1 andrew geschke honours thesis 2015
TRANSCRIPT
The response of urban birds to human population density:
optimally designing cities to enhance native biodiversity
Andrew Phillip Wai-Ming Geschke
School of Life and Environmental Sciences Deakin University
Submitted in partial fulfilment of the degree of
Bachelor of Environmental Science (Honours)
November 2015
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Statement of responsibility
This thesis is submitted in accordance with the regulations of Deakin University in partial
fulfilment of the requirements of the degree Bachelor of Environmental Science (Honours).
I, Andrew Geschke, hereby certify that the information presented in this thesis is the result
of my own research, except where otherwise acknowledged or referenced, and that none of
the material has been presented for any degree at another university or institution.
November 2015
Ethics clearance and permits
This project involved the use of animal subjects and the project was conducted in
accordance with the regulations of the Deakin University Animal Ethics Committee under
Permit No. B10-2015 and in accordance with the Department of Environment, Land, Water
and Planning, Permit No. 10007553.
Principal investigator on both permits: Dr Dale Nimmo
Co-Investigators approved: Mr Andrew Phillip Geschke
November 2015
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Abstract
Urban populations are growing rapidly on a global scale. As urban areas continue to develop
and expand, there is a critical need to understand how urban landscapes can be designed to
produce optimal outcomes for native biodiversity. The concepts of ‘land sharing’ and ‘land
sparing’ provide a framework to conceptualise trade-offs between the human population and
biodiversity. Land sharing integrates both people and biodiversity on the same land, whereas
land sparing separates biodiversity and the human population through compact urbanisation
(high population density) and protecting natural habitats. The biodiversity outcomes under
each allocation method depend on the responses of species to the intensity of urbanisation,
which vary between species. Optimisation modelling can be used to consider the range of
allocations between land sparing and land sharing designs.
In this study, I use human population density as an indicator of urbanisation intensity.
I investigate the relationship between the occurrence of bird species and human population
density, and then use these relationships to evaluate the biodiversity outcomes of land
sharing, land sparing and optimal allocations.
Twenty-eight 25 ha landscapes, along a gradient of human population density, were
surveyed for bird species during the autumn-winter period of 2015. Species responses to
human population density were estimated with generalised additive models (GAMs). Land
allocation methods were evaluated by an index, the geometric mean of relative abundance,
which captures species evenness, total abundance and relative extinction risk.
Human population density was an important driver of occurrence for 28 species. A
variety of species’ response curves were observed, which were used to evaluate species’
occurrence under a land sparing, land sharing or an optimal allocation landscape. For the
current study population, optimal allocation had characteristics of both land sharing and land
sparing. However, in scenarios of increased population, optimal allocation converged upon a
land sparing design. Land sharing performed poorly under all scenarios due to its inability to
support species that depend on large, contiguous patches of native habitat. These results
emphasise the importance of reserves of native vegetation to support native biodiversity that
cannot persist within urbanised areas.
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Acknowledgements
Firstly, I would like to express my gratitude and appreciation to my supervisors, Dale Nimmo
and Andrew Bennett, for their guidance and support this year. Your enthusiasm, commitment
and confidence in the project has made this an incredibly enjoyable and rewarding year.
Specifically, thank you to Dale for your ongoing availability throughout the year despite
moving states, changing jobs and welcoming a new member to your family.
A big thank you to Simon James for his work on the optimisation model and tailoring
its function specifically to my research. Your ongoing support and advice has been greatly
appreciated. Also to Simon Macdonald who assisted with ISO clustering in GIS.
To my parents, thanks for supporting me throughout my education and always being
there if I ever needed help. To the rest of my family, it hasn’t been an easy year with the
passing of my grandmothers, Audrey Geschke and Oi Kwan Chu, but having you all around
really helped me through it. I also must thank my partner, Alice Walker, and closest friends
for their ongoing love and support.
Thank you to my Honours peers, specifically Angelina Siegrist, Caitlin Potts, Anna
Radkovic, Hayley Geyle and Harry Moore, who welcomed me to Deakin, and made every day
in BA a pleasure.
I also would like to thank the environmental technical staff Thomas Schneider,
Clorinda Schofield and Jessica Bywater for their assistance with equipment and vehicles, and
the IT staff Fawzi Elfaidi and Higo Jasser.
Final acknowledgements to the Parks Victoria staff for their assistance at Lysterfield
and Plenty Gorge, Deakin University School of Life and Environmental Sciences for the
scholarship that provided critical financial support, the Deakin University ethics committee
and the Department of Environment Land Water and Planning.
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Table of contents
Statement of responsibility................................................................................................ i
Ethics clearance and permits ............................................................................................. i
Abstract ........................................................................................................................... ii
Acknowledgements ......................................................................................................... iii
Table of contents ............................................................................................................. iv
List of abbreviations ........................................................................................................ vi
1 INTRODUCTION ............................................................................................................. 1
1.1 Urbanisation as a threat to biodiversity .......................................................................... 1
1.2 The response of biodiversity to urbanisation .................................................................. 1
1.3 Land sharing and land sparing framework ...................................................................... 2
1.4 Project aims and objectives ............................................................................................. 8
2 METHODS .................................................................................................................... 10
2.1 Study area ...................................................................................................................... 10
2.2 Study design ................................................................................................................... 11
2.3 Bird surveys .................................................................................................................... 15
2.4 Response and predictor variables .................................................................................. 17
2.5 Analysis .......................................................................................................................... 19
2.6 Optimisation modelling ................................................................................................. 21
2.6.1 Individual species optimisations ............................................................................. 22
2.6.2 Community level optimisations .............................................................................. 23
2.6.3 Optimisation scenarios ........................................................................................... 25
3. RESULTS ..................................................................................................................... 28
3.1 Vegetation area .............................................................................................................. 28
3.2 Human population density ............................................................................................ 36
3.3 Optimisations ................................................................................................................. 42
3.3.1 Individual species optimisation .............................................................................. 42
3.3.2 Community optimisations and alternative scenarios ............................................. 46
4. DISCUSSION ................................................................................................................ 54
4.1 Human population density ............................................................................................ 54
4.2 Optimisation .................................................................................................................. 57
4.2.1 Individual species optimisation .............................................................................. 57
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4.2.2 Community optimisations ....................................................................................... 59
4.3 Limitations ...................................................................................................................... 61
4.4 Future directions ............................................................................................................ 62
APPENDICES ................................................................................................................... 72
The format of this thesis is based upon the journal Journal of Applied Ecology. An example of peer-reviewed article (Soga et al. 2014) can be found here:
http://onlinelibrary.wiley.com/doi/10.1111/1365-2664.12280/epdf
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List of abbreviations
CBD Central business district
GAM Generalised additive linear model
Reserve Large tract of continuous native forest or woodland vegetation
Reserve exclusive Species that were only observed on surveys within landscapes
situated within reserves
Population density Refers to the density of humans within an area
Distance to reserve Distance between study landscapes and the nearest reserve
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1 INTRODUCTION
1.1 Urbanisation as a threat to biodiversity
Urbanisation is a major driver of biodiversity loss on a global scale (Foley et al. 2005; Grimm
et al. 2008; Seto, Güneralp & Hutyra 2012). Urbanisation results in the loss and fragmentation
of native vegetation and the creation of new land uses (Savard, Clergeau & Mennechez 2000;
McKinney 2002), altered hydrological and nutrient cycles (Walsh et al. 2005; Grimm et al.
2008), as well as climatic changes in both temperature and rainfall (Arnfield 2003; Jenerette
et al. 2007; Pickett et al. 2011). These changes affect the composition of biological
communities within urban landscapes worldwide (McKinney 2002; Kowarik 2011; Concepción
et al. 2015). Over half of the world’s human population live in urban areas, a figure expected
to grow rapidly in the coming decades (Pickett et al. 2011; Ramalho & Hobbs 2012). By 2050,
it is estimated that urban areas will house an additional 2.5 billion people (United Nations
2014). As urban areas continue to expand to accommodate more people, it is critical to better
understand how biodiversity conservation can be achieved in urban landscapes (Berkes 2004;
Nelson et al. 2010; Seto, Güneralp & Hutyra 2012; Soga et al. 2014).
1.2 The response of biodiversity to urbanisation
Urban areas represent a gradient in human population density and intensity of ecological
change, ranging from low density suburbs to high density cities (McIntyre, Knowles-Yánez &
Hope 2000; Germaine & Wakeling 2001; Gagné & Fahrig 2010). Plant and animal species
respond to urbanisation in different ways, reflecting differences in ecological traits (Garden
et al. 2006; Kark et al. 2007; Croci, And & Clergeau 2008). Distinct responses to urban intensity
have prompted urban ecologists to describe species as ‘urban avoiders’, ‘urban adapters’ and
‘urban exploiters’ (Blair 1996; McKinney 2002; Kark et al. 2007). This terminology is used to
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describe variation in species persistence and resource use across the gradient in intensity of
urban land-use (McKinney 2006; Kark et al. 2007).
Urban avoiders are species closely associated with remnants of natural vegetation and
depend upon natural habitats (Blair 1996; Johnston 2001). As a result, urban avoiders are
sensitive to human disturbances and do not commonly persist even in low-density suburban
developments (Gagné & Fahrig 2010; Sushinsky et al. 2013). Urban adapters typically occur in
both natural and urbanised environments and benefit from both native and exotic vegetation
(Reichard, Chalker-Scott & Buchanan 2001; McKinney 2002; MacGregor-Fors & Schondube
2012). Elevated resource availability in urban environments often results in urban adapters
peaking in abundance and dominating communities at low to intermediate levels of
urbanisation (Blair 1996; McKinney 2006; Shwartz, Shirley & Kark 2008). In contrast, urban
exploiters depend on urban resources and favour urbanised landscapes (Johnston 2001;
McKinney 2002). Urban exploiters tend to be non-native species and often peak in abundance
at high levels of urbanisation (Blair 2001; Marzluff et al. 2001). This variation in species
responses within urban environments affects how urban landscape should be designed if
biodiversity values are to be optimised (Hulme et al. 2013; Butsic & Kuemmerle 2015).
1.3 Land sharing and land sparing framework
To conceptualise the trade-off between land-use and biodiversity, the concepts of ‘land
sharing’ and ‘land sparing’ provide a useful framework (Green et al. 2005; Phalan et al. 2011b;
Lin & Fuller 2013). Although initially framed in an agricultural context, parallels between
urbanised and agricultural landscapes have seen an increasing number of studies applying the
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land sharing and land sparing framework in urban ecology (Lin & Fuller 2013; Soga et al. 2014;
Caryl et al. 2015; Stott et al. 2015).
