a thesis submitted for the fulfilment for the requirements
TRANSCRIPT
Hollow-Bearing Trees as a Habitat Resource along anUrbanisation 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
Copyright Statement
The author owns the copyright in this thesis, unless stated otherwise.
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.
i
“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.
iv
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
v
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.
vii
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.
viii
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
ix
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.
x
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.
xi
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
xii
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
xiii
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
xiv
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
xv
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
xvi
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
xvii
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
xviii
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
xix
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
xx
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
xxi
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
1
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
2
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).
3
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.
4
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
5
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
6
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,
an agricultural area of 1 million km2, 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
7
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).
8
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
9
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.
10
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.
Study Location Species Minimum
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;
Wormington et al. 2005
Queensland not specified >20 cm
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
11
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 hollows, surrounding vegetation
Lindenmayer et al. 1991a
Mountain brushtail possum
Number of hollows, surrounding vegetation type
Lindenmayer et al. 1996
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 diameter, hollow entrance size and dead trees
Rowston 1998;
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, number of hollows, proximity to other trees with hollows.
Bringham et al. 1998
Barking Owl Tree diameter, diameter of entrance to hollow
Taylor and Kirstin 1999
Gouldian Finch Tree diameter, hollow entrance width, steep slopes, single stemmed trees
Tideman et al. 1992
Striated Pardalote Number of hollows, hollow depth, fire scarring, tree species
Taylor and Haseler 1993
Parrot species generally Dead trees Goldingay 2009
Broad-headed snake Tree health, tree diameter, number of hollows
Webb and Shine 1997
Vertebrate fauna generally number of hollows, tree health, tree diameter
Gibbons et al. 2002
12
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.
13
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%,
14
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).
15
Figure 1.1: Map showing the 45 research sites studied within the Gold Coast, Brisbane,
Logan and Redland Shires, south-east Queensland, Australia.
16
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).
17
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
18
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.
19
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.
20
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.
21
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
22
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
23
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
24
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
25
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.
26
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
27
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,
28
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
29
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.
30
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
31
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.
Council by State
*Council Classification
No: of tabs
Placement of 'Environment' tab on web page
Mean n tabs (excluding environment)
No: of environmental 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.
32
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.
Council by State
*Council Classification
No: of tabs
Placement of 'Environment' tab on web page
Mean n tabs (excluding environment)
No: of environmental 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.
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.
0
5
10
15
20
25
Zero Very low Low Medium High Very High
Num
ber
of C
ounc
ils
Relative importance
33
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
34
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-bearing Trees.
1. Tree Management Report
1.1 Ecological Assessment Report
1.2 Risk assessment
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
Development Control Plan.
1. To protect old growth and significant hollow-bearing trees.
2. Significant Tree Register: Trees of high ecological value as well as other social values.
35
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).
36
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
37
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-
38
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
39
(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.
40
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.
41
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
42
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
43
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.
44
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
45
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
46
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 θ Calculated from GIS layers (Equation 3.2). Aspect (°) Directional orientation. Elevation (m) 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).
47
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.
S5 S6 S7 S8
Alive/dying Alive/dying Dead standing tree Dead stump
S1 S2 S3 S4
Alive/healthy Alive/healthy with Alive with Alive/dying minimal crown damage damaged crown
48
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).
49
∑
∑
=
== n
pp
n
pp
A
HBTD
1
1 (3.1)
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.
𝑡𝑎𝑛θ = 𝑜𝑝𝑝𝑜𝑠𝑖𝑡𝑒𝑎𝑑𝑗𝑎𝑐𝑒𝑛𝑡
(3.2)
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
50
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.
𝑈 = ∑𝑑𝑒𝑛𝑠𝑖𝑡𝑦𝑀𝐴𝑋 𝑑𝑒𝑛𝑠𝑖𝑡𝑦
× 𝐶 (3.3)
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).
51
Figure 3.3: The 250 m buffer placed around sites to capture housing, cadastre and
regional ecosystem data.
52
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
53
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).
54
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
55
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
56
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.
Site name Gradient No:
plots Area (ha) N trees tagged
Dominant species
Dominant HBT Species
Mean dbh of HBT's
N HBT species N HBT's
N Hollows
HBT density 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.
