the noisy native: a miner menace? - birdlife

The noisy native: a

miner menace?

Noisy miner habitat preferences

and influence on woodland bird

species richness

Sarah Chubb

Submitted in partial fulfilment of the requirements for the degree of

Bachelor of Science with Honours

in the Fenner School of Environment and Society,

Australian National University

November 2011

ii

Sarah Chubb The noisy native: a miner menace?

Candidate's Declaration

This thesis contains no material which has been accepted for the award of any other degree

or diploma in any university. To the best of the author’s knowledge, it contains no material

previously published or written by another person, except where due reference is made in the

text.

Sarah Constance Chubb

Date

iii


Acknowledgements

This project has let me immerse myself in a topic that I have been passionate about – and I

have come out of it thoroughly inspired. My inspiration has largely come from the people who

have supported and nurtured my learning experiences, without whom this project would not

have been possible!

To my supervisors, Chris McElhinny and Julian Reid, many thanks for all of your

guidance and support over the past 9 months. You have helped me to shape my research, and

thesis, and have provided me with the encouragement and enthusiasm that helped me to sustain

my interest (and energy) throughout the year. Thank-you Chris, for being such a wonderful

teacher over the past 3 years. You are an inspiration to me.

Without the support and funding of the Cowra Woodland Birds Program, this project could

not have happened. John and Madeline Rankin, Neale and Janeen Coutanche, Malcolm Fyfe,

Maret Vesk and Rosemary Stapleton have been so encouraging and made me feel so welcome

in Cowra. The success of this Program is owed to the efforts of the survey volunteers and the

willingness of the landholders to let the CWBP survey on their property.

The ANU has generously provided funding through the Action Trust scholarship.

To my fieldwork partner, Isabela ‘old man’ Burgher, for the picturesque picnics, your love

of driving and your singing. You have made many a long day in the field fun and enjoyable.

Sam and Clair Johnson and Catherine Bennett have been enormously generous, providing us

with not only a bed, but a home while Isabela and I were in the field.

The Fenner School staff, particularly Matt Brookhouse, Field Services, the IT gurus and

administration, has been tremendously supportive. Assistance from family and friends,

especially my dad, in improving my thesis through hours of proof reading and discussions has

been astounding. I am very grateful to mum, an amazing cook and enough of a sucker to get

coerced into helping out with fieldwork.

To my honours cohort, for the love, laughs, smiles, ciders, cakes and tea breaks. I couldn’t

have chosen a better group of people to spend this year with. Thanks to the mothers of the

honours room for the amazing food, and to those who kept it ‘manly’. Mark, thanks for your

love and support over the past year, I’m looking forward to seeing you a bit more now!

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Abstract

Through its aggressive, competitive behaviour, the noisy miner (Manorina melanocephala)

may be excluding woodland birds from remnant vegetation. This process exacerbates declines

in bird populations already threatened by landscape modification. The aims of this study were to

understand how the noisy miner affects woodland bird species, and to quantify its habitat

preferences to inform effective management strategies. These aims were addressed using the

following research questions:

1. Does noisy miner presence and/or abundance affect bird species richness?

- Which bird species are more susceptible to the effects of noisy miner invasion?

- Is there a density threshold where their effect is more pronounced?

2. Is noisy miner abundance affected by landscape and/or patch-scale variables?

- Which variables are the most powerful in explaining noisy miner abundance?

This research was conducted in 2011 at 33 temperate box-gum woodland sites in the

Cowra Shire of New South Wales, in conjunction with the Cowra Woodland Birds Program,

which has collected bird data since 2002. These data were augmented by detailed landscape and

patch scale habitat data as part of the current project.

Noisy miner impacts on five different categories of bird species richness (Total birds,

Woodland birds, Small woodland birds, Threatened and declining birds and Non-woodland

birds) were analysed using correlation and analysis of variance with categorical values of noisy

miner abundance. This was followed by a more detailed generalised linear model with a Poisson

distribution and a log link function, using continuous values of noisy miner abundance. Habitat

preferences of the noisy miner were identified using correlation analysis and analysis of

variance, followed by a more detailed multivariate least squares regression model in which

average noisy miner abundance, a continuous dependent variable, was modeled in terms of

multiple habitat parameters.

Results of this analysis suggest that noisy miners had a highly significant (p<0.0005)

negative affect on all bird categories except non-woodland birds. Small woodland birds showed

the most significant effects (p<0.0001), with highest species richness in sites where noisy

miners were never present, moderate richness in low and moderate noisy miner sites and very

low richness in high noisy miner sites. Generalised linear modelling indicated that the presence

of just one noisy miner reduced small woodland bird species richness by 40%. In the Cowra

region, it appears that persistent noisy miner presence at a site, even at very low levels, is a

strong predictor of the richness of small woodland birds that will be present at that site.

Noisy miner abundance responded negatively to both landscape and patch scale habitat

variables. A multivariate least squares regression indicated that 50% of variance in noisy miner

abundance could be explained using the parameters of patch area, Callitris regeneration and the

density of hollow trees, all of which reduced noisy miner abundance. Individually, the

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landscape variables of patch area and vegetation cover were the strongest predictors of noisy

miner abundance. Patches greater than 30 ha in size or with more than 20% vegetation cover in

the surrounding regions had lowest noisy miner abundance.

Many of the findings in this study, are comparable with other studies in temperate

woodlands of south eastern Australia, and can be used to inform management practices. These

include:

- Maintaining or increasing woody vegetation surrounding patches because low

vegetation cover is associated with high noisy miner abundance.

- Protecting sites with an intact understorey from modification and degradation.

- Revegetation and restoration efforts in sites with naturally low Eucalyptus

occurrence should avoid increasing eucalypt species density, as this may attract the

noisy miner. Rather, species used in revegetation should be selected on a site-

specific basis.

- Directing revegetation and restoration activities at habitat features that deter patch

utilisation by noisy miners, such as a dense understorey of Callitris or Eucalyptus

regeneration.

Many of these outcomes can be achieved through tree planting or appropriate grazing and

fire regimes, indicating astute management at patch and landscape levels could assist in

reversing further declines in the woodland bird communities of south eastern Australia;

particularly those due to the aggressive behaviour of the noisy miner.

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Table of Contents

Candidate's Declaration ..................................................................................................... ii

Acknowledgements ............................................................................................................ iii

Abstract .............................................................................................................................. iv

Table of Contents ............................................................................................................... vi

List of Figures ................................................................................................................ viii

List of Tables ................................................................................................................. viii

Chapter 1: Introduction ..................................................................................................... 2

1.1 Native invasive species ........................................................................................... 4

1.2 Woodland bird declines in south eastern Australia ................................................. 5

1.3 Research Aims ........................................................................................................ 5

1.4 Thesis Structure ....................................................................................................... 6

Chapter 2: Literature Review ............................................................................................ 8

2.1 Invasive species: the Australian perspective ........................................................... 8

2.2 The noisy miner ....................................................................................................... 9

2.3 How does the noisy miner affect woodland birds? ............................................... 10

2.3.1 Which birds are most at risk? ........................................................................ 12

2.4 Noisy miner habitat preferences ............................................................................ 12

2.5 Conclusions and current knowledge gaps ............................................................. 15

Research Questions ......................................................................................................... 16

Chapter 3: Methods .......................................................................................................... 18

3.1 The Study Area ..................................................................................................... 19

3.1.1 Cowra Woodland Birds Program .................................................................. 19

3.1.2 Climate ........................................................................................................... 20

3.1.3 Landform ........................................................................................................ 20

3.1.4 Land use and vegetation ................................................................................ 21

3.2 Study Sites ............................................................................................................. 22

3.2.1 Site selection .................................................................................................. 24

3.3 Field Survey and Data Collection ......................................................................... 25

3.3.1 Bird surveys ................................................................................................... 25

3.3.2 Patch scale habitat data: stand floristics and structure ................................ 26

3.3.3 Landscape scale habitat data ......................................................................... 29

3.4 Statistical Analysis ................................................................................................ 33

3.4.1 Bird response to noisy miner abundance ....................................................... 33

3.4.2 Noisy miner habitat preferences .................................................................... 34

Hons_thesis_final_27_Oct_v2.doc#_Toc307476042

vii


Chapter 4: Results ............................................................................................................ 37

4.1 Overview of the data ............................................................................................. 37

4.1.1 Bird data ........................................................................................................ 37

4.1.2 Habitat data ................................................................................................... 39

4.2 Bird responses to noisy miner abundance ............................................................. 40

4.2.1 Correlation analysis ....................................................................................... 40

4.2.2 Analysis of Variance ...................................................................................... 40

4.2.3 Generalised linear modelling ........................................................................ 42


4.3.1 Correlation analysis ....................................................................................... 44

4.3.2 Bivariate least squares regression ................................................................. 47

4.3.3 Analysis of Variance ...................................................................................... 48

4.3.4 Multivariate Least Squares Regression analysis ........................................... 50

Chapter 5: Discussion ....................................................................................................... 54

5.1 Bird response to noisy miner abundance ............................................................... 54

5.1.1 The biggest ‘losers’ ........................................................................................ 55

5.1.2 Noisy miner density thresholds ...................................................................... 56


5.2.1 Landscape scale habitat variables ................................................................. 57

5.2.2 Patch scale habitat variables ......................................................................... 58

5.3 Noisy miner multivariate habitat model ................................................................ 61

5.4 Management implications ..................................................................................... 62

5.5 Study limitations and future research questions .................................................... 64

Chapter 6: Conclusion ...................................................................................................... 67

References .......................................................................................................................... 69

Appendix 1: Data collection forms .................................................................................. 76

Appendix 2: Bird species identified in the 33 sites by bird category ............................ 78

Woodland bird species .................................................................................................... 78

Non-woodland bird species ............................................................................................. 79

Exotic species .................................................................................................................. 80

Appendix 3: Raw data ...................................................................................................... 81

Appendix 4: Graphical representation of ANOVA output for bird response to noisy

miner abundance ....................................................................................................................... 82


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List of Figures

Figure 1.1 The agriculturally productive ‘wheat-sheep’ belt of south eastern Australia. ............................. 2 Figure 2.1: Conceptual diagram describing the relationship between landscape modification, noisy miner

dominance and woodland bird declines in temperate Australia. .................................................... 8 Figure 2.2: Distribution of the noisy miner (Manorina melanocephala). ..................................................... 9 Figure 2.3: Where noisy miner habitat and woodland bird habitat intersect, noisy miners can have

significant negative impacts on woodland birds. ......................................................................... 11 Figure 3.1: The study region ...................................................................................................................... 19 Figure 3.2: Long term climate data of Cowra (1966 – 2011) ..................................................................... 20 Figure 3.3: Schematic transect showing the typical change in woodland association with topographic

position and aspect in the Cowra region. Adapted from Wilson (2003) ...................................... 21 Figure 3.4: Map of the Cowra region, displaying the 33 site (yellow)s surveyed for this study. ............... 22 Figure 3.5: Schematic diagram of a typical survey site. ............................................................................. 27 Figure 3.6: Vegetation ‘patches’ derived from a hypothetical landscape (lower left) depend on gap and

spur thresholds definitions. .......................................................................................................... 30 Figure 3.7: The extent of woody vegetation was determined by creating polygons of vegetation using

PatchMorph .................................................................................................................................. 31 Figure 3.8: Circular buffers around survey sites were used to determine the extent of vegetation in the

adjacent region. ............................................................................................................................ 32 Figure 3.9: Schematic summary of the steps used in the statistical analysis for this study. ....................... 35 Figure 4.1: Relative non-woodland bird species richness (BSR) increases with noisy miner abundance. . 41 Figure 4.2: The noisy miner has a negative effect on total bird species richness and species richness of

woodland-dependent birds. .......................................................................................................... 43 Figure 4.3: Graphical representations of the analysis of variance models between noisy miner abundance

and categorical habitat variables. ................................................................................................. 49 Figure 4.4: Graphical representation of the multivariate habitat model presented in Table 4.12. .............. 51

List of Tables Table 3.1: The study sites, and their site selection information. ................................................................ 23 Table 3.2: Definition of noisy miner abundance categories ....................................................................... 24 Table 3.3: Detailed description of the data collected for the patch scale variables. ................................... 28 Table 3.4: Description of the landscape scale habitat data collected.......................................................... 29 Table 4.1: Overview of the bird species richness (BSR) distribution of the different bird categories. ...... 37 Table 4.2: List of the threatened and declining species found in the Cowra region. .................................. 38 Table 4.3: Summary statistics of the continuous patch scale habitat variables. ......................................... 39 Table 4.4: Summary statistics of the landscape scale habitat variables. Both variables displayed

considerable range and were positively (right) skewed. ............................................................... 39 Table 4.5: Correlation between bird response categories and average noisy miner abundance. ................ 40 Table 4.6: All of the bird groups were significantly influenced by average noisy miner abundance

categories. .................................................................................................................................... 41 Table 4.7: The log of noisy miner (lnNM) abundance significantly influences all of the bird response

categories except non-woodland birds. ........................................................................................ 42 Table 4.8: Percent of the potential bird species richness of a site, under different noisy miner abundance

values. .......................................................................................................................................... 43 Table 4.9: Correlation between patch and landscape variables and average noisy miner abundance. ....... 45 Table 4.10: Correlations between continuous explanatory variables for the 33 study sites. ...................... 46 Table 4.11: The effects of continuous individual patch and landscape scale variables on noisy miner

abundance (y). .............................................................................................................................. 47 Table 4.12: Least squares regression analysis identified patch area, the amount of Callitris regeneration

and the number of hollow bearing trees as the best predictors of ln(noisy miner abundance). .... 50
















1


Chapter 1

Introduction

The noisy miner (Manorina melanocephala)

2


Chapter 1: Introduction

Biodiversity loss is a global environmental problem (Sala et al. 2000, Butchart et al. 2010).

The conversion of natural ecosystems for agricultural and urban purposes is considered a key

driver of changes in biodiversity (Sala et al. 2000, Ford et al. 2001), primarily due to habitat

loss and fragmentation (Major et al. 2001, Fischer and Lindenmayer 2007). All taxa have been

affected by landscape modification in some way including plants, birds, mammals, reptiles and

fish (Shine and Fitzgerald 1996, Reid 1999, Yates et al. 2000, Mac Nally and Brown 2001,

Benton et al. 2003, Jenkins 2003, Brown et al. 2008, Pereira 2010). Australia is no stranger to

biodiversity loss with large declines in much of its fauna resulting from the ways in which the

landscape and its constituents have been managed and changed (Reid 1999, Ford et al. 2001,

Radford et al. 2005, Bennett et al. 2006).

Landscape modification in the productive wheat-sheep belt of southern and eastern

Australia (Figure 1.1) has been particularly severe. This belt was once an almost continuous

band of temperate box-gum (eucalypt) woodland stretching from western Victoria north east

through inland New South Wales and south eastern Queensland (Mac Nally et al. 2000b). This

woodland community provides habitat for a rich and unique array of woodland dependent flora

and fauna (Lunt and Bennett 1999, McElhinny et al. 2006b). In many areas, woodlands are now

restricted to fragmented remnant woodland ‘patches’ (a unit of homogeneous area that differs

from its surroundings) which vary in size, quality and isolation (Yates and Hobbs 1997).

Figure 1.1 The agriculturally productive ‘wheat-sheep’ belt of south eastern Australia. This region (shaded in dark grey) has experienced some of the heaviest modification practices in Australia, such as clearing, grazing and changed fire regimes. Adapted from Sherren (2011).

3


More than 80% of native temperate woodland in the wheat-sheep belt has been cleared in

the past 200 years (Robinson and Traill 1996, Major et al. 2001, Chan 2004). In some areas,

more than 95% has been cleared, and what remains has been substantially altered (Robinson and

Traill 1996) through human activities such as grazing and altered fire regimes (Prober and

Thiele 1995, Yates and Hobbs 1997, Yates et al. 2000, Briggs et al. 2008, Howes and Maron

2009). These activities change the way habitat within the landscape is configured.

Changes to landscape configuration can have profound impacts on flora and fauna

assemblages by shaping the distribution and abundance of species (Lindenmayer and Fischer

2006). Some species have benefited from these changes, like the galah (Cacatua roseicapilla)

and the crested pigeon (Ocyphaps lophotes) (Reid 1999). For many species these changes have

had negative effects through habitat loss, degradation and isolation. Landscape modification has

direct impacts on species by reducing the amount of habitat available, the quality of that habitat

or the ability for species to move between remnants. These impacts may result in local or

regional declines of species and communities or even extinctions (Lindenmayer and Fischer

2006). Increases in the time and energy spent foraging, finding a mate and nesting place affect

the success and persistence of species (Zanette et al. 2000, Brooker and Brooker 2002, Fischer

and Lindenmayer 2007). These direct impacts of landscape modification on fauna are

commonly the focus in research (e.g. see Zanette et al. 2000, Manning et al. 2004a, Fischer et

al. 2005), with the indirect effects on many species largely ignored.

Landscape modification also indirectly affects many species. By modifying their habitat,

the way in which species behave and interact may change (Lindenmayer and Fischer 2006).

Species interact in a variety of ways, amongst others habitat enrichment, mutualism, parasitism,

predation and competition are important and common interactions (Soulé et al. 2005). Altered

dynamics of species interactions can become a dominant process within the ecosystem, creating

potentially significant environmental problems.

Processes like predation or competition on native flora and fauna are some examples of the

pervasive impacts of exotic species. In Australia the introduction of the fox (Vulpes vulpes) has

reduced native fauna populations through predation upon many vertebrate species and by

competing with the native spotted-tailed quolls (Dasyurus maculatus) for food and dens. In

addition, the fox has introduced a range of parasites to native marsupials (Saunders et al. 2010).

