The of abandoned farmland, Cumberland and restored : implications for the restoration of an Endangered Ecological Community

Jennifer Kit Fitzgerald

A thesis submitted in fulfilment of the requirements of the degree of Doctor of Philosophy, University of Western , .

July, 2009.

Cumberland Plain Woodland at Hoxton Park. Photo by Jennifer Kit Fitzgerald

Abandoned farmland at . Photo by Jennifer Kit Fitzgerald

ii

Declaration

This thesis does not incorporate, without acknowledgement, any material previously submitted for a degree or diploma in any university and to the best of my knowledge and belief it does not contain any material previously published or written by another person except where due reference is made in the text.

Jennifer Kit Fitzgerald, July 2009.

iii

Abstract

The restoration and management of Woodland, an „Endangered Ecological Community‟ found only in western Sydney, has occurred without a sound understanding of soil-vegetation relationships within this community. Since 1992, large tracts of abandoned farmland, which were originally covered with Cumberland Plain Woodland, have been planted with native trees and to facilitate woodland development. This approach was based on the theory of (small-scale) patch dynamics since it was envisaged that the developing overstorey would facilitate changes to the soil environment, which would advantage native woodland species and disadvantage exotic pasture species.

To date, this approach has had limited success and importantly, the restoration of Cumberland Plain Woodland has ignored: (a) characterisation of the soil environment; (b) how different patch types (e.g. tree and ) influence the soil; (c) how past land use has affected the soil; and (d) the effects of revegetation on soil properties and processes. These issues are of the utmost importance since soil-related barriers to natural regeneration and restoration may exist as a result of a very long history of . This thesis addressed these issues by investigating the of abandoned farmland, Cumberland Plain Woodland and restored areas of various ages. In addition to this, the impacts of various patch types (woodland tree, shrub and open, as well as improved perennial pasture) on soil properties and processes, as well as the ground flora were examined.

Several soil chemical properties and ecological processes were identified as being of particular importance for the ecology of Cumberland Plain Woodland and its restoration on abandoned farmland. The greatest impact on the soil from past agricultural land use was an increase in the concentration of nitrate, ammonium and total nitrogen within the pasture compared to the woodland patch types, although there was an appreciable amount of site-to-site variability. Despite this, data from two different studies, which were carried out over different spatio-temporal scales, suggest that the abandoned pasture and Cumberland Plain Woodland function differently with respect to the cycling of nitrogen and this may hinder restoration efforts.

iv

Acknowledgements

Special thanks to my Supervisor, Dr. E. Charles Morris (UWS), for his support and help in securing much-needed funds for this research. Thanks also to Associate Professor David Eldridge (DNR/UNSW), who co-supervised this work during 2006 and part-way through 2007. The financial support I received from the University of Western Sydney, by way of a Postgraduate Research Award, was invaluable. I am also grateful to the Development Corporation and the Linnaean Society of for funding.

A very big thank you is extended to those who helped with field and laboratory work, namely: Mark Emmanuel (UWS), Dorothy Yu (UNSW) and Chris Myers (UNSW) for their assistance with soil chemical determinations and analytical techniques; Nyree Webster (UNSW) for her untiring field assistance; Adam Birnbaum (UNSW) for his help with respiration measurements and Peter Nichols (UWS) and Alison Hewitt (UNE) for assistance in the field. Thanks also to Frank Hemming (UNSW) for his help with identification, Monique de Barse (UWS) for help with PRIMER, Dani Drewry (UWS) and Penny Watson (UWS) for early discussions on Cumberland Plain Woodland and Graeme Hastwell (UWS) for his input on various topics relating to this research.

I gratefully acknowledge the National Parks and Wildlife Service (Col Davidson and Jonathon Sanders), the Botanic Gardens Trust (Peter Cuneo), the Department of Defence (Marina Peterson and Daryle McKone), the Sydney Catchment Authority (Jane MacCormick) and Greening Australia (Tim Beshara) for allowing me access to land that was under their control. Special thanks to Debra Little, Lotte von Richter and Doug Benson (all Royal Botanic Gardens) for sharing their knowledge on the Cumberland Plain Woodland at Mount Annan.

Very special thanks to my dear friend Carolyn Stonham, my mother Jan Smith and my nanna Kitty Fitzgerald, for their continued support and encouragement. Most importantly, I thank my partner Scott Mooney.

v

This thesis is dedicated to two very strong women, Jan Louise Smith and Kitty Fitzgerald.

I also dedicate this work to my grandfather, Arthur Bridgewater, who was a man well-ahead of his time.

vi

Table of contents

CHAPTER 1: The significance of Cumberland Plain Woodland and the need for soil-based research ...... 1 1.1 The importance of Cumberland Plain Woodland ...... 1 1.2 The New South Wales Threatened Species Conservation Act 1995 ...... 3 1.3 Recovery of threatened species, populations and ecological communities ...... 4 1.4 Previous research on the soils and vegetation of the Cumberland Plain ...... 7 1.5 The attempted restoration of Cumberland Plain Woodland ...... 9 1.6 The impacts of agriculture on the soil and vegetation ...... 13 1.7 Changes to the soil and vegetation during old field succession ...... 15 1.8 Potential effects of fire on the soil environment ...... 18 1.9 The restoration and management of degraded ...... 20 1.10 Aims of this thesis ...... 24

CHAPTER 2: Description of the Cumberland Plain and study sites ...... 25 2.1 The Cumberland Plain ...... 25 2.1.1 Location ...... 25 2.1.2 Climate ...... 26 2.1.3 Physiography ...... 31 2.1.4 Geology ...... 32 2.1.5 Soil associations and soil landscapes ...... 33 2.1.6 Soil types and soil materials ...... 35 2.1.7 European land use history ...... 36 2.1.7.1 Discovery and settlement of the Cumberland Plain 1789-1821 ...... 36 2.1.7.2 Agricultural consolidation of the Cumberland Plain 1821-1858 ...... 42 2.1.7.3 Industrialisation of the Cumberland Plain 1858-1900 ...... 43 2.1.7.4 Urbanisation of the Cumberland Plain 1880-present day ...... 44 2.1.8 Vegetation ...... 46 2.2 The study sites ...... 49 2.2.1 Hoxton Park ...... 49 2.2.1.1 Location ...... 49 2.2.1.2 Climate and physical geography ...... 49 2.2.1.3 Vegetation ...... 51 2.2.1.4 European land use history ...... 51 2.2.2 Mount Annan Botanic Garden ...... 52 2.2.2.1 Location ...... 52 2.2.2.2 Climate and physical geography ...... 52 2.2.2.3 Vegetation ...... 52 2.2.2.4 European land use history ...... 53 2.2.3 Hills Defence Estate ...... 53 2.2.3.1 Location ...... 53 2.2.3.2 Climate and physical geography ...... 53 2.2.3.3 Vegetation ...... 54 2.2.3.4 European land use history ...... 54 vii

2.2.4 ...... 55 2.2.4.1 Location ...... 55 2.2.4.2 Climate and physical geography ...... 55 2.2.4.3 Vegetation ...... 55 2.2.4.4 European land use history ...... 56 2.2.5 Scheyville National Park ...... 56 2.2.5.1 Location ...... 56 2.2.5.2 Climate and physical geography ...... 56 2.2.5.3 Vegetation ...... 57 2.2.5.4 European land use history ...... 57

CHAPTER 3: The soil of abandoned farmland and Cumberland Plain Woodland. 61 3.1 Introduction ...... 61 3.2 Methodology ...... 62 3.2.1 Experimental design ...... 62 3.2.2 Field and soil sampling ...... 63 3.2.3 Soil physical and chemical determinations ...... 69 3.2.3.1 Bulk density ...... 70 3.2.3.2 Soil moisture content ...... 70 3.2.3.3 pH ...... 70 3.2.3.4 Electrical conductivity ...... 71 3.2.3.5 Active C ...... 71 3.2.3.6 Extractable P ...... 72 3.2.3.7 Nitrate and ammonium ...... 72 3.2.3.8 Total C, total N and total S ...... 73 3.2.4 Statistical analyses ...... 73 3.3 Results ...... 74 3.3.1 Bulk density and soil moisture content ...... 74 3.3.2 pH and electrical conductivity ...... 75 3.3.3 Active C and total C ...... 80 3.3.4 Extractable P and total S ...... 82 3.3.5 Nitrate, ammonium and total N ...... 82 3.4 Discussion ...... 87

CHAPTER 4: The ground flora of abandoned farmland and Cumberland Plain Woodland and its relationship with soil chemical properties .. 102 4.1 Introduction ...... 102 4.2 Methodology ...... 104 4.2.1 Experimental design ...... 104 4.2.2 Vegetation and soil sampling ...... 104 4.2.3 Univariate analysis ...... 105 4.2.4 Multivariate analyses ...... 105 4.2.4.1 Examining the floristic similarity of samples using cluster analysis and ordination ...... 106 4.2.4.2 Investigating the effects of site and patch type on ground species composition and cover with analysis of similarity and the SIMPER routine ...... 106

viii

4.2.4.3 Linking the floristic and soil data using the BVSTEP procedure ..... 108 4.3 Results ...... 109 4.3.1 Univariate analysis ...... 109 4.3.2 Multivariate analyses ...... 109 4.3.2.1 Cluster analysis and ordination ...... 109 4.3.2.2 Analysis of similarity and SIMPER analysis ...... 113 4.3.2.3 BVSTEP analysis ...... 118 4.4 Discussion ...... 121

CHAPTER 5: Soil chemical fertility and biotic processes of abandoned farmland, endangered woodland and restored vegetation at Hoxton Park ...... 131 5.1 Introduction ...... 131 5.2 Methodology ...... 133 5.2.1 Site description ...... 133 5.2.2 Experimental design ...... 134 5.2.3 Soil sampling ...... 137 5.2.3.1 Chemical properties and respiration ...... 137 5.2.3.2 Decomposition ...... 139 5.2.4 Statistical analysis ...... 142 5.3 Results ...... 143 5.3.1 Variables measured once during the year ...... 143 5.3.1.1 Bray 1 P ...... 143 5.3.1.2 Total C ...... 143 5.3.1.3 Total N ...... 144 5.3.1.4 C:N ratio ...... 144 5.3.2 Variables measured twice throughout the year ...... 144 5.3.2.1 pH ...... 144 5.3.2.2 Active C ...... 148 5.3.2.3 Respiration ...... 148 5.3.3 Variables measured four times throughout the year ...... 150 5.3.3.1 Soil moisture content ...... 150 5.3.3.2 Nitrate ...... 150 5.3.3.3 Ammonium ...... 153 5.3.4 Decomposition ...... 157 5.4 Discussion ...... 159

CHAPTER 6: The implications of this research for the management and restoration of Cumberland Plain Woodland ...... 167

REFERENCES ...... 172

APPENDICES ...... 204

ix

List of tables

TABLE 1.1 The recovery strategies and priority actions for Cumberland Plain Woodland ...... 6 TABLE 1.2 Previous research on the soils of the Cumberland Plain ...... 8 TABLE 1.3 Previous research on the vegetation of the Cumberland Plain ...... 10 TABLE 2.1 Size of the Local Government Areas associated with the Cumberland Plain and the proportion of their area located within the region ...... 26 TABLE 2.2 The dominant processes contributing to the formation of profile morphology in brown, red and yellow podzolic soils and their degree of development for each soil type ...... 36 TABLE 2.3 Morphological properties of the soil materials from the soil landscape and their limitations ...... 37 TABLE 2.4 The pre-1750 and current (2002) extent of the vegetation communities on the Cumberland Plain and the date they were listed on the TSC Act ...... 46 TABLE 2.5 The diagnostic floral species for the various strata within Hills Woodland and Shale Woodland ...... 48 TABLE 2.6 Attributes and limitations for urban and rural development of the Blacktown and Luddenham soil landscapes ...... 50 TABLE 2.7 Indicators of the extent and nature of agricultural activities on abandoned farmland at Scheyville ...... 59 TABLE 3.1 Sampling dates for each site, along with the mean minimum and maximum temperatures and total rainfall during the four week period (28 days) prior to sampling ...... 68 TABLE 3.2 Mean concentrations and the upper (L2) and lower (L1) 95% confidence limits for the physical and chemical soil properties, averaged over all patch types and soil depths, at each site ...... 76 TABLE 3.3 Mean concentrations and the upper (L2) and lower (L1) 95% confidence limits for the physical and chemical soil properties for the main effects of patch type ...... 78 TABLE 3.4 Mean concentrations and the upper (L2) and lower (L1) 95% confidence limits for the physical and chemical soil properties for the main effects of soil depth ...... 78 TABLE 4.1a Results of the 2-way crossed ANOSIM for the site factor based on ground species composition and cover ...... 114 TABLE 4.1b Results of the 2-way crossed ANOSIM for the patch type factor based on ground species composition and cover ...... 114 TABLE 4.2 The percentage dissimilarity, based on fourth root transformed data, for all pair wise combinations of sites and the individual and cumulative contributions from the top three species for each comparison ...... 115 x

TABLE 4.3 The percentage dissimilarity for all pair wise combinations of patch types and the individual and cumulative contributions from the top three species for each comparison ...... 117 TABLE 4.4 The soil variables that best explained the observed biotic pattern, in terms of ground species composition and cover, across the samples analysed using the BVSTEP procedure ...... 118 TABLE 5.1 The soil chemical properties and ecological processes measured across the abandoned farmland, restored vegetation and remnant Cumberland Plain Woodland at Hoxton Park ...... 138

xi

List of figures

FIGURE 2.1 Map of the Cumberland Plain and surrounding Hawkesbury plateaux showing the locations of the five study sites ...... 25 FIGURE 2.2a Long-term climatic data for selected variables for Badgerys Creek ...... 27 FIGURE 2.2b Long-term climatic data for selected variables for Camden ...... 27 FIGURE 2.2c Long-term climatic data for selected variables for Liverpool ...... 28 FIGURE 2.2d Long-term climatic data for selected variables for Orchard Hills . 28 FIGURE 2.2e Long-term climatic data for selected variables for ..... 29 FIGURE 2.2f Long-term climatic data for selected variables for Picton ...... 29 FIGURE 2.2g Long-term climatic data for selected variables for Prospect ...... 30 FIGURE 2.2h Long-term climatic data for selected variables for Richmond ...... 30 FIGURE 2.3 Block diagram showing the six physiographic units of the Sydney region ...... 32 FIGURE 2.4 Schematic diagram of the Blacktown soil landscape showing changes in soil types and soil materials along the toposequence ...... 35 FIGURE 2.5 Land granted on the Cumberland Plain during the period 1788 to1821 ...... 38 FIGURE 2.6 Crown land on the Cumberland Plain in 1806 ...... 40 FIGURE 2.7 The extent of agricultural land uses in various districts of the County of Cumberland in 1810, 1815 and 1820 ...... 41 FIGURE 2.8 Contemporary land uses for the Cumberland Plain ...... 45 FIGURE 2.9 Historical European land use map for Scheyville ...... 58 FIGURE 3.1 Mean surface soil (0-5 cm) bulk density for the study sites ...... 77 FIGURE 3.2a-e Mean moisture content with depth for the patch types at each site ...... 77 FIGURE 3.3a-e Mean pH with depth for the patch types at each site ...... 79 FIGURE 3.4 Back-transformed mean EC values with depth for the study sites 79 FIGURE 3.5a-e Mean concentration of active C with depth beneath the patch types at each site ...... 81 FIGURE 3.6 Back-transformed total C levels with depth at the study sites ...... 81 FIGURE 3.7a-e Back-transformed mean Bray 1 P concentrations with depth for the patch types at each site ...... 83 FIGURE 3.8 Mean concentration of total S with depth at the study sites ...... 83 FIGURE 3.9a-e Back-transformed mean nitrate concentrations with depth for the patch types at each site ...... 85 FIGURE 3.10 Back-transformed mean ammonium levels with depth for the study sites ...... 85 FIGURE 3.11 Back-transformed mean total N concentrations with depth for the study sites ...... 86