57
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.
Site name Gradient
No:
plots Area (ha)
N trees
tagged
Dominant
species
Dominant HBT
Species
Mean dbh of
HBT's
N HBT
species
N
HBT's
N
Hollows
HBT density per
hectare
Wongawallen Conservation Area R 2 383.67 71 E.microcorys L.confertus 51.3 5 24 65 42.8
Upper Mudgeeraba Conservation Area R 2 193.24 144 C.citriodora.v Dead 40.2 4 5 12 8.9
Trees Road Conservation Area R 2 53.32 203 E.crebra E.carnea 47.4 7 26 47 46.4
Stanmore Park R 1 50.87 111 L.suaveolens E.siderophloia 51.0 2 2 6 7.1
Reedy Creek Conservation Area R 3 44.85 178 L.confertus E.carnea 52.5 8 22 46 26.1
Tallowwood Road R 1 14.24 93 L.confertus E.microcorys 47.8 6 13 32 46.4
Tomewin Road R 1 5.10 51 E.microcorys E.microcorys 77.3 5 16 70 57.1
Gilston Road R 1 4.04 69 L.suaveolens C.citriodora.v 36.2 6 10 14 35.7
Olsen Avenue U 2 38.94 134 A.woodsiana E.pilularis 60.0 2 2 3 3.6
Burleigh Headland National Park U 1 27.32 86 L.confertus L.confertus 57.3 4 26 68 92.8
Burleigh Ridge U 3 17.89 159 L.confertus E.crebra 52.8 5 34 102 40.5
Pacific Highway, Currumbin U 2 16.89 205 L.confertus E.pilularis 60.1 8 33 92 58.9
Brushwood Ridge Reserve U 2 16.87 140 C.citriodora.v E.carnea 38.5 6 22 50 39.2
Herbert Park U 3 16.45 198 C.intermedia E.propinqua 50.3 8 23 56 27.4
Tugun Conservation Area U 1 14.38 67 E.pilularis E.pilularis 78.4 4 9 14 32.1
Summerhill Court Reserve U 1 9.53 52 E.microcorys E.microcorys 54.3 6 14 28 50.0
Kincaid Road Reserve U 1 7.52 50 L.confertus L.confertus 41.8 3 5 5 17.9
Pacific Pines Reserve U 1 5.89 82 E.crebra L.confertus 20.2 2 6 6 21.4
Miami Conservation Area U 1 4.41 126 E.carnea L.confertus 34.9 4 5 6 17.9
Burleigh Knoll National Park U 1 4.15 66 E.racemosa E.racemosa 67.0 3 25 91 89.3
Lakala Street Reserve U 1 3.23 78 C.intermedia E.pilularis 39.3 5 6 9 21.4
Total 45 92 6650.9 6048 14 dominant species 13 HBT species
Mean dbh across all sites 48.5 (±1.84 S.E.)
Mean n of HBT species across all sites 5.7 (±0.4 S.E.) 916 2159
Mean of HBT density per hectare across all sites 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.
58
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
59
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).
Figure 3.4: The number of hollows per hollow size category.
0
200
400
600
800
1000
1200
10 15 20 25 30 35 40 45 50 60 65 70 80 90 100 150
No.
of h
ollo
ws
Hollow size (cm)
60
Butt Branch-end Bayonet Fissure Trunk-main Trunk-top Branch-main
Hollow type
5
10
15
20
25
30
35
40
45H
ollo
w si
ze (c
m)
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.
61
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.
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
200
400
600
800
1000
1200
1400
1600
1800
2000N
o of
tree
s sur
veye
d
dbh category (cm)
Hollow-bearing treesAll trees
62
ButtBranch end
BayonetFissure
Trunk mainTrunk top
Branch main
Hollow type
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7R
elat
ive
abun
danc
e of
hol
low
type
Control Peri urban Rural Urban
Figure 3.7: The relationship between hollow type and the urbanisation gradient (mean ±
S.E.).
63
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.
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
64
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
65
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
66
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
67
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).
69
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
70
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
71
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 θ Calculated from GIS layers (Chapter 3 Equation 3.2) Aspect (°) Directional orientation. Elevation (m) Obtained from GIS mapping layers.
72
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.
