The national attention given to forestry and forest management within Australia (see McAlpine et al. 2007), underpins the conservation of these natural resources, particularly large hollow-bearing trees. This in turn provides a useful platform for the analysis of the potential consequences of policy- implementation gaps at the local level.

Living or dead, hollow-bearing trees are important features of forests (Spies et al. 1988; Newton 1994), providing structural heterogeneity in natural, cultural and recently modified landscapes. Hollow-bearing trees are also globally acknowledged for their value to conserve wildlife (Mawson and Long 1994; Ball et al. 1999; Wormington et al. 2002; Aitken and Martin 2004; Beaven and Tangavi 2012). They provide essential wildlife habitat by offering protection from predators; providing roosting and breeding sites; are used for feeding (Holloway et al. 2007); and offer a stable micro-environment that ameliorates ambient weather conditions (Gibbons and Lindenmayer 2002). Within Australia, the use of this resource by native fauna has received considerable research attention (Comport et al. 1996; Lamb et al. 1998; Gibbons et al. 2000a; Lumsden et al. 2002; Eyre 2005; van der Ree et al. 2006), with many of these studies focusing on threatened species dependence on hollow- bearing trees. Consequently, the continued loss of these habitat features from the landscape is seen as an important conservation management problem (Gibbons and Lindenmayer 1996; Eyre 2007; Goldingay 2009).

The retention and longevity of hollow-bearing trees within the landscape depends on the synergistic effects of multiple factors. These include natural ecological processes contributing to hollow formation, and the extent to which hollow-bearing trees are threatened

by anthropogenic activities, but also the manner in which forests are conserved and managed. Hollow formation is influenced by a variety of environmental factors related to tree damage and decay forming agents such as termites and fungi (Gibbons and Lindenmayer 2002; Adkins 2006), along with tree characteristics such as age and size (Lindenmayer et al. 2000a). Thus, Australian eucalypts require at least 150 years before hollows are likely to be suitable for occupation by vertebrate fauna (Ross 1998). Threatening processes facing hollow-bearing trees include agriculture (Cogger et al. 2003), firewood collection (Driscoll et al. 2000), urban development (Garden et al. 2007b), altered fire regimes (Ross 1999), as well as forestry and timber harvesting (Gibbons and Lindenmayer 1996; Lamb et al. 1998; Adkins 2006). Correspondingly, political inertia in the translation of forest management policy to management actions (Riley et al. 2003; Prest 2004; Musselwhite and Herath 2007), also potentially threatens the conservation of hollow-bearing trees. Therefore, an understanding of the strategic frameworks and policies in place to protect hollow-bearing trees at the local level is required to demonstrate how these achieve regional, state and national biodiversity conservation objectives.

Australia’s natural forest estate is made up of a variety of forest types and land tenures with a corresponding array of management practices (Aenishaenslin et al. 2007). Forestry practices and interests can vary immensely amongst Federal, State and local private landholders, with divergent ideologies resulting in ad hoc programs with no long-term planning or direction. This creates difficulty in formalising conservation objectives that are often incompatible with other potentially competing land uses such as timber harvesting, urban development or agriculture (Dovers 2003). The diversity of historical, cultural, political and physical circumstances within Australia’s states and territories, contributes to decentralised, confusing and often overlapping commercial versus conservation forestry initiatives. However, some consistency arising from the development of ‘best practice’ policies among States has been achieved, giving rise to five basic forestry conservation initiatives. These initiatives allow for 1) afforestation, 2) the creation of wildlife habitats, 3) prevention or reversal of land degradation, 4) establishment of shelter for crops and wildlife as well as, 5) general amenity (Herbohn et al. 2000). However, while some attention has been directed towards retaining habitat features in natural production forests within Australia (Lamb et al. 1998), there is relatively little information available on the implementation of federal conservation policies at a local government level. This failure to amalgamate policies across the nation, is also

found at a state level. For example, a previous review of stakeholder involvement in Victorian forest policy (Musselwhite and Herath 2007) revealed that local governments were not included in the survey, thus highlighting the lack of commitment to include local government agencies.

Given the importance of essential habitat features such as hollow-bearing trees, managing landscapes for their persistence should be considered critical to the success of conservation initiatives. How such conservation actions are implemented depends on the interpretation of policies at various levels of government, but particularly at the local level. This Chapter provides an overview of the current ‘best practice’ policy and guidelines for forest management in eastern Australia. It compares the relative importance of environmental management at the local level and then compares the scalar translation of policy to implementation. It does so by investigating specific actions to conserve hollow-bearing trees and/or habitat trees as essential habitat features within the landscape.

Methods The degree to which the conservation of hollow-bearing trees within Australia is captured within existing legislative frameworks was assessed by reviewing available scientific literature, as well as forestry and nature conservation related legislation, to document the current status of forest management. This process summarised the hierarchy of Federal and State legislation followed by ‘best practice’ forestry policies down to local government policy and planning guidelines. These documents were then searched for any mention of hollow- bearing or habitat trees.

The transfer of guiding policies and recommendations from higher levels of government that inform strategies at the local level are imperative. With this in mind, a two-stage process was followed to assess local level environmental management objectives, and specifically how these translate into the protection of hollow-bearing trees. Information was accessed at the local council level by contacting 86 randomly selected local council areas throughout eastern Australia (Tasmania, Victoria, New South Wales and Queensland). These local councils were categorised at the broad level as either: Urban, Regional, or Rural based on the

23 classification system used by the Department of Regional Australia Local Government, Arts and Sport (2001). In the first stage, web pages of local councils were assessed to determine their environmental emphasis within a broader social responsibility context. This was undertaken across three hierarchical levels. Level 1 entailed enumerating the number of primary council strategies and objectives listed on their respective web pages. This was then taken to a finer scale by searching for any strategies pertaining to the importance of environmental assets and biodiversity and thus hollow-bearing trees by association. Level 2 calculated the relative importance of environmental actions. The total number of menu tabs on each primary council web page was counted and the placement of 'environment' within this structure was determined by recording its order within them. The relative importance was then calculated by dividing the order of the 'environment' tab by the total of all tabs for each council and these figures were normalised by the maximum number of menu tabs across all councils (n=18). Theoretical minima and maxima for the relative importance of ‘environment’ ranged between 1 (lowest score) and 324 (highest score). Finally, Level 3 analyses compared the number of sub-categories within each ‘environment’ tab to those within all other tabs on the primary web page. Collectively, these results give an overall representation of how councils place themselves in a socio-environmental context. As Flemming (1998) explains, in web page design the client is aiming to supply information to their target audience, as well as the message that they are attempting to convey about their business. Therein, it can be seen that by evaluating council web pages placement of tabs on their respective web pages does offer a valid method for analysis. Comparisons of the number of environmental strategies among councils based on their location category were made using a one-way ANOVA.

For the second stage, copies of any policies and/or strategies that captured the conservation of hollow-bearing trees as one of their management actions were requested from council representatives. Documents received from council representatives were then ranked based on the nature of the conservation and management frameworks and actions that councils have in place to conserve hollow-bearing trees. Three broad categories were recognised; (i) council policies that rely upon the Environmental Protection and Biodiversity Conservation Act 1999 (EPBC Act) and other Federal and State legislation with no specific reference to hollow- bearing trees; (ii) council policies that rely on the EPBC Act and other Federal and State legislation with specific reference to the importance of hollow-bearing trees, but with no

specific protection offered; (iii) council nature conservation strategies and management plans with specific mention of the retention/protection of hollow-bearing trees and actions to achieve these objectives. The implications of these findings are discussed in the context of forest conservation and management in Australia with specific reference to the threats facing hollow-bearing trees.

Results

Legislation and the conservation of Australian forests and hollow-bearing trees At the Rio Earth Summit in 1992 the use and management of forests received considerable attention globally, culminating in the production of a number of agreements including the ‘Global Statement of Principles of Forests’ (GSPF) and ‘Agenda 21’. The GSPF made several recommendations for the management, conservation and sustainable development of all forest types globally. Australia endorsed the GSPF and signed a number of conventions relating to biological diversity and climate change, in order to achieve the full range of benefits available from forests (Commonwealth of Australia 1992). Furthermore, Agenda 21 called for global action in regards to sustainable development and was regarded by many as the centrepiece of the Rio accords (Prado-Lorenzo and Sanchez-Garcia 2007). From Agenda 21, Local Agenda 21 was developed that offers support for environmental issues at the local level (Thomas 2007). Local Agenda 21 systems were implemented with the aim of managing for the future while achieving sustainability through integrating planning and policy, focusing on long-term outcomes and involving all sectors of the community (Stenhouse 2004; Thomas 2007).

Australian Federal strategies and objectives were then outlined in the ‘National Forests Policy Statement 1992’ (NFPS) which lay the foundations for forest management in Australia for the next century. The NFPS, signed by the Commonwealth, States, Territories and local governments, provides the framework recognising the need to initiate processes required for changes to occur in order to protect Australia’s forests for ecologically sustainable growth and management (Commonwealth of Australia 1992). At this level however, there are no explicit provisions made that stipulate the management interventions or actions required to protect specific habitat features such as hollow-bearing trees.

Following the endorsement of the NFPS, the Commonwealth and State governments established the framework for Regional Forestry Agreements (RFAs). Twelve RFAs were progressively signed by the Commonwealth and State governments between 1997 and 2001 (Mobbs 2003). Each RFA is a 20 year intergovernmental agreement concerning the security of forests resources, as well as the conservation of environmental heritage and social values (Lane 1999; Aenishaenslin et al. 2007). The key outcomes of RFAs are the allocation of approximately 30% of publicly owned commercial native forests to an expanded reserve system, the strengthening of the codes of practice and increased resource management for the timber industry (McAlpine et al. 2007). Despite the provisions for increasing the extent of native forests within the national reserve system the RFAs do not highlight any measures required to conserve specific structural features within these forests. Furthermore, there has not been widespread adoption of the RFAs by State governments, exacerbating inconsistencies in forest management practices among states and territories. For example, Queensland did not initially sign an agreement and developed its own Queensland Forest Agreement outside of the Federal Government’s RFA guidelines. This was done in conjunction with the Australian Rainforest Conservation Society, the Queensland Conservation Council, The Wilderness Society and the Queensland Timber Board (Mobbs 2003). This alternative agreement, initiated in 1999, failed to fully encompass all forest types in south-east Queensland, favouring rainforest and wet sclerophyll forests in the conservation reserve system (McAlpine et al. 2007). Virtually no allowance was made for the protection of dry eucalypt forests, thereby failing to preserve those ecosystems and species with the highest vulnerability to land clearing (Norman et al. 2004; McAlpine et al. 2007). Similarly, dry sclerophyll forests in New South Wales received little or no recognition and are poorly represented in the New South Wales RFA (Flint et al. 2004). Nevertheless, it has been suggested that RFAs under the NFPS have achieved some positive outcomes, if only for native forests within the national reserves system following the implementation of a higher level of sustainable forestry practices in regards to conservation (Meek 2004).

Nationally, RFAs require that within individual public forest estates a minimum of 15% of each forest type, 60% of remaining old growth forest and at least 90% of high quality wilderness is reserved (Aenishaenslin et al. 2007). While the RFA process has seen a significant reduction in the volumes of timber harvested from within the public estate, in some states this has led to an increase in the importance of private native forests as a source

26 of saw log’s (Norman et al. 2004). Private forests are therefore potentially under greater threat as a consequence of the implementation of the RFAs in various states. This highlights that further engagement at the local level is needed as the current system only ‘encouraged’ land owners of private forests to participate in the policy. Local authorities such as councils are also not readily included in national reserve system planning or management (Lunney 2004).

Private forests have typically been placed in the ‘too hard’ basket by policy makers, legislators, conservationists and foresters as being of low conservation and economic value. For example, the management of private forests has been accompanied by a combination of public ignorance and political and agency inertia, where the laws applicable to private native forests have been formed by inaction (Prest 2004). For example, while the forestry code of New South Wales is highly restrictive in terms of forest harvesting on public land, and management is heavily focused on environmental objectives, little importance is placed on the contribution of private native forests. In Queensland and Victoria codes are commercially driven, while Tasmania attempts to achieve a balance between environmental and commercial objectives (Aenishaenslin et al. 2007).

Equally, small forest patches isolated by fragmentation through urbanisation have been abandoned and considered to be of low conservation value (Alvey 2006; Harper et al. 2008). However, small and often isolated forest patches are valuable to conservation as they are representative of former habitat that was once common to any given area (van der Ree et al. 2003), and also serve to connect habitats within a larger landscape (Lindenmayer and Fischer 2006). Ongoing land conversion will increase the value of private and urban forests (Alvey 2006), as there is a clear link between the condition of private land and biodiversity conservation in Australia (Fitzsimmons and Westcott 2001). The importance of local councils in directing conservation efforts on private land is paramount. For example within Queensland these agencies already administer a number of private conservation initiatives such as the Land for Wildlife schemes which are also available in Victoria, New South Wales and Queensland (Fitzsimmons and Westcott 2001). Local councils therefore act as the interface in providing private landholders with clear policy directions on the conservation and management of forest features such as hollow-bearing trees. Despite these objectives,

conflicts continue to plague the industry and both public and private native forests continue to shrink, emphasising the urgent need for sustainable management of all forests to conserve biodiversity (McAlpine et al. 2007).

Regardless of their long-standing recognition as important forest features, the loss of hollow- bearing trees due to forestry practices was nominated for listing as a key threatening process in 2005 under the Commonwealth Environment Protection and Biodiversity Act 1999. The nomination recognised two parts to the process: activities which remove or destroy hollow- bearing trees and processes which affect tree and seedling recruitment. However, the Endangered Species Scientific Sub-committee found that, where an RFA was in place, a nationally co-ordinated threat abatement plan was neither feasible nor an effective and efficient way to limit the loss of hollow-bearing trees. For regions where no RFA is in place, a range of State forestry management prescriptions, nature conservation and threatened species legislation, vegetation clearance controls and voluntary measures apply. This suggests that RFAs and other legislation have explicit details and plans in place for the conservation of hollow-bearing trees. This is not the case. These documents largely comprise broad sweeping terminologies that cover conservation issues in general, with no specific references to hollow-bearing trees or other important forest structures. Thus, hollow- bearing trees are not specifically protected under the Commonwealth Environment Protection and Biodiversity Act 1999; as there is a belief that sufficient mechanisms are in place within individual state legislation to address the situation. The time frame for management and system reform as determined by the RFAs under the NFPS draws to its conclusion in 2017 and the direction of forestry reform and conservation in Australia thereafter should be closely monitored. Herein lies an opportunity exists for local councils to put policy measures in place to ensure the ongoing conservation of hollow-bearing trees.

Forestry practices within Australia generally identify hollow-bearing trees as being of low commercial value, while also suppressing forest regeneration (Gibbons and Lindenmayer 2002). Despite this, they continue to be harvested primarily as saw logs or at a later stage for fire wood or fence posts (Lamb et al. 1998). In some regions, provisions for the retention of hollow-bearing trees under certain land uses are in place. For example, in Queensland the ‘Habitat Tree Technical Advisory Group’ (HTTAG), was formed by the Environmental

Protection Agency to assist in the development of guidelines for the management of hollow- bearing trees as habitat for wildlife in relation to silviculture (Lamb et al. 1998). Under these guidelines, habitat and recruitment trees are identified as those requiring retention for the purpose of wildlife conservation and may be of either merchantable or non-merchantable value (Queensland Government 2007). Habitat trees are defined as those having one or more hollows >10 cm in entrance diameter, while recruitment trees are suitable trees within a 40 cm diameter at breast height class (Lamb et al. 1998). Recommendations from the HTTAG have also been incorporated into the Code of Practice for Native Timber Production on State Lands (Eyre 2005; Queensland Government 2007). Of those recommendations, the retention of a minimum of six live habitat trees and two recruitment trees per hectare is mandatory throughout harvesting areas (Eyre 2005). Under standard harvesting practice, where habitat trees occur uniformly in an area subject to clearing, additional recruitment trees must be retained where >50% of the basal stand area is to be removed (Lamb et al. 1998). However, these recommendations and guidelines are not applicable outside of production forests, again highlighting the need for local government bodies to establish and implement conservation programs aimed at the maintenance and recruitment of hollow-bearing trees on commercial, public and private lands.

The preceding review highlights that all Acts, Codes, agreements and policies at a Federal and State level within Australia strive for ecological sustainability and the retention of certain vegetation communities at a national scale. Yet, apart from the HTTAG in Queensland there appears to be little documentation regarding the specific management and retention of hollow-bearing trees as a habitat resource to be conserved within the landscape. Therefore, while Federal and State legislation acknowledges the importance of hollow-bearing trees to biodiversity, sufficient mechanisms to halt the ongoing loss of this valuable habitat resource from Australian landscapes appear to be lacking. Without adequate legislation at the national or state levels there is little hope in achieving conservation objectives for hollow-bearing trees at the local level. This study assessed the nature of hollow-bearing tree management strategies at the local level to determine if national level policies are implemented at this scale.

Local level ranking of the importance of biodiversity assets, in particular hollow-bearing trees Of the 86 councils contacted only 46 (53.4%) responded to calls for information on their environmental strategies and plans for the conservation of hollow-bearing trees. These councils were subsequently used in the analysis of broader council social responsibility and how ‘environment’ placed within this operational framework. Of the 46 respondents: 12 were classified as being ‘urban, 23 ‘regional’ and 11 ‘rural’ (Table 2.1).

Level 1 searches of council web pages revealed that only 46.7% of councils had overarching strategies and guiding principles in place relating to environmental protection and the importance of biodiversity assets. The relative importance of ‘environment’ among councils was also highly variable with ranked importance values ranging between 2.3 and 108. Overall, 85% of councils ranked 'environment' low, very low or not at all. Only 2.1% of councils ranked ‘environment’ highly and a further 13% placed a medium emphasis on 'environment’ (Figure 2.1). Finally, Level 3 analyses revealed that the number of environmental programs run by councils in general were significantly less than the mean numbers captured across all other council programs (t=4.5; d.f.=45; P <0.001) (Table 2.1). There was also no difference in the emphasis placed on ‘environment’ when compared among ‘urban’, ‘regional’, or ‘rural’ locations (F=2.32; d.f=2,43; P=0.11).

Information on the management strategies for hollow-bearing trees at the local level provided by 46 councils provided the basis for assessing the degree to which higher level policies are taken up by implementing agencies. Local councils providing information were spread along the south-eastern seaboard of Australia and were represented by Queensland (30.4%), New South Wales (36.9%), Victoria (26.1%) and

Placement of No: 'Environment' Mean n tabs No: of Council *Council of tab on web (excluding environmental by State Classification tabs page environment) strategies NSW 1 Rg 8 4 7.4 1 NSW 2 U 9 4 12.6 28 NSW 3 Rg 6 4 5.6 9 NSW 4 R 5 0 14.5 0 NSW 5 R 13 9 5.3 10 NSW 6 Rg 7 7 11.8 3 NSW 7 U 8 8 9.0 0 NSW 8 R 8 4 5.1 0 NSW 9 Rg 7 1 8.5 1 NSW 10 Rg 5 3 10.8 1 NSW 11 R 8 5 9.6 0 NSW 12 U 3 3 29.0 14 NSW 13 Rg 8 5 11.6 5 NSW 14 Rg 7 3 12.0 8 NSW 15 Rg 5 5 9.5 0 NSW 16 U 7 7 10.2 0 NSW 17 R 9 7 21.9 8 QLD 1 U 8 4 30.3 0 QLD 2 U 7 3 38.8 6 QLD 3 U 8 5 14.4 5 QLD 4 U 7 0 32.5 10 QLD 5 U 6 0 11.2 0 QLD 6 U 9 4 9.3 0 QLD 7 R 7 4 13.3 1 QLD 8 Rg 8 4 9.0 0 QLD 9 Rg 8 4 9.6 3 QLD 10 Rg 8 5 12.9 1 QLD 11 Rg 7 5 23.5 0 QLD 12 Rg 8 0 5.1 1 QLD 13 R 5 0 9.5 3 QLD 14 U 6 0 12.2 4 TAS 1 Rg 5 4 6.5 0 TAS 2 R 9 0 7.1 1 TAS 3 R 7 0 5.8 1 VIC 1 Rg 8 8 22.7 3 *U Urban: Rg regional: R Rural.

Table 2.1 continued: Data from the web pages of local councils in relation to how they place themselves environmentally in a social context. The number of environmental strategies for each council are those listed within each primary ‘Environment’ tab on council web pages, continued.

Placement of No: 'Environment' Mean n tabs No: of Council *Council of tab on web (excluding environmental by State Classification tabs page environment) strategies VIC 2 Rg 6 2 12.2 3 VIC 3 U 17 0 8.0 0 VIC 4 U 7 4 8.3 13 VIC 5 Rg 8 0 24.7 5 VIC 6 U 18 9 10.2 14 VIC 7 Rg 6 0 15.2 2 VIC 8 U 6 3 13.2 8 VIC 9 Rg 14 9 7.2 1 VIC 10 R 5 0 26.0 16 VIC 11 Rg 18 11 7.2 3 VIC 12 R 7 4 9.2 20 *U Urban: Rg regional: R Rural.

Councils of Number 5

0 Zero Very low Low Medium High Very High Relative importance

Figure 2.1: The relative importance placed on the environment by local councils based on the ranking of ‘environment’ tabs in relation to all other tabs on primary council web pages.

Tasmania (6.5%) (Table 2.2). Significantly, 95.6% of councils reported that they had no specific conservation, management policies or guidelines in place for hollow-bearing trees, and that they relied primarily on either State or Federal Acts and legislation to protect vegetation communities within their shires.

Only two councils (4.4%), one each from New South Wales and Queensland had specific measures in place to protect hollow-bearing trees, while a further seven councils (15.2%) mentioned the ecological importance of hollow-bearing trees but did not have any specific conservation measures in place. In the case of the New South Wales council these guidelines were embedded within development control measures along with a significant tree registry. By comparison, the council in Queensland had a series of guidelines targeted to hollow- bearing tree retention. The guidelines implemented by the Queensland local council is a comprehensive document that is entirely committed to the identification, conservation, retention and management of hollow-bearing trees within its boundaries with reference to the Integrated Planning Act 1974 (Table 2.3). The guidelines offer ecological recognition and value to habitat trees within the landscape supported by the ability to undertake on-ground implementation, including measures that consider the implications following the removal of hollow-bearing trees.

Table 2.2: Councils contacted to request information on strategies and policies targeted to hollow-bearing trees at the local level, and number of respondents by state.

State Queensland New South Wales Victoria Tasmania Number contacted 25 26 25 10 Number providing information 14 15 11 2

Table 2.3: Specific measures implemented by two urban local councils to protect and preserve hollow-bearing trees on public and private land in Queensland and New South Wales.

Queensland Council New South Wales Council

Guidelines for the Retention of Hollow- Development Control Plan. bearing Trees. 1. To protect old growth and significant 1. Tree Management Report hollow-bearing trees.

1.1 Ecological Assessment Report 2. Significant Tree Register: Trees of high ecological value as well as other 1.2 Risk assessment social values.

1.3 Hollow-bearing tree assessment

1.4 Selection Criteria: Hollows, Tree health and DBH

2. Annual Management Report

2.1 Maintenance of hollow-bearing trees

2.2 Data reporting and recording

3. Tree Removal Plan

3.1 Removal of wildlife

3.2 Compensatory actions

Discussion Within Australia, amnesia and ad hoc policy formulation is considered to be an ongoing problem at the institutional and organisational foundations of forest policy and management (Dovers 2003). This presents a significant problem for land managers in regards to the implementation of natural resource policy and legislation. The difficulties are in translating the intentions and aspirations of parliamentary statutes, which are intended to protect the environment, into meaningful on-ground actions (Shepheard and Martin 2011). This supported the results here that have revealed how some councils appear to give little consideration for environmental issues within the broader socio-environmental context, and that few gave specific attention to hollow-bearing trees. This is demonstrated by the fact that eight of the 46 councils surveyed appeared to have no information relating to the environment and/or the importance of biodiversity assets displayed on their public websites. Furthermore, when councils did provide information about the environment, this information was often hidden or nested within larger programs or objectives. Nevertheless, results from the three- tiered investigation into council’s web pages suggest that approximately 50% of local councils generally considered the environment and biodiversity as being of some importance. To interpret these results further, they need to be taken in context with the information pertaining to hollow-bearing trees supplied by councils. In doing so the overwhelming lack of translation of overarching national and regional policy and legislation into local conservation action is telling. This lack of local policy implementation is highlighted by the fact that only two councils (4%); both of which were classified as being ‘urban’; had dedicated measures in place to protect hollow-bearing trees. The majority of local councils rely heavily on both existing State and Federal legislation, despite these being vague in their provisions for the preservation of hollow-bearing trees. Thus, the dependence on overarching policies may not ultimately achieve conservation objectives simply because these policies frequently do not have the level of support and detail required to implement targeted conservation strategies.

In the years following the introduction of Local Agenda 21, 117 of Australia’s then 750 councils had either established or were developing local sustainability strategies (McDonald 1998). While this national trend saw the transfer of power and responsibility from Federal government to local authorities. As Local Agenda 21 is a voluntary option for councils, by November 1999 local council involvement had dropped to 75 (Mercer and Jotkowitz 2000).

Whittaker (1997) obtained a deeper insight as to why Australian councils were slow or reluctant to embrace Local Agenda 21; reporting that many had difficulty in deciding whether they were in fact developing a Local Agenda 21 or not and if their management plans could be incorporated into a Local Agenda 21. Ultimately, Local Agenda 21 requires changes to values and patterns of consumption both from within local councils and the community. However, many councils stated that they had great difficulty in raising awareness of Local Agenda 21 and getting the community involved at the time (Whittaker 1997). Furthermore, the apparent lack of support offered to conservation strategies within local councils, as recorded in this study, is further exacerbated by the limited transfer of scientific findings to policy makers or the general public (Pitman et al. 2007). Ecologists generally convey their findings to a limited audience (e.g. other academics) often resulting in key decisions relating to the management of biodiversity and conservation being made without the full benefit of science (Shanley and Lopez 2009). Ironically, the rapid loss of biodiversity continues despite the plethora of information available and the considerable financial investment in research, highlighting the breakdown in communication between science and policy makers. Based on the findings presented here, even where research may have informed the development of higher level conservation policy, these policies are not being enacted at a local level demonstrating the cascading effects of limited information flow pathways.

This study demonstrates this policy implementation breakdown by using hollow-bearing trees as an example and suggests that the problem itself is more deep-seated; thus compromising the effectiveness of a myriad of conservation programs at the local level. Conservation managers at the local level therefore need to make conscious efforts to address ongoing threats to natural habitat and their associated fauna and flora through the development of targeted action plans along with the implementation of hollow-bearing tree protection legislation (e.g. development control measures). The extent to which these problems are entrenched more broadly within other Australian local councils remains to be seen as only 53% of councils responded to this survey. Nonetheless, it is troubling that this initial review may be indicative of a prevailing status quo, particularly in urban regions.

This investigation generated considerable interest amongst councils, with some council respondents expressing their frustration at the lack of protection they felt they were able to

give for hollow-bearing trees and remnant vegetation in general. This is perhaps due to the variable land uses that occur within council jurisdictions such as public parks, bushland, reserve networks and recreational reserves, as well as the range of environmental and social values operating across the landscape (Snep and Opdam 2010). For example, in urban areas a tree is also considered to be hazardous if there is potential for harm to people and property. Accordingly, trees that contain hollows that are readily detectable, and are then judged to be hazardous, are therefore removed (Terho 2009). However, an underlying cause perpetuating inaction appears to be linked with the slow and often hesitant rates of transition to an adaptive management paradigm by local councils. Active adaptive management balances the requirements of management with the need to learn about the system being managed thus leading to better decision making (McCarthy and Possingham 2006). For crisis disciplines such as conservation (Burgman et al. 1993), conservation managers must deal with direct environmental threats in dynamic socio-political environments. The broader social responsibility targets being pursued by local councils, as demonstrated by this study, can be captured within contemporary active adaptive management paradigms to improve management (Cundill and Fabricius 2010). Fostering an adaptive management approach for the conservation of hollow-bearing trees at the local level is dependent on a number of factors. These include an understanding of the conservation development objectives of multiple stakeholders (e.g. councils, landowners, NGOs, traditional landowners), knowledge of the status of the resource, and the identification of defined approaches to achieve these objectives. Calls for conservation planners to develop strategies that will enable them to engage with decision makers in order to integrate biodiversity conservation initiatives into land-use planning (Lagabrielle et al. 2010) are supported by this study.

Conservation challenges and the way forward Australian urban forests contain hollow-bearing trees (Goldingay and Sharpe 2004a; Harper et al. 2005a), providing critical refugia for a variety of native fauna. These forests, however, are characterised by significant time spans separating current hollow-bearing trees and a succession of trees (of suitable sizes) for recruitment and the continuous replacement of hollows (Gibbons and Lindenmayer 2002). Therefore, prioritising forest management policies to preserve a suitable age-structure supply of hollow-bearing trees is required if the biodiversity values of these urban habitats are to be retained in the long-term. This can be achieved through a number of actions that would arguably form the basis of future hollow-

bearing tree conservation strategies; particularly for those councils where these are currently lacking. The following strategies and actions are recommended:

1. Definition of clear management objectives and conservation targets at the outset that will inform future adaptive monitoring efforts;

2. Compiling an inventory of hollow-bearing trees at a local scale to establish a baseline for identifying potential interventions required to meet predefined objectives. These data can then be incorporated with other known data sets on the urbanisation process, such as land clearing, land use, population density etc., to inform holistic land use planning and policy development for the retention of hollow-bearing trees; and

3. Most importantly there is a need to facilitate on-ground action plans for the retention, recruitment and management of hollow-bearing trees at the local level and particularly within rapidly urbanising regions.

Such actions will in turn maintain forest structure and faunal communities as well as increasing their value at a landscape scale. Activities include those that manage the residual natural resources (e.g. minimising fire impacts on large dead hollow-bearing trees), but also those that propose active facilitation to enhance or improve habitat conditions (e.g. supplementation, accelerated hollow formation). While short-term supplementation e.g. erection of nest boxes can assist species conservation efforts in some situations (Beyer and Goldingay 2006; Durant et al. 2009), they cannot replace naturally occurring hollows and their usefulness has been vigorously debated (Spring et al. 2001; Lindenmayer et al. 2002). For hollow-bearing trees any mitigation action should be implemented with a complete understanding of the current status and availability of this resource within the landscape.