xii

FIGURE 4.1a Mean native species richness for the ground layer at the study sites ...... 110 FIGURE 4.1b Mean native species richness for the ground layer of the four patch types ...... 110 FIGURE 4.2a Mean exotic species richness for the ground layer at the study sites ...... 111 FIGURE 4.2b Mean exotic species richness for the ground layer of the four patch types ...... 111 FIGURE 4.2c Mean exotic species richness for the ground layer of the four patch types at each of the study sites ...... 111 FIGURE 4.3 Dendrogram showing the percentage similarity between samples where ground species composition and cover were measured in 10 x 10 m quadrats ...... 112 FIGURE 4.4 nMDS ordination of ground species composition and cover ...... 113 FIGURE 4.5a Native species that had a mean cover greater than or equal to 2% at any one site and their average cover (%) at each site ...... 116 FIGURE 4.5b Exotic species that had a mean cover greater than or equal to 2% at any one site and their average cover (%) at each site ...... 116 FIGURE 4.6a Native species that had a mean cover greater or equal to 2% within any one patch type and their average cover for each patch type ...... 117 FIGURE 4.6b Exotic species that had a mean cover greater or equal to 2% within any one patch type and their average cover for each patch type ...... 117 FIGURE 4.7a nMDS ordination of the samples based on the normalised Euclidean distance for soil moisture content, nitrate, total N and exchangeable Na ...... 119 FIGURE 4.7b nMDS ordination of the samples based on the normalised Euclidean distance for soil moisture content, nitrate, total N and exchangeable Na with superimposed bubbles that represent the soil moisture content for each sample ...... 119 FIGURE 4.7c nMDS ordination of the samples based on the normalised Euclidean distance for soil moisture content, nitrate, total N and exchangeable Na with superimposed bubbles that represent the soil nitrate content for each sample ...... 120 FIGURE 4.7d nMDS ordination of the samples based on the normalised Euclidean distance for soil moisture content, nitrate, total N and exchangeable Na with superimposed bubbles that represent the total N content for each sample ...... 120 FIGURE 4.7e nMDS ordination of the samples based on the normalised Euclidean distance for soil moisture content, nitrate, total N and exchangeable Na with superimposed bubbles that represent the exchangeable Na content for each sample ...... 120 FIGURE 4.8 Positive feedbacks between plant litter chemistry and N mineralization ...... 127 xiii

FIGURE 4.9 The „microbial-N loop‟ ...... 129 FIGURE 5.1 Rainfall and temperature data for Liverpool during the 12 month study of soils at Hoxton Park ...... 134 FIGURE 5.2 Geographic spread of the locations and sampling quadrats at Hoxton Park ...... 135 FIGURE 5.3 Back-transformed mean concentrations of Bray 1 P within the surface soils (0-5 cm) of the four different locations at Hoxton Park ...... 144 FIGURE 5.4 Back-transformed mean concentrations of total C within the surface soils (0-5 cm) of the various patch types within the restored areas and woodland at Hoxton Park ...... 145 FIGURE 5.5 The mean concentrations of total N within the surface soils (0-5 cm) of the various patch types within the restored areas and woodland at Hoxton Park ...... 145 FIGURE 5.6 The mean C:N ratio for the surface soils (0-5 cm) of the four different locations at Hoxton Park ...... 145 FIGURE 5.7a Back-transformed mean pH values for the surface soils (0-5 cm) of the four locations at Hoxton Park in June and December of 2007 ...... 146 FIGURE 5.7b Back-transformed mean pH values for the surface soils (0-5 cm) of the restored areas and woodland at Hoxton Park in June and December in 2007 ...... 146 FIGURE 5.7c Back-transformed mean pH values for the surface soils (0-5 cm) beneath the various patch types within the 6 year-old restored area at Hoxton Park in June and December in 2007 ...... 147 FIGURE 5.7d Back-transformed mean pH values for the surface soils (0-5 cm) beneath the various patch types within the 14 year-old restored area at Hoxton Park in June and December in 2007...... 147 FIGURE 5.7e Back-transformed mean pH values for the surface soils (0-5 cm) beneath the various patch types within woodland at Hoxton Park in June and December in 2007 ...... 147 FIGURE 5.8a Back-transformed mean concentrations for active C within the surface soils (0-5 cm) of the different locations at Hoxton Park in June and December of 2007 ...... 149 FIGURE 5.8b Back-transformed mean concentrations for active C within the surface soils (0-5 cm) of the pasture and control locations at Hoxton Park in June and December in 2007 ...... 149 FIGURE 5.9 Mean soil respiration rates for the surface soils (0-5 cm) of the various patch types within the restored areas and woodland at Hoxton Park ...... 149 FIGURE 5.10a Back-transformed mean soil moisture contents for the surface soils (0-5 cm) of the different locations at Hoxton Park for June, September and December of 2007 and March 2008 ...... 151

xiv

FIGURE 5.10b Back-transformed mean soil moisture contents for the surface soils (0-5 cm) of the pasture and the controls at Hoxton Park for June, September and December of 2007 and March 2008 ...... 151 FIGURE 5.10c Back-transformed mean soil moisture contents for the surface soils (0-5cm) beneath the various patch types within the restored areas and woodland at Hoxton Park ...... 151 FIGURE 5.11a Back-transformed mean nitrate concentrations for the surface soils (0-5 cm) of the different locations at Hoxton Park for June, September and December of 2007 and March 2008 ...... 152 FIGURE 5.11b Back-transformed mean nitrate concentrations for the surface soils (0-5 cm) of the pasture and controls at Hoxton Park for June, September and December of 2007 and March 2008 ...... 152 FIGURE 5.11c Back-transformed mean nitrate concentrations for the surface soils (0-5 cm) of the four locations at Hoxton Park ...... 152 FIGURE 5.11d Back-transformed mean nitrate concentrations for the surface soils (0-5 cm) beneath the various patch types within the restored areas and woodland at Hoxton Park ...... 153 FIGURE 5.12a Back-transformed mean ammonium concentrations of the different locations at Hoxton Park for June, September and December of 2007 and March 2008 ...... 154 FIGURE 5.12b Back-transformed mean ammonium concentrations for the surface soils (0-5 cm) of the pasture and controls at Hoxton Park for June, September and December of 2007 and March 2008 ..... 155 FIGURE 5.12c Back-transformed mean ammonium concentrations for the surface soils (0-5 cm) of the restored areas and woodland at Hoxton Park for June, September and December of 2007 and March 2008 ...... 155 FIGURE 5.12d Back-transformed mean ammonium concentrations for the different locations at Hoxton Park ...... 155 FIGURE 5.12e Back-transformed mean ammonium concentrations for the surface soils (0-5 cm) beneath the various patch types within the 6 year- old restored area at Hoxton Park for June, September and December of 2007 and March 2008 ...... 156 FIGURE 5.12f Back-transformed mean ammonium concentrations for the surface soils (0-5 cm) beneath the various patch types within the 14 year-old restored area at Hoxton Park for June, September and December of 2007 and March 2008 ...... 156 FIGURE 5.12g Back-transformed mean ammonium concentrations for the surface soils (0-5 cm) beneath the various patch types within the woodland at Hoxton Park for June, September and December of 2007 and March 2008 ...... 156 FIGURE 5.13a The percentage mass of calico remaining for the four locations at Hoxton Park ...... 157

xv

FIGURE 5.13b The percentage mass of calico remaining for the tree, shrub and open patch types within the 6 year-old restored area at Hoxton Park ...... 158 FIGURE 5.13c The percentage mass of calico remaining for the tree, shrub and open patch types within the 14 year-old restored area at Hoxton Park ...... 158 FIGURE 5.13c The percentage mass of calico remaining for the tree, shrub and open patch types within the woodland at Hoxton Park ...... 158

xvi

List of plates

FRONTISPIECE Cumberland Plain Woodland at Hoxton Park and abandoned farmland at Scheyville National Park ...... ii PLATES 1 & 3 Cumberland Plain Woodland at Hoxton Park showing a range of patch types, including tree patch types dominated by moluccana individuals ...... 64 PLATE 2 E. moluccana in ...... 64 PLATES 4 & 6 Bursaria spinosa in flower ...... 65 PLATE 5 A shrub patch type dominated by B. spinosa, which was used for soil and vegetation sampling at Hoxton Park ...... 65 PLATES 7 & 8 Aristida vagans and Themeda australis respectively, which are common ground layer species in Cumberland Plain Woodland ...... 66 PLATE 9 An open patch type at Mount Annan dominated by native perennial grasses ...... 66 PLATES 10 & 11 Two common exotic perennial pasture species on the Cumberland Plain, Chloris gayana and Paspalum dilatatum respectively ...... 67 PLATE 12 Abandoned farmland at Mount Annan dominated by P. dilatatum ...... 67

xvii

List of appendices

APPENDIX 1 Summary statistics for the soil analyses presented in Chapter 3 APPENDIX 2 Supporting materials for the analysis of the soil and ground layer attributes presented in Chapter 4 APPENDIX 3 Statistics for the Hoxton Park study

xviii

CHAPTER 1. The significance of Cumberland Plain Woodland and the need for soil-based research

1.1 The importance of Cumberland Plain Woodland

European land use and settlement patterns in the Sydney region have been shaped by the physiography and soils of the Cumberland Plain and the adjoining Hornsby, and Blue Mountains Plateaux (Haworth 2003). The Cumberland Plain is a tectonic depression that extends across all of western Sydney and some parts of the Southern Highlands, with a narrow section that stretches from Parramatta towards the coast (Herbert and Clark 1991). The Cumberland Plain is underlain by Wianamatta Shale and is characterised by plains, low rises and rolling hills with clay-rich soils (Bannerman and Hazelton 1990). The surrounding plateaux are typified by steep slopes, rocky ridges and soils formed from Hawkesbury Sandstone (Chapman and Murphy 1998; DECC 2008d). The shale-derived soils are much more fertile than the sandy soils due to higher nutrient levels and greater water holding capacities (Corbett 1972). As such, the Cumberland Plain has been extensively exploited for agriculture and urban development since early European settlement due to its low-lying terrain and comparatively fertile soils.

For the first 100 years of European settlement, the Cumberland Plain was a rural landscape dominated by livestock grazing and cultivation (Benson and Howell 1990b). This was replaced by urban development at the turn of the 20th Century and the Cumberland Plain is now the focus of Sydney‟s urban sprawl (Proudfoot 1987; WSROC 2005). Over 200 years of extensive land clearance has resulted in highly fragmented and degraded that contain many threatened native species (NPWS 1997). In fact, the native flora and fauna associations of the Cumberland Plain are some of the most threatened and least conserved in New South Wales (NPWS 2002a).

Since 1788, native vegetation cover across the Cumberland Plain has been reduced by 87% and the vast majority of plant communities found in the region are now threatened

JK Fitzgerald Chapter 1 1

with extinction (Tozer 2003). Cumberland Plain Woodland is the dominant vegetation community in the region; at the time of European settlement it covered approximately 125 446 ha but less than 8% of this currently remains (NPWS 2002b).

Cumberland Plain Woodland is endemic to the Cumberland Plain and is comprised of two closely related communities, these being Shale Plains Woodland and Shale Hills Woodland (NPWS 2002b). The most common canopy species are and and the dominant shrub species is Bursaria spinosa. The ground layer is extremely diverse and contains a high cover of native perennial grasses, such as Themeda australis, Aristida ramosa, Aristida vagans and Microlaena stipoides, as well as many small, opportunistic that flower only when conditions are favourable (Benson and Howell 1990b). Cumberland Plain Woodland provides habitat for many native plant and animal species that are of regional, state and national significance (NPWS 1997) and it exhibits high structural and floristic variability, both within and between sites (Tozer 2003).

In 1997, Cumberland Plain Woodland became the first vegetation community to be listed as an „Endangered Ecological Community‟ (EEC) on the New South Wales (NSW) Threatened Species Conservation Act 1995 (TSC Act hereafter; NPWS 2002b). This listing was due to its on-going destruction, high levels of native species diversity and value as habitat for rare species (DECC 2008a). It has also been listed as „Endangered‟ on the federal Environment Protection and Biodiversity Conservation Act 1999 (EPBC Act hereafter; DEWHA 2008b) and Preliminary Determinations have recently been made to list Cumberland Plain Woodland as „Critically Endangered‟ on both of these Acts (DECC 2009; DEWHA 2009).

The TSC Act and EPBC Act are two of the most important pieces of environmental legislation in Australia for the conservation of biodiversity and the sustainable use of natural resources (Messer 1997; DEWHA 2008a). Under both Acts, a species, population or ecological community is listed as endangered if it is likely to become extinct in the near future, while critically endangered means that extinction is imminent (AustLII 2008; 2009).

JK Fitzgerald Chapter 1 2

The Final Determination for the listing of Cumberland Plain Woodland on the TSC Act identified the threats to this community as: clearance for agriculture, grazing, hobby and poultry farms, housing and other developments; invasion by exotic ; and increased nutrient loads due to fertiliser run off from gardens and farmland, dumped refuse or sewer discharge. The TSC Act also lists Cumberland Plain Woodland as being affected by several Key Threatening Processes, these being (DECC 2008c): the establishment of exotic and scramblers; invasion by exotic perennial grasses; clearing of native vegetation; and the ecological consequences of high frequency fires.

1.2 The New South Wales Threatened Species Conservation Act 1995

The objectives of the TSC Act are (AustLII 2008):

 „to conserve biological diversity and promote ecologically sustainable development, and;

 to prevent the extinction and promote the recovery of threatened species, populations and ecological communities, and;

 to protect the critical habitat of those threatened species, populations and ecological communities that are endangered, and;

 to eliminate or manage certain processes that threaten the survival or evolutionary development of threatened species, populations and ecological communities, and;

 to ensure that the impact of any action affecting threatened species, populations and ecological communities is properly assessed, and

 to encourage the conservation of threatened species, populations and ecological communities by the adoption of measures involving co-operative management‟.

To support these aims, the following mechanisms have been established (AustLII 2008):

 the listing of threatened species, populations and ecological communities;  the listing of Key Threatening Processes;  the identification of critical habitat;  the development of recovery plans, threat abatement plans and the Priorities Action Statement;

JK Fitzgerald Chapter 1 3

 the granting of licenses to carry out work or research that impacts listed species, populations and ecological communities;

 the development of Species Impact Statements; and  conservation measures such as stop work orders, joint management agreements, conservation agreements, biodiversity certification and biodiversity banking.

Recovery plans are one of the most important mechanisms for preventing extinctions and downgrading the status of listed entities. With the omission of administrative requirements, a recovery plan must (Wilson 1997; AustLII 2008):

 state what must be done to ensure the recovery of the threatened species, population or community;

 identify critical habitat and state what must be done to protect it;  identify threatening processes;  identify ways to minimise any adverse socio-economic consequences of the plan; and

 state performance indicators for the plan.