Site name No: of plots
HBT density (ha)
Urbanisation Index
Area (ha)
Connectivity (%)
Andrews Reserve (r) 1 28.57 5.22 10.8 44.35
Aqua Promenade (p) 2 60.71 ± 10.15 32.29 13.1 38.80
Austenville Road (r) 3 34.52 ± 8.5 0.02 24.9 71.95
Behms Road (r) 1 39.29 6.94 14.2 0.00
Bonogin Conservation Area (c/u) 6 33.92 ± 7.8 157.16 725.1 73.01
Brushwood Ridge (u) 2 39.28 ± 3.51 522.96 16.8 0.00
Burleigh Headland NP (u) 1 92.85 441.57 27.3 0.00
Burleigh Knoll NP (u) 1 85.71 1412.99 4.1 0.00
Burleigh Ridge (u) 2 53.56 ± 35.71 3195.78 17.89 0.00
Carrington Road (r) 1 39.29 4.18 5.9 77.38
Daisy Hill Forest Reserve (u) 5 23.56 ± 6.6 133.43 432.1 49.97
Elanora Conservation Area (p) 2 71.48 ± 7.14 150.88 13.1 50.62
Elanora Wetlands (u) 1 28.57 90.08 26.6 0.00
Galt Road (p) 1 53.57 115.88 33.6 73.38
Gilston Road (r) 1 35.71 10.23 4.04 85.62
Hellman Street (p) 1 28.57 212.51 9.1 0.00
Herbert Park (u) 3 28.56 ± 7.43 393.57 16.4 0.00
Johns Road (r) 1 39.28 32.50 2.1 45.10
Karawatha Forest Reserve (c/p) 7 25.5 ± 4.63 1299.21 824.3 7.61
Kincaid Park (u) 1 17.86 83.34 7.5 0.00
Kerkin Road (r) 1 42.85 845.68 176.9 17.55
Lakala Street Reserve (u) 1 21.43 760.06 3.2 0.00
Miami Conservation Area (u) 1 10.71 1178.50 4.4 0.00
Mt Nathan Conservation Area (r) 3 33.33 ± 7.8 21.84 266.1 16.52
Nerang Forest Reserve (c/p) 5 61.43 ± 7.93 382.66 1669 15.08
Numinbah Road (r) 2 28.57 ±10.71 3.98 157.6 53.25
Olsen Avenue (u) 2 7.14 807.23 38.9 0.00
Pacific Highway, Currumbin (u) 2 53.57 ± 0 1741.30 16.8 0.00
Pacific Pines Parkland (u) 1 21.42 415.38 5.89 0.00
Piggabeen Road (p) 1 14.28 32.21 2.9 41.60
Pimpama Conservation Area (r) 2 3.57 16.99 565.8 1.28
Reedy Creek Conservation Area (r) 3 26.19 ± 11.72 19.69 44.8 63.74
Reserve Road Parklands (p) 1 17.85 276.44 7.6 0.00
Stanmore Park (r) 1 7.14 15.72 50.8 2.66
Summerhill Court Reserve (u) 1 50 793.33 9.5 0.00
73
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.
Site name No: of plots
HBT density (ha)
Urbanisation Index
Area (ha)
Connectivity (%)
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
74
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.
Site Slope (radians) Aspect Elev.
(m) Stand density (# trees ± S.E.)
Fire index
Logging 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
75
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 θ and slope was converted to
radians prior to analysis. No transformations were made to the response variable
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).
76
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.
77
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
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.
Parameter Estimate
Standard
error
Confidence interval
2.5% 97.5%
Relative variable
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
78
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
79
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
80
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).
81
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.
82
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
83
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.
84
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).
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
Species dbh (cm) 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 - - -
85
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.
𝐺 = (𝐴 × 𝑠) (5.1)
86
Table 5.2: Annual incremental dbh growth rates calculated by bark type. Growth rates
for two common species are also provided.
dbh size
class (cm) E. microcorys E. racemosa
Half-
bark
Iron-
bark
Smooth-
bark
Stringy-
bark
Tessellated-
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.
87
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.
𝑀 = 𝑛d
(𝑛l)𝑡 (5.2)
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.
