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

v

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)

13

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

14

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)

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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’.

17

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

18

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

20

(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

l lan

d

1301-1600

901-1300

501-900

150-500

0

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

rop

ort

ion

of

of

tota

l lan

d

1301-1600

901-1300

501-900

150-500

0

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