As such, the development of a recovery plan requires considerable knowledge about the ecology of a threatened entity but in many cases even a basic ecological understanding has been absent. Recovery plans have thus been very time-consuming and expensive to develop (Adam 2002). The Act was reformed in 2004 to address this problem and it now includes a strategy called the NSW Threatened Species Priorities Action Statement (PAS hereafter), which is the key mechanism for recovery planning and action, although provisions still exist for recovery plans (DECC 2007). It is hoped that the PAS will improve the integration of recovery planning, regional land use planning and natural resource management throughout the state (DECC 2007).

1.3 Recovery of threatened species, populations and ecological communities

Under the PAS, the requirements for the recovery of individual listings are assessed within a regional framework so that recovery planning is integrated with land use planning and natural resource management. The PAS consists of thirty four broad

JK Fitzgerald Chapter 1 4

strategies, such as „habitat management for feral animal control‟ and each listing is assessed for the need to carry out each of these strategies to eliminate threats and to secure its recovery (DECC 2007). The PAS thus uses a top-down approach while recovery plans use a bottom-up (i.e. species-, population- or community-centred) approach to recovery.

The different strategies of the PAS are divided into actions, for example see Table 1.1, which describe how a strategy should be implemented. For example, „habitat management for feral animal control‟ could be achieved by erecting fences and using baits. Actions are prioritised as high, medium or low depending on their perceived necessity for threat abatement and recovery; a high priority action is thought to be essential, a medium priority action is considered to be important and a low priority action is deemed to be desirable but not essential (DECC 2007). A longer-term goal of the PAS is to determine key locations for each threatened species, population and ecological community and to establish site specific actions for these areas (DECC 2007). This appears to be the identification and protection of critical habitat but without a formal declaration being made under Part 3 of the Act.

There are eighteen actions listed on the PAS for the recovery of Cumberland Plain Woodland (Table 1.1). This includes the completion of the Cumberland Plain Endangered Ecological Communities recovery plan as a medium priority, with the now long-expired date for completion being July 2007 (DECC 2008b). As shown in Table 1.1, three of the actions are of low priority, nine have medium priorities and five are seen to be essential (high priority). Since many of the threatening processes for Cumberland Plain Woodland stem from the effects of land clearance and fragmentation, it is not surprising that many of the habitat management strategies contain medium and high priority actions. Given the very poor understanding of Cumberland Plain Woodland ecology however, it is surprising that the facilitation of research is identified as non-essential (Table 1.1). This seems counter-intuitive since the development of effective conservation, management and restoration tools requires a sound understanding of the ecological processes of degradation and restoration (Yates and Hobbs 1997; DECC 2007), neither of which has been studied in any great detail for Cumberland Plain Woodland.

JK Fitzgerald Chapter 1 5

Table 1.1 The recovery strategies (highlighted) and priority actions for Cumberland Plain Woodland. The actions are ranked according to their perceived necessity for threat abatement and recovery (from DECC 2008b). Community and land-holder liaison/ awareness and/or education

Management of EECs is to be included in school environmental management plans where Medium the school land contains EECs. Management of EECs to be included in the conditions for Crown land trusts, lease and Medium licence holders. Prepare and implement community awareness, education and involvement strategy. Medium Support community conservation by providing nursery or other facilities, for regeneration Low activities. Develop and implement protocols and guidelines

Local Govt prepare plans of management in accordance with the Local Government Act for reserves containing EECs, which have conservation as a primary objective, or where High conservation is compatible. Promote best practice management guidelines. Medium Habitat management: ongoing EIA - advice to consent and planning authorities

Incorporate consideration of EEC protection in regional open space planning. High Encourage planning authorities to address EECs in development of environmental planning Medium instruments and, where possible, seek biodiversity certification. Habitat management: other

Manage, to best practice standards, areas of EECs which have conservation as a primary objective, or where conservation is compatible. Priorities are to be based on DEC High conservation significance assessment. Habitat management: site protection e.g. fencing and signage

Encourage and promote best-practice management of EECs on private land. Medium Habitat management: weed control

Develop and implement a coordinated program for removal of African Olive across all High tenures. Ensure the consideration of impacts on EECs when enforcing noxious weed or pest species Medium control in EECs. Habitat Protection (including voluntary conservation agreements etc.)

Develop and implement Cumberland Plain Reservation Strategy and create a protected High bushland network through targeted land acquisition as land becomes available.

Public authorities will promote management agreements to landholders through their Medium ongoing land use planning activities.

Investigate the preparation of a recommendation for the declaration of critical habitat. Low

Monitoring

Investigate the development of a regular monitoring program to assess the change in extent Medium of vegetation across the Cumberland Plain. Recovery Plan Preparation: multi species

Finalise the multi-EEC recovery plan as a State priority in accordance with contractual Medium obligations with DEH by July 2007. Research

Liaise with institutions to facilitate research relevant to the recovery of the Cumberland Low Plain EECs.

JK Fitzgerald Chapter 1 6

1.4 Previous research on the soils and vegetation of the Cumberland Plain

There has been very little research on the soils and vegetation of the Cumberland Plain and most of it has been focused on classification and mapping, as summarised in Table 1.2 and Table 1.3. The ecology of Cumberland Plain Woodland has thus been poorly researched and topics such as land use change, soil-vegetation relationships, vegetation dynamics, disturbance regimes and weed ecology have been addressed by only a handful of studies.

The vast majority of research on the soils of the Cumberland Plain was completed prior to 1990 (Table 1.2) and most of this dealt with classification and mapping, land use impacts and land capability assessments. Many of these studies however, used only a limited number of sites (Jensen 1921; Quilty et al. 1976; Parker and Chartres 1983; Johnston and Hicks 1984; Logan and Luscombe 1984; Hollinger et al. 2001; Chan and Barchia 2007) or samples. Walker (1960) and Corbett (1972) for example, described characteristic soil profiles and physical and chemical soil properties for the Cumberland Plain but this information was based on limited field and laboratory work, including only one or two profiles for each soil series or soil type. Most notably, the only study to focus on the soils of Cumberland Plain Woodland was carried out by Hill et al. (2005), although they didn‟t account for different geologies or soil landscapes in their experimental design.

The vegetation of the Cumberland Plain has received much more attention than the soils, particularly in recent times. The work of Benson and Howell (1990a; 1990b) and Benson (1992) formed the basis for listing Cumberland Plain Woodland on the TSC Act and research on the vegetation of the Cumberland Plain has increased since 1997 (Table 1.3). Most of this work has been focused on mapping the distribution and describing the condition and conservation significance of remnant and regrowth areas throughout the region (James 1997; James et al. 1999; French et al. 2000a; French et al. 2000b; NPWS 2002b and Tozer 2003).

As previously noted, a recovery plan has not been released for Cumberland Plain Woodland but best practice guidelines for the management and restoration of native

JK Fitzgerald Chapter 1 7

Table 1.2. Previous research on the soils of the Cumberland Plain. Topic Author / Date Brief Description - earliest recorded classification of soils in the Sydney Henson (1887) region - soils grouped according to the origin of their parent material - surveyed and mapped the soils of the Hawkesbury Jensen (1921) Agricultural College using physical and chemical characteristics to differentiate the soil types - classified the soils of the Cumberland Plain at a 1:75 000 scale using Great Soil Groups Walker (1960) - soil physical and chemical properties for characteristic profiles of each soil series were measured

Classification - discussed major factors controlling soil formation and mapping - mapped the soils of the Cumberland Plain according to Corbett (1972) Great Soil Groups and described typical profiles - dominant factors affecting profile development and soil formation were discussed Hamilton (1976) - mapped the soils of the Catchment Area at a scale of 1:250 000

Bannerman and Hazelton - mapped the soil landscapes of the Penrith 1:100 000 (1990) map sheet, which covers most of the Cumberland Plain

- mapped the soil landscapes of the Hawkesbury-Nepean DECC (2008d) Catchment, which included new line work for a large area of the Penrith 1:100 000 map sheet - investigated the effects of land use (pasture and Blue Parker and Chartres (1983) Gum High ) on morphological, physical and chemical properties of some red podzolic soils of the Cumberland Plain Hollinger et al. (2001) - measured sediment yields and nutrient losses in runoff from a market garden in Richmond - compared soil loss under different land uses Erskine et al. (2003) (woodland/forest, pasture, cultivated and urbanised) in the South Creek Catchment Land use - examined relationships between anthropogenic impacts Hill et al. (2005) disturbances, soil properties and exotic species in Cumberland Plain Woodland - measured earthworm distribution, abundance and Chan and Barchia (2007) biomass in relation to several soil physical and chemical properties on a single dairy farm located on the Cumberland Plain - investigated the impacts of vegetable farming in the Chan et al. (2007) Sydney region (including some areas of the Cumberland Plain) on soil phosphorus, pH, electrical conductivity and exchangeable cations - mapped and surveyed the soils for constraints to Quilty et al. (1976) engineering and construction works for the South Penrith Housing Project - mapped and surveyed the soils for constraints to Urban and rural Johnston and Hicks (1984) engineering and construction works in the Camden Park land capabilities Development Area - mapped and surveyed the soils for constraints to urban Logan and Luscombe and rural development in the north west sector of (1984) Sydney

…continued over

JK Fitzgerald Chapter 1 8

Table 1.2. Previous research on the soils of the Cumberland Plain (continued). Topic Author / Date Brief Description

Guthrie (1891) - general soil fertility was described and recommendations Agricultural made for liming assessment Jensen (1910) - described the soils most suited for on the Cumberland Plain - described methods to reduce soil erosion and improve Sustainable Beirne (1953) (exotic) pasture quality and quantity for grazing and farming dairying in and around Camden Huston (1953) - outlined how to prevent mass movement on farms in the Camden district Mineralogy - investigated the relationship between mineralogy and Davey et al. (1975) soil colour for the podzolic soils of the Cumberland Plain Soil Walker and Hawkins - examined soil development on the floodplain of the development (1957) - investigated cadmium levels in vegetables and soils in Heavy metals Jinadasa et al. (1997) Greater Sydney, including some areas of the Cumberland Plain

Salinity Collis-George and Evans - looked at why areas of the floodplain of the Hawkesbury (1964) River had saline soils - surveyed the soils and vegetation of the outer north west Nature Forster et al. (1977) region of Sydney to identify areas suitable for conservation recreational and scientific uses vegetation on the Cumberland Plain were published several years ago (DEC 2005). These guidelines are underpinned by very little scientific research on the native vegetation communities of the Cumberland Plain, along with a very poor understanding of their ecology. In spite of this, these guidelines are considered to be a fundamental recovery tool for Cumberland Plain Woodland (DEC 2005).

1.5 The attempted restoration of Cumberland Plain Woodland

Many of the reserves that protect Cumberland Plain Woodland have long agricultural histories and as such, large tracts of abandoned farmland have been earmarked for the improved management and restoration of this endangered vegetation community. Since 1992, the Greening Western Sydney project has been managing and restoring native vegetation on degraded land throughout the region to enhance biodiversity, catchment health, heritage conservation and recreation (Davies and Christie 2001). An important component of this project has been the attempted restoration of Cumberland Plain Woodland on abandoned farmland; these areas were covered with Cumberland Plain Woodland prior to being cleared but they‟re currently dominated by exotic perennial

JK Fitzgerald Chapter 1 9

Table 1.3. Previous research on the vegetation of the Cumberland Plain. Topic Author / Date Brief Description - classified and compared the vegetation of Hawkesbury Sandstone and Wianamatta Shale Pidgeon (1941) - Cumberland Plain Woodland was classified as part of the Eucalyptus hemiphloia - Eucalyptus tereticornis Association - attempted to reconstruct the vegetation of the Sydney Burrell (1972) area at the time of European settlement - formed the basis for the work of Benson and Howell (1990a) - classified the vegetation of the Cumberland Plain based on the dominant canopy species Benson and Howell - mapped the current and pre-European distribution of (1990a) each vegetation community - they were the first to use the term „Cumberland Plain Woodland‟ Benson et al. (1990) - mapped the vegetation of the Bents Basin State Recreation Area - revised Benson and Howell (1990a) but the Classification classification maintained the use of the dominant canopy and mapping Benson (1992) species - mapped the vegetation of the Penrith 1:100 000 map sheet - used Cumberland Plain Woodland as a case study for French et al. (2000a) examining the adequacy of subjective classification systems for endangered vegetation communities French et al. (2000b) - mapped the vegetation of the Holsworthy Military Area using multivariate techniques - the vegetation of the Cumberland Plain was systematically classified for the Western Sydney Native NPWS (2002b) Vegetation Mapping Project - large-scale (cf. Benson 1992) maps were produced to aid land use planning and development control within individual Local Government Areas (LGA) - extended the work of the NPWS (2000) Tozer (2003) - devised a field identification method, based on diagnostic species, for vegetation communities on the Cumberland Plain - documented the destruction of Sydney‟s urban bushland - highlighted the significance of agriculture and Benson and Howell urbanisation for habitat destruction on the Cumberland (1990b) Plain - identified poorly conserved vegetation types and areas of conservation significance within each suburb - compiled species list (flora and fauna) for each LGA, Biodiversity and identified core and complimentary biodiversity areas and conservation James (1997) identified actual and potential vegetation corridors - significant species, conservation needs and major threats to diversity were reported - reviewed various aspects of the ecology of in Myerscough (1998) the Sydney area, with some attention given to the eucalypts of the Cumberland Plain James et al. (1999) - a comprehensive native flora for the Cumberland Plain with over 500 species listed

…continued over

JK Fitzgerald Chapter 1 10

Table 1.3. Previous research on the vegetation of the Cumberland Plain (continued). Topic Author / Date Brief Description - compared 19th century descriptions of the floristics and structure of Cumberland Plain Woodland to contemporary records based on 14 years of monitoring Benson and Howell (2002) at Mount Annan Botanic Gardens Biodiversity and - concerned with the debates surrounding the structure conservation (grassy vs. shrubby) and composition (how many (cont.) species have become extinct since 1788?) of Cumberland Plain Woodland - studied the ecology, specifically the mycorrhizal Darley (2005) infection sites, of orchids on the Cumberland Plain to aid in their conservation - observed the effects of a change from high to low James (1994) frequency mowing on species composition and richness in a regrowth stand of Turpentine- Ironbark Forest - investigated the effects of hazard reduction burning on Thomas (1994) the structure and floristics of Cumberland Plain Woodland at Prospect Reservoir Disturbance Lewis (2001) - observed the impacts of the cessation of mowing on the regimes floristics of a stand of - investigated the effects of fire and grazing on floristics Hill and French (2004) and regeneration of shrubs and eucalypts within the Holsworthy Military Area - investigated the effects of different fire frequencies on Watson (2005) the floristics and structure of Cumberland Plain Woodland - applied fire, tillage and herbicide treatments to a pasture McDonald (1996) that was once Cumberland Plain Woodland, to observe the germination response of the soil seed bank - investigated the size and species composition of soil Wood (2001) seed banks at and Clarendon Paddocks; also looked at germination responses for Seed ecology individual species - analysed the soil seed bank at Holsworthy Military Area Hill and French (2003) and compared it to species richness and abundance of the standing vegetation - tested the germination response of grasses to heat and Clarke and French (2005) smoke, with seeds collected from two stands of Cumberland Plain Woodland - an honours thesis which examined the distribution of Ens (2002) Chilean Needle Grass on the Cumberland Plain and measured the impacts of this species on invertebrates - investigated fire-related germination cues for Pimelea spicata (native endangered shrub with largest remaining Willis et al. (2003) population in Cumberland Plain Woodland) and Asparagus asparagoides (threatening weed), to see if Weed ecology fire could be used to promote the former and eliminate and the later management - examined resistance to weed invasions in monocultures Berryman (2005) versus mixed patches of native ground species after mining at Penrith Lakes - reviewed various aspects of African Olive (e.g. life history attributes and historical significance) that Cuneo and Leishman contribute to its success as an environmental weed (2006) - described a model for invasion using sites on the Cumberland Plain as a case study

…continued over

JK Fitzgerald Chapter 1 11

Table 1.3. Previous research on the vegetation of the Cumberland Plain (continued). Topic Author / Date Brief Description - measured and compared the floristic composition and Wilkins et al. (2003) structure of vegetation along a 9 year chronosequence of restored sites to assess restoration success Restoration - developed a method to evaluate the success of the assessment Nichols (2005) attempted restoration of Cumberland Plain Woodland on abandoned farmland Lomov (2006) - assessed Cumberland Plain Woodland restoration in terms of plant- interactions

pasture species, such as Paspalum dilatatum and Chloris gayana (Davies and Christie 2001; Wilkins et al. 2003).