Artificial hollow formation has also been suggested as an alternative to the use of nest-boxes (Gibbons and Lindenmayer 1996). Accelerating the formation of hollows could be achieved by deliberate attempts to kill or injure a tree (Bull and Partridge 1986), the injection of growth hormones and inoculation with fungi (Connor et al. 1981), tree girdling (Connor et al. 1981), the use of explosives (Smith and Lindenmayer 1988), fire (Adkins 2006) and the combination of these techniques with natural decay agents associated with hollow formation

(Lindenmayer et al. 1993). However, Goldingay (2009) points out there are currently no published studies from Australia that describe experiments to promote or create natural hollows in trees or indeed whether these are successful. Furthermore, the time to accelerated hollow formation in Australian hardwood forests may also be longer than the 3 - 5 years reported from North American softwood forests (Bull and Partridge 1986; Arnett et al. 2010).

Conclusion There are a number of implications for the ongoing management of natural resources at the local level arising from this work, particularly for hollow-bearing trees. Firstly, this study provides empirical evidence of a policy-implementation gap in connection with the management of hollow-bearing trees in Australia. Despite the national emphasis placed on the value of these habitat features within native forests there is little on-ground implementation of conservation actions aimed at their persistence within the landscape. Higher level policy is poorly translated into on-ground action and this requires a shift in current management strategies to adopt a more proactive adaptive approach.

Secondly, a review of, and change in environmental policy is required in order to amalgamate the varying, convoluted and disjointed policies currently in place to provide greater focus to the management of forest resources at various scales. Continuing under current legislative frameworks will be insufficient in preventing the further loss of key resources such as hollow-bearing trees. Furthermore, the potential implications of such largely inadequate and ineffective efforts extend beyond that of hollow-bearing trees and could compromise a number of other forest assets; which is of particular concern in rapidly developing regions.

Thirdly, the role of local government in biodiversity conservation operates at both a regulatory and advisory level in that local councils direct both short and long-term influences on land management and development processes across varied land tenures. As such a review of existing policy is also necessary to acknowledge and guide the conservation contribution within the private sector.

In closing, this review of local council policy provides new evidence demonstrating the lack of connectivity between Federal policy and local implementation. While existing legislation identifies the need to retain ‘forests’, ‘regional ecosystems’, ‘remnant habitat’ or ‘vegetation communities’, it masks the fact that it is largely ineffective in delivering real on-ground conservation actions for finer scale habitat features such as hollow-bearing trees, particularly for those found within urban landscapes and non-protected forests.

Chapter 3: Hollow-bearing trees as a habitat resource within an urbanising landscape.

Introduction The destruction and removal of natural habitats is proceeding globally at a rapid rate (Rankmore and Price 2004; Lewis et al. 2009). Within Australia, the clearing and disturbance of natural vegetation and the subsequent habitat fragmentation is recognised as one of the principle drivers behind declines in biodiversity (Williams et al. 2001). Approximately 50% of Australia’s forests have been lost or severely modified since European settlement, with over 80% of eucalypt forests in particular having been lost to anthropogenic activities (Bradshaw 2012). Consequently, much of the remaining forest cover is severely fragmented (Bradshaw 2012). Small and often isolated habitats are recognised as being valuable to conservation, as they are representative of former habitat that was once common to any given area (van der Ree et al. 2003) and form part of a larger landscape of habitats inclusive of patches and remaining matrix. The landscape is therefore never uniform and disturbances to this are determined by the type and intensity of the land uses found within it (Brady et al. 2009). These disturbances and impacts to patches vary with the time since isolation, distance from other remnants, and the degree of connectivity with other remnants (Saunders et al. 1991; Lindenmayer and Fischer 2006). Within urbanising regions the matrix may be considerably less permeable for biodiversity, thereby reducing the functional potential of natural habitat within these landscapes (Ewers and Didham 2006b). The retention of such habitats as refugia therefore becomes increasingly important in these situations (Harper et al. 2005a).

Within urban landscapes refugia are found in forest patches as well as urban parks and gardens, with their value depending on their ability to provide resources for indigenous flora and fauna (Godefroid and Koedam 2003; Harper et al. 2005a; Alvey 2006). The availability or paucity of one such resource, tree hollows, will generally limit the distribution and abundance of obligate hollow-bearing tree users and general species 41 richness of an area (Gibbons and Lindenmayer 2002). Consequently, the spatial and temporal abundance of hollow-bearing trees within a forest will vary in response to disturbance and stand age (Lindenmayer et al. 1997), with the additional influence of adjacent non forested areas impacting on forest structure and species composition (Harper et al. 2005a). The importance of hollow-bearing trees for a large range of fauna has been demonstrated globally (Braithwaite 1996; Ball et al. 1999; Gibbons and Lindenmayer 2002; Bai et al. 2003; Aitken and Martin 2004; Cameron 2006; Monterrubio-Rico and Escalante-Pliego 2006; Holloway et al. 2007; Blakely et al. 2008; Goldingay 2009). This is particularly so for Australian faunal communities, given the large number of species that are reliant upon them as habitat trees (Braithwaite 1996; Ball et al. 1999; Gibbons and Lindenmayer 2002; Cameron 2006; Goldingay and Stevens 2009). The distribution and abundance of hollow-bearing trees varies across Australian landscapes, suggesting that other factors beyond the tree level are operating to influence the occurrence of trees with hollows (Gibbons and Lindenmayer 2002). These include large scale processes such as anthropogenic disturbance through timber harvesting (Lamb et al. 1998), agriculture (Gibbons and Lindenmayer 2002) and urbanisation (van der Ree and McCarthy 2005). Thus, depending on the prevalence of a range of disturbance processes the density of hollow-bearing trees may vary considerably. Generally, undisturbed forests and woodlands support a higher density of hollow-bearing trees per hectare than disturbed forests (Lindenmayer et al. 1991b; Ross 1999; Koch 2008). Hollow-bearing tree density also appears to be positively correlated with older stands, gullies, areas with no previous logging, and flat terrain (Lindenmayer et al. 1991b).

In urban regions there is a distinct transition in housing density along an urban-rural gradient (van der Ree and McCarthy 2005), with the gradient model being a useful tool for examining the ecological consequences of urbanisation (McDonnell et al. 1997). Previous studies along urban gradients have demonstrated significant effects of such gradients on floral and faunal assemblages where communities are generally depauperate in the more heavily urbanised areas as opposed to those in rural settings (Brady et al. 2009; Hahs and McDonnell 2008; Johnson et al. 2008; Moffat et al. 2004). However, while these studies focus on the species composition along the urban gradient no studies have investigated the impact of the urban gradient on habitat resources such as hollow-bearing trees. Therein, management practices, landscape variables and ecological processes along the gradient will differ with the potential to affect resource 42 availability. Due to the rate of urbanisation urban areas have had less exposure to historical land practices such as logging for a longer period than the more rural areas. As such it is hypothesised that there will be a difference in the size and availability of hollow-bearing trees across the urban gradient where urban patches are expected to have a greater number of these resources. Processes that occur in the urban matrix are poorly understood primarily because forest patches may have been, or currently are, subjected to increasing external pressures. There is also a paucity of information on the distribution of habitat resources, such as hollow-bearing trees, along urbanisation gradients. Since hollow-bearing trees are a highly valued resource within an Australian context an investigation of the availability of hollow-bearing trees would provide a deeper understanding of the impacts of the urbanisation gradient on urban forest patches.

This chapter therefore aims to quantify the distribution and abundance of hollow- bearing trees along an urbanisation gradient by answering the follow questions: 1. How does the urban gradient influence the density of hollow-bearing trees? 2. Is there uniformity in the availability of different types of hollows along the urban gradient? 3. Does the size of the tree influence the availability of hollows? and 4. How important is the urbanisation gradient in influencing the density of hollow-bearing trees compared to other disturbance parameters?

Compiling an inventory of both hollow-bearing and non-hollow-bearing trees and evaluating these patterns against the impacts of disturbance and landscape variables along the urbanisation gradient will answer the above questions. Specifically, it is hypothesised that there will be more hollow-bearing trees within the urban forest remnants due to the reduced time since exposure to historical land practices. Through the empirical analysis of the aforementioned questions this Chapter is then able to expand on the findings to discuss options for management prescriptions for the retention of hollow-bearing trees along an urbanisation gradient.

Methods

Study sites The study was undertaken in patches of natural forest habitat on the Gold Coast, Australia (for a full description see Chapter 1 pages 14-17). Study sites were confined to wet and dry sclerophyll forest types from 21 Regional Ecosystems (Taylor 2003), of which three are threatened and seven are of concern (Appendix 2). Wet and dry sclerophyll forest types were selected as they are widespread across the study area and would guarantee an adequate number of replicate sites. Natural forest patches ≥ 2 ha were identified from within council Conservation Areas, National Parks, State Forests and on private land. Ground-truthing of 50 sites that were deemed to be suitable was undertaken to exclude those that may have access issues, intensive livestock grazing or a mowed understory. Sites with either grazing or mowing (e.g. parklands) were excluded as these were not representative of natural vegetation communities. Forty-five sites were subsequently selected from the highly developed coastal region through to the more natural environments of the hinterland located ≤ 500 m above sea level. The 500 m altitudinal threshold was chosen as rain forests generally dominate the landscape above this elevation. Five large contiguous forest patches (> 500 ha) were chosen as control sites in order to allow for variations found within large forest patches. Three of the controls were located in peri-urban areas while two were within urban areas. Three of the five control sites were established within the southern suburbs of Brisbane, Logan and Redland Shires; to supplement the small number of large contiguous forest patches below 500 m on the Gold Coast. The Gold Coast has 9669.2 ha of open space and reserves that were available as study sites; of which 6650.9 ha was captured within the 45 sites. A systematic grid was placed over all sites with sampling points (plots) identified at all intersection points. The number of plots to be surveyed at each site was determined by patch size using a hierarchical grid cell size, ranging from 250 m in small forest patches to 2000 m in larger sites to allow for sufficient environmental variation. In total 90 plots were selected from all 45 sites (Appendix 3 for GPS locations) composed of 13 urban sites with 20 plots, 14 peri-urban sites with 23 plots, 13 rural sites with 21 plots and five control sites with 31 plots. Sites varied in size (2- 1699 ha), shape, topographic location as well as position along an urban gradient. At each plot a fixed area methodology was used (sensu Gibbons and Lindenmayer 2002), to sample relevant parameters (described below). Fixed area methodology allows for ease in determining if a tree should be sampled or not; plots can be permanently marked and all

44 trees within a plot can be catalogued enabling the estimation of density. A number of environmental and disturbance variables were also quantified in each plot (Table 3.2).

Plot sampling protocol This study quantified the abundance and distribution of all species from within five genera belonging to the family Myrtaceae; Angophora, Corymbia, Eucalyptus, Lophostemon and Syncarpia, hereafter referred to as ‘eucalypts’. At each plot all eucalypt trees within a 30 m radius from the plot midpoint and with a diameter at breast height over bark (dbh) ≥10 cm were identified and measured to determine vegetation community structure and quantify the status of hollow-bearing trees. Eucalypt identification followed Brooker and Kleinig (2004) and Euclid (Centre for Plant Biodiversity 2006), on the basis of bark, fruit bud and leaf characteristics. The area of each circular plot was 0.28 ha resulting in a total survey area of 25.4 ha for all 90 plots.

Hollow-bearing trees detected on each plot were categorised into one of seven senescence types using the framework of Whitford (2002) (Figure 3.1). The senescence types were modified to focus on living trees as opposed to dead trees; therefore, S1 is a completely healthy tree, S2-S6 varying degrees of senescence similar to Whitford (2002) but replacing ‘dead’ with ‘living’. The justification for this was based on the higher value of living trees to fauna than dead trees (Moloney et al. 2002). Tree- hollows in each tree were assessed using binoculars and recorded as one of seven hollow forms (Figure 3.2). A 10 cm diameter entrance threshold was selected based on previous studies that found that tree hollows with entrances of this size were large enough to accommodate the larger species of gliders such as the yellow-bellied glider Petaurus australis and the greater glider Petauroides volans (Kehl and Borsboom 1984; Mackowski 1984; Ross 1998; Eyre 2005; Wormington et al. 2005). While vertebrate fauna do utilise hollows <10 cm, these hollows may not be easily detected using ground based surveys (Harper et al. 2005a). One way to counter this problem would be to fell trees (Gibbons and Lindenmayer 2002) or undertake double sampling in the form of tree climbing (Harper et al. 2004). The first option was not considered appropriate or possible for this study, while time and financial constraints made the latter option unfeasible. It is also possible to overestimate the number of trees hollows due to the fact that a tree will often contain many openings which are blind and not of a suitable depth for occupation (Lindenmayer et al. 1990). Despite the potential bias in estimating

45 hollow presence in trees, ground based surveys are widely used in a number of different Australian environments (Harper et al. 2004; Eyre 2005; Koch 2008).

Table 3.1: Summary of tree, tree-hollow, environmental and disturbance variables and the urbanisation gradient quantified at each plot.

Variable Description of variable Tree variables Tree species Angophora, Corymbia, Eucalyptus, Lophostemon, Syncarpia. dbh ≥10 cm Diameter at breast height for all trees. Tree form & senescence See Figure 3.2. Number of hollows Number of hollows observable in a tree from the ground. Hollow variables Hollow height (m) Height of hollow in tree. Hollow type See Figure 3.3. Hollow entrance size (cm) Approximation of entrance width. Hollow density Equation 3.1 Environmental variables Angle of slope Calculated from GIS layers (Equation 3.2). Aspect (°) Directional orientation. Elevation (m) θ Obtained from GIS mapping layers. Tree disturbance variables Fire scar Presence or absence of fire scars on trees. Termite damage Presence or absence of termite activity. Wind damage Presence or absence of broken limbs. Epicormic growth Presence or absence of epicormic growth. Disturbance index Presence/absence fire and logging history from historical aerial imagery. Urbanisation index Calculated from GIS data on infrastructure, cadastre and regional ecosystems (Equation 3.3).

S1 S2 S3 S4

Alive/healthy Alive/healthy with Alive with Alive/dying minimal crown damage damaged crown

S5 S6 S7 S8

Alive/dying Alive/dying Dead standing tree Dead stump

Figure 3.2: Tree hollow types (Lindenmayer et al. 2000).

The diameter of each hollow entrance was estimated to the nearest 5 cm, using 10 cm as the minimum size. Fissure hollow size estimates were only based on the width (smallest dimension) of each fissure. Tree height and hollow height were measured using a rangefinder (True Pulse 200, Laser Technology Inc.). In some instances (i.e. steep slopes and rugged terrain) it was not possible to use the rangefinder and in these cases tree and hollow heights were estimated using prior knowledge of tree heights measured in other sites. GPS coordinates for each hollow-bearing tree were recorded and each tree was fitted with an aluminium tag carrying a unique identification number.

Hollow-bearing tree density D (number of trees / ha) per site was calculated as the sum of all hollow-bearing trees HBTs from all plots (p) at a site divided by the sum of the total plot area Ap surveyed at each site (Equation 3.1).

n ∑HBT p = p=1 (3.1) D n ∑ Ap p=1

Landscape variables Angle of slope (Ө) was calculated from GIS layers using Equation 3.2, where opposite represents the change in elevation (m) from the lowest to highest contour line at each plot, and adjacent represents the straight line distance (m) between these contour lines. Thus the higher the value the steeper the slope.

= (3.2) 표푝푝표푠푖푡푒 푡푎푛θ 푎푑푗푎푐푒푛푡

Landscape disturbance history (logging and fire) was extrapolated from historical aerial images at 1:100000 supplied by the Queensland Government (Queensland Government 2008; Queensland Government 2011). These data span a 54 year period (1955-2009) and contain 22 years of data related to the current study area (i.e. photography was not available for each year). A disturbance index was calculated from the number of logging/fire events at each site as a function of the 22 years with available site relevant data. Logging was classified where images showed the removal of trees due to clear felling or large canopy gaps from where selective logging had occurred. The same principle was applied to fires across the landscape; where large scale burnt areas were observable from images. These data were then square root transformed before analysis.

Urbanisation gradient It is evident from the published literature that the classification of urban gradients generally weight the importance of commonly used parameters differently. Some rely upon road type and density (Brady et al, 2009), vegetation cover and land development (Germaine and Wakeling 2001), human population density and land-use category (Guntenspergen and Levenson 1997) and the ratio of the density of people divided by the proportion of urban land cover (PEOP/%URB) (Hahs and McDonnell 2009). Therein, for this study the classification of sites along the urbanisation gradient was based on the spatial analysis (using ArcGIS) of a series of land use and development

49 parameters supplied by the Gold Coast, Brisbane, Redland and Logan Councils. Spatially explicit measures of housing density on individual properties (cadastre), land use, service infrastructure networks (e.g. roads, rail, canals, electricity) and remnant vegetation were used to derive an urbanisation index (Appendix 4) (Figure 3.3). The urbanisation index (U) was determined for each site by first placing a 250 m buffer around the periphery of the site and then calculating firstly, the proportional areal extent of transformed habitat (C) within the 250 m buffer (as prescribed by Biodiversity Planning unit 2002). Secondly, the housing density data extracted from the cadastre information within each buffer were summed and then normalised by dividing this total by the maximum housing density across all sites. The transformed extent for each site was then adjusted based on the normalised housing density for built up areas within the sites buffers to calculate the urbanisation index (Equation 3.3), where a greater index value represents a more urbanised site.

= × (3.3) ∑ 푑푒푛푠푖푡푦 푈 푀퐴푋 푑푒푛푠푖푡푦 퐶

Once the urbanisation index had been calculated quantile breaks in ArcGIS were used to identify three primary categories to represent the urban gradient namely; urban, peri- urban and rural. Control sites were assigned a priori but were also subsequently assigned an urban gradient category based on their individual urbanisation indices. Values 0-20 were rural, 21-360 peri urban and >360 urban. Site and tree predictor variables were also included in analyses while landscape disturbance history (logging) was extrapolated from historical 1:100000 aerial images (Queensland Government 2008) to derive a logging index (Table 3.2).

Figure 3.3: The 250 m buffer placed around sites to capture housing, cadastre and regional ecosystem data.

Data analysis Predictor variables known to have a strong biological influence on tree hollows were analysed based on a priori investigations. Spearman rank correlation measures were used to determine similarities between sites using BIOENV in Primer E version 6.1.11 (Clarke and Warwick 2001a). A correlation coefficient value |r| > 0.7 was chosen to identify pairs of highly correlated variables (Garden et al. 2010). BIOENV uses all of the available environmental variables to find the combination that ‘best explains’ the patterns in the biological data (Clark and Ainsworth 1993). No explanatory variables were strongly correlated and thus all were retained. The decision to retain or remove variables was also based on the known or perceived ecological significance of the variables. Significance levels were set at 0.05 for all analyses.

The use of different types of transformations is recommended where explanatory variables are of dissimilar nature (Zuur et al. 2010); such as those used in this analysis. An arcsine square root transformation was undertaken for explanatory variables with percentage values, while a log transformation was undertaken for all other explanatory variables requiring transformation due to large differences in scale. No transformations were undertaken on the dependent variable.

The relationship between dbh and the number of hollows a tree contained used a standard linear regression. Univariate analysis using MANOVA with Tukey’s post-hoc analysis was undertaken in order to compare the variation between the number of different hollow types and urbanisation gradient. Statistical analysis were conducted using Statistica Version 7 (Statsoft. 2005).

The effects of environmental variables, disturbance, and the urbanisation gradient on the density of hollow-bearing trees were analysed using non-metric Multidimensional Scaling (nMDS) and Principle Co-ordinate Analysis (PCO). Non-metric Multidimensional Scaling was used to assess the similarities amongst sites. The nMDS represents the sites in a multidimensional space, so that the distances between the sites accurately represent the original proximity measures. The similarity matrix required for the nMDS was first calculated using the Bray-Curtis similarity index. A Pearson correlation was then applied to the data to find the tree species which were accounting for most of the variation in the density of hollow-bearing trees along the gradient. A Kruskal’s stress test for measuring of goodness of fit was performed prior to conducting 52 the PCO to measure the distances between samples on the ordination to match the corresponding dissimilarities in community structure. Univariate analysis using ANOVA with Fisher Least Significant Difference (LSD) post-hoc analysis was undertaken in order to compare the variations between predictor variables (dbh) and the dependant variables (number of hollow-bearing trees, hollow type, hollow size, number of hollows, tree form and the urbanisation gradient). A Students t-test was used to investigate the relationship between the dbh of trees with or without hollows. Statistical analyses were conducted using Primer E (Clarke and Warwick 2001a) and Statistica (Statsoft. 2005).

Results

Hollow bearing trees A total of 6048 trees from 34 eucalypt species were recorded from the 45 sites (90 plots) (Table 3.2). More than 95% of all trees were one of 17 species, with only five species Lophostemon confertus, Corymbia intermedia, Eucalyptus carnea, Eucalyptus crebra and Eucalyptus propinqua making up 51% of the woody community in the urban forest patches. Corymbia intermedia was the most common species and was found at 98% of all sites. A total of 916 (14.9%) hollow-bearing trees were recorded, containing 2159 hollows across all sites, comprised of 24 eucalypt species including unidentifiable dead trees, with each site containing an average of six species of hollow- bearing tree species (Table 3.3). Most of which were L.confertus (142) followed by standing dead trees (124) and E. crebra (91) (Table 3.2). Along the urbanisation gradient 36% of hollow-bearing trees were contained within rural sites, 29% in control sites, 22% in urban and 11% in peri-urban sites (Table 3.2). Site area, the number of plots per site, number of hollows, hollow-bearing tree species and hollow density per site, reflected the variation in hollow availability along the urbanisation gradient (Table 3.3). Total area of all sites surveyed was 6650.9 ha comprising five control, seven peri- urban, 20 rural and 13 urban sites. Control and rural sites were found to have lower densities of hollow-bearing trees than both peri-urban and urban sites (Table 3.3) however, this was not significant (p = 0.967). Mean hollow-bearing tree density was found to be 37.47 (± 3.13 S.E.) hollow-bearing trees per hectare across all sites. Standing dead trees had the most hollows (15.4%) followed by L.confertus (11%) and E. propinqua (10.1%) (Table 3.4). Lophostemon confertus also represented 45% of all butt-hollows (Table 3.4). 53

Table 3.2: A summary of species dominance and hollow-bearing tree prevalence within eucalypt communities recorded from 45 forest patches along an urbanisation gradient. HBT’s = hollow-bearing trees.

n HBT’s by Gradient Category Tree species Common name N trees N sites N HBT’s Control Peri urban Rural Urban L.confertus Brush box 968 35 142 28 36 44 34 C.intermedia Pink bloodwood 778 44 64 6 5 35 18 E.carnea Broad-leaved white mahogany 563 30 76 21 1 42 12 E.crebra Narrow-leaved iron-bark 527 33 91 22 8 21 40 E.propinqua Small-fruited grey-gum 477 33 80 25 11 34 10 C.citriodora.v Spotted gum 417 23 54 10 4 29 11 C.gummifera Red bloodwood 347 16 35 23 2 10 0 E.tindaliae Queensland white stringy-bark 299 15 36 29 1 4 2 E.microcorys Tallowwood 293 26 71 22 5 38 6 L.suaveolens Swamp box 277 14 20 2 8 4 6 E.pilularis Blackbutt 216 17 29 0 0 8 21 E.siderophloia Grey-leaved iron-bark 162 15 15 7 1 3 4 E.racemosa Scribbly gum 114 6 36 14 6 0 16 Dead Dead 124 39 124 45 11 48 20 E.resinifera Red mahogany 96 10 1 1 0 0 0 A.woodsiana Rough barked apple 61 3 0 0 0 0 0 E.grandis Flooded gum 40 5 2 0 0 2 0 E.longirostrata Grey gum 40 4 0 0 0 0 0 C.trachyphloia Brown bloodwood 36 3 2 1 0 1 0 E.acmenoides White mahogany 30 6 8 8 0 0 0 E.eugenioides Thin-leaved stringy-bark 31 5 4 0 3 0 1 E.tereticornis Forest red gum 25 8 9 0 2 3 4 E.baileyana Bailey’s stringy-bark 26 1 4 4 0 0 0 E.major Grey gum 20 7 9 1 0 4 4

Table 3.2 continued: A summary of species dominance and hollow-bearing tree prevalence within eucalypt communities recorded from 45 forest patches along an urbanisation gradient. HBT’s = hollow-bearing trees.

n HBT’s by Gradient Category Tree species Common name N trees N sites N HBT’s Control Peri urban Rural Urban E.robusta Swamp mahogany 24 1 0 0 0 0 0 E.saligna Sydney blue-gum 14 2 1 0 1 0 0 C.henryi Large-leaved spotted-gum 11 3 1 0 1 0 0 E.moluccana Gum topped box 10 4 0 0 0 0 0 E.biturbinata Large-fruited grey-gum 10 4 1 0 0 1 0 C.tessellaris Moreton Bay ash 4 1 0 0 0 0 0 E.fibrosa Broad-leaved red iron-bark 3 1 0 0 0 0 0 E.seeana Narrow-leaved red-gum 3 2 0 0 0 0 0 E.dura Eucalyptus dura 1 1 0 0 0 0 0 S.glomulifera Turpentine 1 1 0 0 0 0 0 Totals 34 species 6048 916 269 244 194 209

Table 3.3: A description of hollow-bearing (HBT) tree attributes found across all sites sorted by gradient then area. Gradient: R= rural, U=urban, P=peri- urban, C=control. Control sites have also been classified in relation to their position along the urbanisation gradient.

No: N trees Dominant Dominant HBT Mean dbh of N HBT N HBT density Site name Gradient plots Area (ha) tagged species Species HBT's species N HBT's Hollows per hectare Nerang Forest Reserve C (p) 5 1668.76 387 E.carnea E.crebra 42.6 12 86 232 61.4 Karawatha Forest Reserve C (p) 7 824.33 400 E.tindaliae E.tindaliae 38.1 13 53 88 27.0 Bonogin Conservation Area C (u) 6 725.07 301 L.confertus L.confertus 51.2 8 57 99 33.9 Venman Bushland National Park C (p) 6 434.24 236 E.propinqua E.propinqua 52.5 11 40 152 24.4 Daisy Hill Forest Reserve C (u) 5 432.06 235 E.tindaliae Dead 41.6 8 33 67 23.6 Pimpama Conservation Area P 2 565.87 152 C.intermedia C. intermedia 21.5 1 1 1 1 Mt Nathan Conservation Area P 3 266.07 135 C.citriodora.v C.citriodora.v 41.0 6 28 63 33.3 Syndicate Road P 3 185.31 164 C.gummifera E.propinqua 60.4 11 33 73 39.2 Kirken Road P 1 176.87 68 E.racemosa E.racemosa 48.9 4 14 37 50.0 Woody Hill, Hart's Reserve P 2 91.08 117 L.confertus L.confertus 51.0 7 25 44 44.6 Galt Road P 1 33.67 41 E.microcorys E.propinqua 39.6 7 15 32 53.6 Elanora Wetland P 1 26.61 85 L.suaveolens L.suaveolens 42.8 3 8 11 28.6 The Plateau Reserve P 1 26.53 83 L.confertus L.confertus 28.1 8 26 55 92.9 Aqua Promenade P 2 13.10 188 C.intermedia E.propinqua 52.4 8 34 83 60.7 Elanora Conservation Area P 2 13.06 210 L.confertus L.confertus 46.3 8 31 68 55.4 Hellman Street P 1 9.15 53 C.intermedia C.citriodora.v 43.6 4 8 13 28.6 Reserve Road Parklands P 1 7.66 73 C.intermedia E.crebra 43.7 4 5 7 17.9 Piggabeen Road P 1 2.97 72 E.pilularis E.microcorys 45.5 3 4 5 14.3 Johns Road P 1 2.11 85 C.intermedia E.propinqua 38.5 6 12 16 42.9 Numinbah Road R 2 157.55 92 C.gummifera E.microcorys 43.1 6 16 34 28.6 Austenville Road R 3 24.98 318 L.confertus E.microcorys 64.2 6 29 67 34.5 Behms Road R 1 14.29 71 C.intermedia C.intermedia 58.9 4 11 41 39.3 Andrews Reserve R 1 10.82 72 E.pilularis E.pilularis 69.6 4 8 27 28.6 Carrington Road R 1 5.99 47 L.confertus E.propinqua 58.4 5 11 22 39.3

*highlighted areas represent the congruency between the dominant tree species found on a site and the dominant HBT species found on a site.

Table 3.3 continued: A description of hollow-bearing tree (HBT) attributes found across all sites sorted by gradient then area. Gradient: R= rural, U=urban, P=peri-urban, C=control. Control sites have also been classified in relation to their position along the urbanisation gradient.