Less commonly discussed are native species which have become invasive as a result of

changes to their habitat. Native or exotic invasive species that are able to thrive in human-

modified landscapes (often referred to as ‘winners’) can negatively affect species less suited to

that change (‘losers’) through changes in the ways in which the species interact (McKinney and

Lockwood 1999, Low 2002). For example increased herbivory by the introduced rabbit

(Oryctolagus cuniculus) at Cabbage Tree Island in New South Wales has largely removed

shrubby understorey vegetation. Consequent reduction of the structural complexity of the

breeding habitat of the endangered Gould’s petrel (Pterodroma leucoptera leucoptera) leaves

4


nesting petrels and their fledglings exposed to increased levels of predation by the pied

currawong (Strepera graculina) (Priddel et al. 2000). By introducing the rabbit humans have

indirectly modified natural habitat which has changed the way in which these birds would

naturally interact with each other. In this example the pied currawong is a ‘winner’ as a result of

the ways in which human actions have driven modification of the landscape.

1.1 Native invasive species

While the effects of exotic species are important and interesting, my research focuses on an

invasive native species which negatively affect other native species. Invasive native species are

influential in terms of their impacts on other native species, but such studies are under-

represented in mainstream invasive species research.

An invasive species is a species that occurs, “as a result of human activities, beyond its

accepted normal distribution and which threatens valued environmental, agricultural or other

social resources by the damage it causes” (Department of Sustainability Environment Water

Population and Communities 2011). Invasive species can have damaging effects on ecosystem

function. For example, in parts of the north west United States, elk (Cervus elaphus) have

become overabundant as a result of reduced predation by the heavily hunted wolf (Canis lupus).

This has resulted in increased grazing of native plants and long term low recruitment of native

vegetation (Ripple and Larsen 2000). Other North American examples include reduced coyote

(Canis latrans) populations which has released predation pressure on meso-predators (e.g.

house cats, Felis catus), which in turn suppressed populations of small native birds (Soulé et al.

2005).

A relevant avian example of the impact of an invasive native species is the brown-headed

cowbird (Molothrus ater), an edge-favouring nest parasite (Lindenmayer and Fischer 2006).

Prior to the 1800s, the species was found primarily in the central plains and prairies of the

United States (Brittingham and Temple 1983). It was largely absent from the areas of

contiguous forest in eastern North America. As these forests were cleared, more open habitat

was created which allowed for the eastward expansion of the cowbird (Robinson et al. 1995).

With an increase in range and abundance the cowbird has been implicated in reduced

reproductive success of songbirds (Brittingham and Temple 1983, Robinson et al. 1995, Payne

and Payne 1997, Zanette et al. 2005).

Woodland birds are those that rely on woodlands to live and breed (Robinson and Traill

1996). Temperate woodland bird assemblages in south eastern Australia suffer from population

declines as a result of the direct (habitat loss) and indirect (changed species interactions) effects

of landscape modification.

5


1.2 Woodland bird declines in south eastern Australia

Habitat fragmentation can directly affect birds in a variety of ways. Cameron (2006)

describes the importance of tree hollow availability, a declining resource, as a required breeding

habitat attribute for the threatened glossy black cockatoo (Calyptorhynchus lathami). Another

example is the eastern yellow robin (Eopsaltria australis), which is suffering from problems

associated with smaller patch size. In small remnants, the females left their nests more

frequently to find food, had a shorter breeding season, and laid lighter eggs than their

counterparts in larger remnants (Zanette et al. 2000). Zanette et al. attribute these findings to

the scarcity of available food resources per capita in small patches. Changes to the availability

and distribution of resources in the woodlands of south eastern Australia has placed an

additional stress on woodland birds through altering the way that bird species are interacting.

The survival of woodland birds is put further at risk because of the expansion of the

overabundant noisy miner (Manorina melanocephala), a native honeyeater, within its

geographical distribution. Landscape modification has benefitted the noisy miner, allowing it to

interact with bird communities differently. The noisy miner has had significant deleterious

effects on woodland birds throughout temperate woodland remnants as a result of its

competitive behaviour which effectively excludes other birds from woodland remnants (Major

et al. 2001, Piper and Catterall 2003, Maron et al. 2011). In this context, the noisy miner has

become an overabundant invasive native species, and a ‘winner’ in highly modified landscapes.

To moderate the decline of the woodland bird populations of south eastern Australia, the

direct and indirect problems of human activities must be addressed. The major and underlying

threatening process for woodland birds is landscape modification. Addressing this process

requires solutions such as revegetation that are expensive to implement and may take years

before benefits are realised. In the interim, it is important that other causes of declines are

addressed to minimise additional stresses such as those that the noisy miner inflict. To reduce

the effects of the noisy miner on woodland birds we must recognise which features within a

remnant woodland patch attract noisy miner colonies. This can help to focus mitigating efforts

such as habitat restoration or revegetation.

1.3 Research Aims

The overall aims of this study are to provide information on how the noisy miner affects

woodland birds and to quantify noisy miner habitat preferences. The noisy miner presents a

widespread problem across south eastern Australia and my research supplements a growing

body of noisy miner scientific literature. I aim to provide information of value to land and

biodiversity management groups. The ultimate intention of this research is to enable effective,

well-informed management of remnant woodlands for improved bird community conservation

outcomes.

6


1.4 Thesis Structure

This thesis contains a review of current understanding of noisy miner ecology, and

describes my own field study in noisy miner dynamics. I start by reviewing relevant literature in

Chapter 2 to outline the current knowledge of how the noisy miner interacts with resident

woodland bird species, and the habitat preferences of the noisy miner. Chapter 3 describes the

methods that I use in my experimental design, field data collection and statistical analysis

techniques. The results of my data analysis are presented in Chapter 4. This is followed in

chapter 5 by a discussion of the findings of this study and their management implications.

Chapter 6 summarises my key findings and provides recommendations for further research.

7


Chapter 2

Literature Review

An example of a modified landscape. This picture was taken near

‘Liscombe’, in the north east of the Cowra Shire.

8


Chapter 2: Literature Review

The purpose of this chapter is to identify key knowledge gaps in the understanding of noisy

miner ecology for further research. These knowledge gaps will guide the definition of specific

research questions which will be addressed in this thesis. I review literature concerning the

various ways in which the noisy miner affects woodland bird communities and the habitat

preferences of the species. I discuss the noisy miner as an invasive native species, and as a

‘winner’ in the highly modified landscape of south eastern Australia. Finally, I discuss some of

the effects that noisy miner domination has on bird communities and also the habitat

preferences of the noisy miner.

2.1 Invasive species: the Australian perspective

Changing the distribution and availability of habitat and associated resources can have

profound effects on competition for resources. The negative effects of the noisy miner on

woodland birds provide a striking example of how changes to inter specific interactions can

become a dominant process within an ecosystem. Its competitive behavior is widely considered

to be a contributing factor in the decline of native woodland birds in southern and eastern

Australia (Major et al. 2001, Piper and Catterall 2003, Maron et al. 2011). Within its

geographical range, its population may have increased in distribution and abundance over the

past two decades (Barrett et al. 2003, Clarke and Grey 2010) because the species appears to

benefit from the ways in which the landscape has been modified (Catterall 2004, Hastings and

Beattie 2006). The aggressive and competitive behaviour of the species is thought to exacerbate

the decline of already struggling bird populations in these woodlands (Figure 2.1).

Woodland bird

declines

Landscape modification

Noisy miner

dominance

Leads to

Exacerbates

Facilitates

Figure 2.1: Conceptual diagram describing the relationship between landscape modification, noisy miner dominance and woodland bird declines in temperate Australia. In this case, landscape modification encapsulates both habitat fragmentation and degradation.

9


2.2 The noisy miner

The noisy miner is widely considered to be one of the contributing factors in native bird

declines in southern and eastern Australia due to its competitive behaviour. It is a large

(60-90 g, 25-28 cm), native honeyeater that occupies woodland remnants in eastern and

southeastern Australia from northern Queensland to Tasmania (Dow 1976). They are hyper-

aggressive, sedentary birds which operate in communal breeding groups of six to 30 birds (Dow

1976), although colonies of up to several hundred birds have been reported (Dow 1979). Like

many honeyeaters, the noisy miner is a generalist forager, feeding predominantly on

invertebrates, lerps and nectar both in the canopy and on the ground (Dow 1976, Paton 1979,

Hastings and Beattie 2006, Ashley et al. 2009, Maron 2009, Clarke and Grey 2010).

The species is becoming overabundant in its geographical range. The most recent Atlas of

Australian Birds suggests that the reporting rate of the noisy miner has increased by 15% over

the past two decades in NSW, but no significant increase nationally (Barrett et al. 2003, Barrett

et al. 2007, Clarke and Grey 2010). Anecdotal evidence has reached the same conclusions (Low

2002). The increase in abundance of the noisy miner within its geographical range may be

because it benefits from landscape modification. Noisy miners appear to utilise small patches,

edge habitats and degraded sites (discussed in more detail in section 2.5), which are

symptomatic of highly fragmented landscapes.

Figure 2.2: Distribution of the noisy miner (Manorina melanocephala). The species is widely distributed throughout the temperate woodland belt of southern and eastern Australia. While their geographical distribution has not increased, the species is becoming more common within their range. Image sourced from the Birds Australia Atlas (Cited in Australian Bureau of Agricultural and Resource Economics and Sciences 2009)

10


Noisy miners are territorial with distinct home ranges from which they actively exclude

small woodland birds (Dow 1976, Piper and Catterall 2003). Colonies occupy discrete areas,

and most of their activity occurs within this area (Dow 1979). The activity space of an adult is

thought to be between 50 and 200 m in diameter (Dow 1979). Individuals in the colony use loud

repetitive calls, swooping and bill clattering to alarm other species (Arnold 2000). Reasons for

this behavior are not fully understood. They have been observed mobbing potential predators

and competitors (Dow 1979, Arnold 2000), ultimately killing small birds that try to persist in

their territory. Aggressive behavior of the noisy miner is not limited to predators and

competitors, but extends to mobbing water birds, reptiles, mammals and even inanimate objects

(Dow 1979, Arnold 2000). Indeed, they frustrated John Gould when they followed him

“through the entire forest, leaping and flying from branch to branch, they become very tiresome

and annoying” (p. 575 Gould 1865). This aggressive, competitive behaviour is thought to

exacerbate the decline of already struggling bird populations in these woodlands.

2.3 How does the noisy miner affect woodland birds?

Where disturbance-tolerant species are able to thrive in human-modified landscapes, they

can replace more sensitive, specialist species (Noss 1990, Garrott et al. 1993). This can cause

major shifts in population assemblages, often with unique species replaced by widespread

species, known as ‘biotic homogenisation’ (McKinney and Lockwood 1999, Olden et al. 2004),

where landscapes become less species diverse over space and time. The noisy miner behaves as

a biotic homogenisation agent in the temperate woodlands of southeastern Australia. It has been

the recipient of much negative name-calling, from bully bird or snakebird through to soldier

bird or, more extremely, a terrorist. The species appears to be a ‘reverse keystone species’

(Piper and Catterall 2003) throughout much of its geographical range; that is, the noisy miner

needs to be absent or at low densities for a typical assemblage of birds to be present in an area.

There is an extensive body of correlational and experimental evidence that indicates noisy

miner presence reduces the presence and abundance of other birds. Decreased woodland bird

abundance and community diversity are consistently observed when noisy miners are present

(Dow 1979, Major et al. 2001, Mac Nally and Horrocks 2002, Chan 2004, Hastings and Beattie

2006, Clarke and Oldland 2007, Hannah et al. 2007, Clarke and Grey 2010, Hanspach et al.

2011), to the extent that they can become the sole occupants of a patch (Dow 1976). More

frequently though, they are found with a suite of ‘open-country’ birds, generally large birds that

are also expanding in range, such as the eastern rosella (Platycercus eximius), red-rumped parrot

(Psephotus haematonotus), magpie (Cracticus tibicen), galah (Cacatua roseicapilla) and the

pied currawong (Strepera graculina) (Dow 1976, Reid 1999, Major et al. 2001, Piper and

Catterall 2003, Parsons et al. 2006, Clarke and Oldland 2007). These birds appear to endure

noisy miner mobbing and are eventually tolerated by the miner, and in the case of the pied

currawong, even engage in noisy miner mobbing behaviour of common adversaries.

11


While such correlational evidence is a strong indicator of the negative effects of the miner

on woodland birds, it cannot preclude the potential for other factors to be the underlying cause

of such trends. High noisy miner abundance at a site with low bird species richness does not

necessarily imply causation. If noisy miner habitat requirements are different from the

requirements of other birds then low bird species richness in a noisy miner dominated site may

be a result of unsuitable habitat, rather than noisy miner aggression. It is only where noisy miner

habitat and the habitat of other woodland bird species intersect (Figure 2.3) that noisy miner

aggression becomes a problem.

Grey et al. (1997, 1998) addressed this debate by carrying out experimental noisy miner

removal trials in the ironbark-stringybark woodlands. When noisy miners were removed, there

was an influx of small birds, including many threatened species (Grey et al. 1997, Grey et al.

1998). Noisy miner habitat was useful for other birds. This was even the case in small degraded

woodland remnants, which could suddenly support a diverse range of small insectivores and

honeyeaters. Another removal trial in box-gum woodlands in the New England region of New

South Wales had similar results (Debus 2008).

Figure 2.3: Where noisy miner habitat and woodland bird habitat intersect, noisy miners can have significant negative impacts on woodland birds. The challenge is in identifying whether the noisy miner and woodland birds occupy the same habitat, and hence noisy miners are a problem. If the species occupy different habitats, then woodland bird declines cannot be attributed to noisy miner occupation.

Noisy miners have become one of the most important predictors of woodland bird

assemblages throughout a vast area of eastern Australia. What might be ‘suitable’ habitat for

birds is actually not available for use, because noisy miners are excluding them. Noisy miner

colonies have very serious effects on bird assemblages and are a potential problem for bird

conservation efforts. It is important to know which birds are more susceptible to noisy miner

exclusion to get a better understanding of the processes involved in their exclusion.

Noisy miner

habitat

Woodland

bird habitat

Woodland bird

and noisy

miner habitat

12


2.3.1 Which birds are most at risk?

Noisy miner colonies indiscriminately mob animals and objects invading their territory;

some birds are particularly vulnerable to their aggressive behaviour. Affected species tend to be

smaller, and so are more easily excluded by larger aggressive miners (Grey et al. 1997, Piper

and Catterall 2003, Mac Nally et al. 2011). Miner tolerance is strongly associated with size and

diet (Piper and Catterall 2003). The noisy miner shares its dietary preferences with smaller

birds, predominantly feeding on foliage and bark dwelling invertebrates (Dow 1976, Piper and

Catterall 2003, Maron 2009). Piper and Catterall (2003) noted that the noisy miner shared the

same dietary requirements with 75% of smaller species, compared with only 7% of larger

species. Non-discriminatory aggressive behaviour displayed by the noisy miner is an effective

interference competition strategy (Birch 1957, Case and Gilpin 1974), whereby the species

directly interact, interfering with the ability for other species to utilise a common resource.

Because it has a relatively large body size for a diet shared with smaller species (Piper and

Catterall 2003), the noisy miner is able to monopolise food resources with some ease.

2.4 Noisy miner habitat preferences

The increase in noisy miner abundance begs an important question; what makes the species

such a successful ‘winner’? While its behavior obviously allows them to monopolise important

food and habitat resources in the productive woodlands, certain factors must have historically

limited their population. Local limitations to noisy miner abundance and movements would

have let smaller woodland birds persist in those environments.

Early observations indicate that the noisy miner was primarily seen in thinly timbered

forests of the plains and low hills (Gould 1865). Historically, most of the wheat-sheep belt was

covered with woodland and forest. Large scale clearing and modification of the vegetation has

resulted in habitat fragmentation and degradation since European settlement of Australia,

benefiting the noisy miner. Inadvertent provision of preferred habitat may have led to increased

domination of landscapes within its range and an increase in abundance of the noisy miner

(Catterall 2004, Hastings and Beattie 2006).

Recent studies have identified particular habitat features that correlate with noisy miner

abundance and occupation. Their tendency to occupy small remnant patches (< 20 ha), the

edges of large remnants, and those with simplified understories, (Major et al. 2001, Mac Nally

and Horrocks 2002, Martin et al. 2006, Taylor et al. 2008, Oldland et al. 2009) supports the

hypothesis that the noisy miner has benefited from the ways in which humans have modified the

landscape. In the agricultural landscapes where noisy miner colonies persist and dominate,

modification has primarily been caused by land clearing, changed fire regimes and grazing.

These practices have resulted in smaller patches of remnant vegetation scattered throughout the

region and simplified vegetation structure with heavily modified understories (Briggs et al.

2008, Howes and Maron 2009, Fischer et al. 2010).

13


Small remnants and narrow corridors are preferentially occupied by noisy miner colonies

(Mac Nally et al. 2000a, Major et al. 2001, Mac Nally and Horrocks 2002, MacDonald and

Kirkpatrick 2003, Hastings and Beattie 2006). Although these studies used different definitions

for ‘small’ patch area, ranging between 20 ha and 100 ha, a few authors reported patch area

between 30 and 50 ha as being a significant threshold for noisy miner presence (Mac Nally et

al. 2000a, MacDonald and Kirkpatrick 2003). The size of this area threshold may decrease with

increasing site productivity, with noisy miner colonies in drier, less productive sites requiring

larger patches (MacDonald and Kirkpatrick 2003). In preferencing small patches, noisy miner

colonies may be responding to a high edge to area ratio. It is possible that a small patch is more

easily defended against other birds because they can more easily monitor the perimeter of their

territory, letting them monopolise the patch.