Revegetation has been used extensively throughout the region in an attempt to restore native Cumberland Plain Woodland ground species to abandoned farmland. To this end, local provenance tubestock of mostly trees and shrubs are mechanically planted into the pastures following the application of herbicide (Davies and Christie 2001). The plants are spaced at regular intervals in parallel rows that are approximately 3 m apart (pers. obs. 2007). The restored areas therefore, bear little structural resemblance to Cumberland Plain Woodland.

The use of this approach was based on two field observations: firstly, that the removal of livestock in itself did not appear to promote woodland succession on abandoned farmland; and secondly, native grasses could establish beneath the drip-line of individual trees growing in areas with a high cover of exotic grasses (Davies and Christie 2001). It was thus hypothesised that the planted individuals would “improve soil condition and create an intermediate degree of shading” (Davies and Christie 2001 pg. 171), which would lead to the recruitment of native species by reducing pasture growth. Davies and Christie (2001) did not define „soil condition‟ nor did they suggest how the planted individuals would improve it. In addition to this, no physical, chemical or biological attributes of the soil were tested prior to the commencement of restoration (for example see Perkins 1997).

Wilkins et al. (2003) assessed the attempted restoration of Cumberland Plain Woodland using floristics and structure as indicators of success. Using a nine-year chronosequence, they found that the restored sites were similar to abandoned pastures in

JK Fitzgerald Chapter 1 12

terms of native and exotic species richness, while remnant vegetation had more than double the number of native species and significantly less exotic species than the restored vegetation. They also found a weak compositional trend emerging from the restored sites after nine years but it was not in the direction of the remnant vegetation (Wilkins et al. 2003). As such, Wilkins et al. (2003) concluded that either the restoration had been unsuccessful or that nine years was too short to detect the desired successional trajectory. Nichols (2005) described very similar results and reached the same conclusion in a related study that looked at the same area after eleven years of restoration. In addition to the short time frame of these studies, other factors, such as dispersal limitations, depauperate soil seed banks, inappropriate disturbance regimes, drought and unsuitable soil conditions brought about by past land use, may have conceivably affected the regeneration of Cumberland Plain Woodland on abandoned farmland.

1.6 The impacts of agriculture on the soil and vegetation

Intensive and extensive agriculture can directly and indirectly affect the physical, chemical and biological fertility of the soil, which can result in dramatic changes to pre- disturbance conditions (Yates and Hobbs 1997). Since the various components of soil fertility are interrelated (Charman and Roper 2007), a change to one will invariably alter the state of another and this could have adverse implications for native plant growth and productivity, especially if the changes are in response to, or promote the development of, degrading processes. In addition to this, agriculture typically alters the distribution, structure and floristics of the original vegetation, which can ultimately affect both local- and -scale processes, such as propagule dispersal, weed invasion, pollination, nutrient cycling and hydrology (Panetta and Hopkins 1991; Hobbs 1993; Dorrough and Scroggie 2008).

Grazing and cultivation can compact and pulverise the soil and this can lead to structural degradation, which is associated with increased bulk density and reduced porosity, infiltration and aeration (Reiners et al. 1994; Franzluebbers 2002). Compacted soils can impede root penetration and radial growth (Drewry et al. 2008), as well as seed germination (Cole and Lunt 2005). They can also reduce gas exchange by restricting or preventing air flow (Rengasamy and Olsson 1991). Structural degradation

JK Fitzgerald Chapter 1 13

can reduce the water holding capacity of the soil, which can affect plant growth by reducing moisture levels and nutrient availabilities (Passioura 1991). In addition to this, soil structure decline can diminish the biological fertility of the soil by reducing the movement and energy capture of the soil biota (Curry and Byrne 1997; Chan and Barchia 2007).

Nutrient levels and soil pH are directly affected by synthetic fertilisers, manures and other soil amendments (e.g. lime) and indirectly affected by cropping, harvesting and grazing (Burke et al. 1995; Crawford et al. 1995; Havilah et al. 2005). The availability of nutrients and their vertical distribution within the soil profile is also affected by irrigation (Greenwood et al. 2006). In agricultural systems therefore, the rate of nutrient cycling, the size of nutrient pools and the availability of nutrients for plant uptake may be very different to that of the original system (McLauchlan 2006). This could alter the composition, abundance, distribution and activity of the native soil biota (Kulmatiski et al. 2006; Myster 2008), which could also be affected by pesticides and herbicides, as well as the introduction of exotic plants, animals, and microorganisms (Hendrix and Parmelee 1985; Gunapala et al. 1998). Changes to the soil biota may, in turn, affect many different chemical and physical properties and processes within the soil.

Macro- and micro-invertebrates play a key role in decomposition and nutrient cycling through the communition of plant litter and the partial digestion of soil organic matter (Brussaard et al. 2007). They can also affect the pore size distribution of the soil and earthworms are particularly important for soil structure because they create macropores, which can increase infiltration and aeration, as well as provide pathways for root growth and exploration (Fraser et al. 2003; Vallauri et al. 2002). Certain micro-organisms can also have a profound effect on nutrient cycles. Rhizobia and cyanobacteria for example, can increase the concentration and availability of nitrogen within the soil while mycorrhizal fungi can do the same for phosphorus (Eisele et al. 1989; Keith 1997). Importantly, the occurrence and activity of these microorganisms is related to soil pH, moisture and temperature (Attiwill and Leeper 1987).

In many agricultural systems, much of the original vegetation is cleared and replaced with crops and pastures, which can adversely affect how the native vegetation functions (McIntyre and Lavorel 2007). The clearing of trees for example, can alter the hydrology

JK Fitzgerald Chapter 1 14

of an area and lead to secondary salinity (Hobbs 1993), while fragmentation can result in dispersal limitations, reduced genetic diversity and diminished recruitment of native species (Hobbs and Yates 2003). Fragmentation can also enhance edge effects, which can increase the invasibility of an area and disadvantage native species by changing the abiotic (soil, light, temperature, humidity and wind) conditions under which they thrive (Saunders et al. 1991). Under more diffuse agricultural activities, such as the grazing of native pastures, rangelands and woodlands, changes to the vegetation are usually less conspicuous but no less disastrous.

Domestic livestock grazing can result in dramatic changes to the floristics, structure and function of native vegetation communities (Lunt et al. 2007). This typically occurs through a reduction or elimination of palatable species, by the preferential grazing of juvenile plants and via changes to nutrient cycles as a result of dung deposition (Wilson 1990; Wilson 2002). Shrub encroachment can result due to the breakdown of natural processes that sustain a grassy groundcover and a change in the composition and life history of the dominant grasses may result (Archer 1995). This has occurred for example, in some areas of southern Australia where the original ground layer, which was dominated by tall perennial grasses, has been replaced with exotic annual grasses such as Avena and Bromus (Pettit et al. 1995).

1.7 Changes to the soil and vegetation during old field succession

These changes to the soil and vegetation can persist following agricultural abandonment (Flinn and Vellend 2005; McLauchlan 2006). The rate and direction of old field succession therefore, can be affected by the type, intensity and duration of the prior land use or land uses (Noble and Slatyer 1980). Over the past 100 years or so, much effort has been spent trying to understand and predict successional pathways following disturbance and there has been an abundance of old field studies carried out in Europe, North America and the neotropics (Clements 1916; Tansley 1916; Cooper 1926; Egler 1954; Connell and Slatyer 1977; Tilman 1985). In stark contrast to this, very few old field studies have been carried out in Australia (Read and Hill 1983; Onans and Parson 1980; Liangzhong and Whelan 1993; Arnold et al. 1999; Toh et al. 1999; Standish et al. 2006): all of these were of limited areal extent; two were largely qualitative (Onans and Parson 1980; Liangzhong and Whelan 1993); and apart from Standish et al. (2006), they

JK Fitzgerald Chapter 1 15

measured a limited number of soil variables and rarely considered the soil and vegetation at the same time (but see Arnold et al. 1999).

Many old field studies from both temperate and tropical areas have shown that soils recovering from agriculture tend to have elevated pH levels and higher concentrations of exchangeable cations (namely Calcium (Ca), Magnesium (Mg) and Potassium (K)), ammonium, total nitrogen (N), plant-available phosphorus (P), total P and total carbon (C) compared to the original, undisturbed vegetation (Gough and Marrs 1990; Pywell et al. 1994; Reiners et al. 1994; Koerner et al. 1997; Dupouey et al. 2002; Walker et al. 2004; Flinn and Vellend 2005; Flinn et al. 2005; Standish et al. 2006). Lower concentrations of exchangeable aluminium (Al), diminished cation exchange capacity and reduced C:N ratio have also been reported for old field soils (Koerner et al. 1997; Dupouey et al. 2002; Reiners et al. 1994). In addition to this, structural degradation has been reported for the soils of abandoned farmland, as indicated by high bulk densities, low porosities and large surface penetration resistances (Reiners et al. 1994; Motzkin et al. 1996; Hooker and Compton 2003; Garcia et al. 2007).

The impacts of prior cultivation and grazing on the soil and vegetation are often very different (Vitousek et al. 1989) and may persist for varying lengths of time (for example see Jim (2003), Flinn et al. (2005) and Peterken and Game (1984)). This is because cultivation and cropping involve direct physical disturbance to the soil, as well as fertiliser use, while grazing is typically much less invasive to the soil environment. That being said, different cultivation and pastoral activities could also result in different impacts on the environment, for example, dairying typically requires high inputs of fertiliser and the growth of particular pasture species but the rearing of and cattle for wool and beef production is usually less intensive (Havilah et al. 2005).

Koerner et al. (1997) and Dupouey et al. (2002) studied changes to the soil along a gradient of increasing past land use intensity in north eastern France and found that cultivation, cropping and orcharding had much larger impacts on soil chemistry than grazing. This is because the soils of prior gardens and crops typically had higher pH levels and elevated concentrations of total N and total P than areas that were previously grazed, which had similar nutrient levels to ancient forest soils (Koerner et al. 1997; Dupouey et al. 2002). In the tropics, Silver et al. (2000) found that soil C accumulated

JK Fitzgerald Chapter 1 16

faster in areas that were previously grazed rather than cultivated. Similarly, Motzkin et al. (1996) found that formerly cultivated fields in North America had much lower soil C levels than fields that had never been ploughed. The formerly cultivated sites also had compacted subsoils, which prevented the growth of pitch-pine (a tree species) but enabled the recruitment of scrub-oak (a shrub species). Garcia et al. (2007) also found that soil compaction affected the recruitment of different functional groups in old fields throughout Spain, with a greater abundance of grasses in compacted soils and more forbs in well structured soils.

Different types of past land use can also impact species richness in different ways, as highlighted by Koerner et al. (1997). They found higher species richness in areas that were previously fertilised (i.e. croplands and gardens) compared to areas that were grazed, while the latter had similar levels of species richness to ancient . In addition to this, the species assemblages were also very different; nitrophilic plant species prevailed in the more intensively used areas, while acidophilic (or low N demanding) species were far more abundant in the less intensively used areas.

Changes to the soil can persist for decades (Dormaar et al. 1990; Pywell et al. 1994; Jim 2003; Flinn and Marks 2007) or centuries (Flinn and Vellend 2005; Gustavvson et al. 2007) and perhaps even longer (Peterken and Game 1984; Dupouey et al. 2002) following agricultural abandonment. Many studies have shown altered soil nutrient levels to persist for up to 100 years after abandonment (Koerner et al. 1997; Jim 2003; Flinn et al. 2005; Flinn and Marks 2007) while Knops and Tilman (2000) modelled the recovery rate for soil C and N, based on a chronosequence of differently aged fields, to be 180 years and 230 years respectively for a sand plain in Minnesota. In north eastern France, Dupouey et al. (2002) discovered that Roman agriculture during the first and second centuries had altered various chemical and structural properties of the soil which had shaped the floristic composition and structure of present day communities.

In a review focused on the species richness of secondary woodlands and forests in Europe and North America, Flinn and Vellend (2005) concluded that even after centuries of agricultural abandonment and afforestation, these areas do not attain the same level of native species richness that ancient forests possess. Interestingly, a lot of the studies referred to in their review included secondary forests that had been

JK Fitzgerald Chapter 1 17

supplemented with tree plantings (Flinn and Vellend 2005). This contrasts with the tropics where pre-disturbance levels of native species richness are quickly attained during old field succession (Aide et al. 2000). Importantly though, the composition of secondary and primary (ancient) forests remain very different for an extended period of time and Hooper (2008) suggested they may never converge.

There is evidence that factors other than the soil, such as dispersal limitations and plant life history traits, as well as grass competition, are more important in shaping the distribution, composition and abundance of species during old field succession (Inouye et al. 1987; Zimmerman et al. 2000; Bellemare et al. 2002; Hooper et al. 2005; Fraterrigo et al. 2006; Hermy and Verheyen 2007). Nevertheless, the soil is likely to play an important role in old field succession at some point in time and the more recent theories on succession and community assembly highlight the potential for interactions and feedbacks between the abiotic and biotic components of a system following disturbance (for example see Bradshaw (2004), Hobbs and Norton (2004) and Cramer (2007)).

1.8 Potential effects of fire on the soil environment

Fire can directly and indirectly affect the physical, chemical and biological fertility of the soil (Raison 1979; Humphreys and Craig 1981). Immediate changes are largely related to the direct impacts of heat while those that take longer to develop typically occur as a result of changes to the cover, structure and composition of the vegetation (Raison 1979; Greene et al. 1990; Hart et al. 2005). The production of ash may result in both direct and indirect impacts on the soil environment and indirect effects may also stem from changes to the microbial community and macro-fauna populations within the soil profile and on the soil surface (Raison 1979; Tongway and Hodgkinson 1992; Hart et al. 2005).

The way in which fire affects the soil is dependent on a wide range of variables, including: the fire regime, which refers to the frequency, intensity and seasonality of individual fire events (Gill et al. 1981); past and present land use and land management, which can affect fuel loads and fire intensity, as well as the condition (e.g. erodability and nutrient levels) of the soil at the time of burning (Thomas 1994; Hatten et al. 2005);

JK Fitzgerald Chapter 1 18

soil type and various soil properties, namely, moisture content, bulk density, pore size distribution, heat capacity and thermal conductivity (Raison 1979; Humphreys and Craig 1981); and the spatial distribution, quantity, chemical composition and moisture content of the fuel load, which is related to vegetation type, management history, fire regime and topography (Attiwill and Leeper 1987; Tongway and Hodgkinson 1992).

