the response of birds to the fire regimes of mulga woodlands in central australia · 2016-12-22 ·...

The response of birds to the fire

regimes of mulga woodlands in

central Australia

by

Adam Leavesley

Submitted in fulfilment of the requirements for the degree of

Doctor of Philosophy

of the Australian National University

September 2008

ii

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.

Adam Leavesley Date:

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Acknowledgements First of all thanks to the chair of my supervisory panel, Geoff Cary. Geoff maintains the

highest standards of integrity professionally and personally – it was a privilege to be associated

with him. The rest of my panel, Ross Bradstock, Jack Baker, Glenn Edwards, Malcolm Gill and

Jeff Wood have always been willing to front up with the best of advice when required and I am

most grateful. Thanks must also go to my mid-term reviewers Brendan Mackey and Henry Nix

and to Bruce Doran for reviewing my GIS work.

Getting things done: stuff like permits; access to data and equipment; vehicle repairs and

maintenance; liaison with traditional owners; all of that stuff – Grant Allan, Mim Jambrecina,

Dorsey Debney, Andrew Burton and Emma Lee made sustained contributions to the project.

In the field I had a great bunch of volunteers: Peter and Cate Ewin, Sharon Fairclough,

Stuart Rae, Sam Steel, Junko Kondo, Hannah Hueneke and Bernard Nutt – great help and great

company.

And then there so many other people who dropped what they were doing, to do something

for me: from ANPWS: Tracey and Rowan Carboon, Shazza Mallie, Shane Wright, Mick

Starkey, Troy Mallie, Gary McNairn, Hank Schinkel, Thomas Konieczny, Traceylee Forrester,

Shane Forrester and Gordon Waight; Uluru traditional owners: Jimmy Baker, Reggie Uluru and

Norman Tjakaliri; Yulara environment officers: Kane Hardingham and Ella Boyen; from

CSIRO: Julian Reid, Teresa Shanahan and Steve Morton; from NT Parks: Angus Duguid, Chris

Pavey, Catherine Nano, Joe Benshemesh, Steve Eldridge and Chris Brock; from Bushfires NT:

Tony Seceur, Rod Herron and Shane Brumby; from the Desert Knowledge CRC: Craig James,

Jocelyn Davies, Jock Morse, Patrick Hookey and Steve McAlpin; from NSW Parks: Mike

Fleming; from Bushfire CRC: Kellie Watson, David Bruce and Jen Lumsden.

Without cash this project would not have been possible. Thanks to the Bushfire CRC,

Desert Knowledge CRC, Norman Wettenhall Foundation, Stuart Leslie Bird Research Awards

and the NSW Gould League.

At ANU my lab colleagues contributed the best of help and friendship: Rob De Ligt,

Lyndsey Vivian, Carola Kuramotto de Bednarik, Nick Gellie, Amy Davidson; Karen King; Ian

Davies and Jon Marsden-Smedley. Really great group to work with.

Heaps of people in the rest of the university contributed one way or another: Sarah

O’Callaghan, Mark Lewis, Panit Thamsongsana, Zosha Smith, Richard Greene, David

Tongway, Sanjeev Srivastava, Sunil Sharma, Kirsten McLean, Matt Brookhouse, Debbie

Claridge, Clive Hilliker, Piers Bairstow, Mauro Davanzo, Chris Tidemann, Peter Kanowski,

Cris Brack, Sue Holzknecht, Brian Turner, Jake Gillen, Debbie Saunders, Sue Feary, Geraldine

Li, Tran Ha, Liliana Baskorowati, Scott Keogh, Rob Magrath, Andrew Cockburn, Chris Boland,

Mike Double, Nadeena Beck, Golo Maurer, Martin Golman, Sarah Goldin, Liz Noble, Nicki

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Munro, Sue Gould, Ross Cunningham, Emma Knight, Mike Hutchinson, Joern Fisher, David

Lindenmayer, Mayumi Hay, Karl Nissen, John Boland, Steve Leahy and Helen Daniel.

And finally, most important of all, to my family: my parents John and Beryl for all the

love; daughter Hopi for all of her love and for helping me appreciate my parents; my brothers

Matthew, Christian and sister Wendy, for inspiration and belonging. And to Megan Williams,

for bringing out the best in me.

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Abstract The fire mosaic hypothesis is intuitively appealing to scientists and land managers due to

its perceived potential to deliver favourable outcomes for biodiversity conservation and fire

management. However evidence in support of the biodiversity benefits of fine-scaled fire

mosaics is scant. In this thesis I investigate the key assumptions of the fire mosaic hypothesis

using a model system, the mulga woodland/mulga bird community of central Australia. Mulga

woodland is an ideal model system for this question because it is structurally and floristically

simple, yet supports a rich avifauna. I tested how avian diversity (variety and number) was

influenced by 1) time-since-fire; 2) patch size; and 3) the boundary between burnt and unburnt

mulga woodland (pyric edge).

An investigation of time-since-fire is crucial for testing the fire mosaic hypothesis. If there

is no effect of time-since-fire on biodiversity, then the spatial arrangement of different times-

since-fire is irrelevant and the definition of habitat patches and habitat edges based on time-

since-fire is not valid. Patch size and edge effect are potential mechanisms by which a fine-scale

fire mosaic may support greater avian diversity than a coarse-scale fire mosaic. For this to be

the case, avian diversity must increase with decreasing patch size; or be greater at pyric edges

than in the interior of habitats.

Australian arid-zone landscapes are subject to two strong disturbance regimes, recent rain

and fire. The effect of recent rain dominates the distribution of many birds, so much so that the

influence of fire has been difficult to detect. To my knowledge, no properly replicated studies

have succeeded in demonstrating an effect of fire on Australian arid zone birds. My study site is

Uluru-Kata Tjuta National Park and neighbouring Yulara resort in central Australia. The study

site is the subject of the longest running, most detailed fire history in the Australian arid zone. I

minimized the confounding influence of recent rain by conducting space-for-time experiments.

Two time-since-fire experiments were located in landscapes with contrasting geological and

hydrological characteristics – a sheetwash landscape and a dune-swale landscape. I also

conducted an edge experiment in the sheetwash landscape.

The time-since-fire experiments were designed to test the effect of time-since-fire and

patch size on avian diversity. The sheetwash landscape encompassed large areas of mulga

woodland in three age classes – burnt 2002, burnt 1976 and long-unburnt. The dune-swale

landscape encompassed large areas of mulga woodland in two age classes; burnt 2002 and long-

unburnt. A total of 63 patches of mulga woodland of different sizes were selected in the

sheetwash landscape and 34 patches were selected in the dune-swale landscape. Birds were

surveyed in the winter and spring of 2005 and 2006. The habitat structure was measured at all

bird survey sites to help explain the results.

The edge experiment was conducted across 10 edges between patches of mulga woodland

with contrasting times-since-fire - <4 years and >29 years. All sites were located in the

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sheetwash landscape and surveys were conducted in early spring of 2005 and 2006. Habitat

structure was measured either side of the edge to help explain the results.

The habitat structure of mulga woodland varied with time-since-fire. The structure of the

habitat was different in each treatment and followed a predictable pattern in both the sheetwash

and dune-swale landscapes. Fire caused high mortality of the dominant plants in mulga

woodland. The burnt 2002 treatment had no measurable canopy, more groundcover, particularly

grass and less litter cover than the other two treatments. A mulga canopy was present in both the

burnt 1976 and long-unburnt treatments but the characteristics of the canopies were different.

The canopy in the older, long-unburnt treatment was taller, with wider crowns and greater

height diversity than that in the burnt 1976 treatment. The long-unburnt treatment also had more

shrubs that the other two treatments.

Time-since-fire had a strong effect on the distribution of birds. Multivariate tests showed

that a different bird community was present in mulga that was burnt in 2002 than mulga that

was burnt in 1976 and long-unburnt. Univariate tests showed that time-since-fire had no effect

on species richness or bird abundance. The variance in both parameters was greatest in the burnt

2002 treatment. The response of all species to time-since-fire was linear; no species was at

highest density in mulga that was burnt in 1976. Granivores and ground insectivores benefited

from fire at the expense of foliar insectivores.

Patch size had little effect on the distribution of birds. There was no effect of patch size on

species richness or bird abundance. Only two out of 20 species showed an effect of patch size

and both preferred large patches to small. Small patches of mulga woodland do not support

greater avian diversity than large patches. Therefore there is no evidence that patch size is a

mechanism by which a fine-scaled fire mosaic could benefit biodiversity.

Multivariate tests showed that the bird community present at the edge was intermediate

between that present either side. The species present at the edge were a combination of those

present in the habitat interior either side. Univariate tests showed that no species was ecotonal

(present only at edge) and no species was edge conspicuous (preferred the edge). Neither was

bird abundance or species richness greatest at the edge. There was no evidence that edge effect

across a pyric boundary in mulga woodlands provides a mechanism by which a fine-scaled fire

mosaic could benefit avian diversity.

Fire caused a near-complete turnover in the bird community in mulga woodland; however

patch size and edge-effect had little influence on the distribution of birds. When a patch of

mulga woodland last burnt was far more important for avian diversity than the area of the patch

or edge effect. There appears to be no benefit to avian diversity of managing mulga woodland to

create a fine-scale fire mosaic. Furthermore, the coincidence of species responses required so

that a fine-scale fire mosaic may cause an increase in biodiversity appears unlikely.

A positive effect of a fine-scaled fire mosaic on biodiversity cannot be entirely ruled out in

all ecosystems and all circumstances. However against a background of uncritical advocacy for

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such mosaics it makes an important contribution. Uncritical advocacy for fine-scaled fire

mosaics is unjustifiable. It is unreasonable to assume that the imposition of a fine-scaled fire

mosaic will cause an increase in biodiversity. If the justification for the imposition of a fine-

scaled fire mosaic on the landscape is the benefits that it will have for biodiversity, then the

benefits must be demonstrated by evidence.

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Table of Contents Candidate's Declaration..................................................................................................... ii

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

Abstract ............................................................................................................................... v

Table of Contents............................................................................................................. viii List of Figures ................................................................................................................... x List of Tables .................................................................................................................. xii Glossary and Terms....................................................................................................... xvii

Chapter 1: The fire mosaic hypothesis ............................................................................. 1 1.1 Disturbance ............................................................................................................. 4 1.2 Patch size................................................................................................................. 6 1.3 Edge and ecotone .................................................................................................... 8 1.4 Aims and hypotheses............................................................................................. 10

Chapter 2: Fire and birds ................................................................................................ 11 2.1 The response of bird communities to fire.............................................................. 12 2.2 During a fire .......................................................................................................... 12 2.3 Post-fire ................................................................................................................. 13 2.4 Increasing time-since-fire...................................................................................... 15 2.5 Habitat structure .................................................................................................... 16 2.6 Fire severity........................................................................................................... 17 2.7 Burn season ........................................................................................................... 18 2.8 Landscape context ................................................................................................. 18 2.9 Spatial and temporal variability ............................................................................ 19 2.10 The speed of post-fire avian dynamics .............................................................. 20 2.11 Breeding ............................................................................................................ 20 2.12 Conservation...................................................................................................... 21 2.13 Future directions................................................................................................ 22 2.14 Conclusion......................................................................................................... 23

Chapter 3: Background to methods ................................................................................ 25 3.1 Overview............................................................................................................... 25 3.2 Mulga woodland.................................................................................................... 25 3.3 Mulga birds ........................................................................................................... 26 3.4 Mulga birds and fire .............................................................................................. 29 3.5 Selecting the study area......................................................................................... 30 3.6 Study site............................................................................................................... 31 3.7 Principles of experimental design ......................................................................... 34

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3.8 Experimental scale ................................................................................................ 36 3.9 Statistical analysis ................................................................................................. 37

Chapter 4: The experimental landscape......................................................................... 42 4.1 GIS data quality..................................................................................................... 42 4.2 Potentially confounding factors ............................................................................ 42 4.3 Fire history database ............................................................................................. 44 4.4 Mulga mapping ..................................................................................................... 51 4.5 Defining the experimental units ............................................................................ 54 4.6 Selecting the experimental units ........................................................................... 57

Chapter 5: Habitat assessment ........................................................................................ 59 5.1 Methods................................................................................................................. 59 5.2 Time-since-fire study ............................................................................................ 60 5.3 Edge study............................................................................................................. 66 5.4 Summary ............................................................................................................... 69

Chapter 6: Time-since-fire............................................................................................... 71 6.1 Methods................................................................................................................. 71 6.2 Results ................................................................................................................... 76 6.3 Discussion ........................................................................................................... 119 6.4 Conclusion........................................................................................................... 126

Chapter 7: Patch size effect............................................................................................ 127 7.1 Methods............................................................................................................... 127 7.2 Results ................................................................................................................. 128 7.3 Discussion ........................................................................................................... 140 7.4 Conclusion........................................................................................................... 145

Chapter 8: Edge effect.................................................................................................... 146 8.1 Methods............................................................................................................... 146 8.2 Results ................................................................................................................. 149 8.3 Discussion ........................................................................................................... 176 8.4 Conclusion........................................................................................................... 181

Chapter 9: The fire mosaic hypothesis and biodiversity ............................................. 182

References........................................................................................................................ 189

Appendix 1: Survey site details...................................................................................... 204

Appendix 2: Ground-truthing sites ............................................................................... 209

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List of Figures Figure 3-1 Monthly rainfall at Yulara Airport and UKTNP HQ for 2004, 2005 and 2006........................ 33 Figure 4-1 Contour and road map of Uluru Kata-Tjuta National Park and Yulara. Most infrastructure was

centred on Uluru, Kata Tjuta and Yulara Village and the only sealed roads link these locations.43 Figure 4-2 Quaternary soil map of Uluru Kata-Tjuta National Park and Yulara. Grey shading is red earths

in a sheetwash context, black shading is Aeolian red earths in a dune-swale context and white is other soil mostly sand................................................................................................................... 43

Figure 4-3 The extent of fire at the study site in 2002. .............................................................................. 47 Figure 4-4 The extent of fire at the study site from 1977-2001.................................................................. 47 Figure 4-5 The extent of fire at the study site in 1976. .............................................................................. 48 Figure 4-6 The areas of UKTNP and Yulara used for ground-truthing and the randomly selected ground-

truth points. From left to right the polygons are: north-west; bore field; Yulara; and dune-swale...................................................................................................................................................... 49

Figure 4-7 Map of mulga woodland map derived from a 1:25,000 aerial photographic series acquired in 1997.............................................................................................................................................. 53

Figure 4-8 The distribution of mulga woodland patches was right skewed: a) all patches; b) burnt 2002; c) burnt 1976; d) long-unburnt. ........................................................................................................ 56

Figure 4-9 Mulga woodland at the study site, classified by time-since-fire............................................... 57 Figure 5-1 Plot of the first two axes of a principle components analysis showing environmental variables

(natural logarithm transformed) and sites from the sheetwash landscape in the time-since-fire study. A cross = sites burnt 1976, circle = sites burnt 2002 and a square = long-unburnt sites. CCOVER = crown cover, DEPTH = canopy depth, HEIGHT = canopy height, MHD = mulga height diversity, LITTER = phyllode litter coverage, ERE = abundance of Eremophila shrubs, SANTA = abundance of Santalaceae shrubs, MIST = abundance of mistletoe, SEED = abundance of mulga seedlings, SPINIFEX = spinifex coverage, LSHRUBS = low shrub coverage, GRASS = grass coverage............................................................................................. 62

Figure 5-2 Plot of the first two axes of the principle components analysis showing environmental variables (natural logarithm transformed) and sites from the dune-swale landscape in the time-since-fire study. A circle = sites burnt 2002 and a square = long-unburnt sites. CCOVER = crown cover, DEPTH = canopy depth, HEIGHT = canopy height, MHD = mulga height diversity, LITTER = phyllode litter coverage, ERE = abundance of Eremophila spp., SANTA = abundance of Santalaceae shrubs, MIST = abundance of mistletoe, SEED = abundance of mulga seedlings, SPINIFEX = spinifex coverage, GRASS = grass coverage......................................... 65

Figure 5-3 Plot of the first two axes of a principle components analysis showing environmental variables (natural logarithm transformed) and sites. A circle = sites burnt 2002 and a square = long-unburnt sites. CCOVER = canopy cover, DEPTH = canopy depth, HEIGHT = canopy height, MHD = mulga height diversity, LITTER = phyllode litter coverage, ERE = abundance of Eremophila shrubs, SANTA = abundance of Santalaceae shrubs, MIST = abundance of mistletoe, SEED = abundance of mulga seedlings, SPINIFEX = spinifex coverage, LSHRUBS = low shrub coverage, GRASS = grass coverage. ........................................................................... 68

Figure 6-1 Bird survey sites for the time-since-fire study. The cluster of sites at the eastern end of the park is in the dune-swale landscape. .................................................................................................... 72

Figure 6-2 Bi-plot of the first two axes of the redundancy analysis using bird count data from the sheetwash landscape showing environmental variables and sites. Circles are sites burnt 2002, crosses are sites burnt 1976 and squares are sites long-unburnt. MHD = mulga height diversity, CCOV = crown cover, SAN = Santalacea spp. abundance, ERE = Eremophila spp. abundance, MIS = mistletoe abundance, SPIN = spinifex cover. ................................................................... 79

Figure 6-3 Bi-plot of the first two axes of the redundancy analysis using bird count data from the sheetwash landscape showing environmental variables and birds. MHD = mulga height diversity, CCOV = crown cover, SAN = Santalaceae spp. abundance, ERE = Eremophila spp. abundance, MIS = mistletoe abundance, SPIN = spinifex cover. For bird codes see Table 6-4. .................... 80

Figure 6-4 Bi-plot of the first two axes of the canonical correspondence analysis using bird presence/absence data from the sheetwash landscape showing environmental variables and sites. Circles are sites burnt 2002; crosses are sites burnt 1976 and squares are sites long-unburnt. HEI = mulga canopy height, CCOV = crown cover, SAN = Santalacea spp abundance, SPIN = Spinifex cover, SEED = mulga seedling abundance and GRA = grass cover. ............................. 83

Figure 6-5 Bi-plot of the first two axes of the redundancy analysis using bird presence/absence data from the sheetwash landscape showing environmental variables and birds. Circles are sites burnt 2002; crosses are sites burnt 1976 and squares are sites long-unburnt. HEI = mulga canopy height, CCOV = crown cover, SAN = Santalacea spp abundance, SPIN = spinifex cover, SEED = mulga seedling abundance and GRA = grass cover. For bird codes see Table 6-4................................. 84

Figure 6-6 Plot of the first two axes of the detrended correspondence analysis using bird presence/absence data from the dune-swale landscape. The plot shows survey sites and birds. Circles are sites burnt 2002, crosses are sites long-unburnt and birds are represented by codes (see Table 6-4). . 87

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Figure 6-7 Species richness by treatment in the sheetwash landscape showing mean and 95% confidence levels: a) winter 2005, b) spring 2005, c) winter 2006, d) spring 2006........................................ 89

Figure 6-8 Species richness by treatment in the dune-swale landscape showing mean and 95% confidence levels: a) winter 2005, b) spring 2005, c) winter 2006, d) spring 2006........................................ 90

Figure 6-9 Species richness by season in the a) sheetwash and, b) dune-swale landscapes; showing mean and 95% confidence levels........................................................................................................... 91

Figure 6-10 Bird abundance by treatment in the sheetwash landscape showing mean and 95% confidence levels for each survey: a) winter 2005, b) spring 2005, c) winter 2006, d) spring 2006. ............. 93

Figure 6-11 Bird abundance by treatment in the dune-swale landscape showing mean and 95% confidence levels for each survey: a) winter 2005, b) spring 2005, c) winter 2006, d) spring 2006. ............. 94

Figure 6-12 Bird abundance by season in the a) sheetwash landscape and, b) dune-swale landscape, showing mean and 95% confidence levels................................................................................... 95

Figure 6-13 The effect of time-since-fire on Splendid Fairy-wren density (mean and 95% confidence levels). The graph shows data from the sheetwash landscape pooled across seasons. ................. 96

Figure 6-14 The effect of time-since-fire on Chestnut-rumped Thornbill density (mean and 95% confidence levels.). The graph shows data pooled across seasons from the sheetwash and dune-swale landscapes. ......................................................................................................................... 98

Figure 6-15 The effect of time-since-fire on Inland Thornbill density (mean and 95% confidence levels.). The graph shows data from the sheetwash landscape pooled across seasons. ........................... 100

Figure 6-16 The effect of time-since-fire on Slaty-backed Thornbill density (mean and 95% confidence levels.). The graph shows data from the sheetwash landscape pooled across seasons. .............. 102

Figure 6-17 The effect of time-since-fire on Southern Whiteface density (mean and 95% confidence levels). The graph shows data from the sheetwash landscape pooled across seasons. ............... 104

Figure 6-18 The effect of time-since-fire on Spiny-cheeked Honeyeater density (mean and 95% confidence levels). The graph shows data from the sheetwash landscape pooled across seasons.................................................................................................................................................... 105

Figure 6-19 The effect of time-since-fire on Singing Honeyeater density (mean and 95% confidence levels). The graph shows data from the sheetwash landscape pooled across seasons. ............... 107

Figure 6-20 The effect of time-since-fire on Hooded Robin density (mean and 95% confidence levels). The graph shows data from the sheetwash and dune-swale landscapes and the ecotone study pooled across seasons................................................................................................................. 109

Figure 6-21 The effect of time-since-fire on Red-capped Robin density (mean and 95% confidence levels). The graph shows data from the sheetwash landscape pooled across seasons. ............... 110

Figure 6-22 The effect of time-since-fire on Crested Bellbird density (mean and 95% confidence levels). The graph shows data from the sheetwash and dune-swale landscapes pooled across seasons. 112

Figure 6-23 The effect of time-since-fire on Rufous Whistler density (mean and 95% confidence levels). The graph shows data from the sheetwash landscape pooled across seasons. ........................... 113

Figure 6-24 The effect of time-since-fire on Black-faced Woodswallow density (mean and 95% confidence levels). The graph shows data from the sheetwash landscape pooled across seasons.................................................................................................................................................... 115

Figure 6-25 The effect of time-since-fire on Zebra Finch density (mean and 95% confidence levels). The graph shows data from the sheetwash landscape pooled across seasons.................................... 117

Figure 8-1 Bird survey sites for the edge experiment. ............................................................................. 147 Figure 8-2 Plot of the first two axes of a detrended correspondence analysis using bird count data

showing survey sites across a pyric edge in mulga woodland in 2005-06. Sites prefixed B = burnt, E = edge, U = unburnt...................................................................................................... 150

Figure 8-3 Plot of the first two axes of a detrended correspondence analysis using bird count data showing bird species across a pyric edge in mulga woodland in 2005-06. See Table 8-3 for bird codes. ......................................................................................................................................... 151

Figure 8-4 Plot of the first two axes of a principle components analysis using presence/absence data showing survey sites across a pyric edge in mulga woodland in 2005-06. Sites prefixed B = burnt, E = edge, U = unburnt...................................................................................................... 153

Figure 8-5 Plot of the first two axes of a principle components analysis using presence/absence data showing bird species across a pyric edge in mulga woodland in 2005-06. See Table 8-3 for bird codes. Arrows were removed to improve clarity of the figure. .................................................. 154

Figure 8-6 The effect of pyric edge on: species richness by year a) 2005, b) 2006, and bird abundance by year: a) 2005, b) 2006, showing mean and 95% confidence levels............................................ 156

Figure 8-7 Effect of year on a) species richness and, b) bird density across a pyric edge. Graphs show mean and 95% confidence levels. .............................................................................................. 157

Figure 8-8 The effect of edge on the probability of presence of Budgerigars, showing mean and 95% confidence levels........................................................................................................................ 158

Figure 8-9 The effect of edge on the abundance of Splendid Fairy-wrens, showing mean and 95% confidence levels........................................................................................................................ 159

Figure 8-10 The effect of edge on the probability of presence of Splendid Fairy-wrens, showing mean and 95% confidence levels................................................................................................................ 160

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Figure 8-11 The effect of edge on the probability of presence of Chestnut-rumped Thornbills, showing mean and 95% confidence levels. Data from 2005 and 2006 were pooled. ............................... 160

Figure 8-12 The effect of edge on the abundance of Inland Thornbills, showing mean and 95% confidence levels. Data from 2005 and 2006 were pooled. .......................................................................... 161

Figure 8-13 The effect of edge on the probability of presence of Inland Thornbills, showing mean and 95% confidence levels................................................................................................................ 162

Figure 8-14 The effect of edge on the abundance of Slaty-backed Thornbills, showing mean and 95% confidence levels. Data from 2005 and 2006 were pooled......................................................... 162

Figure 8-15 The effect of edge on the probability of presence of Inland Thornbills, showing mean and 95% confidence levels................................................................................................................ 163

Figure 8-16 The effect of edge on the abundance of Southern Whitefaces, showing mean and 95% confidence levels. Data from 2005 and 2006 were pooled......................................................... 163

Figure 8-17 The effect of edge on the probability of presence of Southern Whitefaces, showing mean and 95% confidence levels. Data from 2005 and 2006 were pooled. ............................................... 164

Figure 8-18 The effect of edge on the abundance Spiny-cheeked Honeyeaters, showing mean and 95% confidence levels........................................................................................................................ 165

Figure 8-19 The effect of edge on the probability of presence of Spiny-cheeked Honeyeaters, showing mean and 95% confidence levels. .............................................................................................. 165

Figure 8-20 The effect of edge on the abundance of Singing Honeyeaters, showing mean and 95% confidence levels........................................................................................................................ 166

Figure 8-21 The effect of edge on the probability of presence of Singing Honeyeaters, showing mean and 95% confidence levels................................................................................................................ 166

Figure 8-22 The effect of edge on the probability of presence of Crimson Chats, showing mean and 95% confidence levels........................................................................................................................ 167

Figure 8-23 The effect of edge on the probability of presence of Hooded Robins, showing mean and 95% confidence levels. Data were pooled from 2005 and 2006......................................................... 168

Figure 8-24 The effect of edge on the abundance of Red-capped Robins in 2005, showing mean and 95% confidence levels........................................................................................................................ 168

Figure 8-25 The effect of edge on the probability of presence of Red-capped Robins, showing mean and 95% confidence levels................................................................................................................ 169

Figure 8-26 The effect of edge on the probability of presence of White-browed Babblers, showing mean and 95% confidence levels......................................................................................................... 169

Figure 8-27 The effect of edge on the probability of presence of Crested Bellbirds, showing mean and 95% confidence levels................................................................................................................ 170

Figure 8-28 The effect of edge on the abundance of Rufous Whistlers in 2005, showing mean and 95% confidence levels........................................................................................................................ 171

Figure 8-29 The effect of edge on the probability of presence of Rufous Whistlers, showing mean and 95% confidence levels................................................................................................................ 171

Figure 8-30 The effect of edge on the probability of presence of Grey Shrike-thrushes, showing mean and 95% confidence levels................................................................................................................ 172

Figure 8-31 The effect of edge on the abundance of Willie Wagtails, showing mean and 95% confidence levels. ......................................................................................................................................... 172

Figure 8-32 The effect of edge on the probability of presence of Willie Wagtails, showing mean and 95% confidence levels........................................................................................................................ 173

Figure 8-33 The effect of edge on the probability of presence of Masked Woodswallows in 2005, showing mean and 95% confidence levels. .............................................................................................. 174

Figure 8-34 The effect of edge on the abundance of Black-faced Woodswallows in 2005, showing mean and 95% confidence levels......................................................................................................... 174

Figure 8-35 The effect of edge on the probability of presence of Black-faced Woodswallows in 2005, showing mean and 95% confidence levels................................................................................. 175

Figure 8-36 The effect of edge on the probability of presence of Grey Butcherbirds, showing mean and 95% confidence levels................................................................................................................ 175

Figure 8-37 The effect of edge on the abundance of Zebra Finches in 2005, showing mean and 95% confidence levels........................................................................................................................ 176

Figure 8-38 The effect of edge on the probability of presence of Zebra Finches in 2005, showing mean and 95% confidence levels......................................................................................................... 176

List of Tables Table 3-1 Common mulga bird species, their evolutionary origin and present geographic affinity. ......... 27 Table 3-2 Feeding behaviour, territory size, estimated density and response to proximity of artificial water

of common mulga bird species..................................................................................................... 28

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Table 3-3 Uncommon birds of Northern Territory mulga woodland (Cody, 1994; Recher and Davis, 1997). ........................................................................................................................................... 29

Table 3-4 Arid zone wind seasons following Brookfield (1970) ............................................................... 32 Table 3-5 Factors which can invalidate or cause pseudoreplication in a field ecology experiment

(Hurlbert, 1984) together with features of a valid experimental design (Field ecology method) and alternative procedures for a mensurative space-for-time experiment (Pickett, 1989; Hardgrove & Pickering, 1992, McGarigal & Cushman, 2002). Features of this study are shaded grey. ............................................................................................................................................. 35

Table 4-1 Percentage of the study site burnt during each time period. The resolution of the maps was estimated by measuring the pixels. .............................................................................................. 46

Table 4-2 Area and number of randomly positioned points in the polygons established for ground-truthing the mulga maps and 2002 fire map. ............................................................................................. 49

Table 4-3 Proportion of the area of each ground-truthing polygon burnt in each mapped time-period. Where applicable, management fires and wild fires were combined. .......................................... 49

Table 4-4 Error matrix for the 2002 fire map............................................................................................. 50 Table 4-5 Kappa statistic, producer, user and overall accuracy for the 2002 fire map............................... 51 Table 4-6 Error matrix for the map of mulga woodland map derived from a Landsat 7 image acquired in

2002.............................................................................................................................................. 52 Table 4-7 Kappa statistic, producer, user and overall accuracy for the map of mulga woodland derived

from a Landsat 7 image acquired in 2002. ................................................................................... 52 Table 4-8 Error matrix for the map of mulga woodland derived from a 1:25,000 aerial photographic series

acquired in 1997........................................................................................................................... 53 Table 4-9 Kappa statistic, producer, user and overall accuracy for the map of mulga woodland derived

from a 1:25,000 aerial photographic series acquired in 1997....................................................... 53 Table 4-10 Description of the patches of mulga woodland at the study site by time-since-fire class.

Patches <3ha were excluded from the summary. ......................................................................... 55 Table 5-1 Proportion of canopy plants killed and damaged by fire in the burnt 2002 treatment in the

sheetwash landscape in the time-since-fire study......................................................................... 61 Table 5-2 Summary of a principle components analysis of habitat data in the sheetwash landscape of the

time-since-fire study. ................................................................................................................... 61 Table 5-3 Results of tests for differences in habitat between treatments in the sheetwash landscape of the

time-since-fire study, using Monte Carlo permutations tests with 999 runs. ............................... 62 Table 5-4 Proportion of canopy plants killed, damaged and undamaged by fire in the burnt 2002

treatment. ..................................................................................................................................... 63 Table 5-5 Results of t-tests for differences in habitat parameters between treatments in the sheetwash

landscape. ‘NA’ indicates that a statistical test could not be conducted because the site did not fulfil the criteria for obtaining a measurement. Grey shading indicates a significant difference. 64

Table 5-6 Summary of a principle components analysis of habitat data in the dune-swale landscape of the time-since-fire study. ................................................................................................................... 66

Table 5-7 The effect of time-since-fire on mulga woodland habitat. ‘NA’ indicates that a statistical test could not be conducted because the site did not fulfil the criteria for obtaining a measurement. Grey shading indicates a significant difference............................................................................ 66

Table 5-8 Proportion of canopy plants killed, damaged and undamaged by fire in the burnt treatment. ... 67 Table 5-9 Summary of a principle components analysis of habitat data from the edge study. .................. 67 Table 5-10 Effect of time-since-fire on habitat across a pyric edge in mulga woodland. ‘NA’ indicates that

a statistical test could not be conducted because the site did not fulfil the criteria for obtaining a measurement. Grey shading indicates a significant difference. ................................................... 69

Table 6-1 Wind strength classes for bird surveying in mulga woodland. .................................................. 73 Table 6-2 Summary of a redundancy analysis using bird count data from the sheetwash landscape, CCOV

= crown cover, MHD = mulga height diversity, MIS = mistletoe abundance, ERE = Eremophila spp., SAN = Santalacea spp. abundance, SPIN = spinifex cover. ................................................ 78

Table 6-3 Results of Monte Carlo permutations tests for differences (999 runs) between the bird communities present in each treatment of a redundancy analysis from the sheetwash landscape using bird count data. ................................................................................................................... 78

Table 6-4 Bird codes used in ordination plots, and feeding guilds. Scientific names of all species are listed in Table 3-1 and Table 3-3. .......................................................................................................... 81

Table 6-5 Summary of a canonical correspondence analysis using presence/absence data from the sheetwash landscape, CCOV = crown cover, HEI = canopy height, SEED = mulga seedling abundance, SAN = Santalacea spp. abundance, GRA = grass cover, SPIN = spinifex cover. ..... 82

Table 6-6 Results of Monte Carlo permutations tests for differences (999 runs) between the bird communities present in each treatment of a canonical correspondence analysis using bird presence/absence data from the sheetwash landscape.................................................................. 83

Table 6-7 Canonical correspondence analysis of bird presence/absence data from the sheetwash landscape by season. Grey shading indicates a significant difference. ......................................................... 85

xiv

Table 6-8 Summary of a detrended correspondence analysis of bird presence/absence data in the dune-swale landscape of the time-since-study. ..................................................................................... 86

Table 6-9 Results of GLMM tests of the effect of time-since-fire on species richness showing significant and near-significant terms in the model. ...................................................................................... 88

Table 6-10 Percentage coefficient of variation in species richness; ‘NA’ = not applicable....................... 88 Table 6-11 Results of GLMM tests of the effect of time-since-fire on bird abundance showing significant

terms and interactions in the model.............................................................................................. 92 Table 6-12 Percentage coefficient of variation in bird abundance, NA = not applicable........................... 92 Table 6-13 Summary of the detection functions modelled using Distance 5.0 (Thomas et al., 2006) for the

Splendid Fairy-wren..................................................................................................................... 96 Table 6-14 Splendid Fairy-wren – estimated density (D) with upper and lower 95% confidence levels

(UCL, LCL) and statistical tests. Dark shading indicates a significant results (α < 0.05), light shading indicates a near-significant result (α < 0.08) and ‘NA’ = not applicable. ....................... 97

Table 6-15 Summary of the detection functions modelled using Distance 5.0 (Thomas et al., 2006) for the Chestnut-rumped Thornbill. ......................................................................................................... 98

Table 6-16 Chestnut-rumped Thornbill – estimated density (D) with upper and lower 95% confidence levels (UCL, LCL) and statistical tests. Dark shading indicates a significant result (α < 0.05), light shading indicates a near-significant result (α < 0.08) and ‘NA’ = not applicable................ 99

Table 6-17 Summary of the detection functions modelled using Distance 5.0 (Thomas et al., 2006) for the Inland Thornbill. ........................................................................................................................ 100

Table 6-18 Inland Thornbill – estimated density with upper and lower 95% confidence levels (UCL, LCL) and statistical tests. Shading indicates a significant (α < 0.05) or near-significant result (α < 0.08). ....................................................................................................................................... 101

Table 6-19 Summary of the detection functions modelled using Distance 5.0 (Thomas et al., 2006) for the Slaty-backed Thornbill............................................................................................................... 102

Table 6-20 Slaty-backed Thornbill – estimated density with upper and lower 95% confidence levels (UCL, LCL) and statistical tests. Dark shading indicates a significant result (α < 0.05), light shading indicates a near-significant result (α < 0.08) and ‘NA’ = not applicable. ..................... 103

Table 6-21 Summary of the detection functions modelled using Distance 5.0 (Thomas et al., 2006) for the Southern Whiteface.................................................................................................................... 104

Table 6-22 Summary of the detection functions modelled using Distance 5.0 for the Spiny-cheeked Honeyeater. ................................................................................................................................ 105

Table 6-23 Spiny-cheeked Honeyeater – estimated density with upper and lower 95% confidence levels (UCL, LCL) and statistical tests. Dark shading indicates a significant result (α < 0.05), light shading indicates a near-significant result (α < 0.08) and ‘NA’ = not applicable. ..................... 106

Table 6-24 Summary of the detection functions modelled using Distance 5.0 (Thomas et al., 2006) for the Singing Honeyeater.................................................................................................................... 107

Table 6-25 Singing Honeyeater – estimated density with upper and lower 95% confidence levels (UCL, LCL) and statistical tests. Dark shading indicates a significant result (α < 0.05), light shading indicates a near-significant result (α < 0.08) and ‘NA’ = not applicable. .................................. 108

Table 6-26 Summary of the detection functions modelled using Distance 5.0 (Thomas et al., 2006) for the Hooded Robin. ........................................................................................................................... 109

Table 6-27 Summary of the detection functions modelled using Distance 5.0 (Thomas et al., 2006) for the Red-capped Robin...................................................................................................................... 110

Table 6-28 Red-capped Robin – estimated density with upper and lower 95% confidence levels (UCL, LCL) and statistical tests. Dark shading indicates a significant result (α < 0.05), light shading indicates a near-significant result (α < 0.08) and ‘NA’ = not applicable. .................................. 111

Table 6-29 Summary of the detection functions modelled using Distance 5.0 (Thomas et al., 2006) for the Crested Bellbird. ........................................................................................................................ 112

Table 6-30 Summary of the detection functions modelled using Distance 5.0 (Thomas et al., 2006) for the Rufous Whistler. ........................................................................................................................ 113

Table 6-31 Rufous Whistler – estimated density with upper and lower 95% confidence levels (UCL, LCL) and statistical tests. Dark shading indicates a significant result (α < 0.05), light shading indicates a near-significant result (α < 0.08) and ‘NA’ = not applicable. .................................. 114

Table 6-32 Summary of the detection functions modelled using Distance 5.0 for the Black-faced Woodswallow............................................................................................................................. 115

Table 6-33 Black-faced Woodswallow – estimated density with upper and lower confidence levels (UCL, LCL) and statistical tests. Dark shading indicates a significant result (α= 0.05), light shading indicates a near-significant result (α = 0.08) and ‘NA’ = not applicable. .................................. 116

Table 6-34 Summary of the detection functions modelled using Distance 5.0 (Thomas et al., 2006) for the Zebra Finch, NA = not applicable.............................................................................................. 117

Table 6-35 Zebra Finch – estimated density with upper and lower 95% confidence levels (UCL, LCL) and statistical tests. Dark shading indicates a significant result (α < 0.05), light shading indicates a near-significant result (α < 0.08) and ‘NA’ = not applicable. .................................................... 118

xv

Table 6-36 Classification of bird species by time-since-fire preference. Trend in the data refers to a non-significant difference which if significant would be biologically meaningful. Biologically meaningful is defined as an increase of >50%. .......................................................................... 123

Table 7-1 Results of constrained ordinations with patch area and logarithm of patch area the predictor variables. .................................................................................................................................... 129

Table 7-2 Summary of significant and near-significant results for patch size effect. Grey shading indicates a significant result. ..................................................................................................................... 129

Table 7-3 Tests for patch size effect on species richness in the sheetwash landscape, showing significant terms in the model...................................................................................................................... 130

Table 7-4 Tests for patch size effect on species richness in the dune-swale landscape, showing significant terms in the model...................................................................................................................... 130

Table 7-5 Tests for patch size effect on bird abundance in the sheetwash landscape, showing significant terms in the model...................................................................................................................... 130

Table 7-6 Tests for patch size effect on bird abundance in the dune-swale landscape, showing significant terms in the model...................................................................................................................... 131

Table 7-7 Tests for patch size effect on Splendid Fairy-wren in the sheetwash landscape, showing significant terms in the model. ................................................................................................... 131

Table 7-8 Tests for patch size effect on Splendid Fairy-wren in the dune-swale landscape, showing significant terms in the model. ................................................................................................... 131

Table 7-9 Tests for patch size effect on Variegated Fairy-wren in the sheetwash landscape, showing significant terms in the model. ................................................................................................... 131

Table 7-10 Tests for patch size effect on Redthroat in the sheetwash landscape, showing significant terms in the model................................................................................................................................ 132

Table 7-11 Tests for patch size effect on Yellow-rumped Thornbill in the sheetwash landscape, showing significant terms in the model. ................................................................................................... 132

Table 7-12 Tests for patch size effect on Yellow-rumped Thornbill in the dune-swale landscape, showing significant terms in the model. ................................................................................................... 132

Table 7-13 Tests for patch size effect on Chestnut-rumped Thornbill in the sheetwash landscape, showing significant terms in the model. ................................................................................................... 133

Table 7-14 Tests for patch size effect on Chestnut-rumped Thornbill in the dune-swale landscape, showing significant terms in the model...................................................................................... 133

Table 7-15 Tests for patch size effect on Inland Thornbill in the sheetwash landscape, showing significant terms in the model...................................................................................................................... 133

Table 7-16 Tests for patch size effect on Inland Thornbill in the dune-swale landscape, showing significant terms in the model. ................................................................................................... 133

Table 7-17 Tests for patch size effect on Slaty-backed Thornbill in the sheetwash landscape, showing significant terms in the model. ................................................................................................... 134

Table 7-18 Tests for patch size effect on Slaty-backed Thornbill in the dune-swale landscape, showing significant terms in the model. ................................................................................................... 134

Table 7-19 Tests for patch size effect on Southern Whiteface in the sheetwash landscape, showing significant terms in the model. ................................................................................................... 134

Table 7-20 Tests for patch size effect on Southern Whiteface in the dune-swale landscape, showing significant terms in the model. ................................................................................................... 134

Table 7-21 Tests for patch size effect on Spiny-cheeked Honeyeater in the sheetwash landscape, showing significant terms in the model. ................................................................................................... 135

Table 7-22 Tests for patch size effect on Spiny-cheeked Honeyeater in the dune-swale landscape, showing significant terms in the model...................................................................................... 135

Table 7-23 Tests for patch size effect on Singing Honeyeater in the sheetwash landscape, showing significant terms in the model. ................................................................................................... 135

Table 7-24 Tests for patch size effect on Singing Honeyeater in the dune-swale landscape, showing significant terms in the model. ................................................................................................... 135

Table 7-25 Tests for patch size effect on Hooded Robin in the sheetwash landscape, showing significant terms in the model...................................................................................................................... 136

Table 7-26 Tests for patch size effect on Hooded Robin in the dune-swale landscape, showing significant terms in the model...................................................................................................................... 136

Table 7-27 Tests for patch size effect on Red-capped Robin in the sheetwash landscape, showing significant terms in the model. ................................................................................................... 136

Table 7-28 Tests for patch size effect on Red-capped Robin in the dune-swale landscape, showing significant terms in the model. ................................................................................................... 136

Table 7-29 Tests for patch size effect on White-browed Babbler in the sheetwash landscape, showing significant terms in the model. ................................................................................................... 137

Table 7-30 Tests for patch size effect on Crested Bellbird in the sheetwash landscape, showing significant terms in the model...................................................................................................................... 137

Table 7-31 Tests for patch size effect on Crested Bellbird in the dune-swale landscape, showing significant terms in the model. ................................................................................................... 137

xvi

Table 7-32 Tests for patch size effect on Rufous Whistler in the sheetwash landscape, showing significant terms in the model...................................................................................................................... 137

Table 7-33 Tests for patch size effect on Rufous Whistler in the dune-swale landscape, showing significant terms in the model. ................................................................................................... 138

Table 7-34 Tests for patch size effect on Grey-Shrike-thrush in the sheetwash landscape, showing significant terms in the model. ................................................................................................... 138

Table 7-35 Tests for patch size effect on Grey Fantail in the sheetwash landscape, showing significant terms in the model...................................................................................................................... 138

Table 7-36 Tests for patch size effect on Willie Wagtail in the sheetwash landscape, showing significant terms in the model...................................................................................................................... 139

Table 7-37 Tests for patch size effect on Willie Wagtail in the dune-swale landscape, showing significant terms in the model...................................................................................................................... 139

Table 7-38 Tests for patch size effect on Black-faced Woodswallow in the sheetwash landscape, showing significant terms in the model. ................................................................................................... 139

Table 7-39 Tests for patch size effect on Black-faced Woodswallow in the dune-swale landscape, showing significant terms in the model...................................................................................... 139

Table 7-40 Tests for patch size effect on Zebra Finch in the sheetwash landscape, showing significant terms in the model...................................................................................................................... 140

Table 7-41 Tests for patch size effect on Zebra Finch in the dune-swale landscape, showing significant terms in the model...................................................................................................................... 140

Table 8-1 Summary of detrended correspondence analysis of bird count data from the edge study. ...... 149 Table 8-2 Results of Monte Carlo permutations tests for differences between the bird communities at each

treatment across a pyric edge in mulga woodland...................................................................... 150 Table 8-3 Bird codes used for ordination plots and feeding guilds. See Table 3-1 and Table 3-3 for

scientific names.......................................................................................................................... 152 Table 8-4 Summary of a principle components analysis of bird presence/absence data from the edge

study........................................................................................................................................... 153 Table 8-5 Results of Monte Carlo permutations tests for differences between the bird communities at each

treatment across a pyric edge in mulga woodland. Grey shading indicates a significant result. 154 Table 8-6 The effect of pyric edge on species richness and bird abundance............................................ 155 Table 8-7 Percentage coefficient of variation in species richness and bird abundance across a pyric edge.

................................................................................................................................................... 157 Table 8-8 The effect of pyric edge on Splendid Fairy-wren abundance, showing significant terms in the

model.......................................................................................................................................... 159 Table 8-9 Tests of the effect of edge on Crested Bellbirds showing significant terms in the models. ..... 170 Table 8-10 Summary of habitat preference and edge response by species. ............................................. 178

xvii

Glossary and Terms

ANPWS Australian National Parks and Wildlife Service

BoM Bureau of Meteorology

CCA Canonical Correspondence Analysis

DCA Detrended Correspondence Analysis

GIS Geographic Information System

GPS Geographic Positioning System

GLMM Generalised Linear Mixed Model

PCA Principal Components Analysis

RDA Redundancy Analysis

SD Standard deviation

UKTNP Uluru Kata-Tjuta National Park

1

Chapter 1: The fire mosaic hypothesis The distribution of birds in landscapes has been described using a variety of interactive

factors and processes, including habitat preference, climate, topography, substrate (Ford, 1989;

Krebs, 1985), predators (Odonnell, 1996), competitors (Piper and Catterall, 2003; MacDonald

and Kirkpatrick, 2003), fragmentation (Lindenmayer et al., 2001; Lindenmayer et al., 2003) and

disturbance such as fire (Woinarski and Recher, 1997; Smith, 2000; Smucker et al., 2005;

Tasker et al., 2006), grazing (James et al., 1999; Landsberg et al., 1999) and flood (Knutson and

Klaas, 1996). Of particular interest to land managers and researchers in the fire-prone

landscapes of Australia, is the influence of fire.

A mosaic of vegetation of differing times-since-fire and patch sizes is often referred to as a

“fire mosaic” (Morton, 1990; Bowman, 1998; Gill et al., 2003; Bradstock et al., 2005; Moore,

2005; Parr and Andersen, 2006; Burrows, 2006). The fire mosaic concept probably originated

from observation of the traditional fire-management practices of Aborigines – e.g. ‘fire-stick

farming’ (Jones, 1969; Jones, 1980; Bowman, 1998). The notion is intuitively appealing due to

its perceived potential to deliver favourable outcomes for biodiversity conservation and fire

management (Bolton and Latz, 1978; Morton, 1990; Allan and Baker, 1990; Brooker et al.,

1990; Recher et al., 1991; Garnett and Crowley, 1995; Bowman, 1998; Brooker, 1998;

Woinarski, 1999; Gill, 2000; Ward, 2004; Ward and Paton, 2004; Burrows, 2004; Bradstock et

al., 2005; Moore, 2005; Parr and Andersen, 2006; Burrows, 2006; Kerle et al., 2007). Advocacy

for the creation of fine-scaled fire mosaics has apparently never been accompanied by an

explicit definition (Gill, 2000; Bowman et al., 2004; Parr and Andersen, 2006). Nor have

authors presented a theoretical framework for the supposed benefits. Explanations are limited to

suggestions that advantages may accrue to species due to the close proximity of resources that

can only be obtained from different seral stages of a habitat (Bolton and Latz, 1978). Despite

the lack of definition and theory, advocates appear to suggest that the number and variety of

organisms (Hubell, 2001) will be greater (i.e. greater biodiversity) in landscapes managed as a

fine-scaled fire mosaic than will be present in the absence of variation in time-since-fire.

Implicit is the suggestion that most, if not all species will benefit from the treatment. The loss of

such mosaics from mainland Australia due to the cessation of traditional Aboriginal land

management has been blamed for the decline and extinction of a suite of Australian animals

(Burbidge et al., 1988; Short and Turner, 1994; Franklin, 1999; Woinarski et al., 2001; Parr and

Andersen, 2006).

To my knowledge, few studies have attempted to test the fire mosaic hypothesis. One

reason for the paucity of studies is that Australia appears to be the only jurisdiction where the

scientific literature attributes a positive effect of fine-scale variation in time-since-fire to

biodiversity. The fire ecology literature from other jurisdictions acknowledges the potential

affect of variation in the spatial distribution of fire histories but stops short of assuming a

positive effect of fine-scale, e.g. USA (Hutto, 1995; Kotliar et al., 2002) and the Mediterranean

2

(Pons et al., 2003b; Brotons et al., 2004). Even in South Africa where a similar concept – patch

mosaic burning – has gained traction in the past decade, scientists do not assume particular

affects of scale (Parr and Brockett, 1999; Brockett et al., 2001; Parr and Chown, 2003; Parr and

Andersen, 2006). The focus on the putative affects of fine-scale is therefore largely an

Australian pre-occupation and the cessation of traditional Aboriginal land management, means

that fine-scaled fire mosaics occur rarely, so opportunities to directly test the hypothesis are

scant (Short and Turner, 1994).

The few studies that have set out to test the fire mosaic hypothesis have found no evidence

to support it (Short and Turner, 1994; Letnic, 2003; Letnic and Dickman, 2005). All three of the

tests involved mammals. Short & Turner (1994) assessed the abundance, condition and

reproductive status of three medium-sized marsupials – the Golden Bandicoot (Isodon auratus),

Northern Brush-tailed Possum (Trichosurus vulpecular arnhemensis) and Burrowing Bettong

(Bettongia lesueur) occupying vegetation mosaics of contrasting scales on Barrow Island, an

offshore sanctuary. Areas subject to mining disturbance were classified as fine-scaled and

compared to even-aged vegetation. No effects of the scale of vegetation mosaic were detected.

Letnic (2003) investigated the response of small mammals to the short-term effects (<1 year) of

patch burning (0.9ha-3.0ha) on pastoral properties in the northern Simpson Desert. The species

for which sufficient data were obtained were Australian Hopping Mouse (Notomys alexis),

Sandy Inland Mouse (Pseudomys hermannsburgensis), Brown Desert Mouse (Pseudomys

desertor), Lesser Hairy-footed Dunnart (Sminthopsis youngsoni), Mulgara (Dasycercus

cristicauda) and Wongai Ningaui (Ningaui ridei). The treatments had little effect on the

mammal community and no species showed a strong preference for the treated habitat. A later

study in the same ecosystem (Letnic and Dickman, 2005) compared the response of small

mammals to regenerating patches (aged 1-3 years and 0.3km2-4.0km2 in size) and long-unburnt

(aged >25 years) habitats. Sufficient data were obtained for the same species reported in Letnic

(2003) and the Hairy-footed Dunnart (Sminthopsis hirtipes). The authors found no difference in

species richness or abundance between long-unburnt and regenerating sites and concluded that

none of the species directly benefited from the patch-burning regime.

The lack of a theoretical basis or empirical evidence in support of the fire mosaic

hypothesis is the subject of two recent critiques; Bradstock et al. (2005) and Parr and Anderson

(2006). The authors found that many of the assumptions implicit in the concept lack supporting

evidence or are inconsistent with existing knowledge. They describe five problems with the

concept or its implementation. 1) Theoretical development of the idea is limited to the

assumption that ‘pyrodiversity begets biodiversity’ and this is not supported by empirical

evidence (Bradstock et al., 2005; Parr and Andersen, 2006). 2) Few species demonstrate an

absolute dependence on a particular seral stage, casting doubt on the need to maintain a range of

stages (Bradstock et al., 2005; Parr and Andersen, 2006). 3) Fire ecology theory and empirical

evidence indicate that changes in vegetation structure and composition following fire are

variable and this confounds the concept (Bradstock et al., 2002; Bond and Keeley, 2005; Bond

3

et al., 2005; Bradstock et al., 2005; Parr and Andersen, 2006). 4) There is little data for

quantifying the sizes, shapes, age structures or configurations of patches in relation to fauna so

evidence-based implementation is not possible (Bradstock et al., 2005; Parr and Andersen,

2006). 5) Differences between species mean it is unlikely that a single fire mosaic configuration

will be suitable for all species in a community (Bradstock et al., 2005). The authors of both

papers point out that the distribution of fauna is affected by a range of biotic and abiotic factors

in excess of those addressed by the fire mosaic concept. In addition some of the factors that

affect fauna also feedback into the fire regime with the implication that fire ecology and fire

management is far more complex than can be accommodated within the fire mosaic concept.

While the fire mosaic hypothesis is apparently an Australian idea, the concept that species

may benefit from the juxtaposition of small patches of different vegetation types or different

seral stages of a vegetation type is not uniquely Australian. The resource

complementation/supplementation hypothesis (Dunning et al., 1992) appears to encapsulate the

fire mosaic concept. The hypothesis states that when a species requires resources that can only

be obtained from two vegetation types, where those habitats are in close proximity, larger

populations can be supported in the area of proximity. The authors cite two examples which

support the hypothesis (McIvor and Odum, 1988; Petit, 1989) though neither relates to fire.

Recent reviews suggest the concept has gained acceptance (Farhig, 2003; Ries et al., 2004; Ries

and Sisk, 2004; Turner, 2005) and citations exceed 373 compared to 23 (ISI, 2007) for the first

study to test the fire mosaic hypothesis (Short and Turner, 1994). At least two studies have

investigated the resource complementation hypothesis in pyric landscapes (Pons et al., 2003b;

Brotons et al., 2004), both in the Mediterranean. Pons et al. (2003b) investigated the distribution

of birds in a fine-scale (0.9ha-16.5ha patches) agricultural mosaic consisting of grassland,

shrubland and forest in the Pyrenees. They concluded that the use of multiple adjacent patches

had little influence on the structure of the bird community. The presence of most birds related to

a preference for one or other of the habitats. Brotons et al. (2004) investigated the distribution

of birds in a fire-prone wooded landscape of forest and shrubland. Of 42 species recorded, they

found six that preferred fragments of forest within shrubland and of those, three preferred

smaller fragments to large. They concluded that the resource complementation hypothesis may

explain the distribution of some species but no single hypothesis explained the changes in the

distribution of the bird community from continuous forest to mosaic.

The lack of evidence in support of the fire mosaic hypothesis does not mean that it can be

dismissed in the Australian context. Traditional Aboriginal fire management (Jones, 1969;

Gould, 1971; Latz and Griffin, 1978; Jones, 1980; Singh et al., 1981; Hodgkinson and Griffin,

1982; Kimber, 1983; Griffin and Friedel, 1985; Allan and Griffin, 1986; Burrows and

Christensen, 1990; Latz, 1995b; Bowman, 1998; Bowman et al., 2004) may have altered

Australian fire regimes and consequently influenced the biota (Latz and Griffin, 1978; Griffin,

1984; Griffin and Friedel, 1985; Allan and Griffin, 1986; Burbidge et al., 1988; Burrows and

Christensen, 1990; Allan and Baker, 1990; Walsh, 1990; Bowman, 1998; Gill, 2000; Kershaw

4

et al., 2002). While the characteristics and magnitude of the Aboriginal influence on Australian

fire regimes remains uncertain (Gill, 2000) it is conceivable that the Australian biota may

benefit from fire regimes associated with traditional Aboriginal management (Morton, 1990;

Woinarski and Recher, 1997). This situation could have arisen through selection (Darwin, 1859)

for biota that tolerated change in the fire regime or via adaptation. Recent work has

demonstrated that microevolution can occur in vertebrates in as little as 5,000 years (Hendry

and Kinnison, 1999; Gingerich, 2001; Hendry and Kinnison, 2001; Hairston Jr. et al., 2005;

Keogh et al., 2005; Roca et al., 2006).

In the absence of fine-scale fire mosaics suitable for investigation, an alternative way to

assess the likely response of biota to a fine-scale fire mosaic is by investigating the underlying

assumptions. Implicit within the fire mosaic concept are three assumptions. 1) The distribution

of fauna is influenced by time-since-fire. 2) Faunal diversity increases as the size of patches of

habitat decreases. 3) Faunal diversity is greater at the boundary between patches of different

time-since-fire than it is in the interior of patches. It is also apparently assumed that no faunal

species will be strongly detrimentally affected; certainly no species should be extirpated from a

landscape as a result of the imposition of a fine-scale fire mosaic.

The literature about disturbance and fire ecology is rapidly expanding while that of patch

size and ecotone/edge is voluminous.

1.1 Disturbance The maintenance of biodiversity in the landscape has been attributed to two mechanisms:

niche partitioning and disturbance (Shea et al., 2004). Niche partitioning seeks to explain how

species competing within a stable environment are able to co-exist (Ritchie and Olff, 1999).

Disturbance studies examine the changes in the distribution of species in space and time due to

changes in the environment. Agents of disturbance include predation, herbivory, fires, storms,

floods, drought, waves, landslides, volcanos, fragmentation, mowing, digging, tree fall and any

other process which causes the death or displacement of organisms (Sousa, 1984) or impacts on

the niche relationships of the organisms (Shea and Chesson, 2002). Studies of disturbance have

tended to focus on sessile organisms because these are easier to investigate than vagile

organisms and therefore the literature is biased in this respect (Sousa, 1984). Nonetheless, a

number of broad generalisations have been made.

Central to studies of biodiversity and disturbance is the intermediate disturbance

hypothesis (Connell, 1978; Miller, 1982; Sousa, 1984; Hobbs and Huenneke, 1992; Shea et al.,

2004). The hypothesis states that biodiversity is maximised at intermediate levels of disturbance

because this provides stable habitat suitable for competitive species and a progression of

disturbed environments at different stages of invasion and replacement by colonising species.