Noisy miner domination does occur in large remnants but is usually confined to the edges

(Green and Catterall 1998, Piper and Catterall 2003, Martin et al. 2006, Clarke and Oldland

2007). Since the diameter of the activity space of an adult noisy miner is between 50 – 200 m

(Dow 1979), colonies could be assumed to exist within 200 m of a remnant edge. For this

reason, Piper and Catterall (2003) suggest that remnants between 5 and 10 ha (120 – 180 m

radius) will be entirely dominated by noisy miner colonies. Clarke and Oldland (2007) found

that noisy miner colonies could penetrate between 150 m to more than 300 m into a large

remnant, depending on the habitat type. If the miner can penetrate 300 m into a remnant, a patch

needs to be at least 36 ha before it can have an interior core that is useful for noisy miner

susceptible birds (Clarke and Oldland 2007). Similarly, corridors need to be more than 600 m

wide to contain any noisy miner-free habitat. Noisy miner colonies may use edges because they

provide both open grassland and forest habitat, which enables easy foraging and defence

(Howes and Maron 2009).

The shape of these large remnants can also make a difference, with noisy miner colonies

commonly found in projections of vegetation into the matrix (i.e. corners, corridors or

peninsulas projecting into a paddock), and small clumps of trees within 100 m of the remnant

edge (Taylor et al. 2008). A projection of a patch into a paddock increases the edge to area ratio,

which benefits an edge specialist, like the noisy miner. Larger patches with a geometry that

increases the amount of edge habitat available allows the noisy miner to defend its territory with

ease (Taylor et al. 2008)

Various studies have attempted to identify habitat characteristics that define suitable noisy

miner habitat at the patch scale. Important habitat attributes for noisy miner occupation have

been found in different studies; these vary between study regions and individual studies. Some

of the reported habitat variables include the preference for low understory/woody shrub cover,

certain site floristics (botanical composition), high site productivity and various management

histories.

14


Vegetation with a simple, open structure and low understory cover is probably the most

frequently identified habitat variable associated with the noisy miner. This is thought to be

important in allowing the noisy miner to dominate the site. Where the structure is simple, other

birds are unable to hide or get protection from the aggressive behavior of the noisy miner. Low

shrub density appears to be a very important factor in determining suitability of a site to noisy

miner dominance (Grey et al. 1998, Mac Nally et al. 2000a, MacDonald and Kirkpatrick 2003,

Hastings and Beattie 2006, Kath et al. 2009). There are two possible explanations for their

preference for less dense habitat. Firstly, noisy miners occasionally forage on the ground

(Clarke and Grey 2010), so low structural complexity may provide easier access to the ground.

Secondly, structurally simple patches enable noisy miner colonies to see their predators and

competitors more easily (Maron 2009) and may also prevent competitors from using the shrubs

as a refuge from aggression (MacDonald and Kirkpatrick 2003, Hastings and Beattie 2006).

Some studies found that noisy miner occurrence was not associated with low understorey cover

(Taylor et al. 2008, Oldland et al. 2009). This difference may be a result of prolonged drought

conditions (Oldland et al. 2009) in the study area or it may relate to the vegetation surrounding

the site (Taylor et al. 2008).

Noisy miner abundance is higher in a woodland patch within a pasture or cropping matrix

(the dominant land cover type), than in an uncleared matrix (Martin et al. 2006). The sites of

Taylor et al. (2008) were all adjacent to crops or pastures which may account for the lack of

association between noisy miner occurrence and understorey cover. Perhaps low vegetation

cover in the surrounding landscape offers similar structural features to a low understorey site

thus enabling noisy miner colonies to see their predators and competitors approaching more

easily (Maron 2009). This means that they are better able to defend their territory from

predation and competition thereby reducing the energy required for them to maintain dominance

over the patch.

Stem density is a component of structural complexity but it appears to affect the

occurrence of noisy miner populations inconsistently. Catterall (2004) found that noisy miners

show increased densities in response to thinning of the canopy and creation of sparse eucalypt

cover. This finding supports the hypothesis that simple vegetation structure is advantageous for

noisy miner colonies. Conversely Howes and Maron (2009) found that sites occupied by noisy

miners typically had higher stem density, possibly because this results in lower understorey

cover.

Management history of a woodland remnant may also influence noisy miner site

occupancy. Several studies in contiguous eucalypt forest in the Brigalow Belt bioregion of south

central Queensland have produced different results to those in southeastern Australia (Eyre et al.

2009). Like their southern counterparts, noisy miners exerted a similarly strong negative effect

on abundance of small birds (Eyre et al. 2009). However, noisy miners were found with similar

densities at both edge and interior sites of relatively intact, contiguous forests (Maron and

15


Kennedy 2007). This contrasts with southern woodlands, where the noisy miner was only found

in small remnants or at edge sites of large remnants. Researchers suggest that noisy miner

colonies may be able to dominate interior sites of this forest type because of past management

practices. Burning and grazing have both been used extensively in the Brigalow Belt: the open

habitat promoted by these management practices may enable the noisy miner to exert its

dominance in interior parts of these woodlands. Furthermore noisy miners positively responded

to higher grazing pressure (Martin and McIntyre 2007, Howes and Maron 2009) which is likely

to result from their foraging behavior (Grey et al. 1998). The noisy miner frequently forages on

the ground and preferentially exploits sites with short grass (< 5 cm) (Grey et al. 1998, Clarke

and Grey 2010), making grazed sites with short grass and low shrub cover more valuable as

noisy miner habitat.

Preference for certain site floristics and productivity may also drive variations in noisy

miner abundance (Catterall 2004, Maron 2007, Taylor et al. 2008, Oldland et al. 2009).

Catterall (2004) observed that noisy miners were “encouraged by scattered tall eucalypts… and

nectar rich cultivars of Australian plant species”. In the buloke (Allocasuarina luehmannii)

woodlands of the Wimmera plains in Victoria, noisy miners were more likely to be present

when there were at least 5 eucalypts present per hectare (Maron 2007). In central Victoria, the

most powerful predictors of high noisy miner presence were deep, productive soils and a high

proportion of yellow gum (Eucalyptus leucoxylon) as the dominant overstorey species (Oldland

et al. 2009). Yellow gum is a reliable and prolific nectar producer (Oldland et al. 2009) and is

an attractive site selection feature for a sedentary honeyeater such as the noisy miner. Box-gum

woodland systems at low altitudes, a proxy for productivity, tend to have deeper soils than

higher altitude systems (Taylor et al. 2008). Low altitude was a good predictor of noisy miner

presence (Taylor et al. 2008). This supports the findings of Oldland et al. (2009) that noisy

miners prefer deeper soils. It is unlikely that altitude was related to different climatic extremes

because the altitudinal range was small (142 – 263 m).

2.5 Conclusions and current knowledge gaps

To mitigate woodland bird declines in Australia it is clear that we need to focus on both

landscape modification and its unintended consequences, such as those that the noisy miner

imposes. The literature recognises the effects noisy miner aggression has had on woodland birds

in the temperate agricultural zone of south eastern Australia and it identifies the key habitat

attributes to which the noisy miner appears to respond. Site occupation by a noisy miner colony

has deleterious effects on woodland birds, exacerbating the undesirable impacts of landscape

modification. Understanding what features within a remnant woodland patch a noisy miner

colony is attracted to can help to focus efforts to discourage noisy miner invasions through

habitat modification or other noisy miner controlling measures.

16


The literature suggests that the noisy miner negatively affects bird species richness (Grey

et al. 1997, Major et al. 2001, Piper and Catterall 2003) but this has not been thoroughly studied

in the south west slopes of New South Wales. Hanspach et al. (2011) reported some preliminary

observations on the negative effects of noisy miners on total bird species richness in the region

but did not detail which birds were most affected. There is little if any information regarding a

noisy miner density threshold whereby the effects of noisy miner density are more pronounced.

Identifying an upper-level density threshold of noisy miner individuals would be useful for

future management practices.

There remains insufficient knowledge of noisy miner habitat preferences and how they

vary regionally. Noisy miners may have been successful as a result of favourable habitat being

inadvertently provided by landscape modification. Many studies show that noisy miners

respond to variation in habitat structure, floristics and patch geometry, and these responses vary

geographically. These studies combine to give us a general picture of where noisy miners thrive.

Further research is needed in other landscapes to give us an understanding of their habitat

preferences.

Finally, landscape-level variables have had a limited place in noisy miner ecological

research. The species has been observed to be attracted to a more intensively managed matrix,

such as high-input cropping, because of the low structural complexity associated with such

systems. Complexity is thought to be associated with ease of foraging and defence for the noisy

miner, but this remains to be seen using a spatial geographical analysis.

These knowledge gaps have guided the formulation of specific research questions which

will be addressed in this thesis.

The next chapter will detail the methods and will describe the location and characteristics

of the study region used to address these questions.

Research Questions

1. Does noisy miner presence and/or abundance affect bird species richness?

- Which bird species are more susceptible to the effects of noisy miner invasion?

- Is there a density threshold where their effect is more pronounced?

2. Is noisy miner abundance affected by landscape and/or patch-scale variables?

- Which variables are the most powerful in explaining noisy miner

abundance?

17


Chapter 3

Methods

‘TSR Golden Valley’, a white box/grey box woodland. This site is an

example of a high noisy miner abundance site.

18


Chapter 3: Methods

To answer the research questions, a landscape was required in which temperate woodlands

were the dominant ecosystem because these are the systems where noisy miners occur. This

study landscape needed a distribution of woodland remnants that varied in both size and in the

intensity of land use surrounding the remnants to ascertain how these factors influence the noisy

miner. Furthermore, the noisy miner needed to be present across the landscape at varying

densities.

The Cowra region of New South Wales (NSW) met this suite of requirements. Cowra

Shire is in the south west slopes bioregion, a region which has seen minimal noisy miner related

research. It is home to the Cowra Woodland Birds Program (CWBP), a group which has been

collecting extensive bird data for almost ten years (Reid 2010). I collected detailed habitat data

to use in conjunction with present and historical bird data in the analysis. The CWBP data were

used to gain an insight into the effects of the noisy miner on woodland birds of the Cowra

region. Patch scale and landscape scale habitat data were then collected from the CWBP survey

sites and geographical information system maps respectively, to understand how the noisy

miner responded to different habitat variables.

This chapter details the methods used in this study to investigate the impacts of the noisy

miner on woodland birds and the habitat preferences of the species. I begin with an overview

providing relevant background to the vegetation, landforms and management history of the

study region. This is followed by data sampling and collection procedures. The final section of

this chapter describes the statistical methods used to analyse the bird and habitat data.

19


3.1 The Study Area

Study sites were situated in the Cowra Shire within the Lachlan River catchment (Figure

3.1) of NSW. Cowra (33o50’, 148

o41’) is in the north-east of the NSW South West Slopes

bioregion, about 200 km north of Canberra and 300 km west of Sydney. The region was first

settled by Europeans in the 1830s for grazing and agriculture (Cowra Shire Council). As a result

of the agricultural history of the shire, most of the native vegetation has been cleared for

livestock production and intensive agriculture.

Figure 3.1: The study region Cowra shire (shaded black) in situated to the far west of the Lachlan River catchment (pale grey), on the east of the wheat-sheep belt (dark grey, main) in New South Wales, south eastern Australia. The South Western Slopes Bioregion covers the area within the dashed circle Adapted from Sherren (2011).

3.1.1 Cowra Woodland Birds Program

This study was undertaken as part of the Cowra Woodlands Bird Program (CWBP), an

initiative of Birds Australia. The program was launched in 2001 with the intention of

monitoring and reversing the decline of woodland birds in the Cowra district. Since the program

was launched, scientists and the community have worked together carrying out quarterly bird

surveys and implementing conservation strategies to improve the prospects for local woodland

birds. However noisy miners have become an increasing problem in the region, making bird

monitoring efforts both difficult and unrewarding because they reduce bird diversity which is a

key attraction for volunteer based data collection.

South West Slopes Bioregion

Australian Capital Territory

South eastern temperate grazing region:

The Wheat-sheep belt’ Study location

20


3.1.2 Climate

Cowra has a relatively dry, continental climate with an average annual rainfall of about

600 mm, falling evenly throughout the year (Figure 3.2). The region experiences warm to hot

summers and cool winters. Average temperatures range from 16oC to 32

oC in the summer and

2oC to 14

oC in winter, but extreme temperatures as high as 47

oC and as low as -8

oC have been

recorded in the region (Bureau of Meteorology 2011).

Cowra, along with much of southeastern Australia has experienced a decade of below

average rainfall until 2010. Vegetation structural data was collected in 2011, after a year of

above average rainfall throughout south eastern Australia

Figure 3.2: Long term climate data of Cowra (1966 – 2011) The Cowra region experiences warm to hot summers and cool winters, and about 600 mm of annual rainfall falling evenly throughout the year. Data from the Bureau of Meteorology (2011), taken from the Cowra Airport Comparison site.

3.1.3 Landform

The NSW South West Slopes Bioregion forms the western limit of the Great Dividing

Range. The Cowra Shire is set around the Lachlan River which flows from the south-east to the

north-west of Cowra.

The central parts of the Cowra region are dominated by plains and alluvial valleys (Figure

3.3, 3.4), while hilly areas and isolated ranges predominate in the west (Conimbla National

Park), south west (Koorawatha Nature Reserve) and east (Copperhannia Nature Reserve).

The landscape is geologically diverse, but is typified by granites in the basins and

sedimentary rocks in the hills, with areas of alluvium, sandstone, shale and basalt (Krynen and

Moffitt 1997). Soil patterns in the Cowra region range from shallow, stony soils on the tops of

ridges and hills to strongly textured soils derived from underlying parent material downslope

(Sahukar et al. 2003).

21


3.1.4 Land use and vegetation

The majority of the Cowra Shire is managed as private freehold and leasehold land with

some nature conservation reserves (Conimbla National Park, Koorawatha Nature Reserve), state

forest and remnants of travelling stock reserves (TSRs) (Geoscience Australia 2004). Most of

the nature conservation reserves, state forests and TSRs support native woody vegetation with

large contiguous areas of woodland and forest habitat. In contrast, private land tends to be

sparsely vegetated although remnants of the original vegetation in variable condition are

scattered throughout the region (N.S.W. National Parks and Wildlife Service 2001).

Pressey (2000) estimated that sixteen per cent of original native vegetation remians in the

southwest slopes bioregion in NSW. Within the bioregion the amount of native vegetation

varies between local government areas. Cowra is fairly well vegetated with native vegetation

occurring in 22% of the shire (N.S.W. National Parks and Wildlife Service 2001). There is a

bias towards clearing on the flatter parts of the landscape as these tend to be more fertile and

more suitable for agricultural uses (N.S.W. National Parks and Wildlife Service 2001, Fischer et

al. 2010). For this reason, woodlands of the lower slope country which are dominated by white

box (Eucalyptus albens), grey box (Eucalyptus microcarpa) and yellow box (Eucalyptus

melliodora), have been intensively cleared. These communities are now very scarce with less

than one per cent of intact woodland remaining (Robinson and Traill 1996). In contrast, less

productive woodlands growing on rocky hills or upper slopes were cleared much later and much

larger areas of intact woodland remain. These upper slope communities are dominated by red

stringybark (Eucalyptus macrorhyncha) and red ironbark (Eucalyptus sideroxylon) with black

cypress pine (Callitris endlicheri), kurrajong (Brachychiton spp.) and red box (Eucalyptus

polyanthemos). Figure 3.3 depicts the typical change in vegetation community with topographic

position.

Figure 3.3: Schematic transect showing the typical change in woodland association with topographic position and aspect in the Cowra region. Adapted from Wilson (2003)

http://en.wikipedia.org/wiki/Brachychiton

22


3.2 Study Sites

Thirty-three study sites (Table 3.1) were selected from the CWBP’s survey database of

over 100 survey locations. Sites were distributed throughout the Cowra Shire with a few just

outside the shire boundary. The study sites were 2 ha bird survey areas that sit within a larger

patch of remnant vegetation. Remnant patch sizes varied from about 3 ha to over 400 ha. The

remnants represent a variety of vegetation types found in the region, from sparse open

Eucalyptus dominated woodlands in the valleys and on rocky hilltops, to dense mixed cypress

pine (Callitris spp.) and Eucalyptus forest along ridge lines. Approximately one third of the

sites were situated on public land in TSRs and nature conservation reserves, while the rest were

on private land. Woodland remnant quality on private land ranged from relatively intact to

highly degraded due to livestock grazing and intensive agriculture. Most woodland sites had a

distinctly grassy understorey as little shrubby woodland exists in the agricultural landscapes of

the region.

Figure 3.4: Map of the Cowra region, displaying the 33 site (yellow)s surveyed for this study. The central parts of the Shire are dominated by plains and alluvial valleys, and have been more intensively cleared for agriculture. Towards the east (Rosenberg State Forest), west (Conimbla National Park) and south west (Koorawatha Nature Reserve) of the shire, well vegetated (indicated in green), hilly areas predominate. Map courtesy Isabela Burgher.

23


Table 3.1: The study sites, and their site selection information. Thirty-three sites were sampled in this study, from 8 different strata. Strata were defined using patch area and noisy miner abundance (NMAbn) levels.