88
Table 5.3: The probability of a recruitment tree having a hollow as a function of dbh.
dbh (cm)
Hollow 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
89
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 classification Hollow type
1 butt 2 branch end 3 bayonet 4 fissure 5 trunk main 6 trunk top 7 branch main
90
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
91
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).
Figure 5.1: The dbh range of recruitment trees compared to all trees surveyed.
0
250
500
750
1000
1250
1500
1750
2000
Num
ber
of tr
ees
dbh category (cm)
All treesRecruitment Trees
92
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
93
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
94
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.
0 20 40 60 80 100 120 140 160-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
Prob
abili
tyof
at r
eeha
ving
aho
ll ow
47 80
160
171
134
7841
32
18
138 6 2 1 1
(a)
0 20 40 60 80 100 120 140 160-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
Prob
a bili
tyo f
bayo
neth
ollo
ws
234
8095
9455
24 16
16
6
2
3
0 0
1
(b)
0 20 40 60 80 100 120 140 160-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
Prob
abili
tyfo
rbu
ttho
ll ow
s
21 40 4040
31 1812
8
11
0
54
0 0
1
(c)
0 20 40 60 80 100 120 140 160-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
Prob
a bili
tyo f
bra n
c he n
dho
llow
s3 5 35
56
60
40
19
25
15
12
4
62 1 1
(d)
0 20 40 60 80 100 120 140 1600.0
0.2
0.4
0.6
0.8
1.0
1.2
6 4 7 2 4 4 31
2
0
1
0 0 0 0
Prob
abili
tyo f
fi ssu
reh o
ll ow
s
(e)
0 20 40 60 80 100 120 140 160-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
212 10 7 6 68 4
6
1
2
0
1
0 0Prob
abili
tyof
trun
km
ain
h oll o
ws
(f)
0 20 40 60 80 100 120 140 160
dbh (cm)
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
Prob
abili
tyo f
t run
kto
pho
llow
s
1 2 3 3 2 13
0
11
0 0 0 0 0
(g)
0 20 40 60 80 100 120 140 160
dbh (cm)
0.0
0.2
0.4
0.6
0.8
1.0
1.2
0 0 3 2 2 23
41
3
0 0 0 0 0
Prob
abili
t yof
b ran
chm
ain
h ollo
ws
(h)
95
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.
Parameter Estimate
Standard
error
Confidence
interval 2.5%
97.5%
Relative variable
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).
96
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.
Year Recruitment
trees (n)
Hollow bearing trees (n)
Less Tree
Mortality (0.01)
Less Tree
Mortality (0.05)
Habitat remaining
(ha)
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
0
400
800
1200
1600
2000
20 30 40 50 60 70 80 90 100 110 120 130 140 150 160
No.
tree
s
dbh category (cm)
Year 1
Year 10
Year 50
Year 100
Year 150
97
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.
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
Year 10 Year 50 Year 100 Year 150
Time scale
0
10
20
30
40
50
60
70
80
Perc
enta
ge o
f hol
low
-bea
ring
tree
s lo
st
98
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).
99
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
100
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
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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),
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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;
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• 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.
Site No: of plots
Urban gradient
Area (ha)
HBT density (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 trees
Density of hollow-bearing trees per hectare per site.
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 glider
Petaurus australis Highly active, loud gurgling call, pale-red 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 possum
Trichosurus caninus A cat sized arboreal and terrestrial 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 possum
Trichosurus vulpecula A large heavy-set possum typically of wet 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 possum
Pseudocheirus peregrinus Species in S.E Qld have bright orange face, 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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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.
Species Common name
Survey method
Spotlighting Stagwatching Diurnal Pole Camera Anabat Total No. sites where found
Relative species 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.
Species Common name
Survey method
Spotlighting Stagwatching Diurnal Pole Camera Anabat Total No. sites where found
Relative species 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
117
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).
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.
0
1
2
3
4
5
6
7
02468
101214161820
0 10 20 30 40 50 60 70 80 90 100 110
Mam
mal
ric
hnes
s
Bir
d ri
chne
ss
Effort (hours)
bird
mammal
118
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.
Site Urban
gradient Diurnal Spot-
lighting Stag-
watching Pole
Camera Anabat Total Birds
(n)
Mammals inc. bats
(n) Reptiles
(n) Amphibians
(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
119
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.