Implicit to the hypothesis is the idea that disturbance exists not as a single anomalous and

catastrophic event but as a recurring, predictable feature of the landscape – a disturbance

regime. Such a regime is a natural ecological process leading to a mosaic of habitats and

5

successional stages that can enhance both α and ß diversity (Angelstam, 1998; Brawn et al.,

2001) - α diversity refers to the within-community component of diversity and ß diversity refers

to the between community component of diversity (Loreau, 2000).

Shea et al. (2004) define disturbance regimes by four characteristics: 1) frequency, 2)

extent, 3) intensity, and 4) duration. Frequency refers to the time between disturbance events,

extent refers to the area of the disturbance, intensity refers to the strength of the disturbance

force and duration refers to the period of the disturbance. A simple model of disturbance

incorporating frequency and extent within the same habitat (Miller, 1982) predicts that when the

extent of disturbance is large, maximum biodiversity will be achieved at lower disturbance

frequencies than when the extent of disturbance is small. How species respond to disturbance

varies among ecosystems. In some instances biodiversity may be greatest by maintaining a

transitional habitat created by large, frequent disturbance (Davis et al., 2000; Brawn et al., 2001;

Bond and Keeley, 2005; Bond et al., 2005). In others biodiversity may be maximised by a

mosaic of small disturbed patches of varying age (Angelstam, 1998; Brawn et al., 2001). Some

communities and species depend upon disturbance for regeneration (Pickett and White, 1985).

Disturbance and successional processes have a direct role in structuring avian habitats and

communities (Brawn et al., 2001; Hunter et al., 2001). It appears that some form of disturbance

may be essential for many of the world’s terrestrial birds. Brawn et al. (2001) made a number of

generalisations. Changes in the distribution of birds due to disturbance are usually related to

changes in the structure of habitat. Even-aged regeneration and subsequent succession leads to

the nearly complete turnover of the bird community. In contrast uneven-aged regeneration

causes changes of far lesser magnitude. The landscape context and size of the patch of

disturbance are important for determining the affect on biodiversity. Most information about

disturbance and birds relates to the period soon after disturbance (Brawn et al., 2001).

To my knowledge, the relative importance of natural disturbance regimes such as drought,

fire, flood and storms in Australian ecosystems has never been reviewed. Nonetheless, fire is an

important agent of disturbance in Australia; crucial to the creation and maintenance of the vast

majority of Australian ecosystems (Gill et al., 1981; Bradstock et al., 2002). The framework for

the investigation of the ecological effects of fire is the fire regime (Gill, 1975). Gill et al. (2002)

characterise fire regimes using four parameters: intensity, type, between-fire-interval and

season. Intensity is defined as the amount of energy released by a fire (Byram, 1959), type

refers to whether a fire burns above or below ground, between-fire-interval is the time between

fires usually measured in years, season is a qualification of intra-annual temporal variation and

relates to the timing of plant-phenological or physiological processes which affect

demographics. It is the combination of these factors expressed through time rather than a single,

intense and catastrophic event that is most important for determining the presence or absence of

the biota at any given place (Hobbs and Huenneke, 1992; Morgan et al., 2001; Gill et al., 2002;

Tasker et al., 2006). The response of flora to recurrent fire is predicted by matching the

characteristics of the fire regime with the vital attributes of each species (Noble and Slatyer,

6

1980). The response of fauna to fire regimes is mediated by the response of plants, although the

direct effects can still be expressed via a fire regime. The fire regime is necessarily point-based

because recurrent fire varies in space and time (Gill et al., 2002). The concept focuses on the

characteristics of recurrent fire and excludes the effects of fires on landscapes. The landscape

effects of fires include area burned, distribution of sizes of burned area, shapes and distribution

of unburned patches within a fire’s perimeter, fire severity, the proportion of the landscape at

different stages of development after fire and the fire interval functions (Gill, 1998). The fire

mosaic hypothesis combines aspects both of fire and its effects on landscapes.

1.2 Patch size The effect of patch size on the distribution of animals has been the subject of considerable

study. The effect of area has been investigated on both of the components of diversity - variety

and number (Hubell, 2001). The effect of area on species richness (variety) is called the species-

area relationship (Tjorve, 2003; Kai and Ranganathan, 2005; Turner and Tjorve, 2005). The

effect of area on species density (number) is called the density/area relationship.

Species richness almost always increases with area (MacArthur and Wilson, 1967; Turner

and Tjorve, 2005) and the relationship is so reliable that it is regarded as one of the best

established and well-proven macro-ecological patterns (Lomolino, 2003). A recent review by

Turner and Tjorve (2005) examined factors which may have influenced investigation of the

species-area relationship and the mechanisms behind the relationship. The species/area

relationship has been studied using two formats, described as ‘isolates’ and ‘samples’ (Preston,

1962; Turner and Tjorve, 2005). Isolates are patches within a matrix such as islands (MacArthur

and Wilson, 1967), mountaintops (Kattan and Franco, 2004) or forest remnants (Watson et al.,

2001). Samples are patches defined by the sampling design and include quadrats and political

units such as states (Preston, 1962). Few studies explicitly acknowledge the potential effect that

the experimental format may impose but the direction of the species/area relationship is usually

the same (Turner and Tjorve, 2005). Differences are limited to the shape of the relationship

(Scheiner, 2003)

A number of mechanisms have been proposed to explain the species-area relationship, but

there is no framework for interpreting empirical species/area relationships or predicting species

diversity patterns under different landscape configurations (Turner and Tjorve, 2005). The

mechanisms are: 1) random placement, 2) minimum area effects, 3) habitat heterogeneity, and

4) evolutionary independence. Random placement reflects the fact that the number of

individuals present is dependent on area and this dependence will cause the number of species

present to increase with area as well. Minimum area effects relate to the threshold patch size

that a species population requires to persist in a patch. Habitat heterogeneity refers to the

likelihood that the number of habitats will increase with patch size and thereby support more

species. Evolutionary independence is a factor at the largest spatial scales. At large scales

species richness may be driven by evolution. Further advances in the field are thought likely to

7

come from studies which succeed in isolating the effects of the different mechanisms (Turner

and Tjorve, 2005).

The effect of area on species density is called the density-area effect (Bender et al., 1998;

Connor et al., 2000). Three hypotheses predicting three different outcomes have been advanced

(Connor et al., 2000; Brotons et al., 2003). These are the equilibrium theory of island

biogeography (MacArthur and Wilson, 1967), the density compensation hypothesis (MacArthur

et al., 1972) and the resource concentration hypothesis (Root, 1973). The equilibrium theory of

island biogeography states that species richness increases with area (MacArthur and Wilson,

1967) and presumes that the number of individuals per unit of area remains constant. Critically,

the authors did not explicitly state whether their presumption applied to biotas, faunas,

communities or individual species; however, it has been interpreted to encompass all (Connor et

al., 2000). The density compensation hypothesis assumes that the summed density of species on

mainlands and islands remains the same, so since mainlands support more species than islands,

the density of individual species in small patches must be higher than that in large patches

(MacArthur et al., 1972). The resource compensation hypothesis seeks to explain the common

observation that insect densities reach high levels in patches with large amounts of resources

(Root, 1973). The original explanation for this observation is that large patches are easier to find

than small patches and individuals that find plentiful resources are less likely to emigrate.

Recent modelling suggests this explanation is simplistic and that migration could lead to a wider

range of outcomes depending on the characteristics of the species (Hamback and Englund,

2005). The three theories encompass the full range of potential outcomes of patch size. No

theory suggests that intermediate patches should have higher or lower densities than small or

large patches (Brotons et al., 2003; Connor et al., 2000).

Empirical evidence for or against the three hypotheses is inconclusive. At least three

reviews, including two formal meta-analyses have been conducted. Bowers and Matter (1997)

reviewed the density/area effect on 32 species of mammals using data from 12 studies. Twenty

species exhibited no effect, five showed a positive effect and seven showed a negative effect.

The authors concluded that no consistent density-area relationship operated over the systems

they studied. They further suggested that the concept of habitat patch may be a human construct

rather than a meaningful biological entity. Bender et al. (1998) conducted a formal meta-

analysis of the density-area effect on 134 species of birds, insects and mammals using data from

25 studies. The authors framed their conclusions in relation to the preference of species for edge

or interior habitats. They found that species which exhibit no preference for edge or interior

(generalists) do not show density-area effects. Interior-preferring species show negative density-

area effects and edge-preferring species show positive density-area effects. Connor et al. (2000)

conducted a formal meta-analysis which they describe as similar to that of Bender et al. (1998).

The Connor et al. (2000) study used data from 42 papers and encompassed 287 species and 21

complete faunal assemblages. The authors found that the population densities of individual

species were positively correlated with area and this supports the resource concentration

8

hypothesis. However, area only explained five percent of the variation in animal population

densities and was therefore a moderate to small influence. The summed densities of species

within complete faunal assemblages were not correlated with area, a finding that was consistent

with the equilibrium theory of island biogeography. Connor et al. (2000) address the differences

in the findings between their study and that of Bender et al. (1998). They suggest that the

findings from the two studies are similar and that the main difference is the slightly stronger

effect that they report. They attribute the stronger effect to their larger sample size.

Balancing the evidence, it appears that overall; the density-area relationship is weak, at

least in the generalised sense in which it is presented in the equilibrium theory of island

biogeography, the density compensation hypothesis and the resource compensation hypothesis.

Density-area effects are apparently relatively strong in some species but vary in both strength

and direction. Approaches similar to that taken by Bender et al. (1998) may be more effective at

explaining the distribution of species in landscapes than either of the three hypotheses. Recent

work has demonstrated that the type of matrix in which a patch exists can influence the strength

of the density-area effect (Brotons et al., 2003). This result is consistent with studies

investigating edge effects (Ries et al., 2004). In addition, simulation modelling suggests that

animal population densities within patches may vary in time due to between-generation effects

(Matter, 1999) and that density-dependence can potentially have a strong effect on the density-

area relationship (Matter, 2003). All three of these factors may have confounded past work.

1.3 Edge and ecotone The effect of the juxtaposition of two or more different habitats on biota has long been the

subject of biological study (Ries et al., 2004). The interest in edges and ecotones is due to the

ecological differences between them and interior habitat. Advancement in understanding of

edge and ecotone effects within ecology has been hampered by the imprecise use of the words

(Baker et al., 2002; Strayer et al., 2003; Ries et al., 2004). It is therefore essential that they are

defined. I adopt the definition of Baker et al. (2002). Edge is the boundary between two

ecosystems and ecotone is the zone of transition between them. Recent important contributions

to the field (Strayer et al., 2003; Ries et al., 2004; Ries and Sisk, 2004) have attempted to

standardise the classification of ecotones and edges to better enable synthesis.

Ecotones occur at a wide range of scales depending on how a patch of habitat is defined

(Strayer et al., 2003; Ries and Sisk, 2004). Regardless of scale, the edge effect declines with

distance from edge so small patches and irregularly shaped patches of habitat have a greater

potential edge effect than large, approximately round patches (Ries et al., 2004). Variables –

biotic and abiotic – which increase at edges have a positive edge response, those which exhibit

no pattern have a neutral response and those which decline have a negative response.

A review of more than 900 empirical papers about terrestrial ecotone response produced a

mechanistic model of ecotone-related changes in species abundance, a predictive model of

ecotonal effects on species abundance based on resource distribution and a list of parameters

9

which appear responsible for further variation (Ries et al., 2004; Ries and Sisk, 2004). The

model proposes four underlying mechanisms for ecotone effect (Ries et al., 2004). 1) Ecological

flow of material, organisms or energy between habitats, for example edge-related microclimatic

changes (Matlack, 1993). 2) Optimal access to spatially separated resources such as roosts and

food supply in the case of the common blossom bat (Syconycteris australis) (Law, 1993) or

foraging and breeding resources in the case of the Brown-headed Cowbird (Lowther, 1993). 3)

Resource mapping by an organism so that it’s distribution matches that of its resources, such as

plants responding to increased light (Watkins et al., 2003; Piper and Catterall, 2003). 4) Species

interactions such as mutualism and competition, for example the aggressive competition of the

Noisy Miner (Manorina melanocephala) (Piper and Catterall, 2003). The first two mechanisms

represent fundamental differences between ecotones and habitat interior. The second two

mechanisms are not restricted to ecotones but nonetheless are important for explaining changes

in abundance associated with ecotones (Ries et al., 2004).

Another recent advance in ecotone theory is a predictive model of ecotonal effects on

species abundance based on resource distribution (Ries and Sisk, 2004). The model assumes a

simple landscape of two adjacent patches and considers contrast in the nature and quality of the

resources, either supplementary or complementary. The model predicts the pattern of changes in

the abundance of organisms across an ecotone in five instances. Considering first cases where

resources are concentrated in one patch. 1) If resources in the lower quality patch are

supplementary to those in the higher quality patch, then a transition across the ecotone is

expected. 2) If the resources in the lower quality patch are complementary, then a positive edge

response is predicted in both patches. In other instances resource distribution may be relatively

evenly distributed across patches. 3) Where the resources are supplementary the edge response

is predicted to be neutral. 4) Where the resources are complementary, a positive response is

predicted. Another scenario is where resources are concentrated along an edge. 5) In this

instance, a positive response is predicted (Ries et al., 2004; Ries and Sisk, 2004).

Even within this framework, comparison of empirical papers reveals further variation (Ries

et al., 2004; Ries and Sisk, 2004). When habitat type and species were controlled, it was rare for

the direction of a response to change – e.g. from positive to negative. Most common was a

change in the strength of the effect – e.g. from strongly positive to weakly positive, neutral or

apparently neutral (due to lack of statistical significance). Four factors may account for this

variation. 1) Edge orientation particularly in relation to the sun will affect energy flows across

the ecotone (Matlack, 1993). 2) Temporal effects such as season or time of day due to

temporally-related changes in the resource requirements of organisms (Noss, 1991). 3)

Differences in the degree of habitat fragmentation within the landscape (Moen and Jonsson,

2003). 4) The degree of difference across the edge - edge contrast (Fletcher and Koford, 2002).

A major question hanging over ecotone research is whether the results of relatively small-

scale studies can be extrapolated to landscape or even larger scales (Ries et al., 2004). In

particular, questions remain in two areas: 1) how far does edge influence extend; and 2) what

10

are the effects of multiple edges? While most studies test at distances of a few hundred metres,

effects may occur over many kilometres (Laurance, 2000). Multiple edges are a reality of

landscapes, both natural and anthropogenic and there is evidence of cumulative effects

(Fletcher, 2005). While there is optimism that ecotone and edge effects can be up-scaled (Ries

et al., 2004) these two factors impose an additional layer of complexity to the problem, both in

terms of finding answers and experimental design.

1.4 Aims and hypotheses The aim of this project is to investigate the fire mosaic hypothesis by testing the

assumptions on which it is based. I will investigate three hypotheses in a model system.

1. Time-since-fire affects the distribution of fauna.

2. Faunal diversity is greater in smaller patches of habitat than in larger patches.

3. Fauna diversity is greater at pyric edges between habitats than it is in habitat

interior.

The potential effects of fuel reduction that may accompany prescribed burning are outside the

scope of this study, as is detailed consideration of fire management practice.

An investigation of time-since-fire is essential because an effect of fire is crucial to the fire

mosaic hypothesis (Bradstock et al., 2005; Parr and Andersen, 2006). If there is no effect of fire

history on biodiversity, then the spatial arrangement of different times-since-fire is irrelevant

and the definition of habitat patches and habitat edges based on time-since-fire is not valid. The

aim of the patch size study was to investigate whether the size of a patch of mulga woodland of

the same time-since-fire affected bird diversity. For patch size to function as a mechanism by

which a fine-scaled fire mosaic could increase avian diversity, density of individual species,

combined bird density or species richness must increase with decreasing patch size, or small

patches must support species which are not supported by large patches. Edge effect could also

function as a mechanism by which avian diversity increases in a fine-scale fire mosaic. For this

to be true, pyric edges within a vegetation type must support species that are not supported by

the interior of habitats, or support greater species richness, greater bird density or greater

density of individual bird species than the interior of habitats.

The project comprises five parts. 1) The literature relating to fire and birds was reviewed.

2) The study site was mapped and characterised according to a range of ecological parameters

using ArcGIS 9.1 (ESRI, 2004) and the information was used to design three experiments. 3)

Two replicated time-since-fire experiments were set-up, designed to investigate changes in the

distribution of birds with time-since-fire and patch size. 4) Another experiment was designed to

investigate changes in the distribution of birds across a pyric edge. 5) The habitat structure of

the survey sites used in the experiments was measured to help explain the results.

11

Chapter 2: Fire and birds Most studies examining the response of birds to fire have been undertaken in fire-prone

regions of North America, the Mediterranean and Australia, with fewer studies from southern

Africa, South America and South-east Asia. To my knowledge, a global review has never been

published but continental and regional syntheses have. The most recent of these are; Australia

(Woinarski and Recher, 1997; Woinarski, 1999), North America (Smith, 2000; Kotliar et al.,

2002) and southern Africa (Parr and Chown, 2003).

A high proportion of the early literature about fire and birds suffers serious methodological

problems (Finch et al., 1997; Woinarski, 1999; Kotliar et al., 2002; Parr and Chown, 2003;

Smucker et al., 2005; Tasker et al., 2006). Much of the work is anecdotal or opportunistic

having taken place when fire interrupted another project. Many of the studies either lack

replication, draw comparisons between treatments and sites that may not be comparable, are

short-term, or fail to account for potentially important or confounding factors such as the

characteristics of the fire, fire history, landscape context or salvage logging (Finch et al., 1997;

Woinarski, 1999; Kotliar et al., 2002; Parr and Chown, 2003; Smucker et al., 2005; Tasker et

al., 2006). Fully replicated, long-term studies are scarce.

Another limitation of the literature is the failure to treat fire as a regime (Tasker et al.,

2006). Instead, fire is treated as a one-off event with the implication that it is a catastrophic

perturbation of a climax ecosystem. It is then assumed that the ecosystem invariably recovers

via a predictable process of succession. Also implied, is that fire is bad – not part of the system

– and it follows that the ecosystem would be better off without it. The most celebrated example

of this thinking is the now defunct policy of fire exclusion within national parks in the United

States of America during much of the twentieth century (Hutto, 1995; Lyon et al., 2000a; Lyon

et al., 2000d) . The concept of ‘fire as a catastrophe’ has been superseded by the concept of the

fire regime (Gill, 1975). Far from being catastrophic, fire is an integral part of ecosystems

which occupy more than half the land area of the planet (Woinarski and Recher, 1997; Lyon and

Smith, 2000; Bradstock et al., 2002; Bond et al., 2005). In such ecosystems, fire is part of the

landscape and shapes habitats. For example, if the frequency of fire is high and the extent is

great, then the ecosystem may be permanently maintained in a sub-climactic state (Odum,

1969). In such an instance the successional changes in the biota for a period following a single

fire assume less significance than they do if it is assumed that fire is an aberrant perturbation

from a climax. In what state was the vegetation before the fire? What state is it returning to?

While still valuable, studies which treat fire as a single event provide little of the contextual

information – the history of fire at the site – required to unravel the complexity of the processes.

Despite the limitations, reviewers conclude that generalisations can be drawn because consistent

trends do appear across studies (Finch et al., 1997; Woinarski and Recher, 1997). In addition,

many of the shortcomings noted in previous reviews are not evident in more recent work.

12

2.1 The response of bird communities to fire Time-since-fire is usually the most easily, and therefore most commonly, investigated

effect of fire on biota (Gill and Catling, 2002) and avifauna appears to be no exception. The

impact of fire on bird communities is considerable, with the strength of the effect strongest

immediately post-fire and declining with time (Izhaki and Adar, 1997; Woinarski and Recher,

1997; Huff and Smith, 2000; Smith, 2000; Kotliar et al., 2002; Pons, 2002; Saab et al., 2004;

Smucker et al., 2005; Whelan, 1995). The effects of fire on birds can be placed into three

categories: 1) those associated with combustion at the time of the fire, 2) those associated with

habitat changes caused by fire and, 3) those associated with the development of the habitat after

the fire. The three categories operate at different temporal scales. Many of the direct effects of

burning appear to subside soon after the flames are extinguished, usually lasting only a few days

(Woinarski, 1999; Lyon et al., 2000a). Most of the habitat changes caused directly by fire

usually last no more than a few years, while the development of vegetation to a climax state and

the associated changes in the bird community may take decades or centuries (Woinarski, 1999;

Lyon et al., 2000d; Lyon et al., 2000c; Kotliar et al., 2002; Herrando et al., 2002a).

2.2 During a fire Bird mortality during fires appears to be related to fire extent and intensity (Quinn, 1994;

Whelan, 1995; Woinarski and Recher, 1997; Lyon et al., 2000b; Pons et al., 2003a). Low-

intensity fires apparently cause minimal mortality as do intense fires of limited extent

(Lawrence, 1966; Whelan, 1995; Woinarski and Recher, 1997; Pons, 2002). For example, a

colour-banded population of birds in south-eastern Australian Banksia heathland suffered

minimal mortality during an intense 20ha prescribed fire (Woinarski and Recher, 1997). In

contrast intense, extensive fires appear to cause greater mortality. A wildfire which burnt

20,000ha of south-eastern Australian heathland is believed to have killed most of the resident

birds, thousands of which were carried into the ocean and later washed up on the foreshore

(Fox, 1975; Recher et al., 1975; Woinarski and Recher, 1997). Most of the birds washed up

were smaller species such as fairy-wrens (Maluridae) which weigh approximately 10g (Higgins

et al., 2001) and honeyeaters (Meliphagidae) which weigh <120g (Higgins et al., 2001). Very

few were the size of Pied Currawongs (Strepera graculina) which weigh approximately 280g

(Higgins et al., 2006) or Laughing Kookaburras (Dacelo novaeguineae) which weigh

approximately 340g (Higgins, 1999).

Fires provide foraging opportunities for some species. In particular, carnivores may be

attracted to an easy meal of dead and dying victims and aerial insectivores may be attracted to

prey displaced by the disturbance (Quinn, 1994; Woinarski and Recher, 1997; Lyon et al.,

2000b; Pons, 2002). Such foraging opportunities probably persist for little more than a few days

after fire and appear to have little influence on the longer term composition of the avifauna in

any particular landscape.

13

2.3 Post-fire Birds respond to habitat structure (MacArthur and MacArthur, 1961; Willson, 1974;

Whelan, 2001) and fire can cause a rapid and profound change (Whelan, 1995; Lyon et al.,

2000a). In addition, fire can create favourable growing conditions for a suite of relatively short-

lived plants, which in turn provide resources for invertebrates and birds (Quinn, 1994; Whelan,

1995; Izhaki and Adar, 1997; Woinarski and Recher, 1997; Lyon et al., 2000e; Stuart-Smith et

al., 2002; Smucker et al., 2005). The suite of species present at a site in the period immediately

after a fire is a function of mortality during the fire and the new habitat structure (Lyon et al.,

2000c). The birds present at a site in the period after a fire are likely to be a combination of

those individuals which have survived the fire and are able to persist in the new habitat and

those which emigrate to the site to exploit the pulse of resources released by the fire (Brotons et

al., 2005).

Fire often creates adverse conditions for the pre-fire avifauna. The rates of survival of

seven bird species investigated by mist-net recapture of banded individuals declined following

fire in Mediterranean shrublands (Pons et al., 2003a). The response of some species appeared to

be lagged, probably due to strong individual site fidelity even though the habitat was sub-

optimal. Strong site fidelity has been detected in other studies in the Mediterranean (Pons and

Prodon, 1996; Pons, 2002), Australia (Woinarski and Recher, 1997) and North America (Emlen,

1970). In such circumstances, the foraging and nesting behaviour of individuals may change to

match the available resources (Brooker and Rowley, 1991; Quinn, 1994; Pons and Prodon,

1996; Woinarski and Recher, 1997; Lyon et al., 2000c; Pons, 2002; Pons et al., 2003b; Pons et

al., 2003a; Ward, 2004). Survival and persistence of birds following fire has been attributed to

the proportion of remnant vegetation within the area of the burn (Rowley and Brooker, 1987;

Pons and Prodon, 1996; Woinarski and Recher, 1997; Lyon et al., 2000c; Herrando and

Brotons, 2001; Kotliar et al., 2002; Pons et al., 2003a). A 12-year study of a colour-banded

population of Yellow-rumped Thornbill (Acanthiza chrysorrhoa), a small Australian terrestrial

insectivore, found that survival following fire is positively correlated with the proportion of

remnant vegetation (Rowley and Brooker, 1987). Sardinian Warblers (Sylvia melanocephala)

and Dartford Warblers (Sylvia undata), both European insectivores, can survive on burnt sites

with as little as two percent unburnt vegetation (Herrando et al., 2001). Some species that

survive fires cannot persist in the new habitat (Benshemesh, 1989). In Australian semi-arid

mallee woodland, at least 10 of 11 marked Malleefowl (Leipoa ocellata) survived a wildfire but

within a few months all but four had emigrated or died. Tenacious survivors of fire may persist

on burnt sites but fail to breed (Herrando et al., 2001; Pons et al., 2003a).

Fire often creates a pulse of short-lived resources associated with plants and invertebrates

that take advantage of the favourable environment that is created (Whelan, 1995; Stuart-Smith

et al., 2002). In Australia, this pattern is observed in a number of ecosystems. A suite of

heathland plants shed canopy-stored seed that attracts opportunistic granivores such as

cockatoos (Psittacidae), finches (Estrildidae) and pigeons (Columbidae) (Woinarski and Recher,

http://www.birding.in/birds/Psittaciformes/psittacidae.htm

http://www.birding.in/birds/Columbiformes/columbidae.htm

14

1997). In arid grassland and woodland, a suite of nomadic open-country insectivores and

granivores such as Budgerigar (Melopsittacus undulatus), White-winged Triller (Lalage sueurii)

and Masked Woodswallow (Artamus personatus) invade burnt areas (Reid et al., 1991). Some

plants, notably Xanthorrhea and eucalypts, flower following fire, attracting a suite of

nectarivores (Woinarski and Recher, 1997). In south-eastern Australian eucalypt forests species

that feed on open ground increase in abundance following fire (Loyn, 1997). In North America,

the Olive-sided Flycatcher (Contopus cooperi) is often present immediately after fire (Hutto,

1995; Kotliar et al., 2002). Woodpeckers (Piciformes) and aerial insectivores are early colonists

of burnt patches (Hutto, 1995; Lyon et al., 2000b; Kotliar et al., 2002). Granivores such as

Cassin’s Finch (Carpodacus cassinii), Pine Siskin (Carduelis pinus) and Lazuli Bunting

(Passerina amoena) may respond to short-term increases in seed availability (Smucker et al.,

2005; Leidolf et al., 2007). Granivores and insectivores concentrate in burnt chaparral to feed

on concentrations of seed and exposed insects (Lawrence, 1966). In South America the

Southern Lapwing (Vanellus chilensis) is often recorded feeding in burnt wetlands immediately

after fire (Isaach et al., 2004).

Fire often causes a large, sometimes near-complete turnover of bird species. This occurs in

many North American habitats (Huff and Smith, 2000) including conifer forests (Hutto, 1995;

Finch et al., 1997; Imbeau et al., 1999; Kotliar et al., 2002; Smucker et al., 2005; Schieck and

Song, 2006), oak savannas (Davis et al., 2000) and chaparral (Lawrence, 1966). Similar patterns

have been described in Australian heathlands, Acacia woodlands, hummock grasslands, mallee

woodlands and eucalypt forests and woodlands (Woinarski and Recher, 1997), the forests,

woodlands, shrublands and open habitats of the Mediterranean (Pons and Prodon, 1996;

Herrando et al., 2002a; Herrando et al., 2003), savannah woodlands and shrublands of Southern

Africa (Skowno and Bond, 2003), South American tropical rainforest (Barlow et al., 2002;

Barlow and Peres, 2004; Barlow et al., 2006), southeast Asian tropical rainforest (Slik and van

Balen, 2006; Adeney et al., 2006) and tall grassland of South America (Isacch et al., 2004). In

most instances species with greater habitat breadth (generalists) benefit at the expense of those

with narrow habitat requirements (specialists) (Pons and Prodon, 1996; Woinarski and Recher,

1997; Barlow et al., 2002; Isacch et al., 2004; Pons and Bas, 2005; Adeney et al., 2006). In

Mediterranean habitats, some of the species that occupy burnt country change their diet

seasonally from plant-based to insect-based while those that occupy the unburnt sites rely on

insect food year-round (Pons and Prodon, 1996).

Studies which report little change in the avifauna following fire have taken place in

structurally simple habitats (Huff and Smith, 2000). The bird community composition of

African savannah grasslands remains unchanged following fire (Mills, 2004). The result is

attributed to the short fire return interval. Highland grassland sites with two contrasting fire

management and grazing regimes in South Africa share almost three times as many bird species

than are present under either single regime (Jansen et al., 1999). Burning in North American

http://www.itis.usda.gov/servlet/SingleRpt/SingleRpt?search_topic=TSN&search_value=178140

15

tallgrass prairie had no effect on the species richness or diversity of resident grassland birds, nor

on the density of any species (van Dyke et al., 2007). The bird community composition of

Juncus salt marsh in South America showed little difference between burnt and unburnt plots a

year after fire (Isacch et al., 2004). Differences in species richness were recorded two months

after the treatment but in no later surveys. The rapid convergence of the avifauna present at the

two treatments was attributed to the relative simplicity of the short grass habitat.

2.4 Increasing time-since-fire

Most studies of birds and fire apparently assume that in time, burnt habitats will return to

their pre-fire state and that the bird community will follow (Tasker et al., 2006). Rarely are data

presented to demonstrate that this is the case. Resilience theory, (Elmqvist et al., 2003), the

plant vital attributes concept (Noble and Slatyer, 1980) and empirical evidence suggest that the

assumption is not necessarily valid (Gill, 1975; Noble and Slatyer, 1980; Helle and Monkkonen,

1990; Bowman et al., 1994; Agee, 1998; Duncan et al., 1999; Lyon and Smith, 2000; Platt and

Connell, 2003; Bond et al., 2005; Bradstock et al., 2005; Nano, 2005). Nonetheless, the

vegetation present prior to a fire is the most important factor determining the vegetation present

after a fire (Egler, 1954) so broad generalisations about the dynamics of the vegetation structure

and the bird community appear reasonable. For example, Helle and Monkonen (1990), in a

paper that acknowledges the complexity of disturbance responses, suggested that forest

regeneration is characterised by: 1) increasing vegetation structural complexity; 2) increasing

vegetation height; 3) most vigorous shrub growth in the young or middle stages; and 4) greatest

height diversity in the stage preceding the climax stage. Analogous broad generalisations about

changes in bird communities therefore also appear reasonable.

The composition of bird communities change after fire as the vegetation structure changes

(Raphael et al., 1987; Woinarski and Recher, 1997; Imbeau et al., 1999; Huff and Smith, 2000;

Schieck and Song, 2006). In Australia, open-country birds use burnt eucalypt forest for about

three years until the shrub layer becomes too dense. Where fire has killed the canopy trees,

species typical of old-growth forest may be absent or less abundant for at least 50 years

(Woinarski and Recher, 1997; Loyn, 1997). A similar replacement of open-country birds has

been observed in Australian heath (Woinarski and Recher, 1997). Densities of one heath species

the Slender-billed Thornbill (Acanthiza iredalei) peaked seven years after fire (Ward and Paton,

2004). In contrast, densities of another heath species, the Eastern Bristlebird (Dasyornis

brachyterus) increased with time-since-fire and were highest in vegetation of the oldest fire-age

(Baker, 2000). In the Mediterranean, open-country birds are replaced by shrubland species,

though the timing is variable and the turnover continues as shrublands grow into forests

(Herrando et al., 2002a). In North American forests, foliage-gleaning birds begin returning to

burnt sites by the sapling stage (Imbeau et al., 1999). The density and species richness of

foliage-gleaning birds increases as the volume of foliage increases. At the same time open-

country birds and woodpeckers decline (Raphael et al., 1987; Finch et al., 1997). A big

16

milestone in North American post-fire avian dynamics is the closure of the canopy (Huff and

Smith, 2000). Once this occurs, roughly 40-100 years after fire, the structure of the forest

stabilises and so do the bird communities. The ecosystem may then remain relatively unchanged

for centuries.

Two cases that do not appear to exhibit a gradual return toward the pre-fire bird

community come from rainforest in South America (Barlow and Peres, 2004) and Indonesia

(Adeney et al., 2006). Although both studies were conducted over periods of five years or less,

the bird community in burnt rainforest maintained a trajectory away from the pre-fire structure.

2.5 Habitat structure Changes in bird communities due to fire are often explained in terms of changes in habitat

structure (Comparatore et al., 1996; Izhaki and Adar, 1997; Loyn, 1997; Woinarski and Recher,

1997; Davis et al., 2000; Huff and Smith, 2000; Herrando et al., 2001; Barlow et al., 2002;

Kotliar et al., 2002; Herrando et al., 2003; Pons et al., 2003b; Skowno and Bond, 2003; Barlow

and Peres, 2004; Isacch et al., 2004; Smucker et al., 2005; Pons and Wendenberg, 2005; Slik

and van Balen, 2006; Adeney et al., 2006; Schieck and Song, 2006; Brawn, 2006; Valentine et

al., 2007). Such an explanation is necessarily imprecise because the measurement of habitat

structure is not standardised (McElhinny et al., 2005). Nevertheless, the intent of the term, that

the within plot (small-scale) distribution of foliage and other habitat features is a better predictor

of the composition of the bird community than the presence or absence of floral species, appears

valid.

In Mediterranean landscapes, bird community composition in burnt patches is related to

the amount of shrub cover (Herrando and Brotons, 2002). Years after fire, the presence of

particular species is related to tree height but not tree density or shrub cover. The relationship is

sufficiently reliable that species are often grouped according to habitat structure – e.g. open

country, shrubland or woodland species. In South African mesic savannah, the best predictors of

bird community composition are foliage height diversity, canopy cover and grass height

(Skowno and Bond, 2003). In East African savannah, shrub canopy area was the best predictor

of bird diversity (O'Reilly et al., 2006). In Madagascan dry forest, foliage volume, grass volume

and bare ground explained most of the variation in bird communities (Pons and Wendenberg,

2005). In northern Australian savannah, shrub, tree and vine abundance influences bird

community composition (Valentine et al., 2007). In North American oak savannah, increasing

fire frequency tended to reduce tree density and leaf area (Davis et al., 2000; Brawn, 2006). At

the same time canopy insectivores decreased while omnivorous ground feeders and bark

gleaners increased. In South American tropical rainforest, bird community composition was

most closely related to the amount of canopy cover (Barlow and Peres, 2004) and a similar

result was found in Indonesian rainforest (Adeney et al., 2006).

Despite the popularity of the use of habitat structure to explain changes in the composition

of bird communities after fire, such a metric is unlikely to be universally applicable (Smyth et

17

al., 2002). This may be particularly relevant in ecosystems subject to other disturbance such as

fragmentation or where some plants produce abundant nectar or fruit (Cody, 1993). An

Australian study of the response of hollow-nesting birds to fire and logging found that habitat

structure per se did not account for all the observed variation (Smyth et al., 2002). Similarly, a

study of the threatened Red-cockaded Woodpecker (Picoides borealis) in North American

conifer forest found that group size, clutch size and fledging success was not related to habitat

structure represented by the density and size of trees (James et al., 1997). It was however related

to the composition of the ground cover and the extent of natural pine regeneration both of which

were mediated by fire. The authors concluded that the birds were more productive at more

frequently burnt sites due to nutrient cycling related to fire and a putative increase in the

quantity and quality of prey items.

2.6 Fire severity Fires are variable and the impact on the biota is therefore likely to differ depending on

characteristics such as intensity, rate of spread, continuity of the fire front, season and extent of

the burn (Whelan, 1995). Fire severity is a measure of the impact of fire on an ecosystem

(Simard, 1991). It is often measured in terms of the degree of disturbance, for example the

proportion of ground surface burned or the height of flame scorch (Knapp and Keeley, 2006).

The severity of a fire can influence the composition of the avifauna following fire (Woinarski

and Recher, 1997; Huff and Smith, 2000; Barlow et al., 2002; Kotliar et al., 2002; Mills, 2004;

Saab et al., 2004; Smucker et al., 2005; Adeney et al., 2006; Schieck and Song, 2006; Leidolf et

al., 2007; Kotliar et al., 2007). The proportion of unburnt vegetation can be important for

survival and persistence of individual birds which were present before fire (Woinarski and

Recher, 1997; Huff and Smith, 2000; Barlow et al., 2002; Kotliar et al., 2002; Mills, 2004; Saab

et al., 2004; Smucker et al., 2005; Schieck and Song, 2006).

Studies in North America have demonstrated differences in the fire response of a number

of species depending on fire severity. Kotliar et al. (2007) investigated the response of birds to a

fire severity gradient in conifer forest. Severity was assigned to one of four classes: 1) unburnt;

2) low severity burn; 3) moderate severity burn; and 4) high severity burn. Fire severity

response models were developed for 21 species and covered a broad spectrum of possible

responses. The response classes were: 1) strong decline with increasing fire severity; 2) weak

decline with increasing fire severity; 3) no significant response to fire severity; 4) peak density

at an intermediate fire severity; 5) weak increase with increasing burn severity; and 6) strong

increase with increasing fire severity. The authors concluded that the quantification of burn

severity was important for understanding the fire response of many species. Avian response to

fire severity has also been demonstrated for 10 species in low-elevation conifer forest (Smucker

et al., 2005) and for seven species in montane woodland and conifer forest (Leidolf et al., 2007).

Fire severity may influence breeding success of cavity-nesting species (Saab et al., 2004)

18

because unburnt vegetation may allow earlier re-colonisation of a site by predators, reducing

breeding success.

Effects of fire severity have also been demonstrated in rainforests. In South American

rainforests, different foraging guilds respond in different ways to fire depending on the severity

(Barlow and Peres, 2004). An example is the guild of arboreal-gleaning insectivores which

showed high species turnover that was strongly related to fire severity. Unburnt rainforest

typically supported primary forest arboreal-gleaning insectivores such as the Plain-throated

Antwren (Myrmotherula hauxwelli) and the Long-winged Antwren (Myrmotherula

longipennis). These were replaced by tree-fall gap loving species such as the White-flanked

Antwren (Myrmotherula axillaris) and Warbling Antbird (Hypocnemis cantator) and then by

second-growth and edge-species such as Blackish Antbird (Cercomacra nigrescens) and

Moustached Wren (Thryothorus genibarbis). Fire severity also strongly influences the

composition of bird communities in Sumatran rainforest (Adeney et al., 2006). In particular,

understorey insectivores declined dramatically with increasing burn severity. An instance where

fire severity was found to have little effect was South African savannah grasslands (Mills,

2004). No species were entirely absent from any treatment and since post-fire habitats recovered

quickly it was concluded that not even severe fires disturbed bird communities significantly.

2.7 Burn season Season is a parameter of fire regimes and can influence the impact that a fire has on an

ecosystem (Gill et al., 2002). Season may therefore affect birds but to my knowledge only one

study has investigated this. In northern Australian tropical savannah, the season of a burn

influences the composition of bird communities (Valentine et al., 2007). Within 12 months of

fire, sites burnt in the dry season had more insectivores and granivores but less carnivores than

sites burnt in the wet season. Four years after treatment, the dry-season burn sites had a different

bird community to that present in the wet-season burn site and unburnt controls. The differences

between the burn season treatments were attributed to differences in habitat. Four years after

treatment, the wet season burn sites had developed a vegetation structure similar to the unburnt

controls but the dry season burn sites had not.

2.8 Landscape context Landscape context is an important determinant of the distribution of birds (Forman, 1995)

(Mazerolle and Villard, 1999). However, relatively few studies investigating the effect of fire on

birds have specifically examined it. Within the Mediterranean, studies examining fire and birds

have found that biogeography, fire extent and the characteristics of neighbouring habitats all

influence the distribution of avifauna (Herrando and Brotons, 2002; Brotons et al., 2005; Pons

and Bas, 2005). Birds remained confined to a geographic range regardless of the presence of

apparently suitable fire-mediated habitat in other parts of the region. This suggests that

biogeography is a stronger determinant of the presence of birds than fire (Brotons et al., 2005).

19

Amongst newly burnt sites, large patches contain more species than small patches (Pons and

Bas, 2005). Two reasons are advanced to explain this. Large patches may be easier for birds to

find and are also more likely to contain open-country species prior to fire. Different bird species

are present at burnt sites depending on whether the patch borders woodland, urban development

or agriculture (Pons and Bas, 2005). Species richness in Mediterranean forest (>40 years since

fire) also increases with patch size (Herrando and Brotons, 2002). Patches of forest with a

higher proportion of edge habitat support more species than patches with more interior habitat.

Distance to the nearest-neighbouring patch of similar habitat has no influence on either recently-

burnt or forested habitats (Herrando and Brotons, 2002; Pons and Bas, 2005).

In South American tropical rainforest, the recent fire history of surrounding rainforest

influences the avifauna of neighbouring patches (Barlow and Peres, 2004). The avifauna in both

burnt and unburnt patches appeared to be affected by proximity to different habitats. In North

American oak woodland, Lazuli buntings were present at higher densities at burnt sites and less

than 1,000m from burnt sites than they were more than 1,000m from burnt sites (Leidolf et al.,

2007). The results imply an edge effect and suggest that landscape context is important for this

species. In North American conifer forest the abundance of two birds, Townsend’s Solitaire

(Myadestes townsendi) and Solitary Vireo (Vireo solitarius) decrease with increasing patch size

of a recent burn. The author postulated that the negative patch size responses were due to the

proximity of unburnt vegetation in small burns (Hutto, 1995). Effects of patch size, shape and

proximity to unburnt habitat were listed as potentially confounding factors in a review of studies

of fire and birds in North American conifer forests (Kotliar et al., 2002).

The comparative strength of landscape effects versus fire mediated vegetation structure

appears relatively weak (Pons et al., 2003b; Adeney et al., 2006). A study which investigated

the distribution of birds across a fine-scale mosaic of habitats of different land-use and time-

since-fire in the Mediterranean, found that the bird community was more strongly influenced by

species-specific selection of cover types than by the use of multiple patches (Pons et al., 2003b).

In Sumatran rainforest, fire severity was a better predictor of the bird community than the

relative location in the landscape of the plots (Adeney et al., 2006).

2.9 Spatial and temporal variability Variability in the avifauna, both spatial and temporal, declines with time-since-fire. In the

Mediterranean, bird species richness and abundance is more variable seasonally at recently

burnt sites than unburnt controls (Herrando et al., 2002b). The bird communities at burnt sites

also have greater spatial variability than at unburnt sites (Herrando et al., 2003; Brotons et al.,

2005). In North America, studies indicate a similar pattern. The bird communities of burnt sage

scrub (Stanton, 1986) and burnt Sierra Nevada forest (Raphael et al., 1987) are both more

seasonally variable than the equivalent unburnt controls. In Australian heath, densities of the

Slender-billed Thornbill (Acanthiza iredaleyi) were less variable in heath that was 22 years-

since-fire than in heath that was 3 years or 10 years-since-fire (Ward and Paton, 2004).

20

2.10 The speed of post-fire avian dynamics The rate of change of bird species composition following fire appears to be determined by

the fire response (vital attributes) of the dominant vegetation and the climate. The two factors

combine to determine the rate of change of the habitat structure. The post-fire response of plants

to recurrent disturbance has been classified according to life history characteristics – vital

attributes (Noble and Slatyer, 1980). In habitats in which the dominant vegetation survives fire

and resprouts, a complex habitat structure can be established more quickly than in habitats in

which the dominant vegetation is killed by fire and then grows from seed. Post-fire avian

dynamics proceed more quickly in habitats dominated by sprouting vegetation than they do in

those dominated by seeding vegetation (Prodon et al., 1987; Woinarski and Recher, 1997;

Herrando et al., 2002a). Climate also determines post-fire avian succession. Vegetation in xeric

habitats grows more slowly than in mesic and hydric habitats, so birds associated with recently-

burnt open habitats may occupy a site for longer (Herrando et al., 2002a). Where water is not

limiting, vegetation in cooler climatic zones may grow more slowly than that in warmer climatic

zones (Schieck and Song, 2006).

2.11 Breeding Fire affects the breeding success of birds but this varies among species. A detailed long-

term study of the birds breeding in a heath in south-western Australia (Brooker and Rowley,

1991) found a wide range of effects attributable to fire. Of the 26 species that bred at the site in

the season before a major fire, 21 also attempted to breed in the season immediately following

the fire. Two species bred that had not previously been recorded breeding at the site and another

two species bred in greater numbers than usual. Two species failed to breed for two seasons

following the fire and another species had not bred five years after the fire. The breeding

behaviour of the three most common species - Splendid Fairy-wren (Malurus splendens),

Western Thornbill (Acanthiza inornata) and Yellow-rumped Thornbill (Acanthiza chrysorrhoa)

was examined in detail. Splendid Fairy-wrens and Western Thornbills delayed breeding in burnt

patches possibly because of a shortage of nesting material and a lack of food for egg production.

The nest locations of all three species were affected by the fire and the Splendid Fairy-wrens

experienced a higher level of nest failure during the season.

In North American conifer forest, fire is an important factor for cavity-nesting species

(Saab et al., 2006). Breeding density of cavity-nesters changes with time-since-fire. Two

species, both woodpeckers, achieve maximum breeding densities four years after fire and then

decline. Other factors which influence the presence of cavity-nesting birds include fire extent,

shape and severity (Saab et al., 2004).

The restoration of higher-frequency fire regimes to North American oak savannas

improved the breeding success of six bird species apparently due to a decline in nest predation

(Brawn, 2006). No species experienced a reduction in breeding success. The increased fire

frequency had no effect on nest parasitism. In the Mediterranean, newly fledged Sardinian

21

warblers living on burnt sites had lower body condition than those living on unburnt sites

suggesting that that burnt sites are of overall lower quality (Herrando and Brotons, 2001).

2.12 Conservation Recommendations for the use of fire for maintaining avian diversity vary from habitat to

habitat depending on the fire preference(s) of the bird(s) species which are threatened. In

Australia, inappropriate fire regimes affect 45% of mainland bird species (Garnett and Crowley,

2000). Most threatened Australian birds prefer lower fire frequencies (Woinarski and Recher,

1997; Woinarski, 1999) and such species are described as fire sensitive. Baker (2002) defines

fire-sensitivity in Australian bird populations or species as those which are detrimentally

affected by fire.

“They typically lack one or more of the attributes generally ascribed to birds which would

allow them to avoid or recover from the effects of fire by being ground-dwelling, cover-

dependent, poor fliers, poor dispersers or low in fecundity. Fire may kill individual birds

directly or indirectly, for example by making habitat unsuitable, through loss of food or

sheltering resources or by increased predation. Fire may extirpate entire populations and in the

extreme case may cause the extinction of a species” (Baker, 2002).

Australian species which require, or are most abundant in, long-unburnt vegetation include

Noisy Scrub-bird (Altrichornis clamosus), Western Bristlebird (Dasyornis longirostris) (Smith,

1985) and Malleefowl (Benshemesh, 1989). Adverse affects of fire are not limited to species

which decline as the frequency or extent of fire in the landscape increases. Some species benefit

from the effects of fire in the landscape and in such cases exclusion of fire may lead to

extirpation or extinction. For example, the White-naped Honeyeater (Melithreptus lunatus) and

White-cheeked Honeyeater (Phylidonyris nigra) may benefit from higher fire frequencies

because these species occupy wet-sclerophyll forest which in the absence of fire may be

invaded by rainforest (Chapman and Harrington, 1997).

Within South American rainforest, fire is considered a severe threat and exclusion is

recommended (Barlow et al., 2002; Barlow and Peres, 2004). Similar recommendations are

made for Southeast Asian rainforest (Adeney et al., 2006; Slik and van Balen, 2006). In the

Mediterranean, the use of fire is recommended for maintaining avian diversity (Moreira et al.,

2001; Herrando and Brotons, 2002; Herrando et al., 2003; Brotons et al., 2005). In particular,

fire provides habitat for threatened open country species (Herrando and Brotons, 2002;

Herrando et al., 2003; Brotons et al., 2005; Pons and Bas, 2005) and an increase in its use is

advocated (Moreira et al., 2001). Similarly, in North America the fire exclusion policy in place

for most of the twentieth century has been removed in order to increase fire frequency in many

habitats (Lyon and Smith, 2000f; Davis et al., 2000; Kotliar et al., 2002). A number of North

American bird species appear to prefer or require recently burnt habitats or high fire frequencies

(Hutto, 1995; Shriver and Vickery, 2001). One species, the Black-backed Woodpecker

(Picoides arcticus) is found almost exclusively in early post-fire forest (Hutto, 1995). In

22

southern Africa conservation recommendations span the range of options. In the high fire-

frequency savannah grasslands, avian diversity is robust to all but the most extreme fire regimes

(Mills, 2004). In contrast, where woody-plants are encroaching on savannah, increased fire-

frequency may be required to provide habitat for open-country species (Skowno and Bond,

2003). On Namibian veldt, a lower fire frequency is recommended to maintain populations of

ticks which are the main prey of two declining oxpeckers (Buphagus sp.) (Robertson and Jarvis,

2000). Similarly, populations of highland grassland birds may also require a lower fire-

frequency (Jansen et al., 1999).

A satisfactory general method of classifying birds according to their fire response – i.e.

like plant functional types (Noble and Slatyer, 1980) – remains problematic because of the

inherent complexity of recurrent fire in the landscape and the equally complex interaction

between birds and fire (Tasker et al., 2006). Whelan et al. (2002) propose a method which

incorporates four key processes: 1) mortality caused by fire, 2) recolonisation ability, 3) survival

and establishment of individuals after fire, and 4) post-fire reproduction and population growth.

To adequately describe these processes for a species, information is needed about eight life-

history attributes. These are: 1) microhabitat association; 2) ability to avoid the direct impacts of

fire; 3) breadth of habitat; 4) breadth of diet; 5) susceptibility to competition; 6) susceptibility to

predation; 7) dispersal ability; and 8) reproductive rate (Keith et al., 2002; Whelan et al., 2002;

Tasker et al., 2006). Such information is available for relatively few species partly because fire

ecology has focussed on describing response patterns rather than investigating the mechanisms

of response. A shift in the emphasis of fire ecology to focus on process-based research is

recommended (Whelan et al., 2002).

2.13 Future directions While understanding of the effects of time-since-fire has advanced in many ecosystems,

understanding of fire regime effects remains limited. Gill et al. (2002) characterise fire regimes

using four parameters: intensity, type, between-fire-interval and season. Most studies in fire

ecology represent the fire regime using a surrogate parameter. Time-since-fire is a common

surrogate because it is relatively easy to investigate and is usually the strongest effect that fire

imposes on biota (Gill and Catling, 2002). This generalisation, that time-since-fire is the

strongest effect of fire, appears to be true of birds. However the changes in habitat that are

caused by fire may also be related to fire severity (a surrogate for fire intensity), season of burn

and between fire interval.

Assuming that the same ecosystem persists following a fire, the greater the severity, the

greater the potential difference in a bird community through time as the habitat regenerates.

Season of burn may influence birds because the disruption to factors such as flowering, fruiting

and breeding will be differentially affected. Between-fire-interval may influence birds because

it influences the composition of the plant community (Cary and Morrison, 1995; Morrison et al.,

1995) and therefore potentially the habitat structure and the availability of other resources.

23

Commencing an investigation of the response of birds to fire in a new ecosystem, it appears

wise to focus on time-since-fire. However a greater emphasis on the effects of fire regime

would advance understanding. Fire severity has a strong effect on birds and season of burn is

potentially influential. To my knowledge, no studies involving birds have controlled for time-

since-fire in order to investigate fire frequency or similar parameters such as minimum inter-fire

interval. Such studies would be valuable.

Autecological studies of birds and fire have demonstrated the intricacy of the relationship

(Rowley and Brooker, 1987; Brooker and Rowley, 1991; Brooker, 1998). Inconsistencies in the

pattern of response to fire of some species between study sites are also suggestive of complexity

and cast doubt on the reliability of response patterns for managing bird populations in fire-prone

environments (Gill, 1996; Baker, 2002; Burbidge et al., 2007). Much pattern-oriented research

treats birds as response variates in a study in which the experimental units are habitat subject to

a particular (mostly ill-defined and variable) fire treatment. There is a disjunction in seeking to

infer a relationship between a variable process (fire) and response variates (birds) when the

response variates are responding to the habitat. The conclusions from such research are

necessarily generalised and sometimes contradictory. An example is the threatened Australian

Ground Parrot (Pezoporus wallicus). Fire management recommendations for the Ground Parrot

varied between fire exclusion and a fire frequency of 13 years (Gill, 1996). An explanation for

the inconsistency was that fire influenced the interaction between the graminoid sedge on which

the parrot feeds and shrubs which overtop the sedge and reduce the quality of the habitat. Fire at

an interval appropriate to the growth rate of the shrubs may maintain suitable habitat for the

ground parrot (Gill, 1996; Whelan et al., 2002; Tasker et al., 2006). The inconsistency may

therefore relate to the variability in the growth rate of the shrubs between regions. Process-

oriented research aimed at understanding relationships such as that proposed for the Ground

Parrot is advocated for constructing a fauna vital attributes system similar to that used for plants

(Whelan et al., 2002; Keith et al., 2002; Tasker et al., 2006). In much process-oriented research

the experimental units will be individuals, groups or breeding units of the focal species. While

the fire treatments may still be ill-defined and variable, such studies will reveal more detail of

the variation in species response and the drivers of such response. The data will therefore be

more amenable to predictions about fire response and probably also a range of other

disturbances and processes. Process-oriented research is costlier and more time-consuming than

pattern-oriented research and is therefore probably only likely in a small number of high-

priority species such as those which are threatened.

2.14 Conclusion The magnitude of the change in bird communities due to fire appears to be related to three

predictors: 1) pre-fire complexity of the burnt habitat; 2) historical frequency of disturbance in

the burnt habitat; and 3) severity of the burn.

24

Simply structured habitats such as grasslands appear to experience less change in the

composition of bird communities following fire than complex habitats, such as woodland, forest

and rainforest. The fire effects on the bird communities of simply-structured habitats also

appear to be shorter-lived than those of complex habitats because the pre-fire habitat structure

takes relatively little time to be restored.

The historical frequency of disturbance in a habitat also appears to influence the magnitude

of the change in bird communities following fire. High-frequency disturbance is likely to favour

species with broad niche preferences (generalists) to those with narrow preferences (specialists)

(Wilson and Yoshimura, 1994). Therefore, the species more likely to be present in frequently

disturbed habitats should be less sensitive to disturbance. Fire sensitive Australian rainforest and

mulga woodlands both contain a predictable bird community while that in fire-prone eucalypt

forest and woodland is less so (Cody, 1993; Cody, 1994).

Fire severity influences the magnitude of change in bird communities following fire.

Strong site fidelity amongst many bird species means that where low severity fires leave patches

of unburnt vegetation; species that were present prior to fire often persist. Where fire severity is

high such persistence is less likely.

Broad generalisations about the response of birds to fire are possible between habitats and

avifaunas if the habitat structure and accompanying changes due to fire are analogous. Where fire

kills or defoliates trees and shrubs and an open habitat develops, terrestrial insectivores, granivores

and omnivores, bark gleaners and aerial insectivores (often habitat generalists) tend to benefit at the

expense of foliage-gleaners, nectarivores, frugivores and habitat specialists. Assuming that the

ecosystem is resilient to the fire regime, the bird community will change as the vegetation

regenerates and the structure of the habitat changes. Foliage gleaners return as suitable foliage

develops, nectarivores and frugivores return as the vegetation reaches maturity and other habitat

specialists return as their niches are re-established. Such generalisations should however be treated

with caution for several reasons. 1) Fire is inherently variable and so are its effects. 2) A fire which

is inconsistent with the regime that existed prior could cause a change in the ecosystem; this is a

particular possibility of climate change. 3) Studies show considerable variation in guild responses to

fire within and between habitats.

Most studies attribute changes in bird communities following fire to changes in the structure of

the habitat caused by fire. Such a conclusion is almost inevitable when investigating patterns of fire

response across a bird community because each species is likely to be different. A generalisation

which encompasses all the birds present at a site through time is necessarily very general. In the

absence of better information, predictions based on habitat structure remain useful. However, if

process-based research can provide specific explanations for changes in the distribution of species

due to fire then the focus on habitat structure may be superseded.

Landscape factors such as fire extent and landscape context appear to have a weaker effect on

bird communities than time-since-fire. Studies are few; however the work suggests that the fire

history at a point is more influential in determining the composition of the bird community than the

extent of the fire, or the nature of the neighbouring habitats.

25

Chapter 3: Background to methods

3.1 Overview The project comprised two time-since-fire experiments and an edge experiment. The main

time-since-fire experiment and the edge experiment were set up in the sheetwash landscape

(Tongway and Ludwig, 1990) in the north-western region of UKTNP and Yulara. The other

time-since-fire experiment was set up in the dune-swale landscape (Wasson and Hyde, 1983) at

the south-eastern end of UKTNP (Allan, 1984) (Figure 4-2). The hypotheses were entirely

addressed by the experiments in the sheetwash landscape; however the experiment in the dune-

swale landscape increased the value of the study by allowing comparison of the patterns

observed across contrasting soil and hydrological conditions and informing the degree to which

the results could be generalised across the extent of mulga woodland in Australia. A hypothesis

addressing differences between the landscapes was not invoked because this would not have

addressed the aims of the thesis.

Field work was carried out in the winter and spring of 2005 and 2006. Data were collected

over two years to maximise the statistical power of the study and to allow comparison of

variation attributable to treatments with some inter-annual variation. Inter-annual variation in

Australian arid-zone avifauna can be large (Davies, 1974; Stafford-Smith and Morton, 1990;

Reid et al., 1991; Paltridge and Southgate, 2001; Maron et al., 2005; Burbidge and Fuller, 2007;

Kerle et al., 2007).

3.2 Mulga woodland The model system selected for this study was the mulga woodland/mulga bird community

(Johnson and Burrows, 1994; Cody, 1994) of central Australia. The term mulga is the common

name for the plant Acacia aneura, however it is sometimes also applied to an array of other

Acacia species with a similar growth form (Cody, 1989; Miller et al., 2002). The term is also

used to refer to plant communities dominated by A. aneura or other similar looking acacias

(Cody, 1991; Miller et al., 2002). The taxonomy of Acacia aneura is controversial and the

species is notoriously difficult to identify in the field (Miller et al., 2002). The core group

consists of 10 varieties of A. aneura plus A. minyura, A. Ayersiana and A. paraneura. In this

study, ‘mulga’ refers to A. aneura and I use the term ‘mulga woodland’ to refer to

woodland/shrubland communities dominated by A. aneura.