Noisy miner data

(abundance) Patch Area (ha) Strata

Site name Average Max. Min. Min. Max. (NMAbn/ patch area)

Bydawhile 6.57 17 1 400 >400 High NMAbn/

Altonvale - 1 10.86 25 0 31 100 Large patch

TSR - Golden Valley 7.57 20 0 31 100

Wandella - 2 (control) 11.71 23 4 11 30

Waugoola 11.14 20 3 11 30

TSR - Clements 8.57 20 4 11 30 High NMAbn/

Westville 7.86 13 4 11 30 Small patch

Rosedale 9.29 17 5 3 10

TSR - Applewood 7.86 15 0 3 10

Warrawong - Reserve 4.71 10 1 31 100

Pine Hill 3.86 10 0 31 100 Mod NMAbn/

TSR - Back Creek 3.29 20 0 31 100 Large patch

Penny Royal - Unfenced 3.14 9 1 31 100

Woodstock Cemetery 6.00 12 0 11 30

Morongla Cemetery 3.14 13 0 11 30 Mod NMAbn/

TSR - Bonnie Doone 5.57 10 1 3 10 Small patch

Cucum Creek 5.57 12 0 3 10

Warripendi - Paddock 2.71 4 0 101 400

TSR - Seed Orchard 2.71 6 0 31 100 Low NMAbn/

Penny Royal - Fenced 2.00 7 0 31 100 Large patch

TSR - Wattamondara West 1.71 5 0 31 100

Warrawong - Creek 1.29 2 0 11 30

TSR - Badgery 1.43 3 0 3 10 Low NMAbn/

The Common - 3 (control) 1.29 4 0 3 10 Small patch

Nichols 3.00 5 0 <3 3

Conimbla NP - Wallaby Track 0 0 0 400 >400

Koorawatha NR - 1 0 0 0 400 >400 Never NM/

Wyangala Dam 0 0 0 400 >400 Large patch

McInerneys - Hut 0 0 0 101 400

Bumbaldry Cemetary 0 0 0 11 30

Never NM/

Small patch

Kentucky Mac 0 0 0 11 30

Cucum Hill 0 0 0 3 10

Pine View 0 0 0 3 10

24


3.2.1 Site selection

Noisy miner sites were defined as having an average of more than one noisy miner present

at the site over seven randomly selected spring or summer surveys. Not all CWBP sites were

able to be included in this study due to inconsistencies in survey frequency (ranging from 1 to

16 surveys) and the years in which surveys were conducted. To ensure all sites had equal bird

survey effort, only sites with at least seven bird surveys were considered. A preliminary analysis

indicated that seven surveys captured 85% of bird species richness accumulated over the total

survey period on sites which had been surveyed every season. Seven spring or summer surveys

left a sufficient number of potential sites from which to choose a stratified sample. Where sites

had more than seven spring or summer surveys, seven surveys were selected using a random

number generator to prevent temporal variability biasing the data.

In total there were 37 sites where noisy miners were present. A stratified sample of 33

study sites was selected. Eight strata were used, defined using four levels of noisy miner

abundance and two levels of patch area. Stratification by noisy miner sites used the average

number of noisy miners over the seven survey periods. Sites where the noisy miner was present

were divided into three even groups of high, moderate and low abundance (

Table 3.2). The eight noisy miner free sites had no recorded noisy miners during the seven

surveys. Two levels of patch area were used, defined as ‘large’ or ‘small’. Small patches were

any remnant patch recorded as smaller than 30 ha in the CWBP database. This threshold was

used because the noisy miner appears to respond to patch area thresholds of this size (Mac Nally

et al. 2000a, MacDonald and Kirkpatrick 2003), and also because the patch area records initially

provided by the CWBP had six area classes and 30 ha was the threshold dividing the large

patches from the small patches (as per Birds Australia Atlas habitat forms).

Table 3.2: Definition of noisy miner abundance categories Thresholds between noisy miner categories were defined using the 33.3 and 66.6 percentiles of average noisy miner abundance to evenly divide the sites between three noisy miner abundance categories.

Noisy miner strata Average number of noisy miners

Noisy miner free 0 (never recorded)

Low noisy miner abundance 1 – 3 individuals

Moderate noisy miner abundance > 3 – 6.5 individuals

High noisy miner abundance > 6.5 individuals

I aimed to have four replicates in each stratum. As noisy miner abundance was a key

variable, it was important to ensure that there was a sufficient range of high noisy miner sites

for effective modelling. For this reason, one extra high noisy miner abundance site was

included, giving a total of 33 sites (Table 3.1). Where there were more than four potential sites

25


in a stratum, it was divided into four equal-sized abundance classes and one site was randomly

chosen from each class where possible. One stratum only had 3 sites that fitted the criteria

because of limited high noisy miner/large sites (see Table 3.1). In this case, an extra site in the

same noisy miner (high) class, but with small patch size was added.

3.3 Field Survey and Data Collection

3.3.1 Bird surveys

Bird data have been collected quarterly by the CWBP volunteers since 2002 using a

variation of the ‘active timed area search’ method which is the standard survey method used by

Birds Australia (Barrett et al. 2003). Rather than just recorded species presence, the volunteers

also count the number of individuals. This method was used by the CWBP because it is simple

and time-efficient (see Wilson 2003), a valuable trait for volunteer driven data collection. The

method is also effective in terms of detecting species and individuals (Loyn 1986). Each survey

was conducted in a two hectare site within a patch of vegetation (Figure 3.3), carried out over a

20 minute period between 0700 hrs and 1100 hrs. Species and abundance data have been

recorded by CWBP volunteers in each of the four seasons between 2002 and 2010.

Most sites were 100 m x 200 m quadrats, surveyed either in a zigzag or in two parallel 200

m line transects which were 50 m apart. A few sites were irregularly shaped due to the

configuration of the patch. Birds were recorded as present and counted if they were seen or

heard utilising the plot.

Reducing variability and potential source of bias

Only spring and summer surveys were used in this study to reduce the amount of seasonal

variability over the seven surveys, while still providing sufficient site choice. These seasons

were used because they include both sedentary species in the region and those that only use the

region seasonally such as the Superb Parrot (Polytelis swainsonii) which migrates south to the

Cowra region during its breeding period in spring and summer.

The CWBP conducts bird survey weekends where volunteers congregate and survey all of

the sites over a two day period. All bird surveys are completed before 1100 hrs because birds

are most active in the morning. Conducting the surveys in the same time frame reduced

variability in weather and bird activity.

The potential for observer bias was reduced by pairing experienced with inexperienced

surveyors, and rotating survey groups between sites so that individual sites were not always

surveyed by the same people.

Abundance data in bird surveys always have the potential to be biased as a result of double

counts or different counting methods among individuals. For this reason, this project mainly

26


uses species richness and noisy miner average abundance to minimise the effects observer

variability may have on bird abundance information.

3.3.2 Patch scale habitat data: stand floristics and structure

Patch scale habitat attribute data were collected in the autumn of 2011, guided by

McElhinny et al. (2006a) structural habitat index method. In addition to the 13 attributes used in

this index, I collected data for a further nine variables (Table 3.3).

Vegetation sampling procedure

To ensure vegetation sampling within a bird survey area was unbiased and representative

of the survey site, three vegetation survey plots were laid methodically within the bird survey

area. Vegetation survey plots were 50 m long and 20 m wide. These plots were evenly spaced

throughout the bird survey area (Figure 3.5). Within each vegetation survey plot one 20 m x 20

m subplot was established, depending on the slope of the site, giving a 400 m2 subplot for more

detailed data collection. Each attribute was estimated as the mean of the three different plot

estimates. The 50 m x 20 m plot was used to measure stem density, the size and hollow-bearing

status of overstorey trees species, the amount of coarse woody debris, and the dry weight of

litter. The 20 m x 20 m subplot was used for more detailed data collection such as the number of

lifeforms, plant species richness, and percent ground cover and midstorey cover. For a more

detailed explanation of the variables, refer to Table 3.3 or the methodology of McElhinny et al.

(2006a).

The overstorey species at each plot were also recorded and later categorised into three

main vegetation associations:

- Yellow box / Red gum woodland (YBRG), which were characterised by yellow box

(E. melliodora) and Blakley’s red gum (E. blakleyi) trees in the low lying,

productive areas and valleys. River red gum (E. camaldulensis) communities were

included in this category.

- White box / Grey box woodlands (WBGB) were characterised by the drier footslope

communities dominated by white box (E. albens) and grey box (E. microcarpa) trees.

Other species associated with this vegetation type were the occasional Callitris and

yellow box trees.

- Non-box/gum woodland vegetation (referred to as ‘Hill’ subsequently) was

characterised by the denser iron-bark forests and also hill-top communities.

Predominant species were red ironbark (E. sideroxylon), red stringybark (E.

macrorhyncha), brittle gum (E. mannifera), long-leaved box (E. goniocalyx) and

Callitris species. Hill communities also often included kurrajong (Brachychiton spp.)

trees.

27


Figure 3.5: Schematic diagram of a typical survey site. Each 2 ha site (light blue) had 3 vegetation survey plots (white) within which habitat data collection took place. Insert: A close up of a vegetation survey plot. The hatched area is where the detailed vegetation data (species richness, lifeform count, cover) was collected. Small black squares represent litter collection points.

28


Table 3.3: Detailed description of the data collected for the patch scale variables. ‘Size of plot’ indicates where in the survey plot the data was collected (refer to Figure 3.5).

Patch scale habitat variables

Data collected in each plot Size of plot Unit of measurement

Number of lifeforms Number of plant lifeforms (Appendix 1). 20x20 m Lifeforms/400 m

2

Perennial species richness

Number of native perennial species present

20x20 m Species richness/400 m

2

Ground cover Estimated horizontal ground cover of all vegetation under 0.5 m high. The % cover is the average of the estimates.

4 x (10x10 m2) % ground

cover

Midstorey cover Estimated horizontal ground cover of all vegetation 0.5-6 m high. The % cover is the average of the estimates.

4 x (10x10 m2) % midstorey

cover

Live stem density Number of stems in the plot 20x50 m Stems/ha

Stand basal area Basal area of the stand at breast height of all overstorey individuals ≥ 5 cm dbh

20x50 m m2/ha

Callitris basal area Basal area at breast height of Callitris individuals ≥ 5 cm dbh.

20x50 m m2/ha

Eucalyptus basal area

Basal area at breast height of Eucalyptus individuals ≥ 5 cm dbh.

20x50 m m2/ha

Stand quadratic mean diameter

Quadratic mean diameter of all overstorey individuals in the stand ≥ 5 cm dbh.

20x50 m cm

Callitris quadratic mean diameter

Quadratic mean diameter of all Callitris individuals in the stand ≥ 5 cm dbh.

20x50 m cm

Eucalyptus quadratic mean diameter

Quadratic mean diameter of all Eucalyptus individuals in the stand ≥ 5 cm dbh.

20x50 m cm

Trees > 40 cm Number of tree stems ≥ 40 cm dbh. 20x50 m Stems/ha

Hollow trees Number of hollow bearing stems. Dead and live trees are included.

20x50 m Stems/ha

Total overstory regeneration

Number of regenerating stems (< 5 cm dbh) of all species.

20x50 m Stems/ha

Callitris regeneration Number of regenerating stems (< 5 cm dbh) of Callitris species.

20x50 m Stems/ha

Eucalyptus regeneration

Number of regenerating stems (< 5 cm dbh) of Eucalyptus species.

20x50 m Stems/ha

Dead trees Number of dead stems 20x50 m Stems/ha

Total log length Total length of all coarse woody debris with diameter ≥ 10 cm.

20x50 m m/ha

Large log length Total length of all coarse woody debris with diameter ≥ 30 cm.

20x50 m m/ha

Dry litter weight All dead organic matter less than 10 cm in diameter was collected. Plot litter weight was the sum of the 5 samples

5 x (0.5 x 0.5 m) samples

t/ha

29


3.3.3 Landscape scale habitat data

Landscape scale variables used in this study to identify optimal noisy miner habitat were

measures of patch area and isolation of the patch (Table 3.4). These variables were calculated

using the ArcMap 9.3 add-on ‘PatchMorph’ (Girvetz and Greco 2007) on a SPOT5 layer

covering the study region. This SPOT layer was classified into two categories, woody

vegetation or non-woody vegetation, and was derived using remotely sensed imagery of the

study region from 2004 and 2005.

Table 3.4: Description of the landscape scale habitat data collected.

Identification of a patch is complicated by the fact that species perceive their own

landscapes according to their own criteria; their ‘Umwelt’ (Manning et al. 2004b). PatchMorph

uses a delineation algorithm based on user-specified inputs in order to delineate functional

patches using species-specific parameters. There are three species-specific parameters by which

a patch can be defined (Girvetz and Greco 2007). These parameters are land cover density

threshold, habitat gap maximum thickness (gap threshold) and habitat maximum thickness (spur

threshold). Land cover density threshold is the minimum amount of woody vegetation cover

that defines a given area as ‘patch’, rather than ‘non-patch’. The gap threshold is defined so that

small gaps between habitat cover, which are easily crossed by animals, can be included as part

of the same patch (Figure 3.6). The spur threshold can be defined to exclude very thin corridors

or projections that may not be useful for species dispersal but increase the size of the patch

disproportionately to their value.

Patch Area

Patch area was delineated using settings of 20% for land cover density with a 50 m gap

threshold and a 30 m spur threshold. A woody vegetation cover density threshold of 20%

(equivalent to 10% projected foliage cover) was used as this reflects the minimum woody

vegetation cover required for that patch to be classified as woodland (Yates and Hobbs 1997,

Montreal Process Implementation Group for Australia 2008). Fifty meters was used as the

maximum gap size as patches of woody vegetation within this gap distance were considered to

be part of the same patch. The use of 30 m as a spur threshold ensured that smaller patches of

vegetation were not disproportionately reduced, but that patches joined by corridors less than 30

m wide were considered as distinct patches.

Landscape variable Variable quantified

Patch area Area (ha)

Patch isolation Extent of vegetation cover surrounding the patch within 2 km radius (ha)

30


Increasing Gap

Threshold

Increasing Spur Threshold

Figure 3.6: Vegetation ‘patches’ derived from a hypothetical landscape (lower left) depend on gap and spur thresholds definitions. Vegetation cover is specified in green. The higher the thresholds, the more coarse the delineation of the patch is. Increasing gap threshold (y-axis) incorporates larger gaps into the ‘patch’. Increasing spur threshold (x-axis) means that increasingly large spurs (or peninsulas) will be removed from the ‘patch’. Adapted from Givertz and Greco (2007).

31


Extent of Woody Vegetation Cover

The extent of vegetation cover in the surrounding landscape was determined by creating

polygons of vegetation using PatchMorph settings of 20% land cover density at, a 50 m gap

threshold and the default 3m spur threshold (which is effectively 0 m using a pixel size of 5 m).

Thus, all woody vegetation in the landscape showed up, including scattered trees (Figure 3.7).

Circular buffers comprising a radius of 2 km, using the bird survey plot as the midpoint, were

then extracted from this polygon layer (Figure 3.8). The extent of vegetation was calculated by

summing the total area of woody vegetation contained within the buffer. In their study of

woodland bird responses to landscape scale metrics, Westphal et al. (2003) found that responses

of many species were best explained by vegetation characteristics within a 2 km radius, rather

than 5 km or 10 km.

The GIS analysis was carried out by Isabela Burgher as a component of her honours

project at the ANU (2011).

Figure 3.7: The extent of woody vegetation was determined by creating polygons of vegetation using PatchMorph a) Original binary SPOT layer and, b) the subsequent representation of vegetation in the landscape. Map courtesy Isabela Burgher.

32


Figure 3.8: Circular buffers around survey sites were used to determine the extent of vegetation in the adjacent region. Circular buffers with a 2 km radius were placed around each bird survey plot. Vegetation extent was calculated by summing the total area of vegetation within the buffer. Map courtesy Isabela Burgher.

33


3.4 Statistical Analysis

Statistical analyses were carried out using a two part process (Figure 3.9), guided by the

research questions.

3.4.1 Bird response to noisy miner abundance

After collating a general overview of the bird and habitat data distributions initial analysis

of noisy miner effects on bird species richness (research question 1) were carried out on five

bird categories, using analysis of variance (ANOVA) and correlation. The five bird categories

were:

- Total bird species richness, all of the land bird species found across the 33 sites,

excluding the waterbirds, raptors and nocturnal birds;

- Non-woodland bird species richness, a subset of Total birds.

- Woodland bird species richness, a subset of Total birds.

- Small woodland bird species richness, a subset of Woodland birds, woodland-

dependent birds that weigh less than the noisy miner (65 g), because these birds

are likely to be more susceptible to noisy miner exclusion than larger birds

(Piper and Catterall 2003, Mac Nally et al. 2011).

- Threatened and declining bird species richness, a subset of Woodland birds.

Woodland, small woodland and non-woodland bird categories were determined under

expert advice (Julian Reid, written communication, 03/08/ 2011b; Appendix 2). Threatened and

declining woodland birds in the Cowra region were identified using Reid (1999) and the NSW

threatened species list (Department of Environment and Conservation (NSW) 2005).

This was followed with a more detailed analysis of noisy miner effects using a generalised

linear model, with a Poisson distribution and a log link function, because the dependent

variables (the five bird categories) were in the form of count data.

Given that small woodland birds and the threatened and declining birds are different

subsets of the woodland birds, I will refer to the three categories as the ‘woodland-dependent’

groups. These classes also include migratory woodland birds. For a full species list of the

woodland-dependent species, refer to Appendix 2. The bird categories were used to focus on the

effects of the noisy miner on these groups, so the noisy miner was excluded from these

categories. Avifaunal species richness counts presented here are cumulative species richness

over the seven survey periods. Average noisy miner abundance refers to the average number of

noisy miners recorded at a survey over the same seven surveys.

34


3.4.2 Noisy miner habitat preferences

Research question two, concerning noisy miner habitat preferences, was initially explored

using ANOVA and correlation analysis. This was followed by more detailed least squares

regression modeling in which average noisy miner abundance, a continuous dependent variable,

was modelled in terms of multiple habitat parameters. The natural log (ln) of average noisy

miner abundance was used to normalise their left-skewed distribution. This was an important

step because many statistical models assume a normal distribution; skewed distributions violate

such assumptions. Similarly, many of the habitat attributes distributions were skewed and so

were transformed to a natural log scale. Noisy miner habitat models were developed using a

guided forward step-wise approach. The most parsimonious models were selected as final noisy

miner habitat models. These were models with fewest parameters, best R2 (coefficient of

determination) values, lowest corrected Akaike information criterion (AICc) (a measure of the

goodness of fit) and all parameters in the model were significant.