Land sharing, emphasises the ‘sharing’ of land to simultaneously support the human
population and biodiversity (Lin & Fuller 2013). This is achieved by sprawling, low-intensity
urbanisation which is hypothesised to have less impact on biodiversity than more intensive
land-uses (Fischer et al. 2014; Stott et al. 2015). By distributing people within the landscape
more evenly and at a lower intensity, land sharing theoretically allows for more vegetation to
be retained within urbanised areas (e.g. in large household gardens), supporting greater
biodiversity values (Fig. 1a) (Fischer et al. 2008; Lin & Fuller 2013; Stott et al. 2015). However,
this potentially leaves few or no areas set aside specifically for biodiversity conservation, such
as conservation reserves (Lin & Fuller 2013).
Land sparing, in contrast, emphasises the spatial separation of the human population and
biodiversity values by dedicating some land to high intensity urban land-use while ‘sparing’
other parts to remain in a more natural state (Green et al. 2005; Phalan et al. 2011a; Lin &
Fuller 2013). By concentrating the human population, into the smallest area possible, land
can be ‘spared’ from urbanisation and committed to biodiversity conservation (Fig. 1b) (Soga
et al. 2014; Caryl et al. 2015). While biodiversity in high-intensity urban areas may be greatly
diminished, biodiversity is maintained through the retention of large remnant habitats in
reserves (Sushinsky et al. 2013; Stott et al. 2015).
For a given human population, land sharing requires more land to be urbanised than land
sparing because it distributes people at a lower density (Fig. 1) (Hansen et al. 2005; Soga et
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al. 2014). Therefore, a trade-off exists between the intensity of urbanisation (human
population density) within the landscape and the proportion of the landscape that is
urbanised (Lin & Fuller 2013; Fischer et al. 2014). How this trade-off affects overall landscape
biodiversity will depend upon the responses of individual species to human population
density (Green et al. 2005; Butsic & Kuemmerle 2015).
It is possible to determine which species are favoured by land sharing, land sparing or
alternative allocations by modelling species responses to a gradient of human population
density (Phalan et al. 2011b; Soga et al. 2014; Butsic & Kuemmerle 2015). Identifying the form
of the relationship between a species’ occurrence and density of a human population within
an area indicates which method of land allocation will most benefit a species (Fig. 2) (Phalan
et al. 2011b; Hulme et al. 2013; Soga et al. 2014). As urban avoiders do not persist in even
low density suburbs, they are unlikely to benefit from a land sharing landscape design (Blair
1996; Sushinsky et al. 2013). By contrast, urban adapter and urban exploiter species, which
exhibit tolerance to urban areas, may benefit from a land sharing design (McKinney 2002).
While some studies have identified the best method of allocation by counting the number of
species favoured under scenarios of land sharing or land sparing (Phalan et al. 2011b; Hulme
et al. 2013), others have argued that a simple counting of species preferences fails to
appropriately account for the many trade-offs between human land-use and biodiversity
(Butsic & Kuemmerle 2015). Further, by only considering land sparing or land sharing designs,
the full range of allocation possibilities are ignored. For example, a number of authors have
proposed that an optimal allocation of land is likely to have aspects of both land sharing and
land sparing approaches (Phalan et al. 2011a; Tscharntke et al. 2012; Butsic & Kuemmerle
2015). Furthermore, limiting landscape designs to two polarised options may inadequately
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support species with responses that do not conform to land sparing or land sharing
preferences (Phalan et al. 2011b; Butsic & Kuemmerle 2015).
Optimisation modelling allows for the full range of land-uses to be considered in order to
identify the most efficient allocation (Hodgson et al. 2010; Seppelt, Lautenbach & Volk 2013;
Butsic & Kuemmerle 2015). The use of mathematical optimisation approaches enable
biodiversity values to be maximised while meeting specified landscape targets, such as fitting
a given number of people into an urban area (Moilanen et al. 2011) or achieving a target
agricultural yield in an agricultural landscape (Polasky et al. 2005; Hodgson et al. 2010; Butsic
& Kuemmerle 2015). By considering all possibilities across the gradient in human population,
optimisation methods tailor land-use allocations to species’ response curves, resulting in
context-specific outcomes (Hodgson et al. 2010; Butsic & Kuemmerle 2015). In addition,
optimisation techniques can also incorporate objectives based on biodiversity indices that
represent the entire ecological community within a region, moving beyond consideration of
a single species and towards land allocations that consider the benefit to multiple species or
communities (Di Stefano et al. 2013).
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a) b)
Figure 1. Schematic illustrations of a) land sharing and b) land sparing distributions highlighting differences in landscape designs. Land sharing urbanises the whole landscape at a low-intensity with for vegetation to be interspersed throughout. Land sparing separates intensely urbanised and non-urbanised areas, creating large patches of vegetated area. Grey indicates urbanised area and green represents vegetation (eg. trees and shrubs). Shading indicates the relative intensity of urbanisation and vegetation characteristics. Adapted from Soga et al. (2014).
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Figure 2. Conceptual examples of different relationships between species abundance and urbanisation intensity (species response curves), identifying the responses favouring land sharing or land sparing. Land sparing favours species with convex functions: maximum abundance at high or very low (natural habitat) urban intensity but decline sharply at intermediate or low urban intensity (A and B). Land sharing favours species with concave functions: abundant at intermediate levels but decline at either low or high urban intensity (C and D). The response observed in A would be expected from an urban adapter, while the response of B or D would be expected from an urban exploiter. Adapted from Phalan et al. (2011b) and Soga et al. (2014).
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1.4 Project aims and objectives
In this study, I investigate how bird species respond to human population density in the urban
region of Melbourne. This region has experienced significant development and urban
expansion during the last century resulting in a gradient of residential population densities
(Hahs & McDonnell 2006). The region offers a number of large and continuous tracts of native
vegetation (here in termed ‘reserves’) enabling areas of semi-natural, native habitat to be
observed in an urban context. The large number of bird species, including residents and
migrants, provide examples of various body sizes, feeding guilds and behaviours with the
potential to produce a variety of difference responses to urbanisation (White et al. 2005;
Conole & Kirkpatrick 2011). The availability of high-resolution national census data (Australian
Bureau of Statistics 2012) provides an opportunity to compare human population density with
data on bird species occurrence to better understand how species respond to urbanisation.
These data sets allow for land sharing, land sparing and optimal allocation approaches to be
applied in an urban context.
Specifically this project aims to;
1) Systematically survey birds in urban landscapes and develop species response curves
to understand how individual bird species respond to human population density.
2) Using response curves generated in aim 1), identify the optimal allocation of land uses
(based on human population density) within the study area to maximise a
conservation objective under scenarios of current and future Melbourne populations.
Conservation objectives considered are (i) the occurrence of individual bird species,
and (ii) species diversity of birds as measured by the geometric mean of relative
abundance.
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3) Contribute to a developing understanding of the application of the land sharing and
land sparing concept in an urban context.
The outcomes of this research have the potential to assist in guiding conservation orientated
development of Melbourne as it continues to support a growing population.
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2 METHODS
2.1 Study area
The study was conducted in Melbourne, Australia (37.8136° S, 144.9631° E) in urban
residential and urban fringing parklands of Melbourne’s north, east and south-east. The
region experiences a temperate climate with a mean annual rainfall of 665 mm and average
annual maximum and minimum temperatures of 19.9° C and 10.2° C, respectively (Melbourne
regional office) (Bureau of Meteorology 2015). In 2014, the Greater Melbourne region had a
population of approximately 4.44 million people (Australian Bureau of Statistics 2015) . The
study area (Fig. 3) was a section of the Greater Melbourne metropolitan area, which included
963.8 km2 of residential-zoned urban land; but excluding the Central Business District (CBD).
The study area is home to 2.74 million people (ABS 2011). The population density of Greater
Melbourne averages 440 person per km2 (Australian Bureau of Statistics 2015), which is low
on a global scale due to Melbourne’s history of urban sprawl (White et al. 2005; Buxton &
Scheurer 2007). The highest population densities within the Greater Melbourne area are in
the Inner city (12,000 persons/km2) (Australian Bureau of Statistics 2015).
Prior to European settlement, the land now occupied by the Greater Melbourne area
supported a range of vegetation types including forests, woodlands, wetlands, grasslands and
heathlands (White et al. 2005). Although urbanisation has modified vegetation
characteristics, small patches of remnant vegetation still exist within the urban matrix and on
the urban fringe as conservation reserves and recreational parks (White et al. 2005). In the
outer suburbs of Melbourne, large and continuous patches of remnant native vegetation exist
and provide vegetation types comparable to the pre-settlement vegetation.
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2.2 Study design
This study used a landscape-scale approach to survey bird species within urban residential
areas and urban-fringing native forest. A total of 28 study landscapes, each 25 ha in size, were
selected for the study (Fig. 3). Twenty-four ‘residential’ landscapes, each 500 m x 500 m, were
located within a predominately residential housing area of the urban matrix. Urban green
spaces such as parks were avoided. These residential landscapes were carefully selected to
capture a gradient in human population density, and they varied in their distance from the
Melbourne CBD.
Human population data for study landscapes were derived from the Australian Bureau of
Statistics 2011 census (Australian Bureau of Statistics 2011; Australian Bureau of Statistics
2012). Census data provide the number of dwellings and persons residing in each census
geographical unit, called a ‘mesh block’. Mesh blocks were mapped using ArcGIS 10.2 (ESRI
2015). Within the study area, mesh blocks had a maximum population of 782 people and
ranged in size from 725 m2 to 4.5 km2. The population density in a mesh block (persons per
ha) was calculated based on the mesh block area and mesh block population. Mesh blocks
with population density below 0.5 persons/ha were excluded from site selection as they
typically lacked private dwellings and were not used for residential purposes (despite being
zoned as residential). As numerous mesh blocks were not fully encompassed within
landscapes boundaries, population density within each study landscape was estimated using
mesh block density and the mesh block area within the landscape (Equation 1). Human
population density was strongly correlated with the density of dwellings (r = 0.935) (Appendix
I).
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Equation (1) Landscape population (persons/25ha) =
Σ (Mesh block area within landscape x Mesh block population density)
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Figure 3. Map of the study area within the Greater Melbourne area indicating the location of study landscapes; 24 residential (purple), 4 reserve (green); residential land-use (yellow) and reserves of >200 ha contiguous native habitat (white). The location of the study area in the context of Australia is shown by the red box.