Mulga communities, together with hummock grasslands with a sparse A. aneura

overstorey occupy 1,500,000km2 or about 20 percent of the area of mainland Australia (Johnson

and Burrows, 1994). The distribution of the species is probably determined by the interaction of

climate, fire and soil (Gill, 2000; Williams, 2002; Miller et al., 2002; Nano, 2005; Nano and

Clarke, in press). Acacia aneura is mostly found in regions which receive a mean annual rainfall

of 200mm-500mm but which do not experience a regular seasonal drought (Williams, 2002;

Miller et al., 2002). In central Australia, A. aneura is commonly found on red-earth soils, in the

26

swales between sand-ridges, on slopes and at the base of hills and rocky features (Williams,

1982; Latz, 1995a; Latz, 1995b; van Oosterzee, 1999).

Acacia aneura grows in large continuous stands and in patches that are interspersed with

other plant communities in an intergrove pattern (Tongway and Ludwig, 1990; Bowman et al.,

1994; Williams, 2002). Fire-prone Spinifex (Triodia spp.) hummock grasslands are thought to

influence the distribution of A. aneura which has a low fire tolerance (Williams, 2002; Nano

and Clarke, in press). Acacia aneura can be killed by fires of moderate intensity or greater

which scorch the canopy (Hodgkinson and Griffin, 1982; Latz, 1995b; Gill, 2000). Mulga

woodland is an ideal community in which to study the impact of a fire mosaic on fauna because

the system is relatively simple (Johnson and Burrows, 1994) yet supports a relatively rich bird

community (Reid et al., 1991; Reid et al., 1993; Cody, 1994; Recher and Davis, 1997). The

relative structural and botanical simplicity of the community reduces the potential for

confounding factors, while the relatively high bird diversity increases the likelihood of detecting

a response to variation in environmental parameters (Schodde, 1994; Mac Nally et al., 2004)

such as fire. Another advantage of using birds compared to other vertebrate taxa is that they are

cheaper to survey to a given level of data accuracy (Mac Nally et al., 2004).

3.3 Mulga birds In comparison with other vegetation communities of the Australian arid zone, mulga

supports a rich bird fauna (Reid et al., 1991). Cody (1994) identified 81 avian species that

inhabit mulga and classified 18 species as ‘core’, 28 as ‘peripheral’ and 35 as ‘casual’. The core

species are mostly sedentary (Reid et al., 1991) and occupy common, reliable ecological niches

(Cody, 1994). The remaining nomadic or opportunistic species respond to favourable conditions

following rain (Reid et al., 1991; Cody, 1994; Recher and Davis, 1997).

The core mulga bird species in the Northern Territory are believed to have evolved in the

arid zone (Fisher et al., 1972; Schodde, 1982; Schodde, 1994), do not require free water (Fisher

et al., 1972; Schodde, 1982; Schodde, 1994) are mostly insectivorous (Cody, 1994; Recher and

Davis, 1997) and decline with proximity to artificial water sources (Landsberg et al., 1999;

James et al., 1999). Little is known about the way birds respond to the spatial distribution of

mulga woodlands in the landscape, or the fire regimes associated with mulga woodlands.

Table 3.1 Common mulga bird species, their evolutionary origin and present geographic affinity. Common name Scientific name Cody1 Recher2 Assemblage3 Geographic Affinity4 Rufous Whistler Pachycephala rufiventris 1 1 Bassian - Crested Bellbird Oreoica gutturalis 1 0.86 Eyrean Central Red-capped Robin Petroica goodenovii 1 1 Eyrean Central Western Gerygone Gerygone fusca 1 1 Eyrean Central Little Button Quail Turnix velox 0 1 Eyrean Central Slaty-backed Thornbill Acanthiza robustirostris 0 1 - - Southern Whiteface Aphelocephala leucopsis 0 1 Eyrean Southern Grey Shrike-thrush Colluricincla harmonica 0.86 1 Multifaunal Eastern Spiny-cheeked Honeyeater Acanthagenys rufogularis 0.86 1 Eyrean Central Yellow-rumped Thornbill Acanthiza chrysorrhoa 0.86 1 Multifaunal Eastern Little Crow Corvus bennetti 0.86 0.71 Eyrean Central Singing Honeyeater Lichenostomus virescens 0.86 0.57 Eyrean Pan-austral Diamond Dove Geopelia cuneata 0.86 0.86 Eyrean Central Splendid Fairy-wren Malurus splendens 0.71 0.71 Bassian Southern / western Chestnut-rumped Thornbill Acanthiza uropygialis 0.71 0.86 Multifaunal Central / eastern Willie Wagtail Rhipidura leucophrys 0.71 0.71 Multifaunal Pan-austral Inland Thornbill Acanthiza apicalis 0.57 1 - - White-browed Babbler Pomatostomus superciliosus 0.57 0.29 Bassian Western Crested Pigeon Geophaps lophotes 0.57 0.29 Eyrean Central Zebra Finch Taeniopygia guttata 0.57 1 Eyrean Pan-austral Hooded Robin Melanodryas cucullata 0.57 0.86 Multifaunal Pan-austral Brown Goshawk Accipiter fasciatus - 0.57 Multifaunal Pan-austral Crimson Chat Ephianura tricolor 0 0.57 Eyrean Central Whistling Kite Milvus sphenerus - 0.57 Multifaunal Pan-austral

1. Incidence of bird species in mulga in the Northern Territory following Cody (1994) 2. Relative abundance of bird species in mulga adapted from Recher and Davis (1997) 3. Assemblage refers to the evolutionary origin of the species following Schodde (1994): Bassian = south Australian eucalypt, Eyrean = Australian arid zone,

Multifaunal = extra-Australian or ill-defined/uncertain Australian origin. 4. Geographic affinity refers to the present range of the species following Schodde (1994)

Table 3.2. Feeding behaviour, territory size, estimated density and response to proximity of artificial water of common mulga bird species. Common name1 Food2 Substrate2 Behaviour2 Territorial3 Terr. Size3 Density (birds/ha) 3 Water (NT) 4 Rufous Whistler (female) Insect Ground/shrub Rufous Whistler (male) Insect Canopy Foliage snatcher Yes 1.7ha-32ha 0.03-1.4 Decline

Crested Bellbird Insect Shrub/canopy Gleaner Yes 860ha 0.01-0.4 Decline Red-capped Robin Insect Ground Pounce Yes (Br) 0.5ha 0.006- 0.86 Decline Western Gerygone Insect Canopy Foliage snatcher Unknown 200ha 0.2-0.26 Not determined Grey Shrike-thrush Insect Canopy Foliage gleaner Yes 5-18ha 0.02-0.7 Decline Little Button Quail Seed Ground Gleaner Nomadic N/A Increase Slaty-backed Thornbill Insect Canopy Foliage snatcher Sedentary Unknown Unknown Decline Spiny-cheeked Honeyeater Nectar/fruit Canopy Nomadic N/A N/A Decline Southern Whiteface Seed Ground Gleaner Sedentary Unknown Unknown Decline Yellow-rumped Thornbill Insect Ground/shrub Gleaner No (Br?) 6.5-20ha 0.01-1.3 Not determined Little Crow - - - - - - Increase Singing Honeyeater Nectar/fruit Canopy - Territorial Unknown - Decline Diamond Dove Seed Ground Gleaner Dispersive N/A N/A None Splendid Fairy-wren Insect Ground/shrub Gleaner Territorial 4.4ha 0.575 – 3.8 Not determined Chestnut-rumped Thornbill Insect Shrub/canopy Foliage gleaner Sedentary Unknown 0.006-0.7 Decline Willie Wagtail Insect Ground Pursuer - - - Decline Inland Thornbill Insect Shrub/canopy Foliage gleaner Sedentary Unknown 0.02-5.5 Not determined White-browed Babbler Insect Ground/shrub Probe Yes 5-20ha 0.1-1.5 Decline Crested Pigeon Seed Ground Gleaner Sedentary - - Not determined Zebra Finch Seed Ground Gleaner Nomadic N/A - Not determined Hooded Robin Insect Ground Pounce Sedentary 18-50ha 0.03-0.3 Decline Brown Goshawk Meat - - - - - Not determined Crimson Chat Insect Ground Gleaner Nomadic N/A - Not determined Whistling Kite Meat - - - - - Not determined

1. For scientific names see Table 3.1. 2. Food, feeding substrate and feeding behaviour follow Recher & Davis (1997) 3. Territoriality, territory size and estimated density follow the relevant volumes of the Handbook of Australian New Zealand and Antarctic Birds. 4. The response of mulga birds to proximity to artificial water sources follows Landsberg et. al. (1999)

29

Table 3-3 Uncommon birds of Northern Territory mulga woodland (Cody, 1994; Recher and Davis, 1997).

Common name Scientific name

Black Kite Milvus migrans

Wedge-tailed Eagle Aquila audax

Brown Falcon Falco berigora

Nankeen Kestrel Falco cenchroides

Common Bronzewing Phaps chalcoptera

Galah Eolophus roseicapillus

Major Mitchell Cockatoo Cacatua leadbeateri

Australian Ringneck Barnardius zonarius

Mulga Parrot Psephotus varius

Budgerigar Melopsittacus undulatus

Bourke’s Parrot Neosephotus bourkii

Pallid Cuckoo Cuculus pallidus

Horsfield's Bronze Cuckoo Chrysococcyx basalis

White-browed Treecreeper Climacteris affinis

Weebill Smicronis brevirostris

Yellow-throated Miner Manorina flavigula

Grey-headed Honeyeater Lichenostomus keartlandii

Grey-fronted Honeyeater Lichenostomus plumulus

White-plumed Honeyeater Lichenostomus penicillatus

Brown Honeyeater Lichmera indistincta

White-fronted Honeyeater Phylidonyris albifrons

Black Honeyeater Certhionyx niger

Grey-crowned Babbler Pomatostomus temporalis

White-browed Babbler Pomatostomus superciliosus

Varied Sittella Daphoenositta chrysoptera

Magpie-lark Grallina cyanoleuca

Grey Fantail Rhipidura fuliginosa

Black-faced Cuckoo-shrike Coracina novaehollandiae

White-winged Triller Lalage sueurii

Black-faced Woodswallow Artamus cinereus

Grey Butcherbird Cracticus torquatus

Pied Butcherbird Cracticus nigrogularis

Richards’s Pipit Anthus novaeseelandiae

Mistletoebird Dicaeum hirundinaceum

3.4 Mulga birds and fire Unreplicated evidence from a study of the birds of UKTNP (Reid et al., 1991; 1993)

suggests that fire influences the community composition, species richness and abundance of

birds in mulga woodland.

30

1. Both insectivorous and granivorous nomadic species preferred open habitats created

by recent fire – the patches produced abundant plant life and insects after rain.

2. After rain, most nomads preferred regenerating patches of mulga woodland with

heavy grass growth, to patches of mature mulga woodland with little grass.

3. Some nomadic nectarivorous species preferred mature mulga woodland with

abundant mistletoe.

4. The abundance of many sedentary mulga bird species varied with time-since-fire.

3.5 Selecting the study area The most important criteria for selecting a study site were that it supported large stands of

mulga woodland of different times-since-fire that could be identified from a detailed and long-

running fire history. In the southern Northern Territory, fire histories were available for Uluru

Kata-Tjuta National Park (UKTNP; 1976 - present) and the southern Tanami (1979-1994;

(Allan, 2003; Myers et al., 2004). A pilot study at the two potential study sites was undertaken

in April 2005.

The pilot work involved ground-truthing a Geographic Information System (GIS) database

designed to identify potential study sites, pilot bird surveying, pilot habitat assessment,

arrangement of access permits, familiarisation with the region and equipment testing. A number

of conclusions were reached.

1. Fire histories at UKTNP and the southern Tanami were adequate for the study but

vegetation mapping was inadequate.

2. Anecdotally, the affect on birds of time-since-fire in mulga woodland appeared

consistent with that described by Reid et al. (1991; 1993).

3. There appeared to be greater avian diversity at UKTNP than the southern Tanami.

4. Access to Aboriginal land in the southern Tanami was difficult to arrange because

of the complexity of the consultation process required for a landscape-scale study.

5. Pastoral land in the southern Tanami was excluded because cattle grazing appeared

confounding for two reasons. i) The abundance of many mulga birds apparently

declines with proximity to artificial water (James et al., 1999; Landsberg et al.,

1999). ii) Artificial water points are not randomly distributed in the landscape and

mistletoe was found to be virtually absent from areas remote (>10km from water).

6. Newhaven Reserve in the southern Tanami supported insufficient mulga woodland

to support the study.

7. UKTNP had large areas of accessible mulga woodland in three time-since-fire

classes.

31

8. UKTNP management offered to support the study including provision of liaison

services with the traditional owners, aerial photography, GIS datasets, off-road

vehicles, maintenance facilities and accommodation.

9. It was therefore decided to conduct the first field season at UKTNP.

3.6 Study site The study site was UKTNP (Latitude 25° 20’ Longitude 130° 53’) and the neighbouring

Yulara Resort. The properties cover 1,430km2 - UKTNP is 1,325km2 (ANPWS, 2000), Yulara is

105km2. UKTNP and much of the surrounding land is owned by traditional Aboriginal owners,

who refer to themselves as Anangu. The park is leased from Anangu by the Australian

Government and jointly managed by the traditional owners and the Australian National Parks

and Wildlife Service (ANPWS, 2000). UKTNP is a cultural landscape and management is

determined by Anangu law known as Tjukurpa. Yulara is privately owned and supports tourism

infrastructure associated with UKTNP.

Two major plant formations (Hodgkinson and Griffin, 1982; Beard, 1984; Groves, 1994)

are present at the study site – fire-prone spinifex hummock grasslands and fire-sensitive mulga

Acacia shrublands/woodlands (Griffin, 1984). Other vegetation associations include eucalypt

forest and woodland, Casuarina woodland, mallee, desert myrtle shrubland and Acacia

ammobia woodland (Allan, 1984; Gill, 2000).

Climate and fire are the two most important variables influencing vegetation at the study

site (Griffin, 1984). UKTNP records from 1965 to 2005 yield a mean annual rainfall of 292mm.

This is considered to be higher than the long-term mean because of the short duration of records

and interpretation of unpublished maps of the Bureau of Meteorology (BoM) suggests a long-

term mean of about 220mm (Griffin, 1984). Rainfall variability in the region is classified as

“extreme” on a seasonal basis and “high” on an annual basis (BoM, 2006a). Rainfall records

obtained from the UKTNP (ANPWS, unpublished data) and Yulara Airport (BoM, unpublished

data) from 2004-2006 appear consistent with this description (Figure 3-1). The two sites were

approximately 20km apart and comparison of the records shows little temporal variation in the

occurrence of rain in a month but some large differences in the amount of rain (for further

details of rainfall at the study site see Chapter 3.8). Temperatures range from maxima of >40°C

in summer to minima of <0°C in winter (Griffin, 1984). Relative humidity is highest in winter

and lowest in summer (BoM, 2006b). Winds are predominantly from the south-east with

seasonal mean wind speeds at 3pm ranging from 10-20km/h to 20-30km/h (BoM, 2006c). In the

arid zone, wind strength is generally weakest in winter and strongest in spring (Brookfield,

1973) (Table 3-4). The lowest mean daily wind speed occurs in the early morning. The high

incidence of thunderstorm activity in summer is a major cause of wildfire ignitions at a time

when weather conditions are most conducive to flame-spread (Griffin, 1984). Bird surveys were

carried out under Australian National University Animal Experimentation Ethics Committee

permit number S.RE.03.05. Research at UKTNP was granted under scientific permit issued by

32

the director of national parks, Department of Environment and Heritage and visitor entry permit,

issued by Mutitjulu Community Aboriginal Corporation.

Table 3-4 Arid zone wind seasons following Brookfield (1970) Seasons (months) Wind conditions

March-May Falling winds

June-August Winter, winds slowly increasing

September-November High wind

December-February Summer, moderately high wind

33

Rainfall at the study site in 2004

0

20

40

60

80

100

1 2 3 4 5 6 7 8 9 10 11 12

Month

Rai

nfal

l (m

m)

UKTNP HQ

Yulara Aero


0

20

40

60

80

100

1 2 3 4 5 6 7 8 9 10 11 12

Month

Rai

nfal

l (m

m)

UKTNP HQ

Yulara Aero


0

20

40

60

80

100

1 2 3 4 5 6 7 8 9 10 11 12

Month

Rai

nfal

l (m

m)

UKTNP HQ

Yulara Aero

Figure 3-1 Monthly rainfall at Yulara Airport and UKTNP HQ for 2004, 2005 and 2006.

34

3.7 Principles of experimental design The experiments were designed following the general field ecology method of Hurlbert

(1984) modified for the space-for-time method (Pickett, 1989; Hardgrove and Pickering, 1992;

McGarigal and Cushman, 2002). The space-for-time method is an alternative to long-term

(longitudinal) studies. The method relies on the assumption that spatial and temporal variation is

equivalent and this is usually reasonable when investigating strong successional dynamics. The

effects of time are inferred by comparing locations in space with different known times since

similar comparable events such as fire, logging or flood (Pickett, 1989; Hardgrove and

Pickering, 1992). The space-for-time format was preferred to a longitudinal study for two

reasons. 1) The distribution of fauna in the Australian arid zone is strongly influenced by recent

rain (Davies, 1974; Griffin, 1984; Stafford-Smith and Morton, 1990; Read et al., 2000;

Paltridge and Southgate, 2001; Burbidge and Fuller, 2007). Attempts by the WA Department of

Environment and Conservation (Burbidge and Fuller, 2007), Parks and Wildlife Commission of

the Northern Territory (J. Cole pers. comm.) and the Commonwealth Scientific and Industrial

Research Organisation (S. Morton pers. comm.; M. Fleming pers. comm.) to demonstrate the

affects of fire on the distribution of Australian arid zone birds were hampered in past studies by

the overwhelming affect of recent rain. Other studies have confirmed the importance of this

factor (Paltridge and Southgate, 2001; Letnic, 2003; Letnic and Dickman, 2005). Space-for-time

allows for better minimisation of the affect of recent rain than a longitudinal study. 2) A

comparable longitudinal experiment would have taken a minimum of 60 years. Drawbacks of

the space-for-time method are that: 1) functional dynamics are difficult to investigate; 2) spatial

heterogeneity is ignored or averaged across sites; and 3) it is usually difficult to infer

mechanism (Pickett, 1989; Hardgrove and Pickering, 1992).

The study was conducted in a natural landscape subject to unplanned fire. The

experimental units were patches of mulga woodland and the treatments were wildfires. Mulga

woodland was classified according to time-since-fire and patch size. The response variables at

each experimental unit were the avifauna and the vegetation structure. The use of a natural

landscape and unplanned treatments meant it was impossible to adhere strictly to all aspects of

the general field ecology method of Hurlbert (1984) (Table 3-5). In particular, treatments were

not subject to randomisation among the experimental units, this in turn constrained spatial

interspersion of treatments and temporal interspersion (concomitance) of observations. Within

these constraints, the methods were designed to maintain the independence of samples and

ensure the validity of generalisations drawn from the experiments. The aims and hypotheses

addressed in this study maybe similar to those of the unpublished study of S. Morton and M.

Fleming (Kerle et al., 2007). This study was developed entirely indepedndently of the previous

work.

Table 3.5. Factors which can invalidate or cause pseudoreplication (source of confusion) in a field ecology experiment (Hurlbert, 1984) together with features of a valid, replicated experimental design (Field ecology method) and alternative procedures for a mensurative space-for-time experiment (Pickett, 1989; Hardgrove & Pickering, 1992, McGarigal & Cushman, 2002). Features of this study are shaded grey.

Features of experimental design that reduce or eliminate confusion Source of confusion

Field ecology method Space-for-time alternative

Temporal change Control treatments N/A

Procedure effects Control treatments Potential bias associated with counting birds however effect minimised by consulting the literature and following standard procedures.

Randomised assignment of experimental units to treatments Treatments pre-determined but selection of experimental units randomised. Experimenter bias

Randomisation in conduct of other procedures N/A Experimenter generated variability (random error) Replication of treatments N/A

Replication of treatments N/A Interspersion of treatments Limitations to interspersion of treatments due to pre-determination. Initial or inherent variability among

experimental units Concomitant observations Observations approximately concomitant between treatments due

to their pre-determination. Replication of treatments N/A

Nondemonic intrusion Interspersion of treatments Limitations to interspersion of treatments due to pre-determination.

Demonic intrusion Eternal vigilance, exorcism, human sacrifices etc. N/A

36

3.8 Experimental scale Ecological research is best conducted at an organism-relevant scale (Wiens et al., 1986;

Wiens, 1989; McGarigal and Cushman, 2002). Potential spatial scales for investigations form a

continuum with five convenient reference points: 1) the range or territory occupied by a single

organism; 2) a local patch occupied by many individuals; 3) a region containing many local

patches; 4) a space large enough to contain a closed system — no immigration or emigration;

and 5) the biogeographical scale which encompasses different climates (Wiens et al., 1986). An

organism-relevant scale depends on the question under investigation. For example; a study

aimed at determining affects on individual fitness would be scaled at the normal range of the

organism (excluding events such as dispersal), while a study aimed at determining affects on a

population would be scaled at the intrinsic scale(s) determined by the actual spatial structure of

the population (McGarigal and Cushman, 2002).

The ability to detect ecological patterns is a function of the extent and the grain of the

investigation (Wiens, 1989; Strayer et al., 2003). Extent is the area encompassed by the study,

while grain is the size of the individual units of observation. Scaling a study of a bird

community will always involve some compromise, since the size of bird territories may vary by

orders of magnitude and nomadic birds may range over large areas. Management for arid zone

biota has been recommended at scales exceeding the area of a typical reserve (Dickman et al.,

1995; Kerle et al., 2007) – i.e. >2000km2.

This study investigates the influence of fine-scaled fire mosaics. Although what is meant

by fine-scale has apparently never been explicitly defined (Gill, 2000; Bowman et al., 2004;

Parr and Andersen, 2006), it is clear that such a study requires a fine grain. The extent of the

study area is determined by the need to minimise the confounding effect of recent rain which

has a strong effect on the distribution of birds in central Australia (Davies, 1974; Morton, 1990;

Stafford-Smith and Morton, 1990; Reid et al., 1991; Morton, 1993; Morton et al., 1995;

Paltridge and Southgate, 2001). The spatial distribution of rainfall over any short timeframe is

difficult to measure (Fleming, 1978), and there is limited information of use in addressing the

issue. Rain in arid Australia is described as spotty (Fleming, 1978) and sufficient rain to

stimulate growth can fall in patches as small as 5km2-30km2 (Denny, 1982); a circular rainfall

event of this size would have a diameter of 1km-6km. Rainfall intensity will vary as a function

of the radial distance from the centre of the storm cell (Fleming, 1978). The frequency

distribution of storm size measured at Alice Springs in the centre of the Australian arid zone,

indicated that approximately 15 percent of events had a radius <8.0km and 45 percent of events

had a radius of >24.1km. All events with a radius >32.2km were cyclonic and occurred

unpredictably throughout the year. All events of radius <32.2km were convective and occurred

from November to March (Fleming, 1978). In addition, approximately half the annual rainfall in

central Australia is made up of ineffective falls of less than 12mm (Perry, 1978). Rainfall

records from two locations at the study site are consistent with this generalisation for central

37

Australia. A comparison of daily rainfall records from UKTNP HQ (ANPWS, unpublished

data) and Yulara Airport (BoM, unpublished data) from 2004-2006 (Figure 3-1) shows little

temporal variation in the occurrence of rain but some potentially significant differences in the

amount of rainfall. Monthly totals of >20mm were recorded 10 times from Yulara Airport

during the period. Of these, five months showed differences >20mm between the stations

ranging from 21mm – 65mm. Assuming that the effects of recent rain decline with time-since-

rain, it appears more likely that recent rain will confound studies conducted during the

convective storm season and for a period afterward. The effect of recent rain is probably best

controlled by co-locating experimental units as much as possible, taking care not to compromise

statistical independence, and by interspersing the experimental treatments.

3.9 Statistical analysis

3.9.1 Accounting for detectability Accounting for differences in bird detectability between treatments is critical for the

credibility of the conclusions from studies such as this (Buckland et al., 2001; MacKenzie et al.,

2006; Kotliar et al., 2007). Fire causes changes in habitat structure (Whelan, 1995; Woinarski

and Recher, 1997) (Section 2.5). Habitat structure causes changes in the detectability of birds

(Buckland et al., 2001; MacKenzie et al., 2006). It is therefore essential to account for

detectability when investigating changes in the distribution of birds due to fire (Kotliar et al.,

2007).

Approaches to accounting for detectability address the question using field-based data

collection procedures, statistics, or a combination of both (Buckland et al., 2001; MacKenzie et

al., 2006). Three methods were employed in this study: 1) distance analysis (Buckland et al.,

2001); 2) data truncation (Norvell et al., 2003); and 3) presence/absence analysis (MacKenzie et

al., 2006). Distance analysis was the preferred technique and has been used in at least two other

recent studies of fire and birds (Kotliar et al., 2007; Chaudry et al., 2007). Distance analysis has

two limitations which meant it could not be used for all analyses. These limitations were: 1) the

results could not be easily applied to multi-variate analyses; and 2) the assumption that birds

were randomly distributed in relation to the survey points could not be met in the edge

experiment (the assumptions of Distance analysis are discussed in more detail in Chapter 3.9.4).

Where distance analysis was not suitable one or both of the other two methods were used.

Distance analysis accounts for detectability by fitting a detection function to the data

(Buckland et al., 1993; Buckland et al., 2001). The method yields an unbiased density estimate

which is an ideal metric for testing the hypotheses of this study. In addition, a valid density

estimate has greater intrinsic value than an abundance estimate or an index.

Data truncation accounts for detectability by sampling an area small enough that there is

no difference in detectability between treatments (Norvell et al., 2003). The method yields an

abundance estimate. Data truncation was carried out by limiting the area sampled to a radius of

38

20m (See Chapter 6: for details of the sampling method). A drawback with this method was that

truncation reduced the number of records in the full 50m radius dataset by 60 percent-80 percent

(area sampled was reduced by 84 percent), greatly reducing the power of the analyses and the

number of species about which conclusions could be drawn.

Presence/absence accounts for differences in detectability between treatments by removing

count data. Counts maybe more susceptible to detectability problems because of the need to see

all the individuals present to accurately count them. The vast majority of birds recorded in this

study were detected by acoustic cues. Conversion of count data to presence/absence analysis

can standardise data with distance from the survey point and allow data to be collected from a

bigger area. Presence/absence analysis yields a probability of presence which is a valid basis for

testing the hypotheses (MacKenzie et al., 2006). Presence/absence datasets were derived from

the full distance analysis dataset. This increased the power of the analysis compared to the

truncation method, increased the number of species about which conclusions could be drawn

and provided greater temporal resolution.

3.9.2 Multi-variate and uni-variate analyses Each hypothesis in this study was tested using both multi-variate and uni-variate analyses.

The concept of biodiversity is inherently multi-variate so such tests reflect the aims of the study

and allow conclusions to be drawn at a community level. Uni-variate tests are also conducted

because they demonstrate specific patterns which explain the community-level responses.

3.9.3 Multi-variate analyses Multivariate statistical tests were conducted using the statistical software package

CANOCO 4.53 (Ter Braak, 1986; Ter Braak and Smilauer, 2002). The methods followed Ter

Braak & Smilauer (2002), Leps & Smilauer (2003) and Leps & Smilauer (2005). The software

performed direct and indirect gradient analyses using linear, unimodal and detrended unimodal

response models. The aim of the analyses was to find axes of the greatest variability in the

community composition (in this case of birds) and to visualise using ordination the similarity

structure for the samples (survey sites of different time-since-fire), species (birds) and predictor

variables (habitat parameters at each site). Indirect multi-variate gradient analyses summarise

the relationships between a set of response variables – usually species data. Direct multi-variate

gradient analyses are similar but in addition include one or more predictor variables – usually

environmental variables. Response models are approximations of the relationship of a species

response to an environmental variable. A linear response is the simplest approximation while a

uni-modal response assumes an optimum along the environmental gradient. Over a short

gradient a linear approximation works well, but over a long gradient such an approximation is

likely to be poor. This generalisation can be used to determine the appropriate analysis for a

given set of response variables. An indirect multi-variate gradient analysis with a linear

response model is a principal components analysis (PCA), with a unimodal response model is a

39

correspondence analysis and with a detrended unimodal response model is a detrended

correspondence analysis (DCA). A direct multi-variate gradient analysis with a linear response

model is a redundancy analysis (RDA), with a unimodal response model is a canonical

correspondence analysis (Keith et al.) and with a detrended unimodal response model is a

detrended canonical correspondence analysis. This study explicitly set out to investigate the

relationship between bird species (the response variables) and their environment (the predictor

variables), so direct multi-variate gradient analyses (constrained ordinations) were most suitable

for testing the time-since-fire and patch size hypotheses. Stand alone indirect gradient analysis

was used to ordinate the bird data from the edge experiment and the habitat data because in

these cases there were no predictor variables.

Analyses in CANOCO followed a standard procedure. To determine the appropriate

response model a DCA was conducted on the species variables to obtain the maximum gradient

length of the canonical axes. The gradient length measures the ß-diversity in community

composition (the extent of species turnover) along each axis. The best response model for an

analysis is determined by the maximum gradient length of the canonical axes: <3 SD = linear

model, >4 SD = unimodal model. Where the maximum gradient length was <4 SD but >3 SD

either response model could be suitable, so both linear and unimodal analyses were conducted

and the analysis with the best fit retained.

The predictor variables for constrained ordinations were determined by manual selection of

the best variables. The rejected variables were excluded and the analysis was run with a test of

significance of the first ordination axis and all canonical axes using a Monte Carlo permutations

test with 999 runs (i.e. most significant result possible = 0.01). The Monte Carlo permutations

test operates under the null hypothesis that the species composition is independent of the

environmental variables. If the null hypothesis is true then it does not matter which observation

of species variables is assigned to which observation of environmental variables. The test was

conducted by shuffling the species data in relation to the environmental data and running the

ordination analysis a prescribed number of times. Significance was determined by the

proportion of instances in which a better fit of species variables to environmental variables was

achieved at random than occurred in the real result.

The results of an ordination are usually displayed using an ordination plot. In a plot the

absolute values of objects in ordination space have little meaning. Interpretation is based on the

relative distance, direction or ordering of objects. The axes of ordination plots correspond to the

directions of greatest data variability that is explained by the environmental variables. By

convention, samples (survey sites) are displayed as points in RDA and CCA. Species are shown

by arrows in RDA, with the implication of a linear increase. In CCAs species are shown by

points which represent the species optima. Environmental variables are shown by arrows which

point in the direction that the variable increases. Plots of the results of CANOCO analyses were

created using CANODraw (Ter Braak, 1986; Ter Braak and Smilauer, 2002).

40

Differences between the response variables associated with factors (in this case time-since-

fire) can be tested using a direct gradient analysis with dummy variables to represent each

factor. A Monte Carlo permutations test will determine the significance of any difference.

Where a dataset includes three treatments, the samples (usually survey sites) from one treatment

in turn can be included in the analysis as covariables allowing the significance of any difference

between the remaining two factors to be tested. This is called a partial test and is similar to a

multivariate ANOVA.

3.9.4 Uni-variate analyses Uni-variate tests of the species data were run using distance analysis (Buckland et al.,

1993; Buckland et al., 2001) and generalised linear mixed models (GLMM) (Schall, 1991). A

statistical modelling approach was used because this accommodated uneven sample sizes and

repeated measures. Distance analysis was conducted using the statistical software package

Distance 5.0, release 2 (Thomas et al., 2006) following Buckland et al. (1993; 2001). GLMMs

were conducted in Genstat 8.0 (Payne et al., 2005). T-tests in Genstat 8.0 were used to test for

significance in the habitat data (Snedecor and Cochran, 1989).

Distance analysis is a method of accounting for individuals that are present but not

detected during a biological survey (Buckland et al., 2001). Central to the method is the

detection function, which is a model of the decline in detectability of an object with distance

from the survey point. To produce a valid detection function the survey points must be located

randomly in relation to the objects being sampled. In addition, distance analysis requires three

assumptions:

1. “Objects directly on the line or point are always detected;

2. Objects are detected at their initial location, prior to any movement in response to the observer;

3. Distances are measured accurately or objects are correctly counted in the proper distance interval (Buckland et al., 2001).”

In practice slow movement of objects causes few problems, however responsive movement

to the observer can strongly bias results. A major source of error in distance sampling is

observer variability in the estimation of distance (Buckland et al., 1993; 2001; Rosenstock et al.,

2002). However training, placement of visible markers at fixed distances and use of distance

classes can improve distance estimation.

A GLMM is a parametric regression model with four components: 1) the response variable

and its probability distribution (i.e. the Y-axis variable); 2) the predictor variables (i.e. the X-

axis variable); 3) the link function which links the response and predictor components; and 4)

the random term which accounts for the correlation associated with repeated measures

(McCulloch and Searle, 2001). The response variable must have a distribution from the

exponential family of distributions which includes normal, binomial and Poisson. In general,

continuous variables may have a normal distribution, binomial variables are likely to have a

41

binomial distribution and counts are likely to have a Poisson distribution. For these distributions

there are so-called canonical link functions for which there exist sufficient statistics for the

parameters in the linear predictor. The canonical link function for a normal distribution is

identity, for a binomial distribution is logit and for a Poisson distribution is logarithm. The

predictor variables can be continuous or categorical. In a standard GLMM the dispersion

parameter is constrained to a value implied by the distribution. If the variance of the

observations is greater than this theoretical value, a quasi-binomial or quasi-Poisson model can

be used to estimate the dispersion parameter. Significance can be determined using a Wald

statistic which approximates a χ2 distribution. The Wald statistic overestimates significance

especially with small sample sizes (McCulloch and Searle, 2001; Payne et al., 2005) but this can

be offset by using a conservative α-value (α = 0.01) to reduce type 1 error (Leavesley and

Magrath, 2005).

Habitat variables were analysed using t-tests. A t-test requires two assumptions: 1) that the

samples are from normally distributed populations; and 2) that the observations are sampled

randomly. In practice, the assumption of normality is not crucial because t-tests are robust as

long as the population distributions are not multi-modal or skewed. The Genstat procedure for a

two-sample t-test is to check for equality of means using an F-statistic before conducting the t-

test (Snedecor and Cochran, 1989). If the F-statistic is significant then the t-test proceeds by

estimating separate variances for each sample.

Where a large number of tests are conducted on a single treatment (e.g. Table 5.4 and

Table 7.2) there may be a greater probability of Type 1 error across the set of tests (Perneger,

1998). This issue can be addressed by applying a Bonferroni correction – i.e. adjusting the α-

value. A consequence of applying a Bonferroni correction is that the probability of a Type 2

error is increased, an outcome which is also problematic. Following the recommendation of

Perneger (1998) a Bonferroni correction was not applied however the biological plausibility of

each test result was assessed.

3.9.5 Data entry and checking Data were entered into a Microsoft Excel spreadsheet. All treatment assignments, site

numbers, plot numbers, dates and missing values were checked using pivot tables and corrected

where necessary. Bird data were also entered into Distance 5.0 (Thomas et al., 2006) and the

outputs from the two datasets were compared. Inconsistencies were checked on the data sheets

and corrected. The probability that the same data entry error was made in both datasets was low.

Habitat data were checked by graphing the distribution of each parameter in Microsoft Excel

and looking for outlying values.

42

Chapter 4: The experimental landscape The experimental design required the identification and measurement of patches of mulga

with different times-since-fire. GIS is recommended for designing landscape experiments

because it also provides a means for controlling parameters which may be correlated with

inherent variability in the experimental units (McGarigal and Cushman, 2002). A GIS database

was compiled in ArcGIS 9.1 (ESRI, 2004) containing a map of mulga woodlands, fire history,

soil/geology, sand-ridges, land tenure, roads and infrastructure.

The data for the GIS were available from UKTNP. The fire history of the UKTNP region

was the most comprehensive in central Australia (Allan, 2003). In addition, the layers required

to minimise confounding factors and plan access to the survey sites were also available from

UKTNP. Existing vegetation maps of the park were not suitable for this study. Vegetation was

mapped in communities but did not explicitly delineate mulga woodlands (Griffin, 1984).

Construction of the GIS database therefore required the compilation and ground-truthing of the

GIS data from UKTNP and mapping and ground-truthing mulga woodlands.

4.1 GIS data quality None of the GIS layers provided by UKTNP included meta-data, so information about the

source, accuracy and use for which the data were originally compiled was not readily available.

The most suitable datasets for this study were determined by consulting present and former park

staff and ground-truthing. Improvements to the UKTNP GIS database were undertaken during

the course of this study (V. Chewings, pers. comm.) but the modified datasets were not

available for use in this study.

4.2 Potentially confounding factors When conducting a natural experiment it is necessary to minimise or control for potentially

confounding factors (McGarigal and Cushman, 2002). The distribution of birds in the landscape

is influenced by a wide range of factors (Chapter 1:). Potentially confounding factors at the

study site included recent rain, geology, free water, major roads and infrastructure. The effects

of these factors could be minimised by ensuring that experimental units were remote to the

effects. It was therefore desirable to have GIS data from which the area of influence of

potentially confounding factors could be inferred. The UKTNP GIS database contained layers

for roads, land tenure, contours, drainage, sand ridges, a geology map and a soil map.

The roads layer was the most important for minimising the influence of potentially

confounding factors. The layer was assumed to have been produced by Geoscience Australia

and was used to determine the position of major roads and other infrastructure. Comparison

between the GIS data and a Geographic Positioning System (GPS) tracklog created using 25m

intervals showed the roads data were accurate to within approximately 30m. Road locations

43

were also checked on a 1:25,000 aerial photographic series acquired in 1997. UKTNP staff

confirmed that all major roads at the study site appeared on the map.

Figure 4-1 Contour and road map of Uluru Kata-Tjuta National Park and Yulara. Most infrastructure was centred on Uluru, Kata Tjuta and Yulara Village and the only sealed roads link these locations.

Figure 4-2 Quaternary soil map of Uluru Kata-Tjuta National Park and Yulara. Grey shading is red earths in a sheetwash context, black shading is Aeolian red earths in a dune-swale context and white is other soil mostly sand.

44

The soil map was produced by Geoscience Australia (English, 1998). The map was used to

minimise potential effects of soil type and associated hydrology. The method for determining

soil type was unknown; however the presence of mulga woodland appears to have been used to

assist in identifying the distribution of red earths. The sand-ridge map was derived from a

1:100,000 topographic map series. The map was used to assist in mapping mulga vegetation.

The sand-ridge map was compared with a 1:25,000 aerial photographic series acquired in 1997.

The dataset appeared accurate, though the features appeared slightly truncated when compared

to the aerial photos. The geology, contour, land tenure and drainage maps were assumed to have

been produced by Geoscience Australia. None of these data could be easily ground-truthed

within the scope of this study.

4.3 Fire history database Access to the UKTNP fire history was granted by the Department of Environment and

Heritage under license and delivered in Environmental Systems Research Institute (ESRI)

shapefile format. There was no meta-data associated with the fire history, but people involved

with the development of the database were available for consultation.

The fire history consisted of 43 GIS layers from 1976 to 2003-2004. Files for the years

1976-2002 were produced by Mr Grant Allan while employed by the Northern Territory

Conservation Commission and the Northern Territory Bushfire Council. The files were

projected in Australian Map Grid 1966 (ADG66). Files for 2002-03 and 2003-04 were produced

by Geoimage Pty Ltd and projected in the Geodetic Datum of Australia 1994. The 2002-2003

and 2003-2004 files were re-projected into AGD66. Files from 1976 to 2002 contained the

fields “Area” (m2), “Hectares” and “Perimeter” (m). Files from 1993-2001 contained the fields

“Fire type”, “Year”, “Cause” and “Purpose”. The 2002-2003 and 2003-2004 files contained an

“ID” field only. There was no information about the intensity or severity of fires and little

information of use in determining the season.

The method of production of the data was determined by consultation and inference. No

quantitative ground-truthing is believed to have been conducted on any of the fire maps.

However qualitative ground-truthing by consultation with park staff was reported to have

occurred (G. Allan, pers. comm.; M. Jambrecina, pers. comm.; P. Hookey., pers. comm.; S.

Anderson, pers. comm.). Despite the lack of a quantitative accuracy assessment, confidence in

the quality of the fire history can be drawn from the knowledge that all but two of the fire maps

were produced by the same person, Mr Grant Allan, presently employed in a scientific role by

Bushfires NT. Mr Allan is widely regarded as the leading Australian arid-zone fire scientist and

has considerable specific knowledge of the UKTNP region. Mr Allan contributed to the first

scientific fire management plan for UKTNP (Saxon, 1984) and produced the first vegetation

map for the park (Allan, 1984). In addition to the acknowledged expertise employed in the

production of most of the maps, the ongoing involvement of a single person in the production of

most of the fire history gives confidence that processing of the source material, such as

45

rectification and registration of images has been consistent through time. This gives greater

validity to inter-year comparisons than might otherwise be the case.

The most recent files – 2002-2003 and 2003-2004 – were produced by Geoimage Pty Ltd

using a single LandSat 7 (U.S.G.S, 2007) scene for each file. The area burnt was determined

from a Landsat 7 image (M. Jambrecina, pers. comm.; P. Hookey, pers. comm.). The method of

production of the 2002-2003 and 2003-2004 maps appears less sensitive to patchiness within the

burn, than those produced in the preceding years. The resolution of the maps could not be

determined, but appeared to be of the order of 12.5m x 12.5m resolution. Informal ground-

truthing and consultation with park staff was carried out by former UKTNP staff member Mr P.

Hookey (P. Hookey, pers. comm.).

All maps from 1976-2002 were based on Landsat images (Allan, 2003) and appear to have

been produced by determining the presence/absence of fire on a grid corresponding to the

resolution of the source material. The 2002 map was produced by Mr Allan using a single

LandSat 7 scene acquired 11 December 2002. The presence/absence of fire was determined in a

grid of 12.5m x 12.5m obtained from a panchromatic band in the image (this resolution is

obtained by processing post-acquisition). Resolution of the maps from 2001-1976 is variable as

is the sensitivity to within-burn patchiness (Table 4-1). This variation presumably relates to

changes in the source material used for the mapping.

The area of mulga woodland burnt in a year varied greatly between years (Table 4-1). The

largest extent burnt in a year was 110,738ha in 1976 (Figure 4-3). The sum of the annual areas

of mulga woodland burnt from 1977-2001 was 63,220ha (Figure 4-4) and in 2002; 79,002ha

was burnt (Figure 4-5). The extent of unplanned fire in the Australian arid zone is related to the

cumulative rainfall of the previous three years (Griffin et al., 1983; Gill, 2000; Allan et al.,

2003). UKTNP rainfall records (ANPWS, unpublished data) show that the cumulative rainfall

from 1973-1975 was 1688.1mm and from 1999-2001 was 1,685.2mm. This is almost double the

three year cumulative rainfall expected based on the long-term mean (Chapter 3:).

46

Table 4-1 Percentage of the study site burnt during each time period. The resolution of the maps was estimated by measuring the pixels.

Area burnt (ha) Estimated Resolution Time period

Management Unplanned

% of study area Management Unplanned

1976 0 110,738 77 NA 100m x 100m

1977-1978 No data but fire is believed to be absent or minimal (G. Allan, pers.comm.)

1979 0 328 0 NA 20m x 20m2

1980-1981 No data but fire is believed to be absent or minimal.

1982 791 0 1 100m x 100m 20m x 20m2

1983 52 1131 1 100m x 100m 20m x 20m2

1984 126 78 0 100m x 100m 20m x 20m2

1985 680 56 0 100m x 100m 20m x 20m2

1986 618 17,227 12 100m x 100m 20m x 20m2

1987 681 0 0 100m x 100m 20m x 20m2

1988 No data but fire is believed to be absent or minimal.

1989 2258 120 2 Composite3 40m x 40m

1990 689 15,207 11 40m x 40m 40m x 40m

1991 3,798 1948 4 40m x 40m 40m x 40m

1992 107 1,1021 1 40m x 40m 40m x 40m

1993 182 805 0 40m x 40m 40m x 40m

1994 343 597 0 40m x 40m 40m x 40m

1995 234 115 0 20m x 20m 20m x 20m

1996 488 422 0 20m x 20m 20m x 20m

1997 226 38 0 20m x 20m 20m x 20m

1998 145 0 0 20m x 20m 20m x 20m

1999 34 3 0 20m x 20m 20m x 20m

2000-2001 2,6721 2 20m x 20m

20024 79,0021 55 12.5m x 12.5m

2002-20034 101 0 Not gridded, possibly 12.5m x 12.5m

2003-2004 1,3761 1 Not gridded, possibly 12.5m x 12.5m 1 Type of fire not specified but assumed to be unplanned.

2 Indicates extent of fire but not patchiness within the burn.

3 Data was stored in three files, two resolved at 40m x 40m and one at 100m x 100m. 4 Overlap between these maps was negligible, suggesting that most of the fire in 2002 occurred in

the first half of the year.

47

Figure 4-3 The extent of fire at the study site in 2002.

Figure 4-4 The extent of fire at the study site from 1977-2001.

48

Figure 4-5 The extent of fire at the study site in 1976.

4.3.1 Ground-truthing Maps of recent fires could be ground-truthed because vegetation growth was slow at the

study site and the presence/absence of fire within the previous four years could be reliably

determined. The ground-truthing method followed Jensen (2005). Four polygons encompassing

the area of the park to which access was permitted were established (Figure 4-1; Table 4-2). The

polygons encompassed 26 percent of UKTNP and Yulara and a total of 55 percent of the

ground-truthed area was mapped as burnt in 2002. A total of 220 points were randomly selected

in Arcmap 9.1 (ESRI, 2004) using Hawth’s Analysis Tools for ArcGIS 9.1 (Beyer, 2004; ESRI,

2004). The sample size was determined using binomial probability theory (Jensen, 2005). The

number of points in each polygon was determined according to the proportion of the area

available for ground-truthing within each polygon. Maps from 1999-2004 were considered for

ground-truthing because of the anticipated difficulty differentiating the fire-scars between

consecutive years. Examination of the fire maps allowed those from 1999, 2000-2001, 2002-

2003 and 2003-2004 to be excluded from ground-truthing. This was because only 19ha (0.05

percent) of mulga woodland was mapped as burnt in those years (Table 4-3) and these polygons

were remote to the randomly placed ground-truthing points. Therefore ground-truthing was

applied only to the 2002 fire map.

49

Figure 4-6 The areas of UKTNP and Yulara used for ground-truthing and the randomly selected ground-truth points. From left to right the polygons are: north-west; bore field; Yulara; and dune-swale.

Table 4-2 Area and number of randomly positioned points in the polygons established for ground-truthing the mulga maps and 2002 fire map.

Site Area (ha) Points

Dune-swale 12,244 72

Bore field 10,220 61

Yulara 2,325 14

North-west 12,343 73

Total 37,132 220

Table 4-3 Proportion of the area of each ground-truthing polygon burnt in each mapped time-period. Where applicable, management fires and wild fires were combined.

Mapped area burnt (ha) Site

1999 2000-2001 2002 2002-2003 2003-2004

Dune-swale 0 1 9,322 0 0

Bore field 0 2 4,556 0 0

Yulara 0 5 288 0 0

North-west 0 7 6,160 4 0

Total 0 15 20,326 4 0

Ground-truthing involved visiting each of the random points and recording: 1) whether the

point had been burnt; 2) whether the point was an unburnt patch within the extent of a burn; and

3) if the point was within 50m of a fire boundary, the distance to the boundary. The

50

presence/absence of fire was used to calculate the accuracy of the map and a Kappa statistic,

while the distance to edge and patchiness of the burn was used to explain error. This approach

was conservative because it reduced the number of points recorded as correctly mapped

because: 1) it did not account for location errors that may have been caused by imprecise

rectification or registration of the Landsat scene or GPS imprecision, and 2) it did not allow for

the resolution of the scene – 12.5m x 12.5m. More sophisticated analytical techniques such as

fuzzy logic can account for such factors (Jensen, 2005) but the accuracy value is not necessarily

as important as an understanding of the sources of error. The information in a map can be

redefined following an accuracy assessment to improve the accuracy values. The overall

accuracy of the 2002 fire map was 89 percent with a Kappa statistic of 0.77 (Table 4-4; Table

4-5). An accuracy value of 85 percent is the minimum required for satisfactory land-use data for

resource management (Anderson et al., 1976). The Kappa statistic is a measure of the observed

agreement between map and ground-truthing taking into account agreement that might be

attained by chance. The interpretation of the Kappa value varies between authors. Landis &

Koch, (1977) describe the strength of agreement of a Kappa value of >80 percent as ‘almost

perfect’, 61 percent-80 percent as ‘substantial’, 41 percent-60 percent as ‘moderate’ 21 percent-

40 percent as ‘slight’ and 0 percent-20 percent as ‘poor’. Castilla & Hay (2006) suggest that a

Kappa value of >0.75 is excellent while a value of <0.50 is poor. Producer accuracy and user

accuracy were similar for this map. Land-cover type did not appear to strongly influence the

accuracy of the classification, despite the view of the cartographer that fires were harder to map

in woodlands than they were in other vegetation types (G. Allan, pers. comm.) Most of the

classification errors (22 of 25; 88 percent) occurred at points that were within 30m of mapped

boundaries. In addition, errors were more common in sectors where fire was patchy such as the

dune-swale sector than they were in areas where burning was uniform such as the bore-field.

This suggests that the error may have been caused by imprecision in the rectification and

registration of the Landsat scene. Informal ground-truthing suggested such an error of

approximately 35m between the true position and the mapped position of features in the north-

western sector of the park.

Table 4-4 Error matrix for the 2002 fire map. Ground-truthed

Burnt Unburnt Total

Burnt 105 11 116 Mapped

Unburnt 14 90 104

Total 119 101 220

51

Table 4-5 Kappa statistic, producer, user and overall accuracy for the 2002 fire map.

Proportion of map classified correctly

Producer accuracy User accuracy Overall accuracy

Mulga 0.88 0.91 0.89

Non-mulga 0.89 0.87

Kappa statistic 0.77

4.4 Mulga mapping Maps of mulga woodlands at the study site were produced as part of this project, using the

same Landsat 7 scene that was used to map the fires of 2002. The image was chosen as a map

base because its use precluded registration and rectification problems in the identification of

mulga that was burnt in 2002. Alignment problems between the mulga woodland map and the

rest of the fire history were also less likely because the image was registered and rectified by the

same person who had produced the fire history. Mulga woodland maps were created by two

methods. The first method was a direct interpretation of the Landsat 7 image. The second

method was an interpretation of a 1:25,000 aerial photographic series. In both instances mulga

was delineated by manually creating polygons around the features in Arcmap 9.1 (ESRI, 2004) .

The maximum precision of both maps corresponded to the pixel size of the image which was

12.5m x 12.5m. Two mapping methods were employed to maximise accuracy and provide a

strong basis for inference from the experiments.

4.4.1 Landsat 7 mulga woodland map The Landsat 7 mulga woodland map was compiled in April 2005 by visually identifying

patches of mulga woodland on the image. Classification algorithms were not used. Long

unburnt mulga woodland over red-earth was easily differentiated due to its dark mottled pattern

which appeared to correspond to groves of mulga. The GIS sand-dune layer was used to help

differentiate mulga woodland from patches of Desert Heath Myrtle (Aluta maissonneuvei) since

mulga woodland rarely grows on sand-dunes. More than 50 percent of the park burnt during the

2002 (Table 4-1), including stands of mulga woodland. Burnt mulga woodland was identified

by the remnant groved pattern and by the brown colouration (apparently true colour) that was

darker than burnt grasses and spinifex.

4.4.2 Aerial photographic/Landsat 7 mulga woodland map The aerial photographic mulga woodland map was compiled in June 2006 by identifying

patches of mulga on a 1:25,000 aerial photographic series acquired in 1997. The features

identified as mulga woodland were then located on the Landsat scene and digitised by hand with

the scale set at 1:25,000. Locating the mulga woodland features to be digitised was assisted by

the creation of a layer showing the rough location of the photographic runs and by the same

52

sand-dune layer used to make the first draft of the map. No major fires had burnt within the park

for six years prior to the acquisition of the photographic series (Table 4-1) so there were few fire

scars visible.

4.4.3 Ground-truthing The mulga woodland maps were ground-truthed by visiting the same randomly selected

points that were used to ground-truth the 2002 fire map (Section 4.3.1). The extent of neither

mulga woodland map fully corresponded with that of the ground-truthing sectors, so five points

were excluded from the ground-truth of the draft map (Table 4-6) and one point was excluded

from the ground-truth of the final map (Table 4-8).

The overall accuracy of the Landsat 7 map was 0.73 and the Kappa statistic was 0.66

(Table 4-7). Accuracy was highest in the dune-swale landscape. This was probably because

mulga woodland had not burnt in that sector during the period of the fire history (and probably

for considerably longer), so the mulga plants were large and the formations were easy to

identify on the Landsat scene. Most of the incorrectly classified points in the sector were close

to boundaries so the error may have been due to the imprecision of the rectification or

registration of the Landsat scene. Accuracy was least in the bore fields where mulga woodland

occurred on sand in sparse stands over spinifex. Most of this country had burnt in 1976 and

many of the mulga plants were small and therefore difficult to distinguish from other shrubs.

Another potential cause of misclassification was error identifying burnt mulga woodland.

Table 4-6 Error matrix for the map of mulga woodland map derived from a Landsat 7 image acquired in 2002.

Ground-truthed

Mulga Non-mulga Total

Mulga 88 36 124 Mapped

Non-mulga 23 68 91

Total 111 104 215

Table 4-7 Kappa statistic, producer, user and overall accuracy for the map of mulga woodland derived from a Landsat 7 image acquired in 2002.



Mulga 0.79 0.71 0.73

Non-mulga 0.65 0.75


The overall accuracy of the aerial photographic map was 0.85 and the Kappa statistic was

0.71 (Table 4-9, Figure 4-7). Accuracy was better than the Landsat 7 map in all the sectors

except Yulara. Most of the error in the map was producer error – i.e. classification of mulga

woodland as non-mulga woodland. This occurred in two contexts: 1) where mulga woodland

53

occurred in sparse stands over spinifex on sand plains; and 2) around the edges of correctly

identified mulga woodland. Patches of long-unburnt mulga woodland over red earths were

almost entirely correctly classified, but there was error around the margin. Track-logs created

around patches of mulga woodland in the dune-swale sector indicated that the boundaries of

patches were too tightly drawn.

Table 4-8 Error matrix for the map of mulga woodland derived from a 1:25,000 aerial photographic series acquired in 1997.

Ground-truthed

Mulga Non-mulga Total

Mulga 84 3 87 Mapped

Non-mulga 29 103 132

Total 113 106 219

Table 4-9 Kappa statistic, producer, user and overall accuracy for the map of mulga woodland derived from a 1:25,000 aerial photographic series acquired in 1997.



Mulga 0.74 0.96 0.85

Non-mulga 0.97 0.78


Figure 4-7 Map of mulga woodland map derived from a 1:25,000 aerial photographic series acquired in 1997.

54

The error of classifying sparse, small mulga plants on sand plains that occurred in the

Landsat 7 map also occurred in the aerial photographic map. The same issue of imprecision in

the rectification and registration of the Landsat base image that was described in the fire

mapping ground-truthing may also have influenced accuracy of this map. The results of the

ground-truthing suggest that the true extent of mulga woodland at the study site was greater than

the mapped extent and that mulga woodland over spinifex was under-represented. In addition

the presence of mulga woodland on sand plains and sand dunes and the absence of mulga

woodland on some red earths suggests that the potential extent of mulga woodland may be

considerably greater than the present extent. This finding is consistent with recent work aimed

at understanding mulga woodland/spinifex grassland boundary dynamics (Chapter 3:).

4.5 Defining the experimental units The set of units for this study was defined by overlaying the fire history on the Landsat 7

mulga woodland map. The overlay function produced large numbers of tiny patches which were

smaller in at least one dimension than the resolution of the source material (Table 4-1). These

small patches were retained in calculations to determine that the overlay functions were

performed correctly. After processing, the sum of the areas of mulga allocated to the appropriate

time-since-fire class was equal to that of the area of mulga mapped.

The minimum size of patches suitable for this study was 3ha, so all patches <3ha were

excluded from further consideration. A total of 1,722ha (4.2 percent) were excluded by this rule.

The population of patches of mulga of different times-since-fire was clumped into three age

classes: 1) burnt 2002, 2) burnt 1976 and, 3) long unburnt (Table 4-10). A qualitative

confidence-building assessment (Jensen, 2005) of the mulga age-class map found consistent

differences between these three age-classes suggesting the map was sufficiently accurate to

proceed with the study. Ideally the map would have been ground-truthed by statistical

measurement; however it was impossible to determine the presence or absence of fire at a point

30 years previously. Ground-truthing based on the vegetation structure, would have required a

number of assumptions that would have changed the basis of the treatments and therefore the

conclusions. Three experimental treatments were established coinciding with the three largest

age classes: 1) mulga woodland burnt in 2002 (3-4 years since fire); 2) mulga woodland burnt in

1976 (29-30 years since fire); and 3) long unburnt mulga woodland (not known to have burnt

since records began in 1976) (Figure 4-8). Long unburnt mulga woodland was probably a

minimum of 50 years old since mulga woodland usually only burns during large fire events

following periods of above average rainfall (Griffin et al., 1983; Allan and Southgate, 2002).

The last big fire event prior to records at the study site was in the 1950s (Allan, 1984). The

experimental units for hypothesis testing were selected from this population Figure 4-8. The

area of mulga burnt after 2002 was small and remote to the study site so had no influence on the

study.

55

Table 4-10 Description of the patches of mulga woodland at the study site by time-since-fire class. Patches <3ha were excluded from the summary.