The cementing agents in soils are organic matter, clay minerals and sesquioxides (Corbett 1969) and well structured soils are characterised by high levels of aggregate stability due to the presence of these agents. Vegetative cover also contributes to structural stability since it impedes the erosive forces of wind and rain (Charman and Murphy 2007). The removal of vegetation and the combustion of soil organic matter by fire therefore can increase the surface erodability of the soil (Raison 1980), which can lead to wind and water erosion and a concomitant loss of nutrients from a site (Attiwill and Leeper 1987). Only intense fires are likely to have a direct and immediate impact on soil structure since temperatures less than about 200°C have little impact on soil organic matter (Raison 1979). Soil aggregation within the fine mineral fraction, which includes the clay minerals and sesquioxides, can also occur at very high temperatures and this is thought to be permanent above temperatures of 400°C (Humphreys and Craig 1981). Water repellent surfaces may also result from fire due to the vaporisation of organic, hydrophobic compounds that condense on soil particles and aggregates to form discrete layers (Humphreys and Craig 1981). These hydrophobic layers may increase surface runoff and exacerbate soil erosion and nutrient loss from a site.

In many cases, elevated soil nutrient levels following fire are associated with ash deposition (Attiwill and Leeper 1987), although other factors, such as reduced microbial uptake, may also contribute to this (Bauhus et al. 1993; Hart et al. 2005). Ash can directly and indirectly affect the concentrations of nutrients within the soil profile but wind and water erosion can easily transport ash from a site, resulting in a loss of nutrients (Raison 1979). Typically though, the main components of ash (i.e. Ca, Mg, K, sodium (Na) and P) are directly added to the soil (Tongway and Hodgkinson 1992) and this can increase soil pH, thus enhancing the availability of certain nutrients, for example N and P, for plant and microbial uptake (Attiwill and Leeper 1987). Depressed nutrient levels on the other hand, are generally related to the combustion of soil and

JK Fitzgerald Chapter 1 19

litter constituents, with levels of C and N most likely to decrease following fire (Bauhus et al. 1993; Hatten et al. 2005).

The extent to which fire affects soil chemistry is greatly influenced by soil temperature (Humphreys and Craig 1981) and so fuel load and fire intensity can have large impacts on this component of soil fertility (Tomkins et al. 1991), with higher temperatures generally leading to greater changes in soil chemical properties than lower temperatures (Humphreys and Craig 1981). In general, the greatest changes typically occur within the surface horizons although certain nutrients, namely cations, can be leached down the soil profile by rainfall following fire (Attiwill and Leeper 1987; Tomkins et al. 1991). The persistence of altered states is difficult to determine but severe fires are much more likely to have longer lasting impacts on soil chemistry than low intensity fires like hazard reduction burns (Tomkins et al. 1991; Thomas 1994).

Through the production of heat, changes in soil chemistry and modifications to the microclimate through vegetation clearance, fire can alter the composition and activity of the microbial biomass and can disrupt the occurrence and abundance of the soil macro- fauna (Hart et al. 2005). This can affect biogeochemical cycles and thus decomposition, as well as having adverse implications for soil structure, water movement and gas exchange (Greene et al. 1990). Importantly though, different types of micro-organisms are killed at different temperatures; temperatures above 127°C for example, can completely sterilise the soil while a ten minute exposure at 70°C will kill protozoa, non- sporeforming fungi and some bacteria (Raison 1979). In addition to this, the recovery of microbial communities often results in a very different composition compared to pre- fire conditions (Hart et al. 2005) and changes in the abundance of microbial functional groups could potentially affect processes such as decomposition, nitrification and ammonification (Attiwill and Leeper 1987).

1.9 The restoration and management of degraded woodlands

Knowledge of old field succession, including changes to both the soil and vegetation, is essential for the effective management and restoration of native vegetation communities on degraded land and several monographs have recently highlighted this (Walker and del Moral 2003; Temperton et al. 2004; Cramer and Hobbs 2007). In fact,

JK Fitzgerald Chapter 1 20

understanding the impacts of past land use on the soil is of the utmost importance for natural resource management in general (Odum 1969). For example, Wall and Hytonen (2005), Falkengren-Grerup et al. (2006) and Gachet et al. (2007) highlighted how knowledge of past land use on soil N, P, C and pH could be used to improve forestry operations throughout Europe. They were particularly concerned with soil nutrient status and the presence of toxic elements for stand productivity and profitability (Wall and Hytonen 2005; Falkengren-Grerup et al. 2006), as well as understorey species richness for the conservation of biodiversity (Falkengren-Grerup et al. 2006; Gachet et al. 2007).

In temperate Australia, the removal of livestock and revegetation has frequently been used as restoration tools for degraded woodlands (Yates and Hobbs 1997; Spooner et al. 2002; Prober and Thiele 2005). These measures however, will not result in the unassisted recruitment of native species if they do not support, or reinstate, the ecological processes required for this. For example, Yates et al. (2000b) found that previously grazed Eucalyptus salmonophloia woodlands had significantly lower infiltration rates, elevated nutrient levels and a much greater cover of exotic annuals, with diminished shrub and tree recruitment compared to rarely grazed/ungrazed remnants. The removal of livestock therefore, was not enough to facilitate the natural recruitment of the dominant overstorey species in these areas. In a related study, Yates et al. (2000a) found that deep ripping the soil had a positive effect on the recruitment of shrub and canopy species because an appropriate rate of infiltration was restored to the soil profile.

This has fuelled a move to consider both structure and process in the restoration of degraded ecosystems (Ludwig et al. 1990; Tongway 1991; King and Hobbs 2006). Structure refers to static patterns within the target community i.e. the actual structure (woodland, forest etc.) of the vegetation, while process refers to ecological processes, such as nutrient cycles and propagule dispersal, that impact on structure. In fact, structure and process are interrelated (for example, see Burke et al. 1998) and the existence of feedback relationships between biotic and abiotic components of a system highlight the need to address both structure and process for the improved management and restoration of vegetation communities (Ehrenfeld and Toth 1997).

JK Fitzgerald Chapter 1 21

Prober and Thiele (2005) proposed a framework, based on the integration of process and diversity, to maximise restoration outcomes for temperate woodlands and in Australia. This framework enables the development and application of appropriate restoration techniques by setting clearly defined goals that are underpinned by a sound knowledge of pre-disturbance conditions and an understanding of how and why the system has become degraded. The steps in Prober and Thiele‟s (2005) framework are to: 1. identify what the ecosystem was like prior to degradation; 2. develop an understanding of how and why changes (i.e. degradation) to the system have occurred; 3. reinstate diversity by establishing appropriate processes; and 4. undertake adaptive restoration, which is analogous to adaptive management (sensu Stem et al. 2005).

The first step includes understanding natural processes and natural patterns of diversity and is based squarely on the principles of Landscape Function Analysis, which was developed by David Tongway in the early 1990s as a means to develop appropriate restoration techniques for degraded rangelands in Australia (Tongway 1991). This technique has been applied extensively throughout the country (Ludwig et al. 1994; Tongway and Ludwig 1996; Ludwig et al. 2004; Ludwig et al. 2007), as well as overseas (e.g. Maestre and Cortina 2004) and it is based on identifying and understanding how nutrients, energy and water flow through a landscape and how this affects the vegetation (Tongway and Ludwig 1990; Tongway 1991; Ludwig and Tongway 1995).

Addressing the first two steps outlined above involves comparing the structure and function of degraded ecosystems with reference states or little disturbed systems (Prober and Thiele 2005). This approach has been widely used in Australia to assess how land use change has impacted ecosystem structure and function (Yates and Hobbs 1997), for example see Parker and Chartres (1983), Scougall et al. (1993), Yates and Hobbs (1997), Yates et al. 2000b and Prober et al. (2002b). This approach has also been used extensively in overseas studies that have examined the long term impacts of past land use, namely agriculture, on vegetation communities and their edaphic environment (for example see Flinn and Vellend 2005).

JK Fitzgerald Chapter 1 22

A reference state, or site, is usually the state of the vegetation community prior to disturbance and this is commonly set as pre-European times (or pre-1750) for temperate woodlands in Australia (Austlig 1990). The value of using these types of reference states has been debated in the literature (Egan and Howell 2001; Oliver et al. 2002; Nielson et al. 2007) because pre-disturbance conditions may be inferred rather than known and vegetation communities are dynamic and change over a range of time scales, so aiming for static compositional and structural attributes may be inappropriate (Hobbs and Harris 2001). Prober and Thiele (2005) argued however, that even if restoration to a pre-disturbance state is unrealistic or unwanted, knowledge of relatively undisturbed, self-sustaining ecosystems will result in valuable insights into how or why restoration, or natural succession, may or may not be following a desired trajectory. This view was also expressed by Yates et al. (1994) and Hobbs and Harris (2001).

An important part of understanding the successional pathway is to know the natural variability in the system in terms of micro-habitats or patches. Understanding the natural patterns of diversity and associated processes has been the focus of patch dynamics for many decades (for example see Watt 1947; White 1979; Wu and Loucks 1995). The definition of a patch is context- and scale-specific (White and Pickett 1985; Belsky and Canham 1994). Within a stand of vegetation it generally refers to discrete structural patterns that have been produced from endogenous or exogenous disturbances (White 1979; Brokaw 1985; Loucks et al. 1985; Runkle 1985), or which result as a consequence of the inherent structure (e.g. savanna) of the community (Belsky and Canham 1994; Ludwig and Tongway 1995; Treydte et al. 2007). For example, tree-fall gaps (patches) in tropical or temperate forests may result from windstorms, while inter- canopy areas are characteristic features of woodland communities that tend to occur independently of disturbance events. In these cases, patch dynamics refers to the relationship of different patches within the community, which contribute to the spatial heterogeneity of resources, such as soil and plant attributes. Canopy and inter-canopy areas for example, may affect ecological processes, like nutrient cycling, differently and this could promote the development of different species assemblages (Collins and Pickett 1987; Vetaas 1992; Belsky 1994; Scholes and Archer 1997). At a broader scale, a patch may be an entire stand of vegetation and in this context, patch dynamics refers to the interaction of vegetation patches with their surrounding matrix, be it urban areas, agriculture or another type of land use. These types of interactions are the focus of

JK Fitzgerald Chapter 1 23

landscape ecology (Turner 1989) and meta-population analysis (Freckleton and Watkinson 2002).

Small-scale patch dynamics has been used to study soil-vegetation relationships and vegetation dynamics in tropical (Brokaw 1985; Slocum 2000; McIvor et al. 2005; Gnankambary et al. 2008), temperate (Ryan and McGarity 1983; Collins et al. 1985; Peterson et al. 1990) and semi-arid and arid areas (Obot 1988; Belsky et al. 1989; Garner and Steinberger 1989; Vinton and Burke 1995; Barnes and Archer 1996). This research has found that the physical, chemical and biological attributes of the soil beneath individual trees and shrubs can differ substantially to that of adjacent inter- canopy areas, with differences in microclimate as well (Burke et al. 1995). These differences can affect ground species composition, cover and productivity (Jackson and Ash 1998; 2001) and this has led to the application of small-scale patch dynamics to the restoration of degraded areas (Rhoades et al. 1998; Slocum 2000; Prober et al. 2002b).

1.10 Aims of this thesis

Given the current lack of understanding of Cumberland Plain Woodland ecology and the dire need for the improved management and restoration of this endangered vegetation community, it is imperative that research efforts are increased. In particular, soil-based research relating to both degraded and intact systems is needed to better inform restoration efforts; the vast amount of research on old fields and other degraded systems, both in Australia and overseas, is testament to this. In light of this, this thesis aims to address three fundamental questions:

1. How does the soil and ground layer vegetation of Cumberland Plain Woodland vary in response to canopy and inter-canopy patch types? 2. How has past agricultural land use affected the soil and vegetation of Cumberland Plain Woodland? 3. What are the impacts of restoration of Cumberland Plain Woodland on the soil of abandoned pastures that were once covered in this vegetation community?

JK Fitzgerald Chapter 1 24

CHAPTER 2 Description of the Cumberland Plain and study sites

2.1 The Cumberland Plain

2.1.1 Location

The Cumberland Plain (33°30ˈ-34°30ˈS, 150°30ˈ-151°30ˈE) is synonymous with western Sydney and covers approximately 250,000 ha (NPWS 2002b). The region extends from the Hawkesbury district in the north, to Thirlmere in the south and sweeps westwards from Parramatta to the Hawkesbury-Nepean River. There is also a narrow section that stretches from Parramatta towards the coast (Herbert and Clark 1991; Figure 2.1). Burwood, Camden, Fairfield, Holroyd, Parramatta and Strathfield are located entirely on the Cumberland Plain, while another twenty-four Local Government Areas have a portion of their area within the region (Table 2.1).

Figure 2.1 Map of the Cumberland Plain (low-lying grey area) and surrounding Hawkesbury Sandstone plateaux (elevated green areas) showing the locations of the five study sites. „Reference‟ refers to remnant stands of Cumberland Plain Woodland. Base image from Google Earth (www.googleearth.com).

JK Fitzgerald Chapter 2 Page 25

Table 2.1 Size of the Local Government Areas (LGA) associated with the Cumberland Plain and the proportion of their area located within the region (from NPWS 2002b).

LGA Size of LGA (ha) Area on the Plain (ha) Portion on the Plain (%) Ashfield 827 683 82.6 Auburn 3,236 3,111 96.1 7,744 7,057 91.1 Baulkham Hills 39,958 11,959 29.9 Blacktown 23,934 23,934 100 Burwood 713 713 100 Camden 20,052 20,052 100 Campbelltown 31,037 14,836 47.8 Bay 1,975 557 28.2 Canterbury 3,347 3,145 94.0 Fairfield 10,132 10,132 100 Hawkesbury 276,761 27,005 9.8 Holroyd 4,010 4,010 100 Hornsby 50,537 6,661 13.2 Hunters Hill 562 82 14.6 Hurstville 2,460 1,580 64.2 Kogarah 1,933 231 11.9 Ku-Ring-Gai 8,514 3,395 39.9 1,039 203 19.5 Leichhardt 1,251 245 19.6 Liverpool 30,524 25,827 84.6 Marrickville 1,650 1,331 80.7 Parramatta 6,119 6,119 100 Penrith 40,288 38,372 95.2 Rockdale 3,003 527 17.6 Ryde 4,054 2,730 67.4 South Sydney 1,779 532 29.9 Strathfield 1,385 1,385 100 Sydney 638 72 11.2 Willoughby 2,216 1,070 48.3 Wollondilly 255,029 57,056 22.4

2.1.2 Climate

The Cumberland Plain has a temperate climate with warm wet summers and cold winters with low rainfall (BOM 1991). Climatic summaries for a range of sites located on the Cumberland Plain are given in Figures 2.2a-2.2h. Twice as much rain tends to fall in summer than winter and there is high inter-year variability as well (BOM 2009; Figures 2.2a-2.2h). The medium annual rainfall for the region is about 800 mm but there is a decreasing trend in rainfall from east to west (for example, Figure 2.2e cf. Figure2.2b), which is accompanied by an increase in the frequency and duration of dry

JK Fitzgerald Chapter 2 Page 26

35 120

30 100

25 80 20 60

C)/Number of days rainof C)/Number 15 °

40 (mm) Rainfall 10

5 20 Temperature ( Temperature 0 0

Decile 5 (median) monthly rainfall (mm) for years 1936 to 1996 Mean maximum temperature (Degrees C) Mean minimum temperature (Degrees C) Mean number of days of rain

Figure 2.2a Long-term climatic data for selected variables for Badgerys Creek (BOM 2009).