Residential landscape
Figure key
Study area boundary
Reserve landscape
Residential land use Reserve land
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The 24 residential study landscapes were selected from an initial pool of 53 landscapes
representing a variety of population densities across Melbourne. For each landscape,
distance to the CBD was calculated using ArcGIS 10.2 (ESRI 2015). Across the initial pool of
landscapes, a relationship between human population density and distance to the CBD was
observed (Fig. 4). To avoid sampling spatially clustered landscapes of similar population
density, landscapes were sorted into approximate 10 km distance groups and scatterplots of
population density and distance to CBD were generated to assist selection (Fig. 4). The final
set of landscapes was chosen to represent the gradient in population density within each
distance group from the CBD (Fig. 4). The selected landscapes were between 5 km and 40
km from the Melbourne CBD with residential populations ranging from 146 to 1692 persons.
Figure 4. Scatterplot of the human population density and distance to the Central Business District (CBD) of Melbourne for the initial pool of 53 residential landscapes: selected landscapes (black) and unselected landscapes (grey). Grey bars separate categories of distance from the CBD used in selection. As can be seen, landscapes were selected with low and high population densities for each distance group from the CBD.
Distance to CBD (km)
Hu
man
po
pu
lati
on
de
nsi
ty
(pe
rso
ns/
25
ha)
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In addition, four ‘reserve’ landscapes were selected within native woodlands and forests
fringing the urban matrix. Reserve landscapes were used to represent a human population
density of zero along the gradient of possible human population densities. Reserve landscapes
were chosen for their proximity to residential areas, public accessibility, and their continuity
as part of a large continuous tract of native vegetation (> 200 ha). Reserve landscapes were
also chosen to be as similar to residential landscapes as possible in terms of topography and
their pre-European vegetation character (based on the 1750 ecological vegetation class layer)
(DELWP 2015). Due to difficulty in meeting some aspects of these criteria (e.g. accessibility
and location of remnant patches), reserve landscapes were not restricted to a 500m x 500m
shape but were an identical 25 ha in size to ensure comparability with residential landscapes.
All landscapes were >3.5 km from the coast, and land uses such as golf courses, agricultural
land and shopping centres were avoided to reduce undue influences from adjacent
landscapes. Study landscapes were at least 0.5 km apart, to promote spatial independence,
with the mean minimum distance of 2.6 km to the nearest neighbouring landscape.
2.3 Bird surveys
In each landscape, five 200 m x 50 m (i.e. 1.0 ha) transects were established for bird surveys.
Transects were distributed evenly to enhance representative sampling of landscape
characteristics. Where possible, five sections of the landscape (see Fig. 5) were sampled by at
least one transect to ensure a consistent method of non-random transect distribution. In
residential landscapes, transects followed streets; and in reserve landscapes they followed
existing paths when available.
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Four rounds of surveys were conducted, with each landscape sampled once per round. Within
each landscape, all transects were sampled on the same day. Landscapes were sampled in
groups according to geographic location for logistical reasons, with a maximum of three
landscapes sampled on the same day. The sampling order of landscape groups was
randomised for each survey round to reduce temporal bias and ensure statistical inferences
were reliable (Quinn & Keough 2002).
Bird surveys were conducted during the non-breeding season between May and August of
2015. Each survey involved a 10 min observation period, during which the observer walked
slowly along a transect, recording all birds sighted and heard. Both species identity and the
number of individuals seen or heard were recorded. All surveys were conducted between
dawn and noon, and during favourable weather conditions (i.e. avoiding strong winds and
rainfall). Transects were 50 m wide, and birds observed within 25 m from the street curbs
were recorded as ‘on-site observations’. Observations beyond 25 m from street curb, on the
road surface or flying overhead – but within the study landscape – were recorded as ‘off-site
observations’.
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Figure 5. Method of transect allocation for residential landscapes, with an example of transect layout for the Murrumbeena landscape. Each number denotes a section of the landscape required to be at least in part surveyed to promote representative sampling. Red lines represent transect paths, black box mark the landscape boundary.
2.4 Response and predictor variables
Reporting rates for individual bird species were calculated for each landscape as the summed
number of surveys on which a species was present, resulting in a value out of 20 for each
landscape (i.e. 5 transects x 4 survey rounds per landscape). Reporting rates were used as a
surrogate of species’ abundance (Radford & Bennett 2007). Reporting rates were calculated
for on- and off-site data combined. Species richness and the richness of native species were
also calculated as a response variables, representing the total number of species observed
within each landscape (combining both on- and off-site records).
Several predictor variables were calculated. First, human population density was included as
a measure of the intensity of urban land-use and as a predictor of species reporting rates
(Stankowski 1972; Thompson & Jones 1999; Lin & Fuller 2013). Second, vegetation cover
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within a landscape is often a key predictor of bird occurrence (Radford, Bennett & Cheers
2005). Therefore, tree and shrub cover (collectively ‘vegetation area’) within a landscape
were approximated using 2010 satellite imagery with a resolution of 30 cm. Using ISO cluster
unsupervised classification in ArcGIS 10.2.2 (ESRI 2015), the colour gradient of pixels in each
landscape was independently classified into 20 classes. Classes contributing to vegetation
area were selected, based on the extent to which they correctly classified pixels. These classes
were merged in ArcGIS to generate the vegetation area layer. Compared with residential
landscapes, reserve landscapes had a greater number of classes that corresponded to
vegetation cover due to a narrower pixel colour composition. Where imperfections in
classifications existed due to similarities in pixel colour (e.g. between vegetation and other
urban characteristics such as roofs and lawns), these misclassifications were removed by
manually deleting misclassified pixels from the vegetation area layer. Vegetation area (ha)
was calculated for each landscape, and ranged from 0.32 to 13.99 ha.
A third predictor variable measuring the distance between each study landscape and the
nearest large tract of continuous native forest or woodland vegetation (here termed
‘reserve’) was included to account for the effects of landscape context on bird occurrence
(Melles, Glenn & Martin 2003; Carbó-Ramírez & Zuria 2011; Gilroy et al. 2014). Distance to
reserve was measured in ArcGIS. Six reserves, large tracts of forest or woodland of 200 ha or
greater, were identified: Plenty, Lysterfield Park, the Dandenong Ranges, Warrandyte,
Cardinia and Dandenong Valley parkland. Distance to the nearest reserve ranged from 0 (i.e.
reserve landscapes) to 16 km.
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Finally, to account for biogeographic gradients across the study region, I included a measure
of mean annual rainfall. Data on mean annual rainfall were obtained from the Weather
Station Directory (Bureau of Meteorology 2015). Where possible, mean annual rainfall for
each study landscapes were estimated by the nearest currently operational weather station.
All weather stations used in the study (n = 17) had between 15 and 149 years of rainfall data.
The average distance between the study landscapes and weather stations was 3 km (range
0.7 to 6.4 km). Mean annual rainfall varied across the study landscapes, ranging from 619 to
948 mm (mean = 761 mm).
2.5 Analysis
Generalised additive models (GAMs) were used to generate response curves for species
reporting rates in relation to predictor variables, following Gabriel et al. (2013). I used the
packages nlme (Pinheiro et al. 2015) and mgcv (Wood 2011) in R version 3.0.3 (R Development
Core Team 2014). GAMs were chosen because they allowed for non-normally distributed
response variables that can be fitted with parametric and nonparametric smoothing terms.
This means that both linear and highly non-linear relationships between response and
predictor variables can be modelled (see Nimmo et al. 2012). This was necessary in the
current study because species’ responses to increasing population density were expected to
be non-linear (Fig. 2). Smoothed predictors were given three degrees of freedom to provide
flexibility within the model to generate a range of non-linear response curves, while avoiding
over-fitting.
Since vegetation area and human population density were strongly associated, including both
predictors in the same model violates the model assumption of independent predictors
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(Quinn & Keough 2002). Therefore, species response curves were estimated separately in
relation to vegetation area and human population density, respectively, as smoothed
predictors. The response variable, species’ reporting rate, was a proportion (i.e. number of
surveys observed out of 20 surveys) and as such was specified as having a binomial
distribution of errors. Species that were restricted to reserve landscapes (‘reserve exclusive’
species) typically were recorded only within 3 or 4 landscapes. Since these species were
crucial to the study (i.e. being found only in reserves, they are among the species most
impacted by urbanisation), and were able to be modelled using GAMs, all species with
occurrence in at least three landscapes were modelled. In addition to a smoothed predictor
(vegetation area or human density), species present in more than eight landscapes were also
fitted with linear predictors for mean annual rainfall and distance to nearest reserve. The
deviance explained (D2) was used as measure of model fit. Statistical significance was assessed
as alpha = 0.05 for all species, except for reserve exclusive species for which reliable p-values
were not obtainable due to sample size.
Landscape species richness and native species richness were modelled using the same
method as for species reporting rates, with human population density, rainfall and distance
to reserve as predictors. The relationship between human population density and vegetation
area was also modelled using a GAM, with human population density as the predictor variable
(representing urbanisation) using four degrees of freedom. For these models, response
variables were specified a Gaussian distribution of errors and model fit was again assessed by
deviance explained.
21
2.6 Optimisation modelling
Optimisation modelling was conducted to determine the optimal distribution of people within
a given area that maximises biodiversity variables (i.e. avifaunal species and species diversity
of bird communities). Species that had a significant relationship with human population
density, or which were reserve exclusive species, were included in optimisation models.
Reserve exclusive species were included despite the lack of reliable p-values because they
displayed strong, threshold-type relationships with population density (Fig. 8b).
The optimisation model built for this study (Beliakov et al., in preparation) allows the user to
identify how land should be allocated to land-use categories, in order to maximise ecological
indices under constraints. Land-use categories were the range of human population densities
observed in this study, from 0 to 1600 persons per ha (17 categories). Species were described
by their response curve to human population density (i.e. Table 1). The optimisation uses
species responses to population density to create a distribution of land uses (in this case
defined by population density) that maximises the desired abundance for a species or
diversity metric for the community (see below).
Table 1. Conceptual example of a species’ representation under optimisation modelling with five land-use (human population density) categories. Species report rate is a proportional of surveys (out of 20) in which the species was recorded.
Land-use category 1 2 3 4 5
Human population density (people per 25 ha) 0 200 600 1200 1600
Species reporting rate (per 25 ha) 0.80 0.20 0.05 0.02 0.01
22
The optimal allocation is constrained by the total land area available for allocation and the
total human population required to be achieved. The total human population achieved within
a landscape was the sum product of human population density multiplied by the area of land
allocated to each land-use category. In simple terms, the optimisation reveals how to
distribute a human population of a particular size within a defined area, so that the ecological
index (e.g. a species’ reporting rate or a community indice) is maximised.