Class Area >3ha1 Patches >3ha2 Location3 Area (ha)4

Mulga 40,638 533 NA 41,052

Burnt 2002-04 13 3 Unsuitable 18

Burnt 2002 7060 195 Suitable 7750



Burnt 1976 20,314 278 Suitable 20,656

Long-unburnt 11,410 331 Suitable 11,769 1 Sum of the area of all patches >3ha. 2 Number of patches >3ha

3 Accessible and co-located with other classes

4 Sum of the area of all patches.

56

a)

03-9 9-27 27-81 81-243 >243

Size class (ha)

50

100

150

200

250

Freq

uenc

y

b)

03-9 9-27 27-81 81-243 >243

Size class (ha)

50

100

150

200

250

Freq

uenc

y

c) d)

0

50

100

150

200

250

3-9 9-27 27-81 81-243 >243

Size class (ha)

Freq

uenc

y

0

50

100

150

200

250

3-9 9-27 27-81 81-243 >243

Size class (ha)

Freq

uenc

y

Figure 4-8 The distribution of mulga woodland patches was right skewed: a) all patches; b) burnt 2002; c) burnt 1976; d) long-unburnt.

57

Figure 4-9 Mulga woodland at the study site, classified by time-since-fire.

4.6 Selecting the experimental units Two space-for-time experiments were set up in landscapes with contrasting soil and

hydrological characteristics – sheetwash (Tongway and Ludwig, 1990) and dune-swale (Wasson

and Hyde, 1983) - to test for a time-since-fire effect and density/area effect on birds in mulga

woodlands. In the sheetwash landscape, mulga woodland was classified to one of three time-

since-fire classes: burnt 2002, burnt 1976 and long-unburnt. Within each time-since-fire class,

sites were selected to cover the range of patch sizes in the landscape while standardising for the

potential effects of edge (Helzer and Jelinski, 1999; Ries et al., 2004) using the area-to-

perimeter ratio. The patches of mulga woodland were assigned to a size-class: 3ha - <9ha, 9ha -

<27ha, 27ha - <81ha and >81ha. Five replicates of each size class were selected for each time-

since-fire class. In the 3 - <9ha class the patches with the greatest distance from centre to edge

were selected. In the other size classes the patches were split into sub-classes representing 20

percent of the area range of the class and the patch with the greatest minimum distance from

centre to edge from each sub-class was selected. When all sites had been selected the spatial

distribution was reviewed. Experimental units must be concomitant to reduce the possibility of

non-demonic interference (Hurlbert, 1984) so any isolated sites were excluded and the patch

with the next largest maximum distance to edge substituted. Three extra sites were added to

maximise the overlap in the spatial distribution of the time-since-fire treatments. The dominant

vegetation at all sites was ground-truthed and any that were incorrectly classified as mulga

58

woodland were replaced. A total of 63 experimental units were selected in the sheetwash

landscape, 21 were burnt 2002, 20 were burnt 1976 and 22 were long-unburnt.

Selection of experimental units in the dune-swale landscape followed the same procedure,

but with two differences. A large section of the eastern end of the UKTNP did not burn in 1976

(Figure 4-5) so only two time-since-fire classes were present – burnt 2002 and long-unburnt.

The stands of mulga woodland were smaller in the dune-swale system (Figure 4-7) so only three

size classes were present: 3ha-<9ha, 9ha-<27ha and >27ha. A total of 34 experimental units

were selected, divided equally between the burnt 2002 and long-unburnt treatments.

In the second field season of the project, mulga woodland at the study site was mapped

using the aerial photographic method that was more accurate than the Landsat 7 method that

was used to set up the experiments. The difference in accuracy was relatively large (14 percent)

so the experimental units from the two maps were compared for differences. There were three

main categories of differences between the maps. 1) There were changes in the extent of 11

patches of mulga woodland (eight in the sheetwash landscape and three in the dune-swale

landscape) so that they encompassed two survey sites. 2) There were small differences in the

calculated areas of the other experimental units. 3) There were small differences in the optimal

location of the bird survey plots. Each instance of difference between the two maps was

evaluated and changes were made to the analyses as required. 1) The inclusion of two survey

sites within a single patch of mulga had no effect on the time-since-fire analysis because survey

points within the same large patch were a minimum distance of 400m apart and were therefore

treated as independent. However it was not possible to use the data from all sites to test for

density/area effect without compromising the method (i.e. edge effect was standardised by

maximising the distance to edge within each patch). This was resolved by excluding from

analysis the site with the smaller distance to edge. The dataset for the density/area analysis

therefore included 55 sites in the sheetwash landscape and 31 sites in the dune-swale landscape.

2) The patch sizes of the experimental units were determined from the aerial photographic map,

so improving the accuracy of the independent (x-axis) variables in the density/area regressions.

3) The offset of the bird survey plots from the new centre of patch was either small or

inconsequential to the method because the survey site remained remote to the edge, so no

adjustments were required.

59

Chapter 5: Habitat assessment Habitat was assessed at all the bird survey sites for the time-since-fire and edge studies.

The main aim of the assessments was to characterise the differences in habitat between

treatments in order to explain any differences observed in the distribution of birds. The time-

since-fire study had the additional aim of developing a simple model of vegetation structural

dynamics in mulga woodland following fire.

5.1 Methods An assessment of the vegetation was made at the bird survey plots in each experiment

following the method of Walker & Hopkins (1998) modified to suit the environment. Data were

collected for three strata: the mulga woodland canopy (>2m), the shrub layer (0.5m - <2m) and

the ground layer (<0.5m). Three samples were collected at each bird survey site, one from each

bird survey plot (see Chapter 6: for further details of the bird survey method). The mulga

canopy stratum was characterised by identifying the species of each focal plant and estimating

the height, canopy width, canopy depth and crown separation to within 0.5m by comparison

with a 2m long piece of conduit. Plants were classified as “mulga” if they looked like Acacia

aneura, A. Ayersiana, or A. minyura. Acacia kempeana, A. ramulosa, A. tetragonophylla, and A.

pruinocarpa were distinguished. Other apparently different Acacia species were recorded as

Acacia sp. The growth form was not recorded because mulga varies along a continuum between

tree, mallee and shrub (Miller et al., 2002), so such a classification would have been subjective.

All mistletoe and the vast majority of shrubs and mulga seedlings grew in or under the mulga

canopy or around the base of fire-killed mulga plants. An index was obtained by counting the

number associated with each canopy plant and then multiplying by the percentage canopy cover

of the site. The parameters measured were: 1) number of shrubs (0.5m < height <2.0m), 2)

number of Eremophila shrubs, 3) number of Santalacea shrubs, 4) number of mistletoes, and 5)

number of mulga seedlings. Ground cover and litter were measured using the foliage

interception method (Walker and Hopkins, 1998). Ground cover was classified as grass,

spinifex or shrub (height of <0.5m). The amount of vegetation that intercepted the middle 30m

of the tape was recorded. The coverage of litter that intercepted the full length of the tape (50m)

was recorded. Crown separation ratio (Equation 1) and crown cover (Equation 2) were

calculated following the method of Walker and Hopkins (1998). Mulga height diversity

(Equation 3) for each site was calculated using the Shannon-Weiner index (MacArthur and

MacArthur, 1961). Fire severity was calculated for each site by scoring each canopy plant

according to the degree of fire damage, adding the scores and dividing by the total number of

plants that were scored: fire killed = 1, fire damaged = 0.5, alive with no fire damage = 0.

60

Equation 1 Formula for calculating crown separation ratio (Walker and Hopkins, 1998).

hcrown widtMean gapcrown Mean (C) ratio separationCrown =

Equation 2 Formula for calculating crown cover (Walker and Hopkins, 1998).

6.80k C) (1

k %cover Crown 2

=+

=

Equation 3 Formula for calculating mulga height diversity (MacArthur and MacArthur, 1961)

classheight i theof sindividual all of proportion theclassesheight

ln DiversityHeight Mulga

th

s

1i

=

=

−= ∑=

ps

pp

i

ii

5.1.1 Statistical analysis Multivariate analyses were conducted in CANOCO 4.53 (Ter Braak, 1986; Ter Braak and

Smilauer, 2002; Leps and Smilauer, 2003; Leps and Smilauer, 2005). A Detrended

correspondence analysis (DCA) was conducted on each set of habitat variables to determine the

appropriate response model. The DCA was detrended using 26 segments, rare species were

downweighted and the data were log transformed.

Principal components analysis (PCA) was conducted on the habitat variables. Scaling was

focused on samples. Species scores were divided by the standard deviation because this reduced

the influence of outliers. The habitat data were transformed and centred – centring is mandatory

when the data represent different measures. The samples data were not centred or standardised.

Univariate analysis was conducted using t-tests in Genstat 8.0 (Payne et al., 2005). The

distribution of each variable was examined using a histogram and heavily skewed distributions

were transformed by taking the natural logarithm.

5.2 Time-since-fire study

5.2.1 Sheetwash landscape Habitat parameters were measured at 63 sites in the sheetwash landscape. There were 21

sites in the burnt 2002 treatment, 20 in the burnt 1976 treatment and 22 in the long-unburnt

treatment. Data were collected from 3,093 recognisable canopy plants. Of these, 0.90 were A.

aneura, 0.09 were mulga-like Acacia spp. and 0.01 were eucalypts, Hakea spp. and other

species. Mulga woodland in the burnt 2002 treatment was subject to a mean mortality of 82

61

percent ±17 percent SD (Table 5-1) with a range of 46 percent -100 percent. A further 9 percent

±9 percent SD were alive but fire damaged. As a result there was no measurable canopy in the

burnt 2002 treatment. In the burnt 1976 and long-unburnt treatments there were no burnt plants

and a canopy existed at all sites.

Table 5-1 Proportion of canopy plants killed and damaged by fire in the burnt 2002 treatment in the sheetwash landscape in the time-since-fire study.

Dead Damaged Dead & Damaged Undamaged

Mean S.D. Mean S.D. Mean S.D. Mean S.D.

0.82 0.17 0.09 0.09 0.91 0.11 0.09 0.11

A DCA run on the natural logarithm of the variables returned a maximum gradient length

of 2.267 SD on the first axis; therefore the data were re-analysed using a principal components

analysis (PCA). The first two axes of the PCA accounted for 63.0 percent of the variance (Table

5-2; Figure 5-1). Sites in the burnt 2002 treatment had the most grass cover while those in the

long-unburnt treatment had the tallest, densest, deepest and most structurally diverse canopy,

and most abundant shrubs particularly Eremophila spp. Coverage of spinifex and low shrubs

was orthogonal to the main axis. Partial tests using a redundancy analysis (RDA) with dummy

variables representing the three treatments showed that the habitat in each treatment was

different (Table 5-3).

Table 5-2 Summary of a principal components analysis of habitat data in the sheetwash landscape of the time-since-fire study.

Axis 1 Axis 2 Axis 3 Axis 4 Total variance

Eigenvalues 0.484 0.146 0.123 0.079 1.000

Cumulative variance (%) 48.4 63.0 75.3 83.2

62

-1.0 1.0

-1.0

1.0

CCOVER

HEIGHT

DEPTH

MHD

ERE

SEEDSANTA GRASS

SPINIFEX

LSHRUBS

LITTER MIST

Figure 5-1 Plot of the first two axes of a principal components analysis showing environmental variables (natural logarithm transformed) and sites from the sheetwash landscape in the time-since-fire study. A cross = sites burnt 1976, circle = sites burnt 2002 and a square = long-unburnt sites. CCOVER = crown cover, DEPTH = canopy depth, HEIGHT = canopy height, MHD = mulga height diversity, LITTER = phyllode litter coverage, ERE = abundance of Eremophila shrubs, SANTA = abundance of Santalaceae shrubs, MIST = abundance of mistletoe, SEED = abundance of mulga seedlings, SPINIFEX = spinifex coverage, LSHRUBS = low shrub coverage, GRASS = grass coverage.

Table 5-3 Results of tests for differences in habitat between treatments in the sheetwash landscape of the time-since-fire study, using Monte Carlo permutations tests with 999 runs.

Treatments F-ratio P-value

Burnt 2002 vs Burnt 1976 22.562 0.001

Burnt 2002 vs Long-unburnt 37.766 0.001

Burnt 1976 vs Long unburnt 4.936 0.001

63

Univariate analysis showed the differences in the habitat parameters between the burnt

2002 treatment and the other two treatments were similar (Table 5-5). The burnt 2002 treatment

had no canopy, less coverage of litter, but more groundcover (comprising grass, spinifex and

low shrubs <0.5m). The long-unburnt treatment also had more shrubs (>0.5m and <2m) than the

burnt 2002 treatment.

A mulga-dominated canopy was present in both the burnt 1976 and long-unburnt

treatments however the characteristics of the canopies differed. The long-unburnt treatment had

greater crown cover and was classified woodland (Walker and Hopkins, 1998), while the burnt

1976 treatment was classified open-woodland. The canopy in the long-unburnt treatment was

taller, though both treatments were classified as low (Walker and Hopkins, 1998). The dominant

plants in the long-unburnt treatments had wider crowns, and greater height diversity than in the

burnt 1976 treatment. The long-unburnt treatment also had more shrubs - including Eremophila

spp. and Santalacea spp. and more mulga seedlings. There was no difference in the crown depth,

gap between dominant plants and coverage of ground plants – including grass, spinifex and low

shrubs – or coverage of phyllode litter.

Mistletoe was rare in the landscape, recorded at 11 sites – nine long-unburnt and two burnt

2002. There was more mistletoe in the long-unburnt treatment than the burnt 1976 treatment,

but no difference between the burnt 2002 treatment and the other two treatments.

5.2.2 Dune-swale landscape Habitat parameters were measured at 34 sites in the dune-swale landscape, divided evenly

between the burnt 2002 treatment and the long-unburnt treatment. Data were collected from

1,545 recognisable canopy plants of which 0.99 were A. aneura and other mulga-like Acacia

spp. particularly A. minyura. Mulga woodland in the burnt 2002 treatment was subject to 98

percent ±3 percent SD mortality of the recognisable dominant plants (Table 5-4) with a range of

89 percent – 100 percent. Another 1 percent ±2 percent SD) were still alive but fire damaged.

As a result there was no measurable canopy in the burnt 2002 treatment. In the long-unburnt

treatment none of the dominant plants were burnt in 15 of the 17 sites, however at the other two

sites thin tongues of low intensity fire had killed or damaged some dominant plants (mean

mortality = 3 percent ±8 percent SD). Nonetheless a canopy existed at all sites.

Table 5-4 Proportion of canopy plants killed, damaged and undamaged by fire in the burnt 2002 treatment.



0.98 0.03 0.01 0.02 0.99 0.02 0.01 0.02

Table 5.5. Results of t-tests for differences in habitat parameters between treatments in the sheetwash landscape. ‘NA’ indicates that a statistical test could not be conducted because the site did not fulfil the criteria for obtaining a measurement. Grey shading indicates a significant difference.

Burnt 2002 Burnt 1976 Long-unburnt T-statistic and P-value Parameter

Mean S.D. Mean S.D. Mean S.D. Burnt 2002 vs Burnt 1976

Burnt 2002 vs Long-unburnt

Burnt 1976 vs Long-unburnt

Crown gap (m) NA - 2.7 1.5 2.4 0.8 NA NA T28 = 1.0, p = 0.3

Crown width (m) NA - 2.0 0.4 2.6 0.5 NA NA T40 = -3.9, p <0.001

Crown cover (%) NA - 17.6 8.0 22.9 6.5 NA NA T40 = -2.4, p = 0.02

Crown height (m) NA - 3.2 0.3 3.5 0.4 NA NA T40 = -2.5, p = 0.02

Crown depth (m) NA - 2.5 0.4 2.5 0.3 NA NA T40 = -0.7, p = 0.5

Mulga height diversity 0.27 0.23 0.44 0.09 0.55 0.09 T27 = -3.1, p = 0.004 T26 = -5.1, p <0.001 T40 = -3.7, p <0.001

Mistletoe index 0.3 1.0 0.0 0.0 1.2 1.0 T20 = 1.4, p = 0.2 T28 = -1.6, p = 0.1 T21 = -2.2, p = 0.04

Shrub index 9.7 7.6 12.3 7.1 46.0 25.9 T39 = -1.2, p = 0.3 T25 = -6.3, p <0.001 T24 = -5.9, p <0.001

Eremophila index 1.2 2.5 9.2 6.4 34.0 25.3 T24 = -5.2, p <0.001 T21 = -6.1, p <0.001 T24 = -4.4, p <0.001

Mulga seedling index 1.1 1.9 0.6 0.9 2.4 2.1 T29 = 1.1, p = 0.3 T41 = -0.7, p = 0.5 T33 = -2.1, p = 0.04

Santalacea index 0.1 0.4 0.7 1.1 2.4 2.1 T22 = -2.0, p = 0.06 T22 = -4.9, p <0.001 T33 = -3.3, p = 0.002

Groundcover (%) 25.5 9.9 16.9 11.0 17.2 10.4 T39 = 2.6, p = 0.01 T41 = 2.7, p = 0.01 T40 = -1.7, p = 0.1

Grass cover (%) 15.4 10.7 8.1 7.4 6.5 6.1 T39 = 2.5, p = 0.02 T31 = 3.3, p = 0.002 T40 = 0.8, p = 0.4

Spinifex cover (%) 3.3 5.1 1.6 2.4 2.1 3.7 T29 = 1.4, p = 0.2 T41 = 0.9, p = 0.4 T40 = -0.5, p = 0.6

Shrub cover (%) 6.9 7.0 7.3 8.4 8.6 9.0 T39 = -0.2, p = 0.9 T41 = -0.7, p = 0.5 T40 = -0.5, p = 0.6

Litter cover (%) 6.6 8.2 32.6 13.6 32.6 13.5 T31 = -7.4, p <0.001 T35 = -7.7, p <0.001 T40 = -0.01, p = 1.0

Fire severity index 0.13 0.13 1.00 0.00 1.00 0.00 T39 = -29.0, p <0.001 T41 = -30.4, p <0.001 NA

65


of 2.313 SD on the first axis; therefore the data were re-analysed using a PCA. The first two

axes of the PCA accounted for 81.4% of the variance (Table 5-6). Sites in the burnt 2002

treatment had greater coverage of grass and spinifex than those in the long-unburnt treatment.

Sites in the long-unburnt treatment had higher values for all variables associated with a canopy.

They also had more litter, shrubs and seedlings. Partial tests using an RDA with dummy

variables representing the three treatments showed that the habitat in each treatment was

different (Monte Carlo permutations test, F-ratio = 44.469, P = 0.001).

-1.0 0.6

-0.6

1.0

CCOVER

MHD

HEIGHTDEPTH

ERE

SANTA

SEED

GRASS

SPIN

LITTER

Figure 5-2 Plot of the first two axes of the principal components analysis showing environmental variables (natural logarithm transformed) and sites from the dune-swale landscape in the time-since-fire study. A circle = sites burnt 2002 and a square = long-unburnt sites. CCOVER = crown cover, DEPTH = canopy depth, HEIGHT = canopy height, MHD = mulga height diversity, LITTER = phyllode litter coverage, ERE = abundance of Eremophila spp., SANTA = abundance of Santalaceae shrubs, MIST = abundance of mistletoe, SEED = abundance of mulga seedlings, SPINIFEX = spinifex coverage, GRASS = grass coverage.

66

Table 5-6 Summary of a principal components analysis of habitat data in the dune-swale landscape of the time-since-fire study.


Eigenvalues 0.651 0.162 0.097 0.047 1.000


Univariate analysis showed the burnt 2002 treatment had no canopy, fewer shrubs,

particularly Eremophila spp., less mulga seedlings and less coverage of phyllode litter, but

greater coverage of ground plants than the long-unburnt treatment (Table 5-7). Mistletoe was

recorded at only one site in the landscape.

Table 5-7 The effect of time-since-fire on mulga woodland habitat. ‘NA’ indicates that a statistical test could not be conducted because the site did not fulfil the criteria for obtaining a measurement. Grey shading indicates a significant difference.

Burnt 2002 Long-unburnt Parameter

Mean S.D. Mean S.D. T-statistic & P-value

Crown gap NA - 2.1 1.1 NA

Crown width NA - 3.3 0.3 NA

Crown cover (%) NA - 32.7 10.1 NA

Crown height NA - 4.0 0.1 NA

Mulga height diversity NA - 0.57 0.01 NA

Crown depth NA - 2.4 0.2 NA

Mistletoe index - - - - Insufficient data

Shrub index 6.5 5.6 22.1 14.8 T20 = -4.1, p <0.001

Eremophila index 13.9 16.4 46.1 39.3 T21 = -3.1, p = 0.005

Mulga seedling index 0.19 0.52 1.6 1.3 T21 = -4.2, p <0.001

Santalacea index 0.4 1.4 3.4 4.5 T19 = -2.6, p <0.02

Groundcover (%) 35.0 8.9 23.0 8.3 T32 = 4.1, p = <0.001

Log Spinifex cover (%) 1.6 1.0 0.9 0.9 T32 = 2.2, p = 0.03

Grass cover (%) 28.5 9.7 20.4 7.6 T32 = 2.7, p = 0.01

Litter cover (%) 0.0 0.0 48.2 10.7 T32 = -18.6, p = <0.001

Fire severity 0.0 0.0 1.0 0.1 T32 = -44.1, p <0.001

5.3 Edge study Habitat parameters were measured either side of the pyric edge at 10 sites. Data were

collected from 902 recognisable canopy plants. Of these 0.84 were A. aneura and 0.13 were

mulga-like Acacia spp. Mulga woodland in the burnt treatment was subject to a mean mortality

of 83 percent ±17 percent SD (Table 5-8) with a range of 50 percent to 100 percent. Another 9

percent ±13 percent SD of canopy plants were alive but fire damaged. As a result there was no

measurable canopy in the burnt treatment. In the long-unburnt treatment none of the dominant

plants were burnt in 9 of the 10 sites, however at the other site a tongue of low intensity fire had

67

killed or damaged some dominant plants (mean mortality = 1 percent ±2 percent SD).

Nonetheless a canopy existed at all sites.

Table 5-8 Proportion of canopy plants killed, damaged and undamaged by fire in the burnt treatment.



0.83 0.17 0.09 0.13 0.92 0.08 0.09 0.08


of 1.889 SD on the first axis, therefore the data were re-analysed using a PCA. The first two

axes of the PCA accounted for 67.6 percent of the variance (Table 5-9). Sites in the burnt

treatment had greater coverage of grass, spinifex and low shrubs than those in the unburnt

treatment. There were also more mulga seedlings in the burnt treatment. Sites in the unburnt

treatment had higher values for all variables associated with a canopy however the differences

between the treatments in canopy height and mulga height diversity were less than recorded in

the time-since-fire study. This was a consequence of the lower mortality of canopy plants in the

burnt treatment compared to the time-since-fire study. Canopy height had a roughly orthogonal

relationship with the other canopy variables as did abundance of Eremophila shrubs, Santalacea

shrubs and mistletoe. The composition of the habitats in the two treatments was tested using an

RDA with dummy variables representing the treatments. The habitat in each treatment was

different (Monte Carlo permutations test, F-ratio = 14.576, P = 0.001).

Table 5-9 Summary of a principal components analysis of habitat data from the edge study.


Eigenvalues 0.651 0.162 0.097 0.047 1.000


68

-1.0 1.0

-1.0

1.0

MHD

CCOVER

HEIGHT

DEPTH

MIST

ERE

SEED

SANTA

GRASSSPINIFEX

LSHRUBS

LITTER

Figure 5-3 Plot of the first two axes of a principal components analysis showing environmental variables (natural logarithm transformed) and sites. A circle = sites burnt 2002 and a square = long-unburnt sites. CCOVER = canopy cover, DEPTH = canopy depth, HEIGHT = canopy height, MHD = mulga height diversity, LITTER = phyllode litter coverage, ERE = abundance of Eremophila shrubs, SANTA = abundance of Santalaceae shrubs, MIST = abundance of mistletoe, SEED = abundance of mulga seedlings, SPINIFEX = spinifex coverage, LSHRUBS = low shrub coverage, GRASS = grass coverage.

Univariate analysis showed the burnt treatment had no canopy, less phyllode litter, but

greater ground cover than the unburnt treatment (Table 5-10). Low shrub cover, spinifex cover

and ground cover all showed near-significant differences in the same direction as groundcover.

There were also near-significant differences in the amount of mulga seedlings (more in the

burnt treatment) and Eremophila spp. (more in the unburnt treatment). The interaction between

the two parameters contributed to the lack of difference between the two treatments in the

composite parameter; shrubs. Mistletoe was recorded at four sites and there was no difference

between treatments.

69

Table 5-10 Effect of time-since-fire on habitat across a pyric edge in mulga woodland. ‘NA’ indicates that a statistical test could not be conducted because the site did not fulfil the criteria for obtaining a measurement. Grey shading indicates a significant difference.

Burnt 2002 Long unburnt Parameter

Mean S.D. Mean S.D. T-statistic and P-value

Crown gap (m) NA - 2.7 0.6 NA

Crown width (m) NA - 2.3 0.5 NA

Crown cover (%) NA - 17.9 2.3 NA

Crown height (m) NA - 3.3 0.4 NA

Mulga height diversity NA - 0.5 0.1 NA

Crown depth (m) NA - 2.5 0.3 NA

Mistletoe index 0.04 0.1 0.5 0.8 T9 = 2.0 , p = 0.08

Shrub index 9.4 9.4 17.1 19.5 T13 = -1.1 , p = 0.3

Eremophila index 1.9 3.3 13.4 17.1 T10 = -2.1, p = 0.06

Mulga seedling index 2.7 3.8 0.7 0.8 T10 = 1.7, p = 0.1

Santalacea index 0.2 0.4 1.4 2.2 T10 = 1.7, p = 0.1

Groundcover (%) 35.2 6.9 13.9 1.0 T18 = 5.6, p < 0.001

Shrub cover (%) 6.8 5.4 3.3 2.9 T18 = 1.8, p = 0.08

Grass cover (%) 18.4 11.7 9.1 9.0 T18 = 2.0, p = 0.06

Spinifex cover (%) 6.1 3.9 3.0 3.0 T18 = 2.0, p = 0.06

Litter cover (%) 7.5 5.9 29.9 6.8 T18 = -7.9, p <0.001

Fire severity 0.13 0.12 0.99 0.02 T9 = -23.5, p <0.001

5.4 Summary Fire in mulga woodland caused high mortality of the dominant plants. Following fire, the

coverage of ground plants – mostly grass – peaked. Seedlings of the dead canopy plants

germinated in the open landscape but the abundance of seedlings did not peak at this time.

Where a new mulga woodland canopy developed, the coverage of ground plants declined and

the coverage of phyllode litter increased. The density and height of the mulga woodland canopy

increased with time-since-fire and the structural complexity of the canopy reached its highest

values in the long-unburnt treatment. The development of understorey shrub foliage lagged

behind that of the mulga woodland canopy and appeared to reach its maximum many years after

the pulse of groundcover had subsided. Mistletoe development appeared to follow a similar

pattern to the understorey shrubs.

The results from two parameters, mulga seedling abundance and mistletoe abundance were

inconclusive. The abundance of seedlings in the burnt 2002 treatment varied between

landscapes and studies. This suggests that factors not examined in this study may have affected

germination of mulga seedlings following fire. Potential causes are between-site differences

which may have influenced the degree of fractional release of the seed bank, such as fire

intensity, edaphic conditions and moisture availability (Nano, 2005). Mistletoe was rare at the

70

study site but anecdotal evidence suggested it was most likely to be present in the oldest mulga

plants. Within the long-unburnt treatment, sites that supported mistletoe often also had

senescent or senescing canopy plants. The small numbers of mistletoe recorded in the burnt

2002 treatment were in large, presumably old plants that survived fire. Similarly the mistletoe

present in the burnt 1976 treatment occurred in plants that appeared to have survived the 1976

fire. Factors not examined in this study may affect the distribution of mistletoe in the landscape.

Potential contributory causes are depth to watertable (O’Grady et al., 2006) and nutrient status

(Stafford-Smith and Morton, 1990).

71

Chapter 6: Time-since-fire The aim of this study was to investigate whether time-since-fire causes changes in the

distribution of birds in mulga woodlands and to characterise any change through time. The

composition of the bird community is affected by time-since-fire in many ecosystems (Chapter

2:) and an unreplicated study at UKTNP suggests that this is also true too for birds in mulga

woodlands (Chapter 3.4). An investigation of time-since-fire is essential because an effect of

fire is crucial to the fire mosaic hypothesis (Bradstock et al., 2005; Parr and Andersen, 2006). If

there is no effect of fire history on biodiversity, then the spatial arrangement of different times-

since-fire is irrelevant and the definition of habitat patches and habitat edges based on time-

since-fire is not valid.

I tested the hypothesis:

1. The birds present in mulga woodland vary with time-since-fire. Two space-for-time experiments were set up in landscapes with contrasting soil and

hydrological characteristics – sheetwash (Tongway and Ludwig, 1990) and dune-swale (Wasson

and Hyde, 1983) to help inform the degree to which the results could be generalised. Habitat

was assessed at all the bird survey sites (Chapter 5:). The data were used to help explain any

differences in the distribution of birds between treatments.

6.1 Methods The experimental populations were defined by overlaying a fire history on a map of mulga

woodland in Arcmap 9.1 (Chapter 4:). In the sheetwash landscape, three time-since-fire classes

were identified: burnt 2002, burnt 1976 and long-unburnt. The selection procedure for

experimental units was designed to cover the range of patch sizes in the landscape while

standardising for the potential effects of edge (Helzer and Jelinski, 1999; Ries et al., 2004).

Therefore, the experimental units were selected according to time-since-fire, area and area-to-

perimeter ratio. Different sections of some large linear or irregularly shaped patches were

treated as different patches and this improved interspersion of experimental units and made for

easier access. The patches of mulga woodland were assigned to a size-class: 3ha-<9ha, 9ha-

<27ha, 27ha-<81ha and >81ha. Five replicates of each size class were selected for each time-

since-fire class. In the 3 - <9ha class the patches with the greatest area-to-perimeter ratio were

selected. In the other size classes the patches were split into sub-classes representing 20 percent

of the area range of the class and the patch with the greatest area-to-perimeter ratio from each

sub-class was selected. When all sites had been selected, the spatial distribution was reviewed.

Experimental units must be concomitant to reduce the possibility of bias (Hurlbert, 1984) so any

isolated sites were excluded and the patch with the next largest maximum area-to-perimeter

ratio substituted. The dominant vegetation at all sites was ground-truthed and any that were

incorrectly classified mulga woodland were replaced using the procedure described above.

Three extra sites were added to maximise the overlap in the spatial distribution of the time-

72

since-fire treatments. The dominant vegetation at all sites was ground-truthed and any that were

incorrectly classified as mulga woodland were replaced. A total of 63 experimental units were

selected in the sheetwash landscape, 21 were burnt 2002, 20 were burnt 1976 and 22 were long-

unburnt (Figure 6-1).


but with two differences. A large section of the eastern end of the UKTNP, including the dune-

swale landscape, did not burn in 1976 (Figure 4-5) so only two time-since-fire classes were

present – burnt 2002 and long-unburnt. The stands of mulga woodland were smaller in the

dune-swale system (Figure 4-7) so only three size classes were present: 3ha-<9ha, 9ha-<27ha

and >27ha. A total of 34 experimental units were selected, divided equally between the burnt

2002 and long-unburnt treatments.

Figure 6-1 Bird survey sites for the time-since-fire study. The cluster of sites at the eastern end of the park is in the dune-swale landscape.

6.1.1 Bird counts Bird survey points were positioned in the centre of each experimental unit to standardise

for the effect of edge (Helzer and Jelinski, 1999; Ries et al., 2004). Internal buffers created in

Arcmap 9.1 (ESRI, 2004) at intervals of 100m, 200m, 400m and 800m from the edge, were

used to position the survey points. The points were located by GPS and ground-truthed in

relation to the edge of the experimental unit. A consistent error of approximately 30m was

detected in the smallest experimental units. This was corrected by re-positioning the survey

points by measuring the patch of mulga woodland on the ground with a GPS. The error may

73

have related to the registration and rectification of the Landsat image used as the base for the

mulga map.

Bird surveys were conducted in each landscape in winter 2005, spring 2005, winter 2006

and spring 2006. All surveys were completed in 16 days to approximate synchrony (Field et al.,

2002). Two observers participated in the winter 2005 survey. Estimation of distance by

observers is a major source of error in bird surveys however training can deliver dramatic

improvement (Buckland et al., 1993; Buckland et al., 2001; Rosenstock et al., 2002). Therefore,

training was conducted at the study site prior to each survey to maximise observer accuracy and

minimise differences between observers. In addition, datasheets were checked each day and

visual and acoustic cues were discussed. Comparisons between the two landscapes were limited

to the direction of the effect, because any differences were confounded by the potential effect of

recent rain (Chapter 3:). Surveys were conducted in the same season but not the same dates each

year. This was dictated by rainfall events and was unlikely to bias the results for two reasons. 1)

Data were collected from all treatments in each survey. 2) Recent rain has a strong effect on the

distribution of birds in the arid zone and rainfall at the study site is unpredictable (Chapter 3:).

Birds were counted using the point-interval technique (Recher, 1988). Each point-interval

transect consisted of three survey points located at intervals of 100m. Birds were counted in a

plot of 50m radius centred on each point. Flagging tape was positioned at each point and

equidistant from each point (i.e. 50m) to mark the boundary of each plot and assist with

estimation of distances. Vehicles were parked at least 100m from the survey points to minimise

disruption to birds prior to counting. Sampling at a fine spatial scale is best conducted at a fine

temporal scale (Wiens, 1989), so each point was sampled for five minutes. Five minutes then

was allowed for the observer to traverse to the next point and prepare a new datasheet. Each

record was allocated to a distance class; <10m, 10m-<20m and 20m-<50m. Other data collected

were the start and finish time, whether it was raining, an estimate of cloud cover in octas and an

estimate of wind strength (Table 6-1).

Table 6-1 Wind strength classes for bird surveying in mulga woodland.

Class Description Definition

1 Still No movement of mulga foliage and no noise

2 Light breeze Movement of mulga foliage but no noise

3 Breeze Movement of mulga foliage and some rustling

4 Wind Vigorous movement of mulga branches and excessive rustling

6.1.2 Statistical analyses Multivariate analyses were conducted in CANOCO 4.53 following the procedures

described in Chapter 3.9.3 (Ter Braak, 1986; Ter Braak and Smilauer, 2002; Leps and Smilauer,

2003; Leps and Smilauer, 2005). Differences in detectability between treatments were

74

accounted for using two methods: data truncation and presence/absence (Chapter 3.9.1); running

analyses using both datasets and comparing the results. Uneven sampling effort between sites

can bias multivariate analyses, so the datasets for CANOCO were adjusted to account for this.

Data were pooled from each season (i.e. winter 2005, spring 2005, winter 2006 and spring 2006.

For the count datasets the number of individuals of each species detected at each site was

summed and divided by the effort. For the presence/absence dataset, the first sample from each

site, from each season was retained and any subsequent samples excluded. Presence/absence

from each season at each site was summed to give a binomial total of four. A drawback with the

presence/absence datasets in the time-since-fire experiments was that data were not available for

every site in the spring 2005 survey. Data were unavailable for 11 sites in the sheetwash

landscape and 3 sites in the dune-swale landscape, so both datasets were potentially biased. I

proceeded with the analyses despite the potential bias for 3 reasons. 1) The count datasets were

not biased by uneven survey effort. 2) The main objective was to compare the methods of

accounting for detectability and a small potential bias was unlikely to compromise that

objective. 3) The missing data comprised four percent of the potential sheetwash dataset while

exclusion of the sites from every season comprised 17 percent of the dataset and exclusion of

the season comprised approximately 25 percent.

A detrended correspondence analysis (DCA) was conducted on each set of species

variables to determine the appropriate response model for the constrained ordination. The DCA

was detrended using 26 segments, rare species were downweighted and the data were not

transformed.

Redundancy analysis (RDA) was conducted with scaling focused on samples (i.e. survey

sites). Scaling options change the emphasis of an ordination plot containing samples and species

but do not change the emphasis of a bi-plot containing predictor variables (which is how the

data are presented). Species scores were divided by the standard deviation because this reduced

the influence of outliers. The species data were not transformed. The samples data (survey sites)

were not centred or standardised but the species data were centred (mandatory for an RDA). The

environmental data were automatically standardised to unit variance. The six most significant

predictor variables were included in the analysis and the significance of the first ordination axis

and all canonical axes were tested using a Monte Carlo permutations test with 999 runs (i.e.

most significant result possible = 0.01).

Canonical correspondence analyses (Keith et al.) were conducted with bi-plot scaling

focused on samples. Species data were not transformed, but rare species were downweighted,

because such species could have disproportionately influenced the result. The environmental

data were automatically standardised to unit variance. The six most significant predictor

variables were included in the analysis and the significance of the first ordination axis and all

canonical axes were tested using a Monte Carlo permutations test with 999 runs.

Differences between the response variables associated with the treatments were tested by

conducting a direct gradient analysis with dummy variables to represent each factor. The

75

samples associated with each treatment in turn were entered as covariates so any difference

between the other two treatments could be determined by running a Monte Carlo permutations

test with 999 runs.

The effect of time-since-fire on species richness and bird abundance was tested using

Generalised Linear Mixed Models (GLMM) in Genstat 8.0 (Payne et al., 2005). The data were

analysed at the site level because sites were independent; the three plots within each site were

not. The species richness data were compiled using the presence of species at each site.

Differences in detectability of the presence of a species between treatments were assumed to be

minimal because most records were acoustic (Chapter 3.9.1). The fixed terms in the models

were ‘treatment’ (i.e. time-since-fire) and ‘wind’ (i.e. wind strength), the random term was ‘site’

and the distribution was Poisson with a logarithm link function. The dispersion was estimated

from the data in each test. Both fixed terms and the interaction were included in the initial

models and non-significant interactions and main effects were removed sequentially until only

significant and near-significant terms and interactions remained. Temporal changes in species

richness were tested using similar models but with the fixed term ‘season’ replacing ‘treatment’.

Bird abundance was tested using count data. Differences in detectability between treatments

were accounted for by truncating the data (Chapter 3.9.1). The fixed terms in the models were

‘treatment’ (i.e. time-since-fire) and ‘wind’ (i.e. wind strength), the random term was ‘site’ and

the distribution was Poisson with a logarithm link function. Temporal changes in bird

abundance were tested by replacing the fixed term ‘treatment’ with ‘season’. Significance was

determined using a Wald statistic which approximates a χ2 distribution. The Wald statistic

overestimates significance especially with small sample sizes (McCulloch and Searle, 2001;

Payne et al., 2005) so a conservative α-value was used (α = 0.01) to reduce type 1 error

(Leavesley and Magrath, 2005). Near significance was defined as p < 0.05.

The density of bird species was determined using distance analysis performed in Distance

5.0 Release 2 (Thomas et al., 2006) following Buckland et al. (2001). Separate detection

functions were estimated for each species in each treatment tested because detectability was

assumed to be influenced most by these parameters. Detectability may also have been

influenced by other parameters, in particular the timing of the survey, landscape and observer.

Estimation of separate detection functions to account for these factors was considered, however

there were insufficient observations (Buckland et al., 2001). Instead a common (global)

detection function was estimated using data for each species/treatment combination that was

tested. For some species that were present at low densities, data from the burnt 1976 and long-

unburnt treatments were combined to fit a detection function and a single estimate representing

the density across both treatments was compared with that from the burnt 2002 treatment.

Detection function selection was guided by Akaike’s Information Criterion and visual

inspection of detection probability plots and probability density plots. Particular attention was

paid to ensuring that the combination of visual and acoustic records within a single detection

function did not generate detection anomalies. The data were assigned to distance classes when

76

collected and the cut-points in the analysis were determined by this assignment. Pooling of the

first two distance classes was conducted when a species showed evidence of fleeing the

observer. Data truncation sensu Buckland et al. (2001) was not required. Cluster size estimation

was preferentially calculated using the size bias regression method which was a default option

in Distance 5.0 (Thomas et al., 2006). Where the number of observations was small this

procedure could produce erratic results. In these instances, a global cluster size estimate or

mean cluster size estimate was used instead. Density estimates were obtained for each landscape

using the analytical stratification function in Distance 5.0 (Thomas et al., 2006). Where

sufficient data were obtained, analyses were stratified by survey allowing a time-series of

between-treatment comparisons to be obtained. Between-treatment comparisons were

independent because a separate detection function was estimated for each treatment.

Significance tests were performed using an approximation of either a t-statistic (d.f. <30) or Z-

statistic (d.f. >30). The degrees of freedom were obtained from the Distance 5.0 (Thomas et al.,

2006) output, or calculated using the Satterthwaite approximation.

A rule of thumb is to design a study to try to obtain 30 observations for each detection

function (Buckland et al., 2001). In practice this may be difficult to achieve in a multi-species

study with large differences in the density of some species between treatments. Distance 5 will

fit functions to as few as 10 observations. Functions fit from 10-15 observations are typically

imprecise but have been included because they represent the best estimate obtainable by the

method. The alternative is to assume equal detectability between treatments or to try to model

differences in detectability between treatments using covariates such as habitat structure data (S.

Buckland pers.comm.).

6.2 Results A total of 50 species were detected, 47 in the sheetwash landscape and 38 in the dune-

swale landscape. Sufficient data for univariate analysis were obtained to test for an effect of

time-since-fire on 13 species.

6.2.1 Multivariate analysis The aim of the multivariate analyses was to investigate the response of the bird

community. The sheetwash and dune-swale landscapes were analysed separately. Differences in

detectability between treatments were addressed using two methods and the results compared.

A DCA run on the bird count data from the sheetwash landscape returned a maximum

gradient length of 1.901 SD on the first axis, therefore an RDA was run on the bird counts and

habitat variables. The relationship between birds and habitat variables was significant for the

first axis (Monte Carlo permutation test, F-ratio = 11.469, P = 0.001) and the overall RDA

(Monte Carlo permutations test, F-ratio = 2.991, P = 0.001). The habitat variables explained

24.4 percent of the variance in the bird data (Table 6-2). The first axis accounted for 70.6

percent of the variation in the species-environment relationship. The axis was negatively

77

correlated with mulga height diversity and abundance of Eremophila shrubs. The axis therefore

represents a gradient from tall dense mulga woodland with a variety of plant heights and

abundant shrubs to grassland. The second axis explained 11.9 percent of the species-

environment relationship. The strongest relationship in the axis was spinifex coverage though

this was not significant.

78

Table 6-2 Summary of a redundancy analysis using bird count data from the sheetwash landscape, CCOV = crown cover, MHD = mulga height diversity, MIS = mistletoe abundance, ERE = Eremophila spp., SAN = Santalacea spp. abundance, SPIN = spinifex cover.

Axes 1 2 3 4 Total variance

Eigenvalues 0.172 0.029 0.021 0.010 1.000

Spp- env corr. 0.768 0.505 0.498 0.597 -

Cum % var. sp 17.2 20.1 22.2 23.1 -

Cum % var spp-env 70.6 82.5 91.0 95.0 -

Corr. CCOV - species axis -0.3266 -0.0279 -0.0819 -0.3181 -

Corr. MHD – species axis -0.7202 0.0178 -0.0915 0.0192 -

Corr. MIS – species axis 0.0318 0.1165 0.0228 0.1844 -

Corr. ERE – species axis -0.4899 -0.2141 0.2148 0.0188 -

Corr. SAN – species axis -0.3926 0.1345 0.3436 0.0857 -

Corr. SPIN – species axis 0.1478 -0.2409 -0.0759 0.3423 -

Sum of all eigenvalues 1.000

Sum of all canonical eigenvalues 0.244

Partial tests using dummy variables representing the three treatments - burnt 2002, burnt

1976 and long-unburnt - showed that the bird community in the burnt 2002 treatment was

markedly different to that in the other two treatments (Table 6-3; Figure 6-2). The bird

community associated with the burnt 2002 treatment included all the granivores such as the

Zebra Finch, parrots and Southern Whiteface and most of the terrestrial insectivores such as the

Hooded Robin, Willie Wagtail and Crimson Chat (Figure 6-3). The burnt 1976 and long-

unburnt treatments supported a similar bird community. The species most closely associated

with the two treatments were almost entirely insectivorous such as the canopy foraging Slaty-

backed Thornbill and Grey Shrike-thrush, shrub/canopy foraging Chestnut-rumped Thornbill

and Inland Thornbill, ground/shrub foraging Splendid Fairy-wren and ground-foraging Red-

capped Robin. Aerial insectivores and nectarivore/frugivores were spread across the plot

indicating that members of the guilds could find resources regardless of time-since-fire.

Table 6-3 Results of Monte Carlo permutations tests for differences (999 runs) between the bird communities present in each treatment of a redundancy analysis from the sheetwash landscape using bird count data.


Burnt 2002 vs Burnt 1976 5.909 0.001



79

-1.0 0.6

-1.0

1.0

CCOV

MHD

MIS

ERE

SAN

SPIN

B1

B2

B3

B4

B5

B6

B7

B8B9

B10

B11

B12

B13

B14

B15B16

B17

B18B19

B20B21

R42

R43R44R45

R46

R47R48

R49

R51

R52

R53

R55

R56

R57

R59

R60

R61

R62

R81

R82

M22

M23

M24M25

M26

M27M28

M29

M30

M31

M32

M33

M34

M35

M36

M37

M38

M39

M40

M41

M63

M80

Figure 6-2 Bi-plot of the first two axes of the redundancy analysis using bird count data from the sheetwash landscape showing environmental variables and sites. Circles are sites burnt 2002, crosses are sites burnt 1976 and squares are sites long-unburnt. MHD = mulga height diversity, CCOV = crown cover, SAN = Santalacea spp. abundance, ERE = Eremophila spp. abundance, MIS = mistletoe abundance, SPIN = spinifex cover.

80

-1.0 0.6

-0.6

0.6

BFWS

BOU

BUD

CBB

CHW

CRC

CRTB

GFA

GST

HDRITB

MUL

MWS

RCRRED

RIN

RW

SBTBSCHE

SFW

SHE

SWF

VFW

WBB

WGG

WIL

YRTB

ZEBCCOV

MHD

MIS

ERE

SAN

SPIN

Figure 6-3 Bi-plot of the first two axes of the redundancy analysis using bird count data from the sheetwash landscape showing environmental variables and birds. MHD = mulga height diversity, CCOV = crown cover, SAN = Santalaceae spp. abundance, ERE = Eremophila spp. abundance, MIS = mistletoe abundance, SPIN = spinifex cover. For bird codes see Table 6-4.

81

Table 6-4 Bird codes used in ordination plots, and feeding guilds. Scientific names of all species are listed in Table 3-1 and Table 3-3.

Guild Abbreviation Species

Food Substrate

BFWS Black-faced Woodswallow Insectivore Aerial

BOU Bourke's Parrot Granivore Ground

BUD Budgerigar Granivore Ground

CBB Crested Bellbird Insectivore Shrub/canopy

CHW Chiming Wedgebill Insectivore Ground

CRC Crimson Chat Insectivore Ground

CRTB Chestnut-rumped Thornbill Insectivore Shrub/canopy

GBB Grey Butcherbird Insectivore/carnivore Ground/shrub/canopy

GFA Grey Fantail Insectivore Aerial

GST Grey Shrike-thrush Insectivore Canopy

HDR Hooded Robin Insectivore Ground

ITB Inland Thornbill Insectivore Shrub/canopy

MUL Mulga Parrot Granivore Ground

MWS Masked Woodswallow Insectivore Aerial

RCR Red-capped Robin Insectivore Ground

RED Redthroat Insectivore Ground

RIN Australian Ringneck Granivore Ground

RW Rufous Whistler Insectivore Ground/shrub/canopy

SBTB Slaty-backed Thornbill Insectivore Canopy

SCHE Spiny-cheeked Honeyeater Nectarivore/frugivore Canopy

SFW Splendid Fairy-wren Insectivore Ground/shrub

SHE Singing Honeyeater Nectarivore/frugivore Canopy

SWF Southern Whiteface Granivore Ground

VFW Variegated Fairy-wren Insectivore Ground/shrub

WBB White-browed Babbler Insectivore Ground/shrub

WGG Western Gerygone Insectivore Canopy

WIL Willie Wagtail Insectivore Ground

YRTB Yellow-rumped Thornbill Insectivore Ground/shrub

ZEB Zebra Finch Granivore Ground

A DCA run on the bird presence/absence data from the sheetwash landscape returned a

gradient length of 5.476 SD for the first axis, therefore a CCA was run on the bird

presence/absence data and the habitat variables. The relationship between birds and habitat

variables was significant for the first axis (Monte Carlo permutations test, F-ratio = 5.296, P =

0.005) and the overall CCA (Monte Carlo permutations test, F-ratio = 1.638, P = 0.008). The

habitat variables explained 15.1 percent of the variance in the bird data (Table 6-5). The first

82

axis accounted for 59.1 percent of the variation in the species-environment relationship. The

axis was negatively correlated with canopy height and mulga height diversity and represented a

gradient from tall mulga with a variety of heights to grassland. The second axis explained 14.0

percent of the species-environment relationship. The axis was weakly correlated with spinifex

cover and seedling abundance and there was a weak negative correlation with grass cover.

Table 6-5 Summary of a canonical correspondence analysis using presence/absence data from the sheetwash landscape, CCOV = crown cover, HEI = canopy height, SEED = mulga seedling abundance, SAN = Santalacea spp. abundance, GRA = grass cover, SPIN = spinifex cover.

Axes 1 2 3 4 Total variance

Eigenvalues 0.199 0.047 0.034 0.025 2.231

Spp- env corr. 0.895 0.742 0.673 0.610 -

Cum % var. sp 8.9 11.0 12.6 13.7 -

Cum % var spp-env 59.1 73.1 83.0 90.6 -

Corr. CCOV - species axis -0.5439 0.0673 0.0416 -0.2149 -

Corr. HEI – species axis -0.8310 0.1942 0.1316 0.0571 -

Corr. SEED – species axis 0.1815 0.4749 0.3615 -0.1114 -

Corr. SAN – species axis -0.2521 0.1446 -0.0178 0.4389 -

Corr. GRA – species axis 0.3760 -0.4179 0.3313 -0.1385 -

Corr. SPIN – species axis 0.1775 0.4536 -0.2982 0.1919 -

Sum of all eigenvalues 2.231

Sum of all canonical eigenvalues 0.337

Partial tests using dummy variables representing the three treatments – burnt 2002, burnt

1976, and long-unburnt – showed that the bird community in the burnt 2002 treatment was

different to that in the burnt 1976 and long-unburnt treatments (Table 6-6; Figure 6-4). There

was no difference in the bird community between the burnt 1976 and the long-unburnt

treatments. The bird communities associated with the treatments followed the same pattern

obtained from the analysis of count data. Granivores and ground insectivores were most likely

to be associated with the burnt 2002 treatment (Figure 6-5). Foliar insectivores were most likely

to be associated with the burnt 1976 and long-unburnt treatments. Aerial insectivores and

nectarivore/frugivores were spread across the plot indicating that members of the guilds could

find resources regardless of time-since-fire.

83

Table 6-6 Results of Monte Carlo permutations tests for differences (999 runs) between the bird communities present in each treatment of a canonical correspondence analysis using bird presence/absence data from the sheetwash landscape.

Test F-ratio P-value

Burnt 2002 vs Burnt 1976 4.351 0.001

B2002 vs Long-unburnt 4.872 0.001


-1.0 1.0

-1.0

1.0

CCOV

HEI

SEED

SAN

GRA

SPIN

B1

B2

B3

B4

B5

B6

B7B8

B9

B10

B11

B12

B13

B14

B15

B16

B17

B18

B19

B20

B21

R42

R43

R44

R45

R46

R47

R48

R49

R51

R52

R53

R55

R56

R57

R59 R60

R61

R62

R81

R82

M22

M23

M24

M25

M26

M27

M28

M29

M30

M31

M32

M33

M34

M35

M36

M37M38

M39

M40

M41

M63

M80

Figure 6-4 Bi-plot of the first two axes of the canonical correspondence analysis using bird presence/absence data from the sheetwash landscape showing environmental variables and sites. Circles are sites burnt 2002; crosses are sites burnt 1976 and squares are sites long-unburnt. HEI = mulga canopy height, CCOV = crown cover, SAN = Santalacea spp abundance, SPIN = Spinifex cover, SEED = mulga seedling abundance and GRA = grass cover.

84

-1.0 1.0

-0.6

0.8

BFWS

BOU

BUD

CBBCHW

CRC

CRTB

GBB

GFA

GST

HDR

ITBMUL

MWS

RCR

RED

RINRW

SBTB

SCHESFW

SHE

SWF

VFW

WBB

WGG

WIL YRTB

ZEB

CCOV

HEI

SEED

SAN

GRA

SPIN

Figure 6-5 Bi-plot of the first two axes of the redundancy analysis using bird presence/absence data from the sheetwash landscape showing environmental variables and birds. Circles are sites burnt 2002; crosses are sites burnt 1976 and squares are sites long-unburnt. HEI = mulga canopy height, CCOV = crown cover, SAN = Santalacea spp abundance, SPIN = spinifex cover, SEED = mulga seedling abundance and GRA = grass cover. For bird codes see Table 6-4.

The presence/absence bird data were analysed by season. There was no seasonal variation

in the bird community present at each treatment. The bird community in the burnt 2002

treatment was consistently different to that in the other two treatments (Table 6-7) and there was

no difference between the burnt 1976 and long-unburnt treatments. The results suggest that

factors such as recent rain do not influence the bird communities present in each treatment. This

validates pooling of data across seasons.

85

Table 6-7 Canonical correspondence analysis of bird presence/absence data from the sheetwash landscape by season. Grey shading indicates a significant difference.

Season DCA gradient length Treatments tested F-ratio P-value

Burnt 2002 vs Burnt 1976 2.702 0.001

Burnt 2002 vs Long-unburnt 2.700 0.001 Winter 2005 4.968 SD


Burnt 2002 vs Burnt 1976 2.574 0.001

Burnt 2002 vs Long-unburnt 3.074 0.001 Spring 2005 5.413 SD


Burnt 2002 vs Burnt 1976 2.419 0.001

Burnt 2002 vs Long-unburnt 1.814 0.004 Winter 2006 6.935 SD


Burnt 2002 vs Burnt 1976 1.973 0.001

Burnt 2002 vs Long-unburnt 2.422 0.001 Spring 2006 7.386 SD


There were insufficient data to conduct a meaningful multivariate analysis on the bird

count data from the dune-swale landscape. The data were sparse so that six sites in the burnt

2002 treatment recorded no species or only species that were not recorded in other sites. Such

sites do not relate to the rest of the dataset and analysis can only proceed by excluding them.

This approach was not adopted because of the high proportion (35 percent) of burnt 2002

treatment sites that could not be used.

A DCA run on the bird presence/absence variables returned a maximum gradient length of

7.229 on the second axis; hence a CCA was run on the bird data and habitat variables. The

relationship between birds and the habitat variables was not significant on the first axis (Monte

Carlo permutations test F-ratio = 2.714, P = 0.0550) or on all canonical axes (Monte Carlo

permutations test F-ratio = 1.010, P = 0.426). It is therefore inappropriate to draw inference

about the species/environment relationship from the results. The lack of significance of the

species/environment relationship does not prevent further consideration of the species/site

relationship using a DCA. The first two axes of the DCA accounted for 26.0 percent of the

variance (Table 6-8). A test using a CCA with dummy variables representing the two treatments

– burnt 2002 and long-unburnt – showed that the bird communities in the two treatments were

different (Monte Carlo permutations test, F-ratio = 3.288, P = 0.001). The ordination plot of the

sites shows that the long unburnt treatment clustered tightly in comparison with the burnt 2002

treatment. However approximately half of the sites in the burnt 2002 treatment overlapped in

ordination space with the long unburnt treatment. The composition of the bird community at the

overlapping sites was similar containing most of the species typically found at the long-unburnt

treatment in the sheetwash landscape. The bird community typical of the long-unburnt treatment

86

was dominated by foliar insectivores. Granivores and a carnivore were typically present in the

burnt 2002 treatment sites that were separated from the long-unburnt cluster. Ground

insectivores and aerial insectivores were found distributed across the plot indicating that

members of the guild could find resources regardless of time-since-fire. There was insufficient

data to test for temporal variation in the bird communities associated with time-since-fire in the

dune-swale landscape.

Table 6-8 Summary of a detrended correspondence analysis of bird presence/absence data in the dune-swale landscape of the time-since-study.

Axes 1 2 3 4 Total inertia

Eigenvalues 0.496 0.291 0.189 0.114 3.026

Cum % var. sp 16.4 26.0 32.3 36.0

87

-1 6

-212

BFWS

BUD

CBB

CRTBGBB

GST

HDR

ITB

MUL

MWS

RCR

RIN

RW

SBTB

SCHE

SFWSHE

SWF

WBB

WIL

YRTB

ZEB

Figure 6-6 Plot of the first two axes of the detrended correspondence analysis using bird presence/absence data from the dune-swale landscape. The plot shows survey sites and birds. Circles are sites burnt 2002, crosses are sites long-unburnt and birds are represented by codes (see Table 6-4).

88

6.2.2 Univariate analysis

6.2.2.1 Species richness There was no difference in species richness between treatments in any season in the

sheetwash landscape (Table 6-9; Figure 6-7). The data did show a consistent though non-

significant trend for greatest species richness in the long unburnt treatment. In the dune-swale

landscape, species richness was greater in the long unburnt treatment than in the burnt 2002

treatment in three of the four seasons (Table 6-9; Figure 6-8).

Season had a strong effect on species richness in both the sheetwash landscape (Season: χ2

3

= 20.1, p <0.001, Wind: χ2

1 = 12.2, p <0.001; Figure 6-9) and the dune-swale landscape (χ2

3 =

30.3, p <0.001). Seasonal variation in species richness showed a similar pattern in both (Table

6-10). The percentage coefficient of variation was lowest in the long unburnt treatment in both

landscapes.

Table 6-9 Results of GLMM tests of the effect of time-since-fire on species richness showing significant and near-significant terms in the model.

Landscape Season Fixed terms χ2 df P

Winter 2005 Time-since-fire 5.3 2 0.07

Spring 2005 Time-since-fire 4.6 2 0.1

Time-since-fire 4.6 2 0.1 Winter 2006

Wind 11.0 1 <0.001

Sheetwash


Winter 2005 Time-since-fire 18.0 1 <0.001


Winter 2006 Time-since-fire 41.5 1 <0.001

Time-since-fire 6.7 1 0.01

Dune-swale

Spring 2006 Wind 4.3 1 0.04

Table 6-10 Percentage coefficient of variation in species richness; ‘NA’ = not applicable.