No: N trees Dominant Dominant HBT Mean dbh of N HBT N N HBT density per Gradient plots Area (ha) tagged species Species HBT's species HBT's Hollows hectare Site name R 2 383.67 71 E.microcorys L.confertus 51.3 5 24 65 42.8 Wongawallen Conservation Area R 2 193.24 144 C.citriodora.v Dead 40.2 4 5 12 8.9 Upper Mudgeeraba Conservation Area R 2 53.32 203 E.crebra E.carnea 47.4 7 26 47 46.4 Trees Road Conservation Area R 1 50.87 111 L.suaveolens E.siderophloia 51.0 2 2 6 7.1 Stanmore Park R 3 44.85 178 L.confertus E.carnea 52.5 8 22 46 26.1 Reedy Creek Conservation Area R 1 14.24 93 L.confertus E.microcorys 47.8 6 13 32 46.4 Tallowwood Road R 1 5.10 51 E.microcorys E.microcorys 77.3 5 16 70 57.1 Tomewin Road R 1 4.04 69 L.suaveolens C.citriodora.v 36.2 6 10 14 35.7 Gilston Road U 2 38.94 134 A.woodsiana E.pilularis 60.0 2 2 3 3.6 Olsen Avenue U 1 27.32 86 L.confertus L.confertus 57.3 4 26 68 92.8 Burleigh Headland National Park U 3 17.89 159 L.confertus E.crebra 52.8 5 34 102 40.5 Burleigh Ridge U 2 16.89 205 L.confertus E.pilularis 60.1 8 33 92 58.9 Pacific Highway, Currumbin U 2 16.87 140 C.citriodora.v E.carnea 38.5 6 22 50 39.2 Brushwood Ridge Reserve U 3 16.45 198 C.intermedia E.propinqua 50.3 8 23 56 27.4 Herbert Park U 1 14.38 67 E.pilularis E.pilularis 78.4 4 9 14 32.1 Tugun Conservation Area U 1 9.53 52 E.microcorys E.microcorys 54.3 6 14 28 50.0 Summerhill Court Reserve U 1 7.52 50 L.confertus L.confertus 41.8 3 5 5 17.9 Kincaid Road Reserve U 1 5.89 82 E.crebra L.confertus 20.2 2 6 6 21.4 Pacific Pines Reserve U 1 4.41 126 E.carnea L.confertus 34.9 4 5 6 17.9 Miami Conservation Area U 1 4.15 66 E.racemosa E.racemosa 67.0 3 25 91 89.3 Burleigh Knoll National Park U 1 3.23 78 C.intermedia E.pilularis 39.3 5 6 9 21.4 Lakala Street Reserve Mean n of HBT Mean of HBT species density per Mean dbh across across all hectare across all 14 dominant all sites sites 5.7 sites Total 45 92 6650.9 6048 species 13 HBT species 48.5 (±1.84 S.E.) (±0.4 S.E.) 916 2159 37.47 (±3.13 S.E.) *highlighted areas represent the congruency between the dominant tree species found on a site and the dominant HBT species found on a site.

Table 3.4: Summary of trees species and hollow type with the total number of hollows found across all sites.

Tree species Bayonet Branch end Branch main Butt Trunk main Trunk top Fissure Total Dead 113 119 1 27 24 34 12 330 L.confertus 51 38 0 122 12 4 9 236 E.propinqua 104 93 4 6 8 2 0 217 E.crebra 106 74 0 4 3 0 4 191 E.microcorys 94 57 5 20 6 1 5 188 E.carnea 80 51 1 25 9 4 4 174 C.citriodora.v 81 49 3 10 7 5 2 157 E.racemosa 38 42 6 8 8 2 1 105 C.intermedia 38 34 0 7 4 0 1 84 C.gummifera 49 21 0 3 5 2 0 80 E.tindaliae 40 26 1 8 3 1 1 80 E.pilularis 20 36 1 11 3 0 2 73 E.tereticornis 17 36 1 3 2 0 1 60 L.suaveolens 23 12 0 9 2 0 1 47 E.siderophloia 24 11 0 0 2 0 0 37 E.major 16 6 0 1 0 0 0 23 E.grandis 3 15 0 0 0 1 0 19 E.acmenoides 3 5 2 3 1 1 0 15 E.resinifera 8 3 0 2 0 0 0 13 E.baileyana 2 2 0 0 1 0 0 5 C.trachyphloia 1 4 0 0 0 0 0 5 E.eugenioides 2 0 0 0 0 0 0 2 E.moluccana 1 0 0 0 0 0 0 1 C.henryi 0 1 0 0 0 0 0 1 Total 914 735 25 269 100 57 43 2143

Small hollows within the 10 cm size category were the most abundant in all hollow-bearing trees comprising 50.4% of all hollows detected (Figure 3.4). The relationship between hollow size and type was also significant (F = 114.4; d.f. = 6, 2136; p < 0.01), with all hollow types differing from each other (Figure 3.5). Trunk top hollows were larger than fissures and bayonets were smaller than trunk-main, branch-main, butt and branch end hollows. The relationship between tree form and the presence of a hollow was significant (F = 3.4; d.f. = 6, 5917; p < 0.01) in that forms one and two were significantly different and contained more hollows than forms 3-7. Over 69% of hollow-bearing trees were found to range between dbh 30-60 cm, mean 49.3 cm (± 0.75 S.E.), while 78% of non-hollow bearing trees ranged between dbh 20-40 cm, mean 26.8 cm (± 0.18 S.E.) and the mean dbh range for all trees was 30 cm (± 0.22 S.E.) range 10-159 cm (Figure 3.6). This difference between the dbh of trees with hollows and those with no hollows present was significant (t = - 28.75; d.f. = 879; p < 0.01). Trees with a larger dbh were also found to have more hollows (R2 0.137; p < 0.05).

1200

1000

800

600

No. No. of hollows 400

200

0 10 15 20 25 30 35 40 45 50 60 65 70 80 90 100 150 Hollow size (cm)

Figure 3.4: The number of hollows per hollow size category.

Hollow size (cm) 20

5 Butt Branch-end Bayonet Fissure Trunk-main Trunk-top Branch-main Hollow type

2000

1800

1600

1400

1200

1000 Hollow-bearing trees 800 All trees

No of trees surveyed 600

400

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dbh category (cm)

The impact of environmental variables, disturbance and the urbanisation gradient on hollow- bearing tree species abundance and hollow type Hollow type was not influenced by the urbanisation gradient (F = 0.474; d.f = 18, 280; p = 0.97), however, there was a difference in the number of hollow types more generally in that there were significantly more butt, branch end and bayonet hollow types found (F = 35.8; d.f = 18, 280; p = < 0.05) (Figure 3.7). Site area influenced the pattern within hollow-bearing tree communities. Area of forest patches was strongly linked to control sites (PCO axis 2), while the urbanisation gradient appeared to discriminate urban sites from non-urban sites (PCO axis1) (Figure 3.8). There was no significant difference in the density of hollow- bearing trees among sites along the urbanisation gradient (F = 0.118; d.f. = 3, 41; p = 0.95).

0.7 Control 0.6 Peri urban Rural Urban 0.5

0.4

0.3

0.2

0.1

0.0 Relative abundance of hollow type

-0.1

-0.2 Butt Bayonet Trunk main Branch main Branch end Fissure Trunk top Hollow type Figure 3.7: The relationship between hollow type and the urbanisation gradient (mean ± S.E.).

Discussion The distribution and abundance of habitat features is an important driver of community diversity particularly in areas where species are heavily dependent on structurally complex features (Woinarski et al. 1997; Tanabe et al. 2001). Therefore, the prevalence of hollow- bearing trees in an urbanising landscape may be pivotal to ensuring the persistence of urban diversity. This study found that the density of hollow-bearing trees across all sites averaged approximately 37.47 HBTs/ha but ranged from 1-92.9 HBTs /ha. This figure is considerably higher than the minimum number (4–6 /ha) recommended for managed forests in south-east Queensland (Lamb et al. 1998). This figure is also higher than the 5.8 HBTs/ha found in the only other Australian study on hollow-bearing tree density within an urban landscape (Harper et al. 2005a). However, this average density of hollow-bearing trees may overestimate the

availability of hollows, particularly in terms of their use by fauna, as small, bayonet hollows accounted for 50.4% of all hollows found. While small hollows are a valuable resource (Kehl and Borsboom 1984; Mackowski 1984; Ross 1998; Eyre 2005; Wormington et al. 2005), hollows with a diameter > 10 cm are necessary to meet the requirements of larger hollow-using species (Murphy et al. 2003).

Of the 916 hollow-bearing trees found, standing dead trees were represented by 13.5% (n124) of hollow-bearing trees found, which is substantially lower than the 50% reported for production forests in south-east Queensland (Moloney et al. 2002). The high representation of standing dead trees found during that particular study was likely to have been in response to extensive silvicultural practices including poisoning and ringbarking carried out in these forests (Moloney et al. 2002). While Moloney et al. (2002) suggest that live hollow-bearing trees are of greater importance to wildlife in Australia, standing dead trees are important habitat features. This is widely supported more generally with certain species displaying a preference for stags as denning and nest sites (Lindenmayer et al. 1991a; Eyre 2004; Cameron 2006). Tree-form was found to be related to a tree being hollow-bearing, as forms one and two were significantly different to forms 3-7. This study is consistent with previous studies established that hollows are most likely to occur in large trees with moderately senescent crowns (Whitford 2002). However, fauna have been observed on more occasions (45%) leaving hollows found in the crowns of trees in forms one and two than other forms (Smith and Lindenmayer 1988; Lindenmayer et al. 1991a). This study found that live hollow-bearing trees were found to contain more hollows than standing dead trees. This could be due to the modification of tree senescence from Whitford’s (2002) classification to focus on living trees as opposed to dead trees.

In general, standing dead trees have a higher likelihood of containing hollows than live trees (Eyre 2005; Harper et al. 2005a; Beyer et al. 2008). The persistence of these dead trees across the landscape, however, is likely to be less than that of the living cohort of hollow- bearing trees due to the impacts of fire and wind. Within the dry sclerophyll forests of south east Queensland, the longevity of standing dead trees has been estimated at ~50 years (Moloney et al. 2002), which is significantly less than the time taken for living trees to reach maturity and form hollows (Wormington et al. 2003). Dead trees are also highly susceptible to destruction by fires in dry forests (Lamb et al. 1998). Fire did not appear to be an important factor in influencing distribution of hollow-bearing trees in this study and may

actually be suppressed in urban forest patches given the safety concerns associated with fires and human settlement in Australia (Shea et al. 1981; Jurskis et al. 2003). Consequently, even though standing dead trees are less abundant in urban forest patches they may persist for longer periods than those in managed forests.

Lophostemon confertus dominated the woody tree communities across urban habitat forest patches and were also well represented as hollow-bearing trees. There was a significantly greater prevalence of butt hollows in L.confertus than any other species. Lophostemon confertus is wind dispersed (Lee et al. 2008) and is an early coloniser in rainforests where fire occurrence is low (Burrows 2002). As fire has largely been removed from the urban and peri-urban landscape it is a species that appears to be dominating these sites. Unlike eucalypt species where bark thickness ranges between 13-35 mm, L. confertus with a maximum bark thickness of 3 mm has a reduced regenerative potential, making them susceptible to fire damage (Burrows 2002). This may facilitate the formation of butt hollows in larger L.confertus that have been repeatedly burnt. Furthermore, given that butt-hollows are located at ground level, the likelihood of predation on hollow-using vertebrate fauna would be high, thus making them a less than desirable choice for denning. However, two common brushtail possums Trichosurus vulpecula and a Scaly-breasted Lorikeet Trichoglossus chlorolepidotus were observed leaving small butt-hollows located within L.confertus and Corymbia gummifera respectively, during the course of this study.

Diameter at breast height for hollow-bearing trees ranged from 10-160 cm across all sites, but the mean dbh for hollow-bearing trees was significantly larger than that for the entire wooded community within urban forest patches. These findings support those of previous studies confirming that larger, older trees have a greater likelihood of containing hollows (Wormington et al. 2003; Cameron 2006; Koch et al. 2008b), as well as having a greater number of hollows per tree (Whitford 2002; Wormington et al. 2003). The size class distribution of hollow-bearing trees was also different to that of non-hollow trees with the latter appearing to be of a relatively even age, (78% of all trees found within the 20-40 cm dbh range). This potentially reflects the intense logging history of the region (Chapter 1) where historical forestry practices removed most large mature trees from forest patches and entire fragments were cleared for housing within urban areas. With 92% of Australians living in cities, urban fragments play a critical role in conserving biodiversity (Manning et al. 2006). Town planners should then be able to gain a basic understanding of the patterns and

processes that affect biodiversity as a whole when investigating urban ecosystems (Alvey 2006). Additional options will then become available for the effective management of urban bushland in order to develop an integrated management approach that places conservation reserves in the context of the overall landscape (Saunders et al. 1991). A historical perspective is critical at the site-specific scale as sites that have undergone anthropogenic modifications require an understanding of their current ecology to predict future trends (De Wet et al. 1998).

In this current study, the ‘urbanisation gradient’ (45.5%) was found to be having a stronger effect on hollow-bearing tree density than ‘site area’ (33.1%) supporting the original hypothesis put forward for this chapter, that the urbanisation gradient was having an impact on hollow-bearing tree density. These results are consistent with other studies assessing biodiversity patterns across urban gradients, in that the gradient strongly influences biota (Williams et al. 2005a; McKinney 2006; Brady et al. 2009), and, sites in urban centres appeared more depauperate of species than those in peri-urban or rural areas (Blair 1999; Cornelius and Hermy 2004; Alvey 2006). A structurally complex matrix within a human- modified landscape, however, can provide habitat resources and increase species richness in modified landscapes (Brady et al. 2011).

The urbanisation gradient was found not to have an effect on the type of hollow found. Given that small bayonet type hollows represented 42% of all hollow types along the gradient, it is conceivable that there is a shortage of the larger hollow types required to meet the needs of some vertebrate species. Therein, the somewhat controversial option to address this shortage would be the installation of species appropriate artificial nest-boxes (Goldingay et al. 2007). This is a hotly debated issue (Lindenmayer et al. 1991c; Spring et al. 2001; Harley and Spring 2003; Lindenmayer et al. 2009) as the use of nest boxes has been questioned due to installation and maintenance costs, monitoring and nest-box deterioration. The use of next-boxes in large scale forestry situations has been questioned due to significant logistic constraints and here the retention of hollow-bearing trees is likely to be more viable in the long-term (Lindenmayer et al. 1997; 2002). In contrast, nest-box use has been found to be higher in urban remnants (18.2%) (Harper et al. 2005b; Davis et al. 2013) than in large contiguous forests (7%) (Menkhorst 1984). There are two possible explanations for nest box use patterns. The first is that it reflects a higher abundance of natural hollows in larger and more contiguous forests than urban forest patches. The second is that fauna have become

relatively isolated in fragmented urban forest patches resulting in community overcrowding. There is some support for this from recent work documenting aggression in hollow-nesting birds in the Sydney region of Australia (Davis et al. 2013). However, an understanding of hollow-bearing tree resource availability, the presence of species that require hollow-bearing trees and their species specific hollow requirements is required prior to introducing nest- boxes into any environment (Harper et al. 2005b; Davis et al. 2013).

The Gold Coast as a city with its own unique set of geographical, historical and economic factors, will have different results than cities located in different regions not only in Australia but globally as well. This variation in results between study sites have been well documented in a review of 105 studies across urbanisation gradients (McKinney 2008). From results presented here it seems that many forest patches on the Gold Coast have been left relatively in-tact across the gradient, and have over time, gradually been reduced in size rather than cleared. This may be due to the rapid urbanisation that the Gold Coast has undergone in the last few decades (Spearbritt 2009).

With the understanding of the complexity of hollow-bearing tree management and amelioration across the landscape, inevitably comes the acknowledgement of the time lag between hollow formation and availability. Therefore, retaining the current inventory of hollow-bearing trees is paramount to maintaining biodiversity (Eyre et al. 2010). However, the conservation of hollow-bearing trees alone will only answer the immediate need for this resource, it is therefore imperative to plan for future needs by conserving regrowth as recruitment trees for the long-term sustainability of hollow-bearing trees (Gibbons and Lindenmayer 2002).

Chapter 4: The impacts of landscape variables on hollow-bearing trees along an urbanisation gradient.

Introduction Second only to agriculture, urbanisation is the single most damaging, persistent and confounding form of anthropogenic pressure exerted upon natural systems globally (Vitousek 1997; McKinney 2006; Gaston 2010a). Within Australia, approximately 50% of forests have been lost or severely modified since European settlement, with over 80% of eucalypt forests having suffered from anthropogenic activities, leaving much of Australia’s remaining forest severely fragmented (Bradshaw 2012). In heavily settled regions, landscapes continue to be fragmented due to urbanisation. Consequently, urban bushlands become ‘stepping stones’ within the landscape allowing certain species to move between urban, suburban, rural and forested areas (Manning et al. 2006). The processes driving fragmentation are greater in smaller patches, as they are more likely to be altered by changed environmental parameters (such as urbanisation) to a greater degree than large patches (e.g. edge effects). Such shifts may have implications for habitat structure, ecosystem function and food web interactions in small remnant forest patches (Morgan and Farmilo 2012). Thus, the dynamics of forest patches are principally driven by features from the surrounding landscape (Rowntree 1988; Ewers and Didham 2006a; Morgan and Farmilo 2012). Therefore, the management of, and research on, fragmented ecosystems should be directed at understanding and controlling these external influences as much as the biota of forest patches themselves. This is particularly significant for habitats containing species that utilise specialised resources such as hollow-bearing trees (Lindenmayer 2002; Rowston et al. 2002; Manning et al. 2006).

The urban landscape however, is never uniform, with impacts determined by the type and intensity of the land uses found within it (Brady et al. 2009). These impacts vary with the time since isolation (Saunders et al. 1991), distance from other remnants (Luck and Daily 2003) and degree of connectivity with other remnants (Lindenmayer and 69

Fischer 2006). Consequently, the conservation of hollow-bearing trees within the urban landscape is essential for maintaining ecosystem function by providing areas of refugia and ecosystem services through the conservation of biodiversity in general (McKinney 2006).

Urbanisation intensity correlates with increased disturbance and the structural simplification of remaining vegetation (McKinney 2008). However, it has been found that a complex matrix within a human-modiﬁed landscape can provide supplementary habitat resources (Brady et al. 2011; Threlfall et al. 2011). Thus, the urban landscape can be of benefit to biodiversity when each site is assessed individually (McKinney 2008). Individual species traits also determine how well a species adapts to urbanisation (Luck and Smallbone 2010). So it is important to consider differing species responses to landscape change, to move beyond focusing primarily on spatial attributes (size, isolation), and recognise that landscape change is the most important variable to consider when examining functionality (Holland and Bennett 2009).

The functional ecology of the urbanisation gradient, and specifically that pertaining to the availability of hollow-bearing trees, is what is being investigated in this chapter. In doing so, the ecology in and the ecology of urban areas was explored. The ecology in urban areas is closely affiliated with traditional ecology, in that it investigates ecological patterns and processes in urban areas. The ecology of urban areas is concerned with how those systems function as a whole. Thus, this system-oriented approach will deliver a deeper insight into the role that urbanisation gradients play in conservation management (Gaston 2010a). This chapter quantifies the impacts of urbanisation, landscape and environmental variables on hollow-bearing trees within urban forest patches. The significance of these findings in regards to rapidly changing landscapes, such as those found along the urbanisation gradient, will be of benefit to managers and planners when making decisions about where, and how to best manage for hollow- bearing trees in an urban matrix.

Methods Site establishment and the categorisation of sites along the urbanisation gradient were completed as outlined in Chapter 3. The urbanisation gradient was used to understand

the impacts of urbanisation on hollow-bearing trees. Landscape and environmental variables used for this study along this gradient were chosen to assess the importance of these potential driving forces on forest structure within forest patches (Tables 4.1 - 4.3).

Table 4.1: The urbanisation index, hollow-bearing tree, landscape and environmental variables quantified at each site.

Variable Description of variable Urbanisation Index Calculated as a percentage of site buffer transformed by housing and infrastructure and normalised by the density of housing (Chapter 3 Equation 3.3). Hollow-bearing tree variables Density of hollow-bearing trees per hectare per site. Stand density Stand density was calculated by dividing the sum of all trees per plot by the surveyed area of each plot (0.28 ha). Landscape variables Fire history Number of fire events from historical aerial imagery over time (1955-2009). Logging history Number of logging events from historical aerial imagery over time (1955-2009). Connectivity (%) Percentage of site perimeter connected to other forested areas. Environmental variables Angle of slope Calculated from GIS layers (Chapter 3 Equation 3.2) Aspect (°) Directional orientation. Elevation (m) θ Obtained from GIS mapping layers.

Table 4.2: Landscape variables examined in association with hollow-bearing tree density from 45 urban forest patches sites. Values in brackets represent where a site is located along the urbanisation gradient. Standard errors are supplied for sites with multiple plots. Urbanisation index: urban = high value, rural = low value.

No: of HBT density Urbanisation Area Connectivity Site name plots (ha) Index (ha) (%) 1 Andrews Reserve (r) 28.57 5.22 10.8 44.35 2 Aqua Promenade (p) 60.71 ± 10.15 32.29 13.1 38.80 3 Austenville Road (r) 34.52 ± 8.5 0.02 24.9 71.95 1 Behms Road (r) 39.29 6.94 14.2 0.00 6 Bonogin Conservation Area (c/u) 33.92 ± 7.8 157.16 725.1 73.01 2 Brushwood Ridge (u) 39.28 ± 3.51 522.96 16.8 0.00 1 Burleigh Headland NP (u) 92.85 441.57 27.3 0.00 1 Burleigh Knoll NP (u) 85.71 1412.99 4.1 0.00 2 Burleigh Ridge (u) 53.56 ± 35.71 3195.78 17.89 0.00 1 Carrington Road (r) 39.29 4.18 5.9 77.38 5 Daisy Hill Forest Reserve (u) 23.56 ± 6.6 133.43 432.1 49.97 2 Elanora Conservation Area (p) 71.48 ± 7.14 150.88 13.1 50.62 1 Elanora Wetlands (u) 28.57 90.08 26.6 0.00 1 Galt Road (p) 53.57 115.88 33.6 73.38 1 Gilston Road (r) 35.71 10.23 4.04 85.62 1 Hellman Street (p) 28.57 212.51 9.1 0.00 3 Herbert Park (u) 28.56 ± 7.43 393.57 16.4 0.00 1 Johns Road (r) 39.28 32.50 2.1 45.10 7 Karawatha Forest Reserve (c/p) 25.5 ± 4.63 1299.21 824.3 7.61 1 Kincaid Park (u) 17.86 83.34 7.5 0.00 1 Kerkin Road (r) 42.85 845.68 176.9 17.55 1 Lakala Street Reserve (u) 21.43 760.06 3.2 0.00 1 Miami Conservation Area (u) 10.71 1178.50 4.4 0.00 3 Mt Nathan Conservation Area (r) 33.33 ± 7.8 21.84 266.1 16.52 5 Nerang Forest Reserve (c/p) 61.43 ± 7.93 382.66 1669 15.08 2 Numinbah Road (r) 28.57 ±10.71 3.98 157.6 53.25 2 Olsen Avenue (u) 7.14 807.23 38.9 0.00 2 Pacific Highway, Currumbin (u) 53.57 ± 0 1741.30 16.8 0.00 1 Pacific Pines Parkland (u) 21.42 415.38 5.89 0.00 1 Piggabeen Road (p) 14.28 32.21 2.9 41.60 2 Pimpama Conservation Area (r) 3.57 16.99 565.8 1.28 3 Reedy Creek Conservation Area (r) 26.19 ± 11.72 19.69 44.8 63.74 1 Reserve Road Parklands (p) 17.85 276.44 7.6 0.00 1 Stanmore Park (r) 7.14 15.72 50.8 2.66 1 Summerhill Court Reserve (u) 50 793.33 9.5 0.00

Table 4.2 Continued: Landscape variables used to quantify the impacts of urbanisation on hollow-bearing tree density from 45 urban forest patches. Values in brackets represent where a site is located along the urbanisation gradient. Standard errors are supplied for sites with multiple plots. Urbanisation index: urban = high value, rural = low value.

No: of HBT density Urbanisation Area Connectivity Site name plots (ha) Index (ha) (%)

Syndicate Road (p) 3 39.28 ± 10.71 22.25 185.3 84.93 Tallowwood Road (r) 1 50 2.46 14.2 86.08 The Plateau Reserve (p) 1 92.85 21.06 26.5 24.83 Tomewin Road (r) 1 57.14 17.17 5.1 47.14 Trees Road Conservation Area (r) 2 44.64 ± 12.49 13.31 53.3 81.82 Tugun Conservation Area (u) 1 32.14 684.35 14.3 0.00 Upper Mudgeeraba Conservation Area (r) 2 8.93 ± 1.78 3.02 193.2 92.33 Venman Bushland National Park (c/p) 6 25 ± 5.9 49.52 434.2 86.96 Wongawallen Conservation Area (r) 2 30.35 ±26.78 19.12 383.7 83.28 Woody Hill, Hart's Reserve (p) 2 44.6 ± 12.5 358.72 91.1 0.00

Table 4.3: Environmental variables examined for associations with hollow-bearing trees in each of the 45 forest patches. Standard errors are supplied for sites with multiple plots.

Slope Elev. Stand density Fire Logging Site Aspect (radians) (m) (# trees ± S.E.) index index Andrews Reserve 0.24 -0.17 50 257.14 4.55 0.38 Aqua Promenade Reserve 0.38 -0.04 62 323 ± 41 2.27 0.38 Austenville Road 0.44 0.60 266 370.23 ± 44 4.55 0.21 Behms Road 0.05 0.98 10 242.86 0 0.38 Bonogin Con Area 0.24 0.17 50 175 ± 25 13.64 0.31 Brushwood Ridge Parklands 0.00 1.00 0 242.85 ± 68 2.27 0.38 Burleigh Heads National Park 0.34 -0.51 210 303.57 0 0 Burleigh Knoll Con Park 0.31 -0.21 45 232.14 2.27 0 Burleigh Ridge Park 0.43 0.70 77 273.21 ± 45 2.27 0.31 Carrington Road 0.03 0.64 225 167.86 6.82 0.21 Daisy Hill Con Area 0.08 -0.24 95 158.57 ± 5.5 4.55 0.21 Elanora Con Reserve 0.24 0.00 62 373.21 ± 112.5 0 0.38 Elanora Wetlands 0.00 1.00 0 303.57 0 0.31 Galt Road Park 0.19 -0.64 200 142.86 4.55 0.44 Gilston Road 0.12 -0.17 70 242.86 2.27 0 Hellman Street 0.12 0.98 50 182.14 2.27 0.38 Herbert Park 0.31 0.10 36 233.33 ± 28 2.27 0.21 Johns Road 0.40 0.98 40 300 2.27 0 Karawatha Forest Reserve 0.08 0.00 53 200 ± 40 4.55 0.6 Kincaid Park 0.17 0.94 80 185.7 0 0.38 Kerkin Road 0.05 -0.77 5 225 4.55 0.38 Lakala Street Reserve 0.16 1.00 15 275 0 0.21 Miami Bushland Con Park 0.05 -0.98 20 450 0 0 Mount Nathan 0.41 0.13 193 153.5 ± 9 0 0.38 Nerang State Forest 0.19 0.01 80 271.4 ± 45 2.27 0.31 Numinbah Road 0.32 0.36 180 157.1 ± 11 13.64 0.21 Olsen Avenue 0.07 0.50 20 239.2 ± 11 15.9 0.5 Pacific Highway 0.22 -0.34 47 360.7 ± 3.6 4.55 0.31 Pacific Pines Parkland 0.10 1.00 10 239.86 2.27 0 Piggabeen Road 0.00 1.00 2.5 257.14 0 0 Pimpama Con Park 0.31 0.71 65 271.43 ± 78 6.82 0.44 Reedy Creek 0.33 0.17 60 208.33 ± 56 2.27 0.38 Reserve Road Parklands 0.30 -0.50 69 260.71 0 0.5 Stanmore Park 0.17 0.64 75 396.43 0 0.5 Summerhill Court Reserve 0.05 -0.99 30 178.57 0 0 Syndicate Road 0.21 -0.17 55 192.85 ± 29 0 0.21 Tallowwood Road 0.18 1.71 223 321.43 2.27 0.31 The Plateau Reserve 0.24 -0.17 100 275 2.27 0.38 Tomewin Road 0.44 -0.34 135 178.57 0 0 Trees Road Con Area 0.27 0.01 112 360.7 ± 50 0 0.31 Tugun Conservation Park 0.14 0.50 55 239.29 2.27 0.31 Upper Mudgerabah Cons Area 0.38 0.20 165 253.57 ± 46 2.27 0.21 Venman National Park 0.02 0.48 83 134.52 ± 17 4.55 0.44 Wongawallen 0.45 -0.47 285 125 ± 7 4.55 0.38 Woody Hill 0.01 -0.41 8 205.35 ± 30 0 0

Data analysis Predictor variables known to have strong biological influences were presented for analysis based on a priori investigations from within the literature (Table 4.1). A Spearman’s Rank correlation matrix examined colinearity among variables. Variables that had a correlation coefficient value |r| > 0.7 were considered to be highly correlated (Garden et al. 2010). Mean distance to nearest fragment edge was highly correlated to area (|r| = 0.768) and was removed from further analysis. The decision to retain area over mean distance to nearest fragment edge from plot for inclusion was based on the assumption that area would be more ecologically meaningful for analysis. All other variables were included in subsequent analysis.

Transformation of data was undertaken for two reasons (i) to meet the requirements for homogeneity of variance, i.e. to limit the dominance of outliers and (ii) due to differing scales of data. Arcsine square root transformations were undertaken for explanatory variables with percentage values; while a logarithmic (x+1) transformation was undertaken for all other explanatory variables requiring transformation due to scale differences. Aspect data was transformed using Tan and slope was converted to radians prior to analysis. No transformations were made to the response variable θ hollow-bearing tree density.

The general rule of n/3 (where n = number of sites sampled) was used to determine the maximum number of predictor variables to use for all models (Crawley 2007). The relationship between the response variable (hollow-bearing tree density) and the explanatory variables was examined using pairwise plots to ascertain the strength of the relationship and whether it was linear. Eight predictor variables were modelled to examine their relationships with hollow-bearing tree parameters and those were; stand density, fire history, logging history, connectivity, angle of slope, aspect, elevation and the urbanisation index. Poisson Generalised Linear Mixed Effects Models (GLMM) were used for the analysis; as plots were nested within sites. Interactions between predictor variables were therefore also analysed. The Poisson distribution was chosen as it is generally used for count data. The main advantages are (i) the probability for negative values is 0 and (ii) the mean variance relationship allows for heterogeneity (Zuur et al. 2009). 75

Twenty-eight candidate models including the global model were run to assess landscape and environmental parameters (Appendix 5). An auto-correlation structure was included into the models to account for site variation. Models with random effect of site were then compared using Akaike’s Information Criterion (AIC) (Akaike 1973) with the ‘best’ model having the lowest AIC value (Burnham and Anderson 2002). The interaction of logging and elevation were included as a model as they were the two strongest performing variables when modelled individually. An interaction represents non-additivity in the combined effects of the two interacting factors. The net outcome of the two processes is significantly more (a synergistic effect) or less (an antagonistic effect) than the sum of the two processes operating independently (Didham et al. 2007b).