Analyses were carried out in JMP version 9.0.1. Levels of significance for all analyses

were set at 95% (p ≤ 0.05). Waterbirds, raptors and nocturnal birds were excluded because they

have different resource requirements and operate on different time and space scales than the

noisy miner and woodland birds and may add noise to the data (Julian Reid, written

communication, 30/03/ 2011a). Exotic birds were also excluded because of their urban-biased

distribution (Parsons et al 2003) which may affect species richness counts between sites

differently.

35


Overview of the bird

and habitat data

Initial analysis of noisy miner

effects on bird species richness

using ANOVA and correlation

Initial noisy miner habitat

data analysis using ANOVA

and correlation

Detailed analysis of noisy miner

effects on bird species richness

using generalised linear modelling

Detailed noisy miner habitat

data analysis using least

squares regression

Data management and

preparation for analysis

Research question 1 Research question 2

The next chapter will present the results from this methodological approach. It will give an

overview of the data collected, followed by the results from the statistical analysis of noisy

miner impacts on bird species richness, and their habitat preferences.

Figure 3.9: Schematic summary of the steps used in the statistical analysis for this study.

36


Chapter 4

Results

‘Waugoola’, a highly simplified yellow box / red gum woodland. This site

was a high noisy miner abundance site.

37


Chapter 4: Results

In this chapter, I present analysis of bird species richness against noisy miner abundance in

order to assess the effect of the noisy miner on bird communities. Following this I present the

results of modelling the abundance of noisy miners as a function of landscape and/or patch scale

variables. I begin with an overview of the bird, the patch scale and the landscape scale data

collected for analysis. I then present the results from analysis of noisy miner effects on different

categories of bird species richness. Finally, I show the findings of noisy miner habitat

preferences.

4.1 Overview of the data

4.1.1 Bird data

Across seven surveys at the 33 sites, 104 native bird species were recorded (Appendix 2).

Of the 104 native species, 69 species of woodland birds were recorded and the remaining 35

species were non-woodland or ‘open country’ species. Of the woodland bird species, 59 were

small woodland bird species, and 22 were threatened and declining bird species. Site cumulative

total bird species richness ranged between 12 and 46 species (Table 4.1). Site cumulative

woodland bird species richness ranged between 2 and 31 species. Introduced species and a suite

of water birds, nocturnal birds and raptors were excluded from the original data.

Noisy miner abundance ranged from 0 to 25 individuals. Average noisy miner abundance

over the seven surveys per site ranged from zero (noisy miners were never recorded), up to 11.7

individuals per site. In some cases the noisy miner was analysed as a factor variable, with sites

where the noisy miner was never present, or were present at low, moderate or high levels. These

categories were derived from the original stratification process (Table 3.1).

Table 4.1: Overview of the bird species richness (BSR) distribution of the different bird categories.

Bird species category BSR range Mean BSR Median BSR

Noisy miner abundance Average 0 – 11.7 4.0 3.14

Total bird species Cumulative 12 – 46 26.4 27

Woodland bird species Cumulative 2 – 31 13.6 14

Small woodland birds Cumulative 0 – 27 9.0 9

Non-woodland bird species Cumulative 8 – 21 12.8 12

Threatened and declining bird species

Cumulative 1 – 13 4.3 5

38


Twenty two threatened and/or declining species were observed. Twelve of these were

NSW listed threatened birds, 17 were species of declining birds (Table 4.2). Many of these were

infrequently recorded, with 12 species only observed at 15% or fewer sites. No individuals were

ever recorded at 85% of sites over seven surveys. Of the threatened and declining species, only

the superb parrot and rufous whistler were recorded at more than half of the sites.

Table 4.2: List of the threatened and declining species found in the Cowra region. While the superb parrot and rufous whistler were present at more than half of the sites, half of the threatened and declining species were only present at fewer than 15% of sites over seven surveys.

Species Conservation status Number of

sites present

% of sites present

Black-chinned honeyeater Vulnerable 3 9

Brown treecreeper Declining 15 46

Chestnut-rumped thornbill Vulnerable, Declining 1 3

Crested shrike-tit Declining 9 27

Diamond firetail Vulnerable, Declining 4 12

Dusky woodswallow Declining 14 42

Eastern yellow robin Declining 7 21

Gilbert's whistler Vulnerable 1 3

Grey-crowned babbler Vulnerable, Declining 8 24

Hooded robin Vulnerable, Declining 1 3

Jacky winter Declining 7 21

Little lorikeet Vulnerable 1 3

Red-capped robin Declining 3 9

Restless flycatcher Declining 7 21

Rufous whistler Declining 17 52

Speckled warbler Vulnerable, Declining 5 15

Superb parrot Vulnerable 22 67

Swift parrot Endangered, Declining 1 3

Turquoise parrot Vulnerable 1 3

Varied sittella Vulnerable, Declining 3 9

White-browed babbler Declining 5 15

White-browed woodswallow Declining 7 21

39


4.1.2 Habitat data

Patch scale variables

The patch scale variables had sufficient range in their distribution to be used for modelling.

Variables that were not normally distributed were transformed using the natural log of the

original value. Table 4.3 provides a summary of the distribution of these habitat variables (see

Appendix 3 for the raw data).

Table 4.3: Summary statistics of the continuous patch scale habitat variables.

Patch scale habitat variable Range Mean Median

No. Lifeforms (/400 m^2) 2.0 - 8.3 5.2 4.7

Perennial sp. richness (/400 m^2) 2.3 - 33.6 11.3 10

Ground cover (%) 9.8 - 99.2 63.0 65.8

Midstorey cover (%) 0 - 36.25 5.7 1.6

Live stem density (/ha) 16.7 - 1096.7 329.7 223.3

Stand basal area (m2/ha ) 3.7 - 56.4 19.3 18.0

Callitris basal area (m2/ha) 0 - 13.3 2.0 0

Eucalyptus basal area (m2/ha) 0.9 - 56.4 17.1 17.6

Mean tree basal area (m2) 0 - 0.4 0.1 0.1

Stand quadratic mean diameter (cm) 14.3 - 75.3 35.4 28.3

Callitris quadratic mean diameter (cm) 0 - 32.6 6.2 0

Eucalyptus quadratic mean diameter (cm) 10.9 - 89.7 38.7 33.7

Trees > 40 cm (stems/ha) 0 - 83.3 33.9 33.3

Hollow trees (stems/ha) 0 - 33.3 10 10

Total overstorey regeneration (stems/ha) 0 - 5066.7 400.7 33. 3

Callitris regeneration (stems/ha) 0 - 5023.3 322.0 0

Eucalypt regeneration (stems/ha) 0 - 426.7 45.5 13.3

Number of dead trees (stems/ha) 0 - 406.7 75.1 43.3

Total log length (m/ha) 27.2 - 1039.8 331.6 234.0

Large log length (m/ha) 0 - 103.6 24.6 17.7

Litter weight (t/ha) 1.4 - 13.7 6.5 6.5

Landscape scale variables

The landscape scale variables showed a large level of variability (Table 4.4). Patch area

ranged between 1.5 and 37000 ha, while the extent of vegetation surrounding a site ranged

between about 40 and 1140 ha (equivalent to 3% and 90% vegetation cover within the circular

buffer). Both patch area and extent of woody vegetation were heavily skewed with a small

number of very high values. Both variables were transformed to the natural log and this

transformation was used in subsequent analysis.

Table 4.4: Summary statistics of the landscape scale habitat variables. Both variables displayed considerable range and were positively (right) skewed.

Landscape scale habitat variable Range Mean Median

Patch area (ha) 1.5 - 37000 3918.9 28

Vegetation extent (ha) 40 - 1138

(3 – 90% cover)

314.2 256.6

40


The landscape variables were also converted into factor variables to analyse the variance in

noisy miner abundance according to thresholds of landscape variables. The patch area cut off

was set at 30 ha with patches smaller than 30 ha considered ‘small’ and those larger than 30 ha

were ‘large’ (Mac Nally et al. 2000a, MacDonald and Kirkpatrick 2003). The threshold

determining the extent of vegetation surrounding the site was defined as 20% (or about 250 ha),

with more or less vegetated area considered to have “high” or “low” vegetation extent

respectively.

4.2 Bird responses to noisy miner abundance

4.2.1 Correlation analysis

Simple bivariate relationships between average noisy miner abundance and different bird

response categories were identified using pairwise correlations within a scatterplot matrix

(Table 4.5). The noisy miner appears to negatively affect all bird categories significantly except

for non-woodland birds.

Table 4.5: Correlation between bird response categories and average noisy miner abundance. All of the bird species richness (BSR) categories were negatively correlated with the natural log (ln) of average noisy miner abundance (lnAvgNMAbn), except for non-woodland BSR. 95% confidence intervals (CI) and significance values (p-value) are reported.

Response variable Explanatory variable

Correlation coefficient

Upper 95% CI

Lower 95% CI

p-value

Small woodland bird species richness

lnAvgNMAbn -0.767 -0.879 -0.575 <.0001

Woodland bird species richness

lnAvgNMAbn -0.714 -0.849 -0.491 <.0001

Threatened and declining bird species richness

lnAvgNMAbn -0.620 -0.794 -0.352 0.0001

Total bird species richness lnAvgNMAbn -0.604 -0.785 -0.329 0.0002

Non-woodland bird species richness

lnAvgNMAbn 0.040 -0.308 0.378 0.8274

4.2.2 Analysis of Variance

All bird categories showed significant response to noisy miner abundance as a categorical

explanatory variable (Table 4.6). Species richness of all of the woodland-dependent groups was

significantly lower in high noisy miner abundance levels than any other abundance level.

Small woodland birds showed the most significant effects, with highest species richness in

sites where noisy miners were never present, moderate richness in low and moderate noisy

miner sites and very low richness in high noisy miner sites (P < 0.0001; R2 = 0.57). Woodland

birds, and threatened and declining species show a similar trend but with less distinct changes

41


between noisy miner free and low noisy miner sites (p = 0.0001 and 0.0005 respectively). Total

bird species richness was significantly lower in high noisy miner abundance sites than at any

other site (p = 0.0006).

Non-woodland birds showed a very different trend with lowest species richness in noisy

miner free sites and high noisy miner sites, while low and moderate levels of noisy miner

abundance was associated with higher species richness.

Table 4.6: All of the bird groups were significantly influenced by average noisy miner abundance categories. Different symbols indicate significantly different means. Small woodland birds appear to show the strongest declining trends. This table presents the significance of the result (p-value) and the amount of variance explained by the analysis (R

2). For graphical ANOVA output, with

standard error margins, refer to Appendix 4.

Response variable: Bird species richness (BSR)

Noisy miner abundance p-value R2

Never Low Mod High


17.3 * 11.0 ^ 7.3 ^ 0.3 # <.0001 0.57


21.1 * 16.4 *^ 12.4 ^ 5.6 # 0.0001 0.51


6.4 * 5.5 *^ 4.1 ^ 1.6 # 0.0005 0.45

Total bird species richness 32.0 * 30.8 * 27.5 * 16.6 ^ 0.0006 0.45


10.9 ^ 14.4 * 15.1 * 10.9 ^ 0.0012 0.41

Figure 4.1: Relative non-woodland bird species richness (BSR) increases with noisy miner abundance. As noisy miner abundance increased, all bird species richness decreased. The relative richness of non-woodland bird species (red) became important at high noisy miner abundance levels, contributing to over 65% of all bird species richness. Woodland birds (green) contribute most at low and noisy miner free sites. Data from Table 4.6.

42


4.2.3 Generalised linear modelling

Generalised linear models were used to examine how the abundance of noisy miners affect

bird species richness and to see if threshold effects were apparent.

All of the bird categories except non-woodland birds showed significant responses to noisy

miner abundance as a continuous explanatory variable (Table 4.7; Figure 4.2). All of the

woodland-dependent birds were negatively affected by noisy miner abundance with the

strongest effects in small woodland birds (p < 0.0001 for all groups). These effects were

pronounced at low noisy miner abundance levels (Figure 4.2). Woodland-dependent bird

species richness was reduced by at least 25% and up to 40%, when an average of one noisy

miner was present and by about 50% when noisy miner average abundance reached 3.5

individuals (Table 4.8; Figure 4.2). These effects were the most prominent in the small

woodland bird group, with a reduction of more than 50% of their potential species richness

when just two noisy miner individuals were present.

Table 4.7: The log of noisy miner (lnNM) abundance significantly influences all of the bird response categories except non-woodland birds. This table presents the formula derived from generalised linear models, which are graphically represented in Figure 4.1. Significance is indicated by p ≤0.05. Models are of the form y = e

b+mx,

where b is the intercept, m is a constant and x is the natural log of noisy miner abundance.

Response variable: Bird Species Richness (BSR

Model formula

Y = eb+(m)x

Standard error of b (intercept)

Standard error of m (gradient)

p-value


Exp( 3.137+(-0.473)*lnNM) 0.069 0.054 <0.0001

Total bird species richness

Exp( 3.561+(-0.240)*lnNM) 0.054 0.038 <0.0001


Exp( 1.960+(-0.447)*lnNM) 0.123 0.096 <0.0001


Exp( 2.952+(-0.758)*lnNM) 0.080 0.070

<0.0001


Exp( 2.532+ (0.011)*lnNM) 0.089 0.056 0.8476

43


Figure 4.2: The noisy miner has a negative effect on total bird species richness and species richness of woodland-dependent birds. These were modelled using generalised linear models. All of the models were significant (p < 0.0001) for all groups except the non-woodland birds, which showed no trend or significance. Dotted line ‘a’ (1 noisy miner) represents at least s 25% reduction any (and all) of the woodland-dependent bird species richness categories. ‘b’ (3.5 noisy miners) represents a 50% reduction in the same groups. It is worth nothing that small woodland birds are reduced by 40% and 68% (Table 4.8).

Table 4.8: Percent of the potential bird species richness of a site, under different noisy miner abundance values. Increasing noisy miner abundance reduces the species richness of all bird categories. Small woodland birds showed the largest reductions in species richness. These figures were determined using the formula from generalised linear models (Table 4.7). Bolded rows refer to the dotted lines in Figure 4.2.

Avg. noisy miner abundance

Percent (%) potential species richness

Total birds Woodland birds Threatened and declining birds

Small woodland birds

0 100 100 100 100

1 84.7 72.0 73.3 59.1

2 76.8 59.5 61.2 43.5

3 71.7 51.9 53.8 35.0

3.5 69.8 49.1 51.1 32.1

4 67.9 46.7 48.7 29.5

6 62.7 39.8 41.9 22.9

8 59.0 35.4 37.4 18.9

10 56.2 32.2 34.2 16.2

12 54.0 29.7 31.7 14.3

44



4.3.1 Correlation analysis

Simple bivariate relationships between the continuous explanatory variables at the patch

and landscape scale, and the response variable of average noisy miner abundance were

identified using pairwise correlations within a scatterplot matrix (Table 4.9). All of the

statistically significant correlations negatively affected noisy miner abundance. Noisy miner

abundance was highly significantly (p ≤ 0.005) related to the extent of vegetation in the

surrounding 2 km buffer, patch size, total overstorey regeneration, and Callitris basal area.

Noisy miners were also significantly (p ≤ 0.05) related to the number of lifeforms present, the

amount of Callitris regeneration, perennial plant species richness and Callitris quadratic mean

diameter.

Potential correlations between explanatory variables were also identified using the same

method (Table 4.10) in order to ensure that variables correlated with each other were not

included in the final model together. Variables with correlation coefficients ≥ ±0.9 were not

combined in multivariate regression analysis.

45


Table 4.9: Correlation between patch and landscape variables and average noisy miner abundance. Eight variables were significantly correlated with noisy miner abundance. All variables with coefficients ±0.35 and above were significant, at p ≤ 0.05.

Patch and landscape explanatory variables

Correlation coefficient

Significance level

ln (Vegetation extent) -0.55

p ≤ 0.005; highly

significant

ln (Patch area) -0.54

ln (Total overstorey regeneration) -0.50

ln (Callitris basal area) -0.47

Number of lifeforms -0.40

p ≤ 0.05;

significant

ln (Callitris regeneration) -0.40

ln (Perennial species richness) -0.39

ln (Callitris quadratic mean diameter) -0.37

Ground cover (%) 0.33

p > 0.05;

insignificant

ln (Hollow bearing trees) -0.32

ln (Dead trees) -0.30

ln (Eucalyptus regeneration) -0.29

ln (Stem density) -0.29

ln (Midstorey cover) -0.29

ln (Eucalyptus quadratic mean diameter) 0.25

ln (Stand quadratic mean diameter) 0.23

ln (Large log length) -0.22

ln (Total log length) -0.18

Stand basal area -0.18

ln (Eucalyptus basal area) 0.16

Number of trees > 40 cm 0.15

Litter weight (t/ha) -0.11

Mean tree basal area 0.073

46


Table 4.10: Correlations between continuous explanatory variables for the 33 study sites. All variables with coefficients ±0.35 (in bold) and above were significant, at p ≤ 0.05. Coefficients ±0.9 (grey) were deemed to be too closely correlated and were not used together in subsequent analyses. Average noisy miner abundance was correlated with eight variables (shaded).