Site Urban
gradient Diurnal Spot-
lighting Stag-
watching Pole
Camera Anabat Total Birds
(n)
Mammals inc. bats
(n) Reptiles
(n) Amphibians
(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
120
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.
121
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.
122
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.
Hollow using fauna
Galah
Pale-headed Rosella
Boobook Owl
Sulphur-crested Cockatoo
Rainbow Lorikeet
Scaly-breasted Lorikeet
Laughing Kookaburra
Common brushtail possum
Squirrel glider
Mt. brushtail possum
Sugar glider
Greater glider Bats
Green tree-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
123
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.
Hollow using fauna
Galah
Pale-headed Rosella
Boobook Owl
Sulphur-crested Cockatoo
Rainbow Lorikeet
Scaly-breasted Lorikeet
Laughing Kookaburra
Common brushtail possum
Squirrel glider
Mt. brushtail possum
Sugar glider
Greater glider Bats
Green tree-frog Total
Hollow height (m)
0-5
2
2 4 1 1
2 12 6-10 2 1 2
2
1
1
9
11-15
1
1
2
4 16-20
1
4
5
21-25
1 2
colony
3
124
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
125
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.
Parameter Estimate Standard error
Confidence interval 2.5% 97.5%
Relative variable 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
126
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).
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
0
2
4
6
8
10
12
14
16
18
20
22
24
26
Spec
ies
rich
ness
Figure 6.5: The relationship between tree species diversity and hollow-using
vertebrate species richness across all sites.
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
-0.02
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.18
Spec
ies
occu
panc
y
Figure 6.6: The relationship between tree species diversity and hollow using
vertebrate species rates of occupancy across all sites.
127
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.
128
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).
129
Control Peri urban Rural Urban
Urban Gradient
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.18
Rat
e of
occ
upan
cy Birds Mammals
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.
130
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%)
131
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
132
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
133
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.
134
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.
135
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.
136
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)
137
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
138
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.
139
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
140
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
141
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-
142
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.
143
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.
144
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List of Appendices:
184
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
185
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
186
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
187
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
188
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
189
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
190
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*
191
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)
192
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
193
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
194
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
195
Appendix 4: Cadastre, Infrastructure and Regional Ecosystem data used to calculate urbanisation gradient.
Site name Buffer area
(m) Cadastre
(%) Infrastructure
(%) Regional Ecosystem
(%) 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
196
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)
197
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)
198
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)
199
Model Selection for Chapter 6: M1<glmmML(species~urban+connect+slope+elevation+disturbance+trees+hbtden+stand_den+fire+logging+area,cluster=site,family=poisson)
M2<glmmML(species~urban+connect+slope+elevation+disturbance+trees+hbtden+stand_den+fire+logging,cluster=site,family=poisson)
M3<glmmML(species~urban+connect+slope+elevation+disturbance+trees+hbtden+stand_den+fire,cluster=site,family=poisson)
M4<glmmML(species~urban+connect+slope+elevation+disturbance+trees+hbtden+stand_den,cluster=site,family=poisson)
M5<glmmML(species~urban+connect+slope+elevation+disturbance+trees+hbtden,cluster=site,
family=poisson)
M6<glmmML(species~urban+connect+slope+elevation+disturbance+trees,cluster=site,family=
poisson)
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)
200
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 (dbh in cm)
Tessellated C. intermedia C.trachyphloia E. pilularis Age (yrs) n Rec n HBT N
Age (yrs) n Rec n HBT N
Age (yrs) n Rec n HBT N
Age (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
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 (dbh in cm)
Half-bark Iron-bark E. fibrosa E. crebra Age (yrs) n Rec n HBT N
Age (yrs) n Rec n HBT N
Age (yrs) n Rec n HBT N
Age (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
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 (dbh in cm)
Stringy-bark E. microcorys E. acmenoides C. citriodora Age (yrs) n Rec n HBT N
Age (yrs) n Rec n HBT N
Age (yrs) n Rec n HBT N
Age (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
203
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 (dbh in cm)
Smooth-bark E. propinqua E. racemosa Age (yrs) n Rec n HBT N
Age (yrs) n Rec n HBT N
Age (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
204