Two conservation objectives were considered for optimisation in this study:
- Maximising reporting rates of individual species’ (a proxy for abundance)
- Maximising the geometric mean of abundance for the entire bird community
(see below).
2.6.1 Individual species optimisations
Individual species optimisations focused on maximising the reporting rate of each individual
species within the study area. Individual species optimisation constraints were the current
population (estimated by 2011 census data) and extent of the study area, requiring 2,744,000
people to be allocated within 963.8 km2 (3,855 25 ha landscape units). That is, the
optimisation model seeks to allocate the 2,744,000 people within the 963.8 km2 (3855 x 25
ha units) into population density land-use categories, in a way that maximises the species’
reporting rate based on the species’ relationship with human population density. For
example, if a species cannot persist in urban areas, then the optimal allocation would be a
land sparing approach, fitting all people within as little area as possible (i.e. land-use category
with high population density) and setting aside the remaining area for reserves.
23
Optimisation analyses were performed by using the eco.opti( ) function (Beliakov et al., in
preparation). This function allows the user to set both the population and total land
constraints and identifies the optimal allocation of land units to different land-use categories
(population densities) that maximises the reporting rate of a species. The model uses a linear
solver which provides an easily solvable and a scalable method of optimisation (Beliakov et
al., in prep.). Linear models were preferable as non-linear models do not guarantee a timely
identification of optimal solutions. To optimise species reporting rates, eco.opti( ) requires
estimates of reporting rate for each land-use category. I used the species response curves
produced from GAMs, to estimate reporting rates for each land-use category (i.e. each of the
17 levels of human population density). Rainfall and distance to reserve were held constant
at their average value when predicting reporting rates.
2.6.2 Community level optimisations
One shortcoming of the existing literature on land-use optimisation, particularly within the
debate about land sharing vs sparing, is that studies focus on individual species preferences
in isolation from the broader community (e.g. Phalan et al. 2011b; Hulme et al. 2013). Here,
in addition to individual species, I also optimise an index of species diversity, the geometric
mean of relative abundance (Buckland et al. 2011).
The geometric mean is a community-level metric that responds to changes in species
abundances and increases with greater community evenness (Equation 2) (Buckland et al.
2005; Buckland et al. 2011). Inherently the geometric mean places greater importance on
changes in the abundance of rare or low abundance species than more ubiquitous species
(Buckland et al. 2005). McCarthy et al. (2014) showed that the geometric mean of abundances
24
for a community can predict the proportion of species that go extinct over time. Thus, the
geometric mean of abundance captures a key process of interest to conservation, the
probability of species extinctions. Use of the geometric mean as a biodiversity index is
increasing due these favourable properties (Gregory & Strien 2010; McCarthy et al. 2014). It
is used for bird monitoring in Europe and North America (Butchart et al. 2010; Gregory &
Strien 2010) and is the basis of the Living Planet Index, adopted by the Convention of
Biological Diversity, which summarises trends in the abundance of species on a global scale
(Loh et al. 2005). The geometric mean has been applied to optimisation within fire
management, to identify the optimal composition of vegetation fire ages that maximises
landscape biodiversity (Di Stefano et al. 2013; Kelly et al. 2015).
Equation (2)
Geometric mean = (∏ 𝑥𝑖
𝑚
𝑖=1
)
1𝑚
By optimising the geometric mean, optimisation modelling can identify a solution that
maximises community evenness, total abundance and protects against extinction risk, based
on simultaneous consideration of the responses of all species in a community to human
population density.
For optimisations of the geometric mean, I used the eco.opti.gm( ) function (Beliakov et al.,
in prep.). Similar to the eco.opti( ) function, this model uses a linear solver but optimises the
geometric mean instead of the reporting rate, using piece-wise linear representations. This
25
function enables multiple species to be considered simultaneously and identifies the optimal
allocation of land that maximises the geometric mean. Using min( ) and max( ) functions,
eco.opti.gm( ) allows land allocations to be restricted for specified levels of human population
density (Beliakov et al., in prep.).
2.6.3 Optimisation scenarios
Only native species were included in optimisation of the geometric mean of abundance. The
geometric mean was optimised under the same constraints as for individual species (i.e. the
current human population and area). In addition, I created two further scenarios to examine
how the optimal allocation of land would change under projected changes in the human
population within the Greater Melbourne region. Under each scenario, the total land
constraint was 963.8 km2 (3,855 x 25 ha landscape units), which is the total residential area
within the study region. Total land area remained constant in all scenarios for two reasons.
First, to avoid extrapolating the species’ models beyond the spatial limits of their
development (i.e. not predicting into areas outside the study area). Second, it is consistent
with the adopted permanent Urban Growth Boundary, which has been highlighted under
Melbourne’s development policy since 2003 in Melbourne 2030 (DSE 2003), Melbourne @ 5
million (DPCD 2008), and Plan Melbourne (DTPLI 2014). Scenario populations were based on
population projections for 2050 by the Bureau of Statistics for Greater Melbourne (Australian
Bureau of Statistics 2013). A slower growth rate was used for the study area compared to
Greater Melbourne as residential areas within the study are already developed and unlikely
to have the same growth capacity as the Greater Melbourne region. In total, I optimised the
geometric mean for native bird species for three scenarios:
26
Scenario 1- Current population: Scenario population of 2,744,000 persons, which is the
2011 population of residential land within the study area. Greater Melbourne population was
4.14 million in 2011.
Scenario 2- 2050 Medium-projection: Scenario population of 3,567,000, a 30%
increase from scenario 1. Medium-population projection for Greater Melbourne is 7.65
million for 2050 (85% increase from 2011) (Australian Bureau of Statistics 2013).
Scenario 3- 2050 Upper-projection: Scenario population of 4,390,300, a 60% increase
from scenario 1. Upper-population projection for Greater Melbourne is 8.7 million for 2050
(110% increase from 2011) (Australian Bureau of Statistics 2013).
For each scenario, I compared the optimal solution with a land sharing and land sparing
scenario. While the optimal method of allocation could freely allocate land to any of the 17
land-use categories (human population density), allocations by land sparing and land sharing
methods were restricted. Land sparing was restricted to land allocations at 0 and 1600
persons/25 ha: that is, allocating the specified population within only high density
populations, and setting aside remaining land for reserves. Land sharing was restricted to
allocating land to the two human population density categories that most evenly distributed
people at the lowest density possible, while fitting the specified number of people into the
area. In scenario 3, an additional allocation method was also considered; culturally
constrained allocation, which required 30% of the human population at a population density
of 300 persons/25 ha, to capture a hypothetical effect of cultural demands to protect the
character of existing suburbs and the retention of some lower density housing (DTPLI 2014).
Graphical representations of scenario allocations were simplified by grouping the 17 land-use
27
categories representing population densities into five categories: 0, 150-500, 501-900, 901-
1300 and 1301-1600 persons per 25 ha.
The occurrence of individual species under optimal, land sharing and land sparing allocations
was observed through the ‘summed reporting rate’. The summed reporting rate indicates
the sum of a species reporting rate across the entire study area (i.e. the sum of the reporting
rate for each landscape unit allocated to a human population density). This allowed for the
relative occurrence of each species to be compared within the community and between
allocation designs (i.e. optimal, land sharing and land sparing). It should be noted that
reporting rates are comparable within and between species but are not directly related to
specific population sizes of the species concerned.
28
3. RESULTS
A total 56 species of terrestrial birds were recorded over four survey rounds across the 28
study landscapes. Forty-seven species were native and nine were introduced. Models were
applied to 41 species for which there were observations in three or more landscapes. The Red
Wattlebird (scientific names for all species are given in Table 2) and the Common Myna were
the most commonly recorded species, present in 438 and 394 surveys (out of 560 surveys),
respectively. The Red Wattlebird, Rainbow Lorikeet, Australian Magpie and Little Raven were
recorded in all 28 landscapes. Thirteen species were reserve exclusive (i.e. found only in
reserve landscapes). All reserve exclusive species were native. The number of species
observed within a single landscape ranged from 14 species (Toorak) to 31 species (Narre
Warren North). Twenty-seven native species were observed at the Plenty and Lysterfield
reserve landscapes, the highest of all landscapes. A summary of the species observations is
given in Appendix II.
3.1 Vegetation area
Vegetation area (tree and shrub cover) was an important driver of reporting rates for 25
native species and four introduced species (Table 2). Both rainfall and distance to nearest
reserve contributed in explaining species reporting rates for eight species; Common Myna,
Crimson Rosellas, Eastern Spinebills, Grey Butcherbirds, House Sparrows, Red Wattlebirds,
Spotted Pardalotes and Sulphur-crested Cockatoos (Table 3). In addition, distance to reserves
was associated with reporting rate for another three species and rainfall (but not distance to
reserve) for six species (Table 3). The reporting rates of only two species had a positive
relationship with distance to nearest reserve (i.e. more common further from reserves);
29
House Sparrow and Rock Dove. The reporting rates of nine species (eight native) decreased
as distance to the nearest reserve increased.
The relationship between vegetation area and species reporting rate was variable across
species. Introduce species typically displayed rapid declines in reporting rate with increasing
vegetation area (Figs. 6 a, b). The response curves of native species were more diverse in both
direction and shape (Figs. 6 c, d, e, f). Most introduced species were uncommon in landscapes
of high vegetation area while native species were less common in landscapes of less
vegetation area.
30
Table 2. Summary of generalised additive models (GAMs) of the relationship between species reporting rate and vegetation area (smoothed predictor with 3 degrees of freedom) for 41 species of bird. Rainfall and distance to reserve were included as linear predictors for species observed in more than eight landscapes (see Table 3 for estimates). Significant p-values (α = 0.05) are shown in bold. * denotes species modelled by only vegetation area predictor. edf = estimated degrees of freedom for vegetation area term; D2 (%) = is the percentage deviance explained by the model.