35 120

30 100

25 80 20 60

C)/Number of days rainof C)/Number 15 °

40 (mm) Rainfall 10

5 20 Temperature ( Temperature 0 0

Decile 5 (median) monthly rainfall (mm) for years 1943 to 2008 Mean maximum temperature (Degrees C) Mean minimum temperature (Degrees C) Mean number of days of rain

Figure 2.2b Long-term climatic data for selected variables for Camden (BOM 2009).

JK Fitzgerald Chapter 2 Page 27

35 120

30 100

25 80 20 60

C)/Number of days rainof C)/Number 15 °

40 (mm) Rainfall 10

5 20 Temperature ( Temperature 0 0

Decile 5 (median) monthly rainfall (mm) for years 1962 to 2001 Mean maximum temperature (Degrees C) Mean minimum temperature (Degrees C) Mean number of days of rain

Figure 2.2c Long-term climatic data for selected variables for Liverpool (BOM 2009).

35 120

30 100

25 80 20 60

C)/Number of days rainof C)/Number 15 °

40 (mm) Rainfall 10

5 20 Temperature ( Temperature 0 0

Decile 5 (median) monthly rainfall (mm) for years 1970 to 2008 Mean maximum temperature (Degrees C) Mean minimum temperature (Degrees C) Mean number of days of rain

Figure 2.2d Long-term climatic data for selected variables for Orchard Hills (BOM 2009).

JK Fitzgerald Chapter 2 Page 28

35 120

30 100

25 80 20 60

C)/Number of days rainof C)/Number 15 °

40 (mm) Rainfall 10

5 20 Temperature ( Temperature 0 0

Decile 5 (median) monthly rainfall (mm) for years 1965 to 2008 Mean maximum temperature (Degrees C) Mean minimum temperature (Degrees C) Mean number of days of rain

Figure 2.2e Long-term climatic data for selected variables for Parramatta (BOM 2009).

35 120

30 100

25 80 20 60

C)/Number of days rainof C)/Number 15 °

40 (mm) Rainfall 10

5 20 Temperature ( Temperature 0 0

Decile 5 (median) monthly rainfall (mm) for years 1880 to 2008 Mean maximum temperature (Degrees C) Mean minimum temperature (Degrees C) Mean number of days of rain

Figure 2.2f Long-term climatic data for selected variables for Picton (BOM 2009).

JK Fitzgerald Chapter 2 Page 29

35 120

30 100

25 80 20 60

C)/Number of days rainof C)/Number 15 °

40 (mm) Rainfall 10

5 20 Temperature ( Temperature 0 0

Decile 5 (median) monthly rainfall (mm) for years 1887 to 2008 Mean maximum temperature (Degrees C) Mean minimum temperature (Degrees C) Mean number of days of rain

Figure 2.2g Long-term climatic data for selected variables for Prospect (BOM 2009).

35 120

30 100

25 80 20 60

C)/Number of days rainof C)/Number 15 °

40 (mm) Rainfall 10

5 20 Temperature ( Temperature 0 0

Decile 5 (median) monthly rainfall (mm) for years 1881 to 2008 Mean maximum temperature (Degrees C) Mean minimum temperature (Degrees C) Mean number of days of rain

Figure 2.2h Long-term climatic data for selected variables for Richmond (BOM 2009).

JK Fitzgerald Chapter 2 Page 30

spells, more pronounced temperature extremes and a greater number of frosts (BOM 1991).

The area encompassed by Pitt Town, Penrith, Campbelltown and Liverpool is the dry central core of the region (Benson and Howell 1990b). Throughout this area, the average number of rain days per year is markedly lower than that of more easterly locations and the medium annual rainfall is generally less than 800 mm (BOM 1991). For example, the medium annual rainfall for Parramatta and Badgerys Creek is 961 mm and 740 mm respectively and their corresponding mean number of rain days in a year is 121 and 81 (BOM 2009).

The driest period on the Cumberland Plain is in late winter and early spring (Figures 2.2a-2.2h) when westerly winds prevail. February and March are generally the wettest months of the year (Figures 2.2a-2.2h) and thunderstorm activity is high during this time because storm cells develop over the Great Dividing Range and travel east across the region towards the coast. January and July tend to be the hottest and coldest months respectively (Figures 2.2a-2.2h). The average frost period can exceed 100 days and this usually occurs between May and September (BOM 1991; BOM 2009).

2.1.3 Physiography

The Cumberland Plain is one of the six physiographic units of the Sydney region (Figure 2.3). It is structurally defined by the Cumberland Basin, which is a saucer- shaped tectonic depression that underlies most of western Sydney with a long, narrow extension from Parramatta to (Herbert and Clark 1991). The Cumberland Plain is clearly separated from the Blue Mountains Plateau by the Lapstone Structural Complex, which consists of the Nepean Fault, the Kurrajong Fault and the Lapstone Monocline, as well as many minor thrusts, folds and joints (Herbert 1979). The northern and southern boundaries of the Cumberland Plain are less well-defined, with the Hornsby Warp and South Coast Warp producing gentle transitions to the adjoining plateaux (Herbert and Clark 1991).

The Hawkesbury-Nepean River flows along the Lapstone Structural Complex and drains most of the Cumberland Plain through the South Creek and Eastern Creek

JK Fitzgerald Chapter 2 Page 31

Figure 2.3 Block diagram showing the six physiographic units of the Sydney region (adapted from Bannerman and Hazelton (1990) and Benson and Howell (1990b)). systems. The drains the south eastern section of the Cumberland Plain and its floodplain is generally 1-2 km wide (Young 1991). Quaternary deposits are found along most rivers and creeks but older deposits are relatively rare. In the area between Windsor and Penrith however, there is an extensive deposit of Tertiary alluvium (Gobert 1978).

The Cumberland Plain is characterised by gently undulating plains and low hills that are generally 20-150 masl (Young 1991). In the far southwest of the region however, in the vicinity of the Razorback Range, much higher elevations (~350 masl) are reached (Hazelton and Tille 1990). The undulating terrain is due to the low mass strength of the Wianamatta , which are highly fissured and weather rapidly to produce clay-rich soils (Young 1991).

2.1.4 Geology

The geology of the Cumberland Plain is dominated by the , which consists of three formations that were laid down during a single regressive episode during the Middle (Herbert 1979). The Wianamatta Group is mainly composed

JK Fitzgerald Chapter 2 Page 32

of fine grained rocks, such as claystone and siltstone, although sandstone dominates the smallest formation of the Group. The total preserved thickness is typically less than 150 m but a maximum thickness of 304.2 m has been recorded in the Razorback Range (Herbert 1979).

The three formations of the Wianamatta Group are, in order of decreasing age: ; Minchinbury Sandstone; and . Ashfield Shale was deposited in a lacustrine or shallow marine environment and consists of dark grey to black sideritic claystone and siltstone, dark grey to black siltstone laminite and light grey quartz lithic sandstone laminite (Bembrick et al. 1991). This formation occurs on the northern, south eastern and western margins of the Cumberland Plain and ranges in thickness from 44.6 m to 61.6 m (Herbert 1979). Minchinbury Sandstone is a strandline deposit that is also found on the edge of the Cumberland Plain (Herbert 1979); it is comprised of fine to medium grained quartz-lithic sandstone (Bembrick et al. 1991) and is approximately 4 m thick (Herbert 1979). Bringelly Shale was laid down in a coastal plain environment and is distributed extensively throughout the region, with most deposits being less than 150 m thick ((Bembrick et al. 1991). This formation is dominated by claystone and siltstone but it also contains small amounts of laminite, sandstone, , highly carbonaceous claystone and tuff (Herbert 1979).

2.1.5 Soil associations and soil landscapes

There is a strong relationship between soil profile development and topography on the Cumberland Plain and this has been mapped by Walker (1960) and Bannerman and Hazelton (1990) using the catena concept, which describes soil variation along slopes (Corbett 1969). Walker (1960) mapped seventeen different soil associations, or catenas, for the County of Cumberland, which included most of the Cumberland Plain, as well as large areas of the Hornsby and Woronora Plateaux.

The Cumberland association was the most widespread catena in the County and it consisted of the Warrawee, Cumberland and Austral soil series (Walker 1960). The Warrawee series was a minor component because it was restricted to small areas of shale-capped sandstone on the northern edge of the Cumberland Plain. The Cumberland and Austral soil series on the other hand, were widely distributed throughout the

JK Fitzgerald Chapter 2 Page 33

Cumberland Plain with the former occupying crests and upper slopes and the latter being found on lower slopes and in depressions. The Cumberland series was characterised by red podzolic soils and the Austral series was typified by yellow podzolic soils (Walker 1960). This sequence was later classified, with some minor changes, as the Blacktown soil landscape for the Penrith 1:100 000 map sheet (Bannerman and Hazelton 1990). For the portion of this map sheet located within the Hawkesbury-Nepean catchment however, the distribution of the soil landscapes was recently revised by DECC (2008d).

Soil landscapes are similar to soil associations because they combine information on landform and soil type but soil landscapes also describe soil materials, which represent discrete layers within the soil profile. These layers generally correspond to conventional soil horizons but they may also refer to unconsolidated materials on the soil surface, weathered bedrock and land fill (Atkinson 1993).

The Blacktown soil landscape is the dominant soil landscape of the Cumberland Plain (Bannerman and Hazelton 1990). It is a residual soil landscape, which means that deep soil profiles have formed from the in situ weathering of parent material. It consists of low rises and hills underlain by Wianamatta Shale. These landforms usually have broad (200-600 m) concave crests and simple slopes with convex footslopes (Young 1991). The local relief is 10-50 m and the altitude ranges from 10-202 m. Slopes are generally less than 10% and there is no outcrop (DECC 2008d). The occurrence and relationship of the dominant soil types and soil materials are shown in Figure 2.4. As previously mentioned, red and yellow podzolic soils occupy the upper and lower topographic positions respectively but brown podzolic soils are also found on crests and upper slopes (Bannerman and Hazelton 1990).

The Blacktown soil landscape has an average Rural Land Capability of IV and an Urban Capability that ranges from B to C. Grazing limitations are therefore low and most areas are not well-suited to intense cultivation (Chapman and Atkinson 2007). There are localised hazards for urban development, mostly in the form of reactive subsoils (foundation hazards) and secondary salinity. Localised occurrences of sheet and gully erosion have also been reported for this soil landscape, which can adversely affect both rural and urban development (DECC 2008d).

JK Fitzgerald Chapter 2 Page 34

Figure 2.4 Schematic diagram of the Blacktown soil landscape showing changes in soil types and soil materials along the toposequence (from Bannerman and Hazelton 1990).

2.1.6 Soil types and soil materials

The dominant pedogenic processes operating within podzolic soils are summarised in Table 2.2. The red and yellow podzolic soils are characterised by base depletion, poorly developed O and A2 horizons and no illuviation of humus but a strong illuviation of clay, iron and sesquioxides (Corbett 1969; Corbett 1972). Red podzolic soils form on crests and upper slopes because these well drained positions promote the formation of haematite, which is red in colour. Yellow podzolic soils develop on lower slopes and in depressions because the yellow mineral goethite forms in poorly drained positions (Stace et al. 1968). The brown podzolic soils are somewhat different because they tend to have a greater accumulation of humus throughout the profile, which masks the colour of iron oxides within the subsoil (Corbett 1969).

The morphological properties of the soil materials of the Blacktown soil landscape are listed in Table 2.3, along with their constraints to rural and urban development. The decrease in pH down the soil profile indicates the leaching of basic cations, the change from dark coloured surface layers to light coloured subsoil reflects the lack of humus illuviation and the increase in clay content with depth suggests clay illuviation, although this may also result from the in situ weathering of Wianamatta Shale, as discussed by Bishop et al. (1980).

JK Fitzgerald Chapter 2 Page 35

Table 2.2 The dominant processes contributing to the formation of profile morphology in brown, red and yellow podzolic soils and their degree of development for each soil type: „wd‟ refers to well developed; „min‟ reflects minimal development and a blank space indicates the process does not occur (adapted from Corbett 1969). Illuviation Leaching of O horizon Bleicherde Podzolic soil Iron and bases development formation Humus Clay aluminium Brown wd wd wd wd min

Red wd min wd wd wd

Yellow wd min wd wd wd

On a global scale and in terms of Australian agriculture, the podzolic soils of the Cumberland Plain are strongly acidic and have very low levels of P, N and Ca (Corbett 1972; Bannerman and Hazelton 1990). The subsoils typically have high salt concentrations and secondary salinity is a problem in some areas due to altered drainage patterns, especially on footslopes and in low-lying areas (DECC 2008d).

2.1.7 European land use history

European land use and settlement patterns on the Cumberland Plain have been shaped by the geology and physiography of the Sydney region, as well as the changing socio- economic trends over the past two centuries (Proudfoot 1987; Howarth 2003). The Cumberland Plain was a rural landscape for the first 100 years of European settlement but as the following century progressed, so too did urban and industrial development and since the mid-1970s, the Cumberland Plain has been the focus of Sydney‟s urban sprawl (Benson and Howell 1990b; Kass 2005). Four phases of European settlement have been identified (after Proudfoot 1987) and these highlight the dominant social and land use trends that have occurred since the late 1700s.

2.1.7.1 Discovery and settlement of the Cumberland Plain, 1789-1821

The first agricultural site of the colony was located at Farm Cove, in what is now the Royal Botanic Gardens of Sydney (Australian Gallery Directors Council 1979). Within eight months of settlement, poor crop yields and crop failure threatened the survival of the colony and the Europeans discovered the Cumberland Plain shortly after in their search for arable land. This phase of European settlement was marked by the spread of

JK Fitzgerald Chapter 2 Page 36

Table 2.3 Morphological properties of the soil materials from the Blacktown soil landscape and their limitations (adapted from Bannerman and Hazelton (1990) and DECC (2008d)). Soil Layer morphology Soil limitations material bty 1 Horizon A1 strongly acidic Texture loam to clay loam Colour 10YR 2/2, 5YR 3/2, 10YR 3/4 pH (water) 5.5-7.0 Surface condition friable Ped shape sub-angular blocky Ped size 2-20 mm Ped fabric rough faced and porous Roots common Charcoal fragments uncommon Iron nodules uncommon bty 2 Horizon A2 hardsetting Texture clay loam to silty clay loam low fertility Colour 7.5YR 4/3, 2.5YR 3/3, 10YR 3/3 strongly acidic pH (water) 5.0-6.5 high Al toxicity Surface condition hardsetting & water repellent Ped shape weakly developed sub-angular blocky Ped size 20-50 mm Ped fabric rough faced and porous Roots uncommon Charcoal fragments uncommon Iron nodules common bty 3 Horizon B localised shrink-swell capacity Texture light to medium clay low wet strength Colour 7.5YR 4/6, 2.5YR 4/6, 10YR 4/6 low permeability pH (water) 4.5-6.5 low available water Ped shape polyhedral to sub-angular blocky localised salinity Ped size 5-20 mm localised sodicity Ped fabric smooth faced & dense very low fertility Roots uncommon very strongly acidic Charcoal fragments uncommon very high Al toxicity Mottling red, yellow & grey bty 4 Horizon B3 or C localised shrink-swell capacity Texture silty clay to heavy clay low wet strength Colour 10YR 7/1, 2.5YR 6/2 stoniness pH (water) 4.0-5.5 low permeability Ped shape polyhedral to sub-angular blocky low available water Ped size 2-20 mm localised salinity Ped fabric smooth faced & dense localised sodicity Roots uncommon very low fertility Charcoal fragments uncommon very strongly acidic Iron nodules common very high Al toxicity Mottling red, yellow & grey

JK Fitzgerald Chapter 2 Page 37

agriculture throughout the region, with land use and settlement patterns being established by 1821 (Robinson 1953). Figure 2.5 shows when and where land was granted up until this time. This pattern of land allocations reflects various stages of the colonial administration, as well as the environmental (i.e. soil) constraints on agriculture.