Models were ranked according to their Δi values. The higher the Δi value, the less accurate the model for the given data. The best approximating models that had a Δi ≤ 4 are presented. Because of uncertainty in some of the final model selections, a model averaging approach was applied and variables were ranked according to their relative overall importance by summing the Akaike weight (wi) from all top model combinations where each of the variables occurred. Statistical analyses were conducted using R 3.1 statistical software (R Core Team 2012) and the glmmML (Brøstrom 2012) and MuMIn package (Barton 2012).

Results Analysis of landscape and environmental variables revealed that the model containing the logging index, elevation and the interaction of the logging index*elevation had the best fit (Table 4.4). Logging and elevation were both negatively correlated with the density of hollow-bearing trees (Table 4.4). Parameter estimates for the approximate importance of landscape and environmental variables associated with the density of hollow-bearing trees per hectare revealed that logging was the most important variable. Overall the only coefficient estimates that represented a good fit was the interactive model of the logging index*elevation; all other coefficient estimates did not perform as well (Table 4.5). The urbanisation index was not identified in any of the best performing models, nor did it rank highly in the assessment of the relative importance of parameters influencing hollow-bearing tree density. 76

Table 4.4: Best approximating model comparisons of explanatory landscape and environmental variables associated with the density of hollow-bearing trees. Akaike

models with ∆i values < 4 are presented. The next model (AICc > 4) demonstrates the distance between the two best models and the third.

Model AICc ∆i wi logging index + elevation + (logging index * elevation) 247.54 0.00 0.49 logging index + elevation 250.70 3.15 0.10 logging index + elevation + stand density 251.87 4.33 0.05

Standard Confidence interval Relative variable Parameter Estimate error 2.5% 97.5% importance logging index -2.900 1.857 -6.541 0.740 0.91 elevation -0.063 0.143 -0.344 0.217 0.77 logging history*elevation 0.857 0.355 0.160 1.554 0.50 stand density 0.000 0.000 -0.000 0.001 0.12 fire index -0.008 0.014 -0.035 0.019 0.11 slope 0.250 0.502 -0.734 1.236 0.09 aspect 0.095 0.073 -0.047 0.239 0.05 connectivity 0.002 0.003 -0.004 0.009 0.01 urbanisation index 0.057 0.061 -0.063 0.177 0.01

Discussion Plant community composition is a product of environmental and anthropogenic disturbance regimes (Williams et al. 2005a). Therein, the logging index was found to be a driving factor influencing hollow-bearing density along the urbanisation gradient on the Gold Coast. When the interaction model, logging index*elevation was included it was found to have the greatest effect, in that hollow-bearing tree density decreased where there was increased logging at lower elevations. Given the intensive logging history of south-east Queensland (Chapter 1) it is not surprising that the logging index is the variable of most importance when assessing hollow-bearing trees across the landscape. Previous studies consistently show that the number of hollow-bearing trees is negatively correlated with logging (Lindenmayer et al. 1990; Gibbons and Lindenmayer 1996; Ball et al. 1999; Ross 1999; Lloyd et al. 2006; Law et al. 2013). Importantly, the logging index as well as a number of other environmental parameters was more influential than the urbanisation index. This suggests that on the Gold Coast the effects of urbanisation are not yet discernible within the hollow-bearing tree communities. Previous studies reporting on the impacts of urbanisation gradients tend to focus on specific landscape parameters such as; individual patch dynamics (Blair 1999), land use type (Brady et al. 2009), fragmentation (Crooks et al. 2004) and vegetation communities (Hahs and McDonnell 2007) to name a few. Although historical land use is often mentioned as a variable of importance for biodiversity at a landscape scale (McDonnell et al. 1997; Moffatt et al. 2004; Hahs and McDonnell 2007; Brady et al. 2009), the current study is the first of its kind to quantify the impacts of logging history on hollow-bearing trees, as well as its relative importance compared to other variables along an urbanisation gradient.

These findings are consistent with the view that land use history will significantly impact on the function and dynamics of forest patches (Gibbons and Lindenmayer 2002); with the density and formation of hollow-bearing trees being dependent on site logging history. Logging history in general has resulted in a complex matrix of remnant forest patches (Gibbons and Lindenmayer 2002). At a site level, many of the smaller, highly urbanised sites on the Gold Coast contain high densities of hollow-bearing trees which have remained relatively undisturbed over time (e.g. Burleigh Knoll National Park). They have, however, contracted in size, thus leaving many mature hollow- bearing trees in small remnant forest patches along the gradient. This suggests that for 78

hollow-bearing trees there is a lag response to disturbance given that these resources are relatively long lived. As such the impacts of more recent urbanisation processes may not yet be evident. Conversely, all control sites in this study are ex-forestry lots and have a relative recent logging history, with some sites being logged as recently as the 1990’s (unpublished data. The Department of Environment and Heritage). This is reflected by low numbers of hollow-bearing trees and an evenness in the size class of trees found within these forests, with a mean dbh of 48.5 cm (± 1.84 S.E.) (Chapter 3).

After accounting for logging history and elevation; stand density was the next most important variable in the final models. The stand of trees is the scale at which specific forestry management actions are usually implemented (O'Hara 1998). Therefore, assessing how stands respond to either external or internal factors may be central to the development of adaptive management strategies to improve the functional integrity of urban forest remnants. This could be done by enhancing forest condition through the implementation of protection and restoration initiatives (Garden et al. 2010). Additionally, the arrangement of regrowth forest in the landscape may also aid in remnant forest patch recovery, as connectivity to larger contiguous forests improves stand structure (Bowen et al. 2009; Garden et al. 2010) but also movement of fauna between fragments (Laita et al. 2011). Regrowth and recruitment are therefore both vitally important in ensuring the persistence of particular habitat features such as hollow-bearing trees at the stand level and was the focus of Chapter 5.

The general importance of landscape and environmental variables as drivers of biodiversity has been acknowledged in the literature (Saunders et al. 1991; Lindenmayer et al. 2000b; Ewers and Didham 2006a; Lindenmayer and Fischer 2006; Gaston 2010a). The theoretical management of fragmented landscapes therefore has two components; i) management of the internal dynamics of remnant forest patches, and ii) management of the external influences. It has been suggested that the management of larger reserves focus on the internal dynamics and for smaller forest patches management should focus on the external influences (Saunders et al. 1991). It has also been hypothesised, that external influences are important no matter the size of the forest patch (Janzen 1986). Which supports the findings from this study, in that logging

history was what has been found to determine the vegetation structure and therefore, hollow-bearing trees along the urbanisation gradient on the Gold Coast.

Historical land use practices and their effects have long-lasting impacts on the environment, driving habitat structure and function over time. Whether hollow-using vertebrate fauna utilise these forest patches depends on the availability of specific resources but also on the complexity of the surrounding matrix (Chapter 6). Thus, the long-term viability of native flora and fauna relies upon the ability of local government planners and policy makers to meet the many and varied challenges that are presented along the urbanisation gradient (Chapter 2).

Chapter 5: The retention of recruitment trees and time to hollow formation

Introduction Tree hollow development is a complex process and can take up to 150 years for large hollows to form. In Australia the time taken for hollow formation to occur differs substantially among eucalypt species (Gibbons and Lindenmayer 2002). Large, old trees with a greater diameter at breast height (dbh) are known to contain significantly more hollows than smaller, younger trees (Lindenmayer et al. 1993) with a positive relationship between the diameter of a tree and the presence of hollows reported in many studies (Saunders et al. 1982; Nelson and Morris 1994; Ross 1998; Ross 1999; Wormington and Lamb 1999; Koch 2008; Munks 2009; Goldingay 2011).

The loss of hollow-bearing trees is not easily reversed, as hollow replacement is a slow, incremental process (Gibbons and Lindenmayer 2002). The distribution and abundance of hollow-bearing trees is therefore a function of the retention of trees, but also of the processes facilitating hollow formation in non-hollow-bearing trees. From the few studies examining the long-term trends in numbers of hollow-bearing trees within managed forests; all have predicted a gradual decline (Gibbons and Lindenmayer 1996; Wormington et al. 2005; Koch et al. 2008b; Koch et al. 2009). Failure to halt the loss of hollow-bearing trees can only lead to a prolonged absence of this resource for wildlife, which could result in the possible extinction of species, e.g. Leadbeater’s possum (Smith and Lindenmayer 1992; Harley 2006). Therefore, to assess the impact of such losses for hollow-dependant fauna, it is necessary to gain an understanding of the trajectories of hollow formation as well as patterns of hollow-bearing tree availability. This may be particularly important in regions undergoing rapid habitat transformation, such as within urban areas.

Hollow-bearing eucalypt trees are extremely long-lived, typically living for 300-500 years (Brookhouse 2006) and potentially providing hollows for a further 100 years once the tree had died (Gibbons and Lindenmayer 2002). The age at which tree-hollows are produced usually occurs between 100-230 years (Gibbons et al. 2000b; Whitford 2002; Goldingay 2011). Furthermore, hollows are unlikely to be suitable for fauna if trees are < 120 years old, as large hollows are rare in eucalypts < 220 years old (Gibbons and Lindenmayer 2002). There are also other factors related to age that will influence the presence of hollows in a tree:

• stand competition, equalling suppressed growth (Wormington and Lamb 1999);

• the shedding of large branches is associated with the age at which a tree’s accumulative growth rates slow and begin to decline (Jacobs 1955; Mackowski 1984), the shedding of large branches will increase the potential number of sites on a tree where hollows can form (Gibbons and Lindenmayer 2002);

• the slowing of tree growth rates (ageing) reduces the ability of a tree to occlude wounds, increasing the potential for hollow formation (Jacobs 1955);

• sapwood thickness may decline with age (Florence 1996) thereby increasing a trees suseptability to attack from termites and fungi (Werner and Prior 2007) and fire damage (Adkins 2006) leading to the potential for an increase in hollow formation;

• the ratio of heartwood to sapwood also declines with age, leading to a greater incidence of heartwood decay (Gibbons and Lindenmayer 2002);

• older trees have a greater frequency of exposure to events such as fire and windstorms (Gibbons and Lindenmayer 2002).

While these relationships have all been studied within Australian production forests, how they apply to urban forest patches has received little attention. Furthermore, despite this previous research there is still ongoing debate about the management requirements to achieve a perpetual supply of hollow-bearing trees within the production forests of Australia (Goldingay 2011). In contrast, forest patches within the urban landscape have few, or no, management guidelines for the retention or recruitment of hollow-bearing trees (Chapter 2). However, forest patches within the 82

urban matrix continue to be subjected to variables associated with anthropogenic activities such as land clearing (Davis et al. 2013). Until recently a dismissive management attitude towards small forest patches was taken, as they were not considered to contain sufficient resources to meet the needs of many individual species (Daily 2001). Public safety concerns have also seen the removal of many hollow- bearing trees within urban areas (Rhodes et al. 2005), resulting in a negative social attitudes towards trees from certain sectors of the community (Kirkpatrick et al. 2013). Despite the acknowledged significance of the loss of hollow-bearing trees in urban areas, relatively few studies have investigated the ecological impacts that this will have on faunal communities (Goldingay and Sharpe 2004a; Harper et al. 2005a; Davis et al. 2013) (Chapter 6).

Research aims This chapter aimed to quantify the impact of urbanisation on the persistence and availability of hollow-bearing trees by modelling the influences of selected tree, disturbance and environmental variables on hollow formation from the recruitment eucalypt population in urban forest patches. In particular, the relationship between dbh and hollow type was investigated, as faunal preferences for particular hollow types have been well documented in Australia (Gibbons et al. 2002; Lindenmayer 2002; Goldingay 2009; Koch et al. 2009) but not within urban forest patches. These analyses were completed in conjunction with an investigation into the potential for hollow-bearing tree recruitment and their ability to persist across the landscape.

Methods

Plot sampling protocol To quantify the effects of urbanisation on hollow-bearing trees and the availability of hollows; 45 forest patches along an urbanisation gradient were surveyed. At each site a varying number of plots were sampled to quantify the status of hollow-bearing trees. For a full description of methodologies and site establishment see Chapter 3.

This study evaluated five genera belonging to the family Myrtaceae; Angophora, Corymbia, Eucalyptus, Lophostemon and Syncarpia; hereafter referred to as eucalypts. In each plot all eucalypt trees within a 30 m radius from the plot midpoint and with a dbh ≥ 10 cm were identified and measured to determine vegetation community structure. For each tree the presence of hollows was noted to classify the tree as hollow-bearing or not. In addition, the number and type of hollows in all hollow- bearing trees was recorded (Chapter 3). While dead trees continue to provide a habitat resource for approximately 50 - 100 years (Moloney et al. 2002), they were removed from the data set, as the analyses in this chapter focused on the living cohort of both recruitment trees and existing hollow-bearing trees.

Growth rates for eucalypt species Recruitment trees were classified as being eucalypt trees with a dbh ≥ 10 cm without a hollow. To determine the time frame for recruitment modelling of hollow-bearing-trees it is necessary to estimate tree age at the onset of hollow formation. Tree age can be predicted according to the disturbance history of a site (Bradshaw and Rayner 1997), by radiocarbon dating (Turner 1984), by tree ring counting (Banks 1997) or by tree diameter increment data used in growth models (Wormington and Lamb 1999; Gibbons et al. 2000b; Moloney et al. 2002). Growth models are also widely used as a tree- ageing technique as they are non-destructive and easily applied (Koch 2008). Tree age estimates from wet and dry sclerophyll forests of south-east Queensland reveal the intraspecific variation that occurs between species grown within the same sites (Table 5.1).

dbh (cm) Species 0 - 24.9 25 - 49.9 50 - 74.9 75 - 99.9 100 - 125 125+ E.pilularis 1.2 0.8 0.6 0.5 0.5 0.4 E.microcorys 0.9 0.6 0.45 0.35 0.35 0.3 E.racemosa 0.6 0.5 0.3 0.25 0.24 0.2 C.citriodora 0.4 0.4 0.4 - - -

In growth and yield modelling, dbh is the primary measure of tree age (Avery and Burkhart 1989). Individual tree diameter growth models are used to predict growth rates of trees within a stand (Subedi and Mahadev 2011). Estimates of eucalypt growth rates are available from south-eastern Australia (Bauhus et al. 2002), Tasmania (Koch et al. 2008a) and south-east Queensland (Ross 1999; Wormington and Lamb 1999). For the purpose of this study only data from the managed forests of south-east Queensland (Ross 1999; Wormington and Lamb 1999) were used.

Growth rates in the eucalypt communities in urban fragments The species growth rate data (Table 5.1) were then applied to trees found across the study area. In total 850 trees (16.5%) were represented by the above four species. Remaining species were then classified by bark type i.e. half-bark, tessellated bark, smooth-bark, stringy-bark or iron-bark; to best fit with the data available on the known growth rates of species with similar bark types (Appendix 6). In the case of the iron- barks = Eucalyptus crebra and E. fibrosa; tessellated-barks = Corymbia intermedia and C. trachyphloia; stringy-barks = E. microcorys and E. acmenoides; the mean growth rates of each species per bark type was calculated and then used as the predicted growth rates for other iron, tessellated and stringy-bark species. Smooth-bark growth rates were calculated using C.citriodora as a proxy species. Incremental annual dbh growth rates were then calculated (Table 5.2). To allow for variation in growth rate estimates due to site location, a sensitivity analysis ranging from -5% to 20% was applied (Equation 5.1) where G is growth rate, A is the annual incremental dbh growth rates and s is the sensitivity. It is acknowledged that this is unlikely to give precise growth rate estimates; however, given the lack of data available on the tree species in this study area it offers a justifiable solution.

= ( × ) (5.1)

퐺 퐴 푠

Table 5.2: Annual incremental dbh growth rates calculated by bark type. Growth rates for two common species are also provided.

dbh size Half- Iron- Smooth- Stringy- Tessellated- E. microcorys E. racemosa class (cm) bark bark bark bark bark 0 0.9 0.6 1.2 0.6 0.6 0.6 0.6 25 0.6 0.5 0.8 0.5 0.5 0.5 0.5 50 0.45 0.3 0.6 0.3 0.3 0.3 0.3 75 0.35 0.25 0.5 0.25 0.25 0.25 0.25 100 0.35 0.24 0.5 0.24 0.24 0.24 0.24 125+ 0.3 0.2 0.4 0.2 0.2 0.2 0.2

Mortality rates for eucalypt trees Tree mortality has a fundamental role to play in forest dynamics (Franklin 1987). However, tree mortality remains a very misunderstood process in ecology (Hartman 2010; Sala 2010). The ability to reliably estimate tree mortality rates is difficult due to trees long life span, their large effective population size, geographical ranges (Petit 2006) and to the limitations of data availability (Csilléry 2013). Permanent forest plots and dendrochronological data have been used for probability estimates of mortality for individual trees i.e. the number of dead trees over the total number of individual trees (Peng 2011; Taylor 2007; Wolf 2004) known as the ‘volume mortality rate’. Results from these data reflect mortality from the natural patterns of tree life history, such as competition or senescence (Csilléry 2013) and are well justified (Kurz 2008). Therein, volume mortality rates are used in this current study.

Mortality data from within Native Forest Permanent Plots (NFPP) held by the Queensland Herbarium Department of Science, Information Technology, Innovation and the Arts (unpublished data1) have been used. These data consist of permanently monitored plots in uneven-aged mixed species native forests in Queensland’s State Forests and National Parks. These plots were established between 1936 and 1998 and remeasured every two to 10 years up to 2011. These data were collected from 99 State Forests and National Parks over a 62 year time frame. Data were collected on the species name, date of measure, measure interval in years, a count of all trees measured per site, a count of trees by species and each trees status; being either living, dead by

1 Mortality data from The Queensland Herbarium from unpublished work 1936-2011. 86

natural causes or dead by human causes i.e. logging, treatment or fire. Only trees that had died by natural causes were used for this analysis. As all species of flora are included in this data; initial data exploration required eucalypt species to be extracted; this was further refined to reflect the range of species found within this current study area.

Thus, over 82% of species found within the Gold Coast are represented. The remaining 18% of species were classified by bark type as per the methods outlined in ‘Tree growth rates’. Annual mortality was then calculated at each site for each species (Equation

5.2). Where M is annual mortality, nd is the number of trees dying from natural causes; nl the number of living trees and t represents the number of years between consecutive measurements. The mean mortality rate for each species was then calculated and was found to range between 0.01 - 0.05 (±SE 0.002). This estimate does not take into account stochastic events such as climate or disease.

d = (5.2) ( l) 푛 푀 푛 푡 Recruitment trees and hollow development The differential growth rates (Table 5.2) were then applied to the current cohort of recruitment trees (n = 5132) over 150 years using VLOOKUP tables in Excel based on their known dbh at Year 1. The proportion of the recruitment tree population with hollows was then quantified at 10, 50, 100 and 150 years to monitor trends in hollow- bearing trees over time. Classification of recruitment trees as hollow-bearing or not (i.e. presence/absence of a hollow), in each year was determined based on the probability of a tree having a hollow as a function of dbh. These hollow probabilities were derived from the current hollow-bearing tree cohort using log-linear logistic models (Table 5.3). Loss of trees due to clearing for urban development was also factored into calculations based on the perceived risk at sites along the gradient. The analysis is restricted to the current cohort of recruitment trees (i.e. all non-hollow-bearing trees) as no data are available on recruitment rates or natural mortality to quantify trends in the community as a whole.

Table 5.3: The probability of a recruitment tree having a hollow as a function of dbh.

dbh Hollow (cm) probability 10 - 20 0.05 20 - 30 0.15 30 - 40 0.25 40 - 50 0.42 50 - 60 0.5 60 - 70 0.52 70 - 80 0.65 80 - 90 0.8 90 - 100 0.8 100 - 110 0.9 110 - 120 1 120 - 130 1 130 - 140 1 140 - 150 1 150 - 160 1 160 - 170 1

Loss of hollow-bearing trees due to habitat loss from land clearing Modelling was undertaken to predict the loss of hollow-bearing trees due to habitat loss from land clearing for housing and infrastructure over a 10, 50, 100 and 150 year period. To facilitate this, a risk matrix was constructed to assign risk based on the location of sites along the urban gradient as well as their size as represented by the four primary classes (ha). The total area of remnant vegetation on the Gold Coast (including the three control sites located in Brisbane and Redland Shires) is 63,678 ha. The extent of forest patches was calculated by interrogating ArcGIS regional ecosystem and land use layers supplied by Gold Coast City Council, Brisbane City Council and Redland Shires. Risk values were assigned a priori on a scale from - 0.5 (less risk) to + 0.5 (high risk) along the urbanisation gradient (Table 5.4). Land clearing rates of 442 ha/year were applied uniformly over the study area as this figure has been reported as the Gold Coast City Council clearing rate until 2019 (Gold Coast City Council 2009). The modelling presented here extends well beyond 2019 and predictions should therefore be seen as a minimum threshold as clearing rates could conceivably increase. Therefore, the net rate of remnant habitat loss for the region amounts to 69% per year. This figure was used in all subsequent calculations after adjusting the loss from each 88

site by the relevant risk factor. For example, a small, highly urbanised forest patch may be at a very low risk of clearing given its proximity to high density housing, i.e. narrow streets. By contrast, the larger control sites, some of which have been recently harvested, are at greater risk of further clearing for urbanisation and thus have been weighted accordingly (Table 5.4).

Table 5.4: Risk assessment of sites due to land clearing for urban development and infrastructure. Risk increases down and across (left–right) the table.

Size category for research sites

Gradient Small <5 ha Medium 6-50 ha Large 51-100 ha Very Large > 100 ha

Urban very low (-0.5) low (-0.25) medium low (-0.1) medium (base value)

Peri-urban low (-0.25) medium low (-0.1) medium (base value) medium high (+0.1)

Rural medium low (-0.1) medium (base value) medium high (+0.1) high (+0.25)

Control medium (base value) medium high (+0.1) high (+0.25) very high (+0.5)

Diameter at breast height and bark type as a predictor of hollow type From the current inventory of hollow-bearing trees the probability of a tree containing a hollow of a certain type was calculated based on known dbh and hollow type. Hollow type (Table 5.5) was chosen over hollow size, as apart from bayonet and fissures, hollows in all other categories can be either very large or very small.

Table 5.5: The classification of the seven available hollow types as used in this study.

Hollow type Hollow type classification 1 butt 2 branch end 3 bayonet 4 fissure 5 trunk main 6 trunk top 7 branch main

Data analysis

Univariate determinants of hollow presence by dbh A paired t-test was used in order to determine if the dbh of hollow-bearing trees was greater than non-hollow bearing trees.

Estimating hollow type probability Fifty iterations of a non-linear logistic least squares regression analysis were performed on the hollow-bearing trees sample from all sites to ascertain the relationship between the dependant variable hollow type probability and the independent variable dbh size class. Significance levels were set at 0.05 for all analyses. Calculated probability values for all trees were subsequently used in recruitment cohort modelling to estimate the proportion of trees with hollows over time. Statistical analysis was conducted using Statistica Ver. 7 (Statsoft. 2005).

Tree, disturbance and environmental variables as predictors of a tree containing a hollow Eight predictor variables presented for analysis were selected a priori from those known to have strong biological influences on hollow formation. A correlation matrix was performed to determine colinearity between variables using Spearman’s Rank correlation. A correlation coefficient value |r| > 0.7 was chosen to identify pairs of highly correlated variables (Garden et al. 2010). As expected, tree age was found to be highly correlated to dbh (0.752) and was thus removed from analysis while all other variables were retained. An inspection of the relationship between the response variable (probability of a tree containing a hollow) and the explanatory variables; fire damage, termite damage, epicormic growth, wind damage, dbh, bark type, slope, and elevation was undertaken using pairwise plots to ascertain the strength of the relationships and if they were linear. Poisson Generalised Linear Mixed Effects Models (GLMM) was run as plots were nested within sites.

Interaction models between these variables were included in the analysis and 27 candidate models were run (Appendix 5). Models were ranked according to their Δi

values. The higher the Δi value, the less accurate the model for the given data. The best approximating models that had a Δi ≤ 2 are presented (Burnham and Anderson 2002). Because of uncertainty in some of the final model selections, a model averaging approach was applied. Variables were ranked according to their relative overall importance by summing the Akaike weight (wi) from all model combinations where each of the variables occurred. Statistical analysis was conducted in R version 3.1 statistical software (R Core Team 2012) using the glmmML (Brøstrom 2012) and MuMIn (Barton 2012) packages.

Results A total of 5132 recruitment trees representing 33 eucalypt species was recorded from all sites. Lophostemon confertus accounted for the most recruitment trees (826), followed by Corymbia intermedia (714), Eucalyptus carnea (487), E.crebra (436) and E. propinqua (397) (Table 5.6). Most recruitment trees (85.2%) were within the 10-40 cm dbh size class (Figure 5.1). Trees with hollows had a significantly greater dbh (49.3 ±

0.75 S.E.) than recruitment trees (23.5 ± 0.18 S.E.) (F = 23.08 d.f. = 10.36; p < 0.05).

2000 1750 All trees 1500 Recruitment Trees 1250 1000 750

Number Number of trees 500 250 0

dbh category (cm)

Figure 5.1: The dbh range of recruitment trees compared to all trees surveyed.

Table 5.6: The 33 species of recruitment trees sorted by bark type and absolute abundance in all sites. Bold numbers represent the cumulative total for each bark type.

Species Common name Bark type n N E.microcorys Tallowwood Stringy-bark 222 L.suaveolens Swamp box Stringy-bark 257 E.carnea Broad-leaved white mahogany Stringy-bark 487 E.tindaliae Queensland white stringy-bark Stringy-bark 263 E.resinifera Red mahogany Stringy-bark 94 E.eugenioides Thin-leaved stringy-bark Stringy-bark 27 E.robusta Swamp mahogany Stringy-bark 24 E.acmenoides White mahogany Stringy-bark 22 E.baileyana Bailey's stringy-bark Stringy-bark 22 1418 C.intermedia Pink bloodwood Tessellated bark 714 C.gummifera Red bloodwood Tessellated bark 311 C.trachyphloia Brown bloodwood Tessellated bark 34 A.woodsiana Rough-barked apple Tessellated bark 61 S.glomulifera Turpentine Tessellated bark 1 1121 E.pilularis Blackbutt Half-bark 187 E.moluccana Gum-topped box Half-bark 10 E.tessellaris Moreton Bay ash Half-bark 4 L.confertus Brush box Half-bark 826 1027 C.citriodora.v Spotted gum Smooth-bark 363 E.propinqua Small-fruited grey gum Smooth-bark 397 E.racemosa Scribbly gum Smooth-bark 78 E.longirostrata Grey gum Smooth-bark 40 E.grandis Flooded gum Smooth-bark 38 E.tereticornis Forest red-gum Smooth-bark 16 E.saligna Sydney blue-gum Smooth-bark 14 E.major Grey gum Smooth-bark 11 C.henryi Large-leaved spotted gum Smooth-bark 10 E.biturbinata Grey gum Smooth-bark 9 E.seeana Narrow-leaved red gum Smooth-bark 3 979 E.siderophloia Ironbark Iron-bark 147 E.fibrosa Broad-leaved red iron-bark Iron-bark 3 E.dura Grey iron-bark Iron-bark 1 587 Total 5132

Estimating hollow and hollow type probability For the entire cohort of hollow-bearing trees the chances of a tree having a hollow increased sharply between 50–100 cm dbh. The probability that a tree would definitely have a hollow was reached at a dbh of 140 cm (F = 113444.1; d.f. = 2; p < 0.001). The probability estimate of a hollow-bearing tree containing a certain hollow type based on dbh revealed that the chances for butt (F = 299; d.f. = 2; p < 0.001), branch end (F= 1063.2; d.f. = 2; p < 0.001), bayonet type hollows (F= 706.5; d.f. = 2; p < 0.001), fissure

(F= 34.0; d.f. = 2; p < 0.001), trunk main (F= 121.7; d.f. = 2; p < 0.001), trunk top (F2= 20.7; d.f. = 2; p < 0.001) and branch main (F= 58.4; d.f. = 2; p < 0.001) to be present in a tree increased significantly with dbh (Figure 5.2).

Predictors of trees containing hollows Within all models, dbh and tree disturbance variables (wind damage, epicormic growth and termite damage) influenced the potential of a tree to contain a hollow more than environmental variables (Table 5.7). Individually, dbh and disturbance are the most important variables. Elevation and fire were found to be negatively correlated with the presence of hollow-bearing trees. Confidence intervals performed weakly for all variables except for dbh, epicormic growth, wind and termite damage (Table 5.8).

Table 5.7: Model comparisons for explanatory tree, disturbance and environmental

variables for assessing probability of a tree having a hollow. Only models with a ∆i < 2 are presented.