Species and scientific name edf Chi Squared (χ2) P D2 (%)
Australian Magpie, Gymnorhina tibicen 1.74 3.8 0.139 13.4
Brown Thornbill, Acanthiza pusilla 1.94 68.3 <0.001 54.2
Common Blackbird, Turdus merula 1.92 90.0 <0.001 59.7
Common Myna, Acridotheres tristis 1.49 127.6 <0.001 77.5
Common Starling, Sturnus vulgaris 1.00 98.4 <0.001 71.4
Crested Pigeon, Ocyphaps lophotes 1.00 3.3 0.069 37.8
Crimson Rosella, Platycercus elegans 1.61 18.3 <0.001 55.7
Eastern Rosella, Platycercus eximius 1.85 15.4 <0.001 35.7
Eastern Spinebill, Acanthorhynchus tenuirostris 1.95 23.4 <0.001 46.0
European Goldfinch, Carduelis carduelis* 1.00 3.2 0.075 78.5
Galah, Eolophus roseicapilla 1.47 1.9 0.305 13.7
Golden Whistler, Pachycephala pectoralis* 1.90 6.3 0.043 71.9
Grey Butcherbird, Cracticus torquatus 1.94 33.2 <0.001 84.0
Grey Fantail, Rhipidura fuliginosa* 1.91 87.2 <0.001 61.4
Grey Shrike-thrush, Colluricincla harmonica* 1.90 16.1 <0.001 43.9
House Sparrow, Passer domesticus 1.00 71.8 <0.001 75.4
Laughing Kookaburra ,Dacelo novaeguineae* 1.63 43.3 <0.001 36.8
Little Corella, Cacatua sanguinea* 1.74 2.4 0.278 17.8
31
Little Raven, Corvus mellori 1.00 5.3 0.022 47.3
Little Wattlebird, Anthochaera chrysoptera 1.65 36.6 <0.001 64.2
Long-billed Corella, Cacatua tenuirostris* 1.00 1.6 0.212 13.9
Magpie-lark, Grallina cyanoleuca 1.94 21.6 <0.001 39.2
New Holland Honeyeater, Phylidonyris novaehollandiae* 1.84 26.8 <0.001 18.9
Noisy Miner, Manorina melanocephala 1.95 24.6 <0.001 43.2
Pied Currawong, Strepera graculina 1.97 49.2 <0.001 83.3
Rainbow Lorikeet, Trichoglossus haematodus 1.98 41.4 <0.001 41.1
Red-rumped Parrot, Psephotus haematonotus* 1.00 51.1 <0.001 49.9
Red Wattlebird, Anthochaera carunculata 1.00 1.0 0.313 25.9
Rock Dove, Columba livia 1.82 3.6 0.164 4.2
Silvereye, Zosterops lateralis 1.74 5.9 0.048 87.6
Song Thrush, Turdus philomelos* 1.00 3.5 0.062 20.8
Spotted Pardalote, Pardalotus punctatus 1.00 42.4 <0.001 59.7
Spotted Turtle Dove, Streptopelia chinensis 1.69 70.0 <0.001 56.7
Sulphur-crested Cockatoo, Cacatua galerita 1.63 5.2 0.066 11.4
Superb Fairy-wren, Malurus cyaneus* 1.90 9.7 0.008 19.0
Welcome Swallow, Hirundo neoxena* 1.67 2.3 0.292 41.5
White-browed Scrubwren, Sericornis frontalis* 1.95 39.1 <0.001 98.6
White-eared Honeyeater, Lichenostomus leucotis* 1.65 5.1 0.068 5.9
White-plumed Honeyeater, Lichenostomus penicillatus 1.93 15.0 <0.001 12.7
White-throated Treecreeper, Cormobates leucophaea* 1.92 22.3 <0.001 73.9
Willie Wagtail, Rhipidura leucophrys* 1.00 8.4 0.004 15.7
32
Table 3. Summary statistics (estimate and standard error) for the landscape-scale standardised linear terms (mean annual rainfall, distance to
nearest reserve) in generalised additive models of the relationship between species’ reporting rates and landscape vegetation area. Significant
p-values are shown in bold. S.E = standard error estimate for parameter.
Species Predictor variable Estimate S.E P
Australian Magpie Mean annual rainfall 0.165 0.13 0.212
Distance to reserve -0.103 0.15 0.499
Brown Thornbill Mean annual rainfall 0.473 0.16 0.003
Distance to reserve 0.203 0.19 0.281
Common Blackbird Mean annual rainfall 0.334 0.17 0.044
Distance to reserve -0.079 0.18 0.660
Common Myna Mean annual rainfall -0.533 0.17 0.002
Distance to reserve -0.620 0.21 0.003
Common Starling Mean annual rainfall 0.658 0.20 0.001
Distance to reserve 0.352 0.23 0.119
Crested Pigeon Mean annual rainfall 0.261 0.27 0.334
Distance to reserve -0.113 0.34 0.739
Crimson Rosella Mean annual rainfall 0.587 0.24 0.015
Distance to reserve -0.679 0.32 0.031
Eastern Rosella Mean annual rainfall 0.359 0.23 0.125
Distance to reserve -0.760 0.29 0.009
Eastern Spinebill Mean annual rainfall 0.509 0.20 0.010
Distance to reserve -0.505 0.25 0.042
Galah Mean annual rainfall 0.514 0.27 0.054
Distance to reserve -0.216 0.34 0.523
Grey Butcherbird Mean annual rainfall -0.685 0.19 <0.001
Distance to reserve -0.825 0.19 <0.001
33
House Sparrow Mean annual rainfall -0.413 0.17 0.015
Distance to reserve 0.457 0.22 0.035
Little Raven Mean annual rainfall 0.146 0.13 0.255
Distance to reserve -0.152 0.15 0.308
Little Wattlebird Mean annual rainfall 0.500 0.14 <0.001
Distance to reserve -0.321 0.17 0.058
Magpie-lark Mean annual rainfall 0.736 0.15 <0.001
Distance to reserve -0.298 0.18 0.101
Noisy Miner Mean annual rainfall -0.028 0.15 0.852
Distance to reserve -0.497 0.17 0.004
Pied Currawong Mean annual rainfall 0.118 0.21 0.577
Distance to reserve 0.301 0.24 0.215
Rainbow Lorikeet Mean annual rainfall -0.029 0.14 0.836
Distance to reserve -0.010 0.16 0.953
Red Wattlebird Mean annual rainfall -0.423 0.16 0.007
Distance to reserve -0.941 0.20 <0.001
Rock Dove Mean annual rainfall 0.128 0.25 0.606
Distance to reserve 0.650 0.30 0.028
Silvereye Mean annual rainfall -0.203 0.21 0.331
Distance to reserve 0.038 0.23 0.867
Spotted Pardalote Mean annual rainfall 0.397 0.17 0.021
Distance to reserve -0.606 0.21 0.005
Spotted Turtle Dove Mean annual rainfall -0.190 0.14 0.176
Distance to reserve 0.127 0.16 0.440
Sulphur-crested Cockatoo Mean annual rainfall 0.643 0.22 0.003
Distance to reserve -0.625 0.28 0.026 White-plumed Honeyeater Mean annual rainfall -0.542 0.27 0.043
Distance to reserve 0.545 0.30 0.067
34
Figure 6. Species response curves displaying the relationship between a species reporting rate and vegetation area (ha) in 28 study
landscapes in Melbourne, x-axis = vegetation area (ha). Other variables (rainfall, distance to reserve) are held at their mean value. Solid line =
predictions from a fitted generalised additive model (GAM), grey circles = reporting rate. * denotes species modelled only by vegetation area
predictor.
35
Vegetation area in the study landscapes was strongly related to human population density
(D2 = 64.3%; df = 23, F= 14.24, P = <0.001). The relationship was non-linear, with the transition
from reserve landscapes to residential landscapes coinciding with a marked shift in vegetation
area (Fig. 7). The average vegetation area was 11.3 ha for reserve landscapes and 2.8 ha for
residential landscapes. There was a large range in vegetation areas across the residential
landscapes: the values for the lowest (Roxburgh Park landscape, 0.3 ha) and highest
(Blackburn landscape, 6.8 ha) vegetation areas illustrates this variability.
Figure 7. The relationship between vegetation area and human population density for 28
study landscapes in Melbourne. Solid line = predictions from a fitted generalised additive
model. Grey circles are the raw data for each landscape. Human population density
predictor was smoothed with four degrees of freedom.
Human population density (persons/25 ha)
Ve
geta
tio
n a
rea
(ha)
36
3.2 Human population density
The reporting rates of 28 species (7 introduced, 21 native) were associated with human
population density (Table 4). Significant relationships were observed for 23 species with an
additional five species exclusively occurring in reserves. The White-throated Treecreeper was
considered to be reserve exclusive as 38 of 39 transect observations on which it occurred
were across the four reserve landscapes. Reserve exclusive species were strongly predicted
by human population density, explaining at least 75% of the variability in reporting rate (Table
4). Twenty-three species with reporting rates associated with human population density (i.e.
having significant relationship or being confined to reserves) also had significant relationships
with vegetation area.
A variety of response curves for the relationship between reporting rate and human
population density were observed (Fig. 8). Reserve exclusive species displayed relationships
consistent with the type of responses expected for urban avoiders; this is, a sharp, threshold-
like decrease as human population density increases (Fig. 8b). Species with urban avoider
responses included the Golden Whistler, Grey Shrike-thrush, Superb Fairy-wren, White-eared
Honeyeater and White-throated Treecreeper. Urban adaptive responses (maximum reporting
rates in intermediate population densities) were observed for six species, including both
native and introduced species; Australian Magpie, Common Blackbird, Crimson Rosella,
Eastern Spinebill, Little Wattlebird and Red Wattlebird. Urban exploiter responses (peak
occurrences at the highest population densities) were only observed in three species, all were
introduced: Common Myna, Rock Dove and Spotted Turtle-Dove (Fig. 8d). Quadratic or ‘U-
shaped’ responses, where species occurrence was lowest at intermediate population density,
37
were observed for Brown Thornbill and Spotted Pardalote (Fig. 8c). The full set of response
curves for species responding to human population density are listed in Appendix III.
38
Table 4. Summary of generalised additive models (GAMs) of the relationship between species reporting rate and human population density (smoothed predictor with 3 degrees of freedom) for 41 species of bird. Rainfall and distance to reserve were included as linear predictors for species observed in more than eight landscapes. Significant p-values (α = 0.05) are shown in bold. * denotes species modelled by only vegetation area predictor. edf = estimated degrees of freedom from the smoothed human population density term; D2 (%) = the percentage deviance explained by the model.