Number of lifeforms 1

Ln (Perennial species richness) 0.77 1

Ground cover (%) -0.35 -0.29 1

ln (Midstorey cover) 0.68 0.37 -0.34 1

ln (Stem density) 0.49 0.45 -0.50 0.55 1

Stand basal area -0.17 -0.23 -0.12 0.08 0.24 1

ln (Callitris basal area) 0.60 0.43 -0.55 0.64 0.59 -0.07 1

ln (Eucalyptus basal area) -0.42 -0.28 0.10 -0.31 -0.01 0.75 -0.50 1

Mean tree basal area -0.50 -0.51 0.26 -0.43 -0.80 0.24 -0.45 0.34 1

ln (Stand QMD) -0.62 -0.56 0.40 -0.56 -0.84 0.27 -0.63 0.48 0.92 1

ln (Callitris QMD) 0.72 0.50 -0.43 0.72 0.44 -0.22 0.85 -0.56 -0.47 -0.58 1

ln (Eucalyptus QMD) -0.61 -0.59 0.26 -0.45 -0.70 0.29 -0.54 0.54 0.87 0.90 -0.55 1

Number of trees > 40 cm -0.41 -0.37 0.19 -0.18 -0.07 0.72 -0.40 0.78 0.29 0.49 -0.45 0.51 1

ln (Hollow bearing trees) -0.16 -0.11 -0.16 -0.16 -0.08 0.55 -0.17 0.60 0.43 0.42 -0.32 0.40 0.47 1

ln (Total overstorey regeneration) 0.74 0.62 -0.34 0.68 0.62 -0.33 0.72 -0.63 -0.64 -0.82 0.69 -0.70 -0.56 -0.33 1

ln (Callitris regeneration) 0.64 0.38 -0.48 0.71 0.52 -0.22 0.93 -0.65 -0.46 -0.66 0.89 -0.56 -0.48 -0.32 0.78 1

ln (Eucalyptus regeneration) 0.57 0.64 -0.09 0.39 0.57 -0.16 0.28 -0.18 -0.59 -0.68 0.23 -0.59 -0.34 -0.14 0.72 0.27 1

ln (Dead trees) 0.19 0.36 -0.17 0.06 0.61 0.08 0.25 0.03 -0.66 -0.54 0.20 -0.60 0.04 -0.06 0.28 0.13 0.32 1

Litter weight (t/ha) 0.17 0.27 -0.38 0.07 0.35 0.36 0.18 0.42 -0.13 -0.08 0.10 -0.04 0.27 0.43 -0.04 -0.04 0.08 0.24 1

ln (Patch area) 0.54 0.53 -0.25 0.32 0.54 0.12 0.56 -0.19 -0.43 -0.52 0.47 -0.66 -0.24 0.01 0.52 0.49 0.51 0.43 0.25 1

ln (Vegetation extent) 0.41 0.56 0.09 0.17 0.28 0.14 0.27 -0.06 -0.14 -0.24 0.21 -0.36 -0.16 0.09 0.40 0.23 0.46 0.36 0.00 0.72 1

ln (Average noisy miner abundance) -0.40 -0.39 0.33 -0.29 -0.29 -0.18 -0.47 0.16 0.07 0.23 -0.37 0.25 0.15 -0.32 -0.50 -0.40 -0.29 -0.30 -0.11 -0.54 -0.55 1

Nu

mber o

f lifeform

s

Ln

(Peren

nial sp

ecies

richn

ess)

Gro

un

d co

ver (%

)

ln (M

idsto

rey co

ver)

ln (S

tem d

ensity

)

Stan

d b

asal area

ln (C

allitris b

asal area)

ln (E

uca

lyptu

s basal

area)

Mean

tree basal area

ln (S

tand q

uad

ratic

mean

diam

eter)

ln (C

allitris q

uad

ratic

mean

diam

eter)

ln (E

uca

lyptu

s qu

adratic

mean

diam

eter)

Nu

mber o

f trees > 4

0

cm

ln (H

ollo

w b

earing

trees)

ln (T

otal o

versto

rey

regen

eration

)

ln (C

allitris

regen

eration

)

ln (E

uca

lyptu

s

regen

eration

)

ln (D

ead trees)

Litter w

eight (t/h

a)

ln (P

atch area)

ln (V

egetatio

n ex

tent)

ln (A

verag

e noisy

min

er

abu

nd

ance)

47


4.3.2 Bivariate least squares regression

The impact of the habitat variables on noisy miner abundance was determined by

modelling each significant variable obtained from correlation analysis using least squares

regression. The landscape variables of vegetation extent and patch area had the greatest impact

on noisy miner abundance (Table 4.11): each variable explained about 30% of noisy miner

distribution. These were followed respectively by the patch scale variables of total overstorey

regeneration, Callitris basal area, the number of lifeforms, Callitris regeneration, perennial

species richness and Callitris quadratic mean diameter (in order of significance and explanatory

power).

Table 4.11: The effects of continuous individual patch and landscape scale variables on noisy miner abundance (y). These effects were modelled using least squares regression. The model formula, significance (p-value) and the amount of variation (R

2) explained are presented here. Models are of the form

y = b+mx, where b is the intercept, m is a constant and x is the explanatory variable, in the first column.

Patch and landscape

explanatory variables (x)

Model formula

y = b + m(x)

Standard error of b (intercept)

Standard error of m (gradient)

p-value R2

ln(Vegetation extent) 4.182 - 0.536 * x 0.802 0.147 0.0009 0.30

ln(Patch area) 2.017 - 0.172 * x 0.241 0.048 0.001 0.29

ln(Total regeneration) 1.867 - 0.168 * x 0.226 0.053 0.003 0.25

ln(Callitris basal area) 1.545 - 0.453 * x 0.161 0.151 0.005 0.23

Number of lifeforms 2.268 - 0.188 * x 0.428 0.078 0.02 0.16

ln(Callitris regeneration) 1.512 - 0.125 * x 0.170 0.052 0.02 0.16

ln(Perennial species richness)

2.611 - 0.583 * x 0.572 0.245 0.02 0.15

ln(Callitris quadratic mean diameter)

1.513 - 0.228 * x 0.177 0.104 0.04 0.13

48


4.3.3 Analysis of Variance

One way analysis of variance was used to see how the noisy miner responds to the

categorical habitat variables of vegetation association, patch size and the extent of vegetation

within a 2 km radius of the site. This analysis was conducted using the log of average noisy

miner abundance. Numerical values presented here, the ‘actual means’, refer to natural log

values (as depicted in Figure 4.3) that have been back transformed (expln(mean)

- 1) for tangible

bird numbers.

Average noisy miner abundance was significantly affected by vegetation association

(p=0.0001; R2=0.46), with more individuals occurring at the woodland sites than at the hill sites

(Figure 4.3a). Yellow box/Red gum woodlands had the highest noisy miner abundance (actual

mean=5.1), followed by White box/Grey box woodlands (actual mean=3.8). However, the hill

sites had significantly lower noisy miner abundance (actual mean=0.7). Noisy miner abundance

did not significantly differ between the woodland sites.

Average noisy miner abundance was significantly higher in small patches (less than 30 ha)

than large patches (p = 0.01; R2 = 0.19; Figure 4.2b), with actual mean miner abundance of 4.3

and 1.4 birds respectively.

Average noisy miner abundance was significantly higher when the extent of vegetation

surrounding the site was low (less than 20% cover) than when the extent of cover was high

(p = 0.04; R2 = 0.13; Figure 4.3c), with actual mean noisy miner abundance of 4.0 and 1.7 birds

respectively.

49


(>20%)

a)

c)

b)

Hill

*

*

Figure 4.3: Graphical representations of the analysis of variance models between noisy miner abundance and categorical habitat variables. (a) The box/gum woodland sites both had significantly higher (p=0.0001) noisy miner abundance than the hill sites. YBRG and WBGB refer to yellow box/red gum woodland and white box/grey box woodland communities respectively. Hill sites are non-woodland communities (see section 3.3.2). (b) The noisy miner was more abundant in small (<30 ha) vegetation patches (p=0.01). (c) Sites with low extent of vegetation cover had higher noisy miner abundance (p=0.04). Error bars indicate standard error. Different letters indicate that means are significantly different.

(< 20%)

a

b b

a

a

b

b

50


4.3.4 Multivariate Least Squares Regression analysis

A multivariate least squares regression analysis was used to model the combined effects of

multiple variables on average noisy miner abundance. The most parsimonious model was

selected as the final noisy miner habitat model (Table 4.12), that is, the model with fewest

parameters, best R2 values, lowest corrected Akaike information criterion (AICc), with only

significant parameters in the model.

Noisy miners generally inhabit woodland rather than forest habitats. Because this study

aimed to find which patch and landscape scale habitat variables affect noisy miner abundance

rather than vegetation preferences, the model was developed without using the strong

explanatory power of vegetation association.

Patch area, hollow bearing trees and Callitris regeneration were able to successfully

predict noisy miner abundance (Table 4.12). All of the parameters used in the multivariate

habitat model were significant with p-values of 0.017, 0.0016 and 0.0023 respectively. All of

the variables negatively affected noisy miner abundance. This model was highly significant (p =

0.0002, Table 4.12) and explains about half of the variance in noisy miner distribution (R2 =

0.50, Table 4.12). Small patches with low levels of Callitris regeneration and few hollow

bearing trees had considerably higher noisy miner abundance as shown graphically in Figure

4.4.

Table 4.12: Least squares regression analysis identified patch area, the amount of Callitris regeneration and the number of hollow bearing trees as the best predictors of ln(noisy miner abundance). More noisy miners are present in small patches with low regeneration and few hollow bearing trees. This table presents the model, significance value (p-value) and the amount of variance explained (R

2) by that model. For a graphical representation, using actual average noisy miner

abundance) refer to Figure 4.4.

Response Multivariate habitat model p-value R2

Ln (Average noisy miner abundance)

2.21+Area [S=0.298/L=-0.298] +(-0.15*ln(Callitris Regeneration)) +(- 0.35*ln(Hollow bearing trees))

0.0002 0.50

Actual average noisy miner abundance

e (ln (Average noisy miner abundance))

- 1

51


Figure 4.4: Graphical representation of the multivariate habitat model presented in Table 4.12. All of the variables negatively affected noisy miner abundance. Small patches (<30 ha) tended to have a higher abundance of noisy miners than large patches (> 30 ha).

a) Low hollow bearing tree scenario. Low levels of tree hollows (HT) were set at 0 hollows/ha, 10

th percentile of collected data.

b) High hollow bearing tree scenario. High levels of tree hollows were defined as 22 hollows/ha, 90

th percentile of collected data.

b)

a)

52


This chapter has outlined the data collected and presented the results from statistical

analysis of noisy miner impacts on bird species richness, and their habitat preferences. Chapter

five will discuss these results in the context of the posed research questions in chapter two, and

of the outcomes from other research.

53


Chapter 5

Discussion

‘Warripendi – Paddock’, a ‘hill’ site with high structural complexity. This site

was a low noisy miner site.

54


Chapter 5: Discussion

In this chapter I discuss the two research questions proposed in Chapter 2, and the

relevance of my data analyses in evaluating the collective knowledge of noisy miners. The

responses of woodland birds to noisy miner abundance are evaluated first. This is followed with

a discussion of the bird categories that are most susceptible to noisy miner aggression and the

density threshold of noisy miner abundance where their effect is more pronounced. The habitat

preferences of the noisy miner are then considered at both the patch and landscape scale. Finally

I draw on important findings of my research to identify key management implications and then

discuss some of the limitations of this project and the scope for further research.

5.1 Bird response to noisy miner abundance

Research question 1: Does noisy miner presence and/or abundance affect bird species

richness?

Total bird species richness only responded to high noisy miner abundance. Mean total bird

species richness in noisy miner free, low and moderate sites ranged between 32 and 27.5 species

(Table 4.6). These means were not significantly different from each other but were significantly

higher than at high noisy miner abundance sites, with a mean of 16.6 species. This result is

attributable to the high richness of non-woodland bird species (a subset of total bird species

richness) in the low and moderate noisy miner sites (Figure 4.1). Non-woodland birds accounted

for less than one third of total bird species richness in the noisy miner free site, but about one

half of the species in the low and moderate noisy miner abundance sites (Table 4.6). The

proportion of non-woodland birds to woodland birds increased with increasing noisy miner

abundance (Figure 4.1). More than 65% of the bird species richness at high noisy miner sites

was contributed by non-woodland bird species (c.f. 35% in noisy miner free sites). All of the

bird categories including non-woodland birds had significantly lower bird species richness

under high noisy miner abundance.

Generalised linear modelling presents a different picture (Figure 4.2). An average of just

one noisy miner reduced total bird species richness by 15%, and two individuals reduced total

bird species richness by almost 25% (Table 4.8). Once an average of three noisy miners were

present (equivalent to the cut off between low and moderate noisy miner abundance in this

study), total bird species richness was reduced by 30% and more than half of the species present

were non-woodland birds (Figure 4.2). Non-woodland birds were not affected (Figure 4.2), and

could be buffering the effects of low and moderate noisy miner abundance on total bird species

richness. This suggests we should take a closer look at which suites of birds are the noisy miner

‘losers’ being the birds that are most susceptible to and are being most affected by noisy miner

aggression.

55


5.1.1 The biggest ‘losers’

Research question 1a: Which birds are more susceptible to the effects of noisy miner

invasion and dominance?

The aim of this question was to identify which birds were noisy miner ‘losers’, the birds

that are more vulnerable to the effects of the aggressive behaviour exhibited by the noisy miner.

In this study, all of the bird categories except non-woodland birds were significantly negatively

affected by average noisy miner abundance (Table 4.5, Table 4.8, Figure 4.2). Small woodland

birds appear to be the biggest ‘losers’ with 40% fewer small bird species present when an

average of just one noisy miner is present over time (Table 4.8), and less than half are present

when there are two miner birds. When there was an average of three noisy miner individuals

present, only 35% of the potential small woodland species present were found.

Small woodland birds respond strongly to noisy miner invasion, with substantial

reductions in bird species richness even at very low levels of noisy miner abundance. Small

woodland birds have been identified as those most affected by noisy miner aggression in several

other studies from different regions in south eastern Australia (Grey et al. 1998, Major et al.

2001, Catterall 2004, Hastings and Beattie 2006, Maron et al. 2011). Many of these smaller

birds have similar dietary requirements to the noisy miner, supporting the idea that noisy miners

exclude species that compete for similar resources. Because the noisy miner has a relatively

large body size for its diet and is territorially aggressive (Piper and Catterall 2003), it is able to

exclude competitors with ease and monopolise food resources. This is a classic example of

interference competition.

The threatened and declining birds and woodland birds were also negatively affected

(Table 4.5; Table 4.8; Figure 4.2). Their response was not as strong as in the small woodland

bird category, with 25% fewer species present when an average of one noisy miner was present

and 49% fewer species when an average of 3.5 noisy miner individuals were present (c.f. 40%

and 68% fewer species of small woodland birds) Of the 22 species of threatened and declining

birds, 20 species were also small woodland birds, which may account for the similarities in

responses of the two groups. The superb parrot (Polytelis swainsonii) and the grey-crowned

babbler (Pomatostomus temporalis), the two ‘large’ threatened and declining birds, did not

show any response to noisy miner presence or abundance. These results may account for the

weaker effects of noisy miner abundance on the threatened and declining birds than the small

woodland birds. Many of the birds in the woodland bird category are smaller than the noisy

miner (Appendix 2); this relative sizing may also account for the negative effects of the noisy

miner on this category.

56


5.1.2 Noisy miner density thresholds

Research question 1b: Is there a density threshold where their effect is more

pronounced?

This question aimed to identify a noisy miner density threshold where their effects become

more pronounced. This question was addressed visually by comparing the generalised linear

models of how noisy miner abundance affects bird species richness. In all of the woodland-

dependent groups, the most pronounced negative effects occur between zero and one noisy

miner (Table 4.8, Figure 4.2). This effect tapers off with increasing noisy miner abundance.

Bird species richness continues to decline throughout the noisy miner abundance range.

Analysis of variance of mean small woodland birds species richness also indicates that low

noisy miner abundance (1 – 3 individuals) results in significantly lower small bird species

richness (Table 4.6).

Noisy miner presence has commonly been cited as among the most important predictors of

bird assemblage structure in many eastern Australian landscape (Maron et al. 2011) but very

few studies comment on how many noisy miner individuals can exist in a patch before their

effects become noticeable. This study suggests that an average of just one noisy miner

individual present over time in a two ha patch is enough to have deleterious effects on small

woodland birds, a suite of birds that are exhibiting substantial declines.

Since the noisy miner is a communal, conspicuous bird, finding just one noisy miner at a

site every season is unlikely. An average of one individual may be indicative of either a small

colony present in the patch (only one bird of the colony seen every season), or of transient

miner colony occupation of the patch (7 birds seen in one season, but none in other seasons). In

the first hypothesis, that so few noisy miners need to be present before having deleterious

effects on woodland birds indicates that noisy miner presence rather than density plays a more

important role in determining influence over bird communities. In the second hypothesis, a

larger colony present in only one of the seven surveys, but still negatively affects species over

time is quite different and may indicate some sort of site avoidance memory by the birds. In this

study at all sites where noisy miners were present, they were observed in at least three surveys

and at most sites they were observed in six or seven surveys indicating permanent or semi-

permanent occupation of sites. This indicates that the second hypothesis is unlikely in this

study. Removal experiments in other studies indicate that bird species richness almost

immediately increases after the noisy miner colony is removed from a site (Grey et al. 1997,

Grey et al. 1998, Debus 2008). This suggests that birds do not necessarily remember which sites

to avoid but are deterred when noisy miners are present.

In the Cowra region, it appears that persistent noisy miner presence at a site, even at very

low levels, is a strong inverse predictor of the diversity of small woodland birds that will be

present at that site.

57



Research question 2: Is noisy miner abundance affected by landscape and/or patch-

scale variables?

This research question aimed to determine which landscape scale and patch scale variables

could explain noisy miner abundance. Noisy miner abundance was significantly influenced by

nine habitat variables. I begin with the landscape scale variables because these were the most

influential in explaining noisy miner abundance in bivariate models, followed by the patch scale

variables. Finally I will examine the more complex multivariate habitat model.