Landscape Season Burnt 2002 Burnt 1976 Long unburnt

Winter 2005 36.0% 26.6% 23.8%

Spring 2005 24.5% 27.3% 17.9%

Winter 2006 28.2% 21.3% 18.7% Sheetwash

Spring 2006 34.7% 33.6% 30.8%

Winter 2005 69.6% NA 24.5%

Spring 2005 32.9% NA 21.1%

Winter 2006 95.0% NA 20.6% Dune-swale

Spring 2006 71.5% NA 37.2%

89

a)

0Burnt 2002 Burnt 1976 Long

unburnt

1

2

3

4

5

6

7

8

Spec

ies.

surv

ey-1

b)

0Burnt 2002 Burnt 1976 Long

unburnt

1

2

3

4

5

6

7

8

Spec

ies.

surv

ey-1

c) d)

0

1

2

3

4

5

6

7

8

Burnt2002 Burnt 1976 Longunburnt

Spec

ies.

surv

ey-1

0

1

2

3

4

5

6

7

8

Burnt 2002 Burnt 1976 Longunburnt

Spec

ies.

surv

ey-1

Figure 6-7 Species richness by treatment in the sheetwash landscape showing mean and 95% confidence levels: a) winter 2005, b) spring 2005, c) winter 2006, d) spring 2006.

90

a)

0Burnt 2002 Long unburnt

1

2

3

4

5

6

7

Spec

ies.

surv

ey-1

b)


1

2

3

4

5

6

7

Spec

ies.

surv

ey-1

c) d)

0

1

2

3

4

5

6

7

Burnt 2002 Long unburnt

Spec

ies.

surv

ey-1

0

1

2

3

4

5

6

7


Spec

ies.

surv

ey-1

Figure 6-8 Species richness by treatment in the dune-swale landscape showing mean and 95% confidence levels: a) winter 2005, b) spring 2005, c) winter 2006, d) spring 2006.

91

a)

0

Winter2005

Spring2005

Winter2006

Spring2006

1

2

3

4

5

6

Spec

ies.

surv

ey-1

b)

0

1

2

3

4

5

6

Winter2005

Spring2005

Winter2006

Spring2006

Spec

ies.

surv

ey-1

Figure 6-9 Species richness by season in the a) sheetwash and, b) dune-swale landscapes; showing mean and 95% confidence levels.

6.2.2.2 Bird abundance There was no difference in bird abundance between treatments in any survey in the

sheetwash landscape (Table 6-11; Figure 6-10). There was a strong trend for greatest bird

abundance in the long unburnt treatment in two of the four seasons. Results in the dune-swale

landscape were consistent with, though not identical to those from the sheetwash landscape.

Bird abundance was greater in the long unburnt treatment than in the burnt 2002 treatment in

two of the four seasons (Table 6-11; Figure 6-11 ).

There was a strong trend for bird density to change with season in both the sheetwash

landscape (χ2

3 = 8.6, p = 0.04, Figure 6-12) and the dune-swale landscape (Season: χ2

3 = 9.8, p =

0.02). The coefficient of variation was usually smallest in the long unburnt treatment. This was

92

always the case in the sheetwash landscape and in three of four seasons in the dune-swale

landscape (Table 6-12).

Table 6-11 Results of GLMM tests of the effect of time-since-fire on bird abundance showing significant terms and interactions in the model.

Landscape Survey Fixed terms χ2 df P

Winter 2005 Time-since-fire 1.5 2 0.5


Winter 2006 Time-since-fire 6.1 2 0.05 Sheetwash



Wind 1.1 1 0.3 Winter 2005

Time-since-fire.Wind 4 1 0.05

Time-since-fire 0.2 1 0.7 Spring 2005

Wind 4.0 1 0.05

Time-since-fire 9.2 1 0.002 Winter 2006

Wind 4.3 1 0.04


Dune-swale

Spring 2006 Wind 7.7 1 0.006

Table 6-12 Percentage coefficient of variation in bird abundance, NA = not applicable.

Landscape Season Burnt 2002 Burnt 1976 Long unburnt

Winter 2005 38.2% 49.3% 31.2%

Spring 2005 3245.9% 3254.7% 261.8%

Winter 2006 60.4% 50.7% 23.6% Sheetwash

Spring 2006 624.2% 876.5% 450.7%

Winter 2005 339.2% NA 43.6%

Spring 2005 49.6% NA 65.9%

Winter 2006 860.4% NA 72.3% Dune-swale

Spring 2006 1309.7% NA 230.3%

93

a)

0Burnt2002

Burnt1976

Longunburnt

2

4

6

8

10

12

14

16

18

20

Bird

s.ha

-1

b)

0Burnt2002

Burnt1976

Longunburnt

24

68

10

1214

1618

20

Bird

s.ha

-1

c) d)

0

2

46

8

10

12

1416

18

20

Burnt2002

Burnt1976

Longunburnt

Bird

s.ha

-1

0

2

4

6

8

10

12

14

16

18

20

Burnt2002

Burnt1976

Longunburnt

Bird

s/ha

-1

Figure 6-10 Bird abundance by treatment in the sheetwash landscape showing mean and 95% confidence levels for each survey: a) winter 2005, b) spring 2005, c) winter 2006, d) spring 2006.

94

a)


2

4

6

8

10

12

14

Bird

s.ha

-1

b)


2

4

6

8

10

12

14

Bird

s.ha

-1

c) d)

0

2

4

6

8

10

12

14


Bird

s.ha

-1

0

2

4

6

8

10

12

14


Bird

s.ha

-1

Figure 6-11 Bird abundance by treatment in the dune-swale landscape showing mean and 95% confidence levels for each survey: a) winter 2005, b) spring 2005, c) winter 2006, d) spring 2006.

95

a)

0Winter2005

Spring2005

Winter2006

Spring2006

2

4

6

8

10

12

14

Bird

s.ha

-1

b)

0

2

4

6

8

10

12

14

Winter2005

Spring2005

Winter2006

Spring2006

Bird

s.ha

-1

Figure 6-12 Bird abundance by season in the a) sheetwash landscape and, b) dune-swale landscape, showing mean and 95% confidence levels.

6.2.2.3 Splendid Fairy-wren The Splendid Fairy-wren (Malurus splendens) was present at higher density in the burnt

1976 and long-unburnt treatments than it was in burnt 2002 treatment (Figure 6-13; Table 6-13).

During the breeding season, Splendid Fairy-wrens were present at higher densities in the long-

unburnt treatment than the burnt 1976 treatment, but during winter there was no difference

between those treatments (Table 6-14). Results from the dune-swale landscape were consistent

with the sheetwash landscape.

96

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Burnt 2002 Burnt 1976 Long unburnt

Bird

s.ha

-1

Figure 6-13 The effect of time-since-fire on Splendid Fairy-wren density (mean and 95% confidence levels). The graph shows data from the sheetwash landscape pooled across seasons.

Table 6-13 Summary of the detection functions modelled using Distance 5.0 (Thomas et al., 2006) for the Splendid Fairy-wren.

1. Sources of data used to define the detection function – S/W = sheetwash landscape dataset, D/S = dune-swale landscape dataset, ECT = ecotone dataset.

2. Number of observations used to define the detection function. 3. Number of distance intervals used to define the detection function. 4. EDR = Effective detection range.

Treatment Data source1 N2 Intervals3 Detection function Cluster size EDR4

Burnt 2002 S/W & D/S 32 3 Half normal-cosine Stratified regression 48m

Burnt 1976 S/W 104 3 Half normal-cosine Stratified regression 45m

Long-unburnt S/W & D/S 218 3 Half normal-cosine Stratified regression 46m

Table 6.14. Splendid Fairy-wren - estimated density (D) with upper and lower 95% confidence levels (UCL, LCL) and statistical tests. Dark shading indicates a significant result ( α < 0.05), light shading indicates a near-significant result (α < 0.08) and ‘NA’ = not applicable.

Burnt 2002 vs Burnt 1976 Burnt 2002 vs Long unburnt

Burnt 1976 vs Long unburnt Survey Treatment D

Birds/ha LCL

Birds/ha UCL Birds/ha Z P (2 tail) Z P (2 tail) Z P (2 tail)

Burnt 2002 0.09 0.02 0.4 Burnt 1976 1.6 0.89 3.0 Sheetwash

Winter 2005 Long unburnt 1.3 0.87 2.1

-3.0 0.003 -4.0 0.001 0.5 0.6

Burnt 2002 0.3 0.08 0.9 Burnt 1976 0.6 0.3 1.1

Sheetwash Spring 2005 Long unburnt 1.8 1.2 2.8

1.1 0.3 -3.5 0.0004 -2.8 0.005

Burnt 2002 0.6 0.2 2.0 Burnt 1976 2.0 1.0 3.9

Sheetwash Winter 2006 Long unburnt 3.3 1.9 5.7

-2.0 0.05 -2.9 0.004 -1.1 0.3


Spring 2006 Long unburnt 1.9 1.1 3.2

-0.7 0.5 -2.3 0.02 -1.8 0.06


All surveys Long unburnt 1.8 1.2 2.6

-2.3 0.02 -3.8 0.0002 -1.2 0.2

Burnt 2002 0.02 0.07 0.8 Dune-swale Winter 2005 Long unburnt 1.5 0.9 2.4

NA NA -4.1 0.0 NA NA

Burnt 2002 0.2 0.05 1.0 Dune-swale Spring 2005 Long unburnt 0.6 0.4 1.0


Burnt 2002 0 0 0 Dune-swale Winter 2006 Long unburnt 1.4 0.8 2.5

NA NA -3.4 0.0006 NA NA

Burnt 2002 0 0 0 Dune-swale Spring 2006 Long unburnt 0.8 0.4 1.5

NA NA -3.0 0.003 NA NA

Burnt 2002 0.1 0.03 0.3 Dune-swale All surveys Long unburnt 1.0 0.7 1.5


98

6.2.2.4 Chestnut-rumped Thornbill The Chestnut-rumped Thornbill (Acanthiza uropygialis) was present at higher density in

the long-unburnt treatment than in the burnt 2002 treatment (Figure 6-14; Table 6-15; Table

6-16). There was no difference between the burnt 1976 treatment and the other two treatments.

The trend in the data suggested that Chestnut-rumped Thornbill density increased with time-

since-fire. Results from the dune-swale landscape were consistent with those from the

sheetwash landscape.

0.0

0.2

0.4

0.6

0.8

1.0

1.2


Bird

s.ha

-1

Figure 6-14 The effect of time-since-fire on Chestnut-rumped Thornbill density (mean and 95% confidence levels.). The graph shows data pooled across seasons from the sheetwash and dune-swale landscapes.

Table 6-15 Summary of the detection functions modelled using Distance 5.0 (Thomas et al., 2006) for the Chestnut-rumped Thornbill.


2. Number of observations used to define the detection function. 3. Number of distance intervals used to define the detection function. 4. EDR = Effective detection range





Table 6.16 Chestnut-rumped Thornbill - estimated density (D) with upper and lower 95% confidence levels (UCL, LCL) and statistical tests. Dark shading indicates a significant result (α < 0.05) , light shading indicates a near-significant result (α < 0.08) and ‘NA’ = not applicable.

Burnt 2002 vs Burnt 1976

Burnt 2002 vs Long unburnt


Birds/ha LCL

Birds/ha UCL Birds/ha

Test stat. P (2 tail) Test stat. P (2 tail) Test stat. P (2 tail)

Burnt 2002 0.02 0.00 0.17

Burnt 1976 0.16 0.00 0.62 Sheetwash Winter 2005

Long unburnt 0.41 0.15 1.1

Z = -1.1 P = 0.3 Z = -1.8 P = 0.08 Z = 1.0 P = 0.3

Burnt 2002 0.21 0.01 0.70

Burnt 1976 0.00 - - Sheetwash Spring 2005


NA NA Z = -0.4 P = 0.1 NA NA

Burnt 2002 0.19 0.05 0.67

Burnt 1976 0.51 0.15 1.72 Sheetwash Winter 2006


Z = -0.9 P = 0.4 Z = -1.8 P = 0.07 Z = -1.0 P = 0.3

Burnt 2002 0.10 0.02 0.38

Burnt 1976 0.25 0.07 0.94 Sheetwash Spring 2006


Z = -0.8 P = 0.4 Z = -1.0 P = 0.3 Z = -0.1 P = 0.9

Burnt 2002 0.07 0.01 0.33 Dune-swale Winter 2005

Long unburnt 0.49 0.19 1.29 NA NA t = -1.6

df = 94 P = 0.1 NA NA

Burnt 2002 0.04 0.01 0.27 Dune-swale Spring 2005


df = 69 P = 0.2 NA NA

Burnt 2002 0.14 0.04 0.47 Dune-swale Winter 2006


df = 101 P = 0.07 NA NA

Burnt 2002 0.17 0.05 0.57 Dune-swale Spring 2006


df = 115 P = 0.6 NA NA

Burnt 2002 0.12 0.04 0.33

Burnt 1976 0.23 0.07 0.74 Pooled data: Sheetwash & dune-swale surveys Long unburnt 0.51 0.28 0.96

t = -0.7 0.5 Z = -2.2 0.02 t = -1.3 P = 0.2

100

6.2.2.5 Inland Thornbill The Inland Thornbill (Acanthiza apicalis) was present at a higher density in the long-

unburnt treatment than in the burnt 2002 and burnt 1976 treatments (Figure 6-15; Table 6-17;

Table 6-18). There was no difference between the burnt 2002 and burnt 1976 treatments, though

the trend suggested that Inland Thornbill density increased with time-since-fire. The pattern was

consistent between seasons and across landscapes which indicated that time-since-fire strongly

influenced Inland Thornbill distribution in mulga woodland.

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6


Bird

s.ha

-1

Figure 6-15 The effect of time-since-fire on Inland Thornbill density (mean and 95% confidence levels.). The graph shows data from the sheetwash landscape pooled across seasons.

Table 6-17 Summary of the detection functions modelled using Distance 5.0 (Thomas et al., 2006) for the Inland Thornbill.




Burnt 2002 S/W, D/S, ECT 11 3 Half normal-cosine Stratified regression 36m

Burnt 1976 S/W 33 3 Half normal-cosine Global regression 50m


Table 6.18. Inland Thornbill - estimated density (D) with upper and lower 95% confidence levels (UCL, LCL) and statistical tests. Dark shading indicates a significant result (α < 0.05), light shading indicates a near-significant result (α < 0.08) and ‘NA’ = not applicable.



Birds/ha LCL




-1.2 0.2 -2.2 0.03 -1.3 0.2

Burnt 2002 0.07 0.01 0.42 Burnt 1976 0.16 0.05 0.55


-1.4 0.1 -2.4 0.02 -2.0 0.05

Burnt 2002 0.10 0.02 0.51 Burnt 1976 0.26 0.01 0.77


-0.9 0.4 -2.7 0.008 -2.2 0.03



-0.9 0.4 -2.3 0.02 -2.1 0.03



-1.1 0.3 -3.7 <0.001 -2.9 0.004


NA NA -3.4 <0.001 NA NA

Burnt 2002 0.00 - - Dune-swale Spring 2005 Long unburnt 1.38 0.64 2.95

NA NA NA NA NA NA

Burnt 2002 0.00 - - Dune-swale Winter 2006 Long unburnt 1.39 0.71 2.71

NA NA NA NA NA NA


NA NA NA NA NA NA


NA NA -4.2 <0.001 NA NA

102

6.2.2.6 Slaty-backed Thornbill The Slaty-backed Thornbill (Acanthiza robustirostris) was present at higher densities in

the long-unburnt treatment than the burnt 2002 treatment. There was also a strong trend for the

burnt 1976 treatment to support a higher density than the burnt 2002 treatment. There was no

difference between the long-unburnt treatment and the burnt 1976 treatment. Results from the

dune-swale landscape were consistent with those from the sheetwash landscape.

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Burnt 2002 Burnt 1976 Long Unburnt

Bird

s.ha

-1

Figure 6-16 The effect of time-since-fire on Slaty-backed Thornbill density (mean and 95% confidence levels.). The graph shows data from the sheetwash landscape pooled across seasons.

Table 6-19 Summary of the detection functions modelled using Distance 5.0 (Thomas et al., 2006) for the Slaty-backed Thornbill.







Table 6.20. Slaty-backed Thornbill - estimated density (D) with upper and lower 95% confidence levels (UCL, LCL) and statistical tests. Dark shading indicates a significant result (α < 0.05), light shading indicates a near-significant result (α < 0.08) and ‘NA’ = not applicable.



Birds/ha LCL




-1.7 0.09 -2.3 0.02 0.1 0.9

Burnt 2002 0.08 0.02 0.36 Burnt 1976 0.62 0.21 1.8


-1.5 0.1 -1.5 0.1 0.6 0.6

Burnt 2002 0.10 0.03 0.37 Burnt 1976 0.39 0.13 1.19


-1.2 0.2 -1.8 0.07 0.9 0.4



-1.0 0.3 -0.8 0.4 0.3 0.8



-1.8 0.06 -2.7 0.007 -0.3 0.8


NA NA -3.2 0.002 NA NA




NA NA -3.1 0.002 NA NA


NA NA NA NA NA NA


NA NA -3.8 <0.001 NA NA

104

6.2.2.7 Southern Whiteface Observations were sparse so detection functions were fit using data drawn from the

sheetwash, dune-swale and ecotone datasets (Table 6-21). Insufficient data were obtained to fit

separate detection functions for the burnt 1976 and long-unburnt treatments so these data were

combined in a single detection function. The Southern Whiteface (Acelocephala leucopsis) was

present at similar densities across the treatments in both the sheetwash (Z = 1.0, P = 0.3) and

dune-swale landscapes (Z = 1.4, P = 0.2). There was a trend towards higher densities in the

burnt 2002 treatment (Figure 6-17).

0

0.2

0.4

0.6

0.8

Burnt 2002 Burnt 1976 &Long unburnt

Bird

s.ha

-1

Figure 6-17 The effect of time-since-fire on Southern Whiteface density (mean and 95% confidence levels). The graph shows data from the sheetwash landscape pooled across seasons.

Table 6-21 Summary of the detection functions modelled using Distance 5.0 (Thomas et al., 2006) for the Southern Whiteface.





Burnt 1976 & Long-unburnt S/W, D/S, ECT 17 2 Half normal-cosine Global regression 25m

6.2.2.8 Spiny-cheeked Honeyeater The Spiny-cheeked Honeyeater (Acanthagenys rufogularis) was present at similar densities

across the treatments (Figure 6-18; Table 6-22; Table 6-23) in the sheetwash landscape.

However in the dune-swale landscape Spiny-cheeked Honeyeaters were present at higher

densities in the long-unburnt treatment than the burnt 2002 treatment. Strong seasonal variation

105

within treatments suggested that other factors such as recent rain interacted with fire to

influence the distribution of this species.

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4


Bird

s.ha

-1

Figure 6-18 The effect of time-since-fire on Spiny-cheeked Honeyeater density (mean and 95% confidence levels). The graph shows data from the sheetwash landscape pooled across seasons.

Table 6-22 Summary of the detection functions modelled using Distance 5.0 for the Spiny-cheeked Honeyeater.




Burnt 2002 S/W & D/S 38 3 Half normal-cosine Global regression 46m



Table 6.23. Spiny-cheeked Honeyeater - estimated density (D) with upper and lower 95% confidence levels (UCL, LCL) and statistical tests. Dark shading indicates a significant result (α < 0.05), light shading indicates a near-significant result (α < 0.08) and ‘NA’ = not applicable.


Burnt 1976 vs Long unburnt Survey Treatment Density

Birds/ha LCL

Birds/ha UCL

Birds/ha Z P (2 tail) Z P (2 tail) Z P (2 tail) Burnt 2002 0.33 0.12 0.92 Burnt 1976 0.52 0.18 1.46 Sheetwash


Z = -0.5 P = 0.6 Z = -1.2 P = 0.2 Z = -0.6 P = 0.6

Burnt 2002 0.47 0.17 1.33 Burnt 1976 0.27 0.06 1.18


Z = 0.7 P = 0.5 Z = -0.2 P = 0.8 Z = -1.4 P = 0.2

Burnt 2002 0.64 0.24 1.69 Burnt 1976 0.87 0.34 2.22


Z = -0.4 P = 0.7 Z = 0.7 P = 0.7 Z = 0.7 P = 0.5



Z = -1.2 P = 0.2 Z = 0.0 P = 1.0 Z = 1.2 P = 0.2



Z = -0.4 P = 0.7 Z = 0.4 P = 0.7 Z = 0.1 P = 0.9


NA NA Z = -1.9 P = 0.06 NA NA


NA NA Z = -2.5 P = 0.01 NA NA


NA NA Z = -1.9 P = 0.06 NA NA


NA NA NA NA NA NA


NA NA Z = -2.1 P = 0.04 NA NA

107

6.2.2.9 Singing Honeyeater The Singing Honeyeater (Lichenostomus virescens) was present at similar densities across

all three treatments. There was a trend for the long-unburnt treatment to support a higher density

than the other two treatments (Figure 6-19; Table 6-24; Table 6-25). This may be due to the

higher abundance of Eremophila spp. in long unburnt mulga than in the other two treatments

(Chapter 5:). Eremophila spp. produce nectar-bearing flowers after rain, upon which Singing

Honeyeaters were observed to feed. The magnitude of the variation in density between

treatments in the same survey was low compared to that within treatments between surveys.

This suggests that the density of Singing Honeyeaters was more strongly related to factors other

than time-since-fire, such as recent rain. Results from the two landscapes were consistent.

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4


Bird

s.ha

-1

Figure 6-19 The effect of time-since-fire on Singing Honeyeater density (mean and 95% confidence levels). The graph shows data from the sheetwash landscape pooled across seasons.

Table 6-24 Summary of the detection functions modelled using Distance 5.0 (Thomas et al., 2006) for the Singing Honeyeater.







Table 6.25. Singing Honeyeater - estimated density (D) with upper and lower 95% confidence levels (UCL, LCL) and statistical tests. Dark shading indicates a significant result (α < 0.05), light shading indicates a near-significant result (α < 0.08) and ‘NA’ = not applicable.



Birds/ha LCL

Birds/ha UCL

Birds/ha Test stat. P (2 tail) Test stat. P (2 tail) Test stat. P (2 tail) Burnt 2002 0.47 0.22 1.0 Burnt 1976 0.37 0.17 0.80 Sheetwash


Z = -0.4 0.7 Z = -1.4 0.2 Z = -1.7 0.1

Burnt 2002 0.45 0.21 0.98 Burnt 1976 0.30 0.13 0.71


Z = -0.7 0.5 Z = -0.9 0.4 Z = -1.4 0.2

Burnt 2002 0.38 0.17 0.84 Burnt 1976 0.57 0.26 1.27


Z = -0.7 0.5 Z = -2.1 0.03 Z = -1.4 0.1



Z = 0.4 0.7 Z = 0.4 0.6 Z = 0.03 1.0



Z = 0.2 0.8 Z = -1.9 0.05 Z = -1.7 0.09


NA NA Z = 0.9 0.4 NA NA


NA NA Z = -0.4 0.7 NA NA


NA NA Z = 0.2 0.8 NA NA


NA NA Z = 0.5 0.6 NA NA


NA NA Z = 0.5 0.6 NA NA

109

6.2.2.10 Hooded Robin Observations were sparse so detection functions were fit using data drawn from the

sheetwash, dune-swale and ecotone datasets (Table 6-26). Insufficient data were obtained to fit

separate detection functions for the burnt 1976 and long-unburnt treatments so these data were

combined in a single detection function. The Hooded Robin (Melanodryas cucullata) was

present at similar densities in the treatments (t = 0.6, d.f. = 71, p = 0.5; Figure 6-20).

0.0

0.1

0.2

0.3

0.4

Burnt 2002 Burnt 1976 & Longunburnt

Bird

s.ha

-1

Figure 6-20 The effect of time-since-fire on Hooded Robin density (mean and 95% confidence levels). The graph shows data from the sheetwash and dune-swale landscapes and the ecotone study pooled across seasons.

Table 6-26 Summary of the detection functions modelled using Distance 5.0 (Thomas et al., 2006) for the Hooded Robin.




Burnt 2002 S/W, D/S, ECT 26 3 Half normal-cosine Global mean 36m

Burnt 1976 & Long-unburnt S/W, D/S, ECT 16 3 Half normal-cosine Global mean 29m

6.2.2.11 Red-capped Robin The Red-capped Robin (Petroica goodenovii) was present at similar densities across

treatments in the sheetwash landscape. (Figure 6-21; Table 6-28). There was a trend for density

to increase with time-since-fire. In the dune-swale landscape the density was higher in the long-

unburnt treatment than in the burnt 2002 treatment. This suggests that fire interacts with other

factors such as recent rain to influence the distribution of Red-capped Robins in mulga

woodlands.

110

0

0.2

0.4

0.6

0.8

1


Bird

s.ha

-1

Figure 6-21 The effect of time-since-fire on Red-capped Robin density (mean and 95% confidence levels). The graph shows data from the sheetwash landscape pooled across seasons.

Table 6-27 Summary of the detection functions modelled using Distance 5.0 (Thomas et al., 2006) for the Red-capped Robin.







Table 6.28. Red-capped Robin - estimated density (D) with upper and lower 95% confidence levels (UCL, LCL) and statistical tests. Dark shading indicates a significant result (α < 0.05), light shading indicates a near-significant result (α < 0.08) and ‘NA’ = not applicable.



Birds/ha LCL




-1.6 0.1 -2.9 0.004 -0.7 0.5

Burnt 2002 0.32 0.10 1.0 Burnt 1976 0.13 0.03 0.51


0.9 0.4 -0.5 0.6 -1.9 0.06

Burnt 2002 0.39 0.15 1.0 Burnt 1976 0.33 0.13 0.84


0.2 0.8 -0.5 0.6 0.8 0.4



-1.0 0.3 -0.7 0.4 0.4 0.7



-0.7 0.7 -1.3 0.2 0.5 0.6


NA NA -2.6 0.01 NA NA


NA NA -3.4 <0.001 NA NA


NA NA NA NA NA NA


NA NA -2.7 0.006 NA NA


NA NA -3.9 <0.001 NA NA

112

6.2.2.12 Crested Bellbird Data were sparse so the floodout and dune-swale datasets were pooled. There were

insufficient data to run separate tests for each season. The Crested Bellbird (Oreoica gutturalis)

was present at similar densities across all three treatments (Figure 6-22; Table 6-29): burnt 2002

versus burnt 1976 (t = -0.3, d.f. = 27, p = 0.8); burnt 2002 versus long unburnt (t = -0.9, d.f. =

65, p = 0.4); and burnt 1976 versus long unburnt (t = -1.0, d.f. = 51, p = 0.3).

0.0

0.1

0.2

0.3

0.4


Bird

s.ha

-1

Figure 6-22 The effect of time-since-fire on Crested Bellbird density (mean and 95% confidence levels). The graph shows data from the sheetwash and dune-swale landscapes pooled across seasons.

Table 6-29 Summary of the detection functions modelled using Distance 5.0 (Thomas et al., 2006) for the Crested Bellbird.






Long unburnt S/W & D/S 19 3 Half normal-cosine Global regression 39m

6.2.2.13 Rufous Whistler In the sheetwash landscape, the Rufous Whistler (Pachycephala rufiventris) was present at

a higher density in the burnt 1976 treatment than the burnt 2002 treatment (Figure 6-23; Table

6-31; Table 6-31). There was also a strong trend for a higher density in the long-unburnt

treatment than the burnt 2002 treatment, but no difference between the burnt 1976 and long-

unburnt treatments. In the dune-swale landscape, Rufous Whistlers were at higher density in the

long-unburnt treatment than the burnt 2002 treatment. Inter-seasonal variation within treatment

113

was strong compared to intra-seasonal variation between treatments. This suggests that fire

interacts with other factors such as recent rain to influence the distribution of Rufous Whistlers

in mulga woodlands.

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

Burnt 2002 Burnt 1976 Long Unburnt

Bird

s.ha

-1

Figure 6-23 The effect of time-since-fire on Rufous Whistler density (mean and 95% confidence levels). The graph shows data from the sheetwash landscape pooled across seasons.

Table 6-30 Summary of the detection functions modelled using Distance 5.0 (Thomas et al., 2006) for the Rufous Whistler.




Burn 2002t S/W & D/S 27 2 Uniform-cosine Global regression 50m


Long unburnt S/W & D/S 90 2 Half normal-cosine Global regression 50m

Table 6.31. Rufous Whistler - estimated density (D) with upper and lower 95% confidence levels (UCL, LCL) and statistical tests. Dark shading indicates a significant result (α < 0.05), light shading indicates a near-significant result (α < 0.08) and ‘NA’ = not applicable.



Birds/ha LCL

Birds/ha UCL Birds/ha Test stat. P (2 tail) Test stat. P (2 tail) Test stat. P (2 tail)

Burnt 2002 0.00 - - Burnt 1976 0.12 0.04 0.37 Sheetwash


NA NA NA NA Z = 0.8 0.4

Burnt 2002 0.09 0.03 0.27 Burnt 1976 1.33 0.57 3.08


Z = -1.4 0.2 Z = -2.1 0.2 Z = 0.9 0.3

Burnt 2002 0.05 0.01 0.17 Burnt 1976 0.49 0.17 1.41


Z = -1.6 0.3 Z = -1.6 0.1 Z = 1.0 0.3



Z = 0.3 0.7 Z = -0.0 1.0 Z = -0.3 0.8



Z = -2.1 0.03 Z = -1.8 0.07 Z = 1.4 0.2


NA NA NA NA NA NA


NA NA Z = -1.2 0.2 NA NA


NA NA Z = -2.1 0.04 NA NA


NA NA Z = -1.3 0.2 NA NA


NA NA Z = -2.0 0.05 NA NA

115

6.2.2.14 Black-faced Woodswallow Observations were sparse, so pooling was required to obtain sufficient observations to fit

robust detection functions (Table 6-32). Data were drawn from the sheetwash, dune-swale and

ecotone study datasets. Insufficient data were obtained to fit separate detection functions for the

burnt 1976 and long-unburnt treatments so these data were pooled. The Black-faced

Woodswallow (Artamus cinereus) was present at a higher density in the burnt 2002 treatment

than it was in the combined burnt 1976 and long-unburnt treatments (Figure 6-24; Table 6-33).

0.0

0.2

0.4

0.6

0.8

1.0

Burnt 2002 Burnt 1976 & Longunburnt

Bird

s.ha

-1

Figure 6-24 The effect of time-since-fire on Black-faced Woodswallow density (mean and 95% confidence levels). The graph shows data from the sheetwash landscape pooled across seasons.

Table 6-32 Summary of the detection functions modelled using Distance 5.0 for the Black-faced Woodswallow.

1. Sources of data used to define the detection function – S/W = sheetwash landscape dataset, D/S = dune-swale landscape dataset, ECT = ecotone dataset

2. Number of observations used to fit the detection function 3. Number of distance intervals used to define the detection function 4. EDR = Effective detection range

Treatment Data sources1 N2 Intervals3 Detection function Cluster size EDR4


Burnt 1976 & Long-unburnt S/W, D/S, ECT 10 3 Half normal-cosine Global regression 34m

Table 6.33 Black-faced Woodswallow - estimated density (D) with upper and lower 95% confidence levels (UCL, LCL) and statistical tests. Shading indicates a significant result (α < 0.05) and ‘NA’ = not applicable.

Burnt 2002 vs Burnt 1976 & Long unburnt Burnt 2002 vs Long unburnt

Survey Treatment D Birds/ha

LCL Birds/ha

UCL Birds/ha

Test stat. P (2 tail) Test stat. P (2 tail)

Burnt 2002 0.20 0.07 0.53 Sheetwash Winter 2005 Burnt 1976 & long unburnt 0.08 0.01 0.53

Z = 0.9 0.4 NA NA

Burnt 2002 0.53 0.11 2.56 Sheetwash Spring 2005 Burnt 1976 & long unburnt 0.0 - -

NA NA NA NA

Burnt 2002 0.11 0.03 0.51 Sheetwash Winter 2006 Burnt 1976 & long unburnt 0.0 - -

NA NA NA NA

Burnt 2002 0.38 0.11 1.3 Sheetwash Spring 2006 Burnt 1976 & long unburnt 0.0 - -

NA NA NA NA

Burnt 2002 0.08 0.02 0.30 Dune-swale Winter 2005 Long unburnt 0.0 - -

NA NA NA NA


NA NA t = 1.6, df = 105 0.1


NA NA NA NA


NA NA NA NA

Burnt 2002 0.38 0.18 0.80 Pooled data Sheetwash, Dune-swale & Ecotone

Burnt 1976 & Long unburnt 0.05 0.01 0.19 t = 2.2, df = 43 0.03 NA NA

117

6.2.2.15 Zebra Finch Insufficient data were collected from the burnt 1976 treatment to fit a detection function so

this treatment was excluded from analysis. The Zebra Finch (Taeniopygia guttata) was present

at higher density in the burnt 2002 treatment than in long-unburnt treatment (Figure 6-25; Table

6-35; Table 6-35). The results from the dune-swale landscape were consistent with those from

the sheetwash landscape. The density of Zebra Finches was much lower in 2006 than 2005. This

may have been due to an unusually long, hot period of weather during the previous summer (G.

Edwards, pers. comm.).

0

0.5

1

1.5

2

2.5

3


Bird

s/ha

-1

Figure 6-25 The effect of time-since-fire on Zebra Finch density (mean and 95% confidence levels). The graph shows data from the sheetwash landscape pooled across seasons.

Table 6-34 Summary of the detection functions modelled using Distance 5.0 (Thomas et al., 2006) for the Zebra Finch, NA = not applicable.




Burnt 2002 S/W, D/S, ECT 87 2 Half normal-cosine Global regression 39m

Burnt 1976 S/W 4 NA NA NA NA

Long-unburnt S/W, D/S 21 3 Half-normal-cosine Global mean 39m

Table 6.35. Zebra Finch - estimated density (D) with upper and lower 95% confidence levels (UCL, LCL) and statistical tests. Dark shading indicates a significant result (α < 0.05), light shading indicates a near-significant result (α < 0.08) and ‘NA’ = not applicable.



Birds/ha LCL

Birds/ha UCL

Birds/ha Z P (2 tail) Z P (2 tail) Z P (2 tail) Burnt 2002 2.58 1.13 5.90 Burnt 1976 0.00 - - Sheetwash


NA NA Z = 2.1 P = 0.04 NA NA

Burnt 2002 3.59 1.76 7.31 Burnt 1976 0.00 - -


NA NA Z = 1.8 P = 0.07 NA NA

Burnt 2002 0.77 0.28 2.17 Burnt 1976 0.00 - -

Sheetwash Winter 2006 Long unburnt 0.00 - -

NA NA NA NA NA NA

Burnt 2002 0.92 0.31 2.76 Burnt 1976 0.00 - - Sheetwash


NA NA Z = 1.2 P = 0.2 NA NA

Burnt 2002 1.48 0.77 2.86 Burnt 1976 0.00 - - Sheetwash


NA NA Z = 2.1 P = 0.03 NA NA


NA NA Z = -1.4 P = 0.2 NA NA


NA NA Z = 1.8 P = 0.08 NA NA

Burnt 2002 0.00 - - Dune-swale Winter 2006 Long unburnt 0.00 - -

NA NA NA NA NA NA


NA NA NA NA NA NA


NA NA Z = 0.7 P = 0.5 NA NA

119

6.3 Discussion The bird community in mulga woodlands in the sheetwash landscape varied with time-

since-fire. A different bird community was present in mulga woodland burnt in 2002 than was

present in mulga woodland burnt in 1976 or long-unburnt. Differences in detectability were

accounted for using two methods and the results were consistent. The differences in the bird

communities between treatments therefore reflect real differences rather than differences in

detectability between the treatments.

The presence of a canopy was the most important factor determining the composition of

the bird community in mulga woodlands in the sheetwash landscape. Variables relating to

crown height, crown cover and variation in crown height were the most significant predictor

variables. The habitat varied across the three treatments. The burnt 2002 treatment was

grassland. The burnt 1976 treatment was low mulga woodland of mostly even height and the

dominant growth form shrubby (Walker and Hopkins, 1998). The long-unburnt treatment was

taller mulga woodland with the tallest plants a tree growth form (Walker and Hopkins, 1998), a

diversity of plant heights and with abundant Eremophila shrubs (Chapter 5:). The bird

community in the burnt 1976 and long unburnt treatments was the mulga bird community

(Cody, 1994), while the birds present in the grassland created in the burnt 2002 treatment

included many habitat generalists as defined by Reid et al. (1991; 1993). The presence of a

mulga woodland canopy therefore appears to be good predictor of the presence of mulga birds

(Cody, 1994) in the sheetwash landscape regardless of spatial variation in potentially significant

parameters such as soil nutrient status (Tongway and Ludwig, 1989) or depth to water table

(O’Grady et al., 2006) which were not controlled in this study.

The pattern of community response to time-since-fire in mulga woodland in the sheetwash

landscape was consistent between surveys so the effect was robust to a degree of temporal

variation due to factors such as recent rain (Davies, 1974; Stafford-Smith and Morton, 1990).

Recent rain has a strong effect on the distribution of fauna in the Australian arid zone (Stafford-

Smith and Morton, 1990) and can cause changes in density of an order of magnitude in arid

zone birds (Reid et al., 1991). The differences in habitat structure caused by fire (i.e. grassland

versus mulga woodland canopy) had a stronger effect on the bird communities than recent rain.

Climate change notwithstanding, the consistent patterns of guild response to time-since-fire

(discussed further below) suggest the effect may be robust to a high proportion of the temporal

variation in the experimental landscape.

Multivariate analyses to test for a species/environment relationship in the dune-swale

landscape did not produce meaningful results. A test of the relationship between species and

sites indicated that approximately half of the burnt 2002 treatment sites supported a bird

community similar to that present in the long unburnt treatment. This explains the lack of

significance in the species/environment relationship because the habitat characteristics of the

two treatments were strongly differentiated. Nonetheless, the composition of the bird

120

community in the burnt 2002 treatment was different to that in the long-unburnt treatment. The

bird community associated with the long unburnt treatment comprised the mulga birds (Cody,

1994). Granivores and other generalists (Reid et al., 1991; 1993) dominated the species present

at the burnt 2002 sites that were not close in ordination space, to the long-unburnt cluster.

Examination of the univariate analyses reveals the same patterns of response to time-since-fire

by each species in each landscape (discussed further below). This indicates that some burnt

2002 sites in the dune-swale landscape did not support the suite of species classified as burnt

2002 treatment specialists. The reasons for this spatial variation amongst the burnt 2002

treatment sites in the dune-swale landscape cannot be determined from this study but may be

due to geological (Tongway and Ludwig, 1989; 1990) or hydrological factors (Morton, 1990;

O’Grady et al., 2006).

Drawing the results from the two landscapes together, this study demonstrates that the bird

community in mulga woodland varies with time-since-fire however there is an interaction with

factors that vary spatially. Spatial variation was greater in the dune-swale landscape than the

sheetwash landscape. There was no evidence of temporal variation. Variation was greatest in the

grassland associated with the burnt 2002 treatment (i.e. shortest time-since-fire) and this is

consistent with work from other ecosystems (Chapter 2). Variation in the bird community was

minimal in the two treatments associated with a mulga woodland canopy. This supports the

conclusion of Cody (1994) that mulga woodland supports a predictable bird community (n.b.

Cody’s (1994) conclusion has been disputed (Mac Nally et al., 2004)).

Species richness and bird abundance did not vary with time-since-fire in the sheetwash

landscape, but they did in the dune-swale landscape. The effect of time-since-fire on species

richness and bird abundance varied in time in both landscapes. The variance in species richness

was least in the long-unburnt treatment and tended to be greatest in the burnt 2002 treatment.

The variance in bird abundance was usually least in the long-unburnt treatment. The treatment

with least variance changed in time in the sheetwash landscape.

The guild responses of birds to time-since-fire in mulga woodlands followed a predictable

pattern. Granivores and ground insectivores use mulga woodland for up to five years following

fire. The granivorous species are probably attracted to seed produced in the grassland which

grows at mulga woodland sites after fire. Terrestrial insectivores were also attracted to the post-

fire grassland, though the most abundant member of this guild, the Red-capped Robin was a

time-since-fire generalist. Red-capped Robins and Hooded Robins are perch-and-pounce

predators which use the dead mulga stags when feeding. However none of the other birds

abundant in post-fire grassland appear to require the dead stags, so the bird community is

essentially a grassland community. Some of the species with a preference for grassland, such as

the Budgerigar and Crimson Chat were nomadic, and their presence was most likely following

rain. This contributed to the high variance in species richness in the habitat. The results were

consistent with those of Reid et al. (1991; 1993) who classified most of the species present in

recently burnt habitat as generalists. Studies in other ecosystems have shown that the guild

121

responses to fire are variable (Chapter 2:). Granivores and terrestrial insectivores were attracted

to recently burnt sites in North American chaparral which underwent a change in vegetation

structure similar to that in mulga woodland (Lawrence, 1966). In contrast North American

conifer forest attracted woodpeckers and an exceptionally rich suite of bark-probing insectivores

(Hutto, 1995; Finch et al., 1997) all of which were using the standing dead trees. Granivores

were a smaller proportion of the species present. High variance in the bird community at shorter

times-since-fire has been reported in the Mediterranean (Herrando et al., 2002a; Herrando et al.,

2003; Brotons et al., 2005), North America (Stanton, 1986; Raphael et al., 1987) and Australia

(Ward and Paton, 2004).

The vast majority of the species that prefer the burnt 1976 and long-unburnt treatments

were insectivorous. This included canopy feeders such as the Slaty-backed Thornbill,

shrub/canopy feeders such as the Inland Thornbill and Chestnut-rumped Thornbill, shrub and

ground feeders such as the Splendid Fairy-wren and White-browed Babbler, ground-feeding

Red-capped Robin and the ground/shrub/canopy feeding Rufous Whistler. Many of the species

with a preference for the burnt 1976 and long-unburnt treatments are regarded as sedentary in

other parts of their range (Table 3-1) and were consistently present in mulga though their

densities varied between surveys. The return of insectivorous species with the return of the

mulga canopy was also reported by Reid et al. (1991; 1993). The main difference between that

study and this, was that the grass and grassland species reported by Reid et al. (1991; 1993) in

mulga woodland that was 14 years-since-fire were not recorded in this study 29 years-since-fire.

The return of foliar insectivores with the re-establishment of the pre-fire vegetation structure is

a common theme of studies of fire and birds in forest and woodland (Chapter 2:). The

generalisation has been reported from Mediterranean conifer shrublands and forests (Herrando

et al., 2002a), North American conifer forests (Raphael et al., 1987; Finch et al., 1997; Imbeau

et al., 1999) and oak savannah (Davis et al., 2000; Brawn, 2006).

Two guilds, the nectarivores/frugivores and aerial insectivores, included species that were

present in all treatments. The nectarivores/frugivores were represented by Spiny-cheeked

Honeyeaters and Singing Honeyeaters, both of which were time-since-fire generalists. This

generality was probably facilitated by both species ability to feed on insects. This finding was

consistent with that of Reid et al. (1991; 1993) who described both species as habitat generalists

(present in a number of vegetation types). A study in Australian eucalypt forest produced

similar findings to this study. Smyth et al. (2002) found that the presence of some nectarivores

and frugivores was not related to vegetation structure. The independence of

nectarivores/frugivores from fire is not universal. A review of fire and birds in Australia

reported a number of studies in which wildfires stimulated plants to flower, in some cases

attracting species which do not usually feed on nectar (Woinarski and Recher, 1997). Other

studies reported that fire disrupted flowering and fruiting leading to declines in the abundance

of frugivores and nectarivores. Similarly, nectarivores and frugivores of tropical rainforest show

a range of responses to time-since-fire (Barlow and Peres, 2004; Adeney et al., 2006).

122

Aerial insectivores were represented by the Black-faced Woodswallow, Grey Fantail and

Masked Woodswallow. Black-faced Woodswallows are specialists of recently burnt mulga

woodland and Grey Fantails are mulga foliage specialists. The Masked Woodswallow feeds in

flocks several tens of metres above the ground and does not appear to be influenced by the

vegetation type or structure. Of the three species, only the Black-faced Woodswallow was

regularly present during the study. This suggests that despite the presence of aerial insectivores

across the treatments, the burnt 2002 treatment provided the aerial insect resource most

conducive to exploitation by birds. This finding is consistent with a review of fire and birds in

North American conifer forests that found that aerial insectivores were attracted to burnt sites

(Kotliar et al., 2002).

Reid et al. (1991; 1993) concluded that the nomadic nectarivore/frugivore, the White-

fronted Honeyeater was most abundant in long-unburnt mulga, attracted by the nectar and fruit

of mistletoe. Very little data were collected for the species in this study however the conclusion

seems doubtful, or at least requires qualification for two reasons. 1) Mistletoe was rare in the

landscape and although at greatest abundance in long-unburnt mulga woodland in the sheetwash

landscape, was virtually absent from the dune-swale landscape. This suggests that the presence

of mistletoe in mulga woodland is related to factors independent of time-since-fire. 2) Mistletoe

was not observed to flower or fruit at any of the survey sites throughout the study and no

honeyeaters were observed feeding from it. This raises the possibility that the pattern recorded

by Reid et al. (1991; 1993) was a rare coincidence in time and space and not generalisable.

All species exhibited a monotonic response to time-since-fire with no species at highest

density in the burnt 1976 treatment. Of the 13 species for which sufficient data were available,

six showed no difference between treatments and were classified ‘generalists’. Four species

avoided the burnt 2002 treatment but showed no preference between the burnt 1976 and long-

unburnt treatments and these were classified ‘mulga foliage’ specialists. Two species preferred

grasslands and were classified ‘recently burnt’ specialists and one species preferred the long-

unburnt treatment and was classified ‘long-unburnt’ specialist. The distribution of species

responses suggests that a high proportion of the birds present in mulga woodland do not respond

strongly to time-since-fire. However such a conclusion is not supported by the evidence. Of the

six species which were classified generalists, five showed biologically meaningful differences

between treatments. I define biologically meaningful as an increase in density of >50% (Table

6-36). In addition, of the four mulga foliage specialists, two showed a biologically meaningful

difference between the burnt 1976 and long-unburnt treatments. One of these, the Rufous

Whistler, was at highest density in the burnt 1976 treatment. Two factors contributed to the lack

of discrimination between the treatments. 1) The 1976 fire did not burn much of the dune-swale

landscape, so there were less data from this treatment with which to model a detection function

and this meant the coefficient of variation was large in comparison with the other two

treatments (e.g. Rufous Whistler, Chestnut-rumped Thornbill and Singing Honeyeater). 2) The

density of many species which were recorded most often in the burnt 2002 treatment was low

123

and the coefficient of variation was high (e.g. Southern Whiteface), so it was difficult to

demonstrate significant differences. The distribution of species responses to time-since-fire

based on trends comprises one generalist, three mulga foliage specialists, four recently burnt

specialists, four long-unburnt specialists and one species in a new classification, ‘intermediate-

age mulga’ specialist (Table 6-36).

Table 6-36 Classification of bird species by time-since-fire preference. Trend in the data refers to a non-significant difference which if significant would be biologically meaningful. Biologically meaningful is defined as an increase of >50%.

Classification Species

Statistically significant Trend in the data

Splendid Fairy-wren Mulga foliage Mulga foliage

Chestnut-rumped Thornbill Mulga foliage Long-unburnt

Inland Thornbill Long-unburnt Long-unburnt

Slaty-backed Thornbill Mulga foliage Mulga foliage

Southern Whiteface Generalist Recently burnt

Spiny-cheeked Honeyeater Generalist Generalist

Singing Honeyeater Generalist Long-unburnt

Hooded Robin Generalist Recently burnt

Red-capped Robin Generalist Mulga foliage

Crested Bellbird Generalist Long unburnt

Rufous Whistler Mulga foliage Intermediate age mulga

Black-faced Woodswallow Recently burnt Recently burnt

Zebra Finch Recently burnt Recently burnt

Vegetation structure is a good predictor of the composition of the bird community across

the time-since-fire treatments in mulga woodland. Many other studies of fire and birds have

found a similar relationship between vegetation structure and birds (Chapter 2:). The drivers of

the relationship in this study are members of the two most numerous guilds, the granivores and

the insectivores. Both guilds are strongly associated with particular structural properties of the

vegetation. The granivores all have a preference for grassland and the insectivores partition the

habitat by preferentially feeding at certain heights above the ground (Recher and Davis, 1997).

The popularity of the use of vegetation structure to explain the effects of time-since-fire on birds

is a function of the nature of such studies. When investigating a community of birds, it is

virtually certain that a generalised explanation for the changes observed will be broad. The

problem with this approach is that the information does not address the mechanism through

which a species vegetation structural preference is expressed and therefore may not be

applicable across ecosystems or at different latitudes of the same ecosystem where the resources

a species is accessing may be different (Gill, 1996). Extrapolation of the results of this kind of

pattern-oriented research should therefore be done with caution (Whelan et al., 2002).

124

Bearing in mind the caution expressed above, the conclusions from this work are

potentially applicable to a large proportion of the mulga woodlands in Australia. The mulga bird

community is stable and most of the same species are present across the continent (Cody, 1993;

1994). In addition, this study found that birds showed consistent habitat preferences across two

landscapes with contrasting soil and hydrological systems. Therefore, in the absence of specific

information it would be reasonable to anticipate a degree of continuity of response across

Australia.

A simple model of the dynamics of bird communities in mulga woodland can be

constructed from the results of this study. Bird communities in mulga woodland are affected by

time-since-fire, but there is an interaction with factors which vary spatially such as geology and

hydrology. The habitat preferences of individual species appear stable at the time scales

investigated in this study, however the species richness and abundance of birds varies in time

due to factors such as recent rain. It therefore appears that the distribution of birds in mulga

woodland is related to time-since-fire, variability between the sites which support mulga

woodland and stochastic variation such as recent rain.

Many factors may cause the effect of time-since-fire on birds in mulga woodland to vary

from the pattern described here. Work in other ecosystems has found that the effects of fire can

interact with other processes such as salvage logging to change the pattern of response (Kotliar

et al., 2002). A common interacting process in mulga woodlands is grazing (James et al., 1999;

Landsberg et al., 1999). Many mulga birds decline with proximity to an artificial water source

for stock. Stock may influence the pattern of response of mulga birds to time-since-fire in

several ways. 1) Stock may disrupt grass seeding and reduce the amount available for

granivores (Franklin, 1999). 2) An artificial water supply may improve the habitat for

aggressive competitors and predators of mulga birds (James et al., 1999; Landsberg et al.,

1999). 3) Grazing may influence the germination and growth to maturity of mulga plants after

fire (James et al., 1999; Landsberg et al., 1999). 4) Grazing may reduce the severity of fires in

mulga woodland by reducing the fuel load. Fire severity has been shown to affect the bird

community in other ecosystems (Chapter 2:). Caution should be exercised when extrapolating

the results of this study to land subject to grazing, particularly heavy grazing.

Further investigation of the effect of time-since-fire on bird communities should focus on

the period during which the post-fire grassland is replaced by a mulga woodland canopy. At

UKTNP this is the period approximately 6-28 years-since-fire. Theoretically, the rate of change

in the vegetation structure at a mulga woodland site is likely to be highest during a fire and

decline with time-since-fire (Whelan, 1995). Evidence from this study supports the theory

(Chapter 5:). This study has demonstrated a link between vegetation structure and the

composition of the bird community in mulga woodland, and has also shown that the rate of

change in the composition of the bird community in mulga woodland mirrors the rate of change

in the vegetation structure. The bird community in the burnt 1976 and long-unburnt treatments

was the same so further investigation of times-since-fire of >30 years is less likely to yield new

125

information. The period during which the post-fire grassland is replaced by a mulga woodland

canopy is likely to be the most important for understanding how the dynamics of bird

communities in mulga woodland are affected by time-since-fire.

Time-since-fire is a surrogate for fire regime. Fire regimes are characterised by the

frequency, intensity, type and season of recurrent fire (Gill, 1975; Gill et al., 2002). The effects

of these parameters on mulga birds have not been investigated however they could potentially

change the pattern of response of birds to time-since-fire. Mulga woodlands are fire sensitive

and increased fire frequency could lead to the loss of A. aneura propagules from the site

creating a grassland (Noble and Slatyer, 1980; Nano, 2005; Nano and Clarke, in press) that may

remain until propagules disperse to the site from elsewhere. In that instance the bird community

at the site is likely to resemble that found at the burnt 2002 treatment (Reid et al., 1991; Reid et

al., 1993). Fire intensity may also affect the response of birds to time-since-fire, however

intensity is difficult to measure and not a parameter in any arid zone fire histories (Allan, 2003).

A surrogate for fire intensity when investigating the effect of fire on an ecosystem is fire

severity. Fire severity can affect the response of birds to fire (Smucker et al., 2005; Kotliar et

al., 2007). In practice fire severity may be less important in mulga woodland ecosystems

because mulga is fire sensitive and suffers high rates of mortality when burnt (Hodgkinson and

Griffin, 1982; Nano, 2005). Therefore variation in fire severity may not be high and this may

limit the degree of the potential affect. Fire type will not affect the response of mulga birds to

time-since-fire because only one fire type occurs in regions where mulga woodlands occur.

Little is known about the effect of burn season on mulga woodland and it is therefore difficult to

predict how the effect of time-since-fire on mulga birds could be influenced by it. Burn season

can affect the response of birds to time-since-fire (Valentine et al., 2007), however the only

published study took place in a relatively fast-growing tropical savannah ecosystem with a

strong seasonal effect due to the monsoon. I postulate that seasonal effects are not as strong in

the arid zone as they in the tropics and that therefore any effect of burn season on time-since-

fire in mulga birds will be relatively weak.

Fire frequency and fire severity are good prospects for further investigation of the response

of birds to fire in mulga woodlands. To my knowledge an effect of fire frequency has not been

demonstrated in birds. Essentially the study would be looking for changes in the extent of mulga

woodlands or changes in the vegetation structure that correlate with particular fire frequencies

similar to work by Banfai and Bowman (2005) and Brook and Bowman (2006). The relatively

long period between fires in mulga woodlands requires a long-term fire history. Also valuable is

a map of the extent of mulga woodlands preferably prior to the beginning of the fire history.

The longest running fire history in the Australian arid zone is for UKTNP (Allan, 2003), which

following recent work, includes a map of the big fire events in the 1950s (G. Allan, pers.

comm.; V. Chewings, pers. comm.). The fire history therefore encompasses three major fire

events: 1950s; 1976 and 2002; and is the best resource for examining the feasibility of such a

study. Another useful tool would be a map of the extent of red earth soils produced

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independently of the extent of mulga woodland. Such a map could provide a context for any

changes in the distribution of mulga woodland that occurred during the period of the fire

history.

Few studies have investigated the response of birds to fire severity (Chapter 2:) and no

such studies have been conducted in Australian ecosystems. Fire severity is a relatively simple

parameter to investigate because it requires only one large-scale fire event to provide the

experimental units. A fire severity index was calculated in this study and analysis of the data

could be fruitful.

An area of further research of practical importance for conservation is an investigation of

the interaction between fire and grazing in mulga woodlands on the response of birds to fire.

Grazing occurs over most of the mulga woodlands in the southern Northern Territory (James et

al., 1999; Landsberg et al., 1999). Many arid zone species cannot be effectively managed at the

scale of the typical reserve (Dickman et al., 1995; Kerle et al., 2007), so an understanding of the

interaction with grazing is important to understand the dynamics of mulga birds at a scale

relevant to conservation.

6.4 Conclusion The bird community in mulga woodlands varied with time-since-fire. A different bird

community was present in mulga woodland burnt in 2002 than was present in mulga woodland

burnt in 1976 or long-unburnt. The result was robust to different methods of accounting for

differences in detectability between treatments. The presence of a canopy was the most

important factor determining the bird community. Variables relating to crown height, crown

cover and variation in crown height were the most significant predictor variables. Abundance of

Eremophila shrubs was also significant. The pattern of community response to time-since-fire

was consistent between surveys so the effect was relatively robust to temporal variation.

Time-since-fire affected bird species richness and bird abundance in the dune-swale

landscape but not in the sheetwash landscape. Variation in both parameters declined with time-

since-fire in most instances. Temporal variation, which encompassed parameters such as recent

rain, was a stronger influence on bird species richness and bird abundance than time-since-fire.

Individual species showed preferences for different times-since-fire (Table 6-36). Four

response models were identified: 1) preference for mulga canopy (burnt 1976 and long-unburnt

treatments), e.g. Splendid Fairy-wren; 2) prefers long-unburnt mulga, i.e. Inland Thornbill; 3)

prefers a grassland, e.g. Zebra Finch; and 4) shows no preference, e.g. Spiny-cheeked

Honeyeater.

Time-since-fire affects the bird community in mulga woodlands, individual species and to

a lesser extent species richness and bird abundance. The hypothesis that the birds present in

mulga woodland vary with time-since-fire is supported.

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Chapter 7: Patch size effect The aim of the patch size study is to investigate whether the size of a patch of mulga

woodland of the same time-since-fire affects bird diversity (number and variety). Patch size can

affect bird density and usually affects bird species richness (Chapter 1:). For patch size to

function as a mechanism by which a fine-scaled fire mosaic could support greater diversity than

a coarse-scale fire mosaic, density of individual species, combined bird density or species

richness must increase with decreasing patch size.

The effect of patch size on density was investigated using count data. These give an

estimate of abundance, so assuming a direct relationship between abundance and density it is

possible to test for a density/area effect (Chapter 1:). There is no reason for bird detectability to

change with the area of a patch so this assumption is reasonable. An effect of density/logarithm

of area was also investigated. A density/area effect refers to a direct (untransformed)

relationship between density and area (Connor et al., 2000; Kai and Ranganathan, 2005). The

effect of area on species richness – the species/area effect – was investigated using

presence/absence data. The hypotheses tested were:

1. Bird density in patches of mulga woodland increases as the size of the patch

decreases.

2. Bird species richness in patches of mulga woodland increases as the size of the

patch decreases.