Model AICc ∆i wi dbh + epicormic growth + fire scar + bark type + termite damage + wind damage 4443.7 0.00 0.407 dbh + epicormic growth + fire scar + slope + termite damage + wind damage 4443.8 0.14 0.379

1.2 1.2 (a) (b) 6 2 1 w 8 1 o 1.0 1.0 1 l 13 l s o h w

18 o a l 0.8 l g 0.8 o 16 n h i 32 t v e a n h

41 o 0.6 e 0.6 y

e 78 a r t 134 b f a 3 o f 0.4 0.4 o 55 6 y

t 94 y i l t 171 i i l b i 24 16 a b 160 0.2 95 2 a

0.2 b o b 80 r o 80 34 r 47 P 2 P 0.0 0.0 0 0

-0.2 -0.2 0 20 40 60 80 100 120 140 160 0 20 40 60 80 100 120 140 160

1.2 1.2 (c) (d) 2 1 1 1 1.0 1.0 6 s s w

w 12 o l o l l l 0.8 o 0.8 o h h 15 d t t 4 n u 5 e b 0.6 h 0.6 r c 25

o 11 n

f 4 a r y t b i l 0.4 0.4 f i o b

a 40 y t b i o l

i 60 19 r 0.2 12 0.2 b P 31 18 8 a 56

40 b

21 40 40 o 35

r 3 5 0 0.0 0 0 P 0.0

-0.2 -0.2 0 20 40 60 80 100 120 140 160 0 20 40 60 80 100 120 140 160

1.2 1.2 (e) (f)

s 1.0 1.0 w o s l l w o o h l

l 0.8 n o

0.8 i h a e m r u k 0.6 s n s i

u 1 f 0.6 r f t o f o y 0.4 t i y l t i i

l 6 b 2 0.4 i a b b a 0.2 o b r o 8 4 r P 6 1 0.2 P 12 2 10 7 6 0 0 0 1 0.0 2 4 3 6 4 7 2 4 1 0 0 0 0 0 0.0 -0.2 0 20 40 60 80 100 120 140 160 0 20 40 60 80 100 120 140 160

1.2 (g) 1.2 (h) s

1.0 w s o l 1.0 l w o o l h l o n

0.8 i h a p 0.8 o m t h k c

n 0.6 n u a r r

t 0.6 b f f o

0.4 o y y t i t l i i l

i 0.4 b b a a b 0.2 b o o r 1 3 3 r P 1 2 3 3 2 1 1 P 0.2 0.0 0 0 0 0 0 0 4 3 1 0 2 2 2 0 3 0 0 0 0 0 0.0 -0.2 0 20 40 60 80 100 120 140 160 0 20 40 60 80 100 120 140 160 dbh (cm) dbh (cm) Figure 5.2: Probability of a tree having a hollow for (a) all hollow types; (b) bayonet; (c) butt; (d) branch end; (e) fissure; (f) trunk main; (g) trunk top and (h) branch main hollows. Values represent the number of hollow-bearing trees in each dbh size class category.

Confidence Standard interval 2.5% Relative variable Parameter Estimate error 97.5% importance wind damage 0.570 0.139 0.297 0.843 1.00 termite damage 0.224 0.094 0.040 0.409 1.00 epicormic growth 0.062 0.135 -0.202 0.327 1.00 dbh 0.026 0.002 0.022 0.030 1.00 fire scar -0.050 0.082 -0.212 0.111 0.81 bark type 0.013 0.009 -0.004 0.032 0.62 slope 0.043 0.331 -0.606 0.693 0.21 elevation -0.022 0.040 -0.102 0.057 0.06

Recruitment trees, hollow development and the influence of land clearing Data are presented on the current cohort of 5132 recruitment trees (i.e. non hollow- bearing trees) showing a change over time in the numbers of trees within each dbh category (Figure 5.3). At Year 1, the majority of trees are ≤ 40 cm dbh (85%), with only five trees >90 cm dbh. The mean dbh is 26.9 cm (± 0.19 S.E.). The number of hollow-bearing trees increases to almost 1200 by year 10 and peaks at 1643 in year 50 before declining to 1184 at year 150 (Table 5.9). Due to the predicted land clearing rate of 69%, approximately 71% of habitat will be lost along the urbanisation gradient over the next 150 years (Figure 5.4).

Year 1 2000 Year 10 Year 50 1600 Year 100 Year 150 1200 No. trees 800

400

0 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 dbh category (cm) Figure 5.3: The predicted number of trees by dbh category over 10, 50, 100 and 150 years showing the increasing dbh through time of the current cohort of recruitment trees.

Table 5.9: Summary of the current cohort of recruitment trees transitioning to hollow- bearing trees over 150 years from the 45 sites within the study area. The loss of hollow- bearing tree due to natural mortality are presented as well as the loss of remnant habitat over time.

Less Less Recruitment Hollow Habitat Tree Tree Year trees bearing remaining Mortality Mortality (n) trees (n) (ha) (0.01) (0.05) 1 5132 0 0 0 6650.96 10 3555 1164 (25%) 1152 1106 6048.07 50 1737 1643 (49%) 1627 1561 4142.37 100 782 1450 (65%) 1436 1378 2590.66 150 295 1184 (80%) 1173 1125 1627.15

20 Percentage of hollow-bearing trees lost trees of hollow-bearing Percentage 10

0 Year 10 Year 50 Year 100 Year 150 Time scale

Discussion

Factors influencing hollow formation The distribution and abundance of hollow-bearing trees within the landscape is influenced by various disturbance processes (Lindenmayer et al. 1997; van der Ree and McCarthy 2005; Adkins 2006) which alter their availability. Therefore, knowledge of the factors affecting the presence of hollows in trees in the face of disturbance is important for their management. Results indicate that the presence of hollows in trees was significantly influenced by dbh, where larger trees were more likely to have hollows. Estimations of the probability of a tree containing a certain hollow type based on dbh, demonstrated that all hollow types are potentially present in any given dbh size class but that this probability generally increased with dbh. While this is consistent with previous findings (Fox et al. 2008; Koch et al. 2009; Goldingay 2011; Lindenmayer et al. 2012), the models run in this study found that within urban systems a complex mix

of tree disturbance and environmental variables influence hollow development. Hollows are more likely to form in trees with a large dbh consistent with other studies (Mackowski 1984; Lindenmayer et al. 1993; Gibbons et al. 2000b); although the probability of a tree with a large dbh containing a hollow is in part a function of decay resistance in ageing trees (Rudman 1965). Diameter at breast height was the most important predictor of hollow-bearing trees. However, the probabilities of hollow formation in fissure, trunk-main, trunk-top and branch-main hollows was less than other hollow types. This pattern is likely due to the relatively small number of recorded observations for these hollow types (combined n = 225) compared to butt, branch end and bayonet hollow types (combined n = 1918).

The significance of hollow types and probability of formation is important as preferences for specific hollow types by vertebrate fauna have been well documented in the literature (Lindenmayer 2002; Goldingay 2009; Koch et al. 2009; Goldingay 2011). Findings from this current study support this, in that most species were observed using small (10 cm) bayonet type hollows (Chapter 6). This study also found that butt, branch end and bayonet hollows have a strong relationship to dbh; i.e. with increased dbh there was a greater probability a tree containing one of these hollow types. However, tree species differ in their tendency for hollow formation (Wormington et al. 2003; Todarella and Chalmers 2006; Fox et al. 2009), as a consequence of differing growth rates, susceptibility to decay and site management history (Eyre 2005). For example, 45% of butt-hollows were found in Lophostemon confertus. Lophostemon confertus is a fire sensitive species; which may facilitate the formation of butt-hollows in larger trees that have been repeatedly burnt (Chapter 3).

The disturbance variables; termite damage, wind damage and epicormic growth (as a surrogate measure of disturbance) also influenced the presence of hollows in trees and were equally important as dbh. Typically termites gain entry at the base of a tree where damage has occurred or via a fungal infection (Werner and Prior 2007). Epicormic growth is a response by trees to disturbances such as fire (Waters et al. 2010), logging (Hooper and Sivasithamparam 2005), insect (Stone and Coops 2004) and wind damage (Staben and Evans 2008) with wind damage being a primary cause of hollow formation in urban areas (Harper et al. 2005a). 98

Within the literature fire has been found to play an important role in hollow-formation (Inions et al. 1989b; Adkins 2006; Haslem et al. 2012) but the exclusion of fires from urban forest patches may explain why fire was not an important variable in the models analysed here. Alternatively, the influence of fire may have been underestimated in the models as it has also been found to remove large numbers of hollows from the landscape, depending on fire intensity (Inions et al. 1989). Furthermore, smooth-barked eucalypts shed their fine outer layer of bark annually (Brooker and Kleinig 2004) removing signs of non-destructive fires. Trees with this bark type account for 73% of hollow-bearing trees recorded in this study; possibly resulting in the underestimation of fire damage and hence its importance in hollow formation in urban forest patches. The acknowledgment and understanding of fire ecology is significant for all forest types across Australia. However, there have been relatively few studies conducted within the forests of south-east Queensland. Therefore, currently there are no sound published recommendations for fire management for any vegetation community within the region (Tran and Wild 2000). However, hazard reduction burns are carried out on the Gold Coast on a regular basis (Gold Coast City Council 2009).

Hollow-bearing tree recruitment The Gold Coast City council has committed to making land available on the Gold Coast for housing and infrastructure until 2019 (Gold Coast City Council 2009). The amount of land cleared could be as much as 442 ha per annum across the shire based on past historical clearing rates on the Gold Coast. There is no mention if this is a target to be reached, or if it is a maximum allowed; nor is there any reference as to where clearing will/can take place. Therein, while there is some recruitment of hollow-bearing trees into the population in forest patches (the majority of the current recruitment cohort having hollows by the year 150), there is also considerable loss of trees through land clearing during this time (up to 70%). With these predicted rates of loss, the implications to urban biodiversity in general may in some cases be extreme. The immediate consequences of habitat loss results in the formation of forest patches of varying size and shape (Fahrig 2002; Aguilar et al. 2008) such as those found along the urbanisation gradient within this study. Loss of habitat coincides with a reduction in population size and an increase in the degree of isolation across the landscape, with these phenomena recognised as being globally significant driving forces in biodiversity loss (Cook et al. 2002; McGarigal and Cushman 2002; Villard 2002; Lindenmayer and 99

Fischer 2006). This also appears to be a limiting factor for hollow-bearing trees along the gradient in the future. However, habitat loss is only one disturbance factor and this may act in synergy with others with corresponding management implications.

Management implications of hollow-bearing tree recruitment It is generally accepted that declines in the distribution and abundance of species coincide with habitat loss (Cameron 2006; Lindenmayer and Fischer 2006; Didham et al. 2007a). As demonstrated above, these patterns also hold for structural habitat features such as hollow-bearing trees. Ensuring the persistence of those features in urban landscapes may therefore require active management.

Without adaptive management plans in place, hollow-bearing trees and tree-hollows will continue to decline. Coinciding with this decline in hollow-bearing trees will be the decline in the fauna that relies upon this resource (Goldingay and Stevens 2009; Lindenmayer et al. 2009; Manning et al. 2012). The question should not be how many trees to retain but, how many hollow-bearing trees to manage for (Mackowski 1984)? This current study has shown that tree disturbance and environmental variables are important predictors of hollows in trees, as well as type of hollow. However, the long length of time taken for hollow formation may be superseded by other factors of a much shorter time frame e.g. changes in priorities by managers as well as changing legislation and policy (Chapter 2). Therefore, in formulating active adaptive management strategies for hollow-bearing trees in urban landscapes managers will firstly need to define a series of clear objectives in relation to the density, type and size requirements for hollow-bearing trees. These objectives should be based on the known distribution and abundance of the resource within forest patches (as identified by this study) as well as green space and gardens. Monitoring of the distribution and dynamics of the hollow- bearing tree resources as well as their use by fauna will therefore inform the review of objectives over time. The measures needed to define goals for the conservation of the resource in the long term, may require active intervention such as artificial hollow formation, erection of nest boxes and habitat restoration. Subsequent monitoring of natural and artificial hollow use as well as the ability to adapt and change policy and management guidelines is required. Progressive silviculture management techniques may promote hollow-bearing tree formation. For example, planting eucalypt trees 100

within a mixed species stand has been found to facilitate growth rates e.g. interactions between E.pilularis and E.grandis which were found to have increased yields ranging between 10-30% in production forests (Forrester and Smith 2010). As the findings of this study have demonstrated that the presence of hollows in trees is significantly influenced by dbh; therein, the ability to increase growth rates would decrease the time taken to hollow formation.

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Chapter 6: Forest patch occupancy and use of hollow-bearing trees by vertebrates along the urbanisation gradient.

Introduction In Australia and elsewhere, increased awareness of conservation and biodiversity objectives being solely restricted to National Parks has been reassessed and urban environments are now being recognised for their ability to provide a range of ecosystem functions and services with the potential to enhance and broaden conservation objectives (Lunney and Burgin 2004a; Davison and Ridder 2006). In 2001, the Australia State of the Environment Report, stated that, ‘Overall, the condition of biodiversity in Australia today is poorer than it was in 1996’ (Lunney and Burgin 2004b). The Australia State of the Environment Report 2011, delivers a similar message, ‘Human activities have the potential to further reduce genetic, species and ecosystem biodiversity, which will seriously affect the delivery of environmental benefits to Australians and reduce our quality of life’(Hatton et al. 2011). With 90% of the Australian population living in urban centres by 2050 (Zipperer and Pickett 2001), urban ecology in regards to biodiversity and conservation should be an important consideration for policy makers and city planners now and into the future (Lunney and Burgin 2004b). As to whether policy makers and planners will take up the challenge remains to be seen. The results from Chapter 2 suggest otherwise, in that biodiversity conservation and hollow-bearing trees in particular did not feature prominently on council websites potentially reflecting a lower order priority.

Within Australia, areas that once provided habitat for many species have often become major centres of urbanisation (Lindenmayer and Fischer 2006). It is widely acknowledged that urbanisation has caused many species to be displaced (Gaston 2010b), species diversity has decreased (Luck and Smallbone 2010) and localised extinctions have occurred and will continue to do so (Gaston and Fuller 2007). Since European settlement, certain species of Australian native fauna have proven to be resilient to habitat disturbance (Braithwaite 2004), as predicted by McKinney (2006), 102

these species are urban ‘winners’, able to exploit the surrounding anthropogenic landscape.

Urbanisation typically results in a highly fragmented landscape containing a complex matrix of remnant vegetation that is sometimes connected to larger contiguous forested areas (Bunnell 1999; Davis et al. 2013; Harrisson et al. 2013). Faunal responses to urbanisation and the associated fragmentation of natural habitats can be at times difficult to interpret as cities are not generally known for their wild animals or places. This can, and often does, lead to false social and political perspectives about urban wildlife (Lunney and Burgin 2004b). Since most Australians live in urban environments, urban wildlife is what will be encountered on a daily basis (Lunney 2004). However, ’urban wildlife’ may elicit different responses from different people, depending on the circumstances surrounding any interactions (Lunney 2004). Perceptions of how urban wildlife responds to urbanisation may therefore be influenced by these perspectives, and a change in perspective, may be what is required to conserve urban wildlife.

Previous studies have reported both positive and negative responses by fauna to the effects of urbanisation (Low 2003; Ross 2004; Durant et al. 2009; Taylor and Goldingay 2009; Cardilini et al. 2013). Recent interest in species adaptation to urbanisation has revealed a significant divergence between urban and rural conspecific populations demonstrating a wide range of responses depending on the traits being investigated (Evans 2010). Nevertheless, species responses to the impacts of urbanisation have been broadly categorised as being either ‘matrix-occupying’ (winners) or ‘matrix-sensitive’ (losers) based on similarities between species and their ability to persist within the urban environment as well as the surrounding natural environments (McKinney 2002; Catterall 2004; Garden et al. 2006; Didham et al. 2007a). Within urban environments there are four main mechanisms driving spatial variation which are likely to generate intraspecific responses (Evans et al. 2010) and these include;

• Differences in the history of urban development;

• biotic factors, such as potential colonial species; 103

• the quality of urban habitats i.e. human attitudes to urban wildlife as well as environmental factors; and

• the ability of populations (e.g. rural) to acquire specific traits that increase their ability to adapt to urban environments.

Faunal responses to urbanisation are often strongly associated with changes in their habitats (Rankmore and Price 2004; Luck and Smallbone 2010). Urbanisation has resulted in the fragmentation of native vegetation into small isolated remnants in many regions (Alberti and Marzluff 2004) with the urban matrix being the dominant, most extensive and often the most modified landscape type (Lindenmayer and Fischer 2006). The resultant fragmented landscape alters species interactions, the trophic structure of communities, and movement of individuals through the landscape as well as resource flow between habitats (Lindenmayer et al. 2000b; Ewers and Didham 2006b; Brearley et al. 2010). Natural habitat remnants are therefore important features of fragmented urban landscapes with parks or reserves having direct value for urban biodiversity (Cornelius and Hermy 2004).

The ecological value of remnants to local fauna is frequently dependent upon the provision of habitat resources (Harper 2005; Harper et al. 2008; Goldingay 2011). The distribution and abundance of these resources are affected by urbanisation, as has been shown for hollow-bearing trees (Harper et al. 2005a). Studies conducted in Australia have shown that there is a general paucity of hollow-bearing trees in Australia’s urban environments (Harper et al. 2005a; Harper et al. 2008; Luck and Smallbone 2010). Consequently, urban areas within Australia do not generally support a high diversity of hollow-using species (Garden et al. 2006). There is however, a knowledge gap in relation to studies of fauna and their relationship with hollow-bearing trees along an urban gradient. Only two published papers were found within Australia (Harper et al. 2005a; Harper et al. 2008), which highlights the need for further research in this area.

Against this backdrop this chapter quantifies the richness and relative occupancy of hollow-using vertebrate fauna found within forest patches along an urban gradient on

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the Gold Coast in south-east Queensland, a rapidly urbanising region of Australia. This chapter tests the hypothesis that smaller, isolated urban habitats would support more depauperate communities dominated by common species, compared to larger and more contiguous forest patches. Specifically, this chapter will determine whether hollow dependent fauna are able to persist within the urban environment, and whether their richness and occupancy rates are a function of the features of forest patches along an urban gradient. This information will contribute to our understanding of forest patches in various urban settings and their ability to support habitat specialist fauna.

Methods A full description of methodologies for the establishment of sites and their classification along the gradient can be found in Chapter 3. For this particular Chapter, 34 sites (including the five control sites), were selected from the original 45 sites established for the hollow-bearing trees and vegetation assessment component of this project Chapter 3 (Table 6.1 and Appendix 3 for GPS locations). The subset of 34 sites (78 plots) was chosen from within the original data due to varying access and safety issues that were revealed during the establishment of sites.

Various survey methodologies have previously been advocated to ensure that representative samples of multiple taxonomic groups are captured (Garden et al. 2007a). Many studies report on the limitations or relative success of different methods for detecting mammal and/or reptiles; such as pit-fall traps (Catling et al. 1997), Elliot traps (Clemann et al. 2005), wire cage traps (Catling et al. 1997), direct observations and active searches (Ryan et al. 2002), hair tubes, vocalisations and direct signs such as tracks, scats, diggings or scratches (Mills et al. 2002). All studies unanimously agreed that different survey methods are useful for sampling particular fauna species, and that no single approach will capture all species within a community. The selection of methods used however, is a decision that will influence the accuracy of the survey outcomes (Garden et al. 2007a).

A number of different survey methods were used in this study to capture the range and taxonomic diversity of hollow using vertebrate fauna along the urbanisation gradient. These include; (i) point count diurnal surveys, (ii) spotlighting, (iii) stagwatching, (iv)

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Anabat and (v) pole camera surveys. Broader landscape variables such as logging and fire indices as well as a tree disturbance index that captured estimates of epicormic growth, wind, termite and fire damage were quantified at a site level. An urbanisation index was calculated along the gradient (see Chapter 3) as well as environmental and tree variables (Table 6.2).

Table 6.1: Area and hollow-bearing tree density of sites used for fauna surveys. Standard errors are supplied for sites with multiple plots. c = control, p = peri-urban, r = rural, u = urban.

No: of Urban Area HBT density Site plots gradient (ha) (ha) Nerang Forest Reserve 5 c/p 1668.76 61.43 ± 7.93 Karawatha Forest Reserve 7 c/p 824.33 25.5 ± 4.63 Bonogin Conservation Area 6 c/u 725.07 33.92 ± 7.8 Venman Bushland National Park 6 c/p 434.24 25 ± 5.9 Daisy Hill Forest Reserve 5 c/u 432.06 23.56 ± 6.6 Mt Nathan Conservation Area 3 p 266.07 33.33 ± 7.8 Syndicate Rd 3 p 185.31 39.28 ± 10.71 Kerkin Rd 1 p 176.90 42.85 Woody Hill, Hart's Reserve 2 p 91.08 44.6 ± 12.5 The Plateau Reserve 1 p 26.53 92.85 Aqua Promenade 2 p 13.10 60.71 ± 10.15 Elanora Conservation Area 2 p 13.06 71.48 ± 7.14 Piggabeen Rd 1 p 2.97 14.28 Wongawallen Conservation Area 2 r 383.67 30.35 ± 26.78 Numinbah Rd 2 r 157.55 28.57 ± 10.71 Trees Road Conservation Area 2 r 53.35 44.64 ± 12.49 Stanmore Park 1 r 50.87 7.14 Reedy Creek Conservation Area 3 r 44.85 26.19 ± 11.72 Austenville Road 3 r 24.98 34.52 ± 8.5 Behms Road 1 r 14.29 39.29 Tallowwood Rd 1 r 14.24 50 Andrews Reserve 1 r 10.82 28.57 Carrington Rd 1 r 5.99 39.29 Tomewin Rd 1 r 5.10 57.14 Gilston Rd 1 r 4.04 35.71 Olsen Ave 2 u 38.94 7.14 Burleigh Ridge 2 u 17.89 53.56 ± 35.71 Pacific Highway 2 u 16.89 53.57 ± 0 Brushwood Ridge 2 u 16.87 39.28 ± 3.51 Herbert Park 3 u 16.45 28.56 ± 7.43 Tugun Hill Conservation Area 1 u 14.38 32.14 Summerhill Court Reserve 1 u 9.53 50 Miami Conservation Area 1 u 4.41 10.71 Burleigh Knoll National Park 1 u 4.15 85.71

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Table 6.2: Landscape, environment and tree variables quantified at each plot.

Variable Description of variable Landscape variables Site area Area in hectares for each forest patch. Logging Index Presence/absence of logging history from historical aerial imagery. Fire Index Presence/absence of fire history from historical aerial imagery Connectivity (%) Percentage of site perimeter connected to other forested areas. Environmental variables Angle of slope (°) Calculated from GIS layers (Chapter 3. Equation 3.2). Aspect (°) Directional orientation. Elevation (m) Obtained from GIS mapping layers. Urbanisation Index Calculated from GIS data on infrastructure, cadastre and regional ecosystems (Chapter 3. Equation 3.3). Density of hollow-bearing Density of hollow-bearing trees per hectare per site. trees Tree disturbance index Combined tree disturbance parameters i.e. fire, termites, wind damage and epicormic growth. Stand density Density of all trees per hectare per site. Tree species diversity Tree species diversity calculated using the Shannon Wiener diversity index H = – ∑ (pi) (ln pi).

Diurnal surveys The point count method was used to identify hollow-using avian fauna observed or heard at each plot. Point count methods are generally highly efficient in terms of data quality and survey effort. This method allows for data to be collected in relation to habitat use and can be used to measure relative and absolute abundance. Point counts are also frequently used to survey bird communities in various habitats (Sewell and Catterall 1998; Luck and Daily 2003; Sutherland 2006; Elliott et al. 2010). Surveys were undertaken between 24/11/2009 and 14/03/2011. Start times at each site were staggered, but always took place between 9.00 a.m. and 11 a.m. Surveys were undertaken for 30 minutes from the central point of each plot. There was an initial settling in period of 10 minutes prior to each survey to allow birds to become accustomed to human presence. One observer would then identify birds by sight and

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individual calls heard. Any other hollow-using vertebrate fauna observed were also recorded.

Spotlighting Spotlighting is frequently used to survey nocturnal fauna in Australian environments (Ward 2000; Eyre 2006) and can be applied to both plot (Garden et al. 2007a) and transect surveys (Eyre 2006). For this study all plots were surveyed a minimum of three times between 12/07/2011 and 29/05/2012 with spotlighting start times being rotated across each site and plot per visit. To account for variable species behaviour plots were extensively searched for hollow-using fauna by walking slowly through the plot for 10 minutes using a 50-100 watt variable spotlight. Travel times and any species observed while walking to plot points were also recorded. All hollow-using mammalian fauna observed were identified to species level assisted by individual variations in eyeshine, as well as size, colour and markings, gliding style and individual character traits (Table 6.3). If an animal was observed leaving a hollow the species of tree was also recorded. Hollow-using fauna heard were also recorded at each plot. As the resultant survey effort for each site was a combination of travel and survey times, fauna abundance measures were standardised to the number of observations per hour of survey effort prior to analysis.

Stagwatching Stagwatching is commonly used to observe or detect hollow using vertebrate fauna in Australia (Smith et al. 1989; Lindenmayer et al. 1990; Lindenmayer et al. 1994a; van der Ree and Loyn 2002). Previous stagwatching studies make use of large numbers of observers (Smith et al. 1989), which were not available for this study. Two observers were stationed at the plot centroid, each scanning one half of the plot for signs of wildlife emerging from hollows. Stagwatching began approximately 10 minutes before sunset and ended 15 minutes after sunset with each plot having a total survey effort of 50 person minutes. Any hollow-using fauna observed or heard were identified to species level. Tree species and hollow characteristics that animals were observed emerging from were also recorded. All plots were surveyed once between 12/07/2011 and 30/05/2012.

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Table 6.3: Features that can be used to distinguish the different species of gliders and possums (Menkhorst and Knight 2001; Lindenmayer 2002).

Common name Scientific name Distinguishing features Feathertail glider Acrobates pygmaeus Small size, pen-quill-like tail, white underbelly fur and rapid movements. Eyeshine: tiny brilliant white. Sugar glider Petaurus breviceps Medium-sized body,' yap-yap' call. Tail is often white-tipped. Smaller than the Squirrel glider and face is shorter. Ears are shorter and more oval than the Squirrel glider. Eyeshine: pale red. Squirrel glider Petaurus norfolcensis Similar to Sugar glider but larger and the tail is larger and more thickly furred. Tail is sometimes black-tipped but never white- tipped. Belly fur is always white; deep guttural gurgle call reminiscent of the last part of the Yellow-bellied glider call. Eyeshine: pale red. Yellow-bellied Petaurus australis Highly active, loud gurgling call, pale-red glider eyeshine. Ears not heavily furred like the Greater glider. Body fur is olive-grey and black on the limbs. Eyeshine: pale red. Greater glider Petauroides volans Pendulous tails, bright white eye-shine, slow movements and prolonged periods of inactivity. Largely silent. Ears heavily furred. Eyeshine: brilliant white. Common brushtail Trichosurus caninus A cat sized arboreal and terrestrial possum: possum ears obviously longer than broad. Fur colour, length and density variable in E. Australia mostly uniform silver grey above with cream underparts; belly often stained yellow-orange; tail black, only slightly brushy. Call a characteristic loud series of rattling nasal coughs and hisses. Eyeshine: red/orange. Mountain brushtail Trichosurus vulpecula A large heavy-set possum typically of wet possum forests; upper parts uniformly dark grey, but can be blackish, dark brown or reddish: underparts cream or yellow-orange: tail thick and black. Ears broadly oval. Call a short sequence of sharp grunts of coughs, not a rattling series as in the common brushtail possum. Eyeshine: red. Common ringtail Pseudocheirus peregrinus Species in S.E Qld have bright orange face, possum limbs and flanks with rufous underparts and white patches behind and below ears; blackish flecked rufous back. Tail rufous with white terminal quarter, short-haired, tapering, prehensile, often carried in a coil. Call, soft, high-pitched, insect-like chirruping and harsher ‘zip zip’. Eyeshine: red.

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Anabat surveys A ‘space for time’ approach (Fahrig et al. 1995; Germaine and Wakeling 2001) was used to rapidly sample micro-bat species richness from all sites between August 2011 and November 2011 using an Anabat II™ detector (Titley Electronics, Ballina, Australia). Active monitoring with Anabat II™ recorded echolocation calls digitally to a 2 Mb flash card. The detector was placed at ground level, angled at 45° with a researcher monitoring and saving calls as they were detected for a period of 20 minutes. No sampling was conducted during rain events or periods of high winds. Plots were surveyed on three occasions with each plot having a total survey effort of 1 hour. Start times were rotated across each plot per visit; the earliest surveys starting at dusk 17:00 hours and the latest surveys commencing at 20:00 hours.

Microbat surveys are routinely undertaken using harp traps (Hourigan et al. 2008) or echolocation recording (Duffy et al. 2000) and subsequent identification. While some studies suggest that restricting methods to acoustic surveys alone has certain limitations (Barclay 1999; Reardon 2009; Adams et al. 2010), others have found that these provide reliable measures of micro-bat communities (Dixon 2012). During a study undertaken in Brisbane, a highly urbanised region in south-east Queensland, significantly more species were identified using acoustic surveys alone than either harp traps alone or the two methods used in conjunction (Hourigan et al. 2008). The space for time methodology used in this survey is expected to provide a valid assessment of insectivorous bat assemblages within the urban landscape.

Bat call identification Echolocation calls, stored as individual files, were identified to species level using Analook™, Bat Calls of New South Wales (Pennay et al. 2004), and Key to Bat Calls of south-east Queensland and north-east New South Wales (Reinhold et al. 2001). Positive identifications were only made where a minimum of three pulses classified to the same species were identified. As part of this process pulses were excluded if they were unable to be identified to species level. Species richness was calculated by the assessment of presence/absence data from each site for all nights surveyed.

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Pole camera Previous studies have used pole mounted cameras to monitor wildlife in a variety of elevated microhabitats e.g. nest boxes, nests and tree hollows (Purcell 1997; Richardson et al. 1999; Huebner and Hurteau 2007; Luneau and Noel 2010). For this study a small, lightweight infra-red security camera (model: CCG8110 from OzSpy Ltd.) hard wired to a rechargeable 9 volt power source was mounted to a flexible arm at the top of a 12 m fiberglass telescopic pole. Video footage (3GP format) from the camera was transmitted via a cable to a monitor and digital recording device (2.4" TFT-LCD monitor with 640 x 240 pixel resolution). At each accessible hollow from all sites surveyed (i.e. those ≤ 12 m n = 500 hollows) the pole was extended to the height of the hollow and the camera was manoeuvred into position to obtain a clear view of the hollow cavity. For each hollow surveyed, the presence or absence of any vertebrate fauna was recorded. Any animals found were identified to species level, as well as the number of occupants. Other signs of hollow-use such as eggs, feathers or fur were also recorded. Surveys were conducted between 16/01/2012 and 11/05/2012 with each hollow being surveyed once.

Species occupancy The presence of a species at a site over time provides information about the relative value of the habitat for each species. Therefore, the occupancy of sites i.e. the proportion of those where a species is present (MacKenzie et al. 2006), provides a useful means to assess the value of these sites for hollow-dependent fauna. To evaluate the presence or absence of a species at any given location with any confidence requires more than two surveys be undertaken (Wintle et al. 2005). A minimum of eight surveys per site were undertaken during this study, determined by site area and the number of plots within each site. Site occupancy was determined by quantifying the presence of individual species from all surveys which were then grouped as either birds or mammals to broadly compare the occupancy between these two taxonomic groups.