5.2.1 Landscape scale habitat variables

At the landscape scale the extent of vegetation surrounding the patch and the patch area

both significantly influenced noisy miner abundance. Given that these variables were correlated

with each other and that both are likely to influence the ability for noisy miners to dominate that

patch of woodland, these findings were expected.

Extent of vegetation in adjacent area

The extent of vegetation surrounding the patch could explain 30% of the distribution of

noisy miner abundance (Table 4.11) with higher noisy miner abundance when the extent of

vegetation was low (p < 0.001). Noisy miner abundance was significantly more likely to be high

when the extent of vegetation surrounding a patch was less than 20% (Figure 4.3). This finding

was expected for two reasons.

Firstly, noisy miners have commonly been associated with highly modified landscapes.

The extent of vegetation surrounding a patch is indicative of the intensity of land use and the

level of modification (Radford et al. 2005). For example in a given region a more intensively

managed cropping production system has fewer trees than a native pasture grazing system

which has less woody vegetation than a reserve or national park. In this study noisy miners were

more abundant in the sites with low extent of vegetation cover, which tend to be the more

heavily modified systems, than they were at sites with high vegetation cover like reserves and

national parks.

Secondly, noisy miner behaviour lends them to defending open areas more easily (Martin

et al. 2006, Taylor et al. 2008). Where the extent of vegetation is lower - in human altered or

natural systems - noisy miner colonies can see potential predators and competitors more easily

(Maron 2009). They are thus better able to exclude other birds when there is low woody

vegetation cover in the areas adjacent to their home range. More intensive land use with

scattered remnant vegetation is likely to encourage noisy miner invasions.

58


Patch area

Similarly, noisy miner abundance was significantly higher in small patches (< 30 ha) than

in larger patches (p = 0.001; Figure 4.3). Patch size could explain 29% of variation in their

abundance distribution (Table 4.11). This supports an extensive body of literature that suggests

noisy miner colonies tend to dominate in small patches (Mac Nally et al. 2000a, Major et al.

2001, Mac Nally and Horrocks 2002, MacDonald and Kirkpatrick 2003, Hastings and Beattie

2006). Like the extent of vegetation in the surrounding area, patch area relates to the ability of

the noisy miner to defend its territory. With a high edge to area ratio, a small patch is more

easily defended against other birds because noisy miners can monitor the perimeter of their

territory. This allows them to see potential predators and competitors more easily thereby

helping them to monopolise the patch resources. Furthermore, small patches with low

vegetation cover in the area are more prone to edge effects which facilitate processes like weed

invasion and further degrade the site (Hobbs 2001), another factor that appears to attract the

noisy miner (section 5.2.2).

5.2.2 Patch scale habitat variables

At the patch scale, eight habitat variables had significant influence over noisy miner

abundance distribution (Table 4.11). Vegetation association, total overstorey regeneration and

Callitris basal area were highly significant (p < 0.005). The number of lifeforms, Callitris

regeneration, perennial species richness and Callitris quadratic mean diameter were also

significant (p < 0.05). Measures of ground cover, midstorey cover, stem density, Eucalyptus

size and density, logs and litter did not influence noisy miner abundance distributions in this

study.

The seven significant explanatory variables were split into three key sections, because

some of these variables are likely to have broadly similar effects on noisy miner ability to utilise

the site. The first of these, vegetation association, includes both a discussion on the overall

vegetation type, including Callitris measures of basal area and quadratic mean diameter, as

these are constituents of the vegetation association. Secondly, total overstorey regeneration and

Callitris regeneration are included together, as overstorey regeneration, because they are both

midstorey structural features. Finally, I discuss how the number of lifeforms and perennial

species richness may be an indicator of overall site disturbance and ‘health’ thus influencing

noisy miner abundance.

Vegetation association

The strongest patch scale variable influencing noisy miner abundance was habitat

association with yellow box/red gum woodlands and white box/grey box woodlands sustaining

significantly higher levels of noisy miners than hill sites (Figure 4.3). This may be a

manifestation of woodlands tending to be smaller in area. In this study, however, the definition

59


of small patch size (< 30 ha) meant that there was even representation of large woodlands, with

seven large woodland sites and nine large hill sites. This indicates that vegetation association

was important, irrespective of patch size.

One of the key differences between the woodland and hill sites is topographic position,

which has many influences on the types of vegetation that will grow at that site. The woodlands

occur in the more productive, lower lying parts of the landscape, a factor that has been found to

be important for noisy miner presence (Catterall 2004, Taylor et al. 2008, Oldland et al. 2009).

Woodlands are also generally higher in Eucalyptus density which is a valuable and prolific food

resource for the noisy miner (Maron 2007). These sites hold many more of the structural habitat

features generally associated with noisy miner site preferences such as low stem density, low

understorey cover, and lower extent of vegetation cover. Conversely, hill sites were either

densely forested, (e.g. Conimbla National Park), or were sparsely treed grasslands on rocky

hilltops rather than woodland. Both of these formations offer vastly different resources from

typical woodland in terms of structure and resource availability, attributes that appear to define

noisy miner site occupation. The more open hill sites (treed grasslands) would be structurally

easy to defend, but they probably do not have sufficient canopy cover and food resources

available to sustain a whole noisy miner colony.

Influence of Callitris

The basal area and quadratic mean diameter of Callitris trees within the stand both

significantly negatively influenced noisy miner abundance (p = 0.004, 0.04 respectively). These

are quantitative measures of the amount of Callitris and the average diameter of the Callitris

trees at the site. In both cases noisy miner abundance decreased with increasing measures of

Callitris. When a site has more Callitris trees and larger Callitris trees, it becomes less desirable

to noisy miner colonies.

The noisy miner colonies did not use dense Callitris woodlands and forests. There are two

key reasons that the noisy miner may be avoiding these sites. Firstly, their requirements for

carbohydrate rich dietary resources (like lerps) make any Callitris dominated woodland less

suitable for noisy miner occupation. This is similar to Maron’s (2007) finding in the buloke

woodland of the Wimmera Plains in western Victoria, where at least five eucalypts per hectare

were required for a 50% chance of a noisy miner colony being present. Secondly, the hill sites

of the Cowra region, where Callitris generally occurs, are structurally very different to eucalypt

woodlands with a high stem density and high extent of vegetation in the surrounding regions.

These characteristics are not ideal for noisy miner occupation because colonies are not able to

defend the site as easily and the food resource is much lower than in the woodlands.

In the Cowra region, it is possible that both the vegetation structure and low productivity

that are associated with Callitris dominated vegetation associations may deter the noisy miner

from inhabiting the hill sites.

60


Overstorey regeneration

Overstorey regeneration encompasses ‘total overstorey regeneration’ and Callitris

regeneration, and refers to any stem less than 5 cm in diameter. Total overstorey regeneration

includes any species that has the potential to become an overstorey species, that is, any species

of Eucalyptus, Callitris, Brachychiton, and some Acacia and Casuarina species. The most

common regenerating species were Eucalyptus, Callitris and Brachychiton. Callitris

regeneration is a subset of total regeneration relating only to Callitris individuals. Total

overstorey regeneration and Callitris regeneration were both significantly negatively related to

noisy miner abundance (p = 0.003, 0.02 respectively). I include total regeneration and Callitris

regeneration together in this section because they both add a level of structural complexity in

the midstorey level of a site. Midstorey cover per se was not a significant variable in this study,

but this may be because there was a small range in midstorey cover distribution and it was

heavily skewed towards lower midstorey cover (Table 4.3). Furthermore, regeneration may be

more objectively measured because it was count data rather than taken from site percentage

estimates.

Noisy miner colonies can more easily dominate simple sites which may explain their

avoidance of sites with high regeneration. Where the understorey vegetation at a site is complex

as it is with high levels of regeneration, other birds are able to take refuge when noisy miner

colonies try to mob them (Grey et al. 1998, Mac Nally et al. 2000a, MacDonald and Kirkpatrick

2003, Hastings and Beattie 2006, Kath et al. 2009). It is thought that complex structures at a site

make it harder for the noisy miner to see and mob its competitors. Furthermore, simple site

structure may enable the noisy miner to gain easier access to the ground, an important feeding

substrate (Clarke and Grey 2010). Structurally simple sites are more cost-effective to dominate

and provide easier access to an important foraging substrate, and thus may be more attractive to

noisy miner colonies.

Site quality

The final patch scale variables that influence noisy miner abundance are the number of

lifeforms at the site (Appendix 1) and the species richness of the perennial woody plants at the

site (p = 0.02, 0.04 respectively). Both of these variables impact noisy miners negatively with

higher lifeform richness and higher species richness resulting in lower noisy miner abundance.

Lifeform richness and perennial species richness are both indicative of broader site quality, from

a bird conservation perspective. These variables are indicative of the level of weediness

(McElhinny et al. 2006a), the structural complexity of the site, disturbance history and overall

site health (Prober and Thiele 1995).

Lifeform richness ranged between two lifeforms (i.e. tree and non-tussock grass), to more

than eight lifeforms (which may include shrubs, tussock grass, regeneration and mistletoe,

among others). This variable is both a measurement of complexity (discussed above), and the

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level of site disturbance. For example, a healthy woodland which is regenerating properly and

still has an intact midstorey and grassy layer may, have a lifeform richness of as high as six to

eight lifeforms. Many of the high noisy miner sites had only two lifeforms, which were treed

sites with a tussock grass understorey. These sites are easy for noisy miner colonies to

dominate.

Native perennial plant species richness tends to be higher in good quality woodland

remnants, that is, those with a history of light grazing and no cultivation or fertiliser application

with fewer native perennial species present as the history of use becomes more intensive (Yates

and Hobbs 1997, Rawlings et al. 2010). Furthermore higher native perennial species richness is

also indicative of lower exotic plant cover (McElhinny et al. 2006a) which is another indicator

of overall site health. The fact that noisy miner colonies are negatively associated with higher

plant species richness is perhaps another manifestation of the preference of the species to more

modified and hence less intact landscapes.

These bivariate relationships have important implications for understanding noisy miner

habitat preferences within the Cowra landscape but none was individually able to explain more

than 30% of variation in noisy miner abundance. I used a multivariate model to see how the

combined explanatory power of some of these variables could predict noisy miner abundance.

5.3 Noisy miner multivariate habitat model

Patch area, hollow bearing trees and Callitris regeneration were able to successfully

predict noisy miner abundance (refer to the multivariate habitat model, Table 4.12; Figure 4.4a,

Figure 4.4b). The multivariate habitat model was highly significant and explained about 50% of

the variance in noisy miner distribution. Small patches with low levels of Callitris regeneration

and few hollow bearing trees had considerably higher noisy miner abundance (Figure 4.4a). All

three habitat variables negatively affect noisy miner abundance. This means that a large patch

with many hollow bearing trees and high Callitris regeneration is less likely to be occupied by a

noisy miner colony.

This model predicts that a small patch (< 30 ha) with low levels of hollow bearing trees (1

hollow) and low regeneration (1 stem) will have about twice as many noisy miners as a large

patch (> 30 ha) with the same patch scale variables (refer to low hollow scenario, Figure 4.4a).

The same small patch will have 10 times more noisy miners than a large patch with high levels

of hollow trees (e.g. 20 hollows) and low regeneration (high hollow scenario, Figure 4.4b). If

we were to also increase regeneration in that large patch (150 stems); noisy miner abundance

would approach zero while the small patch would have an average of over 11 (high abundance)

individuals present.

Patch size and Callitris regeneration, discussed above as bivariate relationships, have clear

effects on noisy miner ability to monopolise food and space resources within a site. Hollow

bearing trees did not have a significant influence on noisy miner occupation on their own (Table

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4.9). However hollow bearing trees may be indicative of the broader management history and

quality of the site. Removal of big hollow bearing trees for timber has long lasting effects on

present day tree demography (Martin and McIntyre 2007). Past substantial clearing results in

fewer hollow bearing trees in the present. In addition, ongoing loss of hollow-bearing trees from

agricultural landscapes as a result of tree clearing, die-back and lack of hollow tree recruitment,

are a manifestation of degradation at those sites (Manning et al. 2004a). Sites with lower levels

of hollow bearing trees are probably those that are the most heavily modified and most suitable

for noisy miner colonies thereby most likely to be dominated by noisy miners.

5.4 Management implications

This study demonstrates that noisy miner abundance has a strong negative influence on

woodland bird species richness. Small woodland birds are at risk from noisy miner expansion

through domination of remaining woodland patches in temperate Australia. While it is

recognised that the underlying cause of woodland bird declines in Australia are related to the

direct effects of habitat modification, the indirect changes that occur between species are also

important to consider. Landscape modification has facilitated noisy miner colonisation of many

woodland remnants in the wheat sheep belt of Australia and has exacerbated woodland bird

declines in the region. The amount of available habitat for noisy miner invasion should be

minimised to discourage further domination of the noisy miner.

This study has identified habitat variables that can affect noisy miner abundance in

woodland patches in temperate Australia; noisy miner colonies avoid large patches of vegetation

with lots of surrounding vegetation, high levels of structural complexity, high Callitris presence

and sites that are in good condition. Noisy miners attain their highest abundance in small,

productive, low-lying Eucalyptus dominated woodlands that are in poor condition.

It follows that to reduce the favourability of remnant woodland patches for noisy miners,

revegetation efforts should aim to maximise the habitat attributes that are unattractive to the

species. Increasing the area of small remnants to a minimum of 30 ha should effectively reduce

the likelihood of noisy miner domination, because the species preferentially occupies small

remnants. Furthermore, larger patches should have a greater core area unoccupied by the

species, which will enable small woodland birds to persist in that space. Using revegetation

methods that enhance regeneration within the site, such as through tree planting or more

appropriate grazing and fire regimes, will further improve results by increasing structural

complexity.

The most parsimonious multivariate habitat model (Table 4.12; Figure 4.4a, Figure 4.4b)

used Callitris regeneration over other measures of regeneration in discouraging noisy miner

occupation. However, noisy miner colonies also responded to total overstorey regeneration

individually (explaining 25% of the regression variation, Table 4.11), which is significantly

correlated with the regeneration of Eucalyptus species (Table 4.10). This suggests that

63


encouraging regeneration of either Callitris or Eucalyptus would have the desired effect of

deterring noisy miner occupation, but should be implemented on a site-specific basis. Sites

should not be revegetated with species that do not naturally occur there. For example,

Eucalyptus woodland, with no Callitris present should be revegetated with Eucalyptus species.

Inadvertent creation of more noisy miner habitat should be avoided. Sites that naturally

have low eucalypt presence and are dominated by species of Callitris, Acacia, Brachychiton and

Casuarina, such as the hill sites in this study, should be maintained in this state because they

provide excellent habitat for many woodland bird species. Eucalypt plantings at sites that do not

naturally have high eucalypt densities may just encourage site utilisation by noisy miner

colonies, negating any potential benefits from increased food resources provided by the

eucalypts. The extent of vegetation within a 2 km radius around a patch is negatively associated

with noisy miner abundance - clearing of woody vegetation within 2 km of patches in good

condition should be avoided. Similarly sites with an intact understorey should be protected from

modification. Grazing, fertilizer input and fire can significantly alter the understorey where not

applied properly and such management practices should be very carefully implemented. Low

input rotational grazing systems appear to improve plant species richness and vegetation

structural complexity, both of which deter noisy miners (Dorrough and Scroggie 2008, Fischer

et al. 2009). Fire regimes based around 8-15 year frequency intervals during spring or autumn,

can improve plant species richness, promote shrub and native grass growth and aid in weed

management (Prober et al. 2005, Rawlings et al. 2010) which may deter noisy miners from

occupying the site.

Revegetation efforts should be ramped up at landscape scale and regional scales as a

potential long term solution to reduce the domination of noisy miner colonies in the Cowra

region. Since many woodland bird species in temperate Australia are experiencing significant

declines at present, more localised direct mitigation strategies may be a viable, immediate way

to benefit these birds. Removal trials in Victoria have demonstrated the value of small, degraded

woodland patches for small woodland birds after noisy miners have been translocated (Grey et

al. 1997, Grey et al. 1998, Debus 2008). Small scale, targeted removal of noisy miners from

sites may be a cost effective method which immediately frees up important and declining habitat

for vulnerable woodland birds. Culling noisy miners where their colonies are excluding a

threatened species from a critical resource is being promoted as an effective solution to these

problems (Clarke and Grey 2010). For example, noisy miners have been implicated in the

decline of the endangered regent honeyeater (Anthochaera phrygia) by excluding them from

woodland remnants (Grey et al. 1997, Menkhorst et al. 1999). Oliver (2000) reported that red

ironbark was the most important foraging species for regent honeyeaters. Removal of noisy

miners from small patches (<10 ha) of ironbark woodland that the regent honeyeater uses

greatly benefitted the regent honeyeater (Grey et al. 1997). Furthermore, because the noisy

miner appears to prefer the low lying, productive woodlands, and these are the vegetation types

64


preferentially cleared for agriculture, these systems are less likely to be available for other birds

that may preferentially select these productive sites. Localised removal in areas close to a source

patch (such as a larger remnant) would improve the potential for that productive site to be used

by other species (Grey et al. 1997).

The implications of not acting may result in further domination of the noisy miner in

temperate woodlands resulting in lower species diversity in the woodland bird communities of

southern and eastern Australia (Noss 1990, Garrott et al. 1993).

5.5 Study limitations and future research questions

As in any volunteer data collection research project, there are limitations that come with

the many positive aspects of the Cowra Woodland Birds Program dataset. The experience of the

bird observers, timing of data collection and irregularity of site visits are a few of the limitations

that this method of data collection may have had on this project. These variations have been

minimised by the Cowra Woodland Birds Program organisers and scientists, but detection of

small inconspicuous birds may have been inconsistent. Furthermore some sites that were

surveyed in the initial stages of the program (2002/2003) were not deemed ‘important’ because

they had low bird species richness and had low conservation value and so were omitted in later

survey years. Coincidentally and perhaps tellingly, these sites were also dominated by the noisy

miner and would have been a valuable addition to this study.