7.1 Methods Two space-for-time experiments were set up in contrasting landscapes to test for an effect

of patch size on the density of birds in mulga woodlands. The experimental population was

defined by overlaying a fire history on a map of mulga woodland in Arcmap 9.1 (Chapter 4:). In

the sheetwash landscape, three time-since-fire classes were identified: burnt 2002; burnt 1976;

and long-unburnt. The selection procedure for experimental units was designed to cover the

range of patch sizes in the landscape while standardising for the potential effects of edge

(Helzer and Jelinski, 1999; Ries et al., 2004). Therefore the experimental units were selected

according to time-since-fire, area and area to perimeter ratio. The patches of mulga woodland

were assigned to a size-class: 3ha-<9ha, 9ha-<27ha, 27ha-<81ha and >81ha. A maximum of

five replicates of each size class were selected for each time-since-fire class. In the 3-<9ha class

the patches with the greatest area to perimeter ratio were selected. In the other size classes the

patches were split into sub-classes representing 20% of the area range of the class and the patch

with the greatest area to perimeter ratio from each sub-class was selected. When all sites had

been selected the spatial distribution was reviewed. Experimental units must be concomitant to

reduce the possibility of non-demonic interference (Hurlbert, 1984) so any isolated sites were

excluded and the patch with the next largest maximum distance to edge substituted. The

dominant vegetation at all sites was ground-truthed and any that were incorrectly classified

128

mulga woodland were replaced. A total of 63 experimental units were selected in the sheetwash

landscape. Re-mapping of the mulga woodland subsequent to the site selection necessitated the

exclusion of eight sites from analysis (Chapter 4:) leaving a total of 55; 18 were burnt 2002, 18

were burnt 1976 and 19 were long-unburnt (Appendix 1).


but with three differences. Only two time-since-fire classes were present: burnt 2002; and long-

unburnt. There were three size classes: 3ha-<9ha, 9ha-<27ha and >27ha. A total of 34

experimental units were selected, however re-mapping necessitated the exclusion of three from

analysis (Chapter 4:) leaving a total of 31; 16 in the burnt 2002 treatment and 15 in the long-

unburnt treatment (Appendix 1).

7.1.1 Bird counts Bird surveying followed the methods described in Chapter 6.

7.1.2 Statistical analyses Multivariate analyses followed the methods described in Chapter 6.

The effect of area and logarithm of area on species richness, bird abundance and

abundance of bird species was tested using Generalised Linear Mixed Models (GLMM) in

Genstat 8.0 (Payne et al., 2005). The data were analysed at site level because sites were

independent; the three plots within each site were not. Data from each treatment were analysed

separately so there was no need to account for detectability between treatments in any tests.

Consequently the records from all three distance classes (0m–10m, 10m–20m and 20m–50m)

were retained in the dataset. The models contained the fixed terms ‘patch size’ or ‘log of patch

size’ and ‘wind’ (i.e. wind strength). The random term was site and the distribution was Poisson

with a logarithm link function. The dispersion was estimated from the data in each test. All

fixed terms and the interactions were included in the initial models and non-significant

interactions and main effects were removed sequentially until only significant and near-

significant terms and interactions remained. Significance was determined using a Wald statistic

which approximates a χ2 distribution. The Wald statistic overestimates significance especially

with small sample sizes (McCulloch and Searle, 2001; Payne et al., 2005) so a conservative α-

value is used (α = 0.01) to reduce type 1 error (Leavesley and Magrath, 2005). Near significance

was defined as p < 0.05.

7.2 Results A total of 46 species were recorded in 235 surveys in the sheetwash landscape and 36

species were recorded in 153 surveys in the dune-swale landscape.

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7.2.1 Multivariate analysis All species and surveys were included in the multivariate tests. There was no affect of area

or logarithm of area on the composition of the bird community in any of the treatments. None of

the environmental variables were significantly related to the species data in any of the analyses

(Table 7-1). Nor were tests of the first axis or all canonical axes significant for any treatment in

either landscape.

Table 7-1 Results of constrained ordinations with patch area and logarithm of patch area the predictor variables.

1st canonical axis All canonical axes Landscape Treatment DCA maximum

gradient length Test F-ratio P-value F-ratio P-value

Burnt 2002 4.636 SD CCA 1.158 0.483 1.123 0.259

Burnt 1976 3.012 SD RDA 2.357 0.090 1.434 0.149 Sheetwash

Long-unburnt 2.844 SD RDA 0.745 0.744 0.457 0.804

Burnt 2002 6.424 SD CCA 1.153 0.535 1.189 0.233 Dune-swale

Long-unburnt 1.795 SD RDA 1.110 0.549 0.861 0.471

7.2.2 Univariate analysis Univariate tests for density/area effect and density/logarithm of area effect were conducted

on 20 species, plus the parameters, species richness and bird abundance. Of 65 tests, 5

species/treatment combinations returned a significant or near-significant result. In the burnt

2002 treatment the Splendid Fairy-wren was in greater abundance in larger patches than small

(Table 7-2). In the burnt 1976 treatment there were no significant results, though the Splendid

Fairy-wren showed a near-significant increase in abundance in large patches. In the long-

unburnt treatment, the Slaty-backed Thornbill was present in greater abundance in large

patches. There were near-significant increases in abundance of Zebra Finch in large patches and

Singing Honeyeater in small patches. Species richness and bird abundance were unaffected by

area or logarithm of area (Table 7-3 – Table 7-6). For results of univariate tests see Table 7-7 -

Table 7-41. N is defined as the number of observations.

Table 7-2 Summary of significant and near-significant results for patch size effect. Grey shading indicates a significant result.

Treatment Species Parameter P-value Direction

Splendid Fairy-wren Patch size 0.05 Positive Burnt 2002

Splendid Fairy-wren Log patch size 0.003 Positive

Burnt 1976 Splendid Fairy-wren Patch size 0.03 Positive

Slaty-backed Thornbill Patch size 0.03 Positive

Slaty-backed Thornbill Log patch size 0.01 Positive

Singing Honeyeater Log patch size 0.05 Negative Long-unburnt

Zebra Finch Patch size 0.03 Positive

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7.2.3 Species richness Table 7-3 Tests for patch size effect on species richness in the sheetwash

landscape, showing significant terms in the model. Treatment N Model Fixed terms χ2 df P

Patch size Patch size 2.6 1 0.1 Burnt 2002 301

LogPatch size LogPatch size 3.4 1 0.06



Patch size 0.7 1 0.4 Patch size

Wind 4.3 1 0.04

LogPatch size 2.0 1 0.2 Long-unburnt 420

LogPatch size Wind 4.0 1 0.04

Table 7-4 Tests for patch size effect on species richness in the dune-swale landscape, showing significant terms in the model.

Treatment N Model Fixed terms χ2 df P




Wind 5.3 1 0.02



7.2.4 Bird abundance Table 7-5 Tests for patch size effect on bird abundance in the sheetwash






Patch size Patch size 0.1 1 0.8 Long-unburnt 950


131

Table 7-6 Tests for patch size effect on bird abundance in the dune-swale landscape, showing significant terms in the model.






7.2.5 Splendid Fairy-wren Table 7-7 Tests for patch size effect on Splendid Fairy-wren in the sheetwash

landscape, showing significant terms in the model.

Treatment N Model Terms χ2 df P

Patch size 3.9 1 0.05

Wind 0.7 1 0.4 Patch size

Patch size.Wind 4.1 1 0.04 Burnt 2002 36






Table 7-8 Tests for patch size effect on Splendid Fairy-wren in the dune-swale landscape, showing significant terms in the model.

Treatment N Model Terms χ2 df P


Wind 19.1 1 <0.001

LogPatch size 0.6 1 0.4 Burnt 2002 18

LogPatch size Wind 19.3 1 <0.001



7.2.6 Variegated Fairy-wren Table 7-9 Tests for patch size effect on Variegated Fairy-wren in the sheetwash



Burnt 2002 8 Insufficient data



Wind 4.8 1 0.03



132

7.2.7 Redthroat Table 7-10 Tests for patch size effect on Redthroat in the sheetwash landscape,

showing significant terms in the model. Treatment N Model Fixed terms χ2 df P





Wind 8.2 1 0.004



7.2.8 Yellow-rumped Thornbill Table 7-11 Tests for patch size effect on Yellow-rumped Thornbill in the

sheetwash landscape, showing significant terms in the model. Treatment N Model Fixed terms χ2 df P


Wind 14.3 1 <0.001 Patch size

Patch size.Wind 6.6 1 0.01

LogPatch size 2.1 1 0.1

Wind 14.4 1 <0.001

Burnt 2002 28

LogPatch size

LogPatch size.Wind 10.4 1 0.001



Wind 11.9 1 <0.001 Patch size

Patch size.Wind 6.6 1 0.01 Long-unburnt 11


Table 7-12 Tests for patch size effect on Yellow-rumped Thornbill in the dune-swale landscape, showing significant terms in the model.





133

7.2.9 Chestnut-rumped Thornbill Table 7-13 Tests for patch size effect on Chestnut-rumped Thornbill in the







Wind 4.3 1 0.04



Table 7-14 Tests for patch size effect on Chestnut-rumped Thornbill in the dune-swale landscape, showing significant terms in the model.






Burnt 2002 23




7.2.10 Inland Thornbill Table 7-15 Tests for patch size effect on Inland Thornbill in the sheetwash

landscape, showing significant terms in the model. Treatment N Model Fixed terms χ2 df P Burnt 2002 8 Insufficient data




Wind 7.1 1 0.008



Table 7-16 Tests for patch size effect on Inland Thornbill in the dune-swale landscape, showing significant terms in the model.





Patch size.Wind 4.8 1 0.03 Long-unburnt 58


134

7.2.11 Slaty-backed Thornbill Table 7-17 Tests for patch size effect on Slaty-backed Thornbill in the




Log Patch size Log Patch size 0.01 1 0.9



Table 7-18 Tests for patch size effect on Slaty-backed Thornbill in the dune-swale landscape, showing significant terms in the model.





7.2.12 Southern Whiteface Table 7-19 Tests for patch size effect on Southern Whiteface in the sheetwash






Wind 2.8 1 0.09

Burnt 2002 39

LogPatch size

Log patch size.Wind 14.1 1 <0.001




Table 7-20 Tests for patch size effect on Southern Whiteface in the dune-swale landscape, showing significant terms in the model.



Wind 17.2 1 <0.001



Long-unburnt 3 Insufficient data

135

7.2.13 Spiny-cheeked Honeyeater Table 7-21 Tests for patch size effect on Spiny-cheeked Honeyeater in the








Table 7-22 Tests for patch size effect on Spiny-cheeked Honeyeater in the dune-swale landscape, showing significant terms in the model.





7.2.14 Singing Honeyeater Table 7-23 Tests for patch size effect on Singing Honeyeater in the sheetwash







Wind 13.5 1 <0.001


Wind 13.2 1 <0.001

Long-unburnt 72

LogPatch size


Table 7-24 Tests for patch size effect on Singing Honeyeater in the dune-swale landscape, showing significant terms in the model.






136

7.2.15 Hooded Robin Table 7-25 Tests for patch size effect on Hooded Robin in the sheetwash




Burnt 1976 5 Insufficient data Long-unburnt 4 Insufficient data

Table 7-26 Tests for patch size effect on Hooded Robin in the dune-swale landscape, showing significant terms in the model.


Patch size Patch size 0.0 1 0.9

LogPatch size 0.1 1 0.7 Burnt 2002 12 LogPatch size

Wind 19.8 1 <0.001


7.2.16 Red-capped Robin Table 7-27 Tests for patch size effect on Red-capped Robin in the sheetwash



Wind 7.4 1 0.007







Table 7-28 Tests for patch size effect on Red-capped Robin in the dune-swale landscape, showing significant terms in the model.




Wind 5.6 1 0.02



137

7.2.17 White-browed Babbler Table 7-29 Tests for patch size effect on White-browed Babbler in the




Wind 13.4 1 <0.001





7.2.18 Crested Bellbird Table 7-30 Tests for patch size effect on Crested Bellbird in the sheetwash

landscape, showing significant terms in the model. Treatment N Test Fixed terms χ2 df P



Patch size 0.07 1 0.8 Patch size Wind 5.7 1 0.02




Table 7-31 Tests for patch size effect on Crested Bellbird in the dune-swale landscape, showing significant terms in the model.

Treatment N Test Fixed terms χ2 df P




7.2.19 Rufous Whistler Table 7-32 Tests for patch size effect on Rufous Whistler in the sheetwash









138

Table 7-33 Tests for patch size effect on Rufous Whistler in the dune-swale landscape, showing significant terms in the model.






Wind 5.0 1 0.03

Burnt 2002 15

LogPatch size






Long-unburnt 49


7.2.20 Grey Shrike-thrush Table 7-34 Tests for patch size effect on Grey-Shrike-thrush in the sheetwash




Wind 6 1 0.01





7.2.21 Grey Fantail Table 7-35 Tests for patch size effect on Grey Fantail in the sheetwash





Wind 6.0 1 0.01



139

7.2.22 Willie Wagtail Table 7-36 Tests for patch size effect on Willie Wagtail in the sheetwash






Table 7-37 Tests for patch size effect on Willie Wagtail in the dune-swale landscape, showing significant terms in the model.



LogPatch size Log patch size 1.3 1 0.2

Patch size Patch size 0.1 1 0.8

LogPatch size 1.0 1 0.3 Long-unburnt 19 LogPatch size

Wind 4.0 1 0.05

7.2.23 Black-faced Woodswallow Table 7-38 Tests for patch size effect on Black-faced Woodswallow in the





Long-unburnt 0 Insufficient data-

Table 7-39 Tests for patch size effect on Black-faced Woodswallow in the dune-swale landscape, showing significant terms in the model.





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7.2.24 Zebra Finch Table 7-40 Tests for patch size effect on Zebra Finch in the sheetwash






Wind 5.1 1 0.02 Long-unburnt 22


Table 7-41 Tests for patch size effect on Zebra Finch in the dune-swale landscape, showing significant terms in the model.





Wind 6.2 1 0.01



7.3 Discussion Multivariate tests showed no significant relationship between mulga birds, area and

logarithm of area. Within each treatment, the same community of birds was present regardless

of the size of the patch of mulga woodland. The ordinations incorporated species richness,

species abundance and species composition and so were comprehensive measures of diversity.

Bird abundance showed no effect of area or logarithm of area. Within each treatment, the

same number of birds was likely to be present regardless of patch size. Two species showed a

significant response to patch size; the Splendid Fairy-wren in the burnt 2002 treatment and the

Slaty-backed Thornbill in the long-unburnt treatment. Both species responded positively to

patch size so the results did not support the hypotheses. Three species showed weak effects of

patch size (i.e. near-significant). Two of the three showed a positive effect, but one, the Singing

Honeyeater, showed a negative effect. This is the only evidence, albeit weak, in support of the

hypotheses. Therefore, assuming that bird abundance has a linear relationship to bird density,

the hypothesis that bird density increases as patch size decreases is rejected. Patch size effect is

not a mechanism by which the number of birds in mulga woodland would increase if the

landscape was managed to create a fine-scaled fire mosaic.

The four species which showed significant or near-significant effects of patch size were

from different guilds. The Splendid Fairy-wren is a ground/shrub insectivore (Recher and

Davis, 1997) which was present at highest density in mulga woodland that was >29 years-since-

141

fire (Chapter 6:). The species showed a positive density/area effect in mulga woodland that was

burnt 2002. The burnt 2002 treatment is atypical habitat which only appeared to be occupied

during the breeding season (Chapter 6:). Birds in the burnt 2002 treatment appeared to be

associated with remnant live mulga plants so it is possible that patch size was confounded by

fire severity. Therefore this result should be treated with caution. The species also showed a

weak positive density/area effect in the burnt 1976 treatment.

The Slaty-backed Thornbill is classified as a mulga canopy insectivore (Recher and Davis,

1997), however results from this project suggest it also forages in shrubs (Chapter 6:). The

species is at highest density in mulga woodland that is >29 years-since-fire. In the long-unburnt

mulga woodland it showed a positive density/area response, but there was no corresponding

effect in mulga woodland that was 29-30 years-since-fire. This is puzzling because there was

little difference in density between the two treatments and is a potential avenue for further

research.

The Singing Honeyeater is a nectarivore/frugivore (Recher and Davis, 1997) and a time-

since-fire generalist (Chapter 6:). Like most honeyeaters it also feeds on insects so the guild

classification could be misleading. Nonetheless, nectarivory/frugivory may partly explain the

insensitivity to time-since-fire, because flowering shrubs and nectar are both present in mulga

woodland that is long-unburnt and 3-4 years-since-fire. Flowers are also present in other major

vegetation types at the study site (Allan, 1984). The species showed a weak negative patch size

effect in long-unburnt mulga woodland. A potential explanation is that the species preferentially

feeds on nectar and therefore is not strongly attracted to mulga woodland except for short

periods when the Eremophila shrubs are flowering.

The Zebra Finch is a terrestrial granivore (Recher and Davis, 1997) present at highest

density in mulga woodland burnt 2002 (Chapter 6:). The species showed a weak positive

density/area effect in long-unburnt mulga woodland. Zebra Finches use long-unburnt mulga

woodland for roosting and breeding and may use large patches because they provide more

protection from predation than small patches.

To my knowledge only one other study has investigated a density/area effect in a pyric

landscape. A study in North American conifer forest concluded that the presence of most

species in patches of burnt forest was independent of the size of the burn (Hutto, 1995). Of 47

species only two showed an effect of burn size. The abundance of Townsend’s Solitaire

(Myadestes townsendi) and Solitary Vireo (Vireo solitarius) was negatively correlated with burn

size. Townsend’s Solitaire is a ground feeding insectivore with a broad habitat tolerance and a

preference for disturbed habitat. The Solitary Vireo is a foliage gleaning insectivore with a

preference for pine and broadleaf forests. The author postulated that the negative patch size

responses were due to the proximity of unburnt vegetation in small burns – i.e. an edge effect.

The relatively large minimum patch sizes in that study (40ha) may have masked density/area

effects at lower size ranges (Kotliar et al., 2002).

142

The weak and variable effect of area on the density of birds in pyric patches is consistent

with the results of studies investigating other types of patches and taxa. A formal meta-analysis

by Connor et al. (2000) found a positive density/area effect. This was consistent with the

resource concentration hypothesis (Root, 1973). However area only accounted for 5% of the

variation in the population density of animal species. The result differed from an earlier meta-

analysis by Bender et al. (1998) which found no overall effect of area on density. Bender et al.

(1998) found strong negative density/area effects for edge specialist species, strong positive

density/area effects for interior specialist species and weak or nil effect for edge neutral

(generalist) species. They postulated that the density/area effect may be a function of the edge

effect (Ries et al., 2004; Ries and Sisk, 2004). Another review paper by Bowers and Matter

(1997) concluded that the density/area relationship was inconsistent between ecosystems and

appeared to be dependent upon scale. They claimed that patches were a construct of human

convenience rather than meaningful biological entities. Connor et al. (2000) address the

differences in the conclusions of previous studies and theirs. They suggest that their work is not

inconsistent with that of Bender et al. (1998) and that the differences are due to differences in

the sample sizes. Connor et al. (2000) also found no effect of scale. Despite the work to date,

the variability of density/area responses has been difficult to explain within the current

theoretical framework and consensus has not been achieved (Hamback and Englund, 2005). The

lack of pattern in the density/area literature leaves little scope to draw conclusions in

comparison with this study. What the literature does suggest however, is that a consistent

negative or neutral density/area effect is unlikely in a community of species. Therefore, based

on the present empirical evidence, the likelihood that a community of species would show a

uniform density/area response of sufficient strength to function as the mechanism by which a

fine-scaled fire mosaic could increase biodiversity is slim.

Species richness showed no effect of area or logarithm of area. Within each treatment, the

same number of species was likely to be present regardless of patch size. Therefore the

hypothesis that species richness increases with decreasing patch size is rejected.

That species richness did not vary with patch size is a surprising result because it is

inconsistent with the small number of studies that have investigated the species-area

relationship in birds in pyric environments and with the voluminous literature that has

investigated the species-area relationship in general. A study of open habitat birds in recently

burnt areas in the Mediterranean found a positive correlation between size of burn and species

richness (Pons and Bas, 2005). The regression equation fit to the data suggests that five species

are expected in burns of 100ha and for each increasing order of magnitude the number of

species increases to 9, 12-13 and 15-16. The result was attributed to three main factors. 1) Birds

were more likely to discover large newly burned patches. 2) Large patches were more likely to

contain suitable habitat than smaller patches. 3) Large patches were more likely to support pre-

fire populations of open-habitat species associated with rocky, grassy or bare ground (Pons and

Bas, 2005). A similar effect was found in Mediterranean Aleppo pine (Pinus halepensis) forests,

143

the size of which was mediated by fire and ranged from 0.4ha to 311ha. Larger forest fragments

contained more species than smaller fragments (Herrando and Brotons, 2002). The result was

attributed to two factors. 1) Smaller patches may have been too small to support some species.

2) Smaller patches may have contained a lower diversity of habitats than large patches and

hence supported less species (habitat diversity hypothesis).

Species richness almost always increases with area (MacArthur and Wilson, 1967; Turner

and Tjorve, 2005) and the relationship is so reliable that it is regarded as one of the best

established and well-proven macro-ecological patterns (Lomolino, 2003). An exception to the

pattern therefore begs the question why? There are several potential explanations. 1) Variability

in species richness across the landscape was too high to achieve a significant result. This could

occur if the distribution of birds in mulga woodlands is strongly affected by factors which are

independent of time-since-fire such as geological or hydrological conditions (Stafford-Smith

and Morton, 1990; O’Grady et al., 2006) and which vary across the study site. 2) The scale of

the study was inappropriate for detecting an effect of area. For example, the potential effect of

area may have been cancelled out by the potential effect of edge. 3) The classification of

patches by time-since-fire reduced the potential for habitat diversity within a patch, so

preventing this mechanism from contributing to increased species richness. 4) Most of the birds

in the landscape travel freely between patches and use multiple patches, multiple treatments or

other vegetation types when the area of mulga woodland of a particular treatment is small.

Essentially this would mean that the birds fail to perceive patches as they were defined in this

study. In particular, birds using the burnt 2002 treatment may have failed to perceive any

difference between the burnt mulga and burnt spinifex vegetation types. 5) The sampling

strategy was inappropriate for detecting a positive species-area effect (Mac Nally and Horrocks,

2002; Watson, 2003). This is discussed further below. Regardless of the reasons that a positive

species-area relationship was not detected, a negative species-area relationship is rare (Chapter

1:). Therefore considering the evidence from this study and the literature, the likelihood that the

species-area relationship would function as the mechanism by which a fine-scaled fire mosaic

could increase biodiversity is slim.

A lack of evidence in support of a hypothesis is not proof that a hypothesis is false. The

patch size response of most species/treatment combinations remains undefined. Two aspects of

the Australian arid zone mitigate against achieving a statistically significant result in natural

experiments. 1) Ecological variability – both spatial and temporal - is high. 2) The mean density

of many species is low. The possibility remains that the densities of the birds of mulga

woodland are affected by patch size. Clearly however, the results of this study suggest that any

such effects are weak or inconsistent. It is also possible that the species richness of birds of

mulga woodland changes with patch size, but again any effects are likely to be weak or

inconsistent.

A potential criticism of this study is that the bird counting method was unsuitable for

testing for differences in species richness with patch size (Mac Nally and Horrocks, 2002;

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Watson, 2003). This is because the species richness associated with a patch was determined by

counting the birds in a fixed time, fixed area plot in the centre of the patch. This meant that a

much larger proportion of the small patches was sampled, than of the large patches and

consequently the completeness of the results may have varied according to the size of the patch.

Critics suggest it is illogical to infer differences in species richness with patch size from such a

method. Alternatives such as adjusting sampling effort in proportion to the area of a patch (Mac

Nally and Horrocks, 2002), or using a results-based stopping rule are proposed (Watson, 2003).

The alternative methods are more conducive to recording more species in bigger patches

because they have the scope to incorporate different vegetation types and structures which may

be present in a large patch. This consideration was less pertinent in this study because a patch

was defined by the vegetation type and by an important source of variation in vegetation

structure – time-since-fire. Therefore the survey site was representative of the habitat of the

patch. Rare species may still have been missed in large patches, but thorough consideration of

these was beyond the means of this study to investigate. Most importantly, if the views of Mac

Nally and Horrocks (2002) and Watson (2003) were accepted, then the method favoured a false

positive result because the completeness of the samples from the small patches was greater than

for the larger patches (Turner and Tjorve, 2005). The criticism is therefore not relevant in the

context of this study.

All of the significant or near-significant density/area relationships were recorded from data

collected in the sheetwash landscape. No effects were obtained from the dune-swale landscape.

Little can be inferred from this because the data from the two landscapes are not comparable

because of the potential effects of recent rain. Nonetheless, the result begs the question of

whether or not the combination of landscapes may offer research opportunities. The prospects

for a study which compared the two landscapes for the purpose of investigating patch size

effects are poor. There are two main differences between the sheetwash and dune-swale

landscapes. 1) The largest patches in the dune-swale landscape are considerably smaller than the

largest patches in the sheetwash landscape (Chapter 3:). 2) Productivity is expected to be higher

in the sheetwash landscape (Chapter 3:). Comparison of differences in density/area response

based on the differences in the patch sizes between landscapes would be an unnecessarily

difficult way to investigate the question. The question would be better addressed within a single

landscape. Questions about the differential effect of productivity would need to be carried out in

contrasting conditions similar to that offered by the two landscapes. Ideally though, such a study

should not be confounded by differences in the sizes of the patches or differences in the

underlying geological or hydrological conditions (i.e. sheetwash versus dune-swale). Given the

present state of knowledge and the obvious opportunities, use of the two landscapes for the

purposes of investigating patch size effects is likely to be inefficient and is unlikely to deliver

robust results.

Advancement of understanding of patch size effects in mulga woodland could be achieved

by accounting for some of the potentially confounding effects listed above (Turner and Tjorve,

145

2005). A first step could be to redefine patches according to a different set of rules. For

example, the burnt 1976 and long-unburnt mulga could be assigned to a single class and all

vegetation types burnt during the same time period could also be assigned to a single class. The

scale of the study could be adjusted, particularly by incorporating smaller patches than was the

case in this study. Detailed geological and hydrological mapping undertaken using techniques

that are independent of vegetation type could allow some of the landscape variation to be

controlled. In addition, consideration could be given to using different methods to test for a

species-area relationship (Mac Nally and Horrocks, 2002; Watson, 2003).

7.4 Conclusion A negative patch size effect is potentially a mechanism by which the imposition of a fine-

scaled fire mosaic on a landscape could increase biodiversity. The bird communities in mulga

woodland of different times-since-fire did not change with patch size. Only two of 20 species

showed an effect of patch size in mulga woodland and neither was negative. Bird density did

not change with patch size, therefore the management of mulga woodland to maintain small

patches of the same time-since-fire did not of itself function to increase the density of any of the

species tested. Species richness also showed no relationship to patch size - the number of

species present at a bird survey site in the centre of a patch of mulga woodland remained the

same regardless of the size of the patch. Patch size effect is therefore not a mechanism by which

the number and variety of birds in mulga woodland could be increased by managing the

landscape to create a fine-scaled fire mosaic.

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Chapter 8: Edge effect Small patches of habitat are more strongly influenced by edge effects than large patches

(Chapter 1:). The fire mosaic hypothesis predicts that biodiversity will be greater in a fine-

scaled fire mosaic than in a coarse-scaled fire mosaic. Edge effect could facilitate this

occurrence if the effect of edge on biodiversity is positive; that is if pyric edges support species

that are not supported by habitat interior, or if species are present at higher densities at an edge

than they are in habitat interior.

The aim of the edge study is to investigate whether avian diversity increases at pyric edges

within mulga woodland. The hypotheses tested were:

1. Pyric edges in mulga woodland support a different bird community than is present in the interior of patches of mulga woodland.

2. Pyric edges in mulga woodland support different bird species than the interior of patches of mulga woodland.

3. Pyric edges in mulga woodland support bird species in greater abundance than the interior of patches of mulga woodland.

4. Pyric edges in mulga woodland support greater species richness than the interior of patches of mulga woodland.

5. Pyric edges in mulga woodland support greater bird density than the interior of patches of mulga woodland.

8.1 Methods The population of pyric edges between burnt and unburnt mulga woodlands suitable for

study were identified using GIS database described in Chapter 4. Suitable sites for this study

were edges between patches of burnt and unburnt mulga woodland which were large enough to

accommodate the bird survey method and were approximately straight. Each site consisted of

three treatments: ‘burnt’ (mulga woodland burnt in 2002); ‘unburnt’ (mulga woodland ≥29

years-since-fire; N.B. the term “unburnt” is used for simplicity but is not strictly correct because

it is improbable that the sites have never burnt); and edge (the area 50m either side of the

boundary between the burnt and unburnt treatments). Ten suitable sites were identified and all

were used in the study (Figure 8-1). The limited number of suitable experimental units meant

that the effects of a range of potentially confounding factors (Ries et al., 2004) such as edge

contrast, orientation and landscape context could not be controlled or minimised in the study. In

addition it was not possible to control for floristic composition or fire severity, though the

differences between experimental units were quantified (Chapter 5:) to help explain the results.

Therefore different sites had different times-since-fire on the unburnt side, varying proportions

of non-A. aneura canopy species such as A. kempeana and A. tetragonophylla and varying fire

severities. Another consequence of the limited number of suitable study sites was the use of two

pairs of sites that shared large patches of mulga burnt in 2002. Survey plots did not overlap

however the minimum distance between plots in ecotone sites 3 and 4 was 310m and between

plots 5 and 7 was 380m. The sites were treated as independent because of differences in time-

147

since-fire or substrate between the replicates. The minimum distance between all the other

survey sites was 1400m.

Figure 8-1 Bird survey sites for the edge experiment.

An assessment of the vegetation was made at the bird survey plots following the method

described in Chapter 5. Data were collected from the burnt and unburnt treatments but not the

edge treatment. This was because a pyric edge is the boundary between burnt and unburnt

vegetation; it is not a separate vegetation class.

8.1.1 Bird counts Birds surveys were conducted at the edge and either side using the point-interval technique

(Recher, 1988) consisting of three parallel transects with three plots per transect – i.e. a matrix

of nine points per site. The points were located by finding the boundary identified from Arcmap

9.1 (ESRI, 2004) using a GPS. The three boundary points were located along the pyric edge

100m apart using a GPS. The burnt and unburnt plots were located 100m either side of the

boundary and 100m apart by triangulation using a GPS.

Bird count methods followed those described in Chapter 6 with some additional

procedures. Within each site, treatments were visited in a randomised order. Five minutes was

allowed to move from one treatment to another treatment within the same site, so each survey

was completed in 85 minutes. Data were collected in early spring of 2005 and 2006.

8.1.2 Statistical analyses Multivariate analyses were conducted in CANOCO 4.53 following the procedures

described in Chapter 3.9.3 and Chapter 6. (Ter Braak, 1986; Ter Braak and Smilauer, 2002;

Leps and Smilauer, 2003; Leps and Smilauer, 2005). Differences in detectability between

148

treatments were accounted for using two methods: data truncation and presence/absence

(Chapter 3.9.1), running analyses using both datasets and comparing the results. Uneven

sampling effort between sites can bias multivariate analyses, so the datasets for CANOCO were

adjusted to account for this. Data were pooled from each year (i.e. 2005 and 2006). For the

count datasets the number of individuals of each species detected at each site was summed and

divided by the effort. For the presence/absence dataset, the first four samples from each season

were retained and any subsequent samples excluded. Presence/absence from each season at each

site was summed to give a binomial total of eight.

The effect of edge on species richness and bird abundance was tested using Generalised

Linear Mixed Models (GLMM) in Genstat 8.0 (Payne et al., 2005). The data were analysed at

the site level because these were independent. The species richness data were compiled using

the presence of species at each site. Differences in detectability between treatments were

assumed to be minimal (Chapter 3.9.1). The fixed terms in the models were ‘treatment’ and

‘wind’ (i.e. wind strength), the random term was ‘site’ and the distribution was Poisson with a

logarithm link function. The dispersion was estimated from the data in each test. Both fixed

terms and the interaction were included in the initial models and non-significant interactions and

main effects were removed sequentially until only significant and near-significant terms and

interactions remained. Temporal changes in species richness were tested using similar models

but with the fixed term ‘year’ replacing ‘treatment’. Bird abundance was tested using count

data. Differences in detectability between treatments were accounted for by truncating the data

(Chapter 3.9.1), by excluding the 20m-50m distance class. The fixed terms in the models were

‘treatment’ and ‘wind’ (i.e. wind strength), the random term was ‘site’ and the distribution was

Poisson with a logarithm link function. Temporal changes in bird abundance were tested by

replacing the fixed term ‘treatment’ with ‘year’. Significance was determined using a Wald

statistic which approximates a χ2 distribution. The Wald statistic overestimates significance

especially with small sample sizes (McCulloch and Searle, 2001; Payne et al., 2005) so a

conservative α-value was used (α = 0.01) to reduce type 1 error (Leavesley and Magrath, 2005).

Near significance was defined as p < 0.05.

Changes in the distribution of bird species across a pyric edge were also tested using

GLMMs and a Wald statistic. Changes in density across the edge were tested using count data.

Differences in detectability between treatments were accounted for by truncating the data

(Chapter 3.9.1), excluding the 20m-50m distance class. The fixed terms in the models were

‘treatment’ and ‘wind’ (wind strength), the random term was ‘site’ and the distribution was

Poisson with a logarithm link function. The dispersion parameter was estimated from the data in

each test. Both fixed terms and the interaction were included in the initial models and non-

significant interactions and main effects were removed sequentially until only significant and

near-significant terms and interactions remained. Changes in the probability of presence across

an edge were tested using presence/absence data. Differences in detectability between

treatments were assumed to be minimal (Chapter 3.9.1). The models were the same as those

149

used to analyse the count data except that the distribution was binomial with a logit link

function.

8.2 Results A total of 52 species were recorded in the ecotone survey over both years of the study. The

count data consisted of 231 observations of 40 species. The presence/absence data consisted of

495 observations of 52 species.

8.2.1 Multivariate analysis There were insufficient count data from 2006 to perform a test without deleting samples so

the data from both years were pooled. A detrended correspondence analysis (DCA) returned a

maximum gradient length of 4.039 SD on the first axis. The eigenvalue for the first axis was

0.562 and for the second axis was 0.283 (Table 8-1). The sum of all eigenvalues was 3.348, so

the first two axes accounted for 25.2% of the variance. The plot of the DCA indicates that the

bird community in the edge treatment is intermediate to that present in the other two treatments

(Figure 8-2). Paired tests for differences between the bird communities present in each

treatment were performed using a canonical correspondence analysis (Keith et al.). The same

bird community was present in the burnt and edge treatments and this community was different

to that present in the unburnt treatment (Table 8-2). The guild structure of the birds across the

edge showed a pattern in ordination space (Figure 8-3). Aerial insectivores, granivores and

specialist terrestrial insectivores were clustered around the burnt sites. Canopy insectivores and

shrub insectivores were clustered around the unburnt sites. Presence in the intermediate

ordination space indicates a relatively even abundance either side of the edge. The guilds that

occupied this space were the canopy nectarivores/frugivores, an omnivore and a carnivore.

Table 8-1 Summary of detrended correspondence analysis of bird count data from the edge study.


Eigenvalues 0.562 0.283 0.159 0.111 3.348


150

-1 5

-14

B01B02

B03

B04

B05

B06

B07

B08B09

B10

U01

U02

U03

U04

U05

U06

U07

U08

U09

U10

E01

E02

E03

E04

E05

E06

E07

E08E09E10

Figure 8-2 Plot of the first two axes of a detrended correspondence analysis using bird count data showing survey sites across a pyric edge in mulga woodland in 2005-06. Sites prefixed B = burnt, E = edge, U = unburnt.

Table 8-2 Results of Monte Carlo permutations tests for differences between the bird communities at each treatment across a pyric edge in mulga woodland.


Burnt vs Edge 0.873 0.602

Burnt vs Unburnt 2.996 0.001

Edge vs Unburnt 2.172 0.002

151

-1 5

-15

BFWS

BOU

BUD

CBB

CRC

CRTB

GBB

HDR

ITB

LCW MUL

MWS

RCR

RW

SBTB

SCHE

SFW

SHESWF

WBB

WILWWT

ZEB

Figure 8-3 Plot of the first two axes of a detrended correspondence analysis using bird count data showing bird species across a pyric edge in mulga woodland in 2005-06. See Table 8-3 for bird codes.

152

Table 8-3 Bird codes used for ordination plots and feeding guilds. See Table 3-1 and Table 3-3 for scientific names.

Guild Abbreviation Species

Food Substrate

BCU Brush Cuckoo Insectivore Shrub/canopy

BFWS Black-faced Woodswallow Insectivore Aerial

BOU Bourke's Parrot Granivore Ground

BUD Budgerigar Granivore Ground

CBB Crested Bellbird Insectivore Shrub/canopy

CHW Chiming Wedgebill Insectivore Ground

CRC Crimson Chat Insectivore Ground

CRTB Chestnut-rumped Thornbill Insectivore Shrub/canopy

GBB Grey Butcherbird Insectivore/carnivore Ground/shrub/canopy

GST Grey Shrike-thrush Insectivore Canopy

HDR Hooded Robin Insectivore Ground

ITB Inland Thornbill Insectivore Shrub/canopy

LBQ Little Button-quail Granivore Ground

MUL Mulga Parrot Granivore Ground

MWS Masked Woodswallow Insectivore Aerial

PPT Richard's Pipit Insectivore Ground

RCR Red-capped Robin Insectivore Ground

RED Redthroat Insectivore Ground

RIN Australian Ringneck Granivore Ground

RSL Rufous Songlark Insectivore Ground

RW Rufous Whistler Insectivore Ground/shrub/canopy

SBTB Slaty-backed Thornbill Insectivore Canopy

SCHE Spiny-cheeked Honeyeater Frugivore/nectarivore Canopy

SFW Splendid Fairy-wren Insectivore Ground/shrub

SHE Singing Honeyeater Frugivore/nectarivore Canopy

SWF Southern Whiteface Granivore Ground

VFW Variegated Fairy-wren Insectivore Ground/shrub

WBB White-browed Babbler Insectivore Ground/shrub

WGG Western Gerygone Insectivore Canopy

WIL Willie Wagtail Insectivore Ground

WWT White-winged Triller Insectivore Ground

YRTB Yellow-rumped Thornbill Insectivore Ground/shrub

YTM Yellow-throated Miner Insectivore Ground/shrub/canopy

ZEB Zebra Finch Granivore Ground

The presence/absence data from both years were pooled to produce an analysis that was

comparable to that produced using the count data. A DCA returned a maximum gradient length

of 2.074 SD on the first axis. Therefore the data were re-analysed using a principal components

153

analysis (PCA). The eigenvalue for the first axis was 0.338 and for the second axis was 0.151

and the first two axes accounted for 48.9% of the variance explained by the analysis (Table

8-4). The plot of the PCA indicates that the bird community at the edge treatment is

intermediate to that present in the other two treatments (Figure 8-4). Paired tests for differences

between the bird communities present in each treatment were performed using a redundancy

analysis. The bird communities in each treatment were different (Table 8-5). The guild structure

of the birds across the edge showed a pattern in ordination space (Figure 8-5). Aerial

insectivores and granivores were clustered around the burnt sites. Specialist terrestrial

insectivores showed a similar though weaker tendency. Canopy insectivores, shrub insectivores

and canopy nectarivores/frugivores were clustered around the unburnt sites. The meat-eating

Grey Butcherbird was in the centre of axis-1 in ordination space.

Table 8-4 Summary of a principal components analysis of bird presence/absence data from the edge study.


Eigenvalues 0.338 0.151 0.113 0.062 1.000

Cumulative variance 33.8 48.9 60.2 66.4

-1.5 1.5

-0.8

0.8

B01

B02

B03

B04

B05

B06

B07

B08

B09

B10

U01

U02

U03

U04U05

U06

U07

U08

U09

U10

E01

E02

E03

E04

E05

E06 E07

E08E09

E10

Figure 8-4 Plot of the first two axes of a principal components analysis using presence/absence data showing survey sites across a pyric edge in mulga woodland in 2005-06. Sites prefixed B = burnt, E = edge, U = unburnt.

154

Table 8-5 Results of Monte Carlo permutations tests for differences between the bird communities at each treatment across a pyric edge in mulga woodland. Grey shading indicates a significant result.


Burnt vs Edge 3.684 0.001

Burnt vs Unburnt 10.557 0.001

Edge vs Unburnt 2.638 0.004

-0.6 1.0

-0.8

0.6

BCU

BFWS

BOU

BUD

CBB

CHW

CRC

CRTB

GBB

GST

HDR

ITB

LBQ

MUL

MWSPPT

RCR

RED

RIN

RSL

RW

SBTB

SCHE

SFW

SHE

SWF

VFW

WBB

WGG

WIL

WWT

YRTB

YTM

ZEB

Figure 8-5 Plot of the first two axes of a principal components analysis using presence/absence data showing bird species across a pyric edge in mulga woodland in 2005-06. See Table 8-3 for bird codes. Arrows were removed to improve clarity of the figure.

The two methods of accounting for detectability produced consistent results. The bird

community present at the edge was intermediate between that present in the burnt and unburnt

treatments in both analyses.

The two analyses differed in one respect. Significance tests performed on the count data

showed that the same bird community was present in the edge and burnt treatments and this

community was different to that present in the unburnt treatment. In contrast, the

presence/absence data showed that the bird communities present in each treatment were all

155

different to each other. These results are not contradictory. The presence/absence dataset was

larger and therefore had greater statistical power.

The distribution of birds and guilds in ordination space was consistent between analyses.

The presence/absence dataset contained more observations and more species than the count

dataset. This means that the profile of common species is more evenly spread across the sites of

the treatments in which they are common. In addition, many of the species not recorded in the

count data were more likely to be present at burnt sites. The combined effect of these two

features is that a cluster of species is present around the burnt sites and there is greater

differentiation in ordination space among the species more likely to be present at unburnt sites.

8.2.2 Univariate analysis Of the 52 species recorded in the edge study, sufficient data were obtained to test the

abundance of 11 species for an edge effect. Tests of the probability of presence across an edge

were also conducted for these species and nine others. Species richness and bird abundance

were also tested for an edge effect.

8.2.2.1 Species richness Species richness was higher in the edge and unburnt treatments than it was in the burnt

treatment in both years (Table 8-6; Figure 8-6). There was no difference between the edge and

unburnt treatments. Ignoring treatment, year had a strong effect on species richness (Year: χ2

1 =

11.1, p <0.001, Wind: χ2

1 = 5.8, p = 0.02; Figure 8-7), 2005 having greater species richness than

2006. The coefficient of variation was highest in the burnt treatment in both years. In

comparison the difference in variance between the edge and unburnt treatments was relatively

small and the rank changed between years (Table 8-7).

Table 8-6 The effect of pyric edge on species richness and bird abundance. Test Year Fixed terms χ2 df P

2005 Edge effect 18.5 2 <0.001

Edge effect 15.0 2 <0.001 Species richness 2006

Wind 8.8 1 0.003

2005 Edge effect 0.9 2 0.6

Edge effect 15.3 2 <0.001 Bird density 2006

Wind 7.5 1 0.006

156

a)

0Burnt Edge Unburnt

1

2

3

4

5

6

7

8

Spec

ies.

surv

ey-1

b)

0Burnt Edge Unburnt

1

2

3

4

5

6

7

8

Spec

ies.

surv

ey-1

c)

d)

0

5

10

15

20

25

30

Burnt Edge Unburnt

Bird

s.ha

-1

0

5

10

15

20

25

30

Burnt Edge Unburnt

Bird

s.ha

-1

Figure 8-6 The effect of pyric edge on: species richness by year a) 2005, b) 2006, and bird abundance by year: a) 2005, b) 2006, showing mean and 95% confidence levels.

157

a)

0

1

2005 2006

2

3

4

5

6

7

Spec

ies.

surv

ey-1

b)

0

2

4

2005 2006

6

8

10

12

14

16

18

Bird

s.ha

-1

Figure 8-7 Effect of year on a) species richness and, b) bird density across a pyric edge. Graphs show mean and 95% confidence levels.

Table 8-7 Percentage coefficient of variation in species richness and bird abundance across a pyric edge.

Test Year Burnt Edge Unburnt

2005 24.9 16.5 17.8 Species richness

2006 30.4 22.1 19.9

2005 24.8 19.6 32.0 Bird abundance

2006 68.3 85.5 26.9

8.2.2.2 Bird abundance Edge influenced bird abundance, but the effect changed between years. In 2005 there was

no difference in bird abundance between treatments (Table 8-6; Figure 8-6). However in 2006

bird abundance was higher in the unburnt treatment than it was in the edge and burnt treatments.

There was no difference between the edge and burnt treatments. The coefficients of variation

across the edge showed no pattern (Figure 8-7). Bird abundance changed between years (χ2

1 =

7.1, p = 0.008; Table 8-7 ) and was greater in 2005.

8.2.2.3 Budgerigar Budgerigars were more likely to be present in burnt and edge treatments than in the

unburnt treatment in 2005 (χ2

2 = 12.8, p = 0.002; Figure 8-8). There was no difference between

the burnt and edge treatments. In 2006 there was no difference between treatments (χ2

2 = 3.8, p

= 0.2). The species was a burnt treatment specialist and edge-neutral in 2005 and an edge-

neutral generalist in 2006.

158

0.0

0.1

0.2

0.3

0.4

0.5

0.6

Burnt Edge Unburnt

Pr (p

rese

nce)

2005

2006

Figure 8-8 The effect of edge on the probability of presence of Budgerigars, showing mean and 95% confidence levels.

8.2.2.4 Splendid Fairy-wren There was no difference in the abundance of the Splendid Fairy-wrens between treatments

in 2005 (Figure 8-9; Table 8-8). However in 2006 Splendid Fairy-wrens were more abundant in

the unburnt treatment than in the edge and burnt treatments. The species was an unburnt

treatment specialist and edge-avoider in 2006. In 2005 it was an edge-neutral generalist.

Splendid Fairy-wrens showed a 14-fold increase in abundance in the unburnt treatment

between 2005 and 2006. The increase may seem unrealistic and requires explanation.

Comparison of the results with those from the sheetwash landscape in the time-since-fire study

(Chapter 6:) shows that: 1) the direction of the change was consistent; 2) the magnitude of the

change was large in both, 3) the 2005 result for the unburnt treatment was unusually low, and 4)

the 2006 result for the unburnt treatment was not unusually high. When the Splendid Fairy-wren

population was low in 2005, the edge habitat was occupied at low density. Following breeding

in spring 2005, the density of Splendid Fairy-wrens increased and it appears that the birds were

forced to occupy edge habitat during the 2006 breeding season.

159

0

1

2

3

4

5

6

7

Burnt Edge Mulga

Bird

s.ha

-1

2005

2006

Figure 8-9 The effect of edge on the abundance of Splendid Fairy-wrens, showing mean and 95% confidence levels.

Table 8-8 The effect of pyric edge on Splendid Fairy-wren abundance, showing significant terms in the model.

Year Model terms χ2 df p

Treatment 1.0 1 0.3

Wind 0.2 1 0.7 2005

Treatment.Wind 8.9 1 0.003

Treatment 9.1 1 0.003 2006

Wind 4.3 1 0.04

The probability of presence of Splendid fairy-wrens across an edge differed between all

three treatments in both years (2005: χ2

2 = 35.2, p <0.001; 2006 - χ2

2 = 41.8, p <0.001; Figure

8-10). The species was most likely to be present in the unburnt treatment and least likely to be

present in the burnt treatment. In both years the final model contained one fixed term -

treatment. Splendid Fairy-wrens were mulga treatment specialists and edge avoiders.

160

0

0.2

0.4

0.6

0.8

1

Burnt Edge Unburnt

Pr (p

rese

nce)

2005

2006

Figure 8-10 The effect of edge on the probability of presence of Splendid Fairy-wrens, showing mean and 95% confidence levels.

8.2.2.5 Chestnut-rumped Thornbill There was no difference in the probability of presence of Chestnut-rumped Thornbills

between treatments (χ2

2 = 8.0, p = 0.02; Figure 8-11) however there was a strong trend

indicating a higher probability of presence in the unburnt and edge treatments than in the burnt

treatment. Data were pooled across 2005 and 2006 because the number of observations was

small and consistent between years. The final model contained one fixed term – treatment. The

near-significant result suggested the species was an unburnt treatment specialist and edge

neutral.

0.00

0.05

0.10

0.15

0.20

0.25

0.30

Burnt Edge Unburnt

Pr (p

rese

nce)

Figure 8-11 The effect of edge on the probability of presence of Chestnut-rumped Thornbills, showing mean and 95% confidence levels. Data from 2005 and 2006 were pooled.

161

8.2.2.6 Inland Thornbill There was no effect of edge on the abundance of Inland Thornbills (χ2

2 = 6.5, p = 0.04;

Figure 8-12). However there was a strong trend indicating the density in the unburnt treatment

was higher than the burnt treatment. Data were pooled across years because the number of

observations was small and consistent between years. The near-significant result suggested that

the species was an unburnt specialist and edge neutral.

0

0.1

0.2

0.3

0.4

0.5

0.6

Burnt Edge Unburnt

Bird

s.ha

-1

Figure 8-12 The effect of edge on the abundance of Inland Thornbills, showing mean and 95% confidence levels. Data from 2005 and 2006 were pooled.

Inland Thornbills were most likely to be present in the unburnt treatment and least likely to

be present in the burnt treatment in both years of the study (2005 - χ2

2 = 22.0, p < 0.001; 2006 -

χ2

2 = 20.1, p < 0.001; Figure 8-13). The final model in both years contained one term -

treatment. The Inland Thornbill was an unburnt treatment specialist and an edge avoider.

162

0.0

0.2

0.4

0.6

0.8

Burnt Edge Unburnt

Pr (p

rese

nce)

2005

2006

Figure 8-13 The effect of edge on the probability of presence of Inland Thornbills, showing mean and 95% confidence levels.

8.2.2.7 Slaty-backed Thornbill There was no effect of edge on the abundance of Slaty-backed Thornbills (Treatment: χ2

2 =

4.8, p = 0.09; Wind: χ2

2 = 6.2, p = 0.01; Figure 8-14). Data were pooled across years because the

number of observations was small and consistent between years. The species was an edge

neutral generalist.

0.00

0.05

0.10

0.15

0.20

0.25

Burnt Edge Unburnt

Bird

s.ha

-1

Figure 8-14 The effect of edge on the abundance of Slaty-backed Thornbills, showing mean and 95% confidence levels. Data from 2005 and 2006 were pooled.

There was no difference in the presence of Slaty-backed Thornbills across a pyric edge in

either year (2005: χ2

2 = 1.3, p = 0.5; 2006: χ2

2 = 3.8, p = 0.1; Figure 8-15). The final model for

163

both years contained one fixed term – treatment. The Slaty-backed Thornbill was an edge

neutral generalist.

0.0

0.1

0.2

0.3

0.4

Burnt Edge Unburnt

Pr (p

rese

nce)

2005

2006

Figure 8-15 The effect of edge on the probability of presence of Inland Thornbills, showing mean and 95% confidence levels.

8.2.2.8 Southern Whiteface There was no difference in Southern Whiteface abundance between treatments (χ2

2 = 3.5, p

= 0.2; Figure 8-16). Data were pooled across years because the number of observations was

small and consistent between years. The species was an edge neutral generalist.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Burnt Edge Unburnt

Bird

s.ha

-1

Figure 8-16 The effect of edge on the abundance of Southern Whitefaces, showing mean and 95% confidence levels. Data from 2005 and 2006 were pooled.

Southern Whitefaces were more likely to be present in the burnt and edge treatments than

in the unburnt treatment (χ2

2 = 25.0, p < 0.001; Figure 8-17). There was no difference between

164

the burnt and edge treatments. Insufficient data was collected in 2006 to conduct an analysis.

The final model contained one fixed term – treatment. The Southern Whiteface was a burnt

treatment specialist and edge neutral.

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Burnt Edge Unburnt

Pr (p

rese

nce)

Figure 8-17 The effect of edge on the probability of presence of Southern Whitefaces, showing mean and 95% confidence levels. Data from 2005 and 2006 were pooled.

8.2.2.9 Spiny-cheeked Honeyeater Spiny-cheeked Honeyeaters were more abundant in the unburnt treatment and edge

treatment than in the burnt treatment in 2005 (χ2

2 = 13.7, p = 0.001; Figure 8-18). There was no

difference between the unburnt and edge treatments. In 2006 there was no difference in density

between the treatments (χ 2

2 = 2.4, p = 0.3). In 2005 the species was an unburnt treatment

specialist and edge avoider. In 2006 it was a generalist and edge neutral.

165

0.0

0.5

1.0

1.5

2.0

2.5

Burnt Edge Unburnt

Bird

s.ha

-1

2005

2006

Figure 8-18 The effect of edge on the abundance Spiny-cheeked Honeyeaters, showing mean and 95% confidence levels.

Spiny-cheeked Honeyeaters were more likely to be present in unburnt and edge treatments

than in the burnt treatment in 2005 (χ2

2 = 13.2, p = 0.001; Figure 8-19). There was no difference

between the unburnt and edge treatments. In 2006, there was no difference between treatments

(χ2

2 = 4.2, p = 0.1). In both years the final model contained one fixed term - treatment. Spiny-

cheeked Honeyeaters were unburnt treatment specialists and edge neutral in 2005, but in 2006

were edge neutral generalists.

0

0.2

0.4

0.6

0.8

1

Burnt Edge Unburnt

Pr (p

rese

nce)

2005

2006

Figure 8-19 The effect of edge on the probability of presence of Spiny-cheeked Honeyeaters, showing mean and 95% confidence levels.

166

8.2.2.10 Singing Honeyeater There was no difference in the abundance of Singing Honeyeaters between treatments in

2005 (χ2

2 = 0.9, p = 0.6) or 2006 (χ2

2 = 3.3, p = 0.2; Figure 8-20). The species was an edge

neutral generalist.

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Burnt Edge Unburnt

Bird

s.ha

-1

2005

2006

Figure 8-20 The effect of edge on the abundance of Singing Honeyeaters, showing mean and 95% confidence levels.

Singing Honeyeaters were more likely to be present in the unburnt treatment than the burnt

treatment in 2005, however there was no difference between the edge treatment and the other

two treatments (χ2

2 = 12.0, p = 0.002: Figure 8-21). In 2006 there was no difference in the

probability of presence between treatments (χ2

2 = 1.0, p = 0.6). In both years the final model

contained one fixed term - treatment. Singing Honeyeaters were unburnt treatment specialists

and edge neutral in 2005 and edge neutral generalists in 2006.

0

0.2

0.4

0.6

0.8

1

Burnt Edge Unburnt

Pr (p

rese

nce)

2005

2006

Figure 8-21 The effect of edge on the probability of presence of Singing Honeyeaters, showing mean and 95% confidence levels.

167

8.2.2.11 Crimson Chat There was no difference in the probability of the presence of Crimson Chats between

treatments in 2005 (χ2

2 = 9.0, p = 0.01; Figure 8-22) however there was a strong trend indicating

a higher probability of presence in the burnt treatment than in the edge and unburnt treatment.

The final model contained one fixed term – treatment. Insufficient data were collected in 2006

for analysis. The near-significant result suggested the species was a burnt treatment specialist

and an edge avoider.

0.0

0.1

0.2

0.3

0.4

0.5

Burnt Edge Unburnt

Pr (p

rese

nce)

Figure 8-22 The effect of edge on the probability of presence of Crimson Chats, showing mean and 95% confidence levels.

8.2.2.12 Hooded Robin Hooded Robins were more likely to be present in the edge and burnt treatments than in the

unburnt treatment (Treatment: χ2

2 = 9.3, p = 0.01; Wind: χ2

2 = 8.5, p = 0.004; Figure 8-23).

There was no difference between the edge and burnt treatments. Data were pooled from 2005

and 2006 because the number of observations was small and consistent between the years. The

Hooded Robin was a burnt treatment specialist and edge neutral.

168

0

0.1

0.2

0.3

0.4

Burnt Edge Unburnt

Pr (p

rese

nce)

Figure 8-23 The effect of edge on the probability of presence of Hooded Robins, showing mean and 95% confidence levels. Data were pooled from 2005 and 2006.

8.2.2.13 Red-capped Robin Red-capped Robins were more abundant in the unburnt treatment than in the edge

treatment (χ2

1 = 8.4, p = 0.004; Figure 8-24) in 2006. No birds were recorded in the burnt

treatment. Insufficient data were collected in 2005 to conduct an analysis. The species was an

unburnt treatment specialist and edge avoider.

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

Burnt Edge Unburnt

Bird

s.ha

-1

Figure 8-24 The effect of edge on the abundance of Red-capped Robins in 2005, showing mean and 95% confidence levels.

Red-capped Robins were most likely to be present in the unburnt treatment, and least likely

to be present in the burnt treatment in 2006 (χ2

2 = 21.2, p <0.001; Figure 8-25). In 2005 there

was no difference in the probability of presence of the species between treatments (Treatment:

169

χ2

2 = 5.0, p = 0.08; Wind: χ2

2 = 9.1, p = 0.003). The Red-capped Robin was an edge neutral

generalist in 2005 and an edge avoiding unburnt treatment specialist in 2006.

0.0

0.2

0.4

0.6

0.8

Burnt Edge Unburnt

Pr (p

rese

nce)

Figure 8-25 The effect of edge on the probability of presence of Red-capped Robins, showing mean and 95% confidence levels.

8.2.2.14 White-browed Babbler White-browed babblers were more likely to be present in unburnt and edge treatments than

in the burnt treatment in 2005 (χ2

2 = 11.1, p = 0.004; Figure 8-26). There was no difference

between the unburnt and edge treatments. In 2006 there was no difference in the probability of

presence between treatments (χ2

2 = 5.0, p = 0.08). In both years, the final model contained one

fixed term – treatment. White-browed Babblers were unburnt treatment specialists and edge

neutral in 2005 and edge neutral generalists in 2006.

0.0

0.1

0.2

0.3

0.4

0.5

0.6

Burnt Edge Unburnt

Pr (p

rese

nce)

2005

2006

Figure 8-26 The effect of edge on the probability of presence of White-browed Babblers, showing mean and 95% confidence levels.

170

8.2.2.15 Crested Bellbird There was no difference in the probability of presence of Crested Bellbirds between

treatments in 2005 or 2006. The species was an edge neutral generalist.

0.0

0.1

0.2

0.3

0.4

Burnt Edge Unburnt

Pr (p

rese

nce)

2005

2006

Figure 8-27 The effect of edge on the probability of presence of Crested Bellbirds, showing mean and 95% confidence levels.

Table 8-9 Tests of the effect of edge on Crested Bellbirds showing significant terms in the models.