Species edf Chi Squared (χ2) P D2 (%) Australian Magpie 1.85 11.3 0.004 22.9 Brown Thornbill 1.98 88.5 <0.001 62.9 Common Blackbird 1.95 33.6 <0.001 27.7 Common Myna 1.92 97.6 <0.001 53.4 Common Starling 1.98 70.0 <0.001 25.4 Crested Pigeon 1.87 7.3 0.026 26.9 Crimson Rosella 1.93 14.7 <0.001 58.2 Eastern Rosella 1.55 7.6 0.019 52.3 Eastern Spinebill 1.91 16.8 <0.001 45.8 European Goldfinch* 1.00 1.4 0.241 4.3 Galah 1.00 2.0 0.161 37.2 Golden Whistler* 1.00 0.0 1.000 80.3 Grey Butcherbird 1.00 6.7 0.010 45.9 Grey Fantail* 1.20 10.1 0.003 78.2 Grey Shrike-thrush* 1.00 0.0 1.000 93.2 House Sparrow 1.96 39.6 <0.001 26.8 Laughing Kookaburra* 1.00 20.4 <0.001 57.2 Little Corella* 1.71 3.7 0.147 11.0 Little Raven 1.71 3.1 0.192 10.7 Little Wattlebird 1.91 29.3 <0.001 33.3 Long-billed Corella * 1.88 5.5 0.063 34.4 Magpie-lark 1.53 2.9 0.188 30.8 New Holland Honeyeater* 1.85 6.5 0.038 3.1 Noisy Miner 1.00 0.5 0.495 10.6 Pied Currawong 1.83 22.1 <0.001 27.3 Rainbow Lorikeet 1.00 0.0 0.999 0.9 Red-rumped Parrot* 1.66 1.5 0.4410 74.5 Red Wattlebird 1.95 32.2 <0.001 11.9 Rock Dove 1.00 6.4 0.012 27.8 Silvereye 1.61 7.6 0.019 17.3 Song Thrush* 1.75 2.1 0.335 8.1 Spotted Pardalote 1.96 42.4 <0.001 69.7 Spotted Turtle Dove 1.94 87.1 <0.001 67.0 Sulphur-crested Cockatoo 1.00 0.5 0.481 33.1 Superb Fairy-wren* 1.00 0.0 1.000 98.4 Welcome Swallow* 1.83 3.6 0.160 51.9 White-browed Scrubwren* 1.00 17.3 <0.001 54.4 White-eared Honeyeater* 1.00 0.0 1.000 84.5 White-plumed Honeyeater 1.96 22.2 <0.001 43.6 White-throated Treecreeper * 1.08 2.4 0.146 78.5 Willie Wagtail * 1.67 2.3 0.283 3.7
39
Figure 8. Species response curves displaying the relationship between species reporting rate and human population density, x-axis = human
population density (persons/25 ha). Examples of species sensitive to urbanisation (a, c), urban avoider (b), urban exploiter (d) and urban
adapter (e, f) responses. Solid line = predictions from a fitted generalised additive model (GAM), grey circles = reporting rate. * denotes species
modelled only by vegetation area predictor.
40
Overall species richness (i.e. all bird species) per landscape was negatively associated with
human population density (GAM, F = 8.77, P = 0.002, n = 28). As population density increases,
species richness decreased from an average of 28 species in reserve landscapes, to 17 species
at the highest population density (1692 persons/25 ha). Neither rainfall nor distance to
nearest reserve contributed to explaining variation in species richness between landscapes
(rainfall: GAM, t = 1.48, P = 0.152, n = 28; distance to reserve: GAM, t = -1.27, P = 0.216, n =
28).
Species richness of native species also declined with increasing population density in the study
landscapes (GAM, F = 17.39, P = <0.001, n = 28) (Fig. 9) and was negatively associated with
distance to nearest reserve (GAM, t = -2.13, P = 0.010, n = 28). Rainfall was not identified as
an important predictor of landscape native species richness (GAM, t = -0.72, P = 0.474, n =
28). The maximum native species richness (27 species) was observed at two reserve
landscapes (0 population density): Plenty and Lysterfield. The highest native species richness
in residential landscapes was 25 in Blackburn, which also had the highest vegetation area of
all residential landscapes.
41
Figure 9. The relationship between native species richness and human population density (smoothed predictor with three degrees of freedom) for 28 study landscapes in Melbourne. Solid line = predictions from the fitted model (GAM) holding rainfall and distance to reserve constant at their average values. Grey circles = reporting rates for study landscapes.
Human population density (persons/25 ha)
Nat
ive
sp
eci
es
rich
ne
ss
42
3.3 Optimisations
Twenty-eight species were identified as having reporting rates strongly related to human
population density, and were considered for individual species optimisations.
3.3.1 Individual species optimisation
Optimising of the reporting rates of introduced species resulted in an identical distribution of
population densities for six of the seven species considered. For these species, all available
land was allocated to land-use categories with population densities between 500 and 900
persons per 25 ha (Fig. 10). The only exception was the Rock Dove, which had an optimal
reporting rate when 44.5% of the area was allocated to 1600 persons/25 ha and the remaining
land to reserves.
For native species, optimal land allocations displayed a wider variety of allocation types.
Similar to introduced species, the Australian Magpie, Little Wattlebird, New Holland
Honeyeater and Red Wattlebird were favoured by ‘land sharing’ allocations that resulted in
100% of the landscape distributed at lower human population densities of between 500 and
900 persons/25 ha (Fig. 11). Thirteen native species were identified as having allocations
akin to ‘land sparing’, with land divided into high density areas of 1600 persons/ 25 ha and
areas with no humans (i.e. reserves) (Fig. 11). The Crimson Rosella, Eastern Spinebill and
Pied Currawong also displayed preferences for low human population densities. For these
species, land was allocated at 1600 persons/25 ha to meet population constraints allowing
for the remaining area to be allocated at 500 persons/25 ha (400 persons/25 ha for Pied
Currawong). These allocations would not occur under land sharing or land sparing
approaches. Uniquely, the White-plumed Honeyeater was optimised by allocating the entire
43
human population at 900 persons/25 ha, with remaining land allocated to reserves despite
reserves contributing little to the species modelled reporting rate (see Appendix III for
White-plumed Honeyeater species curve).
44
Figure 10. Optimal land allocation to maximise the reporting rate of introduced bird species. Constraints in the optimisation model were the study area population of 2,744,000 and total area of 963.8 km2 (3885 x 25 ha units). The 17 land-use categories representing population densities are grouped into five categories here: 0 (green), 150-500 (yellow), 501-900 (orange), 901-1300 (dark orange) and 1301-1600 persons per 25 ha (red).
0.00
0.20
0.40
0.60
0.80
1.00P
rop
ort
ion
of
tota
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d
1301-1600
901-1300
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150-500
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Human population density
(persons/25 ha)
45
Figure 11. Optimal land allocation to maximise the reporting rate of native bird species. Constraints in the optimisation model were the study area population of 2,744,000 and land area of 963.8 km2 (3885 x 25 ha units). The 17 land-use categories representing population densities are grouped into five categories here: 0 (green), 150-500 (yellow), 501-900 (orange), 901-1300 (dark orange) and 1301-1600 persons per 25 ha (red). * denotes species modelled by only the human population density predictor.
0.00
0.20
0.40
0.60
0.80
1.00P
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of
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1301-1600
901-1300
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150-500
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Human population density
(persons/25 ha)
46
3.3.2 Community optimisations and alternative scenarios
The geometric mean of abundance was optimised considering the 21 native species for that
displayed an association with population density (Fig. 11).
Scenario 1- Current human population
Under scenario 1, the optimal allocation of land which maximised the geometric mean
allocated land to human population densities of 0, 1000 and 1600 persons per 25 ha (Fig.
12a(i)). Optimal allocation had a marginally higher geometric mean of abundance compared
to the land sparing allocation and a substantially higher geometric mean compared to the
land sharing allocation (Fig. 13).
Although it is the community measure (in this instance the geometric mean of abundance)
being optimised, it is also possible and important to consider how individual species fair under
the various allocations (i.e. optimal, land sparing, land sharing), as species of conservation
concern will not always benefit from the optimising of the community indice (Kelly et al.
2015). For all species except the White-plumed Honeyeater, species reporting rates under the
optimal allocation were closely mirrored by that of the land sparing approach (Fig. 14). Under
land sparing allocation, the five urban avoider species were poorly represented within the
community, with summed reporting rates below 0.04 (Fig. 14)
47
Figure 12. Diagrammatic illustration of land allocations generated with optimal, land sparing, land sharing and cultural allocation methods under three population scenarios: scenario 1 (2,744,000 persons), scenario 2 (3,567,000 persons) and scenario 3 (4,390,300 persons). Indicates proportion of landscape allocated to human population density land-uses. All scenarios had the same land constraint 963.8 km2 (3885 x 25 ha units). Each diagram represents 100% allocation of the available land. The 17 land-use categories representing population densities are grouped into five categories here: 0 (green), 150-500 yellow, 501-900 (orange), 901-1300 (dark orange) and 1301-1600 per 25 ha (red).
1301-1600
901-1300
501-900
150-500
0
Human population density (persons/25 ha)
48
Figure 13. Value of the geometric mean under the optimal (grey), land sparing (green), land
sharing (orange) and cultural (yellow, scenario 3 only) allocation methods for three
population scenarios; scenario 1 with current population (2,744,000 persons), scenario 2
with 2050 medium-projection population (3,567,000 persons) and scenario 3 with 2050
upper-projection population (4,390,300 persons). All scenarios had the same land constraint
of 963.8 km2 (3885 x 25 ha units).
0
100
200
300
400
500
600
Scenario 1 Scenario 2 Scenario 3
Geo
met
ric
mea
n
Optimal
Land sparing
Land sharing
Cultural
Allocation Method
49
Figure 14. Summed reporting rate in scenario 1 (current population of 2,744,000 persons)
for native species under land allocations that optimise the geometric mean of relative
abundance (grey), as well as land sparing (green) and land sharing (orange). Summed
reporting rate indicates the sum of a species reporting rate across the entire study area
(3855 x 25 ha landscape units allocated to human population densities categories) under
each allocation method. * denotes species modelled by only the human population density
predictor.
Allocation Method
50
Scenario 2- 2050 middle projection
The optimal allocation of land under scenario 2 was similar to a land sparing model, with
large areas dedicated to either reserves or high density populations (Fig. 12b(i) and b(ii)).
Under both allocations, land allocated to reserves decreases compared to scenario 1, while
allocation to the land-use category of 1600 persons/25 ha increases to meet population
constraints. The optimal allocation that maximised the geometric mean of abundance
reduces the land allocated to 1000 persons/25 ha compared to scenario 1. Land-use under
the land sharing allocation becomes more densely populated, with land allocated to 900 and
1000 persons/25 ha (Fig. 12b(iii)). The geometric mean of relative abundance for the land
sharing allocation remained low, decreasing from 0.229 (scenario 1) to 0.175 (Fig. 13); with
the reporting rate for six species falling below one (Fig. 15). The geometric mean for the
optimal and land sparing allocations were very similar (Fig. 10; 473 and 468, respectively);
however, both have fell relative to scenario 1. Reporting rates for species were similar
between the optimal and land sparing methods, except for the White-plumed Honeyeater
(Fig. 15).