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Statistical analysis

Determinants of species richness and relative occupancy of hollow-using vertebrate fauna along an urbanisation gradient Eleven predictor variables were modelled; site area, the urbanisation index, connectivity, slope, aspect, elevation, individual tree disturbance index (i.e. the presence/absence of fire, termite, wind damage and epicormic growth), tree species diversity, hollow-bearing tree density per hectare, fire and logging indices. Predictor variables used in the models were selected a priori from those known to have strong biological influences. A correlation matrix was performed to determine colinearity between variables using Spearman’s Rank correlation. Variables with a correlation coefficient value |r| > 0.7 were considered to be highly correlated (Garden et al. 2010). The decision to retain or remove variables was based on its known/perceived ecological significance as well as the degree of colinearity. Aspect was removed as an environmental variable for two reasons. Firstly, it was found to perform weakly in the models and secondly there was insufficient data in the case of wildlife use of tree hollows to undergo rigorous analysis. Remaining predictor variables (Table 6.3) were retained. Data transformation is recommended where explanatory variables are of dissimilar nature (Zuur et al. 2010); such as those used in this analysis. An arcsine square root transformation was undertaken for proportional explanatory variables while a logarithmic transformation (ln[x+1]) was undertaken for all other explanatory variables due to scale differences. No transformations were made to the response variable.

As plots were nested within sites, Poisson Generalised Linear Mixed Effects Models (GLMM) were used to assess the importance of tree, landscape and environmental variables on the relative abundance of hollow-using species. Interaction models between predictor variables were included in analyses and a total of 26 candidate models were run including the global model (Appendix 5). Models were ranked according to their Δi AIC values. The higher the Δi value, the less accurate the model for the given data, thus the best approximating models that had a Δi ≤ 4 are presented (Burnham and Anderson 2002). Model averaging was applied to remove uncertainty in some of the final model selections. Variables were ranked according to their relative overall importance by summing the Akaike weight (wi) from all top model

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combinations where each of the variables occurred. Statistical analyses were conducted using R 3.1 statistical software (R Core Team 2012) and packages glmmML (Brøstrom 2012) and MuMIn (Barton 2012).

A paired t-test was used to compare the difference between mammal and bird occupancy across all sites. Only these two taxonomic groups were chosen as there were insufficient data for other faunal groups. Analysis was conducted using the SPSS statistical program (IBM. Corporation. 2012). Linear regressions were performed to determine whether mammal and bird occupancy by site was a function of tree species diversity. A MANOVA comparing four factors (occupancy, taxon, urbanisation index and site) was then run in Statistica (Statsoft. 2005) to compare the difference in rates of occupancy between birds and mammals along the urbanisation gradient.

A Chi-square (χ²) goodness-of-fit test compared avian and mammalian species occupancy observed along the urbanisation gradient to expected occupancy based on the assumption that each part of the gradient was utilised in proportion to habitat availability, α was set at 0.05 for all occupancy analyses.

Non-metric Multidimensional Scaling (nMDS) assessed similarities in species occupancy along the urbanisation gradient using the Bray-Curtis similarity index. A Kruskal’s stress test for measure of fit was performed prior to undertaking a Principle Coordinates Analysis (PCO) to measure the distances between sites on the ordination to match the corresponding dissimilarities in community structure. A Pearson rank correlation with a threshold |r| value of 0.6 was then used to identify those species accounting for the variation in occupancy across the gradient. A total of nine sites had less than four species and were excluded from analysis. This analysis was performed using the statistical program Primer-E (Clarke and Warwick 2001b).

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Results Forty-two native hollow-using vertebrate species were observed during this study. These species included 13 mammals, 19 birds, five frogs and four reptiles (Table 6.6). The Rainbow Lorikeet, Trichoglossus haematodus (250) and the Little Corella, Cacatua sanguinea (230) were found in very high numbers at two sites due to the presence of roosts. When individual counts of species are taken into consideration the common brushtail possum, Trichosurus vulpecula (58) and Laughing Kookaburra, Dacelo novaeguineae (39) represented the highest number of hollow-using species across all sites. Spotlighting as a survey method recorded the greatest number of species (26) followed by diurnal surveys (21) (Table 6.4). Karawatha Forest Park and Venman Bushland National Park had the highest diversity of hollow-using fauna with 21 and 18 species respectively (Table 6.5), while 11 other sites (including four control sites) had 10 or more species recorded.

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Survey method Total No. sites Relative species Species Common name Spotlighting Stagwatching Diurnal Pole Camera Anabat where found occupancy Mammals Petaurus breviceps Sugar glider 5 4 0 1 - 10 (13) 0.21 Petaurus norfolcensis Squirrel glider 5 3 0 0 - 8 (17) 0.18 Trichosurus vulpecula Common brushtail possum 10 0 0 1 - 11 (58) 0.47 Trichosurus caninus Mountain brushtail possum 2 0 1 0 - 3 (5) 0.09 Petauroides volans Greater glider 1 1 0 0 - 2 (3) 0.06 Pseudocheirus peregrinus Common ringtail possum 3 0 0 0 - 3 (3) 0.09 Rattus fuscipes Bush rat 2 0 0 0 - 2 (~21) 0.06 Insectivorous bats Chalinolobus gouldii Gould's wattled bat 0 0 0 0 13 13 (22) 0.32 Vespadelus pumilus Eastern forest bat 0 0 0 0 5 5 (9) 0.12 Scotorepens greyii Little broad-nosed bat 0 0 0 0 4 4 (5) 0.15 Saccolaimus flaviventris Yellow-bellied sheath-tail bat 0 0 0 0 4 4 (3) 0.19 Austronomus australis White-striped free-tail bat 0 0 0 0 3 3 (1) 0.03 Vespadalus darlingtoni Large forest bat 0 0 0 0 2 2 (3) 0.09 Birds Ninox strenua Powerful Owl 1 0 0 0 - 1 (2) 0.03 Ninox conivens Barking owl 2 0 0 0 - 1 (2) 0.06 Ninox novaeseelandiae Southern Boobook Owl 12 1 0 1 - 14 (29) 0.50 Aegotheles cristatus Australian Owlet Nightjar 6 0 0 0 - 6 (6) 0.18 Colluricincla harmonica Grey Shrike Thrush 0 0 1 0 - 1 (2) 0.06 Cacatua galerita Sulphur Crested Cockatoo 4 1 3 0 - 8 (25) 0.26 Calyptorhynchus funereus Yellow-tailed Black Cockatoo 0 0 1 0 - 1 (6) 0.03 Calyptorhynchus lathami Glossy Black Cockatoo 0 0 2 0 - 2 (7) 0.06 Cacatua roseicapilla Galah 1 1 2 0 - 4 (~29) 0.15 Cacatua sanguinea Little Corrella 2 0 0 0 - 2 (~230) 0.06 Alisterus scapularis King Parrot 0 1 3 0 - 4 (10) 0.02 Trichoglossus chlorolepidotus Scaly Breasted Lorikeet 0 0 2 0 - 2 (4) 0.09

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Table 6.4 continued: The number of sites where vertebrate species were observed using each survey method. The total number of sites where a species was found as well as the total number of individuals recorded (in brackets) is also presented. Relative species occupancy across all sites is also estimated. Roosting sites or large numbers of individuals recorded at night are estimates and have a ~ next to their values.

Survey method Total No. sites Relative species Species Common name Spotlighting Stagwatching Diurnal Pole Camera Anabat where found occupancy Trichoglossus haematodus Rainbow Lorikeet 2 0 3 0 - 5 (~250) 0.24 Glossopsitta pusilla Little Lorikeet 0 0 1 0 - 1 (2) 0.06 Dacelo novaeguineae Laughing Kookaburra 11 0 2 0 - 13 (39) 0.47 Cormobates leucophaeus White Throated Tree Creeper 1 0 1 0 - 2 (4) 0.12 Todiramphus sanctus Sacred Kingfisher 1 0 3 0 - 4 (11) 0.09 Platycercus adscitus Pale Headed Rosella 0 0 2 0 - 2 (7) 0.03 Platycercus eximus Eastern Rosella 3 0 1 0 - 4 (11) 0.06 Chenonetta jubata Australian Wood Duck 1 1 1 0 - 2 (10) 0.09 Frogs Litoria fallax Eastern dwarf tree frog 8 0 0 0 - 8 (9) 0.63 Litoria tyleri Tyler's tree frog 4 0 0 0 - 5 (3) 0.38 Litoria caerulea Green tree frog 1 0 1 1 - 3 (2) 0.25 Litoria peronii Emerald tree frog 2 0 0 0 - 2 (5) 0.13 Litoria gracilenta Graceful tree frog 1 0 0 0 - 1 (1) 0.00 Reptiles Morelia spilota Carpet python 1 0 0 0 - 1 (1) 0.13 Dendrelaphis punctulatus Green tree snake 0 0 2 0 - 2 (2) 0.25 Boiga irregularis Brown tree snake 0 0 1 0 - 1 (1) 0.13 Varanus varius Lace monitor 0 0 2 0 - 2 (3) 0.25 Pogona barbata Eastern bearded dragon 0 0 2 0 - 2 (2) 0.25 Total number of species for each survey method 26 8 21 4 6

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Diurnal surveys For diurnal surveys 21 hollow-using species were recorded across all sites (Table 6.5) the Rainbow Lorikeet T. haematodus, Sacred Kingfisher Todiramphus sanctus, Australian King- Parrot Alisterus scapularis, and Sulphur-crested Cockatoo Cacatua galerita, were all seen at 3 sites. The greatest diversity of reptiles was also recorded during diurnal surveys, with only one mammal and one frog species being detected (Table 6.5).

Spotlighting Twenty-two species of hollow-using vertebrate fauna were found across all sites from 108 hours of survey effort (Figure 6.1). Species richness for mammals and birds reached a plateau after 62.5 and 94 hours respectively of spotlighting effort. The common brushtail possum T.vulpecula, Southern Boobook Owl Ninox novaeseelandiae and Laughing Kookaburra D. novaeguineae were the species most often recorded across all sites (Table 6.6).

20 7 18 6

16 14 5 12 4 10 bird 8 3 6 mammal 2 Bird richness 4

1 richnessMammal 2 0 0 0 10 20 30 40 50 60 70 80 90 100 110 Effort (hours)

Figure 6.1: The number of bird and mammal species found to occupy all sites as a function of cumulative survey effort during spotlighting surveys.

Stagwatching Eight hollow-using species were found during stagwatching surveys, with the sugar glider, Petaurus breviceps being the most common species found (4 sites in total) (Table 6.5). The greatest number of species found on one site during stagwatching was two; the common brushtail possum T.vulpecula and the squirrel glider Petaurus norfolcensis. The majority of sites (71%) recorded zero species using this method.

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Mammals Urban Spot- Stag- Pole Birds inc. bats Reptiles Amphibians Site gradient Diurnal lighting watching Camera Anabat Total (n) (n) (n) (n) Karawatha Forest Reserve c 4 13 1 0 3 21 11 6 1 1 Venman Bushland National Park c 2 13 1 0 2 18 8 6 1 2 Woody Hill, Hart's Reserve p 2 4 2 3 3 14 3 6 0 1 Nerang Forest Reserve c 4 6 0 0 3 13 6 4 1 1 Daisy Hill Forest Reserve c 1 6 2 0 3 12 6 6 0 0 Austenville Road r 1 9 1 0 0 11 5 2 0 2 Syndicate Rd p 3 6 1 0 1 11 5 1 0 1 Burleigh Knoll National Park u 5 2 1 0 1 9 8 0 0 0 Mt Nathan Conservation Area p 4 2 0 0 1 7 5 1 1 0 Numinbah Rd r 2 5 0 0 0 7 3 1 0 1 Kerkin Rd p 0 4 0 0 2 6 3 3 0 0 Miami Conservation Area u 4 2 0 0 0 6 4 1 0 0 Bonogin Conservation Area c 2 3 0 0 1 6 4 1 1 0 Brushwood Ridge u 2 4 0 0 0 6 3 1 1 1 Reedy Creek Conservation Area r 1 3 1 0 0 5 4 0 0 0 Aqua Promenade p 0 4 0 0 1 5 3 2 0 0 Elanora Conservation Area p 1 3 0 0 1 5 2 1 1 0 Behms Road r 2 3 0 0 0 5 1 1 1 2 Tallowwood Rd r 0 4 0 0 0 4 2 2 0 0 Tomewin Rd r 3 1 0 0 0 4 2 1 0 0 Carrington Rd r 0 3 0 0 1 4 0 1 0 2 The Plateau Reserve p 1 2 0 0 1 4 2 2 0 0 Andrews Reserve r 1 1 1 0 1 4 1 1 0 0

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Table 6.5 continued: The number of species recorded during surveys at each site; sorted by site with the highest number of species found. Some species were found by more than one method. Therefore, in some instances taxon totals will be different from method totals. Species tallies during spotlighting include those observed in transit to and from plots.

Mammals Urban Spot- Stag- Pole Birds inc. bats Reptiles Amphibians Site gradient Diurnal lighting watching Camera Anabat Total (n) (n) (n) (n) Pacific Highway u 0 3 0 0 0 3 3 0 0 0 Trees Road Conservation Area r 0 2 0 0 1 3 2 1 0 0 Piggabeen Rd p 0 4 0 0 0 4 2 0 0 2 Burleigh Ridge u 0 0 1 0 1 2 1 1 0 0 Wongawallen Conservation Area r 1 1 0 1 0 3 1 1 0 0 Herbert Park u 0 2 0 0 0 2 0 1 0 1 Gilston Rd r 0 2 0 0 0 2 0 0 0 2 Olsen Ave u 0 2 0 0 0 2 1 0 0 1 Summerhill Court Reserve u 0 2 0 0 0 2 1 1 0 0 Stanmore Park r 0 1 0 0 0 1 1 1 0 0 Tugun Hill Conservation Area u 0 1 0 0 0 1 0 1 0 0 Total species richness 21 22 8 4 6 20 13 5 5

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Anabat survey The detector recorded echolocation frequencies over 75 nights for 75 hours of survey effort. Six hollow-using insectivorous bat species were recorded across all sites; an additional three species of non-hollow using insectivorous bat were also found along the urbanisation gradient. Gould’s wattled bat, Chalinolobus gouldii was the most common species with 22 echolocations recorded, followed by the eastern forest bat, Vespadelus pumilus with nine echolocations recorded. All other species had five or fewer echolocations recorded (Table 6.5).

Pole camera A total of 196 hollows (9% of all hollows) were investigated. Only four observations of four species were made at two sites using the pole camera. A common brushtail possum, T. vulpecula, was observed occupying a 15 cm trunk-top hollow at 4.7 m in a Eucalyptus siderophloia (Figure 6.2) and a sugar glider, P. breviceps was found in a 15 cm trunk-main hollow at 6.2 m in a L. confertus. Two eggs, from a pair of Southern Boobook Owl, N. novaeseelandiae were found in a large ~40 cm branch-end hollow at 9.6 m in a Eucalyptus tereticornis (Figure 6.3). Eggs were identified upon observing the parents guarding the hollow during spotlighting surveys. A green tree-frog, Litoria caerulea was observed in a water-filled bayonet hollow at 1 m located in a dead tree (Figure 6.4). Overall, the results from the pole camera survey were poor with 97% of all hollows checked being unoccupied when surveyed. Ten hollows of the 196 investigated were found to be false hollows, in that they did not have openings, or where openings were present, these did not extend beyond one or two centimetres.

Figure 6.2: Trichosurus vulpecula found occupying a Eucalyptus siderophloia hollow.

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Figure 6.3: The eggs of Ninox novaeseelandiae found in a large E. tereticornis hollow.

Figure 6.4: Litoria caerulea in water filled hollow.

*The frog was discovered while using the pole camera. Given the location of the hollow (1m) this image was taken with an SLR camera (Photo: N. Rakotopare).

Tree species, hollow type and use Of the 916 available hollow-bearing trees 24 (2.6%) were found to be occupied. Hollow using fauna were observed entering or leaving 11 species of eucalypt trees including standing dead trees. Tree species use was dominated by three species (48%) in which fauna were observed entering or leaving hollows. Standing dead trees were found to be the most utilised tree type for vertebrate fauna (n = 8; 24%), followed by C. citriodora (12%) and L.confertus (12%). Trunk main (36%) was the most commonly used hollow type followed by trunk top (24%). A hollow entrance diameter of 10 cm was the size in which most wildlife (33%) were observed utilising, at a height between 0-5 m (36%). Seven species of birds, six mammals (including bats) and one amphibian were observed using hollows (Table 6.6). No reptiles were observed using hollows in this study.

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Hollow using fauna Pale- Sulphur- Scaly- Common Mt. Green headed Boobook crested Rainbow breasted Laughing brushtail Squirrel brushtail Sugar Greater tree- Galah Rosella Owl Cockatoo Lorikeet Lorikeet Kookaburra possum glider possum glider glider Bats frog Total Tree species E. racemosa 2 2 C. intermedia 1 1 1 3 L. confertus 2 1 1 4 E. grandis 1 1 2 C. citriodora.v 1 1 2 4 E. tereticornis 2 2 E. microcorys 1 1 1 3 dead 1 6 1 8 C.gummifera 1 1 E. carnea 1 1 E. pilularis 2 1 colony 3 Hollow type Bayonet 0 Butt hollow 1 2 3 Branch end 1 3 1 1 6 Branch main 2 1 1 4 Trunk main 3 2 2 1 1 1 1 1 12 Trunk top 1 5 2 colony 8 Fissure 0 Hollow size (cm) 10 2 2 2 2 2 10 15 2 1 2 1 6 20 1 2 2 2 7 30 2 4 1 7 50 1 2 colony 3

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Table 6.6: The tree species, hollow type and size that hollow-using fauna were found in, from all surveys. Highlighted rows represent the highest number of hollow-using species found in each category. Numbers are a representation of the number of each species found in each hollow.

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Drivers of hollow-using vertebrate species richness along an urbanisation gradient Site area was the most important variable influencing the distribution of hollow-using species. Of all 26 models tested, the model with the combined variables site area and tree species diversity was the best fit when explaining the distribution of hollow-using species, followed by the model containing site area as a single variable (Table 6.7). Site area and tree species diversity were both positively correlated with the species richness of hollow using vertebrate fauna, whereas logging, fire, stand density and slope were negatively correlated with species richness. Apart from site area and tree species diversity, confidence intervals were poor in regards to all other variables along the urbanisation gradient (Table 6.8). As a reflection of the urbanisation gradient the urbanisation index did not explain any patterns in species richness along the gradient.

Table 6.7: Best approximating model comparisons of landscape and the urbanisation gradient variables associated with the number of hollow-using vertebrate species found across all sites. Only Akaike models with ∆i values ≤ 4 are presented.

Model AIC ∆i wi site area + tree species diversity 70.2 0.00 0.455 site area 71.8 1.62 0.203 site area + fire index + tree species diversity 72.8 2.64 0.120 tree species diversity 73.0 2.85 0.110 tree disturbance + tree species diversity 73.9 3.76 0.069

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Standard Confidence interval Relative variable Parameter Estimate error 2.5% 97.5% importance site area 0.167 0.066 0.037, 0.298 0.78 tree species diversity 0.687 0.316 0.066, 1.308 0.77 fire index -0.007 0.026 -0.058, 0.044 0.13 tree disturbance index 1.337 1.053 -0.726, 3.402 0.08 logging index -0.470 0.877 -2.191, 1.249 0.02 hbt density/ha 0.044 0.171 -0.291, 0.379 0.02 slope -0.824 0.856 -2.502, 0.853 0.01 urbanisation gradient 0.057 0.072 -0.085, 0.200 0.01 stand density -0.001 0.001 -0.004, 0.001 0.01 elevation 0.000 0.002 -0.003, 0.005 0.01 connectivity 0.005 0.005 -0.004, 0.015 0.00

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The species richness of hollow-using vertebrate fauna increased with tree species diversity (R2 0.577; p < 0.05) (Figure 6.5). Conversely, occupancy rates of hollow- using vertebrate fauna decreased with tree species diversity (R2 0.623; p < 0.05) (Figure 6.6).

26 24 22 20 18 16 14 12 10 Species richness 8 6 4 2 0 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 Tree species diversity

Figure 6.5: The relationship between tree species diversity and hollow-using vertebrate species richness across all sites.

0.18

0.16

0.14

0.12

0.10

0.08

0.06 Species occupancy 0.04

0.02

0.00

-0.02 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 Tree species diversity

Figure 6.6: The relationship between tree species diversity and hollow using vertebrate species rates of occupancy across all sites.

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Vertebrate assemblages and relative occupancy of fauna along the urbanisation gradient. The Australian Owlet Nightjar, squirrel glider, common brushtail possum and Gould’s wattled bat account for 26.9% of species variation observed among sites, with all of these species corresponding to non-urban areas of the urbanisation gradient (Figure 6.7). The relative abundance of birds and mammals was high along the urbanisation gradient compared to all other taxa but there was no difference in the occupancy rates between birds and mammals across all sites (paired t-test = 1.3; d.f. = 33; p = 0.197). There was however, a difference in occupancy rates between birds and mammals along the urbanisation gradient (F = 11.66; d.f. = 3, 110; p < 0.01). Control sites had lower occupancy rates than all other sites along the urbanisation gradient with occupancy being similar between urban, rural and peri-urban sites (Figure 6.8). Despite area being a significant determinant of species richness in the models, there was no difference in observed occupancy rates along the urbanisation gradient compared to expected rates for either taxon (χ² = 0.0037, d.f. = 3, 0.25 > p > 0.10). There was also no relationship between bird (R2 = 0.005; p = 0.7807) or mammal (R2 = 0.004; p = 0.7501) occupancy and site area.

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0.18 Birds 0.16 Mammals

0.14

0.12

0.10

0.08

Rate of occupancy 0.06

0.04

0.02

0.00 Control Peri urban Rural Urban Urban Gradient Figure 6.8: Occupancy rates of birds and mammals along the urbanisation gradient.

Discussion

General summary This study recorded 42 hollow-using vertebrate species along an urbanisation gradient, representing 34% of hollow-using species that are known to occur regionally in south- east Queensland and 15% nationally. Overall more species were found during spotlighting surveys (n = 22), confirming that spotlighting is an effective technique when surveying arboreal mammals (Catling et al. 1997; Goldingay and Sharpe 2004b; Goldingay and Sharpe 2004c). As no further species were found after 94 (birds) and 62.5 (mammals) hours of spotlighting effort this suggests that the species accumulation threshold for forest patches along the urbanisation gradient has been reached. While stagwatching was not overly successful during this study, other studies have found stagwatching to be effective when examining the relationship between tree species used as nest sites, the height of hollows and the type of hollow used by arboreal marsupials (Smith et al. 1989; Lindenmayer et al. 1990; Lindenmayer et al. 1994b; Lindenmayer 2002). In this regard stagwatching was able to provide in indication of the type of hollow used by vertebrate fauna for this study. 129

The application of extendable pole cameras to investigate tree hollows has received mixed reviews. All studies report similar problems associated with height restrictions, precluding many hollows from investigation thus impacting results (Purcell 1997; Richardson et al. 1999; Huebner and Hurteau 2007; Luneau and Noel 2010). During the current study 500 hollows were identified as potential survey hollows for use with the pole camera. However, many were inaccessible due to the location of the tree/hollow; or the hollow entrance was at an awkward angle, while some were false hollows. As a result only 39% of the 500 potential hollows could actually be surveyed. False hollows are tree deformities (particularly branch hollows located on the top side of branches) or had openings that did not have a depth beyond one or two centimetres. In addition, the classification of hollows in this study was made during ground based surveys where the limitations of such surveys have been acknowledged (Harper et al. 2004; Koch 2008; Stojanovic et al. 2012). Nevertheless, they are useful for providing relative, rather than true hollow abundance surveys (Koch 2008).

Tree species, hollow type and use Standing dead trees are recognised as an important structural feature for hollow-using fauna within Australian forests (Moloney et al. 2002; Koch et al. 2009) and this study supports these findings in that standing dead trees accounted for the most observations of use by fauna (24%). This is likely to be due to the fact that there is a greater likelihood of dead trees to contain hollows (Eyre 2005; Harper et al. 2005a; Beyer et al. 2008). The findings from this study concurred with these previous studies, in that dead trees accounted for the most hollow-bearing trees across all sites (13.5%), as well as containing the most hollows (15.4%) (Chapter 3).

Most hollows in eucalypts form in branches (i.e. bayonet and branch end) due to the process of branch shedding and consequently these hollow types account for most hollows found in open forests (Gibbons and Lindenmayer 2002). Hollows with a relatively small entrance size i.e. 10 cm are often preferred by hollow-using vertebrate fauna and were found to have 44% occupancy rates in the forests of south-eastern Australia (Gibbons et al. 2002) and 34% in Tasmania (Koch et al. 2008b). A similar pattern in hollow sizes was found in this study where hollows with an entrance size of 10 cm were used by 33% of species found utilising hollows, while the majority (36%) 130

of hollows used by fauna in this study were located at a height ranging between 0-5 m. Apart from one frog and bird species, all species utilising hollows at this height were possums and gliders. This suggests that hollow height is not a constraint for these species, the findings of which have been reported in a similar study (Traill and Lill 1997). This provides valuable information when evaluating the suitability of hollow- bearing trees for retention (Gibbons et al. 2002; Koch et al. 2008b; Goldingay 2011). Ogden (2009) undertook a study of hollow-use in Karawatha Forest Park (a control site in this current study) and found the occupancy rate to be 10%. A similar pattern was found in this study with an overall low occupancy rate of 2.6% across all sites.

The impacts of the urbanisation gradient on species richness Within urban environments common species are often more prevalent than specialists (Pearman and Weber 2007; Gaston 2010b; Luck and Smallbone 2010). A similar pattern was observed in this study where the two species most frequently found across the urban gradient were the common brushtail possum and Laughing Kookaburra. These species are known to be locally ‘common’ and would be categorised as urban winners. Common species have the ability to exclude other species from resources (Laurance 1991) as well as being able to move through, and live within, the urban matrix, therefore tending to dominate these environments (Garden et al. 2006). Common species however, contribute much of the structure, biomass, and energy turnover to the majority of terrestrial systems and can exert a significant influence on environmental conditions; thus enabling other species to co-exist (Pearman and Weber 2007; Gaston 2010b). Gaston and Fuller (2007) argue that even a small decline in the abundance of common species can result in large absolute losses of individuals and biomass in general, and as such, common rather than rare species are the principle drivers of spatial variation and species richness. Potentially, this is more of a problem in environments that have a greater degree of fragmentation and isolation (Tilman et al. 1994; Gaston 2010b) such as urban ecosystems.

Studies of species along urbanisation gradients often report a peak in diversity at moderate levels of development with reduced species richness occurring at urban centres (Sewell and Catterall 1998; Blair 2004; Smith and Wachob 2006). For hollow- using fauna it appears that this pattern does not hold, as this study found no relationship 131

between species richness overall and the degree of urbanisation. It is also important to note that comprehensive bird surveys were not completed in this study, and results may therefore not provide a useful comparison with other studies assessing bird communities along urban gradients. Findings from the current study also suggest that some species are better able to exploit the urban gradient, in particular Gould’s wattled bat, Chalinolobus gouldii. Chalinolobus gouldii favours large flyways and low levels of forest clutter (Lloyd et al. 2006) and is therefore highly adaptable to foraging in cities, making them one of the most common city bats in Australia (Richards et al. 2012). The study did find, however, that micro-bats responded differently to general predictions, as species that occupy the forest niche high in the canopy, fly fast and have little need for great manoeuvrability, adapt well to the uncluttered structure of urbanised areas (Threlfall et al. 2011) (unpublished data). Micro-bats are common in urban environments and many species (60%) are dependent on hollow-bearing trees as roost sites (Richards et al. 2012), highlighting the importance of this resource for this class of mammals alone. They are however; still highly reliant on hollow-bearing trees and thus the retention of forest patches is crucial to their survival.

The effects of the urbanisation gradient, landscape and environmental variables on fauna The urbanisation gradient was not found to have an impact on species richness, despite control sites having the greatest species richness overall. This is due in part to all species found during the course of this study being classified as ‘common’ and being able to adapt to the urban landscape. Control sites were larger forest patches with some having greater connectivity to surrounding areas and it could be expected that these sites would therefore support more species. Landscape ecology theory predicts that such sites would not only have a greater number of species but also greater abundance (van der Ree et al. 2003; Rhodes et al. 2006). This is because larger patches may be less disturbed from matrix variables such as edge effects, isolation or urbanisation and thus capture more environmental variability (Lindenmayer and Fischer 2006). In the current study, site area was found to be the most important variable in relation to species richness along the urbanisation gradient. Previous studies have unanimously agreed that site area is the driver behind species richness (Brotons et al. 2003; Lindenmayer and Fischer 2006; Ewers et al. 2007; Catterall et al. 2010). This highlights that the

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retention of species rich communities in urbanising landscapes is inextricably linked to the retention of larger, contiguous forest patches.

Other deterministic factors beyond the site area may also be in operation, particularly at the site scale. Plant communities frequently regulate ecological processes, community structure and environmental services (Hooper and Vitousek 1997; Symstad et al. 1998; Kahmen et al. 2005; Young et al. 2011). Previous research has identified that vegetation structure, composition and plant species richness strongly influences overall species richness and species rates of occupation along the urbanisation gradient (Gibbons and Lindenmayer 2002; Ross et al. 2002; Catterall 2004; McElhinny et al. 2005). Vegetation is an important resource for many faunal species, with the drivers of species diversity for both flora and fauna changing depending on unique site-specific environmental conditions (Tews et al. 2004). This is particularly pertinent for Australian environments where fauna are heavily dependent on the availability of hollow-bearing trees (Goldingay 2009; Ball et al. 2011; Goldingay 2011; Davis et al. 2013). Within this study, site area and tree species diversity were the most significant variables when accounting for species richness.

Tree species diversity had a strong positive influence on the species richness of hollow- using fauna. Conversely, tree species diversity had a negative influence on occupancy rates of fauna along the urbanisation gradient. This may however, be due to the low numbers of individuals found on many sites. These results highlight the importance of a holistic approach when planning conservation and biodiversity management strategies, based on the individual site requitements (Felton et al. 2010) and not focusing on spatial elements such as area and isolation alone (Holland and Bennett 2009).