Figure 2.5 Land granted on the Cumberland Plain during the period 1788 to 1821 (from Robinson 1953).

During Governor Phillips term (1788-1796), small areas of land were granted along the , around Prospect and on the floodplain of the Hawkesbury River in the

JK Fitzgerald Chapter 2 Page 38

northwest of the region (Robinson 1953). The first farm on the Cumberland Plain was established at Rose Hill (now Rosehill, near Parramatta) and its initial harvest was extremely successful because it produced about ten times the quantity of wheat and barley than Farm Cove for the same season (Proudfoot 1987). The settlements of Windsor, Pitt Town, Richmond, Wilberforce and Castlereagh (the „Macquarie Towns‟) were established soon after (ca.1790) to exploit the fertile floodplain of the Hawkesbury River. At this time, these settlements were composed of many small (~12 ha) land grants located next to the river that were intensively cultivated using the double- cropping method (Atkinson 1826). By 1803, severe soil erosion had occurred due to the clearance of native vegetation and inappropriate farming practices (Proudfoot 1987).

Governor Hunter and Governor King continued the trend of allocating small-sized grants (generally less than 20 ha) on alluvial soils during the period 1796-1806. Most of the land granted during this time was: along South Creek; beside various northern, southern and western sections of the Hawkesbury-Nepean River; and adjacent to the Georges River in the Bankstown district. Grants were also allocated along the track (now called Old Windsor Road) linking Parramatta with the Macquarie Towns (Robinson 1953).

It soon became apparent that the soils of the Cumberland Plain, especially those located away from the drainage lines, were much more suited to grazing than cropping (Murray and White 1988) and by 1806, large tracts of land had been reserved by the government to supplement food crops and to increase stock numbers (Figure 2.6). Government farms were established at Toongabbie and Castle Hill in 1791 and 1801 respectively, while government-owned cattle herds were raised on 6800 ha near Rooty Hill (Nicolaidis 2000). Commons were also established at various locations throughout the region to enable subsistence farmers to raise a small number of cattle and sheep.

During the early years of Governor Macquarie‟s term (i.e. 1806-1813) grants continued to be made along the rivers and major creeks but they were larger than those allocated by the preceding governors (except for the Crown reserves) and as such, they were not confined to the floodplains (Robinson 1953). Many grants were made to the northwest and west of Parramatta but extensive areas of land were also being granted in the

JK Fitzgerald Chapter 2 Page 39

southwest of the region (Figure 2.5). These grants were made to people who could invest money in livestock and this fuelled the development of the pastoral industry on

Figure 2.6 Crown land on the Cumberland Plain in 1806 (from Robinson 1953). the Cumberland Plain (Kass 2005).

Governor Macquarie continued to allocate large grants from 1813 to 1821 and this was focused on developing new pastoral lands in the southwest (Keating 1996). In the Liverpool district for example, there were 8554 head of sheep and 3743 head of cattle in 1814 but within three years, this had increased to 12,667 and 7291 head of sheep and

JK Fitzgerald Chapter 2 Page 40

Figure 2.7 The extent of agricultural land uses in various districts of the County of Cumberland in 1810, 1815 and 1820 (from Robinson 1953). cattle respectively (Keating 1996). The prevalence of grazing throughout the Cumberland Plain is highlighted by Figure 2.7, which shows how many acres were under pasture, in fallow and cropped in the Sydney, Parramatta, Hawkesbury and Liverpool districts in 1820, by which time overgrazing had become a serious problem in some areas (Proudfoot 1987). Several government reserves were also dissolved during this time and the land was granted to individuals (Robinson 1953).

Excluding government farms and Commons, there were 1665 farms on the Cumberland Plain by the end of 1821 and the vast majority of these (75%) were less than 40 ha, 20% were between 40 ha and 200 ha, and 5% ranged in size from 200 ha to 2000 ha (Robinson 1953). In general, the smaller properties supported subsistence farming along

JK Fitzgerald Chapter 2 Page 41

rivers and large creeks while the larger grants were used for domestic livestock grazing and broad-acre cropping, which were profit-driven activities (Kass 2005).

2.1.7.2 Agricultural consolidation of the Cumberland Plain, 1821-1858

The role of the government in food production and pastoralism declined during this phase of European settlement and the private sector expanded (Benson and Howell 1990b). This is marked by the demise of the government farms and the acquisition of additional parcels of land by rich land holders (Atkinson 1826). The pastoral industry continued to flourish for much of this phase and broad-acre cropping was also prevalent during this time. Changing socio-economic conditions and environmental constraints however, contributed to a change in land use and settlement patterns towards the end of this period.

Compared to the initial phase of European settlement, the rate and extent of vegetation clearance increased dramatically during this phase, with at least five government clearing gangs working across the Cumberland Plain (Proudfoot 1987). An extensive network of tracks, analogous to Travelling Stock Reserves, was also established in the early 1800s and many of these are still evident today. Importantly, the , the Northern Road and Cowpasture Road formed the basis of this network (Kass 2005). Holding paddocks were established along these tracks at key locations, such as Penrith, Richmond, St. Mary‟s and Liverpool. These provided convenient resting places for stock and their handlers as they travelled to and from the Sydney markets (Proudfoot 1987).

During the 1830s and 1840s the pastoral industry became established on more fertile pastures in the Hunter Valley and west of the Great Dividing Range (Keating 1996). This reduced the general prosperity of the pastoral industry on the Cumberland Plain which, combined with a severe drought in the late 1830s and an economic depression during the 1840s, spurred many large landholders to move the bulk of their operations to the North Coast and of NSW (Keating 1996; Kass 2005).

Wheat was the most common broad-acre crop grown on the Cumberland Plain during this time and Campbelltown was the most successful wheat-growing district in the

JK Fitzgerald Chapter 2 Page 42

region (Bayley 1974). The industry was destroyed by stem rust in the 1860s however and this helped to fuel the development of new industries, such as dairying and orcharding, which gained prominence in the proceeding phases of European settlement (Kass 2005).

2.1.7.3 Industrialisation of the Cumberland Plain, 1858-1900

This phase marks the beginning of industrial and urban development on the Cumberland Plain, although agriculture continued to play an important role in the region‟s economy throughout this time. The construction of the railway line from Sydney to Windsor, which occurred during 1855-1864, was instrumental in fuelling the development of the Cumberland Plain‟s industrial sector (Proudfoot 1987). Importantly, the railway promoted the expansion of pre-existing industries, as well as the development of new ones. The railway also made the region more accessible and this, coupled with new employment opportunities, led to population growth (Benson and Howell 1990b).

The fruit growing industry, which was centred on the north western margin of the Cumberland Plain, thrived during this time because more produce could be transported to the Sydney markets (Kass 2005). Similarly, abattoirs could now operate in the region because the meat could be transported to the markets without spoiling. The timber- getting industry also became established during this time, with sawmills being built next to most railway stations and sidings (Proudfoot 1987). Ironbarks and other hardwood species, which had long been used to build rooves and fences, were now being exploited for railway sleepers (Benson and Howell 1990b). Softwoods, such as , were also being collected to meet the ever-increasing demand for firewood. In addition to this, decurrens and Acacia parramattensis were harvested for their bark, which was used in leather tanning solutions (Benson and Howell 1990b).

During the construction of the railway it was very common for subsistence farmers, farmhands and labourers to gain short-term contracts for fencing and timber-getting. Excavating was another additional source of income for people during this time, as several quarries were established between Prospect and Penrith to mine sand, , shale and blue metal (Proudfoot 1987).

JK Fitzgerald Chapter 2 Page 43

2.1.7.4 Urbanisation of the Cumberland Plain, 1880-the present day

The subdivision of rural land for urban purposes first occurred in the 1840s but urban development didn‟t really get underway until the turn of the 20th Century (Proudfoot 1987). Agriculture continued to play an important role during this phase of European settlement however, particularly up until the 1960s (Keating 1996) and large areas in the northwest and southwest continue to be the focus of rural activities (Figure 2.8).

After the demise of the pastoral and wheat industries during the previous phase of settlement, there was a shift to smaller and more intensive agricultural operations (Keating 1996; Kass 2005). Importantly though, some areas have sustained large-scale pastoral activities to the present day, or until very recently, especially in the southwest around Bringelly, Luddenham and Camden (Figure 2.8).

Dairying came to the fore in the late 1800s, aided by the development of new cooling technologies and improved transportation (Kass 2005). Poultry farming also proliferated throughout the region during 1900-1960 and by the 1950s, poultry was the most common agricultural enterprise on the Cumberland Plain (Keating 1996). Orcharding, market gardening and viticulture were also important industries during this phase of European settlement. In 1945 for example, the agricultural sector of the Cumberland Plain accounted for 17% of the citrus market in NSW, 75% of the States lettuce supply and about 15% of the grapes produced in NSW. The Cumberland Plain also contained nearly three-quarters of the States poultry farms and produced about 18% of the milk made in NSW at that time (Proudfoot 1987).

The initial spate of urban development was focused on railway stations. In 1855 and 1904 for example, residential developments occurred next to Parkes Platform (now Werrington railway station) and Quakers Hill station respectively (Proudfoot 1987). Toongabbie was also at the centre of several residential developments during 1910-1922 and in 1931, the construction of the East Hill railway line instigated intense urban development in the southwest (Benson and Howell 1990b).

The increasing availability of cars between the two World Wars promoted settlement away from the public transport corridors (Proudfoot 1987). During this period there was

JK Fitzgerald Chapter 2 Page 44

Figure 2.8 Contemporary land uses for the Cumberland Plain; grazing occurs in the areas coloured light green, while pink identifies residential and industrial areas (from NPWS 2002a). major population growth within the Canterbury-Bankstown district and in the area between Concord and Parramatta. The post-World War II period also had a strong impact on land use and settlement patterns in the region, particularly with regards to the baby-boomers, who instigated a wave of urban development in the 1970s that has continued until the present day (Benson and Howell 1990b).

JK Fitzgerald Chapter 2 Page 45

2.1.8 Vegetation

Over 200 years of land clearance on the Cumberland Plain has reduced native vegetation cover by 87% and the vast majority of communities are now threatened with extinction (Tozer 2003; Table 2.4). Cumberland Plain Woodland is the most widespread vegetation community on the Cumberland Plain but more than 91% of its pre-European extent had been cleared by 2002 (NPWS 2002b; Table 2.4).

Table 2.4 The pre-1750 and current (2002) extent of the vegetation communities on the Cumberland Plain and the date they were listed on the TSC Act (from NPWS 2002b). Vegetation community Pre-European (ha) Current (ha) Remaining (%) TSC Act Shale Sandstone Transition 43 990.10 9949.80 22.6 11/09/1998 Forest Castlereagh 12 185.40 1011.60 8.3 10/05/1998 Ironbark Forest Castlereagh Woodland 1006.00 616 61.2 24/12/1999 Castlereagh Scribbly Gum 5852.40 3083.30 52.7 24/12/1999 Woodland Agnes Banks Woodland 615.2 97.8 15.9 3/11/2000 Cumberland Plain Woodland 125 446.30 11 054.50 8.8 13/06/1997 Sydney Coastal River-flat 39 161.80 5446.10 13.9 12/02/1999 Forest Western Sydney Dry Rainforest 1281.80 338.2 26.4 24/03/2000 Moist Shale Woodland 2033.60 604.1 29.7 19/04/2002 Sydney Turpentine-Ironbark 26,516.40 1181.70 4.5 16/10/1998 Forest Freshwater 1552.40 664.2 42.8 22/12/2000 Elderslie Scrub Forest Not modelled 13.4 n/a 9/10/1998 Shale/Gravel Transition Forest 5427.40 1721.20 31.7 19/04/2002 Blue Gum High Forest 3720.10 167.8 4.5 3/09/1997 TOTAL 268 789.00 35 949.70 13.4

Cumberland Plain Woodland has been variously defined since the 1940s when Pidgeon (1941) classified it as part of the Eucalyptus hemiphloia – Eucalyptus tereticornis Association. It wasn‟t until much later that Benson and Howell (1990a) first coined the term „Cumberland Plain Woodland‟ when they mapped the pre-European and the then current distributions of vegetation communities located in the region. This classification was subsequently revised (Benson 1992; NPWS 2002b; Tozer 2003) and French et al. (2000) addressed the difficulties of classifying Cumberland Plain Woodland due to its high levels of floristic diversity, both within and between sites.

JK Fitzgerald Chapter 2 Page 46

The latest classification schemes have divided Cumberland Plain Woodland into two closely related communities that are distinguished by topography, they being Shale Hills Woodland and Shale Plains Woodland (NPWS 2002b; Tozer 2003). Shale Hills Woodland is largely restricted to the southern half of the Cumberland Plain and is found on steeper slopes and at higher elevations than Shale Plains Woodland, which generally occurs north of Prospect Reservoir and Orchard Hills. For this research however, Shale Hills Woodland and Shale Plains Woodland were treated as one community (viz. the Cumberland Plain Woodland of Benson and Howell (1990a)) because the study sites had similar soil landscapes and therefore topographies.

The most common diagnostic species for Shale Hills Woodland and Shale Plains Woodland are listed in Table 2.5. E. moluccana and E. tereticornis are the most common and widespread tree species throughout the region (Myerscough 1998; Tozer 2003) and they‟re the dominant canopy species for Cumberland Plain Woodland (Benson 1992; Tozer 2003). E. moluccana tends to prefer well drained slopes and ridges while E. tereticornis generally prevails on lower slopes and in depressions. may become co-dominant towards the edge of the Cumberland Plain and similarly, and are very common in certain areas (Benson 1992; Benson and McDougall 1998). A small tree stratum occurs at about 58% of sites and typically consists of and eucalypts with a mean height of 10 m (Tozer 2003). Current distributions and occurrences of canopy species may be skewed from pre-European times. The selective felling of E. crebra for construction materials for example, is thought to have resulted in a substantial local decline of this species (Benson and Howell 1990a).

According to the structural classification scheme of Specht et al. (1995), Cumberland Plain Woodland should have a projective foliage cover of 10-30% with individual crowns that do not overlap (see also Yates and Hobbs 1999). Benson (1992) estimated the canopy cover for Grey Box Woodland, which largely corresponds to Shale Plains Woodland (NPWS 2002b), to be in the range of 10-73%. These figures include woodland, open-forest and closed-forest formations and the last two represent stands with vigorous regeneration following disturbance.