51
Figure 15. Summed reporting rate in scenario 2 (medium-projection 2050 population of
3,567,000 persons) for native species under optimal (grey), land sharing (orange) and land
sparing (green) allocation methods. Summed reporting rate indicates the sum of a species
reporting rate across the entire study area (3855 x 25 ha landscape units allocated to human
population densities categories) under each allocation method. * denotes species modelled
by only the human population density predictor.
Allocation Method
52
Scenario 3:- 2050 Upper projection
With a hypothesised further increase in the human population under scenario 3, and land
constraints remaining constant, the optimal allocation continues to converge upon the land
sparing allocation (Fig. 18); only 0.5% of the total area is allocated to 1000 persons/25 ha with
the remaining land allocated either to reserves (28.6%) or the land-use category of highest
human population density (1600 persons/25 ha) (70.9%). The optimal and land sparing
allocations achieve the same geometric mean of 373 (Fig. 13). In contrast, land sharing
allocates land to categories of 1100 and 1200 persons/25 ha (Figure 12c(iii)) resulting in nine
species with reporting rates of less than one and a geometric mean of just 0.09 (Figs. 13 and
16).
Optimisation under the cultural constraints, with 30% of the population allocated at 300
persons/25 ha, achieved a geometric mean of 211 by allocating land to reserves (Fig. 12c(iv)
and 13). Land allocated to reserves under optimal, land sparing and culturally constrained
allocations, contributed to a higher geometric mean than land sharing allocation because
reserves support urban avoiders and species sensitive to urban areas within the landscape
(Fig. 13).
Across the three scenarios, the optimal allocation converges on a land sparing allocation as
population constraints increase (Fig. 12). As the total human population increases, land
sharing allocations become more densely populated, but remain evenly distributed
throughout the landscape. Further, as the human population increases, the geometric mean
consistently declines under all methods of allocation (Fig. 13). This is a result of decreasing
community evenness and lower summed reporting rates (Fig. 14, 15 and 16).
53
Figure 16. Summed reporting rate under optimal (grey), land sparing (green), land sharing (orange) and culturally constrained (yellow) allocation methods for scenario 3 (upper-projection 2050 population of 4,390,300 persons). Summed reporting rate indicates the sum of a species reporting rate across the entire study area (3855 25 ha landscape units allocated to human population densities categories) under each allocation method. * denotes species modelled by only the human population density predictor.
Allocation Method
54
4. DISCUSSION
Modelling the response of bird species to human population density in urban landscapes
shows that bird conservation in urban areas requires aspects of both land sparing and land
sharing. However, the optimal approach was more similar to a land sparing than a land sharing
allocation, suggesting that concentrating people in high density populations and setting aside
large areas for reserves free of human housing will be most beneficial to bird conservation.
As the human population of the study region increased, the optimal allocation of land further
converged upon a land sparing allocation.
4.1 Human population density
Human population density was a key driver of the reporting rates of two-thirds of the species
modelled. This suggests that human population density captures important aspects of the
urban environment to which bird species are responding. This is likely to include the density
of housing and increases in impervious cover (Stankowski 1972; Gagné & Fahrig 2010), as well
as decreasing vegetation cover and human presence (Brazel et al. 2000; Hahs & McDonnell
2006; Luck & Smallbone 2011).
Several types of response-curves to human population density were evident, including those
typically expected for urban avoiders, urban adapters and urban exploiters (Blair 1996;
McKinney 2002; Kark et al. 2007). Five species (including the Golden Whistler and Superb
Fairy-wren) occurred only in reserves, displaying distinct urban avoider characteristics with
threshold responses to urbanisation (Blair 1996; Parsons, French & Major 2009). For these
species, large remnant patches of contiguous, native vegetation are required for their
persistence (McKinney 2002; Sushinsky et al. 2013). Six species, including the Eastern Spinebill
55
and Common Blackbird, displayed urban adapter response-curves; that is, occurring in
reserves but increasing in frequency of occurrence at low to intermediate urban population
densities. The three distinctly urban exploiter species – species most common in landscapes
with the highest densities of people –– were introduced species; Spotted-turtle Dove, Rock
Dove and Common Myna (Shwartz, Shirley & Kark 2008; Van Rensburg, Peacock & Robertson
2009).
Nine species, including Silvereye and Eastern Rosella, displayed sensitivity to human
population density, with occurrences peaking in reserves but the species still persisting
(although less frequently) at low and intermediate population density. All species sensitive to
urbanisation were native species. Two species were observed only at low and intermediate
population density (New Holland Honeyeater, White-plumed Honeyeater), suggesting these
species depend upon resources (e.g. flowering trees and shrubs in gardens) which are
abundant in residential landscapes (Daniels & Kirkpatrick 2006; Carbó-Ramírez & Zuria 2011;
Rayner et al. 2014). The wide range of species responses to human population density reflect
differences in ecological traits such as nesting requirements, diet and tolerance to humans
(Garden et al. 2006; Kark et al. 2007; Croci, And & Clergeau 2008).
Total species richness and the richness of native species were both negatively associated with
human population density. As the density of humans in the landscape increases, the number
of species recorded within that landscapes declined. High density residential landscapes
supported approximately 40% fewer species than reserve landscapes. A reduction in species
richness with increasing urbanisation intensity has been observed across a number of taxa
including birds (Chace & Walsh 2006; Møller 2009), butterflies (Blair 1999; Marzluff 2001) and
56
beetles (Soga et al. 2014). Native species richness was greatest in reserves, emphasising the
importance of natural habitats free of urban pressures for urban bird conservation (Palmer et
al. 2008; Phalan et al. 2011a; Kang et al. 2015).
One of the key mechanisms underpinning the loss of species from urban landscapes is the
loss of native vegetation (Blair 1996; McKinney 2006). On average, reserve landscapes had
approximately four times the vegetation area of residential landscapes. Even in landscapes
with the lowest population density, only 16% of the vegetation area of the average reserve
landscape was observed, and such vegetation in the residential landscapes is primarily non-
native vegetation (French, Major & Hely 2005; Daniels & Kirkpatrick 2006). The substantially
lower vegetation area of residential landscapes relative to reserve landscapes, indicates that
the transition from reserve to residential land-use results in a large shift in tree and shrub
cover (Germaine et al. 1998; McKinney 2008; Pickett et al. 2011). The loss of native vegetation
affects bird species by altering vegetation structure which affects food availability, protection
from predators and nesting resources (Marzluff & Ewing 2001; White et al. 2005; Rousseau,
Savard & Titman 2015). Of the 28 species responding to human population density, the
reporting rates of 23 species were associated with vegetation area confirming that vegetation
is a key mechanism driving the negative association with human population density.
Considering only the residential landscapes, there was much variation in vegetation area. In
some instances, landscapes with large differences in human population density had similar
amounts of vegetation cover. This suggests there is great scope for increasing the amount of
vegetation cover, even in densely populated areas. Once urbanised, vegetation
characteristics of landscapes may be influenced by factors such as private landholder values,
57
council management or the age of the suburb (Nassauer 1995; White et al. 2005; Jenerette
et al. 2007). Therefore, opportunities may exist to increase the occurrence of urban tolerant
species by increasing the amount of vegetation cover within residential landscapes (Daniels
& Kirkpatrick 2006; Goddard, Dougill & Benton 2010).
In addition to human population density and vegetation area, the extent to which urban areas
are isolated from large, continuous tracts of native vegetation influences species occurrence
(Melles, Glenn & Martin 2003; Parsons, French & Major 2003; Gilroy et al. 2014). Native
species with reporting rates associated with distance to nearest reserve declined in
occurrence with increasing distance, as did the richness of native species. This suggests that
large areas of native vegetation are important reservoirs for the persistence of native species
within urban areas (Hostetler & Knowles-Yanez 2003; Sandström, Angelstam & Mikusiński
2006; Palmer et al. 2008). Such reserves may comprise of core habitat resources for species
while residential landscapes provide supplementary resources (Melles, Glenn & Martin 2003;
Parsons, French & Major 2003).
4.2 Optimisation
4.2.1 Individual species optimisation
Under the current scenario of 2,744,000 people in 964 km2, the optimal allocation of land for
most native species was a land sparing allocation - condensing the human population at high
population density and allocating ‘spared’ land to reserves. Land sparing favoured species
that were scarce in urbanised landscapes but peaked in occurrence in reserves (Green et al.
2005; Phalan et al. 2011b; Sushinsky et al. 2013). By contrast, the reporting rates of
introduced species were often highest under a land sharing approach, with the entire
58
landscape urbanised at intermediate population densities. This is consistent with numerous
studies indicating that introduced species are favoured by urbanised environments (Van
Rensburg, Peacock & Robertson 2009; Kowarik 2011; Pickett et al. 2011).
Preferences for types of land allocation that would not occur under a strictly land sharing or
land sparing approach were also apparent (Hulme et al. 2013). Several species favoured low
population density allocations (e.g. Pied Currawong, Eastern Spinebill), and potentially would
benefit from concentrating part of the human population to allow for most of the available
land to be allocated to low population density. The occurrence of the White-plumed
Honeyeater was also optimised by an allocation approach combining aspects of land sharing
and land sparing, with some land allocated to reserves and most allocated to intermediate
population density. Low population density is typically associated with larger properties and
larger residential gardens, potentially elevating the abundance of favourable resources
available to urban tolerant species through increased flowing trees and shrubs (Chamberlain,
Cannon & Toms 2004; French, Major & Hely 2005; Parsons, Major & French 2006).
Species showing preferences for allocations other than land sharing or land sparing
approaches highlight the need to consider the range of possible allocation types in between
land sharing and land sparing (Phalan et al. 2011a; Butsic & Kuemmerle 2015; Stott et al.
2015). Limiting allocation designs to just land sharing and land sparing approaches is unlikely
to adequately support such species. This also suggests there is value in understanding the
types of resources available in low and intermediate landscapes to facilitate greater
occurrence of urban tolerant species (Daniels & Kirkpatrick 2006).
59
4.2.2 Community optimisations
By using optimisation modelling to maximise the geometric mean of relative abundance, I
have considered how allocation design affects community evenness, total abundance and
extinction risk for a set of native species with a range of responses to human population
density (Buckland et al. 2005; McCarthy et al. 2014). This provides a sophisticated analysis of
the land sharing and land sparing allocations, moving beyond previous studies (Phalan et al.
2011b; Hulme et al. 2013) that only consider individual species’ responses. By repeating the
optimisation across both current and future scenarios of human population growth, it allows
us to infer the allocation that would benefit the entire avian community to the greatest extent
under different population constraints.