Most of the bird data were collected during heavy drought years, while the patch scale

habitat data were collected following a reasonably wet season. This may have influenced the

way in which we view what constitutes a noisy miner or non-noisy miner site. Habitat variables

were carefully selected such that it was unlikely that they were influenced by short-term

moisture variations. Tree diameter, numbers of tree hollows, lifeform richness and perennial

species richness are stable attributes and will not change after one wet season. Attributes used in

other survey methods like canopy cover are more susceptible to short term moisture variation

and were not used in this study. Ground cover may potentially be affected, but I estimated the

cover of perennial species (as opposed to all species) which are present over multiple seasons.

The variables that the noisy miner responded to were not attributes that would be significantly

altered following an abnormally wet season.

In investigating the influence of average noisy miner abundance on the species richness of

woodland birds over seven seasons, this study may have missed some of the subtleties of

individual species that may or may not respond to noisy miner dominance. Individual bird

species that are vulnerable to the impacts of the noisy miner and many that are not disturbed by

noisy miners have been identified in various studies. This study takes a different perspective,

indicating that the persistence of noisy miner colonies, even small colonies, at a site reduces

woodland bird species richness. By implication, individual bird species will also be affected.

The Cowra Woodland Bird Program dataset could be used examine this issue further by

65


comparing surveys within a site over time to investigate how short-term movements or

fluctuations in abundance of noisy miners may influence the presence and abundance of other

bird species.

Large threatened and declining bird species in this study, such as the grey-crowned

babbler, did not show any response to noisy miner presence or abundance in this study. This

does not mean that they are not being affected. It is likely that the babbler would have to expend

energy in interacting with the noisy miner, and further research into how this may affect other

life cycle aspects of the babbler should be explored. For example, does increased time spent in

avoidance of the noisy miner result in less time spent foraging, or decreased breeding success?

In terms of landscape scale variables, this study only looked at the influence of patch size

and the extent of vegetation cover in surrounding areas. Other aspects of landscape scale

research remain a significant gap in the collective knowledge of noisy miners and how they are

distributed across fragmented landscapes. Further information is now needed on habitat

connectivity, and whether greater connectivity may buffer the effects of the noisy miner on

woodland birds. For example, this study suggests that noisy miner colonies are deterred by high

levels of vegetation cover around a patch. Does this have a two-fold benefit for woodland birds

by increasing the amount of habitat available to them and by reducing the dominance of noisy

miner colonies? It may be difficult to disentangle these two mechanisms, but noisy miner

removal experiments would shed some light on this issue.

66


Chapter 6

Conclusion

Conimbla National Park, to the west of the Cowra Shire. This site is an

example of a noisy miner free site. It has high perennial species richness,

lifeform richness, regeneration and structural complexity.

67


Chapter 6: Conclusion

Noisy miner abundance has a significant and deleterious impact on bird species richness in

the Cowra region. Woodland birds, and in particular small woodland birds, showed the

strongest negative response to noisy miner abundance. Persistent noisy miner presence, even at

very low levels, is a very strong predictor of the richness of small woodland birds that will be

present at a site. This study has identified landscape and patch scale attributes which are

influential in determining the presence and size of noisy miner colonies and their ability to

monopolise a site. These attributes can directly inform management practices in two ways.

Firstly, an understanding of noisy miner habitat preferences allows landholders and land

management agencies to avoid inadvertently creating noisy miner habitat in the Cowra region.

Remnants that are not currently utilised by noisy miner colonies should be kept in a state that

does not encourage infestation. Clearing of woody vegetation surrounding a good condition

patch should be avoided, because low vegetation cover surrounding a patch is associated with

high noisy miner abundance. Similarly, sites with an intact understorey should be protected

from modification, such as inappropriate grazing or fire regimes. High input, intensive grazing,

and very frequent fire regimes reduce understorey structure, lifeform richness, perennial species

richness and promote weed invasion, all characteristics associated with high noisy miner

abundance.

Secondly, management practices should focus on incorporating and promoting habitat

attributes that make sites and landscapes unattractive to noisy miner colonies. This may

discourage the species from utilising and monopolising currently unoccupied areas and may

help to reduce their abundance at occupied sites. Revegetation methods should be used on a

landscape scale to reduce the domination of noisy miner colonies in entire landscapes.

Increasing the area of the patch and the amount of woody vegetation surrounding the patch with

revegetation methods that enhance Callitris or Eucalyptus regeneration within the site, such as

through tree planting or more appropriate grazing and fire regimes, reduces the likelihood of

noisy miner colonies using that remnant. Low input, rotational grazing systems and cool burns

every 8-15 years (site-specific), may perpetuate good structural complexity and perennial

species richness of understorey species, in doing so limiting the noisy miners inclination to

colonise the site. Revegetation and restoration efforts in sites with naturally high Callitris

presence should avoid increasing eucalypt species density, as this may attract the noisy miner.

Revegetation is a very long-term landscape transformation strategy with no guarantees of

success. Many woodland bird species in temperate Australia are currently experiencing

significant declines. Localised direct mitigation strategies may be a viable, immediate way to

benefit these woodland birds. Culling of noisy miners in targeted areas may represent a cost

effective, immediate and humane way to reduce their impacts. Because the noisy miner

preferences the heavily cleared, low lying productive woodlands, these vegetation types are less

68


likely to be available for woodland birds across large parts of the landscape, making them a

candidate for effective removal activities. Grey et al. (1997) propose that removing the noisy

miner in circumstances where they are excluding a species which is already threatened due to

loss of habitat, such as the regent honeyeater, in targeted areas may be one way to sustain

populations that are susceptible to noisy miner aggression. Removal of the noisy miner in small

red ironbark woodlands could free up an important food resource for the regent honeyeater.

Many of the findings in this study, both in terms of noisy miner effects on woodland birds,

and their habitat preferences, are comparable with those found generally in temperate

woodlands of south eastern Australia. While individual habitat attributes may vary between

study locations and methods, broader themes and consistent patterns are emerging from studies

of noisy miner habitat preferences. Importantly, patch size, amount of vegetation cover

surrounding the patch, vegetation association, structural complexity, and general site quality are

characteristics that appear to influence noisy miner abundance. These are all characteristics that

can be managed, in a variety of ways, to mitigate some of the impacts of noisy miners on

woodland birds. The implications of not acting, both in the short and long term will probably

permit the continuing and increasing domination of the noisy miner in temperate woodlands,

likely resulting in further declines of the woodland bird communities of southern and eastern

Australia.

69


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PLOT 1 1. NUMBER OF LIFEFORMS

(In a 20mx20m subplot if a lifeform is present enter 1 next to its category. Leave blank if absent)

tussock grass non-tussock grass low shrub 0-0.5m tall shrub >0.5m sedges / rushes ferns

vines xanthorrhoea mistletoe regeneration < 2m regeneration > 2m tree

2. NUMBER OF PERENNIAL SPECIES

(Enter the total number of perennial species in a 20mx20m subplot)

Tally TOTAL

3. VEGETATION COVER < 0.5M

(Enter % cover for each 10mx10m subplot)

subplot 1 subplot 2 subplot 3 subplot 4

4. VEGETATION COVER 0.5-6M (Enter % cover for each 10mx10m subplot)

subplot 1 subplot 2 subplot 3 subplot 4

7. NUMBER OF TREES > 40CM DIAMETER (Enter total number of large trees in a 50mx20m plot)

Tally TOTAL

8. NUMBER OF HOLLOW BEARING TREES (Enter total number of hollow trees in a 50mx20m plot)

Tally TOTAL

9. OVERSTOREY REGENERATION

(Enter total number of regenerating stems in a 50mx20m plot)

Callitris Eucalyptus Kurrajong Acacia Casuarina

10. NUMBER OF DEAD TREES

(Enter total number of dead standing trees in a 50mx20m plot)

Tally TOTAL

11. LENGTH OF ALL LOGS > 10CM DIAMETER – INCLUDING LARGE LOGS

(Enter total length of all logs in a 50mx20m plot, including large logs)

Tally TOTAL

12. LENGTH OF LARGE LOGS > 30CM DIAMETER

(Enter total length of large logs in a 50mx20m plot)

Tally TOTAL

13. LITTER DRY WEIGHT

(Enter litter dry weight for each sample)

Sample 1

Appendix 1: Data collection forms

77


PLOT 1 5. BASAL AREA

6. QUADRATIC MEAN DIAMETER

(Enter the total number of live stems in each diameter class)

Diameter class Callitris Eucalyptus Kurrajong Acacia Casuarina Other TOTAL

5 - 20

20 - 30

30 - 40

40 - 50

50 - 60

60 - 70

70 - 80

80 - 90

90 - 100

100 - 110

110 - 120

120 - 130

130 - 140

140 - 150

150 - 160

160 - 170

170 - 180

180 - 190

190 - 200

200 - 210

210 - 220

220 - 230

78


Appendix 2: Bird species identified in the 33 sites by bird category

A2.1: Full list of the bird species identified at the 33 sites, over seven Cowra Woodland Bird Program bird surveys. Mass, as advised by (Julian Reid, written communication, 03/08/ 2011b). Bird categories were T, Total bird species (not to be confused with ‘threatened’); W, woodland bird species; SWB, small woodland bird species; T&DWB, threatened and declining woodland bird species; and non-W, non-woodland bird species. Note that all species fall within Total bird species, and are wither woodland or non-woodland birds.

Woodland bird species

Common name Scientific name Mass (g) Bird category

Apostlebird Struthidea cinerea 122 T, W

Black-chinned Honeyeater Melithreptus gularis 19 T,W, SWB, T&DWB

Brown Thornbill Acanthiza pusilla 7 T,W, SWB

Brown Treecreeper Climacteris picumnus 32 T,W, SWB, T&DWB

Brown-headed Honeyeater Melithreptus brevirostris 15 T,W, SWB

Buff-rumped Thornbill Acanthiza reguloides 8 T,W, SWB

Chestnut-rumped Thornbill Acanthiza uropygialis 6 T,W, SWB, T&DWB

Cicadabird Coracina tenuirostris 69 T, W

Common Bronzewing Phaps chalcoptera 615 T, W

Crested Shrike-tit Falcunculus frontatus 28 T,W, SWB, T&DWB

Crimson Rosella Platycercus elegans 132 T, W

Diamond Firetail Stagonopleura guttata 18 T,W, SWB, T&DWB

Dollarbird Eurystomus orientalis 130 T, W

Double-barred Finch Taeniopygia bichenovii 9 T,W, SWB

Dusky Woodswallow Artamus cyanopterus 35 T,W, SWB, T&DWB

Eastern Spinebill Acanthorhynchus tenuirostris 11 T,W, SWB

Eastern Yellow Robin Eopsaltria australis 20 T,W, SWB, T&DWB

Fan-tailed Cuckoo Cacomantis flabelliformis 48 T,W, SWB

Fuscous Honeyeater Lichenostomus fuscus 18 T,W, SWB

Gilbert's Whistler Pachycephala inornata 32 T,W, SWB, T&DWB

Golden Whistler Pachycephala pectoralis 26 T,W, SWB

Grey Butcherbird Cracticus torquatus 96 T, W

Grey Fantail Rhipidura albiscapa 8 T,W, SWB

Grey Shrike-thrush Colluricincla harmonica 64 T,W, SWB

Grey-crowned Babbler Pomatostomus temporalis 75 T,W, T&DWB

Hooded Robin Melanodryas cucullata 22 T,W, SWB, T&DWB

Horsfield's Bronze-Cuckoo Chalcites basalis 22 T,W, SWB

Jacky Winter Microeca fascinans 15 T,W, SWB, T&DWB

Laughing Kookaburra Dacelo novaeguineae 334 T, W

Leaden Flycatcher Myiagra rubecula 12 T,W, SWB

Little Friarbird Philemon citreogularis 64 T,W, SWB

Little Lorikeet Glossopsitta pusilla 39 T,W, SWB, T&DWB

Masked Woodswallow Artamus personatus 36 T,W, SWB

Noisy Friarbird Philemon corniculatus 104 T, W

Olive-backed Oriole Oriolus sagittatus 95 T, W

Peaceful Dove Geopelia striata 50 T,W, SWB

Red-browed Finch Neochmia temporalis 10 T,W, SWB

Red-capped Robin Petroica goodenovii 9 T,W, SWB, T&DWB

Restless Flycatcher Myiagra inquieta 20 T,W, SWB, T&DWB

Sacred Kingfisher Pachycephala rufiventris 24 T,W, SWB, T&DWB

79



Shining Bronze-Cuckoo Todiramphus sanctus 44 T,W, SWB

Silvereye Chalcites lucidus 24 T,W, SWB

Speckled Warbler Zosterops lateralis 11 T,W, SWB

Spotted Pardalote Chthonicola sagittata 14 T,W, SWB, T&DWB

Striated Thornbill Pardalotus punctatus 9 T,W, SWB

Striped Honeyeater Acanthiza lineata 7 T,W, SWB

Superb Fairy-wren Plectorhyncha lanceolata 39 T,W, SWB

Superb Parrot Malurus cyaneus 10 T,W, SWB

Swift Parrot Polytelis swainsonii 154 T,W, T&DWB

Tree Martin Lathamus discolor 65 T,W, SWB, T&DWB

Turquoise Parrot Petrochelidon nigricans 14 T,W, SWB

Varied Sittella Neophema pulchella 41 T,W, SWB, T&DWB

Variegated Fairy-wren Daphoenositta chrysoptera 13 T,W, SWB, T&DWB

Weebill Malurus lamberti 8 T,W, SWB

Western Gerygone Smicrornis brevirostris 6 T,W, SWB

White-bellied Cuckoo-shrike Gerygone fusca 6 T,W, SWB

White-browed Babbler Coracina papuensis 64 T,W, SWB

White-browed Scrubwren Pomatostomus superciliosus 41 T,W, SWB, T&DWB

White-browed Woodswallow Sericornis frontalis 14 T,W, SWB

White-eared Honeyeater Artamus superciliosus 35 T,W, SWB, T&DWB

White-naped Honeyeater Lichenostomus leucotis 22 T,W, SWB

White-throated Gerygone Melithreptus lunatus 14 T,W, SWB

White-throated Treecreeper Gerygone albogularis 7 T,W, SWB

White-winged Chough Cormobates leucophaeus 21 T,W, SWB

White-winged Triller Corcorax melanorhamphos 355 T, W

Yellow Thornbill Lalage sueurii 25 T,W, SWB

Yellow-faced Honeyeater Acanthiza nana 6 T,W, SWB

Yellow-tufted Honeyeater Lichenostomus chrysops 17 T,W, SWB

Lichenostomus melanops 25 T,W, SWB

Non-woodland bird species


Australasian Pipit Anthus novaeseelandiae 24 T, non-W

Australian Magpie Cracticus tibicen 299 T, non-W

Australian Raven Corvus coronoides 592 T, non-w

Australian Reed-Warbler Acrocephalus australis 17 T, non-w

Black Honeyeater Sugamel niger 10 T, non-w

Black-faced Cuckoo-shrike Coracina novaehollandiae 124 T, non-w

Black-faced Woodswallow Artamus cinereus 35 T, non-w

Blue-faced Honeyeater Entomyzon cyanotis 100 T, non-w

Brown Quail Coturnix ypsilophora 91 T, non-w

Brown Songlark Cincloramphus cruralis 47 T, non-w

Cockatiel Nymphicus hollandicus 93 T, non-w

Crested Pigeon Ocyphaps lophotes 207 T, non-w

Diamond Dove Geopelia cuneata 31 T, non-w

Eastern Rosella Platycercus eximius 104 T, non-w

Galah Eolophus roseicapillus 334 T, non-w

Little Corella Cacatua sanguinea 462 T, non-w

Little Raven Corvus mellori 544 T, non-w

Magpie-lark Grallina cyanoleuca 88 T, non-w

Mistletoebird Dicaeum hirundinaceum 9 T, non-w

Noisy Miner Manorina melanocephala 65 T, non-w

Pallid Cuckoo Cacomantis pallidus 89 T, non-w

Pied Butcherbird Cracticus nigrogularis 132 T, non-w

80


Pied Currawong Strepera graculina 301 T, non-w

Rainbow Bee-eater Merops ornatus 27 T, non-w

Red Wattlebird Anthochaera carunculata 116 T, non-w

Red-rumped Parrot Psephotus haematonotus 65 T, non-w

Rufous Songlark Cincloramphus mathewsi 30 T, non-w

Spiny-cheeked Honeyeater Acanthagenys rufogularis 45 T, non-w

Striated Pardalote Pardalotus striatus 11 T, non-w

Stubble Quail Coturnix pectoralis 101 T, non-w

Sulphur-crested Cockatoo Cacatua galerita 520 T, non-w

Welcome Swallow Hirundo neoxena 15 T, non-w

White-plumed Honeyeater Lichenostomus penicillatus 18 T, non-w

Willie Wagtail Rhipidura leucophrys 19 T, non-w

Yellow-rumped Thornbill Acanthiza chrysorrhoa 9 T, non-w

Exotic species

Common Name Scientific Name Mass (g)

Common Blackbird * Turdus merula 95

Common Starling * Sturnus vulgaris 73

European Goldfinch * Carduelis carduelis 15

House Sparrow * Passer domesticus 27

81


Appendix 3: Raw data

Refer to the c.d. at the back of this thesis.

82


Appendix 4: Graphical representation of ANOVA output for bird response to

noisy miner abundance

A3.1: Graphical output of an analysis of variance, showing noisy miner effects on bird species richness. Different letters indicate that means are significantly different. Bird species richness numerical means are shown in Table 4.6.

a b

a

b

c

a

b

b

c

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a

a b

b

c

a a

a

b

b b

a a

the noisy native: a miner menace? - birdlife

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