Hollow-bearing trees as a resource face ongoing pressures through urbanisation (Chapter 5) and the effects of this pressure will be obvious at the landscape scale. For example, fauna in urban forest patches may be isolated to such a degree that relict populations now exist at higher densities than in more contiguous environments. The data presented here provide some support for this theory given that the occupancy rates of fauna where generally greater in smaller more urbanised forest patches than larger control sties. Fauna were therefore more readily detected in smaller urban fragments.

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Disturbance in the form of human landscape modification typically leads to local or regional declines in species richness (Shochat et al. 2006). This is also greatly influenced by human land-use policy along the gradient which may be solely responsible for species richness at any given location (Chapter 2) (Luck and Smallbone 2010). In general, the results presented here concur with the ‘habitat heterogeneity hypothesis’ (Wintle et al. 2005), which assumes that structurally complex habitats may provide more environmental resources and in turn increase species diversity. The presence of keystone structures, such as hollow-bearing trees, is therefore positively correlated to the species diversity-habitat heterogeneity relationship (Tews et al. 2004).

This chapter set out to quantify the richness and occupancy of hollow-using vertebrate fauna found within forest patches along an urban gradient. In the sampled population the hypothesis was; that smaller, isolated, urban habitats would support more depauperate communities, dominated by common species. While the predictions about the dominance of common species along the gradient hold, richness was not strongly influenced by the gradient for these hollow-using fauna. It is important to realise that common species can sometimes be at risk of rapid decline and that complacency in their conservation can be problematic (Lindenmayer et al. 2011). There is a need to identify, monitor and alleviate significant events that lead to declines in species numbers (such as the loss of hollow-bearing trees) which would involve paying more attention to the needs of common species (Gaston and Fuller 2007). Therefore, in terms of the implications for conservation it is paramount to undergo continual assessment of current management strategies to ensure the constant supply of tree hollows into the future for all fauna. The findings here have highlighted the range of variables that are at play along the urbanisation gradient. Therein, caution is necessary to avoid making generalised statements about the value of forest patches to hollow-using vertebrate fauna based purely on their size and location on the urbanisation gradient.

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Chapter 7: General Discussion

The importance of hollow-bearing trees to faunal communities is generally acknowledged globally (Wormington et al. 2002; Aitken and Martin 2004; Eyre 2005; Wormington et al. 2005; Ball et al., 2011; Beaven and Tangayi 2012), although there is scant reference to this resource in relation to urban forest patches. Therefore, this study aimed to identify the biotic and abiotic factors influencing hollow-bearing tree availability, density and use within forest patches along an urbanisation gradient at a landscape scale. While I acknowledge that hollow-bearing trees do exist in other parts of the landscape such as, parks, back yards and within non-indigenous tree species, this thesis has been restricted to trees belonging to the Myrtaceae family within forest patches. Therein, this thesis had a number of objectives and the findings presented in the discussions of Chapters 2-6 provide the details about how these objectives have been met. It is the purpose of this chapter to expand on these findings and how they pertain to wider ecological and urban planning theory.

In the first instance a literature review was undertaken to highlight the importance of hollow- bearing trees globally and the threats associated with their conservation. The review then focused on the importance of this habitat resource within the Australian context. While the importance of hollow-bearing trees is documented in the production forests of Australia (Eyre 2005; Koch et al. 2008b; Johnstone et al. 2013), there is relatively little literature available from urban landscapes, with only a handful of studies investigating the impacts of urbanisation on hollow-bearing trees (Harper et al. 2005a; Harper et al. 2008). As such, the findings from the current study provide an important contribution to our understanding of the relationship between hollow-bearing trees as a habitat resource and impacts of urban development.

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A number of general themes emerge from this work, building on previous studies assessing biotic responses to urbanisation. The first key outcome reported in Chapter 2 highlighted the policy/implementation gap in providing for the conservation and management of hollow- bearing trees at the local level, particularly for non-production forests (Treby et al. 2014). This is an important issue, as the failure to apply conservation actions that preserve important habitat features contributing to the functional integrity of urban ecosystems at the local level fundamentally undermines the purpose of broader overarching legislative provisions. While many council respondents voiced their frustrations about not being able to ‘do much’ for hollow-bearing trees this appears to be driven more by institutional inertia than by the lack of policy (McAlpine et al. 2002; Calver and Wardell-Johnson 2004). Institutional inertia is widely acknowledged as a hurdle to conservation efforts globally (Karr 1990; Norman et al. 2004; Beunen 2006). Further implications of such ineffective policy measures pertain to the future planning provisions in urban regions. Urban planners need to consider various elements in their appraisal and delivery of development guidelines for urban areas, to meet the needs of present and future generations (Niemelä 1999; Albers 2006; McDonnell 2007; Gordon et al. 2009; Snep and Opdam 2010). Recognising the need to retain natural habitat remnants may be just one of these elements for sustainable urban development.

Linked to the concept of sustainable urban development is the need to understand how species, communities and specific habitat features respond to urbanisation. This is necessary to determine if there is any change in the functional capacity of the urban landscape to support biodiversity. Here the urban gradient has been proposed as a theoretical concept to facilitate the monitoring of these changes (McDonnell 2007) and much work has been done to assess how faunal communities respond to these gradients (Germaine and Wakeling 2001; Crooks et al. 2004; Johnson et al. 2008; Pillsbury and Miller 2008). These studies highlight some general patterns, such as biotic homogenisation (Knight 1999; Hooper and Sivasithamparam 2005; McKinney 2006). Homogenisation results in a reduction of available resourse within the landscape (Mckinney 2006), therefore the retention of hollow-bearing trees will provide structural complexity to the environment. The concepts of urban winners and losers in relation to the sensitivity to, and resilience of, some species to the urban transformation process (Threlfall et al. 2012; Cardilini et al. 2013; Davis et al. 2013) has also been revealed. The current study provides a novel contribution in that it quantified the distribution and abundance of an important habitat feature (i.e. hollow-bearing trees)

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highlighting that some of the patterns reported for wildlife may not hold for habitat features that are remnants of past land use practices.

Providing for the retention of functional elements of biodiversity in urban landscapes is complex. An important point for consideration is the time-scale at which various elements including policy, conservation practice and ecological processes operate (Fallding 2004). This study highlights the mismatch between these elements and considers the implications of these disparities for urban biodiversity. Each of the aforementioned issues is discussed in further detail below.

Policy-implementation gaps The analysis of the existing forms of legislative provisions and statutory regulations at National, State and local government levels completed in Chapter 2 revealed an overwhelming lack of policy transition into conservation action. It also found considerable amounts of confusion surrounding biodiversity, conservation policy and legislation at all levels of government within Australia, but particularly in relation to the conservation and management of hollow-bearing trees. This failure of the Australian administrative system to integrate broader level policy into local level urban planning has previously been noted by (Humphries 1989) and the current findings suggest that little has changed in the more than 20 years that have passed. These problems are in no way restricted to the Australian environment and similar situations have been reported from other countries. For example, in North America Karr (1990) revealed that the narrowly defined goals of the legislation designed to protect endangered species and ecosystems were largely inefficient in reducing the impacts of humans at a landscape scale. Elsewhere, a break down in the communication of legislation into on-ground implementation was also found to cause problems for conservation efforts in the Netherlands (Beunen 2006). In the United Kingdom, Brown and Grant (2005), list the divide between policy and implementation as one of the major barriers limiting the integration of biodiversity values into urban planning.

It should be noted that there were a small number of local councils surveyed in the current study that did have specific provisions in place for the conservation and management of

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hollow-bearing trees. This suggests that it is possible for local councils to enact broader policy and legislative provisions. This has also been demonstrated in the past where urban planning provisions in local council areas have assigned remnant habitats with high ecological values ‘special environmental amenity’ status to provide protection for these environments within urbanising landscapes (Humphries 1989). Therefore, while the current research has shown that policy implementation at the local level seems to be a perpetual and pervasive problem in certain areas, this may not be due to the lack of specific provisions such as tree preservation orders (Llewellyn 1984) or similar development controls.

Humphreys (1989) suggested a number of reasons behind the failure of statutory regulations to protect trees in urban environments. Amongst these is the policy-implementation gap, as demonstrated here, for the conservation of hollow-bearing trees. Additional issues were the failure of planning provisions to account for the ecological functioning of remnant habitats as well as the failure of regulatory procedures to engage with communities. The current study also provides a useful basis offering some perspectives to the long-term impacts of dysfunctional or weak legislation that fails to account for ecosystem functioning. Within Australia, the age at which a tree produces a large hollow usually occurs at 150 years (Ross 1998). During this time there could potentially be over 35 changes in Federal government, with as many, or more, changes in governance at the local level. This temporal scale mismatch between development planning and ecological analysis has previously been reported (Fallding 2004). This study highlights that; conservation legislation at all levels of government will need to be robust enough to defend itself from radical changes brought about by individual party policies (e.g. the proposed reintroduction of logging concessions within national parks in Queensland and world heritage sites in Tasmania). Furthermore, future legislation will also need to focus more on implementation and decision making processes, particularly at the local level, to achieve positive conservation outcomes within contemporary urban planning. While it appears that Local Agenda 21 provides the legislative backdrop for local action, the results from Chapter 2 suggest that far greater engagement with this process is required by local councils.

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Contemporary urban planning Global human populations are non-randomly distributed, with a bias towards urban centres (Zipperer and Pickett 2001; Gaston et al. 2010). Human population projections for Australia are also substantial and this is expected to have significant effects on both human quality of life and natural environments (Zipperer and Pickett 2001; Byrne et al. 2010; Bekessy et al. 2012). Despite these projections, there appears to be considerable ad hoc planning in regards to both the integration of biodiversity values (Fallding 2004; Bekessy et al. 2012) as well as the retention of urban resident amenity values (Byrne et al. 2010).

Chapter 2 highlighted the poor implementation of conservation actions targeting hollow- bearing trees at the local level within Australia. However, the specific issues raised above, extend beyond hollow-bearing trees and are symptomatic of a larger biodiversity planning problem. As Niemelä (1999) argued, there are three key pre-requisites needed to capture biodiversity into planning provisions. The first is an improved basic knowledge of urban nature. The second is an understanding of urban ecological processes and the third is the need of ecosystem-specific management schemes. As such the current study provides valuable information pertaining to the value of urban forests in each of these three areas. First, it provides the managers and planners of the Gold Coast with an inventory of hollow- bearing trees, their distribution and relative abundance but also how these resources are affected by urbanisation (Chapters 3 and 4). Second, it demonstrates how this particular keystone resource is likely to change over time considering future urban growth projections and highlights the importance of considering these longer time frames in future planning (Chapter 5). Finally, it highlighted the value of urban forest patches for the protection of not only hollow-bearing trees but also the fauna that are reliant upon these (Chapter 3 and 6).

While these findings can now be used in future urban planning decision making it is important that they are considered early in the development process as this has been a key limitation in the past (Fallding 2004). However, it is important to note that the current study focuses on the values of only one type of urban landscape element and its associated biota, i.e. natural forest patches in various urban settings. Within the urban context there is marked variation in the concept of urban greenspace and various typologies of park lands (Byrne and Sipe 2010). Additionally, there are differing social values placed on the importance of trees

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and natural areas where these are perceived as being sacred, utilitarian, decorative or hazardous with some sectors of the community being indifferent to them (Breuste 2004; Kirkpatrick et al. 2013).

These results highlight the various socio-political and ecological management facets that local councils need to address when managing for trees and natural habitats in general, within an urbanising landscape. Recent advances advocate the use of systematic conservation planning, or reverse conservation planning (i.e. identification of the areas most suited to urban development) to assist decision making for urban planners (Gordon et al. 2009; Bekessy et al. 2012). However, as Bekessey et al. (2012) highlight these systematic planning tools should be used to assist in decision making rather than being seen to provide prescriptive recommendation for urban planning. They also emphasised the importance of robust baseline data that are used to develop the planning models. It is important to note that an element not yet captured in these conservation planning tools is the human dimension, and in particular the social and amenity value placed on natural remnants by urban residents. Undeniably improved urban planning is required. One way forward lies with strategic and active adaptive management (McCarthy and Possingham 2006; Armitage et al. 2009). Here the various and often contradictory values from differing stakeholders can be addressed in the early stages of the planning domain in order to derive common objectives.

The opportunity exists for local Australian councils, with the support of overarching policy directives, to implement a more community based model for the management of urban forests. Environmental planners could therefore potentially follow a set of prescribed targets offering ecological justification for specific planning goals within urban environments that would enhance biodiversity as well as accommodating the interests and needs of community stakeholders.

Natural resource management: gradients, biotic responses and temporal scales The current study has revealed that urban ecosystems are important to biodiversity, and more importantly, that people and their activities create a unique set of circumstances for natural resource management within the urban landscape. These findings confirm the value of using

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the urban-rural gradient as a model to test the effects of the urbanisation process (McDonnell et al. 1993; McDonnell et al. 1997; Hahs and McDonnell 2006; Qureshi et al. 2013). Urbanisation creates a number of shifting effects depending on patch size, the loss and degradation of habitat and the amount and type of human disturbance (McKinney 2008). The urbanisation process has seen a reduction in species richness for birds, reptiles and amphibians, while for mammals there has been a shift to medium sized, generalist species globally (Ǻs 1999; Germaine and Wakeling 2001; Devictor et al. 2007; Evans 2010). Generally, plant communities have shown in an increase in species richness, depending on the level of urbanisation (Guntenspergen and Levenson 1997; Ross et al. 2002; McKinney 2008). Importantly, the responses detected for hollow-bearing trees did not follow predicted patterns (e.g. intermediate disturbance hypothesis) in that there was little variability in the density of this resource along the gradient. Conversely, the faunal responses reported were similar to those published previously (Germaine and Wakeling 2001; Crooks et al. 2004; Johnson et al. 2008) suggesting that the urbanisation gradient is exerting a much stronger influence on these taxa than their specific habitat requirements (i.e. for tree hollows).

In addition to the changing pace of urbanisation as a driver behind the variations detected in natural forest patches, was the importance of historical land use practices. The logging history of any given region results in a disjointed array of forest patches across the landscape (Gibbons and Lindenmayer 2002; Andrieu et al. 2011), and also alters the availability of resources within these disturbed landscapes (Flynn et al. 2011; Law et al. 2013). The importance of past logging on the Gold Coast was evident in this study and these practices were the driving factor influencing the density of hollow-bearing trees in urban forests (Chapter 4). The enduring effects of these past logging practices will need to be addressed by natural resource managers well into the future given the length taken before hollow formation occurs in eucalypt trees.

Previous work has shown that the combined effects of urbanisation and logging results in a reduction in overall stand density in urban remnants (Pickett et al. 2001). However, the mean density of hollow-bearing trees found in the current study (37.47 trees/ha) was greater than that reported from other urban and managed forests in Australia (Lamb et al. 1998; Moloney et al. 2002; Harper et al. 2005a; Eyre et al. 2010). Despite the greater prevalence of hollow-

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bearing trees on the Gold Coast this figure may overestimate the functional value of the resources since the majority of these hollows were small, potentially limiting their use by hollow-dependant fauna, as larger hollows are generally more valuable for wildlife (Saunders et al. 1982; Goldingay 2009; Johnstone et al. 2013). This result indicates that the absolute hollow-bearing tree densities reported here should not simply be taken at face-value but that their functional significance within the landscape should be considered. This has implications for management and future research directions.

Recommendations and further research The loss of hollow-bearing trees from across the landscape has been documented globally (Lindenmayer et al. 1991b; Mazurek and Zielinski 2004; Holloway et al. 2007; Politi and Hunter 2009; Smith et al. 2009). Past land use practices and urbanisation continue to threaten the persistence and availability of hollow-bearing trees in urban landscapes. As noted previously the density of hollow-bearing trees and hence individual hollows may be inflated on the Gold Coast due to the dominance of small and possibly unusable hollows within urban forest patches. While these trees may yet develop larger hollows, this process is a lengthy one (Whitford 2002; Goldingay 2011) and may not meet the immediate wildlife requirements. Future biodiversity planning for the retention of forest patches and their habitat features should therefore consider the need to supplement the existing resource.

Nest-boxes that target species-specific requirements are already being used within Australian urban landscapes as a means, of sustaining biodiversity until there are enough large hollows to support native fauna (Harper et al. 2005b; Durant et al. 2009). In some areas the deployment of nest-boxes did not appear to improve conditions for arboreal marsupials (Ball et al. 2011) highlighting that local conditions may play an important factor in nest-box use. On the Gold Coast nest-boxes are already being used as a means of remedial mitigation in the development of residential estates in peri-urban regions (Castley, pers. comm.). Therefore, nest-boxes may have a role to play in the management and conservation of hollow-using vertebrate fauna within the urban context (see Chapter 3). Further research is required that quantifies the success of such measures, or indeed, whether they are necessary in certain regions.

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Future research is also required in order to understand the relationship between hollow type and use by faunal species along the urbanisation gradient. While a number of previous studies have investigated hollow use in the managed forests of Australia (Lindenmayer et al. 1990; Lindenmayer 2002; Koch et al. 2009), very few have investigated the number and type of hollows required by individual wildlife species along the urban gradient. More research is required to improve our understanding of (i) the abundance and distribution of existing hollows, (ii) what species are using them, (iii) whether intra- and interspecific co-occupancy occurs, and (iv) the levels of intra and interspecific competition for hollows.

The current study focused on the ecological value of one particular feature of urban forest patches, i.e. hollow-bearing trees. However, as noted previously these habitat features comprise only one of the myriad urban landscape elements (Byrne and Sipe 2010). Research that quantifies the attitudes of local communities towards these forest patches as well as other urban green-spaces are urgently required to capture the needs of residents within a larger active adaptive management framework. Furthermore, Watts and Selman (2004) have shown that an improved understanding of the barriers limiting the integration of biodiversity aspects into urban planning can lead to better local implementation. Therefore, further research should strive to determine what limits such integration in the Australian urban context.

Finally, the advent of social media may provide a ready means to obtain opinion and feedback about urban planning projects (Schroeter and Houghton 2011). Social media platforms not only provide opportunities for research but also wider engagement among stakeholders. For example, sites such as ‘LinkedIn’, readily make the qualifications and attributes of individual stakeholders publically available. With this comes the potential ability to network and share ideas between all stakeholders, theoretically resulting in strong, cohesive active adaptive management plans. The utility of using these new technologies in guiding co-management strategies requires further study.

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List of Appendices:

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Appendix 1: Hollow using fauna of Australia

Table 1: Frogs recorded from hollows, and other arboreal or semi arboreal frogs that may use hollows.

■ indicates the species is confirmed to use hollows (Gibbons and Lindenmayer 2002). ▲ indicates fauna occurring in south-east Queensland. ♦ indicates conservation concern

Species Name Common Name Frogs ■ Lechriodus fletcheri Fletcher's Frog Litoria adelaidensis Slender Tree Frog Litoria bicolor Northern Dwarf Tree Frog ■ ▲ Litoria caerulea Green Tree Frog ▲ Litoria chloris Red-eyed Tree Frog Litoria citropa Blue Mountains Tree Frog ▲ Litoria denata Bleating Tree Frog ■ Litoria ewingii Brown Tree Frog, Whistling Tree Frog ▲ Litoria fallax Eastern Sedge Frog Litoria genimaculata New Guinea Tree Frog ■ ▲ Litoria gracilenta Dainty Green Tree Frog Litoria infrafrenata Giant Tree Frog, White-lipped Tree Frog Litoria jervisiensis Jervis Bay Tree Frog Litoria littlejohni Littlejohn's Tree Frog Litoria nyakalensis Nyakala Frog, Mountain Mist Frog ■ ▲ Litoria peronii Peron's Tree Frog Litoria personata Masked Frog ▲ Litoria phyllochroa Leaf Green Tree Frog ■ Litoria piperata Peppered Tree Frog ■ ▲ Litoria rothii Roth's Tree Frog ■ ▲ Litoria rubella Desert Tree Frog Litoria subglandulosa Glandular Frog Litoria splendida Magnificent Tree Frog ▲ Litoria tyleri Tyler's Tree Frog Litoria xanthomera Orange-thighed Frog Nyctimystes dayi Australian Lace-lid ▲ Cophixalus ornatus Ornate Frog

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Table 2: Reptiles recorded that use/may use hollows in Australia Species Name Common Name Geckos Cyrtodactylus louisiadensis Ring Tailed Gecko Diplodactylus ciliaris Spiny-tailed Gecko Diplodactylus intermedius Eastern Spiny-tailed Gecko Diplodactylus rankini Diplodactylus spinigerus Western Spiny-tailed Gecko Diplodactylus strophurus Diplodactylus taenicauda Golden -tailed Gecko Diplodactylus wellingtonae ■ Diplodactylus williamsi Gehyra australis Northern Dtella Gehyra baliola Gehyra catenata Gehyra dubia Gehyra oceanica Oceanic Gecko Gehyra purpurascens ■ Gehyra variegata Tree Dtella Hemidactylus frenatus House Gecko Lepidodactylus lugubris Mourning Gecko Lepidodactylus pumilus Oedura castelnaui Northern Velvet Gecko Oedura marmorata Marbled Velvet Gecko Oedura monilis Ocellated Velvet Gecko ■ Oedura reticulata Reticulated Velvet Gecko Oedura rhombifer ▲ Oedura robusta Robust Velvet Gecko ▲ Phyllurus caudiannulatus Phyllurus cornutus Northern Leaf-tailed Gecko Pseudothecadactylus australis Lizards Caimanops amhiboluroides ■ Chlamydosaurus kingii Frilled Lizard Diporiphora australis Diporiphora superba Hypsilurus boydii Boyd's Forest Dragon ▲ Hypsilurus spinipes Southern Angle-headed Dragon Lopthangus gilbertii Gilbert's Dragon Lophognathus longirostris Long-nosed Water Dragon ▲ Pogona barbata Bearded Dragon Pogona minor Dwarf Bearded Dragon Pogona mitchelli Pogona nullarbor Pogona vitticeps Goannas & Monitors ■ Varanus caudolineatus ■ Varanus gilleni Pygmy Mulga Monitor ■ Varanus scalaris Spotted Tree Monitor Varanus mitchelli Mitchell's Water Dragon Varanus semiremex Rusty Monitor

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Table 2: Reptiles in Australia recorded using tree hollows continued. Species Name Common Name Goannas & Monitors ■ Varanus timorensis Spotted Tree Monitor ■ ▲ Varanus tristis ■ ▲ Varanus varius Lace Monitor Skinks Crytoblepharus carnabyi Crytoblepharus plagiocephalus ■ ▲ Crytoblepharus virgatus Egernia formosa Egernia napoleonis ■ Egernia saxatilis Black -rock Skink ▲ Egernia striolata Tree Skink Emoia longicauda Eulamprus heatwolei ▲ Eulamprus martini Eulamprus sokosoma ■ Eulamprus tenuis ■ Eulamprus tigrinus Rainforest Water Skink Pseudemoia coventryi Pseudemoia orocrypta ■ Pseudemoia spenderi Spencers Skin Snakes ■ Morelia viridis Green Python ▲ Antaresia childreni Children's Python Antaresia maculosus Antaresia stimsoni ■ Morelia amethistina Amethystine Python Morelia bredli Centralian Carpet Python ■ ▲ Morelia spilota Carpet/Diamond Python ■ Boiga irregularis Brown Tree Snake ■ ▲ Dendrelaphis calligastra Northern Tree Snake ■ ▲ Dendrelaphis punctulata Common Tree Snake Hoplocephalus bitorquatus Pale-headed Snake ■ Hoplocephalus bungaroides Broad-headed snake ■ Hoplocephalus Stephensii Stephen's banded Snake

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Table 3: Birds that use tree hollows in Australia. The list does not include taxa that feed in hollows but do not otherwise use them (e.g. some species of corvids). Species Name Common Name Birds ▲ Phaethon lepturus White -tailed Tropic bird ▲ Tadorna tadornoides Australian Shelduck ▲ Tadorna radjah Radjah Shelduck ▲ Nettapus pulchellus Green Pygmy-goose ▲ Nettapus coromandelianus Cotton Pygmy-goose ▲ Chenonetta jubata Maned Duck ▲ Malancorhynchus membranaceus Pink-eared Duck ▲ Anas gracilis Grey Teal ▲ Anas castanea Chestnut teal ▲ Anas superciliosa Pacific Black Duck Anas platyrhynchos Mallard (Introduced) ▲ Anas thynchotis Australian Shoveler ▲ Gallirallus philippensis Buff-banded Rail ▲ Falco cenchroides Australian Kestrel ▲ Falco peregrinus Peregrine Falcon ▲ Geopelia cuneata Diamond Dove ▲ Probiscar aterrimus Palm Cockatoo ▲ Calyptorhynchus banksii Red-tailed Black Cockatoo ▲♦ Calyptorhynchus lathami Glossy-black Cockatoo ▲ Calyptorhynchus funereus Yellow-tailed Black Cockatoo Calyptorhynchus latirostris Short-billed Black Cockatoo Calyptorhynchus baudinii Long-billed Black Cockatoo Callocephalon fimbriatum Gang Gang Cockatoo ▲ Cacatua roseicapilla Galah Cacatua tenuirostris Long-billed Corella Cacatua pastinator Western Corella ▲ Cacatua sanguinea Little Corella Cacatua leadbeateri Major Mitchell's Cockatoo ▲ Cacatua galerita Sulpher-crested Cockatoo ▲ Nymphicus hollandicus Cockateil ▲ Trichoglossus haematodus Rainbow Lorikeet ▲ Psitteuteles versicolor Varied Lorikeet ▲ Trichoglossus chlorolepidotus Scaly-breasted Lorikeet ▲ Glossopsitta concinna Musk Lorikeet ▲ Glossopsitta pusilla Little Lorikeet Glossopsitta porphyrocephala Purple-crowned Lorikeet ▲ Eclectus roratus Eclectus Parrot Geoffrous geoffroyi Red-cheeked Parrot ▲♦ Cyclopsitts diophthalma Double-eyed Fig Parrot ▲ Alisterus scapularis Australian King Parrot ▲ Aprosmictus erythropterus Red-winged parrot ▲ Polytelis swainsonii Superb Parrot Polytelis anthopeplus Regent Parrot Polytelis alexandrea Princess Parrot Platycercus caledonicus Green Rosella ▲ Platycercus elegans Crimson Rosella ▲ Platycercus eximius Eastern Rosella ▲ Platycercus adscitus Pale-headed Rosella

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Table 3. Birds continued. Species Name Common Name Platycercus venustus Northern Rosella Platycercus icterotis Western Rosella Barnadius zonarius Australian Ringneck Purpureicephalus spurius Red-capped Parrot ▲ Northiella haematogaster Blue Bonnet ▲♦ Lathamus discolor Swift parrot ▲ Psephotus haematonotus Red-rumped Parrot ▲ Psephotus varius Mulga Parrot ▲ Mesopsitta cusundulatus Budgerigar ▲ Neophsephotus bourkii Bourke's Parrot ▲ Neophema chrysostoma Blue-winged Parrot Neophema elegans Elegant Parrot Neophema chrysogaster Orange-bellied Parrot ▲ Neophema pulchella Turquoise Parrot ▲ Neophema splendid Scarlet-chested Parrot ▲♦ Ninox strenua Powerful Owl ▲ Ninox rufua Rufous Owl ▲ Ninox conivens Barking Owl ▲ Ninox novaeseelandiae Southern Boobook ▲♦ Tyto tenebricosa Sooty Owl Tyto multipunctata Lesser Sooty Owl ▲♦ Tyto novaehollandiae Masked Owl ▲ Tyto alba Barn Owl ▲ Aegotheles cristatus Australian Owlet-nightjar ▲ Alseco pusilla Little Kingfisher ▲ Dacelo novaeguineae Laughing Kookaburra ▲ Dacelo leachii Blue-winged Kookaburra ▲ Syma torotoro Yellow-billed Kingfisher ▲ Todiramphus macleayii Forest Kingfisher ▲ Todiramphus pyrrhopygia Red-backed Kingfisher ▲ Todiramphus sanctus Sacred Kingfisher ▲ Todiramphus chloris Collared Kingfisher ▲ Eurystomus orientalis Dollarbird ▲ Cecropis nigricans Tree Martin Hirundo ariel Fairy Martin Zoothera heinei Russet Ground Thrush Zoothera lunulata Australian Ground Thrush Petroica rodinogaster Pink Robin Petroica multicolor Scarlet Robin ▲ Petroica phoenica Flame Robin ▲ Melanodryas cucullata Hooded Robin Melanodryas vitta Dusky Robin ▲ Colluricincla harmonica Grey Shrike-thrush Oreocia gutturalis Crested Bellbird ▲ Aphelocephala leucopsis Southern Whiteface Aphelocephala nigricinta Banded Whiteface ▲ Acanthiza reguloides Buff-rumped Thornbill Acanthiza inornata Western Thornbill ▲ Acanthiza uropygialis Chestnut-rumped Thornbill

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Table 3. Birds continued. Species Name Common Name ▲ Cormobates leucophaea White-throated Treecreeper ▲ Climacteris melanura Black-tailed Treecreeper Climacteris rufa Rufous Treecreeper ▲ Climacteris erythrops Red-browed Treecreeper ▲ Climacteris affinis White-browed Treecreeper ▲ Climacteris picumnus Brown Treecreeper Paralotus quadragintus Forty-spotted Pardalote ▲ Pardalotus striatus Striated Pardalote ▲ Poephila guttata Zebra Finch ▲ Neochima phaeton Crimson Finch ▲ Erythrura gouldiae Gouldian Finch ▲ Poephila cincta Black-throated Finch ▲ Aplonis metallica Metallic Starling Passer domesticus House Sparrow (Introduced) Passer montanus Tree Sparrow (Introduced) Sturnus vulgaris Common Starling (Introduced) Acidotheres tristis Common Myna (Introduced) ▲ Artamus minor Little Woodswallow ▲ Artamus cyanopterus Dusky Woodswallow ▲ Artamus personatus Masked Woodswallow ▲ Artamus supercilliosus White-browed Woodswallow ▲ Artamus leucorhynchus White-breasted Woodswallow