Year Model terms χ2 df p

2005 Treatment 3.2 2 0.2

Treatment 5.1 2 0.1

Wind 1.9 1 0.2 2006

Wind.Treatment 10.0 2 0.007

8.2.2.16 Rufous Whistler Rufous Whistlers were more abundant in the unburnt treatment than in the burnt treatment

in 2005 (χ2

2 = 8.7, p = 0.01; Figure 8-28). There was no difference between the edge treatment

and the other two treatments. Insufficient data was collected in 2006 to conduct an analysis. The

species was an unburnt treatment specialist and edge neutral.

171

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Burnt Edge Unburnt

Bird

s.ha

-1

Figure 8-28 The effect of edge on the abundance of Rufous Whistlers in 2005, showing mean and 95% confidence levels.

The probability of presence of Rufous Whistlers differed between all three treatments in

2005 (χ2

2 = 26.8, p <0.001; Figure 8-29). The species was most likely to be present in the

unburnt treatment and least likely to be present in the burnt treatment. In 2006, Rufous

Whistlers were more likely to be present in the unburnt treatment than the other two treatments

(χ2

2 = 26.8, p <0.001). There was no difference between the unburnt and edge treatments. In

both years the final model contained one fixed term - treatment. Rufous Whistlers were unburnt

treatment specialists in both years but edge avoiders in 2005 and edge neutral in 2006.

0.0

0.2

0.4

0.6

0.8

Burnt Edge Unburnt

Pr (p

rese

nce)

2005

2006

Figure 8-29 The effect of edge on the probability of presence of Rufous Whistlers, showing mean and 95% confidence levels.

172

8.2.2.17 Grey Shrike-thrush There was no difference in the probability of presence of Grey Shrike-thrush between

treatments (χ2

2 = 6.8, p = 0.03; Figure 8-30), however there was a strong trend indicating a higher

probability of presence in unburnt and edge treatments than in the burnt treatment. Data were pooled

from 2005 and 2006 because the number of observations was small and consistent between the

years. Treatment was the only fixed term in the model. The near-significant result suggested that the

Grey Shrike-thrush was an unburnt treatment specialist and edge neutral.

0.00Burnt Edge Unburnt

0.05

0.10

0.15

0.20Pr

(pre

senc

e)

Figure 8-30 The effect of edge on the probability of presence of Grey Shrike-thrushes, showing mean and 95% confidence levels.

8.2.2.18 Willie Wagtail There was no difference in the abundance of Willie Wagtails between treatments (χ2

2 = 0.4, p =

0.8; Figure 8-31). Data were pooled across years because the number of observations was small and

consistent between years. The species was an edge neutral generalist.

0Burnt Edge Unburnt

0.1

0.2

0.3

0.4

Bird

s.ha

-1

Figure 8-31 The effect of edge on the abundance of Willie Wagtails, showing mean and 95% confidence levels.

173

In 2005 Willie Wagtails were more likely to be present in unburnt and edge treatments

than in the burnt treatment (χ2

2 = 16.1, p < 0.001: Figure 8-32). There was no difference between

the unburnt and edge treatments. In 2006 there was no difference in the probability of presence

between treatments (Treatment: χ2

2 = 0.5, p = 0.8; Wind: χ2

2 = 4.5, p = 0.04). The Willie Wagtail

was an unburnt treatment specialist and edge neutral in 2005 and an edge neutral generalist in

2006.

0

0.1

Burnt Edge Unburnt

0.2

0.3

0.4

0.5

0.6

0.7

Pr (p

rese

nce)

2005

2006

Figure 8-32 The effect of edge on the probability of presence of Willie Wagtails, showing mean and 95% confidence levels.

8.2.2.19 Masked Woodswallow There was no difference in the probability of presence of Masked Woodswallows between

treatments in 2005 (Treatment: χ2

2 = 5.0, p = 0.08; Wind: χ2

2 = 8.1, p = 0.005; Figure 8-33).

However the trend suggested the species was more likely to be present in the burnt and edge

treatments than in the unburnt treatment. In 2006, insufficient data was obtained to conduct an

analysis. The species must be classified as an edge neutral generalist however it maybe a burnt

treatment specialist and edge neutral.

174

0.0

0.1

Burnt Edge Unburnt

0.2

0.3

0.4

0.5

Pr (p

rese

nce)

Figure 8-33 The effect of edge on the probability of presence of Masked Woodswallows in 2005, showing mean and 95% confidence levels.

8.2.2.20 Black-faced Woodswallow Black-faced Woodswallows were present at higher density in the burnt treatment than in

the edge treatment in 2005 (χ2

1 = 6.6, p = 0.01; Figure 8-34). No birds were recorded in the

unburnt treatment. Insufficient data were collected in 2006 to conduct an analysis. The species

was a burnt treatment specialist and edge avoider.

0.0

0.2

Burnt Edge Unburnt

0.4

0.6

0.8

1.0

Bird

s.ha

-1

Figure 8-34 The effect of edge on the abundance of Black-faced Woodswallows in 2005, showing mean and 95% confidence levels.

Black-faced Woodswallows were more likely to be present in burnt and edge treatments

than in the unburnt treatment in 2005 (χ2

2 = 10.5, p = 0.005; Figure 8-35). There was no

difference between the burnt and edge treatments. Insufficient data were collected in 2006 for

analysis. The species was a burnt treatment specialist and edge neutral.

175

0.00

0.10

Burnt Edge Unburnt

Pr0.20

0.30

0.40

(pre

senc

e)

Figure 8-35 The effect of edge on the probability of presence of Black-faced Woodswallows in 2005, showing mean and 95% confidence levels.

8.2.2.21 Grey Butcherbird There was no difference in the probability of presence of the Grey Butcherbird between

treatments (χ2

2 = 0.7, p = 0.7; Figure 8-36). Data were pooled from 2005 and 2006 because the

number of observations was small and consistent between the years. Treatment was the only

fixed term in the model. The Grey Butcherbird was a generalist and edge neutral.

0.00

0.05

0.10

Burnt Edge Unburnt

Pr (p

re

0.15

0.20

senc

e)

Figure 8-36 The effect of edge on the probability of presence of Grey Butcherbirds, showing mean and 95% confidence levels.

8.2.2.22 Zebra Finch There was no difference in the abundance of Zebra Finches between treatments in 2005 (χ2

2

= 1.5, p = 0.5; Figure 8-37). Insufficient data were collected in 2006 for analysis. The species

was an edge neutral generalist.

176

0

0.1

0.2

Burnt Edge Unburnt

Bird

s

0.3

0.4

0.5

.ha-1

Figure 8-37 The effect of edge on the abundance of Zebra Finches in 2005, showing mean and 95% confidence levels.

Zebra Finches were more likely to be present in burnt and edge treatments than in the

unburnt treatment in 2005 (χ2

2 = 15.4, p = 0.002: Figure 8-38). There was no difference between

the burnt and edge treatments. Insufficient data were obtained in 2006 to conduct an analysis.

Zebra Finches were burnt treatment specialists and edge neutral.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Burnt Edge Unburnt

Pr (p

rese

nce)

Figure 8-38 The effect of edge on the probability of presence of Zebra Finches in 2005, showing mean and 95% confidence levels.

8.3 Discussion The bird community present at the edge treatment was intermediate between that present at

the burnt and unburnt treatments. Ordinations showed that the sites representing burnt and

unburnt treatments were clustered separately indicating that the composition of the bird

communities was different as would be expected from the time-since-fire study (Chapter 6). The

177

bird community at the edge treatment consisted of a combination of species that were also

present in the burnt and unburnt treatments. Significance tests on the bird communities present

across the edge produced different but not contradictory results depending on the method used

to account for detectability. The count data shows that the bird community at the edge was the

same as that present at the burnt treatment and these were both different to the unburnt

treatment. The result reflects the greater abundance of birds present in 2005 - most of the

difference in bird abundance between years was due to a greater abundance of birds in the burnt

and edge treatments. The presence/absence dataset shows significant differences between all

three treatments. An important reason for the difference in the significance tests is the difference

in the quantity of data. Taken together, the evidence supports the hypothesis that the community

of birds present at a pyric edge in mulga woodland is different to that present in the interior

burnt and unburnt habitat either side of the edge.

The abundance of all species at the edge was intermediate (i.e. either edge neutral or edge

avoider) between the interior burnt and unburnt habitats (Table 8-10). The habitat preference

and edge effect of some species changed between abundance tests and presence/absence tests.

This was due to differences in the statistical power of the tests. The pattern of response across

the edge was similar for all species regardless of the analytical method and there were no

contradictory results. No species was ecotonal (present only at the edge) so the hypothesis that

pyric edges in mulga woodland support different species to those present in the interior of burnt

or unburnt habitat was rejected. In addition no species were edge conspicuous (present in

greatest abundance at the edge). Therefore the hypothesis that pyric edges support birds in

greater abundance than interior habitat was also rejected.

178

Table 8-10 Summary of habitat preference and edge response by species. Species Analysis Year Habitat preference Edge response

2005 Burnt Neutral Budgerigar Presence

2006 Generalist Neutral

2005 Generalist Neutral Abundance

2006 Unburnt Avoider

2005 Unburnt Avoider Splendid Fairy-wren

Presence 2006 Unburnt Avoider

Chestnut-rumped Thornbill Presence 2005-06 Unburnt Neutral

Abundance 2005-06 Unburnt Neutral

2005 Unburnt Avoider Inland Thornbill Presence


Abundance 2005-06 Generalist Neutral

2005 Generalist Neutral Slaty-backed Thornbill Presence


Abundance 2005-06 Generalist Neutral Southern Whiteface

Presence 2005 Burnt Neutral

2005 Unburnt Avoider Abundance


2005 Unburnt Neutral Spiny-cheeked Honeyeater

Presence 2006 Generalist Neutral

2005 Generalist Neutral Abundance


2005 Unburnt Neutral Singing Honeyeater

Presence 2006 Generalist Neutral

Crimson Chat Presence 2005 Burnt Avoider

Hooded Robin Presence 2005-06 Burnt Neutral

Abundance 2005-06 Unburnt Avoider

2005 Generalist Neutral Red-capped Robin Presence


2005 Unburnt Neutral White-browed Babbler Presence


2005 Generalist Neutral Crested Bellbird Presence


Grey Shrike-thrush Presence 2005-06 Unburnt Neutral

Abundance 2005-06 Unburnt Neutral

2005 Unburnt Avoider Rufous Whistler Presence

2006 Unburnt Neutral

Abundance 2005-06 Generalist Neutral

2005 Unburnt Neutral Willie Wagtail Presence


Masked Woodswallow Presence 2005 Generalist Neutral

Abundance 2005 Burnt Avoider Black-faced Woodswallow

Presence 2005 Burnt Avoider

Grey Butcherbird Presence 2005-06 Generalist Neutral

Abundance 2005 Generalist Neutral Zebra Finch

Presence 2005 Burnt Neutral

179

Species richness varied across the edge; however, the edge treatment did not support

greater species richness than both the other two treatments. Therefore the hypothesis that

species richness is greatest at pyric edges was rejected. Bird abundance also varied across the

edge; however the edge treatment did not support greater bird abundance than both the other

two treatments. Therefore the hypothesis that bird abundance was greatest at pyric edges was

also rejected. Taken together the results of this study show that edge effect is not a mechanism

by which the imposition of fine-scaled fire mosaic on mulga woodland could increase avian

diversity.

The differences in the bird communities in the burnt and unburnt treatments were similar

to those found in the time-since-fire study (Chapter 6:) and can be explained by differences in

habitat structure (Chapter 5:). The unburnt treatment had a mulga woodland canopy, greater

coverage of phyllode litter but less groundcover particularly grass, than the unburnt treatment.

Granivores and terrestrial insectivores were most likely to be present in the open, grassy habitat

of the burnt treatment. Canopy insectivores and shrub insectivores were more likely to be

present in the foliage of the unburnt treatment while nectarivore/frugivores were generalists in

most analyses. Only one guild, the aerial insectivores showed a result inconsistent with the

time-since-fire study (Chapter 6:). In the edge study, members of the guild were clustered

around the burnt treatment sites, however in the time-since-fire study the members were

distributed across ordination space. The difference was due to differences in the habitat

occupied by the Masked Woodswallow and the lack of data about the Grey Fantail in the edge

study. The difference in the habitat preference of the Masked Woodswallow was due to

differences in behaviour between the studies. In the time-since-fire study the species was

usually recorded feeding above the canopy at a height of >50m and appeared unaffected by

vegetation type or structure. During the edge study it was often recorded roosting and this

always occurred in the burnt or edge treatments. The consistency of the guild responses to

habitat across the two studies suggests that habitat structure is more important for determining

the presence and abundance of birds at a site than edge effect.

A model of edge effects (Ries and Sisk, 2004) predicts the species response based on the

resource complementation/supplementation hypothesis (Dunning et al., 1992). The bird

communities present in the burnt and unburnt habitats were different, with most species

showing a strong preference for one habitat. This suggests that most species perceive a

difference in the quality of resources available in burnt and unburnt habitats (Ries et al., 2004)

and therefore that a pyric edge in mulga woodland is real sensu (Strayer et al., 2003). The bird

community at the edge comprised a combination of species from either side of the edge and the

pattern of edge response was overwhelmingly transitional. A transitional edge response is

predicted when one habitat is of lower quality than the other and the resources of the two are

supplementary (Ries and Sisk, 2004) – i.e. there are no resources available in one habitat which

are not available in the other. A transitional edge response also implies that birds perceived no

180

habitat enhancement at the edge – i.e. there were no resources available at the edge that were

not available elsewhere.

The pattern of species richness across the edge was consistent between years, however bird

abundance was not. Fewer birds were present in the burnt and edge habitats in 2006 than there

were in 2005. This was due to changes in the abundance of six nomadic, semi-nomadic or

irruptive species which showed a preference or a trend for preference for burnt habitat in 2005.

Zebra Finch, Masked Woodswallow, Black-faced Woodswallow, Crimson Chat and Southern

Whiteface were present in such low numbers in 2006 that an analysis of edge effect was not

possible. Budgerigars were also less likely to be present. In contrast, the populations of unburnt

specialists appear more stable. Taken together the results suggest that the composition of the

bird community present at the edge changes as the abundance of birds in the burnt habitat

changes. When burnt habitat specialists are in great abundance, the edge is dominated by these.

However when they leave, the edge community is mostly composed of the unburnt habitat

specialists.

The results of an edge study are contingent on the concept of edge and the method used to

test it. The edge treatment in this study encompassed both sides of the edge – burnt and unburnt.

This concept of edge is valid and has been used elsewhere (Sisk and Margules, 1993). However

some studies have collected and analysed data from each side of an edge separately (Baker et

al., 2002). Such an approach is not appropriate for this study because the aim is to examine the

consequences of landscape management on avian diversity. It is not possible to manage

landscape to create one side of an edge, so it is not valid to classify a species response to edge in

such a way and then use the information to inform landscape management.

Views about the consistency of the main result of this study with the edge literature are

divided. Ries and Sisk (2004) suggest that positive edge responses are more common than

neutral and negative responses. However Baker et al. (2002) conclude that there is no evidence

to support an edge effect of increased density and species richness across natural edges and little

evidence from anthropogenic edges. I am unaware of any other studies of avian response across

a pyric edge.

The failure to detect a positive edge effect could be due to the scale at which the study was

conducted (Paton, 1994; Laurance, 2000; Ries et al., 2004). Theoretically the edge treatment

could have been too big or the interior burnt and unburnt treatments too close to detect an edge

effect. That a 40m wide edge treatment is too big is rejected because a smaller scale is

inappropriate for measuring bird abundance and can yield misleading results (Terborgh, 1985).

In addition the probability of presence that was measured over a 100m wide edge treatment

produced comparable results to those measured over 40m. A larger scale edge effect was

difficult to test in this landscape because sites in which the patches from both treatments

included sufficient habitat >150m from a suitable pyric edge were rare. The scale of this study is

therefore appropriate for this landscape, a conclusion borne out by the detection of edge effects

in many species. Nonetheless, the possibility remains that some species may respond differently

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to edge at a larger scale, so caution should be exercised when extrapolating the results of this

study to a landscape in which the interior habitat is more remote to edge than was the case in

this study (Laurance, 2000). Differences in edge response due to scale are more likely to be in

the magnitude (e.g. edge neutral to edge avoider) rather than direction (e.g. edge avoider to edge

conspicuous) - differences in the direction of an edge effect are rare (Ries et al., 2004).

Temporal effects on edge responses are often considered nuisance parameters, however

understanding the cause could help explain observed variability in edge response (Ries et al.,

2004). Some species showed differences in habitat preference and edge effect between years. In

most instances this was due to differences in the abundance of a species between years because

the likelihood of obtaining a significant result increased with abundance. Where data were

sparse the classification of a species defaulted to edge neutral generalist. The pattern of response

across an edge was the same for most species between years and no species showed a

significant interaction between years. The consistency across years suggests that the main

finding of the study may be robust to temporal variation. Inconsistencies in edge response are

common, however changes in the direction of edge response are rare (Ries et al., 2004). Some

assurance of temporal stability in an ecological generalisation in the Australian arid zone is

valuable because variation due to recent rain is strong (Davies, 1974; Griffin, 1984; Stafford-

Smith and Morton, 1990; Read et al., 2000; Paltridge and Southgate, 2001; Burbidge and Fuller,

2007).

8.4 Conclusion There is no evidence that pyric edges in mulga woodland support greater avian diversity

than interior habitat. No species were ecotonal or edge conspicuous in mulga woodland so there

is no evidence that managing a mulga woodland landscape to increase the proportion of edge

habitat would increase avian diversity. There is therefore no evidence that edge effect is a

mechanism by which avian diversity could increase in a fine-scaled fire mosaic in mulga

woodland.

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Chapter 9: The fire mosaic hypothesis and biodiversity The aim of this thesis is to investigate the fire mosaic hypothesis by testing three key

assumptions implicit in the concept (Chapter 1:). 1) The distribution of birds is affected by time-

since-fire (Chapter 6:). 2) Bird diversity (number and variety of birds) is greater in smaller

patches of the same time-since-fire than they are in larger patches (Chapter 7:). 3) Birds

diversity is greater at pyric edges between patches of different time-since-fire than in the

interior of patches (Chapter 8:).

The first hypothesis, that the distribution of birds is affected by time-since-fire was

investigated because such effects of fire are crucial to the fire mosaic hypothesis (Bradstock et

al., 2005; Parr and Andersen, 2006). If there is no effect of time-since-fire on biodiversity, then

the spatial arrangement of fire histories is irrelevant and the definition of habitat patches and

habitat edges based on time-since-fire is not valid. The second and third hypotheses investigate

attributes of fire mosaics that are putative mechanisms that may affect biodiversity. For patch

size to act to increase biodiversity in a fine-scale fire mosaic, biodiversity must increase as

patch size decreases. For edge effect to act to increase biodiversity in a fine-scale fire mosaic,

edge habitat must support greater biodiversity than interior habitat. The hypotheses were tested

in the mulga bird/mulga woodland (Cody, 1994; Johnson and Burrows, 1994) model system.

Time-since-fire affects the distribution of birds in mulga woodland (Chapter 6:). Fire

causes a near-complete turnover in the bird community. Following fire, the bird community in

mulga woodland is dominated by a suite of generalist and nomadic species (Reid et al., 1991)

mostly granivores and ground insectivores. Twenty-nine years after fire, the bird community in

mulga woodland was dominated by the mulga birds (Cody, 1994) which are mostly foliar

insectivores (Recher and Davis, 1997). The bird community present in long-unburnt mulga

woodland is similar to that present 29 years after fire (Chapter 6:). Fire in mulga woodland,

therefore, has a profound effect on the composition of the bird community and this conclusion is

consistent with the literature on fire and birds (Chapter 1:). Differences in the response of birds

to time-since-fire validates the definition of landscape patches using this parameter for many

species.

The effect of patch size on the density of birds in mulga woodland was tested using

patches defined by time-since-fire (Chapter 7:). Patch size had no effect on species richness or

bird abundance and no species increased in density with decreasing patch size. Therefore,

smaller patches of mulga woodland did not support greater avian diversity than larger patches.

Consequently there was no evidence that patch size may act to increase avian diversity in a fine-

scaled fire mosaic compared to a coarse-scaled fire mosaic. This result is consistent with the

literature on the species/area relationship and the density/area relationship (Chapter 1:).

The edge response across a pyric edge was tested for the birds present in mulga woodland.

The community of birds present at the edge was intermediate between that present in the interior

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habitat either side of the edge (Chapter 8:). Species richness was not highest at the edge nor was

bird abundance. No species was present only at the edge nor was any species most abundant at

the edge. Therefore pyric edge did not support greater avian diversity than patch interior.

Consequently increasing the amount of pyric edge by managing mulga woodland as a fine-

scaled fire mosaic will not act to increase avian diversity compared to a coarse-scale fire

mosaic. To my knowledge this is the only study to have investigated edge effect at a pyric edge;

however the result is consistent with the literature describing the edge effect on birds at other

natural edges (Chapter 8:).

Taken together, the three studies show that fire has a strong affect on birds in the model

system, but the spatial distribution of fire does not. Birds are affected by time-since-fire in

mulga woodland, but the size of patches is of little consequence and there is no positive effect

of edge. There is therefore no evidence that the imposition of a fine-scaled fire mosaic will

increase avian diversity in the model system compared to a coarse-scale fire mosaic. These

results are consistent with the voluminous literature relating to the effects of: 1) fire on birds; 2)

patch size; and 3) edge effect (Chapter 1:, Chapter 2:). This study is also consistent with other

investigations of the fire mosaic hypothesis or the analogous concepts; the vegetation mosaic

hypothesis and the resource complementation/supplementation hypothesis (Short and Turner,

1994; Letnic, 2003; Pons et al., 2003b; Brotons et al., 2004; Letnic and Dickman, 2005). In all

of the studies, the authors concluded that fauna were most strongly affected by time-since-fire

and the putative affects of mosaic were weak (Chapter 1:).

In a critical review of the fire mosaic paradigm Bradstock et al. (2005) state that it is

unlikely that a single fire mosaic configuration will suit all species at all times. This study is one

of those unlikely instances. No species was present at highest density in the intermediate time-

since-fire class so a seral succession within the fire mosaic was not required to maximise avian

diversity. The two significant density/area responses were both positive and so required larger

patches and species richness did not vary with patch size. The edge response of species varied

between neutral and negative (i.e. edge avoider). Species that are edge neutral are indifferent to

the amount of edge habitat in the landscape while those with a negative edge response will be at

higher densities in a landscape with less edge habitat. It is therefore possible to optimise the

mosaic configuration for density/area effect and edge response by maximising the patch size

and mean area-to-perimeter ratio of each time-since-fire class. This conclusion is contingent on

a number of factors. 1) Greater statistical power will not produce any results inconsistent with

the conclusion. 2) Greater temporal resolution in the time-since-fire classes will not reveal any

unimodal responses. 3) Patch size and edge effects do not vary in time. 4) No other ecological

processes affect the density of birds in mulga woodland fire mosaics. None of these

contingencies can be ruled out. In particular, with greater statistical power, Rufous Whistlers

may be shown to prefer the intermediate time-since-fire class (Chapter 6:), Hooded Robins and

Slaty-backed Thornbills may be edge positive (Chapter 8:) and Singing Honeyeaters may have a

negative density/area relationship (Chapter 7:). Despite the conclusion from this study, the

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results serve to reinforce the view of Bradstock et al. (2005) that the coincidence of consistent

results required from all species in a community, so that a single mosaic configuration will be

optimal for all, is unlikely.

The other critical contingency to the conclusion is the definition of biodiversity (Hubell,

2001). Biodiversity maybe defined in a number of different ways depending on the aims of

management and the technology available for measuring it. Where biodiversity is defined as the

variety of species or as the variety of ecosystems including seral stages of ecosystems (N.

Burrows, pers. comm.), the results of the time-since-fire experiment support the fire mosaic

hypothesis. In my opinion these two definitions are less informative than number and variety of

species. Where biodiversity is defined as the variety of species, a landscape with 100

individuals of 10 species would be of equal biodiversity to that with 10 individuals of 10

species. In my opinion such a result is misleading. When biodiversity is defined as the variety of

ecosystems including seral stages, the question becomes circular and maybe misleading because

it is analogous to the questionable assumption that pyrodiversity begets biodiversity (Bradstock

et al., 2005; Parr and Andersen, 2006).

A limitation of this study is the reductionist, patch-based approach. The method focuses on

particular factors that were selected a priori and precludes other factors that may nonetheless be

significant. It remains possible that the avifauna in a patch of mulga woodland of a certain size

may be different if the habitat patches around it are different. Another limitation of this study is

the scale. Patch sizes in this study encompass the full range present at the study site (3ha-

3,000ha), but fires in central Australia reach an extent in excess of 1,000,000ha (Gill, 2000;

Allan and Southgate, 2002). It is probable that different processes operate at different scales and

therefore possible that results would differ (Laurance, 2000). Fire is a landscape scale factor and

ideally the fire mosaic hypothesis would be tested using landscape as the experimental unit. The

treatments – fire mosaics of contrasting scale – would be applied to each experimental unit.

Such a study, comparing mulga woodland landscapes with fire mosaics of contrasting scales

would add greater certainty to existing knowledge. Preferably the coarse-scale landscape would

be composed of patches with a greater mean area and greater maximum area than was the case

in this study. The variance in the effect due to environmental stochasticity could be quantified

by conducting the study over a number of years and targeting periods of extreme rainfall.

Replication would need to be at a landscape level with ideally, no less than four replicates of

each mosaic configuration (R. Cunningham, pers. comm.). To my knowledge, the cessation of

traditional aboriginal fire management means there are few such landscapes which may

potentially support such an experiment. Though an apparently unreplicated attempt to test the

fire mosaic hypothesis in a landscape is underway in Jarrah (Eucalyptus marginate Sm) forest in

Western Australia (Burrows, 2004; Burrows, 2006). The key challenge for such a landscape

study is to account for differences between the landscapes that affect the distribution of fauna

such as rainfall, soil nutrient status, hydrology and vegetation (Bradstock et al., 2005), so that

any differences can be reliably attributed to the treatments.

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This study cannot rule out the possibility that a fine-scale fire mosaic may in some

circumstances and some ecosystems benefit biodiversity. However against a background of

uncritical advocacy for such mosaics it makes an important contribution. Uncritical advocacy

for a fine-scaled fire mosaic is unjustifiable. It is unreasonable to assume that a fine-scale fire

mosaic will increase biodiversity. If the justification for the imposition of a fine-scaled fire

mosaic on the landscape is the benefits it will have for biodiversity, then the benefits must be

demonstrated by evidence.

A common justification for the advocacy of fine-scale fire mosaics to maintain biodiversity

is the prevention of homogenisation of the time-since-fire of large areas of habitat by a single

large fire event during extreme fire weather (Bradstock et al., 2005). Burning of large areas of

vegetation is regarded as undesirable because it is likely to favour some species and

disadvantage or exclude others (Bradstock et al., 2005); (Chapter 2:, Chapter 6:) thereby

reducing biodiversity. This view incorrectly conflates fire ecology and fire management (which

is outside of the scope of this thesis) and it is the imprecision of understanding relating to this

conflation which affords the fire mosaic concept some of its appeal. A prescribed ecological fire

and a prescribed fuel reduction fire are likely to have different ecological consequences and

should be distinguished. Fire ecology refers to the effects of the fire regime on biota. It follows

that a prescribed ecological burn pertains to the maintenance or establishment of a fire regime

suitable for the biota or a portion of the biota in the area burnt (Marsden-Smedley and

Kirkpatrick, 2000). Examples of such a fire would be to burn a patch of vegetation dominated

by a population of a serotinous plant which was dependent on fire for recruitment and which

was senescing. Alternatively a population of a fire-sensitive weed may be burnt to promote

growth of other plants (Robertson et al., 1999; Tran and Williams, 2007). In contrast, fire

management refers to activities intended to influence future fire behaviour. The aim is usually to

reduce the fuel load adjacent to an asset that it is undesirable be burnt. Such an asset could be

anthropogenic or ecological. An example is prescribed burning in fire-prone Ngarkat

Conservation Park in South Australia (Henderson and Wouters, 2007) where prescribed burning

is conducted adjacent to long-unburnt vegetation which is the favoured habitat of the Southern

Emu-wren (Stripiturus malachurus). Despite the intention to protect an ecological asset this is

not an ecological burn because the aim is to influence fire behaviour (Marsden-Smedley and

Kirkpatrick, 2000). Maintenance of fuel loads at levels below what occurs without prescribed

burning probably represents a change in three of the four fire regime parameters (Gill et al.,

2002); frequency, intensity and season. The prescribed fire regime will probably therefore have

ecological consequences for the flora (Noble and Slatyer, 1980) and fauna (Chapter 2:) present

in the area subject to prescribed fire. In effect the ecosystem in the area of the prescribed burn is

being sacrificed for the ecosystem adjacent yet both fires are described as ecological burns. The

lack of precision in the language of fire ecology and fire management that is represented in this

scenario and in the fire mosaic concept reflects poorly-conceived policy and management. The

adoption of clear definitions for the types and function of prescribed fires would improve

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understanding of the operational outcomes potentially attributable to the implementation of fine-

scaled fire mosaics in the landscape.

Although fire management, both European and Aboriginal, is outside the scope of this

thesis, the apparent origin of the fire mosaic concept in European perceptions of the Aboriginal

fire management and its landscape effects, it is pertinent to make some comment here based on

relevant literature.

A common assumption of Australian ecologists is that the cessation of traditional

Aboriginal fire management has changed Australian fire regimes – i.e. frequency, intensity,

season and type of fires (Gill, 1975; Gill et al., 2002) – and therefore the effect of fire on the

landscape – i.e. burn pattern, time-since-fire pattern, fire-interval pattern and inter-ecosystem

pattern (Gill, 1998). Furthermore, it is hypothesised that such changes are of detriment to the

Australian biota particularly small mammals (Burbidge et al., 1988; Short and Turner, 1994).

This point-of-view is extrapolated from the knowledge that fire was an important land

management tool of traditional Aborigines and that traditional Aboriginal fire management has

ceased across most of Australia. Implicit in the assumption is the view that traditional

Aboriginal fire management: 1) involved setting small, low intensity fires at high frequency

across a high proportion of the landscape, 2) reduced fuel load and fuel continuity such that the

fire regimes were characterised by fires of higher frequency, lower intensity and that these fires

were of smaller size than occurs in its absence, 3) resulted in the creation of a mosaic of patches

of different time-since-fire at a finer scale than occurs in its absence, and 4) that this fine-scale

fire mosaic benefited biodiversity. The assumption begs three questions: 1) what were the fire

regimes and landscape effects of fire of pre-European Australia; 2) to what extent did traditional

Aboriginal fire management influence those regimes and effects; and 3) how did the assumed

change in the fire regimes and effects influence the biota?

Direct comparison of pre-European and present-day fire regimes and landscape effects of

fire, is not possible because pre-European fire regimes and their effects are unquantified. Instead

the question has been addressed indirectly using a combination of ethnohistorical, ethnographic

and ecological data (Bowman, 1998; Gammage, 2008). This effort is comprehensively reviewed

by Bowman (1998) who concluded that traditional Aboriginal fire management: 1) played a

central role in the maintenance of landscapes subsequently colonised by Europeans, 2) affected

the geographic range and demographic structure of many vegetation types, and 3) was important

in creating habitat mosaics that favoured the abundance of some mammals. At the same time

Bowman concedes that the question of whether traditional Aboriginal fire management strongly

affected the pre-European landscapes is “complex and vexatious”. The ethnohistorical record is

regarded by some scholars as too biased and unreliable to provide useful information (Gill,

1977; Horton, 1982). At best such evidence is difficult to use to accurately determine the spatial

extent of fires, the vegetation type which was burnt, and the reasons for burning (Bowman,

1998). Of the ethnographic record, Bowman says that fire management is a blind spot and that

187

the details have not been adequately documented. Of the ecological evidence he concedes that

most studies are circumstantial.

“What is required is an advance from the poetic concept of fire-stick farming (Jones, 1969)

to a coherent scientific analysis of Aboriginal burning that can be used to buttress land

management prescriptions (Bowman, 1998).

The review leaves the impression of a fragmentary, disparate and hotly contested body of

evidence that is open to interpretation. The only uncontested conclusion being that Aborigines

across the continent made regular use of fire for a wide variety of purposes.

A recent example of the nature of the debate comes from Tasmania (Marsden-Smedley and

Kirkpatrick, 2000; Gammage, 2008; King et al., 2008). Gammage (2008) presented a series of

anecdotes which illustrate a skilful and widespread use of fire by Tasmanian Aborigines but fall

short of quantifying the fire regime or demonstrating broad-scale landscape effects of fire.

Marsden-Smedley and Kirkpatrick (2000) postulated that Tasmanian Aborgines frequently

burnt small patches of buttongrass moorland in south west Tasmania and that this minimised the

occurrence of large high-intensity fires. King et al. (2008) pointed out that known historical

deviations from the present climate may have contributed to hypothesised differences between

the pre-European and present day fire regimes.

That the use of fire by Aborigines markedly altered the fire regimes of pre-European

Australia and changed the effects of fire across the landscape to the benefit of the biota is an

extrapolation of varying degree from what is known about traditional Aboriginal fire

management in most Australian ecosystems. A comparison of fire regime simulation models by

Cary et al. (2006) found that greater than 30% of the landscape needs to be in a fuel reduced

state to affect the behaviour of unplanned fire. That Aboriginal people had the inclination and

capacity to undertake a task of this magnitude without deliberately lighting fires of large spatial

extent has not been established. The larger the spatial extent of fires lit by Aborigines, the lesser

the potential difference between the postulated pre-European fire regimes and those of the

present day. In addition, the dominant role of weather and climate (Kershaw et al., 2002; Cary

et al., 2006; Power et al., 2008), rather than the characteristics of fuel (Fernandes and Botelho,

2003), in determining fire behaviour casts doubt on the assumed impact that Aboriginal fuel

reduction may have had on the spread of large intense fires during extreme weather. The pattern

of the effects of fire in the landscape are dominated by large fire events (Strauss et al., 1989;

Edwards et al., 2008), which homogenise time-since-fire across the landscape – i.e. create

coarse-scale mosaics. That traditional Aboriginal fire management had more than localised

effect on the landscapes colonised by Europeans in the eighteenth century is not clear.

Implementation of the fire mosaic hypothesis in Australian ecosystems on the basis of its

perceived connection to traditional Aboriginal fire management practices is therefore

questionable.

The application of the conclusions of this study to contemporary fire management

practices is dependent on the aims of land management (Keith et al., 2002) and the scale at

188

which such management is applied (Wiens et al., 1986; Wiens, 1989). As discussed above the

aims of fire management fall into two broad categories: 1) enhancement of biodiversity; and 2)

protection of assets, including ecological assets (Gill et al., 2002). Where the area under

management is small compared to the extent of fires in the landscape, prescribed burning may

increase the variety of species in a given management unit. Where such an outcome is the aim

of management, the evidence from this study and the literature (e.g. Chapter 2:) generally

support the use of prescribed burning. Under such circumstances the extent of the area burnt

will probably be more important than the patch size or landscape context of the burn. That said,

two points should be noted. 1) The landscape effects of fire may vary with parameters of the fire

regime such as frequency, intensity, season and type of fire (Gill, 1975), so these must be

considered when planning a prescribed fire. 2) How such management affects biodiversity at

different scales is dependent on the specific circumstances, but it is not logical to assume that

biodiversity will increase at all scales (Wiens et al., 1986; Wiens, 1989). By definition, species

are different (Mayr, 1942; Ridley, 1996). It is therefore impossible to implement a single fire

regime to impose an effect on the landscape which benefits all species at once. Fire

management for biodiversity must therefore focus on priority species such as those which are

threatened, or implement a trade-off in which as few species as possible are disadvantaged to

the point of extirpation or ultimately extinction. Prescribed burning may affect fire regimes and

cause changes in the landscape effects of fires (Cary et al., 2006). For example where an aim of

management is to reduce the probability of fire damage to property or reduce the probability of

fire sensitive ecosystems being burnt, targeted prescribed burning may help achieve that

outcome. In such instances the fire regime in areas subject to prescribed burning is altered in

order to change the fire regime in neighbouring patches of land. That such a management

regime may act to enhance biodiversity is a function of the definition of biodiversity (Hubell,

2001), the characteristics of the fires, the effectiveness of the prescribed burning operation in

achieving the aims of the fire plan, and the characteristics of the affected ecosystems – i.e. those

that are prescribed to be burnt and those they are affected by the imposition of the burn (Gill,

1996). However in such instances the effect upon biodiversity in the area/s burnt is implicitly of

secondary importance within the prescribed burning plan. It is recommended that the

philosophy underpinning a fire management plan aimed at asset protection, also consider the

effect on biodiversity in the area/s subject to prescription.

189

References Adeney, J. M., Ginsberg, J. R., Russell, G. J. and Kinnaird, M. F., 2006. Effects of an ENSO-

related fire on birds of a lowland tropical forest in Sumatra, Animal Conservation, 9: 292-301.

Agee, J. K., 1998. The landscape ecology of western forest fire regimes, Northwest Science, 72(Special issue): 24-34.

Allan, G. and Baker, L., 1990. Uluru (Ayers Rock-Mt Olga) National Park: an assessment of a fire management programme, Proceedings of the Ecological Society of Australia, 16: 215-220.

Allan, G. and Griffin, G. F. (Eds.), 1986. Fire ecology of the hummock grasslands in central Australia Proceedings of the fourth Australian Rangeland Society Conference, Armidale, NSW.

Allan, G. E., 1984. Fire history and land units of Uluru, In Anticipating the inevitable: a patch-burn strategy for fire management at Uluru (Ayers Rock-Mt Olga) National Park(Ed, Saxon, E. C.) CSIRO, Melbourne, pp. 13-24.

Allan, G. E., 2003. Fire regimes in Australia's rangelands: a lack of information, In Biodiversity Monitoring in the Rangelands A Way Forward, Report to Environment Australia, Vol. 1 (Eds, Smyth, A., James, C. D. and Whiteman, G.) Environment Australia and CSIRO.

Allan, G. E., Phillips, N. R. and Hookey, P. (Eds.), 2003. Learning Lessons from an Exceptional Period of Fires in Central Australia: 1999 to 2002 Proceedings of the 3rd International Wildland Fire Conference, Sydney, 3-6 October.

Allan, G. E. and Southgate, R. I., 2002. Fire regimes in the spinifex landscapes of Australia, In Flammable Australia: the fire regimes and biodiversity of a continent(Eds, Bradstock, R. A., Williams, J. E. and Gill, A. M.) Cambridge University Press, Cambridge.

Anderson, J., Hardy, E., Roach, J. and Witmer, R., 1976. A land use and land cover classification system for use with remote sensor data, U.S Geological Survey, Washington D.C.

Angelstam, P. K., 1998. Maintaining and restoring biodiversity in European boreal forests by developing natural disturbance regimes Journal of Vegetation Science, 9(4): 593-602.

ANPWS, 2000. Uluru Kata-Tjuta Plan of Management, Canberra, Department of Environment and Heritage.

Baker, J., 2000. The Eastern Bristlebird: cover-dependent and fire-sensitive, Emu, 100(4): 286-298.

Baker, J. (Ed.) 2002. Fire-sensitive birds and adaptive management Proceedings of the NSW Nature Conservation Council Bushfire Conference, Sydney.

Baker, J., French, K. and Whelan, R. J., 2002. The edge effect and ecotonal species: bird communities across a natural edge in southeastern Australia, Ecology, 83(11): 3048-3059.

Banfai, D. S. and Bowman, D. M. J. S., 2005. Dynamics of a savanna-forest mosaic in the Australian monsoon tropics inferred from stand structures and historical aerial photography Australian Journal of Botany, 53(3): 185-194.

Barlow, J., Haugaasen, T. and Peres, C. A., 2002. Effects of ground fires on understorey bird assemblages in Amazonian forests, Biological Conservation, 105(2): 157-169.

Barlow, J. and Peres, C. A., 2004. Avifaunal responses to single and recurrent wildfires in Amazonian forests, Ecological Applications, 14(5): 1358-1373.

Barlow, J., Peres, C. A., Henriques, L. M. P., Stouffer, P. C. and Wunderle, J. M., 2006. The responses of understorey birds to forest fragmentation, logging and wildfires: An Amazonian synthesis, Biological Conservation, 128: 182-192.

Beard, J. S., 1984. The vegetation of the Australian arid zone, In Arid Australia(Eds, Cogger, H. G. and Cameron, E. E.) Australian Museum, Sydney.

Bender, D. J., Contreras, T. A. and Fahrig, L., 1998. Habitat loss and population decline: a meta-analysis of the patch size effect, Ecology, 79(2): 517-533.

Benshemesh, J., 1989. Management of Malleefowl - With Regard to Fire, In The Mallee Lands: A Conservation Perspective, Proceedings of the National Mallee Conference, Adelaide(Eds, Noble, J. C., Joss, P. J. and Jones, G. K.) CSIRO, Melbourne.

Beyer, H. L. (2004) Spatial Ecology.com. http://www.spatialecology.com/htools.

http://www.spatialecology.com/htools

190

Bolton, B. L. and Latz, P. K., 1978. The Western Hare-wallaby, Lagovchestes hirsutus (Gould) (Macropodidae), in the Tanami Desert, Australian Wildlife Research, 5: 285-293.

BoM, 2006a. Three month and annual rainfall variability maps Bureau of Meterorology, Canberra.

BoM, 2006b. Climate graphs and maps - average daily relative humidity for Yulara, Bureau of Meteorology, Canberra.

BoM, 2006c. Wind speed and direction rose for Giles, Bureau of Meteorology, Canberra. Bond, W. J. and Keeley, J. E., 2005. Fire as a global 'herbivore': the ecology and evolution of

flammable ecosystems, Trends in Ecology and Evolution, 20(7): 387-394. Bond, W. J., Woodward, F. I. and Midgley, G. F., 2005. The global distribution of ecosystems

in a world without fire, New Phytologist, 165(525-538). Bowers, M. A. and Matter, S. F., 1997. Landscape ecology of mammals: relationships between

density and patch size, Journal of Mammalogy, 78(4): 999-1011. Bowman, D. M. J. S., 1998. Tansley Review No. 101 - The impact of Aboriginal landscape

burning on the Australian biota, New Phytologist, 140: 385-410. Bowman, D. M. J. S., Latz, P. K. and Panton, W. J., 1994. Pattern and Change in an Acacia

aneura Shrubland and Triodia Hummock Grassland Mosaic on Rolling Hills in Central Australia, Australian Journal of Botany, 43: 25-37.

Bowman, D. M. J. S., Walsh, A. and Prior, L. D., 2004. Landscape analysis of Aboriginal fire management in Central Arnhem Land north Australia, Journal of Biogeography, 31: 207-223.

Bradstock, R. A., Bedward, M., Gill, A. M. and Cohn, J. S., 2005. Which mosaic? A landscape approach for evaluating interactions between fire regimes, habitats and animals, Wildlife Research, 32: 409-423.

Bradstock, R. A., Williams, J. E. and Gill, A. M. (Eds.), 2002. Flammable Australia : the fire regimes and biodiversity of a continent, Cambridge University Press, Cambridge, New York.

Brawn, J. D., 2006. Effects of restoring oak savannas on bird communities and populations, Conservation Biology, 20(2): 460-469.

Brawn, J. D., Robinson, S. K. and Thompson, F. R., 2001. The role of disturbance in the ecology and conservation of birds, Annual review of ecology and systematics, 32: 251-276.

Brockett, B. H., Biggs, H. C. and van Wilgen, B. W., 2001. A patch mosaic burning system for conservation areas in southern African savannas, International Journal of Wildland Fire, 10: 169-183.

Brook, B. W. and Bowman, D. M. J. S., 2006. Postcards from the past: charting the landscape-scale conversion of tropical Australian savanna to closed forest during the 20th century Landscape Ecology, 21(8): 1253-1266.

Brooker, M. G., 1998. Fire and birds in a Western Australian heathland, Emu, 98: 276-287. Brooker, M. G., Braithwaite, R. W. and Estbergs, J. A., 1990. Foraging Ecology of Some

Insectivorous and Nectarivorous Species of Birds in Forests and Woodlands of the Wet-Dry Tropics of Australia, Emu, 90(215-230).

Brooker, M. G. and Rowley, I., 1991. Impact of wildfire on the nesting behaviour of birds in heathland, Wildlife Research, 18: 249-263.

Brookfield, M., 1973. Winds of arid Australia, CSIRO, Melbourne. Brotons, L., Herrando, S. and Martin, J. L., 2004. Bird assemblages in forest fragments within

Mediterranean mosaics created by wildfires, Landscape Ecology, 19(663-675). Brotons, L., Monkkonen, M. and Martin, J. L., 2003. Are fragments islands? Landscape context

and density-area relationships in Boreal forest birds, The American Naturalist, 162(3): 343-357.

Brotons, L., Pons, P. and Herrando, S., 2005. Colonization of dynamic Mediterranean landscapes: where do birds come from after fire?, Journal of Biogeography, 32: 789-798.

Buckland, S. T., Anderson, D. R., Burnham, K. P. and Laake, J. L., 1993. Distance sampling: estimating abundance of biological populations, Chapman & Hall, London, New York.

Buckland, S. T., Anderson, D. R., Burnham, K. P., Laake, J. L., Borchers, D. L. and Thomas, L., 2001. Introduction to distance sampling: estimating abundance of biological populations, Oxford University Press, Oxford.

191

Burbidge, A. A. and Fuller, P. J., 2007. Gibson Desert birds: responses to drought and plenty, Emu, 107: 126-134.

Burbidge, A. A., Johnson, K. A., Fuller, P. J. and Southgate, R. I., 1988. Aboriginal knowledge of the mammals of the central deserts of Australia, Australian Wildlife Research, 15(9-39).

Burbidge, A. H., Rolfe, J., McNee, S., Newbet, B. and Williams, M., 2007. Monitoring population change in the cryptic and threatened Western Ground Parrot in relation to fire, Emu, 107: 79-88.

Burrows, N. D. (2004) In Are we Prepared for Future Challenges?: Program & Abstracts: the 11th Annual AFAC Conference and Inaugural Bushfire CRC ConferenceBushfire CRC, Perth, pp. 19-25.

Burrows, N. D., 2006. Burning for biodiversity: investigating fine grain fire mosaics in south-west Australia, Australasian Plant Conservation, 14(4): 5-6.

Burrows, N. D. and Christensen, P. E. S. (Eds.), 1990. A survey of Aboriginal fire patterns in the Western Desert of Australia Fire and the Environment: Ecological and Cultural Perspectives. Proceedings of an International Symposium, Knoxville, Tennessee. USDA Forest Service General Technical Report SE-69.

Byram, G. M., 1959. Combustion of forest fuels, In Forest fires: control and use(Ed, Davis, K. P.) McGraw-Hill, New York, pp. 61-89.

Cary, G. J., Keane, R. K., Gardner, R. H., Lavorel, S., Flannigan, M. D., Davies, I. D., Li, C., Lenihan, J. M., Rupp, T. S. and Mouillot, F., 2006. Comparison of the sensitivity of landscape-fire-succession models to variation in terrain, fuel pattern, climate and weather., Landscape Ecology, 21: 121-137.

Cary, G. J. and Morrison, D. A., 1995. Effects of fire frequency on plant species composition of sandstone communities in the Sydney region: combinations of inter-fire intervals., Australian Journal of Ecology, 20: 418-426.

Castilla, G. and Hay, G. J., 2006. Uncertainties in land use data, Hydrology and Earth Systems Sciences Discussions, 3: 3439-3472.

Chapman, A. and Harrington, G. N., 1997. Responses by birds to fire regime and vegetation at the wet sclerophyll/tropical rainforest boundary, Pacific Conservation Biology, 3: 213-220.

Chaudry, T. A., Dreissen, M. M. and Richardson, A. M. M. (Eds.), 2007. Avifaunal habitat use and potential availability of arthropod prey resources in relation to post-fire succession of buttongrass moorlands in the Tasmanian wilderness World Heritage Area. Proceedings of the buttongrass moorlands fire management conference, Hobart.

Cody, M. L., 1989. Morphological variation in mulga. 1. Variation and covariation within and among Acacia aneura populations, Israel Journal of Botany, 38(4): 241-257.

Cody, M. L., 1991. Morphological variation in mulga. 2. Covariation among morphology, phenology, and spatial patterns in Acacia-dominated vegetation, Israel Journal of Botany, 40(1): 41-59.

Cody, M. L., 1993. Bird Diversity Components within and between Habitats in Australia, In Species Diversity in Ecological Communities: Historical and Geographical Perspectives(Ed, Ricklefs, R. E., Schluter, D.) University of Chicago Press, Chicago, pp. 147-158.

Cody, M. L., 1994. Mulga bird communities: species composition and predictability across Australia, Australian Journal of Ecology, 19: 206-219.

Comparatore, V. M., Martinez, M. M., Vassallo, A. I., Barg, M. and Isacch, J. P., 1996. Abundance and relationships with the habitat of birds and mammals in Paspalum quadrifarium grasslands managed by fire (Buenos-Aires Province, Argentina), Interciencia, 21(4): 228-237.

Connell, J. H., 1978. Diversity in tropical rain forests and coral reefs, Science, 199(24): 1302-1310.

Connor, E. F., Courtney, A. C. and Yoder, J. M., 2000. Individuals-area relationships: the relationship between animal population density and area, Ecology, 81(3): 734-748.

Darwin, C. R., 1859. On the origin of species by means of natural selection / a facsimile of the first edition, Harvard University Press, Cambridge, Massachusetts.

192

Davies, S. J. J. F., 1974. Environmental variables and the biology of Australian arid zone birds, In Proceedings of the 16th International Ornithological Congress(Ed, Frith, H. J., Calaby, J.H.) Australian Academy of Science, Canberra.

Davis, M. A., Peterson, D. W., Reich, P. B., Crozier, M., Query, T., Mitchell, E., Huntington, J. and Bazakas, P., 2000. Restoring savanna using fire: Impact on the breeding bird community, Restoration Ecology, 8(1): 30-40.

Denny, M. J., 1982. Adaptations of the red kangaroo and euro (Macropodidae) to aridity, In Evolution of the flora and fauna of arid Australia(Eds, Barker, W. R. and Greenslade, P. J. M.) Peacock Publications, Adelaide, pp. 179-183.

Dickman, C. R., Predavec, M. and Downey, F. J., 1995. Long-range movements of small mammals in arid Australia: Implications for land management, Journal of Arid Environments, 31(4): 441-452.

Duncan, B. A., Boyle, S., Breininger, D. R. and Schmalzer, P. A., 1999. Coupling past management practice and historical landscape change on John F. Kennedy Space Center., Landscape Ecology, 14: 291-309.

Dunning, J. B., Danielson, B. J. and Pulliam, R. H., 1992. Ecological processes that affect populations in complex landscapes, Oikos, 65(1): 169-175.

Edwards, G. P., Allan, G. E., Brock, C., Duguid, A., Gabrys, K. and Vaarzon-Morel, P., 2008. Fire and it's management in central Australia, Rangeland Journal, (109-121).

Egler, F., 1954. Vegetation science concepts I. Initial floristic composition, a factor in old-field vegetation development., Vegetatio, 4: 412-417.

Elmqvist, T., Folke, C., Nystrom, M., Peterson, G., Bengtsson, J., Walker, B. and Norberg, J., 2003. Response diversity, ecosystem change, and resilience, Frontiers in Ecology and the Environment, 1: 488-494.

Emlen, J. T., 1970. Habitat selection by birds following a forest fire, Ecology, 51: 343-345. English, P., 1998. Cainozoic geology and hydrogeology of Uluru-Kata Tjuta National Park,

Northern Territory, Australian Geological Survey Organisation, Canberra. ESRI, 2004. ArcGIS 9.1, ESRI, Redlands. Farhig, L., 2003. Effects of habitat fragmentation on biodiversity, Annual review of ecology and

systematics, 34: 487-515. Fernandes, P. M. and Botelho, H. S., 2003. A review of prescribed burning effectiveness ib fire

hazard reduction, International Journal of Wildland Fire, 12: 117-128. Field, S. A., Tyre, A. J. and Possingham, H. P., 2002. Estimating bird species richness: How

should repeat surveys be organised in time?, Austral Ecology, 27: 624-629. Finch, D. M., Ganey, J. L., Yong, W., Kimball, R. T. and Sallabanks, R. (Eds.), 1997. Effects

and interactions of fire, logging and grazing, U.S. Department of Agriculture, Forest Service, Rocky Mountain Forest and Range Experiment Station, Fort Collins, CO.

Fisher, C. D., Lindgren, E. and Dawson, W. R., 1972. Drinking patterns and behaviour of Australian desert birds in relation to their ecology and abundance, The Condor, 74: 111-136.

Fleming, P. M., 1978. Types of rainfall and local rainfall variability, In Studies of the Australian arid zone III: water in rangelends(Ed, Howes, K.) CSIRO, Melbourne, pp. 18-28.

Fletcher, R. J., 2005. Multiple edge effects and their implications in fragmented landscapes, Journal of Animal Ecology, 74: 342-352.

Fletcher, R. J. and Koford, R. R., 2002. Habitat and landscape associations of breeding birds in restored and native grasslands., Journal of Wildlife Management, 66: 1011-1022.

Ford, H. A., 1989. The Ecology of Birds, Surrey Beatty and Sons, Chipping Norton, NSW. Forman, R. T. T., 1995. Land mosaics: the ecology of landscapes and regions, Cambridge

University Press, Cambridge. Fox, A., 1975. The '72 fire of Nadgee Nature Reserve, Parks and Wildlife, 2: 5-24. Franklin, D. C., 1999. Evidence of disarray amongst granivorous bird assemblages in the

savannas of northern Australia, a region of sparse human settlement, Biological Conservation, 90(1): 53-68.

Gammage, W., 2008. Plain facts: Tasmania under Aboriginal management, Landscape Research, 33(2): 241-254.

Garnett, S. and Crowley, G., 1995. The decline of the black treecreeper Climacteris picumnus melanota on Cape York Peninsula, Emu, 95: 65-68.

193

Garnett, S. and Crowley, G. M., 2000. The Action Plan for Australian Birds, Environment Australia, Canberra.

Gill, A. M., 1975. Fire and the Australian Flora, Australian Forestry, 38: 5-30. Gill, A. M., 1977. Management of fire-prone vegetation for plant species conservation in

Australia, Search, 8: 20-26. Gill, A. M. (Ed.) 1996. How fires affect biodiversity Proceedings of the Conference, Fire and

Biodiversity: the Effects and Effectiveness of Fire Management., Footscray, Melbourne. Department of Environment, Sport and Territories.

Gill, A. M., 1998. An hierachy of fire effects: impacts of fire regimes on landscapes, In 3rd International Conference of Forest Fire Research, 14th Conference on Fire and Forest Meteorology, Vol. 1 (Ed, Viegas, D. X.) University of Coimbra, Coimbra, Portugal, pp. 129-144.

Gill, A. M., 2000. Fire pulses in the heart of Australia: fire regimes and fire management in central Australia, A report to Environment Australia.

Gill, A. M., Allan, G. and Yates, C., 2003. Fire-created patchiness in Australian savannas, International Journal of Wildland Fire, 12: 323-331.

Gill, A. M., Bradstock, R. A. and Williams, J. E., 2002. Fire regimes and biodiversity: legacy and vision, In Flammable Australia: the fire regimes and biodiversity of a continent(Eds, Bradstock, R. A., Williams, J. E. and Gill, A. M.) Cambridge University Press, Cambridge.

Gill, A. M. and Catling, P. C., 2002. Fire regimes and biodiversity of forested landscapes of southern Australia, In Flammable Australia: the fire regimes and biodiversity of a continent(Eds, Bradstock, R. A., Williams, J. E. and Gill, A. M.) Cambridge University Press, Cambridge.

Gill, A. M., Groves, R. H. and Noble, I. R. (Eds.), 1981. Fire and the Australian Biota, Australian Academy of Science, Canberra.

Gingerich, P. D., 2001. Rates of evolution on the time scale of evolutionary process, Genetica, 112-113: 127-144.

Gould, R. A., 1971. Uses and effects of fire among the Western Desert Aborigines of Australia, Mankind, 8: 14-24.

Griffin, G. F., 1984. Climate, vegetation, man and fire, In Anticipating the inevitable: a patch-burn strategy for fire management at Uluru (Ayers Rock-Mt Olga) National Park(Ed, Saxon, E. C.) CSIRO, Melbourne, pp. 7-12.

Griffin, G. F. and Friedel, M. H., 1985. Discontinuous change in central Australia: some implicationsd of major ecological events for land management, Journal of Arid Environments, 9: 63-80.

Griffin, G. F., Price, N. F. and Portlock, H. F., 1983. Wildfires in central Australia 1970-1980, Journal of Environmental Management, 17: 311-323.

Groves, R. H., 1994. Australian Vegetation, Cambridge University Press, Cambridge. Hairston Jr., N. G., Ellner, S. P., Geber, M. A., Yoshida, T. and Fox, J. A., 2005. Rapid

evolution and the convergence of ecological and evolutionary time, Ecology Letters, 8: 1114-1127.

Hamback, P. A. and Englund, G., 2005. Patch area, population density and the scaling of migration rates: the resource concentration hypothesis revisited, Ecology Letters, 8: 1057-1065.

Hardgrove, W. W. and Pickering, J., 1992. Pseudoreplication: a sine qua non for regional ecology, Landscape Ecology, 6(4): 251-258.

Helle, P. and Monkkonen, M. (Eds.), 1990. Forest successions and bird communities: theoretical aspects and practical implications, SPB Academic Publishing, The Hague.

Helzer, C. J. and Jelinski, D. E., 1999. The relative importance of patch area and perimeter-area ratio to grassland breeding birds, Ecological Applications, 9(4): 1448-1458.

Henderson, M. and Wouters, M. (Eds.), 2007. Prescribed burning in South Australia Ecological Society of Australia Conference, Perth.

Hendry, A. P. and Kinnison, M. T., 1999. The pace of modern life:measuring rates of contemporary microevolution, Evolution, 53(6): 1637-1653.

Hendry, A. P. and Kinnison, M. T., 2001. An introduction to microevolution: rate, pattern, process, Genetica, 112-113: 1-8.

194

Herrando, S. and Brotons, L., 2001. Fluctuating asymmetry in Sardinian Warblers Sylvia melanocephala inhabiting two shrublands affected by fire, Bird Study, 48: 180-187.