Under the current population scenario, optimal allocation consists of both land sharing and
land sparing characteristics, as also proposed by a number of authors (Phalan et al. 2011a;
Tscharntke et al. 2012; Butsic & Kuemmerle 2015). Optimised allocation distributed more
than half of the landscape area to reserves (52%), some (9%) to intermediate population
density and the remaining land (39%) to the highest population density land-use. By not
restricting allocations to just land sparing or sharing approaches, an optimal allocation
approach has the flexibility to allocate land to support rare or vulnerable species that may
have differing preferences from other species.
Since most native species that were modelled favoured the land sparing allocation, the
difference between the geometric mean index for land sparing and for the optimal solution
was relatively small, especially when compared with the difference between the optimal
solution and land sharing. Without land allocated to reserves, land sharing inadequately
60
supports urban avoider species, resulting in a low geometric mean. The poor performance of
the land sharing approach emphasises the importance of reserves within the urban landscape
for native bird diversity (Parsons, French & Major 2003; Sandström, Angelstam & Mikusiński
2006; Sushinsky et al. 2013).
As the human population increases, as depicted by the alternative scenarios, the optimal
approach converged on a land sparing approach. That is, although land sparing is not currently
the best option, as the population grows it becomes closer to optimal. Despite having the
capacity to tailor allocations based on species response curves, the optimal allocation
approach has decreasing flexibility with greater human population constraints. While a small
amount of land was still allocated to intermediate population density, the main finding was
to concentrate the human population at high population density to maximise the area
available for reserves to provide for urban avoider species (McKinney 2002; Parsons, French
& Major 2003).
In all future scenarios modelled, land sharing performed poorly because urban avoider
species were inadequately represented within the landscape. Even a relatively small
allocation to reserves, as in the optimisation model when cultural constraints were
considered, prevents the loss of urban avoider species (Sushinsky et al. 2013). With increasing
human population, a trend of a decreasing geometric mean was observed for all allocation
methods. This suggests that meeting future conservation goals and managing extinction risk
of species will become more challenging with growing human population pressures (Nelson
et al. 2010; McCarthy et al. 2014).
61
4.3 Limitations
Optimisation modelling conducted in this study did not consider the geospatial distribution
of land-use allocations. However, due to the design of this study, estimated species
occurrence within reserves assumes that reserves are large, contiguous patches of native
habitat. While the optimisation model has the capacity to consider allocations to any land-
use categories, including reserves of different sizes, these land-use types must be sampled.
Since study landscapes representing reserves were part of a large continuous tract, the
biodiversity value of fragmented native habitats within the urban matrix cannot be
considered. Previous studies have suggested that the biodiversity value of large continuous
habitats is greater than that of small fragmented urban parks, and therefore we do not advise
that reserves to be allocated as fragmented patches (Friesen, Eagles & Mackay 1995; Hanski
1999; Palmer et al. 2008). Further, urban avoider species that benefit most strongly from
reserves are likely to be sensitive to edge effects associated with small patches (McKinney
2002; Palmer et al. 2008). Therefore, despite not directly considering the geospatial
configuration of land-use categories, the data used for modelling and the findings in the
literature suggest reserve allocations should be distributed as large, contiguous patches
where possible.
Despite practical limitations to optimisation modelling, optimal allocations have inherent
theoretical value in understanding how the study area might ideally be designed to maximise
species diversity (Fischer et al. 2014). Existing distributions of land-uses, infrastructure and
vegetation largely commit Melbourne to a particular distribution of people in the future. The
prospect of dramatically reworking the configuration of Melbourne’s land use is unlikely and
would be costly. These models, however, provide a means to conceptualise an ideal
62
configuration of an urban environment such as Melbourne, and how it could developed in the
future to support a growing population with minimal impact on biodiversity. More
importantly, optimisation modelling emphasises the importance of reserves in any future
scenario to support a set of species unable to persist within urban areas.
4.4 Future directions
This study shows that human population density serves as a surrogate to capture urban
effects to which bird species respond. Further research opportunities exist to use such high
resolution population data from the widely accessible national census data in urban ecology
for a range of taxa including amphibians, reptiles and invertebrates (McKinney 2008; Pickett
et al. 2008) . With such data there is also scope to further understand trade-offs that exist
between meeting the demands of the human population and ecosystem service provision
(e.g. pollination, pest control) (Seppelt, Lautenbach & Volk 2013; Kremen 2015; Stott et al.
2015).
While optimisation modelling has been demonstrated to yield useful insights into species
diversity outcomes given current population constraints, optimisation outcomes are context
dependent (Hodgson et al. 2010; Butsic & Kuemmerle 2015). Here, I have highlighted that
land sparing becomes more optimal with increasing population pressure: however, for
ecological communities consisting of a different set of species responses, different optimal
allocations are possible. Further research is required to understand whether land sparing is
consistently favoured as population demands increase, and whether large continuous native
habitats have similar importance in other urbanised landscapes.
63
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APPENDICES
Appendix I. The relationship between landscape human population density and dwelling
density in residential landscapes in the 28 study landscapes in Melbourne.
Human population density (persons/25 ha)
Dw
elli
ng
den
sity
(h
ou
ses/
25
ha)
73
Appendix II. List of all 56 terrestrial bird species indicating the number of landscapes each species was observed in and the number of surveys the species occurred on, ordered by number of landscape observations. Each landscape was surveyed 20 times over four survey rounds. Species observed in less than 3 landscapes were not modelled in the study. * indicates species found exclusively in reserve landscapes
Species (Common name, scientific name) Landscapes observations
Survey observations
Bell Miner*, Manorina melanophrys 1 2
Eastern Yellow Robin*, Eopsaltria australis 1 1
Gang-gang Cockatoo, Callocephalon fimbriatum 1 1
Striated Thornbill*, Acanthiza lineata 1 2
Varied Sittella*, Daphoenositta chrysoptera 1 1
White-naped Honeyeater*, Melithreptus lunatus 1 1
Australian King Parrot, Alisterus scapularis 2 4
Black-faced Cuckoo-shrike* , Coracina novaehollandiae
2 3
Grey Currawong*, Strepera versicolor 2 8
Musk Lorikeet, Glossopsitta concinna 2 3
Red-browed Finch, Neochmia temporalis 2 4
Scarlet Robin*, Petroica multicolor 2 2
Striated Pardalote, Pardalotus striatus 2 3
Yellow-tailed Black Cockatoo , Calyptorhynchus funereus
2 4
Yellow-faced Honeyeater*, Lichenostomus chrysops 2 8
Golden Whistler*, Pachycephala pectoralis 3 12
Long-billed Corella , Cacatua tenuirostris 3 11
White-eared Honeyeater*, Lichenostomus leucotis 3 4
Grey Shrike-thrush*, Colluricincla harmonica 4 31
Laughing Kookaburra, Dacelo novaeguineae 4 23
Red-rumped Parrot, Psephotus haematonotus 4 7
Superb Fairy-wren*, Malurus cyaneus 4 38
Welcome Swallow, Hirundo neoxena 4 7
White-browed Scrubwren, Sericornis frontalis 5 28
White-throated Treecreeper , Cormobates leucophaea
5 39
European Goldfinch, Carduelis carduelis 6 10
Song Thrush, Turdus philomelos 6 13
Willie Wagtail , Rhipidura leucophrys 6 20
Grey Fantail, Rhipidura fuliginosa 7 51
74
Little Corella , Cacatua sanguinea 7 14
New Holland Honeyeater, Phylidonyris novaehollandiae
7 62
Crimson Rosella, Platycercus elegans 9 57
White-plumed Honeyeater, Lichenostomus penicillatus
11 63
Crested Pigeon, Ocyphaps lophotes 12 24
Eastern Rosella, Platycercus eximius 12 63
Eastern Spinebill, Acanthorhynchus tenuirostris 12 60
Pied Currawong, Strepera graculina 15 77
Rock Dove, Columba livia 16 42
Sulphur-crested Cockatoo, Cacatua galerita 16 57
Galah, Eolophus roseicapilla 17 30
House Sparrow, Passer domesticus 17 184
Spotted Pardalote, Pardalotus punctatus 18 127
Brown Thornbill, Acanthiza pusilla 19 147
Common Starling, Sturnus vulgaris 20 270
Magpie-lark, Grallina cyanoleuca 21 140
Noisy Miner, Manorina melanocephala 22 150
Silvereye, Zosterops lateralis 23 71
Grey Butcherbird, Cracticus torquatus 24 152
Little Wattlebird, Anthochaera chrysoptera 24 160
Common Blackbird, Turdus merula 26 360
Spotted Turtle Dove , Streptopelia chinensis 26 349
Common Myna, Acridotheres tristis 27 394
Australian Magpie, Gymnorhina tibicen 28 342
Little Raven, Corvus mellori 28 332
Rainbow Lorikeet, Trichoglossus haematodus 28 375
Red Wattlebird, Anthochaera carunculata 28 438
75
Appendix III. Species response curves displaying the relationship between species’ reporting
rate and human population density for species that were strongly related to human
population density. * denotes species modelled by only the human population density
predictor
Australian Magpie, Gymnorhina tibicen
Brown Thornbill, Acanthiza pusilla
Common Blackbird, Turdus merula
Common Myna, Acridotheres tristis
76
Common Starling, Sturnus vulgaris
Crested Pigeon, Ocyphaps lophotes
Crimson Rosella, Platycercus elegans
Eastern Rosella, Platycercus eximius
Golden Whistler*, Pachycephala pectoralis
Eastern Spinebill, Acanthorhynchus tenuirostris
77
Grey Butcherbird, Cracticus torquatus
Grey Fantail*, Rhipidura fuliginosa
House Sparrow, Passer domesticus
Grey Shrike-thrush*, Colluricincla harmonica
Laughing Kookaburra*, Dacelo novaeguineae
Little Wattlebird, Anthochaera chrysoptera
78
New Holland Honeyeater*, Phylidonyris novaehollandiae
Pied Currawong, Strepera graculina
Red Wattlebird, Anthochaera carunculata
Rock Dove, Columba livia
Silvereye, Zosterops lateralis
Spotted Pardalote, Pardalotus punctatus
79
Spotted Turtle Dove, Streptopelia chinensis
Superb Fairy-wren*, Malurus cyaneus
White-browed Scrub-wren*, Sericornis frontalis
White-eared Honeyeater*, Lichenostomus leucotis
White-plumed Honeyeater, Lichenostomus penicillatus
White-throated Treecreeper*, Cormobates leucophaea