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Table 4: Microbats that use tree hollows in Australia Species Name Common Name Mammals/Microbats ▲ Saccolaimus flaviventris Yellow -bellied Sheathtail-bat Saccolaimus mixtus Cape York Sheathtail-bat Saccolaimus saccolaimus Bare-rumped Sheathtail-bat Taphozous kapalgensis Arnhem Sheathtail-bat Rhinolophus phillippinensis achilles Greater Horseshoe Bat* Rhinolophus phillippinensis maros Large-eared Horseshoe Bat* Hipposideros ater Dusky Leaf-nosed Bat Hipposideros diadema reginae Diadem Leaf-nosed Bat* Hipposideros semoni Semon's Leaf-nosed Bat Rhinonycteris aurantius Orange Leaf-nosed Bat ▲ Chalinolobus gouldii Gould's Wattled Bat ▲ Chalinolobus morio Chocolate Wattled Bat ▲ Chalinolobus nigrogriseus Hoary Wattled Bat Chalinolobus picatus Little Pied Bat Falsistrellus mackenziei Western Falsistrelle ▲♦ Falsistrellus tasmaniensis Eastern Falsistrelle ▲♦ Myotis adversus Large-footed Myotis ▲ Nycophilus bifax Northern Long-eared Bat ▲ Nyctophilus geoffroyi Lesser Long-eared Bat ▲ Nyctophilus gouldi Gould's Long-eared Bat ▲ Nyctophilus timorensis Greater Long-eared Bat Pipistrellus adamsi Cape York Pipistrelle Pipistrellus murrayi Christmas Island Pipistrelle Scoteanax ruepellii Greater Broadnosed Bat ▲♦ Scotorepens balstone Inland Broadnosed Bat ▲♦ Scotorepens greyii Little Broadnosed Bat ▲♦ Scotorepens orion Eastern Broadnosed Bat Scotorepens sanborni Northern Broadnosed Bat Vespadelus baverstocki Inland forest Bat ▲♦ Vespadelus darlingtoni Large Forest Bat Vespadelus pumilus Eastern Forest Bat ▲♦ Vespadelus regulus Southern Forest Bat ▲♦ Vespadelus vulturnus Little Forest Bat ▲ Austronomus australis White-striped Freetail Bat Charephon jobensis Northern Freetail Bat ▲ Mormopterus beccarii Beccari's Freetail Bat ▲♦ Mormopterus norfolcensis East-coast Freetail Bat Mormopterus sp. Eastern Freetail Bat Mormopterus sp. Hairy-nosed Freetail Bat Mormopterus loriae Little Northern Freetail Bat* Mormopterus planiceps (small penis sp) Inland Freetail Bat* Mormopterus planiceps (eastern long penis sp) Southern Freetail Bat* Mormopterus planiceps (western long penis sp) Western Freetail Bat*

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Table 5: Arboreal and scansorial mammals that have been recorded using hollows in Australia. Species Name Common Name Mammals ▲♦ Dasyurus maculatus Spotted -tailed Quoll Dasyurus geoffroii Western Quoll (Chuditch) ▲♦ Phascogale tapoatafa Brush-tailed Phascogale Phascogale calura Red-tailed Phascogale ▲ Antichinus flavipes Yellow-footed Antechinus (Mardo) Antichinus agilis Agile Antechinus Antichinus bellus Fawn Antechinus ▲♦ Antechinus stuartii Brown Antechinus ▲ Antechinus swainsonii Dusky Antechinus Antechinus leo Cinnamon Antechinus Sminthopsis dolichura Little Long-tailed Dunnart Sminthopsis murina Common Dunnart Sminthopsis leocopus White-footed Dunnart Myrmecobius fasciatus Numbat ▲♦ Pseudocheirus peregrinus Common Ringtail Possum Pseudocheirus occidentalis Western Ringtail Possum ▲ Pseudocheirus herbertensis Herbert River Ringtail Possum ▲ Pseudocheirus archeri Green Ringtail Possum ▲ Hemibelideus lemuroides Lemuroid Ringtail Possum ▲♦ Petauroides volans Greater Glider ▲♦ Petaurus australis Yellow-bellied Glider ▲ Petaurus breviceps Sugar Glider ▲♦ Petaurus norfolcensis Squirrel Glider ▲♦ Petaurus gracilis Mahogony Glider Gymnobelideus leadbeateri Leadbeater's Possum ▲ Dactylopsila trivirgata Striped Possum ▲ Trichosurus vulpecula Common Brushtail Possum ▲ Trichosurus arnhemensis Northern Brushtail Possum ▲ Trichosurus caninus Mountain Brushtail Possum ▲ Phalanger orientalis Grey Cuscus ▲♦ Cercartetus nanus Eastern Pygmy Possum Cercartetus consinnus Western Pygmy Possum Cercartetus lepidus Little Pygmy Possum ▲ Cercartetus caudatus Lont-tailed Pygmy Possum ▲ Acrobatus pygmaeus Feathertail Glider Tarsips rostratus Honey Possum ▲ Uromys causimaculatus White-tailed Rat ▲ Conilurus penicaillatus Brush-tailed Rabbit-rat ▲ Mesembriomys gouldii Black-footed Tree-rat ▲ Rattus fuscipes Bush Rat Rattus rattus Black Rat (Introduced) Rattus exulans Polynesian Rat (Introduced) Mus musculus House mouse (Introduced) Funambulus pennati Five-striped Palm Squirrel (Introduced) Mustela furo Ferret (Introduced) Vulpes vulpes European red fox (Introduced) Felis cat Cat (Introduced)

191

Appendix 2: Regional ecosystems containing wet and dry sclerophyll communities and their status found on the Gold Coast (Queensland Government 2009). Regional Ecosystem Status Short Description

12.3.2 Of concern E. grandis open forest 12.3.3 ENDANGERED E. tereticornis open forest 12.3.7 Not of concern E. tereticornis on alluvial plain 12.3.11 Of concern Eucalyptus spp. coastal E. tereticornis, E.siderophloia, C.intermedia, L. suaveolens 12.5.3 ENDANGERED E. tereticornis coastal 12.8.2 Of concern E. oreades open forest 12.8.8 Of concern E. saligna open forest 12.8.8a Of concern E. siderophloia open forest 12.8.14 Not of concern Eucalyptus spp. open forest E.eugenioides, E.biturbinata, E melliodora 12.9.4 Not of concern E. racemosa open forest 12.9/10.7a Of concern Eucalyptus spp. open forest E.tereticornis. E.crebra, E.siderophloia, C.intermedia, L. suaveolens 12.9/10.19a Not of concern Eucalyptus spp. open forest C.henryi, E.fibrosa, C.citriodora, E.acmenoides, A. leiocarpa. E.major 12.11.2 Not of concern Eucalyptus spp. open forest E.saligna, E.grandis. E.microcorys, E.acmenoides, L. confertus 12.11.3 Not of concern Eucalyptus spp. open forest E.siderophloia, E.propinqua, L. confertus 12.11.5 Not of concern Eucalyptus spp. open forest C.citriodora, E.siderophloia, L. confertus 12.11.5a Not of concern Eucalyptus spp. open forest E.tindaliae, E.carnea, C.citriodora, E.crebra, E.major, C.henryi, C.trachyphloia, E.siderophloia, E.microcorys, E.racemosa, E.propinqua 12.11.5j Not of concern Eucalyptus spp. open forest E.seeana, C.intermedia, E.siderophloia, C.citriodora, E.pilularis. L. suaveolens 12.11.5k Not of concern Eucalyptus spp. open forest C.henryi, E.fibrosa, C.citriodora, E.carnea, E.tindaliae, E.propinqua 12.11.9 Of concern E. tereticornis open forest 12.11.18 Not of concern E. moluccana open forest 12.11.23 ENDANGERED E. pilularis coastal

192

Appendix 3: Site and plot number with GPS co-ordinates. Site name Plot No: Point X Point Y Andrews Reserve 1 540694.906555 6889735.367500 Aqua Promenade 1 543257.308090 6883229.676150 Aqua Promenade 2 543486.139761 6883479.078970 Austenville Road 1 530666.301243 6881462.468190 Austenville Road 2 530680.325252 6881733.416220 Austenville Road 3 530400.165131 6881961.965800 Behms Road 1 533165.752583 6927484.785180 Bonogin Forest Reserve 1 534633.094556 6886460.527840 Bonogin Forest Reserve 2 534552.811936 6885552.921390 Bonogin Forest Reserve 3 535540.313051 6887455.392450 Bonogin Forest Reserve 4 535253.426648 6884450.604270 Bonogin Forest Reserve 5 536542.029379 6884461.419280 Bonogin Forest Reserve 6 535521.355264 6885454.544230 Brushwood Ridge 1 535463.239208 6908060.224510 Brushwood Ridge 2 535361.124621 6908301.810920 Burleigh Headland NP 1 544864.684547 6892205.053510 Burleigh Knoll NP 1 543154.255047 6893677.694360 Burleigh Ridge 1 544005.036810 6892144.909970 Burleigh Ridge 2 544387.718958 6892372.062960 Carrington Road 1 537423.106852 6887339.083980 Daisy Hill Forest Reserve 1 515129.010051 6944705.488550 Daisy Hill Forest Reserve 2 515274.707680 6945640.440550 Daisy Hill Forest Reserve 3 516120.289081 6944481.220770 Daisy Hill Forest Reserve 4 517053.987953 6944356.571600 Daisy Hill Forest Reserve 5 516977.879368 6943430.255700 Elanora Conservation Area 1 543351.664747 6886452.523860 Elanora Conservation Area 2 543417.600948 6886270.312100 Elanora Wetland 1 543576.186581 6889471.948350 Gelt Road 1 525705.860191 6918287.600500 Gilston Road 1 529615.672771 6898301.197930 Hellman Street 1 534782.659501 6906647.004210 Herbert Park 1 543237.979633 6891289.330760 Herbert Park 2 542997.138556 6891254.724850 Herbert Park 3 543253.691349 6891030.915890 Johns Road 1 531669.381782 6891612.371050 Karawatha Forest Reserve 1 507727.607731 6942529.452500 Karawatha Forest Reserve 2 508728.667171 6943502.908220 Karawatha Forest Reserve 3 506785.617820 6943369.089470 Karawatha Forest Reserve 4 507927.601614 6944422.802160 Karawatha Forest Reserve 5 506744.915788 6944399.440000 Karawatha Forest Reserve 6 507751.468544 6943379.635020 Karawatha Forest Reserve 7 508775.185812 6944306.744620 Kincaid Park 1 532202.735559 6900780.800900 Kirken Road 1 532162.520897 6922919.666220 Lakala Street 1 538479.489186 6905674.962470 Miami Conservation Area 1 542168.767812 6894693.885270 Mt Nathan Conservation Area 1 526332.928915 6903271.678560 Mt Nathan Conservation Area 2 525847.763866 6904131.802350 Mt Nathan Conservation Area 3 525576.926372 6905136.382180

193

Appendix 3: continued.

Site name Plot No: Point X Point Y Nerang Forest Reserve 1 532028.965643 6905420.456540 Nerang Forest Reserve 2 530263.009672 6905505.184520 Nerang Forest Reserve 3 529955.625783 6907263.104300 Nerang Forest Reserve 4 527720.308972 6905414.606190 Nerang Forest Reserve 5 528038.795086 6907334.221670 Numinbah Road 1 521672.286258 6883086.616550 Numinbah Road 2 521275.101355 6883727.335790 Olsen Avenue 1 537341.899703 6906520.653280 Olsen Avenue 2 537247.375321 6906106.382430 Pacific Highway, Currumbin 1 547483.722075 6887359.276130 Pacific Highway, Currumbin 2 547452.517681 6887686.574000 Pacific Pines Parklands 1 531600.463863 6909873.633300 Pimpama Conservation Area 1 535199.746344 6924216.326470 Pimpama Conservation Area 2 534347.183290 6924287.300750 Reedy Creek Conservation Area 1 537562.243502 6889388.059000 Reedy Creek Conservation Area 2 538085.796694 6888847.692140 Reedy Creek Conservation Area 3 538066.910600 6889393.403100 Reserve Road Parklands 1 528448.699157 6917116.209830 Stanmore Park 1 521764.900688 6930177.034770 Summerhill Court Reserve 1 535309.338108 6894408.814870 Syndicate Road 1 539047.034743 6883822.279950 Syndicate Road 2 538528.754842 6883205.819700 Syndicate Road 3 538218.744518 6882973.473620 Tallowwood Road 1 531120.025094 6886628.940760 The Plateau Reserve 1 523029.078759 6925041.471360 Tomewin Road 1 541158.644162 6881541.226480 Trees Road 1 540575.611833 6884050.718910 Trees Road 2 541002.687432 6884233.426600 Tugun Hill Conservation Area 1 547329.453671 6886315.700510 Upper Mudgeeraba Conservation Area 1 533680.658976 6890020.044500 Upper Mudgeeraba Conservation Area 2 534350.212959 6890505.841910 Venman Bushland National Park 1 519480.294031 6944925.190920 Venman Bushland National Park 2 519408.855771 6943972.463510 Venman Bushland National Park 3 520443.786257 6943875.546520 Venman Bushland National Park 4 520398.854195 6942926.637070 Venman Bushland National Park 5 519420.690871 6942938.186230 Venman Bushland National Park 6 518520.007062 6944989.216650 Wongawallen Conservation Area 1 523358.236927 6918341.414670 Wongawallen Conservation Area 2 525058.095543 6917380.770640 Woody Hill, Hart's Reserve 1 537191.808597 6896953.136380 Woody Hill, Hart's Reserve 2 536983.990140 6896764.950610

194

Appendix 4: Cadastre, Infrastructure and Regional Ecosystem data used to calculate urbanisation gradient.

Buffer area Cadastre Infrastructure Regional Ecosystem Site name (m) (%) (%) (%) Andrews Reserve 560779.61 50.87 6.59 42.54 Aqua Promenade Reserve 806637.29 41.40 5.86 52.74 Austenville Road 690016.43 8.10 1.53 90.37 Behms Road 674678.36 82.96 8.14 8.90 Bonogin Conservation Area 5210152.79 24.76 6.20 69.04 Brushwood Ridge Parklands 984271.29 60.99 11.23 27.78 Burleigh Heads National Park 883416.40 28.64 57.46 13.90 Burleigh Knoll Conservation Park 405382.51 80.52 19.00 0.49 Burleigh Ridge Park 714007.17 56.53 29.90 13.57 Carrington Road 452760.66 24.31 7.76 67.92 Daisy Hill Conservation Area 2765383.93 30.27 2.82 66.91 Elanora Conservation Reserve 713406.75 34.87 4.91 60.22 Elanora Wetlands 926847.43 38.79 35.06 26.15 Galt Road Park 839228.49 39.22 0.33 60.46 Gilston Road 437194.44 31.84 4.27 63.88 Hellman Street 612001.21 41.21 26.29 32.50 Herbert Park 694176.02 46.12 17.66 36.22 Johns Road 407443.15 54.95 10.22 34.83 Karawatha Forest Reserve 4270380.63 53.91 22.70 23.38 Kincaid Park 534169.22 61.30 13.90 24.79 Kirken Road 1692360.99 69.70 5.85 24.45 Lakala Street Reserve 399861.10 43.39 23.29 33.31 Miami Bushland Conservation Park 424732.47 79.81 16.05 4.14 Mount Nathan 3486450.10 35.34 10.12 54.54 Nerang State Forest 6181506.57 36.43 9.25 54.32 Numinbah Road 1610309.71 34.39 2.62 63.00 Olsen Avenue 1019031.21 47.03 29.80 23.17 Pacific Highway 699636.88 54.93 20.84 24.24 Pacific Pines Parkland 538435.24 56.93 15.28 27.78 Piggabeen Road 382837.93 77.37 5.44 17.20 Pimpama Conservation Park 3726843.55 49.05 20.95 30.00 Reedy Creek 1007140.33 21.47 1.49 77.04 Reserve Road Parklands 511487.84 57.57 11.72 30.72 Stanmore Park 990738.01 35.07 11.04 53.89 Summerhill Court Reserve 507236.42 68.27 21.24 10.48 Syndicate Road 2347051.35 33.48 4.56 61.95 Tallowwood Road 599332.43 22.85 1.91 75.24 The Plateau Reserve 1250117.45 41.70 2.86 55.44 Tomewin road 494248.65 47.11 5.50 47.39 Trees Road Conservation Area 908002.79 23.13 2.70 74.18 Tugun Conservation Park 569598.42 48.50 37.52 13.98 Upper Mudgeeraba Conservation Area 1874057.58 16.25 4.08 79.68 Venman National Park 3488388.60 27.00 3.52 69.48 Wongawallen Conservation Area 3260992.65 19.03 2.25 78.73 Woody Hill, Hart's Reserve 1710937.66 94.67 2.05 3.28

195

Appendix 5: Model selection Model Selection for Chapter 4: M1<-glmmML(density~gradient+connectivity+fire+logging+slope+elevation+aspect+stand_den,cluster

=site.no,family=poisson,data=env)

M2<-glmmML(density~connectivity,cluster=site.no,family=poisson,data=env)

M3<-glmmML(density~logging,cluster=site.no,family=poisson,data=env)

M4<-glmmML(density~slope,cluster=site.no,family=poisson,data=env)

M5<-glmmML(density~elevation,cluster=site.no,family=poisson,data=env)

M6<-glmmML(density~fire,cluster=site.no,family=poisson,data=env)

M7<-glmmML(density~aspect,cluster=site.no,family=poisson,data=env)

M8<-glmmML(density~stand_den,cluster=site.no,family=poisson,data=env)

M9<-glmmML(density~logging+elevation+stand_den,cluster=site.no,family=poisson,data=env)

M10<-glmmML(density~logging+elevation+fire,cluster=site.no,family=poisson,data=env)

M11<-glmmML(density~logging+fire,cluster=site.no,family=poisson,data=env)

M12<-glmmML(density~fire+logging+slope,cluster=site.no,family=poisson,data=env)

M13<-glmmML(density~logging+slope+elevation,cluster=site.no,family=poisson,data=env)

M14<-glmmML(density~slope+aspect+stand_den,cluster=site.no,family=poisson,data=env)

M15<-glmmML(density~gradient+connectivity+fire,cluster=site.no,family=poisson,data=env)

M16<-glmmML(density~gradient+connectivity+fire+logging,cluster=site.no,family=poisson,data=env)

M17<-glmmML(density~slope+elevation,cluster=site.no,family=poisson,data=env)

M18<-glmmML(density~logging+elevation,cluster=site.no,family=poisson,data=env)

M19<-glmmML(density~logging+aspect,cluster=site.no,family=poisson,data=env)

M20<-glmmML(density~logging+stand_den,cluster=site.no,family=poisson,data=env)

M21<-glmmML(density~elevation+stand_den,cluster=site.no,family=poisson,data=env)

M22<-glmmML(density~logging+slope,cluster=site.no,family=poisson,data=env)

M23<-glmmML(density~fire+logging,cluster=site.no,family=poisson,data=env)

M24<-glmmML(density~fire+stand_den,cluster=site.no,family=poisson,data=env)

M25<-glmmML(density~fire+elevation,cluster=site.no,family=poisson,data=env)

M26<-glmmML(density~fire+slope,cluster=site.no,family=poisson,data=env)

M27<-glmmML(density~fire+aspect,cluster=site.no,family=poisson,data=env)

M28<-glmmML(density~logging*elevation,cluster=site.no,family=poisson,data=env)

196

Model Selection for Chapter 5: M1<-glmmML(hollows~dbh+species+fire+termites+epicorms+wdamage+slope+elevation+aspect,cluster=site, family=binomial)

M2<-glmmML(hollows~dbh+species+fire+termites+epicorms+wdamage+slope+elevation,cluster=site,family= binomial)

M3<-glmmML(hollows~dbh+species+fire+termites+epicorms+wdamage+slope+aspect,cluster=site,family

=binomial)

M4<-glmmML(hollows~dbh+species+fire+termites+epicorms+wdamage+elevation+aspect,cluster=site,family

=binomial)

M5<-glmmML(hollows~dbh+species+fire+termites+epicorms+slope+elevation+aspect,cluster=site,family

=binomial)

M6<-glmmML(hollows~dbh+species+fire+termites+wdamage+slope+elevation+aspect,cluster=site,family

=binomial)

M7<-glmmML(hollows~dbh+species+fire+epicorms+wdamage+slope+elevation+aspect,cluster=site,family

=binomial)

M8<-lmmML(hollows~dbh+species+termites+epicorms+wdamage+slope+elevation+aspect,cluster=site,family

=binomial)

M9<-glmmML(hollows~dbh+termites+epicorms+wdamage+slope+elevation+aspect,cluster=site,family=

binomial)

M10<-glmmML(hollows~species+fire+termies+epicorms+wdamage+slope+elevation+aspect,cluster=site,

family=binomial)

M11<-glmmML(hollows~dbh,cluster=site,family=binomial)

M12<-glmmML(hollows~species,cluster=site,family=binomial)

M13<-glmmML(hollows~fire,cluster=site,family=binomial)

M14<-glmmML(hollows~termites,cluster=site,family=binomial)

M15<-glmmML(hollows~epicorms,cluster=site,family=binomial)

M16<-glmmML(hollows~wdamage,cluster=site,family=binomial)

M17<-glmmML(hollows~slope,cluster=site,family=binomial)

M18<-glmmML(hollows~elevation,cluster=site,family=binomial)

M19<-glmmML(hollows~aspect,cluster=site,family=binomial)

M20<-glmmML(hollows~dbh+fire,cluster=site,family=binomial)

197

M21<-glmmML(hollows~dbh+termites,cluster=site,family=binomial)

M22<-glmmML(hollows~dbh+epicorms,cluster=site,family=binomial)

M23<-glmmML(hollows~dbh+wdamage,cluster=site,family=binomial)

M24<-glmmML(hollows~dbh+slope,cluster=site,family=binomial)

M25<-glmmML(hollows~dbh+elevation,cluster=site,family=binomial)

M26<-glmmML(hollows~dbh+aspect,cluster=site,family=binomial)

M27<-glmmML(hollow~dbh+species,cluster=site,family=binomial)

198

Model Selection for Chapter 6: M1

M7<-glmmML(species~urban+connect+slope+elevation+disturbance,cluster=site,family=poisson)

M8<-glmmML(species~urban+connect+slope+elevation,cluster=site,family=poisson)

M9<-glmmML(species~urban+connect+slope,cluster=site,family=poisson)

M10<-glmmML(species~urban+connect,cluster=site,family=poisson)

M11<-glmmML(species~urban,cluster=site,family=poisson)

M12<-glmmML(species~connect,cluster=site,family=poisson)

M13<-glmmML(species~slope,cluster=site,family=poisson)

M14<-glmmML(species~elevation,cluster=site,family=poisson)

M15<-glmmML(species~disturbance,cluster=site,family=poisson)

M16<-glmmML(species~trees,cluster=site,family=poisson)

M17<-glmmML(species~hbtden,cluster=site,family=poisson)

M18<-glmmML(species~stand_den,cluster=site,family=poisson)

M19<-glmmML(species~fire,cluster=site,family=poisson)

M20<-glmmML(species~logging,cluster=site,family=poisson)

M21<-glmmML(species~area,cluster=site,family=poisson)

M22<-glmmML(species~area+trees,cluster=site,family=poisson)

M23<-glmmML(species~area+fire+trees,cluster=site,family=poisson)

M24<-glmmML(species~area+disturbance+stand_den,cluster=site,family=poisson)

M25<-glmmML(species~trees+fire+disturbance,cluster=site,family=poisson)

M26<-glmmML(species~fire+disturbance+stand_den,cluster=site,family=poisson)

199

Appendix 6: Tree growth rates

Table 1: The predicted age of all tree species categorised by dbh. Species are further categorised as being either a recruitment tree (Rec) or hollow-bearing tree (HBT).

Size class Tessellated C. intermedia C.trachyphloia E. pilularis (dbh in cm) Age Age Age Age (yrs) n Rec n HBT N (yrs) n Rec n HBT N (yrs) n Rec n HBT N (yrs) n Rec n HBT N 10-20 38.49 109 1 110 44.19 221 6 227 32.79 15 0 15 24 27 1 28 21-30 76.98 110 5 115 88.38 257 4 261 65.57 18 0 18 36 55 2 57 31-40 117.67 54 7 61 131.79 128 13 141 103.55 1 1 2 42 33 1 34 41-50 164.15 23 5 28 190.84 64 9 73 137.45 0 0 46 38 3 41 51-60 225.52 7 8 15 251.09 30 14 44 199.95 1 1 61 10 4 14 61-70 263.24 3 4 7 293.19 6 6 12 233.28 0 78 18 3 21 71-80 317.72 4 2 6 376.52 4 5 9 258.92 0 92 4 4 8 81-90 383.55 1 2 3 473.70 3 2 5 293.40 0 109 2 2 4 91-100 458.74 1 1 584.71 1 1 2 332.77 0 135 2 2 101-110 543.30 1 1 709.57 1 1 377.04 0 155 2 2 111-120 638.74 851.48 1 1 426.20 175 4 4 121-130 737.50 1021.78 1 1 453.22 196 1 1 131-140 885.00 1226.14 0 0 543.87 218 141-150 1062.00 1471.36 0 0 652.64 245 151-160 1274.50 1765.64 1 1 783.17 270 160+ 1529.50 2118.76 939.80 283 Total 311 36 347 714 64 778 34 2 36 187 29 216

200

Table 1 continued: The predicted age of all tree species categorised by dbh. Species are further categorised as being either a recruitment tree (Rec) or hollow-bearing tree (HBT).

Size class Half-bark Iron-bark E. fibrosa E. crebra (dbh in cm) Age Age Age Age (yrs) n Rec n HBT N (yrs) n Rec n HBT N (yrs) n Rec n HBT N (yrs) n Rec n HBT N 10-20 24 420 20 440 33.33 37 0 37 23.80 1 0 2 66.67 145 3 148 21-30 36 244 26 270 90.31 56 0 56 47.62 2 0 1 133.33 124 5 129 31-40 42 95 31 126 132.39 23 3 26 75.61 189.17 83 20 103 41-50 46 42 22 64 173.05 24 5 29 101.08 245.01 49 33 82 51-60 61 13 12 25 226.60 4 6 10 129.25 323.96 25 17 42 61-70 78 8 14 22 290.14 3 1 4 157.29 422.53 9 7 16 71-80 92 4 8 12 363.72 1 1 186.70 540.74 1 3 4 81-90 109 2 2 4 450.95 223.29 678.61 2 2 91-100 135 2 3 5 542.53 265.06 820.38 1 1 101-110 155 1 1 648.01 312.03 984.45 111-120 175 1 1 772.59 364.19 1181.25 121-130 196 2 2 919.27 421.55 1417.45 131-140 218 0 1080.04 459.08 1700.85 141-150 245 0 1295.95 550.90 2041.05 151-160 270 1555.04 661.08 2449.25 160+ 283 1866.14 793.29 2939.05 Total 830 142 972 148 15 163 3 0 3 436 91 527

201

Table 1 continued: The predicted age of all tree species categorised by dbh. Species are further categorised as being either a recruitment tree (Rec) or hollow-bearing tree (HBT).

Size class Stringy-bark E. microcorys E. acmenoides C. citriodora (dbh in cm) Age Age Age Age (yrs) n Rec n HBT N (yrs) n Rec n HBT N (yrs) n Rec n HBT N (yrs) n Rec n HBT N 10-20 39.34 456 11 467 43 70 1 71 35.68 5 0 5 34.72 154 2 156 21-30 59.18 403 16 419 47 58 3 61 71.37 7 0 6 69.43 122 8 130 31-40 81.52 212 32 244 53 38 12 50 110.04 4 4 3 103.39 53 8 61 41-50 104.50 106 32 138 61 28 16 44 148.07 5 1 6 137.49 24 11 35 51-60 134.43 41 23 64 80 13 15 28 188.86 1 1 1 172.47 7 12 19 61-70 165.13 10 10 20 102.5 6 8 14 227.77 2 216.01 2 6 8 71-80 198.75 5 9 14 126.5 5 3 8 271.01 261.36 1 2 3 81-90 233.78 1 7 8 154 3 7 10 313.57 314.79 1 1 91-100 272.33 1 1 2 182.5 0 2 2 362.16 375.82 3 3 101-110 313.90 0 0 0 211 1 3 4 416.80 444.45 1 1 111-120 463.49 1 1 2 239.5 0 0 477.49 520.68 121-130 390.18 269.5 1 1 510.86 554.22 131-140 457.50 302 613.03 665.06 141-150 535.31 335 735.63 798.08 151-160 625.66 368.5 882.83 957.69 160+ 722.50 386 1059.00 1149.23 Total 1236 142 1378 222 71 293 22 8 21 363 54 417

202

Table 1 continued: The predicted age of all tree species categorised by dbh. Species are further categorised as being either a recruitment tree (Rec) or hollow-bearing tree (HBT).

Size class Smooth-bark E. propinqua E. racemosa (dbh in cm) Age Age Age (yrs) n Rec n HBT N (yrs) n Rec n HBT N (yrs) n Rec n HBT N 10-20 34.72 40 0 40 38.46 156 3 159 49 13 0 13 21-30 69.43 51 0 51 76.92 129 9 138 68.6 25 2 27 31-40 103.39 33 5 38 116.53 72 17 89 72 15 6 21 41-50 137.49 18 4 22 154.99 27 21 48 82 12 9 21 51-60 172.47 6 4 10 198.94 7 14 21 109 8 9 17 61-70 216.01 2 3 5 231.73 5 5 10 142.5 1 3 4 71-80 261.36 0 1 1 273.40 0 3 3 177.5 3 1 4 81-90 314.79 1 1 2 321.96 1 5 6 215.5 1 1 2 91-100 375.82 2 2 377.42 1 1 255.5 2 2 101-110 444.45 1 1 439.78 0 0 295.5 2 2 111-120 520.68 1 1 453.63 0 0 335.5 0 0 121-130 554.22 543.60 1 1 356 1 1 131-140 665.06 653.20 1 1 427.2 141-150 798.08 738.88 512.6 151-160 957.69 940.65 615.2 160+ 1149.23 1128.78 738.2 Total 151 22 173 397 80 477 78 36 114

203

204