Herrando, S. and Brotons, L., 2002. Forest bird diversity in Mediterranean areas affected by wildfires: a multi-scale approach, Ecography, 25(2): 161-172.

Herrando, S., Brotons, L., Del Amo, R. and Llacuna, S., 2002a. Bird community succession after fire in a dry Mediterranean shrubland, Ardea, 90(2): 303-310.

Herrando, S., Brotons, L. and Llacuna, S., 2002b. Does fire increase the seasonal variability of bird communities? A case in Mediterranean shrublands, Revue D Ecologie-La Terre Et La Vie, 57(2): 151-163.

Herrando, S., Brotons, L. and Llacuna, S., 2003. Does fire increase the spatial heterogeneity of bird communities in Mediterranean landscapes?, Ibis, 145(2): 307-317.

Herrando, S., del Amo, R., Brotons, L. and Llacuna, S., 2001. Factors influencing post-fire dynamics of Sardinian and Dartford Warblers in Mediterranean shrublands, Ornis Fennica, 78(4): 168-174.

Higgins, P. J. (Ed.) 1999. Handbook of Australian, New Zealand and Antarctic Birds. Volume 4. Parrots to Dollarbird, Oxford University Press, Melbourne.

Higgins, P. J., Peter, J. M. and Cowling, S. J. (Eds.), 2006. Handbook of Australian, New Zealand and Antarctic Birds. Volume 7. Boatbills to Starlings, Oxford University Press, Melbourne.

Higgins, P. J., Peter, J. M. and Steel, W. K. (Eds.), 2001. Handbook of Australian, New Zealand and Antarctic Birds. Volume 5. Tyrant Flycatchers to Chats., Oxford University Press, Melbourne.

Hobbs, R. and Huenneke, L. F., 1992. Disturbance, diversity, and invasion: implications for conservation, Conservation Biology, 6(3): 324-337.

Hodgkinson, K. C. and Griffin, G. F. (Eds.), 1982. Adaptation of shrub species to fires in the the arid zone, Peacock Publications, Adelaide.

Horton, D. R., 1982. Aborigines, fire and Australian ecosystems, Mankind, 13: 237-251. Hubell, S. P., 2001. The Unified Neutral Theory of Biodiversity and Biogeography, Princeton

University Press, Princeton. Huff, M. H. and Smith, J. K., 2000. Fire effects on animal communities, In Wildland fire in

ecosystems: effects of fire on fauna. Gen. Tech. Rep. RMRS-GTR-42, Vol. 1 (Ed, Smith, J. K.) U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station., Ogden, UT.

Hunter, W. C., Buehler, D. A., Canterbury, R. A., Confer, J. L. and Hamel, P. B., 2001. Conservation of disturbance-dependent birds in eastern North America, Wildlife Society Bulletin, 29(2): 440-455.

Hurlbert, S. H., 1984. Pseudoreplication and the design of ecological field experiments, Ecological Monographs, 54(2): 187-211.

Hutto, R. L., 1995. Composition of Bird Communities Following Stand-Replacement Fires in Northern Rocky-Mountain (USA) Conifer Forests, Conservation Biology, 9(5): 1041-1058.

Imbeau, L., Savard, J. L. and Gagnon, R., 1999. Comparing bird assemblages in successional black spruce stands originating from fire and logging, Canadian Journal of Zoology, 77: 1850-1860.

Isaach, J. P., Holz, S., Ricci, L. and Martinez, M. M., 2004. Post-fire vegetation change and bird use of a salt marsh in coastal Argentina, Wetlands, 24(2): 235-243.

Isacch, J. P., Holz, S., Ricci, L. and Martinez, M. M., 2004. Post-fire vegetation change and bird use of a salt marsh in coastal Argentina, Wetlands, 24(2): 235-243.

ISI (2007) (Ed, http://portal.isiknowledge.com/portal.cgi/wos?Init=Yes&SID=4BGC934iF89eGJ6PeDa).

Izhaki, I. and Adar, M., 1997. The effects of post-fire management on bird community succession, International Journal of Wildland Fire, 7(4): 335-342.

James, C. D., Landsberg, J. and Morton, S. R., 1999. Provision of watering points in the Australian arid zone: a review of effects on biota, Journal of Arid Environments, 41: 87-121.

http://portal.isiknowledge.com/portal.cgi/wos?Init=Yes&SID=4BGC934iF89eGJ6PeDa)

http://portal.isiknowledge.com/portal.cgi/wos?Init=Yes&SID=4BGC934iF89eGJ6PeDa)

195

James, F. C., Hess, C. A. and Kufrin, D., 1997. Species-centered environmental analysis: Indirect effects of fire history on red-cockaded woodpeckers, Ecological Applications, 7(1): 118-129.

Jansen, R., Little, R. M. and Crowe, T. M., 1999. Implications of grazing and burning of grasslands on the sustainable use of francolins (Francolinus spp.) and on overall bird conservation in the highlands on Mpumalungma province, South Africa, Biodiversity and Conservation, 8: 587-602.

Jensen, J. R., 2005. Introductory Digital Image Processing, Prentice Hall. Johnson, R. W. and Burrows, W. H., 1994. Acacia open forests, woodlands and shrublands., In

Australian Vegetation(Ed, Groves, R. H.) Cambridge University press, Cambridge. Jones, R., 1969. Fire-stick farming, Australian Natural History, 16: 224-228. Jones, R., 1980. Cleaning the country: The Gidgingali and their Arnhemland Environment, BHP

Journal, 1: 10-15. Kai, M. A. C. and Ranganathan, J., 2005. Testing the importance of patch scale on forest birds,

Oikos, 111(3): 606-610. Kattan, G. H. and Franco, P., 2004. Bird diversity along elevational gradients in the Andes of

Colombia: area and mass effects Global Ecology and Biogeography, 13(5): 451-458. Keith, D. A., McCaw, W. L. and Whelan, R. J., 2002. Fire regimes in Australian heathlands and

their effects on plants and animals., In Flammable Australia: the fire regimes and biodiversity of a continent(Eds, Bradstock, R., Williams, J. E. and Gill, A. M.) Canbridge University Press, Cambridge, pp. 401-425.

Keogh, J. S., Scott, I. A. W. and Hatyes, C., 2005. Rapid and repeated origin of insular gigantism and dwarfism in Australian tiger snakes, Evolution, 59(1): 226-233.

Kerle, J. A., Fleming, M. R. and Foulkes, J. N., 2007. Managing biodiversity in arid Australia: a landscape view, In Animals of arid Australia out on their own?(Eds, Dickman, C. R., Lunney, D. and Burgin, S.) Royal Zoological Society of NSW, Sydney.

Kershaw, A. P., Clark, J. S., Gill, A. M. and D'Costa, D. M., 2002. A history of fire in Australia, In Flammable Australia: the fire regimes and biodiversity of a continent(Eds, Bradstock, R. A., Williams, J. E. and Gill, A. M.) Cambridge University Press, Cambridge.

Kimber, R. G., 1983. Black lightning: Aborigines and fire in central Australia and the Western Desert, Archaeology in Oceania, 18: 38-45.

King, K. J., Bradstock, R. A., Cary, G. J., Chapman, J. and Marsden-Smedley, J. B., 2008. The relative importance of fine-scale fuel mosaics on reducing fire risk in south-west Tasmania, Australia, International Journal of Wildland Fire, 17(3): 421-430.

Knapp, E. E. and Keeley, J. E., 2006. Heterogeneity in fire severity within early-season and late-season prescribed burns in a mixed conifer forest, International Journal of Wildland Fire, 15(1): 37-45.

Knutson, M. G. and Klaas, E. E., 1996. Declines in abundance and species richness of birds following a major flood on the Upper Mississippi River, Auk, 114(3): 367-380.

Kotliar, N. B., Hejl, S. J., Hutto, R. L., Saab, V. A., Melcher, C. P. and McFadzen, M. E., 2002. Effects of fire and post-fire salvage logging on avian communities in conifer-dominated forests of the western United States, Studies in Avian Biology, 25: 49-64.

Kotliar, N. B., Kennedy, P. L. and Ferree, K., 2007. Avifaunal responses to fire in southwestern montane forests along a burn severity gradient, Ecological Applications, 17(2): 491-507.

Krebs, C. J., 1985. Ecology, the Experimental Analysis of Distribution and Abundance, Harper and Row, New York.

Landis, J. R. and Koch, G. G., 1977. Measurement of observer agreement for categorical data, Biometrics, 33(1): 159-174.

Landsberg, J. J., James, C. D., Morton, S. R., Bobbs, T. J., Stol, J., Drew, A. and Tongway, H., 1999. The Effects of Artificial Sources of Water on Rangeland Biodiversity, Canberra, Environment Australia.

Latz, P., 1995a. Fire in the desert: increasing biodiversity in the short term, decreasing it in the long term., In Country in Flames. Biodiversity Series, Paper No. 3, Biodiversity Unit(Ed, Rose, D. B.) Commonwealth of Australia and North Australia Research Unit, pp. 77-86.

196

Latz, P., 1995b. Bushfires and Bushtucker: Aboriginal Plant Use in Central Australia, IAD Press, Alice Springs.

Latz, P. K. and Griffin, G. F., 1978. Changes in Aboriginal land management in relation to fire and to food plants in central Australia, In The nutrition of Aborigines in relation to the ecosystem of central Australia(Eds, Hetzel, B. B. and Frith, H. J.) CSIRO, Melbourne, pp. 77-85.

Laurance, W. F., 2000. Do edge effects occur over large spatial scales?, Trends in Ecology and Evolution, 15: 134-135.

Law, S., 1993. Roosting and foraging ecology of the Queensland blossom bat (Syconycteris australis) in north-eastern New South Wales: flexibility in response to seasonal variation., Wildlife Research, 20: 419-431.

Lawrence, G. E., 1966. Ecology of vertebrate animals in relation to chaparral fire in the Sierra Nevada foothills, Ecology, 47(2): 278-291.

Leavesley, A. J. and Magrath, R. D., 2005. Communicating about danger: urgency alarm calling in a bird, Animal Behaviour, 70: 365-373.

Leidolf, A., Nuttle, T. and Wolfe, M. L., 2007. Spatially scaled response of a Lazuli bunting population to fire, Western North American Naturalist, 67(1): 1-7.

Leps, J. and Smilauer, P., 2003. Multivariate Analysis of Ecological Data using CANOCO, Cambridge University Press, Cambridge.

Leps, J. and Smilauer, P., 2005. Multivariate Analysis of Ecological Data – Course Materials, Faculty of Biological Sciences, University of South Bohemia, Ceske Budejovice.

Letnic, M., 2003. The effects of experimental patch burning and rainfall on small mammals in the Simpson Desert, Queensland, Wildlife Research, 30: 547-563.

Letnic, M. and Dickman, C. R., 2005. The responses of small mammals to patches regenerating after fire in the Simpson Desert, central Australia, Austral Ecology, 30: 24-39.

Lindenmayer, D. B., Cunningham, R. B., MacGregor, C., Tribolet, C. and Donnelly, C. F., 2001. A prospective longitudinal study of landscape matrix effects on fauna in woodland remnants: experimental design and baseline data, Biological Conservation, 101: 157-169.

Lindenmayer, D. B., McIntyre, S. and Fischer, J., 2003. Birds in eucalypt and pine forests: landscape alteration and its implications for research models of faunal habitat use, Biological Conservation, 110: 45-53.

Lomolino, M. V., 2003. Ecology's most general yet protean pattern: the species-area relationship, Journal of Biogeography, 27: 17-26.

Loreau, M., 2000. Are communities saturated? On the relationship between alpha, beta and gamma diversity, Ecology Letters, 3: 73-76.

Lowther, P. E. (Ed.) 1993. Brown-headed cowbird, American Ornithologists Union, Philadelphia.

Loyn, R. H., 1997. Effects of an extensive wildfire on birds in far eastern Victoria, Pacific Conservation Biology, 3: 221-234.

Lyon, L. J., Bown, J. K., Huff, M. H. and Smith, J. K., 2000a. Introduction, In Wildland fire in ecosystems: effects of fire on fauna. Gen. Tech. Rep. RMRS-GTR-42, Vol. 1 (Ed, Smith, J. K.) U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station., Ogden, UT.

Lyon, L. J., Hooper, R. G., Telfer, E. S. and Schreiner, D. S., 2000e. Fire effects on wildlife foods, In Wildland fire in ecosystems: effects of fire on fauna. Gen. Tech. Rep. RMRS-GTR-42, Vol. 1 (Ed, Smith, J. K.) U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station., Ogden, UT.

Lyon, L. J., Huff, M. H. and Smith, J. K., 2000d. Fire effects on fauna at landscape scales, In Wildland fire in ecosystems: effects of fire on fauna. Gen. Tech. Rep. RMRS-GTR-42, Vol. 1 (Ed, Smith, J. K.) U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station., Ogden, UT.

Lyon, L. J., Huff, M. H., Telfer, E. S., Schreiner, D. S. and Smith, J. K., 2000c. Fire effects on animal populations, In Wildland fire in ecosystems: effects of fire on fauna. Gen. Tech. Rep. RMRS-GTR-42, Vol. 1 (Ed, Smith, J. K.) U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station., Ogden, UT.

197

Lyon, L. J. and Smith, J. K., 2000. Management and research implications, In Wildland fire in ecosystems: effects of fire on fauna. Gen. Tech. Rep. RMRS-GTR-42, Vol. 1 (Ed, Smith, J. K.) U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station., Ogden, UT.

Lyon, L. J. and Smith, J. K., 2000f. Management and research implications, In Wildland fire in ecosystems: effects of fire on fauna. Gen. Tech. Rep. RMRS-GTR-42, Vol. 1 (Ed, Smith, J. K.) U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station., Ogden, UT.

Lyon, L. J., Telfer, E. S. and Schreiner, D. S., 2000b. Direct effects of fire and animal responses, In Wildland fire in ecosystems: effects of fire on fauna. Gen. Tech. Rep. RMRS-GTR-42, Vol. 1 (Ed, Smith, J. K.) U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station., Ogden, UT.

Mac Nally, R., Ellis, M. and Barrett, G., 2004. Avian biodiversity monitoring in Australian rangelands, Austral Ecology, 29: 93-99.

Mac Nally, R. and Horrocks, G., 2002. Proportionate spatial sampling and equal-time sampling of mobile animals: A dilemma for inferring areal dependence, Austral Ecology, 27: 405-415.

MacArthur, R. H., Diamond, J. M. and Karr, J. R., 1972. Density compensation in island faunas, Ecology, 53: 330-342.

MacArthur, R. H. and MacArthur, J. W., 1961. On bird species diversity, Ecology, 42(3): 594-598.

MacArthur, R. H. and Wilson, E. O., 1967. The theory of island biogeography, Princeton University Press, Princeton.

MacDonald, M. A. and Kirkpatrick, J. B., 2003. Explaining bird species composition and richness in eucalypt-dominated remnants in subhumid Tasmania, Journal of Biogeography, 30(9): 1415-1426.

MacKenzie, D. I., Nichols, J. D., Royle, J. A., Pollock, K. H., Bailey, L. L. and Hines, J. E., 2006. Occupancy estimation and modelling: inferring patterns and dynamics of species occurence, Elsevier, Oxford.

Maron, M., Lill, A., Watson, D. M. and Mac Nally, R., 2005. Temporal variation in bird assemblages: How representatitve is a a one-year snapshot?, Austral Ecology, 30: 383-394.

Marsden-Smedley, J. B. and Kirkpatrick, J. B., 2000. Fire management in Tasmania's wilderness world heritage area: ecosystem restoration using Indigenous-style fire regimes?, Ecological Management and Restoration, 1(3): 195-203.

Matlack, G. R., 1993. Microenvironment variation within and among forest edge sites in the eastern United States, Biological Conservation, 66: 185-194.

Matter, S. F., 1999. Population density and area: the role of within- and between-generation processes over time, Ecological Modelling, 118: 261-275.

Matter, S. F., 2003. Modeling the density-area relationship in a dynamic landscape: an examination fir the beetle Tetraopes tetraophthalmus and a generalized model, Ecological Modelling, 169: 103-117.

Mayr, E., 1942. Systematics and the origin of species from the viewpoint of a zoologist, Columbia University Press, New York.

Mazerolle, M. J. and Villard, M.-A., 1999. Patch characteristics and landscape context as predictors of species presence and abundance: A review, Ecoscience, 6(1): 117-124.

McCulloch, C. E. and Searle, S. R., 2001. Generalized, Linear, and Mixed Models, John Wiley & Sons, New York.

McElhinny, C., Gibbons, P., Brack, C. and Bauhus, J., 2005. Forest and woodland stand structural complexity: its definition and measurement., Forest Ecology and Management, 218: 1-24.

McGarigal, K. and Cushman, S. A., 2002. Comparative evaluation of experimental approaches to the study of habitat fragmentation effects, Ecological Applications, 12(2): 335-345.

McIvor, C. C. and Odum, W. E., 1988. Food, predation risk and microhabitat selection in a marsh fish assemblage, Ecology, 69: 1341-1351.

Miller, J. T., Andrew, R. A. and Maslin, B. R., 2002. Towards an understanding of variation in the Mulga complex (Acacia aneura and relatives) Conservation Science, Western Australia, 4(3): 19-35.

198

Miller, T. E., 1982. Community diversity and interactions between the size and frequency of disturbance, American Naturalist, 120(4): 533-536.

Mills, M. S. L., 2004. Bird community response to savanna fires: should managers be concerned?, South African Journal of Wildlife Research, 34(1): 1-11.

Moen, J. and Jonsson, B. G., 2003. Edge effects on liverworts and lichens in forest patches in a mosaic of boreal forest and wetland, Conservation Biology, 17(380-388).

Moore, P., 2005. A guide to plants of inland Australia, Reed New Holland, Sydney. Moreira, F., P.G., F., Rego, F. C. and Bunting, S., 2001. Landscape changes and breeding bird

assemblages in northwestern Potugal: the role of fire, Landscape Ecology, 16: 175-187. Morgan, P., Hardy, C. C., Swetnam, T. W., Rollins, M. G. and Long, D. G., 2001. Mapping fire

regimes across time and space: understanding coarse and fine-scale fire patterns, International Journal of Wildland Fire, 10: 329-342.

Morrison, D. A., Cary, G. J., Pengelly, S. M., Ross, D. G., Mullins, B. J., Thomas, C. R. and Anderson, T. S., 1995. Effects of fire frequency on plant species composition of sandstone communities in the Sydney region: Inter-fire interval and time-since-fire., Australian Journal of Ecology, 20: 239-247.

Morton, S. R., 1990. The impact of European settlement on the vertebrate animals of arid Australia: a conceptual model, Proceedings of the Ecological Society of Australia, 16: 201-213.

Morton, S. R., 1993. Determinants of diversity in animal communities of arid Australia, In Species Diversity in Ecological Communities: Historical and Geographical Perspectives(Ed, Ricklefs, R. E., Schluter, D.) University of Chicago Press, Chicago, pp. 158-169.

Morton, S. R., Stafford Smith, D. M., Friedel, M. H., Griffin, G. F. and Pickup, G., 1995. The stewardship of arid Australia: ecology and landscape management, Journal of Environmental Management, 43: 195-217.

Myers, B., Allan, G., Bradstock, R., Dias, L., Duff, G., Jacklyn, P., Landsberg, J., Morrison, J., Russell-Smith, J. and Williams, R., 2004. Fire Management in the Rangelands, Darwin, Tropical Savannas CRC.

Nano, C. E. M. (2005) In School of Environmental Sciences and Natural Resource managementUniversity of New England, Armidale.

Nano, C. E. M. and Clarke, P. J., in press. Variegated desert vegetation: covariation of edaphic and fire variables provides a framework for understanding mulga-spinifex coexistence, Austral Ecology.

Noble, I. R. and Slatyer, R. O., 1980. The use of vital attributes to predict successional plant changes in plant communities subject to recurrent disturbance., Vegetatio, 43: 5-21.

Norvell, R. E., Howe, F. P. and Parrish, J. R., 2003. A seven-year comparison of relative-abundance and distance-sampling methods, Auk, 120(4): 1013-1028.

Noss, R. F., 1991. Effects of edge and internal patchiness on avian habitat use in an old-growth Florida hammock., Natural Areas Journal, 11: 34-47.

O'Reilly, L., Ogada, D., Palmer, T. M. and Keesing, F., 2006. Effects of fire on bird diversity and abundance in an East African savanna, African Journal of Ecology, 44: 165-170.

O’Grady, A. P., Cook, P. G., Duguid, A. and Knapton, A. (Eds.), 2006. Water use by Corymbia opaca trees over a depth to water table gradient, Ti Tree, Central Australia Proceedings of the Groundwater Surfacewater Interaction in the Arid Zone Conference, Alice Springs, Alice Springs. International Association of Hydrogeologists, Northern Territory Branch.

Odonnell, C. F. J., 1996. Predators and the decline of New Zealand forest birds - An introduction to the hole-nesting bird and predator programme, New Zealand Journal of Zoology, 23(3): 213-219.

Odum, E. P., 1969. The strategy of ecosystem development, Science, 164: 262-270. Paltridge, R. and Southgate, R. I., 2001. The effect of habitat type and seasonal conditions on

fauna in two areas of the Tanami Desert, Wildlife Research, 28: 247-260. Parr, C. L. and Andersen, A. N., 2006. Patch mosaic burning for biodiversity conservation: a

critique of the pyrodiversity paradigm, Conservation Biology, 20(6): 1610-1619. Parr, C. L. and Brockett, B. H., 1999. Patch-mosaic burning: a new paradigm for savanna fire

management in protected areas, Koedoe, 42(2): 117-130.

199

Parr, C. L. and Chown, S. L., 2003. Burning issues for conservation: A critique of faunal fire research in Southern Africa, Austral Ecology, 28: 384–395.

Paton, P. W. C., 1994. The effect of edge on avian nest success: how strong is the evidence?, Conservation Biology, 8(1): 17-26.

Payne, R. W., Harding, S. A., Murray, D. A., Soutar, D. M., Baird, D. B., Welham, S. J., Kane, A. F., Gilmour, A. R., Thompson, R., Webster, R. and Tunnicliffe Wilson, G., 2005. GenStat Release 8 Reference Manual, VSN International, Oxford.

Perneger, T. V., 1998. What's wrong with Bonferroni adjustments?, British Medical Journal, 316: 1236-1238.

Perry, R. A., 1978. Climate and landforms in central Australia, In The nutrition of Aborigines in relation to the ecosystem of central Australia(Eds, Hetzel, B. B. and Frith, H. J.) CSIRO, Melbourne, pp. 73-76.

Petit, D. R., 1989. Weather-dependent use of habitat patches by wintering woodland birds, Journal of Field Ornithology, 60: 241-247.

Pickett, S. T. A., 1989. Space-for-time substitution as an alternative to long-term studies, In Long-term studies in ecology(Ed, Likens, G. E.) Springer-Verlag, New York, pp. 110-135.

Pickett, S. T. A. and White, P. S. (Eds.), 1985. The ecology of natural disturbance and patch dynamics, Academic Press, Orlando.

Piper, S. D. and Catterall, C. P., 2003. A particular case and a general pattern: hyperaggressive behaviour by one species may mediate avifaunal decreases in fragmented Australian forests, Oikos, 101(3): 602-614.

Platt, W. J. and Connell, J. H., 2003. Natural disturbances and directional replacement of species, Ecological Monographs, 73(4): 507-522.

Pons, P., 2002. The population responses of birds to fire in Mediterranean ecosystems, In Fire, landscape and biodiversity: an appraisal of effects and effectiveness(Eds, Pardini, G. and Pinto, J.) Servei de Publicacions de la Universitat de Girona, Girona, pp. 57-68.

Pons, P. and Bas, J. M., 2005. Open-habitat birds in recently burned areas: The role of the fire extent and species' habitat breadth, Ardeola, 52(1): 119-131.

Pons, P., Henry, P. Y., Gargallo, G., Prodon, R. and Lebreton, J. D., 2003a. Local survival after fire in Mediterranean shrublands: combining capture-recapture data over several bird species, Population Ecology, 45(3): 187-196.

Pons, P., Lambert, B., Rigolot, E. and Prodon, R., 2003b. The effects of grassland management using fire on habitat occupancy and conservation of birds in a mosaic landscape, Biodiversity and Conservation, 12(9): 1843-1860.

Pons, P. and Prodon, R., 1996. Short term temporal patterns in a Mediterranean shrubland bird community after wildfire, Acta Oecologica-International Journal of Ecology, 17(1): 29-41.

Pons, P. and Wendenberg, C., 2005. The impact of fire and forest conversion into savanna on the bird communities of West Madagascan dry forests, Animal Conservation, 8: 183-193.

Power, M. J., Marlon, J., Ortiz, N., Bartlein, P. J., Harrison, S. P., Mayle, F. E., Ballouche, A., Bradshaw, R. H. W., Carcaillet, C., Cordova, C., Mooney, S., Moreno, P. I., Prentice, I. C., Thonicke, K., Tinner, W., Whitlock, C., Zhang, Y., Zhao, Y., Ali, A. A., Anderson, R. S., Beer, R., Behling, H., Briles, C., Brown, K. J., Brunelle, A., Bush, M., Camill, P., Chu, G. Q., Clark, J., Colombaroli, D., Connor, S., Daniau, A.-L., Daniels, M., Dodson, J., Doughty, E., Edwards, M. E., Finsinger, W., Foster, D., Frechette, J., Gaillard, M.-J., Gavin, D. G., Gobet, E., Haberle, S., Hallett, D. J., Higuera, P., Hope, G., Horn, S., Inoue, J., Kaltenrieder, P., Kennedy, L., Kong, Z. C., Larsen, C., Long, C. J., Lynch, J., Lynch, E. A., McGlone, M., Meeks, S., Mensing, S., Meyer, G., Minckley, T., Mohr, J., Nelson, D. M., New, J., Newnham, R., Noti, R., Oswald, W., Pierce, J., Richard, P. J. H., Rowe, C., Goni, M. F. S., Shuman, B. N., Takahara, H., Toney, J., Turney, C., Urrego-Sanchez, D. H., Umbanhowar, C., Vandergoes, M., Vanniere, B., Vescovi, E., Walsh, M., Wang, X., Williams, N., Wilmshurst, J. and Zhang, J. H., 2008. Changes in fire regimes since the Last Glacial Maximum: an assessment based on global synthesis and analysis of charcoal data, Climate Dynamics, 30(887-907).

Preston, F. W., 1962. The canonical distribution of commonness and rarity: part II, Ecology, 43(3): 410-432.

200

Prodon, R., Fons, R. and Athias-Binche, F., 1987. The impact of fire on animal communities in Mediterranean area, In The role of fire on ecological systems(Ed, Trabaud, L.) SPB Academic Publications, The Hague, pp. 121-157.

Quinn, R. D., 1994. Animals, fire and vertebrate herbivory in Californian chaparral and other Mediterranean-type ecosystems, In The role of fire in Mediterranean-type ecosystems(Eds, Moreno, J. M. and Oechel, W. C.) Springer-Verlag, New York.

Raphael, M. G., Morrison, M. L. and Yoder-Williams, M. P., 1987. Breeding bird populations during twenty-five years of postfire succession in the Sierra Nevada, Condor, 89: 614-626.

Read, J. L., Reid, N. and Venables, W. N., 2000. Which birds are useful bioindicators of mining and grazing impacts in arid South Australia?, Environmental Management, 26(2): 215-232.

Recher, H. F., 1988. Counting terrestrial birds: use and application of census procedures in Australia, Australian Zoological Review, 1: 25-45.

Recher, H. F. and Davis, W. E., 1997. Foraging ecology of a mulga bird community, Wildlife Research, 24: 27-43.

Recher, H. F., Kavanagh, R. P., Shields, J. M. and Lind, P., 1991. Ecological associations of habitats and bird species during the breeding season in southeastern New South Wales, Australian Journal of Ecology, 16: 337-352.

Recher, H. F., Lunney, D. and Posamentier, H., 1975. A grand natural experiment - the Nadgee wildfire, Australian Natural History, 18: 150-163.

Reid, J. R. W., Kerle, J. A. and Morton, S. R., 1991. The distribution and abundance of vertebrate fauna of Uluru (Ayers Rock-Mount Olga) National Park, CSIRO Division of Wildlife and Ecology.

Reid, J. R. W., Kerle, J. A. and Morton, S. R., 1993. Uluru Fauna: the distribution and abundance of vertebrate fauna of Uluru (Ayers Rock-Mount Olga) National Park, N.T., Australian National Parks and Wildlife Service, Canberra.

Ridley, M., 1996. Evolution, Blackwell Science, Cambridge, Massachusetts. Ries, L., Fletcher, R. J., Battin, J. and Sisk, T. D., 2004. Ecological responses to habitat edges:

mechanisms, models and variability explained, Annual review of ecology and systematics, 35: 491-522.

Ries, L. and Sisk, T. D., 2004. A predictive model of edge effects, Ecology, 85(11): 2917-2926. Ritchie, M. E. and Olff, H., 1999. Spatial scaling laws yield a synthetic theory of biodiversity,

Nature, 400: 557-560. Robertson, A. and Jarvis, A. M., 2000. Oxpeckers in north-eastern Namibia: recent population

trends and the possible negative impacts of drought and fire, Biological Conservation, 92: 241-247.

Robertson, D. C., Morgan, J. W. and White, M., 1999. Use of prescribed fire to enhance control of English broom (Cytisus scoparius) invading a sub-alpine woodland in Victoria, Plant Protection Quaterly, 14(2): 51-56.

Roca, C. P., Cuesta, J. A. and Sanchez, A., 2006. Time scales in evolutionary dynamics, Physical Review Letters, 97: 158701.

Root, R. B., 1973. Organization of a plant-arthropod association in simple and diverse habitats: the fauna of collards (Brassica oleracea), Ecological Monographs, 43: 556-586.

Rosenstock, S. S., Anderson, D. R., Giesen, K. M., Leukering, T. and Carter, M. F., 2002. Landbird counting techniques: current practices and an alternative, The Auk, 119(1): 46-53.

Rowley, I. and Brooker, M. G., 1987. The response of a small insectivorous bird to fire in heathlands, In Nature conservation: the role of remnants of native vegetation(Eds, Saunders, D. A., Arnold, G. W., Burbidge, A. A. and Hopkins, A. J. M.) Surrey Beatty, Chipping Norton, pp. 211-218.

Saab, V. A., Dudley, J. and Thompson, W. L., 2004. Factors influencing occupancy of nest cavities in recently burned forests, The Condor, 106: 20-36.

Saab, V. A., Russell, R. E. and Dudley, J. G., 2006. Nest densities of cavity-nesting birds in relation to postfire salvage logging and time-since-wildfire., Condor, 109: 97-108.

Saxon, E. C. (Ed.) 1984. Anticipating the inevitable:a patch-burn strategy for fire management at Uluru (Ayers Rock-Mt Olga) National Park, CSIRO, Melbourne.

201

Schall, R., 1991. Estimation in generalized linear models with random effects., Biometrika, 78: 719-727.

Scheiner, S. M., 2003. Six types of species-area curves., Global Ecology and Biogeography, 12: 441-447.

Schieck, J. and Song, S. J., 2006. Changes in bird communities throughout succession following fire and harvest in boreal forests of western North America: literature review, Canadian Journal of Forest Research-Revue Canadienne De Recherche Forestiere, 36: 1299-1318.

Schodde, R., 1982. Origin, adaptation and evolution of birds in arid Australia, In Evolution of the flora and fauna of arid Australia(Ed, Barker, W. R., Greenslade, P.J.M.) Peacock Publications, South Australia, pp. 191-224.

Schodde, R., 1994. The bird fauna of western New South Wales: geography and status, In Future of the fauna of Western New South Wales(Ed, Lunney, D., Hand, S., Reed, P., Butcher, D.) Royal Zoological Society of NSW, Mosman.

Shea, K. and Chesson, P., 2002. Community ecology theory as a framework for biological invasions Trends in Ecology and Evolution, 17(4): 170-176.

Shea, K., Roxburgh, S. H. and Rauschert, E. S. J., 2004. Moving from pattern to process: coexistence mechanisms under intermediate disturbance regimes, Ecology Letters, 7: 491-508.

Short, J. and Turner, B., 1994. A test of the vegetation mosaic hypothesis: a hypothesis to explain the decline and extinction of Australian mammals, Conservation Biology, 8(2): 439-449.

Shriver, W. G. and Vickery, P. D., 2001. Response of breeding Florida grasshopper and Bachman's sparrows to winter prescribed burning, Journal of Wildlife Management, 65(3): 470-475.

Simard, A. J., 1991. Fire severity, changing scales, and how things hang together, International Journal of Wildland Fire, 1(1): 23-34.

Singh, G., Kershaw, A. P. and Clark, R., 1981. Quaternary vegetation and fire history in Australia, In Fire and the Australian Biota(Eds, Gill, A. M., Groves, R. H. and Noble, I. R.) Australian Academy of Science, Canberra.

Sisk, T. D. and Margules, C. R., 1993. Habitat edges and restoration: methods for quantifying edge effects and predicting results of restoration efforts, In Reconstruction of fragmented ecosystems(Ed, Saunders, D. A., Hobbs, R.J., Ehrlich, P.R.) Surrey Beatty and Sons, pp. 59-68.

Skowno, A. L. and Bond, W. J., 2003. Bird community composition in an actively managed savanna reserve, importance of vegetation structure and vegetation composition, Biodiversity and Conservation, 12(11): 2279-2294.

Slik, J. W. F. and van Balen, S., 2006. Bird community changes in response to single and repeated fires in a lowland tropical rainforest of eastern Borneo, Biodiversity and Conservation, 15: 4425-4451.

Smith, G. T., 1985. Fire effects on populations of the Noisy Scrub-bird (Atrichornis clamosus), Western Bristlebird (Dasyornis longirostris) and Western Whipbird (Psophodes nigrogularis), In Fire ecology and management in Western Australian ecosystems(Ed, Ford, J. R.) Western Australian Institute of Technology, Perth.

Smith, J. K. (Ed.) 2000. Wildland fire in ecosystems: effects of fire on fauna., U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station., Ogden, UT.

Smucker, K. M., Hutto, R. L. and Steele, B. M., 2005. Changes in bird abundance after wildfire: Importance of fire severity and time since fire, Ecological Applications, 15(5): 1535-1549.

Smyth, A., Mac Nally, R. and D., L., 2002. Comparative influence of forest management and habitat structural factors on the abundances of hollow-nesting bird species in subtropical Australian eucalypt forest, Environmental Management, 30(4): 547-559.

Snedecor, G. W. and Cochran, W. G., 1989. Statistical Methods (eighth edition), Iowa State University Press, Ames.

Sousa, W. P., 1984. The role of disturbance in natural communities, Annual review of ecology and systematics, 15: 353-391.

Stafford-Smith, D. M. and Morton, S. R., 1990. A framework for the ecology of arid Australia, Journal of Arid Environments, 18: 255-278.

202

Stanton, P. A., 1986. Comparison of avian community dynamics of burned and unburned coastal sage scrub, Condor, 88: 285-289.

Strauss, D., Bednar, L. and Mees, R., 1989. Do one percent of forest fires cause ninety-nine percent of the damage?, Forest Science, 35(2): 319-328.

Strayer, D. L., Power, M. E., Fagan, W. F., Pickett, S. T. A. and Belnap, J., 2003. A classification of ecological boundaries, Bioscience, 53(8): 723-729.

Stuart-Smith, K., Adams, I. T. and Larsen, K. W., 2002. Songbird communities in a pyrogenic habitat mosaic, International Journal of Wildland Fire, 11: 75-84.

Tasker, E. M., Baker, J. and Whelan, R. J. (Eds.), 2006. Applying recent advances in vital attributes analysis to management of fire for biodiversity conservation Life in a fire-prone environment: translating science into practice, Brisbane.

Ter Braak, C. J. F., 1986. Canonical correspondence analysis: a new eigenvector technique for multivariate direct gradient analysis, Ecology, 67(5): 1167-1179.

Ter Braak, C. J. F. and Smilauer, P., 2002. CANOCO reference manual and CanoDraw for Windows user’s guide: Software for Canonical Community Ordination (version 4.5), Microcomputer Power, New York.

Terborgh, J., 1985. The role of ecotones in the distribution of Andean birds., Ecology, 66: 1237-1246.

Thomas, L., Laake, J. L., Strindberg, S., Marques, F. F. C., Buckland, S. T., Borchers, D. L., Anderson, D. R., Burnham, K. P., Hedley, S. L., Pollard, J. H., Bishop, J. R. B. and Marques, T. A. (2006) Research Unit for Wildlife Population Assessment, University of St. Andrews, UK, http://www.ruwpa.st-and.ac.uk/distance/, .

Tjorve, E., 2003. Shapes and functions of species-area curves: a review of possible models, Journal of Biogeography, 30: 827-835.

Tongway, D. J. and Ludwig, J. A., 1989. Mulga log mounds: fertile patches in the semi-arid woodlands of eastern Australia., Australian Journal of Ecology, 14: 263-268.

Tongway, D. J. and Ludwig, J. A., 1990. Vegetation and soil patterning in semi-arid mulga lands of Eastern Australia, Australian Journal of Ecology, 15: 23-34.

Tran, C. and Williams, P. (Eds.), 2007. Prescribed burning in Queensland: an inconvenient truth Ecological Society of Australian Conference, Perth.

Turner, M. G., 2005. Landscape ecology in North America: past, present and future, Ecology, 86(8): 1967-1974.

Turner, W. R. and Tjorve, E., 2005. Scale-dependence in species-area relationships, Ecography, 28(6): 721-730.

U.S.G.S, 2007. U.S.G.S Landsat Project, United States Geological Service. http://landsat.usgs.gov/index.php

Valentine, L. E., Schwarzkopf, L., Johnson, C. N. and Grice, A. C., 2007. Burning season

influences the response of bird assemblages to fire in tropical savannas, Biological Conservation, 137: 90-101.

van Dyke, F., Schmeling, J. D., Starkenburg, S., Yoo, S. H. and Stewart, P. W., 2007. Responses of plant and bird communities to presecribed burning in tallgrass prairies, Biodiversity Conservation, 16: 827-839.

van Oosterzee, P., 1999. A field guide to central Australia, J.B. Books Australia, Marleston. Walker, J. and Hopkins, M. S., 1998. Vegetation, In Australian soil and land survey(Eds,

McDonald, R. C., Isbell, R. F., Speight, J. G., Walker, J. and Hopkins, M. S.) CSIRO, Melbourne.

Walsh, F. J., 1990. An ecological study of traditional Aboriginal use of "country": Martu in the Great and Little Sandy Deserts, Western Australia, Proceedings of the Ecological Society of Australia, 16: 23-37.

Ward, M. J. and Paton, D. C., 2004. Responses of Slender-billed Thornbills Acanthiza iredalei hedleyi in Ngarkat Conservation Park, South Australia. 1 Densities, group sizes, distribution and management issues, Emu, 104: 157-167.

Ward, M. J., Paton, D.C., 2004. Responses of Slender-billed Thornbills Acanthiza iredalei hedleyi in Ngarkat Conservation Park, South Australia. 2. Foraging behaviour, Emu, 104: 169-175.

Wasson, R. J. and Hyde, R., 1983. Factors determining desert dune type, Nature, 304: 337-339.

http://www.ruwpa.st-and.ac.uk/distance/

http://landsat.usgs.gov/index.php

203

Watkins, R. Z., J., C., Pickens, J. and Brosofske, K. D., 2003. Effects of roads on understory plants in a managed hardwood landscape, Conservation Biology, 17: 411-419.

Watson, D. M., 2003. The 'standardised search': an improved way to conduct bird surveys, Austral Ecology, 28: 515-525.

Watson, J., Freudenberger, D. and Paull, D., 2001. An assessment of the focal-species approach for conserving birds in variegated landscapes in southeastern Australia, Conservation Biology, 15(5): 1364-1373.

Whelan, C. J., 2001. Foliage structure influences foraging of insectivorous forest birds: an experimental study, Ecology, 82(1): 219-231.

Whelan, R. J., 1995. The ecology of fire, Cambridge University Press, Cambridge. Whelan, R. J., Rodgerson, L., Dickman, C. R. and Sutherland, E. F., 2002. Critical life cycles of

plants and animals: developing a process-based understanding of population changes in fire-prone landscapes, In Flammable Australia: the fire regimes and biodiversity of a continent(Eds, Bradstock, R., Williams, J. E. and Gill, A. M.) Canbridge University Press, Cambridge, pp. 94-124.

Wiens, J. A., 1989. Spatial scaling in ecology, Functional Ecology, 3(4): 385-397. Wiens, J. A., Addicott, J. F., Case, T. J. and Diamond, J., 1986. Overview: the importance of

spatial and temporal scale in ecological investigations, In Community Ecology(Eds, Diamond, J. and Case, T. J.) Harper & Row, New York.

Williams, J., 2002. Fire regimes and their impacts in the mulga (Acacia aneura) landscapes of central Australia, In Australian Fire Regimes: Contemporary Patterns (April 1998 - March 2000) and Changes Since European Settlement(Eds, Russell-Smith, J., Craig, R., Gill, A. M., Smith, R. and Williams, J.) Department of the Environment and Heritage, Canberra.

Williams, O. B. (Ed.) 1982. The vegetation of present-day arid Australia: a biotic appraisal, Peacock Publications, Adelaide.

Willson, M. F., 1974. Avian community organisation and habitat structure, Ecology, 55: 1017-1029.

Wilson, D. S. and Yoshimura, J., 1994. On the coexistence of specialists and generalists, American Naturalist, 144: 692-707.

Woinarski, J. C. Z., 1999. Fire and Australian birds: a review, In Australia's Biodiversity - responses to fireEnvironment Australia, Canberra.

Woinarski, J. C. Z., Milne, D. J. and Wanganeen, G., 2001. Changes inmammal populations in relatively intact landscapes of Kakadu National park, Northern Territory, Australia, Austral Ecology, 26: 360-370.

Woinarski, J. C. Z. and Recher, H. F., 1997. Impact and response: a review of the effects of fire on the Australian avifauna, Pacific Conservation Biology, 3: 183-205.

Appendices

Appendix 1 Survey site details Table A1-1 Details of bird survey sites in the sheetwash landscape. W2005 = winter 2005, S2005 = spring 2005, W2006 = winter 2006, S2006 = spring 2006. Patch size study indicates the sites excluded from the patch size analysis. Datum is UTM WGS84, Zone 52S.

Number of surveys by season Coordinates Site Treatment Area (ha) W2005 S2005 W2006 S2006 Patch size study Easting Northing 1 Burnt 2002 16 1 1 1 1 - 675328 7214599 2 Burnt 2002 9 1 1 1 1 - 671906 7214323 3 Burnt 2002 4 1 1 1 1 - 670013 7220585 4 Burnt 2002 (166) 2 1 1 2 (=Site 17) 668473 7215827 5 Burnt 2002 8 2 1 1 1 - 672654 7214239 6 Burnt 2002 8 1 1 1 1 - 671526 7207005 7 Burnt 2002 27 1 1 1 1 - 693360 7203735 8 Burnt 2002 13 3 1 1 1 - 671288 7215153 9 Burnt 2002 9 2 0 1 1 - 674972 7211867 10 Burnt 2002 26 2 1 1 1 - 674549 7212158 11 Burnt 2002 21 1 1 1 1 - 673793 7210702 12 Burnt 2002 37 1 1 1 1 - 675032 7216333 13 Burnt 2002 26 1 1 1 1 - 676444 7211678 14 Burnt 2002 7 1 1 1 1 - 685815 7200388 15 Burnt 2002 (118) 1 1 1 1 (=Site 18) 673166 7206644 16 Burnt 2002 108 2 0 1 1 - 696454 7206091 17 Burnt 2002 166 2 1 1 1 - 668741 7214976 18 Burnt 2002 57 1 1 1 1 - 672763 7206814 19 Burnt 2002 118 2 1 2 1 - 670426 7213033 20 Burnt 2002 1407 1 0 1 1 - 675469 7206725 21 Burnt 2002 (1407) 1 0 1 1 (=Site 20) 676340 7208371 22 Long-unburnt 6 1 1 1 1 - 683190 7198018 23 Long-unburnt 5 1 1 1 1 - 674171 7217034 24 Long-unburnt 9 1 1 1 1 - 671964 7211933 25 Long-unburnt 6 1 1 1 1 - 673545 7210029

Number of surveys by season Patch size study Coordinates Site Treatment Area (ha) W2005 S2005 W2006 S2006 Easting Northing 26 Long-unburnt 4 1 1 1 1 - 695674 7213934 27 Long-unburnt (231) 1 1 1 1 (=Site 32) 669566 7217332 28 Long-unburnt 90 1 1 1 1 - 686010 7203389 29 Long-unburnt 59 2 1 1 1 - 669208 7218334 30 Long-unburnt 74 1 1 1 1 - 673885 7216047 31 Long-unburnt 51 1 1 1 1 - 669842 7212518 32 Long-unburnt 231 2 1 1 1 - 669988 7216536 33 Long-unburnt 416 1 1 1 1 - 684388 7202866 34 Long-unburnt (416) 1 1 1 1 (=Site 33) 682531 7203813 35 Long-unburnt 593 1 1 1 1 - 696897 7211504 36 Long-unburnt 311 1 1 1 1 - 695094 7209109 37 Long-unburnt 22 1 1 1 1 - 681317 7203021 38 Long-unburnt 13 1 0 1 1 - 674838 7212995 39 Long-unburnt 19 1 1 1 1 - 691930 7204080 40 Long-unburnt 37 2 1 1 1 - 670574 7211922 41 Long-unburnt 1236 2 1 1 1 (=Site 63) 668174 7213974 42 Burnt 1976 347 2 0 1 1 - 672089 7215328 43 Burnt 1976 92 1 1 1 1 - 670364 7211163 44 Burnt 1976 263 1 1 2 1 - 672566 7208225 45 Burnt 1976 2759 1 1 1 1 - 683756 7201420 46 Burnt 1976 3290 2 0 1 1 - 697395 7209545 47 Burnt 1976 98 2 1 1 1 - 670094 7218810 48 Burnt 1976 117 1 1 1 1 - 675975 7211434 49 Burnt 1976 23 2 0 1 1 - 696876 7213291 50 Burnt 1976 NA - - - - - - - 51 Burnt 1976 (117) 1 1 2 1 (=Site 47) 669618 7217726 52 Burnt 1976 94 1 1 1 1 - 682547 7204161 53 Burnt 1976 17 1 0 1 1 - 675943 7213243 54 Burnt 1976 NA - - - - - - - 55 Burnt 1976 27 2 0 1 2 - 674817 7212634 56 Burnt 1976 12 2 1 1 1 - 674700 7213610 57 Burnt 1976 8 1 0 1 2 - 675534 7207245

Number of surveys by season Patch size study Coordinates, AGD 1966 Site Treatment Area (ha) W2005 S2005 W2006 S2006 Easting Northing 58 Burnt 1976 NA - - - - - - - 59 Burnt 1976 11 1 1 1 1 - 671755 7211821 60 Burnt 1976 9 1 1 1 1 - 670178 7220188 61 Burnt 1976 9 1 1 1 1 - 671771 7213253 62 Burnt 1976 (3290) 1 1 1 1 (=Site 46) 692674 7203943 63 Long-unburnt (1236) 1 1 1 1 (=Site 41) 668631 7213534 80 Long-unburnt 10 1 1 1 1 - 669683 7215865 81 Burnt 1976 42 2 1 1 1 - 669970 7215547 82 Burnt 1976 21 1 1 1 1 - 683274 7204227

Table A1-2 Details of bird survey sites in the dune-swale landscape. W2005 = winter 2005, S2005 = spring 2005, W2006 = winter 2006, S2006 = spring 2006. Patch size study indicates the sites excluded from the patch size analysis.

Number of surveys by season Coordinates, AGD 1966 Site Treatment Area (ha) W2005 S2005 W2006 S2006

Patch size study Easting Northing

103 Burnt 2002 47 1 1 1 1 X 735648.7 7191869 104 Burnt 2002 33 1 1 1 2 X 737735.6 7191798 105 Burnt 2002 27 1 1 1 1 X 734468.4 7188731 106 Burnt 2002 (162) 1 1 1 1 ( =Site 107) 734890.5 7187944 107 Burnt 2002 162 1 1 1 1 X 736309.3 7188207 110 Long-unburnt 53 1 1 1 1 X 735732.5 7192453 111 Long-unburnt 73 2 2 1 2 X 737386.4 7194898 112 Long-unburnt 112 2 1 1 1 X 738090.8 7195435 114 Long-unburnt 73 1 1 1 1 X 734194.6 7190882 117 Long-unburnt 33 1 1 2 1 X 736419.6 7192921 121 Burnt 2002 28 1 1 1 2 X 734465.3 7191867 122 Burnt 2002 7 1 1 1 1 X 732263.7 7196216 123 Burnt 2002 24 1 0 1 2 X 738269.7 7193584 124 Long-unburnt (73) 2 2 1 2 (=Site 111) 736608.1 7194819 125 Burnt 2002 21 2 1 1 2 X 736367.9 7193691 126 Long-unburnt 8 2 1 1 2 X 736166.3 7194343 127 Long-unburnt 18 1 1 1 1 X 735908.4 7193902 133 Long-unburnt 10 1 1 1 1 X 733283.4 7190519 135 Burnt 2002 39 1 1 1 2 X 737892.2 7191327 136 Long-unburnt 8 1 1 2 1 X 732999 7195370 139 Burnt 2002 5 1 1 1 1 X 735407 7188973 140 Burnt 2002 13 2 1 2 2 X 730417.8 7198043 141 Long-unburnt 9 2 1 2 2 X 730999.1 7198268 142 Burnt 2002 13 2 1 2 2 X 730859.2 7197148 143 Burnt 2002 9 1 1 2 2 X 734946.3 7197223 144 Long-unburnt 7 1 2 2 2 X 734299.5 7195774 145 Burnt 2002 9 1 1 1 1 X 733887.1 7190328 146 Burnt 2002 31 1 1 1 1 X 733440.2 7191024 148 Burnt 2002 25 1 1 1 1 X 736069.9 7190505 149 Long-unburnt 12 1 1 1 2 X 737418.7 7191831

Number of surveys by season Patch size study Coordinates, AGD 1966 Site Treatment Area (ha) W2005 S2005 W2006 S2006 Easting Northing 150 Long-unburnt 9 1 1 1 1 X 735734.5 7190350 151 Long-unburnt 23 1 1 1 2 X 738601.4 7193601 152 Long-unburnt (112) 1 1 1 1 (=Site 112) 737372 7196058 153 Long-unburnt 7 1 1 1 1 X 735305.9 7189992 Table A1-3. Details of edge study survey sites. Datum = WGS84 UTM Zone 52S.

Surveys Coordinates Site 2005 2006 Easting Northing 1 4 4 685696.1 7200296

2 4 4 671393.8 7215168

3 5 4 668725.4 7214629

4 5 4 674491.4 7212001

5 5 4 674598.3 7211492

6 6 4 669925.6 7213087

7 5 4 674903.6 7216402

8 5 4 670070.4 7213656

9 6 4 693346.7 7203419

10 5 4 696253.1 7206084

209

Appendix 2: Ground-truthing sites Table A2-1 Details of ground-truthing sites from the sheetwash polygon. The

datum is UTM WGS84 Zone 52S. Coordinates Coordinates

Site Easting Northing

Site Easting Northing

NW01 668254.8 7210962 NW38 669339.3 7211135 NW02 675049.2 7218268 NW39 669925.8 7213811 NW03 673712.4 7220238 NW40 673321.1 7207262 NW04 671745.3 7217832 NW41 674395.7 7219726 NW05 673589.2 7214389 NW42 673598.4 7213859 NW06 669726.3 7206948 NW43 668310.2 7213817 NW07 672298.7 7211557 NW44 675307 7208308 NW08 676331.4 7210399 NW45 673865.6 7208553 NW09 670084.3 7207681 NW46 670337.4 7208684 NW10 669724.4 7215649 NW47 669262.4 7213362 NW11 672028.7 7217352 NW48 672459.7 7205671 NW12 671624.2 7215546 NW49 670837.4 7212703 NW13 674082.5 7214961 NW50 670969.2 7216332 NW14 671701.6 7210394 NW51 675218.5 7206915 NW15 676252.4 7212474 NW52 669989.9 7208648 NW16 671060.9 7213722 NW53 670423.8 7212450 NW17 672807.8 7207967 NW54 671368.6 7206981 NW18 669312.9 7217441 NW55 674120.5 7206715 NW19 669647.7 7211701 NW56 676372.9 7209226 NW20 670450.8 7219763 NW57 673690 7209535 NW21 670328.5 7216821 NW58 672763.8 7211043 NW22 674101.9 7212509 NW59 668665.7 7205845 NW23 672539.4 7218041 NW60 674099.3 7219010 NW24 668867.7 7216423 NW61 673474.6 7219771 NW25 675021.9 7219262 NW62 669201.3 7208391 NW26 669538.5 7216779 NW63 672127.9 7215160 NW27 669866.4 7215345 NW64 673994.5 7217525 NW28 672828 7206076 NW65 675282.3 7206588 NW29 670325.1 7207103 NW66 668438.1 7209588 NW30 673695.3 7215660 NW67 670751.8 7211588 NW31 673858.1 7220006 NW68 669256.5 7218681 NW32 675399.1 7218097 NW69 676391.3 7206571 NW33 669055 7208393 NW70 671049.7 7218971 NW34 668214.1 7210488 NW71 669715.5 7219369 NW35 669714.4 7214492 NW72 673061.2 7219084 NW36 668734.1 7212855 NW73 670032 7218689 NW37 675802.2 7206174

210

Table A2-2 Details of ground-truthing sites from the dune-swale polygon. The datum is UTM WGS84 Zone 52S.

Coordinates Coordinates Site

Easting Northing Site

Easting Northing SE01 735567.7 7189186 SE37 734692.9 7187465 SE02 738368.9 7190316 SE38 730433.4 7188479 SE03 737112.8 7199544 SE39 738311.7 7198862 SE04 730017.9 7200534 SE40 735743.1 7189866 SE05 736068.4 7200406 SE41 732861.1 7197421 SE06 738168 7191492 SE42 738111.8 7198778 SE07 729525.1 7195343 SE43 732172.5 7193532 SE08 737777.7 7190293 SE44 735099.1 7196647 SE09 731091 7195727 SE45 733720.6 7189079 SE10 733544.2 7200580 SE46 732563.5 7195402 SE11 729298.6 7189154 SE47 736489.8 7192704 SE12 733175.2 7199393 SE48 731655.8 7187482 SE13 730627.3 7191521 SE49 733684.7 7198263 SE14 732095.2 7190922 SE50 736394.2 7192893 SE15 731631.6 7195364 SE51 735170.4 7193404 SE16 737029.2 7196677 SE52 736084 7189147 SE17 729947.1 7188474 SE53 737313.8 7198260 SE18 736987.8 7195464 SE54 735761.7 7191781 SE19 737582.7 7189246 SE55 731702.6 7200398 SE20 731424.2 7200556 SE56 734041.2 7190984 SE21 732467.3 7191666 SE57 734446.3 7200213 SE22 733236.5 7192983 SE58 730688.5 7198659 SE23 731201.6 7193970 SE59 735435.2 7192725 SE24 737465 7192006 SE60 736484.2 7191743 SE25 736030.7 7190487 SE61 732215.4 7192889 SE26 738015.5 7195096 SE62 730487.3 7192725 SE27 729841.1 7193596 SE63 729560.2 7197320 SE28 737521.7 7188182 SE64 729356.5 7192895 SE29 737479.2 7192109 SE65 732180.1 7189522 SE30 732051.3 7199402 SE66 730892.7 7199397 SE31 733845.2 7193307 SE67 730572.9 7188619 SE32 734689.8 7195574 SE68 733048.3 7191937 SE33 737228.3 7188014 SE69 734898.5 7194352 SE34 737377.7 7196183 SE70 736929.4 7189923 SE35 731122.7 7199330 SE71 737959.6 7188957 SE36 729920.6 7189929 SE72 733788.8 7189085

211

Table A2-3 Details of ground-truthing sites from the bore field polygon. The datum is UTM WGS84 Zone 52S.



Easting Northing C01 691606.4 7203980 C32 682981.5 7199572 C02 686809.1 7200697 C33 688811.5 7197929 C03 685928 7199703 C34 681728.1 7203639 C04 692548.8 7198214 C35 687729.6 7197696 C05 690061.9 7201992 C36 683906.5 7200120 C06 688106.3 7202580 C37 681444.3 7199298 C07 687943.3 7202653 C38 687200.2 7199260 C08 690314.8 7198589 C39 690708.6 7199938 C09 686297.7 7200126 C40 693673.7 7203723 C10 681491.2 7200038 C41 682518 7197699 C11 687391.2 7200568 C42 687516 7201041 C12 683695 7198035 C43 689300.1 7200294 C13 691734.9 7197816 C44 682674.2 7204028 C14 689877.4 7203332 C45 691805.1 7197365 C15 690804.5 7201339 C46 693940.8 7201010 C16 691546.6 7198474 C47 691701 7203776 C17 689713.3 7197014 C48 684742.2 7200080 C18 683135.9 7199330 C49 680996.6 7197370 C19 693275.5 7201941 C50 691322.1 7198512 C20 683012.8 7203390 C51 681355 7196804 C21 682085.3 7198354 C52 683997.4 7200158 C22 691252.8 7201410 C53 693547.2 7203145 C23 685112.1 7199949 C54 684684.9 7204046 C24 690679.6 7198632 C55 690172.1 7197103 C25 682560.1 7197311 C56 688064.7 7202586 C26 691171.3 7199560 C57 691579.6 7199610 C27 685119.9 7197375 C58 692574.4 7197043 C28 684106 7202316 C59 685371.2 7203808 C29 683422 7203396 C60 681479.7 7199573 C30 693324.5 7202601 C61 682354.5 7201074 C31 694007.3 7196833

Table A2-4 Details of ground-truthing sites from the Yulara polygon. The datum is UTM WGS84 Zone 52S.



Easting Northing Y01 695590.7 7208394 Y08 696603.3 7213813 Y02 696624.8 7208897 Y09 695952.1 7209640 Y03 696402.6 7206177 Y10 696310.9 7205407 Y04 695535.7 7209265 Y11 694809.7 7208433 Y05 696738.8 7206801 Y12 697050.4 7210280 Y06 696953.9 7210250 Y13 695905.3 7208409 Y07 695033 7213061 Y14 694962 7205741

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