Bursaria spinosa is the dominant shrub species on the Cumberland Plain (Benson and

JK Fitzgerald Chapter 2 Page 47

Table 2.5 The diagnostic floral species for the various strata within Shale Hills Woodland and Shale Plains Woodland. Common species are denoted with a black circle and less frequent species are indicated by a white circle (from NPWS 2002b). Stratum Shale Hills Woodland Shale Plains Woodland  Eucalyptus moluccana  Eucalyptus moluccana  Eucalyptus tereticornis  Eucalyptus tereticornis Tree o Eucalyptus crebra o Eucalyptus crebra o Eucalyptus eugenioides o Corymbia maculata  Acacia implexa  Acacia decurrens Eucalyptus eugenioides Acacia parramattensis subsp. Small Tree    parramattensis  Corymbia maculata  Exocarpus cupressiformus  Bursaria spinosa  Bursaria spinosa o Acacia falcata Shrub o Breynia oblongifolia o Indigophera australis o Dodonea viscosa subsp. cuneata  Aristida ramosa  Aristida vagans  Brunoniella australis  Brunoniella australis  Cheilanthes sieberi spp. sieberi  Desmodium varians Desmodium varians Dichelachne micrantha Ground     Dichondra repens  Microlaena stipoides var.  Microlaena stipoides var. stipoides stipoides  Opercularia diphylla  Themeda australis  Themeda australis

Howell 2002; Tozer 2003) and it is an integral component of Cumberland Plain Woodland, which has a shrub stratum at most (95-100 %) sites (Tozer 2003). B. spinosa is a multi-branched spiny shrub that can reach up to 3 m high (Carolin and Tindale 1993). This species occurs as individuals and commonly grows in clumps or thickets, especially in the absence of fire or following the cessation of grazing and mowing (James 1994; Watson 2005). The lateral expansion of these thickets is thought to be quite slow (Benson and Howell 2002), which is in stark contrast to the smothering effect of the exotic shrub Olea europaea subsp. cuspidata, which is highly invasive and threatens the integrity of Cumberland Plain Woodland in extensive areas in the south and southwest of the region (Cuneo and Leishman 2006).

More than 500 species have been recorded for Cumberland Plain Woodland and most of these are found in the ground layer (James et al. 1999). This high floristic richness may or may not be readily evident due to inconspicuous vegetative forms and sporadic flowering times. Many annual, biennial and perennial members of the Asteraceae, Epacridaceae, Fabaceae and Liliaceae for example, die back and resprout when conditions are favourable (James 1997). The ground layer typically

JK Fitzgerald Chapter 2 Page 48

has a very high cover of perennial grasses and Aristida ramosa, Aristida vagans, Microlaena stipoides and Themeda australis are the most common grass species (Table 2.5).

Cumberland Plain Woodland is thus both shrubby and grassy (sensu Clarke 1999), although the understorey structure of individual stands may be highly dynamic over short timeframes. Benson and Howell (2002) for example, monitored the floristics and structure of a remnant of Cumberland Plain Woodland for 14 years and observed the development of more open and less open phases in response to disturbances such as fire, grazing and drought. The relative abundance of shrubs and grasses prior to European settlement however, is very difficult to determine. This is because historical descriptions are limited and can be variously interpreted and no areas of undisturbed (virgin) woodland remain for analysis and comparison.

2.2 The study sites

2.2.1 Hoxton Park

2.2.1.1 Location

Hoxton Park (33°54ˈS, 150°49ˈE) is located 10 km due south of Prospect Reservoir and 18 km southwest of Parramatta in the Liverpool LGA (Figure 2.1). This site forms part of the , which is a multipurpose open space corridor that stretches from Quakers Hill in the north to West Hoxton in the south (DIPNR 2004). The site is bound by Elizabeth Drive in the north, McIvor Avenue in the south and Cowpasture Road in the east. A water supply channel runs along the western edge of the site and the M7 Sydney Orbital was recently constructed near the eastern boundary.

2.2.1.2 Climate and physical geography

The nearest and longest running meteorological station to Hoxton Park is located at the Liverpool Whitlam Centre. The median annual rainfall for this site is 871.6 mm and the highest (10.9) and lowest (7.1) mean number of rain days occur in March and July respectively (Figure 2.2c). July is the coldest month of the year, with a mean minimum

JK Fitzgerald Chapter 2 Page 49

daily temperature of 4.7°C and a mean maximum daily temperature of 17.3°C. January tends to be the hottest month, with the mean minimum and maximum temperatures being 17.6°C and 28.2°C respectively (BOM 2009).

Hoxton Park is underlain by Bringelly Shale and includes large areas of the Blacktown and Luddenham soil landscapes. These soil landscapes are closely related to each other and they frequently intergrade at Hoxton Park (Bannerman and Hazelton 1990). The Luddenham soil landscape tends to occur at higher elevations and has larger slope gradients than the Blacktown unit. The rural and urban land capabilities are very similar, although more limitations and hazards tend to occur within the Luddenham soil landscape due to the localised occurrences of steep slopes (Table 2.6).

The Blacktown and Luddenham soil landscapes are dominated by red and yellow podzolic soils. The Luddenham unit may also contain additional soil types, which are massive red earths on crests and prairie soils in depressions. The former occurs infrequently while the latter may form in valleys between adjacent hills within the unit (Bannerman and Hazelton 1990).

Table 2.6 Attributes and limitations for urban and rural development of the Blacktown and Luddenham soil landscapes (from DECC 2008d). Attribute/Limitations Blacktown Luddenham Landforms Rises and low hills Low hills and hills Local relief 10-50 m 30-100 m Altitude 10-20 masl 10-104 masl Slope gradient 0-9% 5-20% Rock outcrop nil nil Rural Land Capability IV (II, VI) IV (VI) Grazing limitations Low Low-moderate Cultivation limitations Low-moderate Low-high Urban capability B(C) B(C) Occurrence of steep slopes Not observed Localised Mass movement hazard Not observed Localised Occurrence of seasonal waterlogging Localised Localised Occurrence of permanent waterlogging Not observed Not observed Flood hazard Not observed Localised Foundation hazard Localised Widespread Salinity hazard Localised Localised Sheet erosion Localised Widespread Gully erosion Localised Localised

JK Fitzgerald Chapter 2 Page 50

2.2.1.3 Vegetation

Hoxton Park contains a large (~32 ha) remnant of Cumberland Plain Woodland that has been used, in conjunction with the remnant at Prospect Reservoir, as a reference area for this endangered vegetation community by several other studies (Wilkins et al. 2003; Lomov 2006; Nichols 2005). The woodland at this site has been classified as Shale Hills Woodland and Shale Plains Woodland (Tozer 2003) but it differs from many other sites throughout the region because it is dominated by both C. maculata and E. moluccana (Benson 1992). This site also contains extensive areas of abandoned farmland dominated by exotic perennial grasses, as well as restored areas of vegetation.

2.2.1.4 European land use history

Hoxton Park covers two adjacent parcels of land that were granted to John Wylde and Barron Field in 1817 and 1818 respectively, for beef and wool production (Cannon 1997). The grants were 2000 acres each and the northern property, Cecil Hills, was granted to John Wylde (Donald 1997), who was contracted by the government to produce 6000 pounds of beef within the first year of operation (Liverpool City Council 2007). This property retained a strong focus on cattle grazing until the late 1800s when the last remaining member of the Wylde family became too sick to manage the business. The estate was then sold to the Pye family in 1905 and was run as a sheep and cattle property until 1972 (Liverpool City Council 2007). The property was subsequently purchased by the State Government and was leased for horse agistment and cattle and sheep grazing for many years (Perkins 1997).

The southern property was called Hinchinbrook, the boundary of which remained largely unchanged until the 1940s when the Hoxton Park Aerodrome was built in the south western corner, near Cowpasture Road (Liverpool City Council 2007). The remnant of Cumberland Plain Woodland is located on this allotment and while grazing was excluded in ca. 1999 (pers. comm. T. Beshara 2006), the area is still very occasionally grazed by cattle and kangaroos have also been observed in the area (pers. obs. 2007). Grazing is also excluded from the restored areas (pers. comm. T. Beshara 2006).

JK Fitzgerald Chapter 2 Page 51

2.2.2 Mount Annan Botanic Garden

2.2.2.1 Location

Mount Annan Botanic Garden (34°03ˈS, 150°46ˈE; Mount Annan hereafter) is situated in the southwest of the Cumberland Plain, about 35 km southwest of Parramatta and 4 km due west of Campbelltown (Figure 2.1). The areas used for this research were the Woodland Conservation Area and adjoining pasture, which are located along Mt Annan Drive, to the south of Narellan Road.

2.2.2.2 Climate and physical geography

The dominant geological formation for the site is Bringelly Shale and the area is characterised by the Blacktown soil landscape (Hazelton and Tille 1990). The closest meteorological station is located at Camden Airport. The median annual rainfall is 814.6 mm and February is the wettest month while August is the driest month (BOM 2009). The mean number of rain days ranges from 7.7 for July to 11 for February. The lowest mean minimum daily temperature (2.9°C) occurs in July and the highest mean maximum daily temperature (29.3°C) occurs in January (Figure 2.2b).

2.2.2.3 Vegetation

The Woodland Conservation Area is a very well protected remnant of Cumberland Plain Woodland that has a high level of floristic diversity (Benson and Howell 2002). This remnant has been classified as Shale Plains Woodland (Tozer 2003) and is habitat for many rare and regionally significant plant species including Pimelea spicata, Rhodanthe anthemoides, Sorghum leiocladum and Ranunculus lappaceus (Benson and Howell 2002). The structure and floristics of this remnant have been monitored since 1988 and experiments involving ecological burns and exclosure plots (for the prevention of grazing) have been carried out by ecologists from the Botanic Gardens Trust. Both the woodland and pasture were last grazed by domestic livestock in ca. 1986. The pasture is dominated by exotic perennial grasses, with a very high cover of Paspalum dilatatum (Benson and Howell 2002).

JK Fitzgerald Chapter 2 Page 52

2.2.2.4 European land use history

This site was originally part of a very large (3000 acres) grant allocated to William Howe in 1818 (Cannon 1997). He called the property Glenlee and under the ownership of James Fitzpatrick, who purchased the property in the 1850s, it became one of the most productive dairy farms in the region (Bayley 1974). The Fitzpatrick‟s ran the farm until 1978 and while dairying was the core business, other activities, such as orcharding and cropping, were also carried out. Rye, oats and barley for example, were grown as fodder crops in the late 1800s to the early 1900s and in 1905, parts of the estate were leased and run as a sheep farm (Bayley 1974). In 1984, the Botanic Gardens Trust acquired the southern-most portion of the estate and the Garden was opened four years later (Spackman and Mossop 2000).

2.2.3 Orchard Hills Defence Estate

2.2.3.1 Location

The Orchard Hills Defence Estate (33°48ˈS, 150°43ˈE; Orchard Hills hereafter) is situated in a rural area within the Penrith LGA about 26 km west of Parramatta (Figure 2.1). The northern boundary of the site is marked by Wentworth Road, which was the original cadastral boundary for this parcel of land (Cannon 1997), while the southern perimeter is marked by the supply pipeline. The site is also bound by the Northern Road in the west and large private rural holdings in the east. The estate covers approximately 2029 acres.

2.2.3.2 Climate and physical geography

The site is underlain by Bringelly Shale and the Blacktown unit is the dominant soil landscape (Bannerman and Hazelton 1990). The closest weather station is located at the Orchard Hills Sewage Treatment Plant, which is adjacent to the Defence Estate. The median annual rainfall for the site is 740.4 mm (BOM 2009) and most of the rain falls between January and March, with little rain falling in winter. The coldest and hottest months of the year tend to be July and December, which have mean maximum temperatures of 17.2°C and 28.5°C respectively (Figure 2.2d).

JK Fitzgerald Chapter 2 Page 53

2.2.3.3 Vegetation

The remnant Cumberland Plain Woodland at this site has been classified as both Shale Hills Woodland and Shale Plains Woodland (Tozer 2003). This remnant was listed on the Register of the National Estate in 2002 because it‟s one of the best remaining examples of Cumberland Plain Woodland and is a core area for the conservation of biodiversity on the Cumberland Plain (Australian Heritage Commission 2009a). Pellow and French (2003) surveyed the flora of the site and found 71 species that were of regional significance including Dillwynia tenuifolia, juniperina spp. juniperina and Pultenaea parviflora. The pastures developed for cattle grazing are dominated by exotic perennial grasses (Parsons Brinckerhoff 2002).

2.2.3.4 European land use history

This site encompasses the 2000 acres that was granted to Gregory Blaxland in 1809 (Cannon 1996). Blaxland was a pioneer of the Australian cattle industry and he had a huge scientific interest in pasture improvement (Buttrey 2006). His property, Lee Holme, was used mostly for cattle grazing, which persisted to varying degrees until the turn of the 21st Century. The estate was owned by the Wentworth family for a large part of the 19th Century and was leased to John Lackey in 1879 (Paul Davies Pty. Ltd. 2007). Several artefacts that remain on or near the site reflect the significance of livestock grazing for Lee Holme during the early to mid 1900s, they being, cattle saleyards and horse exercise yards, which were associated with the livestock dealers William Inglis and Sons (Paul Davies Pty. Ltd. 2007). In fact a Rotunda, which was originally built as a cattle exercise yard in the 1920s, is of state historical significance (Paul Davies Pty. Ltd. 2007). In addition to this, the cultural landscape of Lee Holme is of local significance to the Penrith LGA as it highlights the long pastoral history of the district (Murray and White 1988; Paul Davies Pty. Ltd. 2007).

The Department of Defence acquired the site in the 1940s and established a depot for the storage and maintenance of ammunition (Parsons Brinckerhoff 2002). Livestock continued to graze extensive areas of the site until the year 2000 (pers. comm. M. Peterson 2006) and for at least 20 years prior to this time, the site was grazed by approximately 30 cattle and 40 horses (pers. comm. M. Peterson 2006). The site is

JK Fitzgerald Chapter 2 Page 54

currently grazed by Eastern Grey Kangaroos and wallabies, as well as several feral animals, namely and hares (pers. obs. 2006).

2.2.4 Prospect Reservoir

2.2.4.1 Location

Prospect Reservoir (33°49ˈS, 150°53ˈE; Prospect hereafter) is located near the eastern margin of the Cumberland Plain, approximately 10 km west of Parramatta (Figure 2.1). The surrounding areas are comprised largely of rural-residential developments and industrial estates, although urban expansion is occurring to the north of the site. This site includes the reservoir and associated infrastructure, as well as large areas of native and introduced vegetation that aid in the control of water quality and which regulate access to the area (Thomas 1993). A transmission easement cuts through the woodland located on the northern shore of the reservoir and this has been used to delineate hazard reduction burns (Thomas 1994).

2.2.4.2 Climate and physical geography

The dominant geological formation and soil landscape for this site are Bringelly Shale and Blacktown respectively (Bannerman and Hazelton 1990; Jones and Clark 1991). Compared to the other four sites, the climatic conditions of Prospect are moderated by its proximity to the eastern margin of the Cumberland Plain. It has, for example, the highest mean minimum daily temperature for July, the lowest mean maximum temperature for January and along with nearby Hoxton Park, it has the highest median annual rainfall of the five sites (BOM 2009). Prospect still displays however, the broad climatic trends described in Section 2.1.2 for the Cumberland Plain (Figure 2.2f).

2.2.4.3 Vegetation

Both Shale Hills Woodland and Shale Plains Woodland (Tozer 2003) occur at this site, which contains the largest remnant of Cumberland Plain Woodland in the Blacktown LGA (James 1997). The site has a high level of native species diversity and protects at least one hundred plant species that are inadequately conserved on the Cumberland

JK Fitzgerald Chapter 2 Page 55

Plain; it is also core habitat for the threatened species A. pubescens and P. spicata (James 1997).

2.2.4.4 European land use history

The reservoir was constructed from 1878 to 1888 on 2000 acres of land that was granted to John Brabyn in the early 1800s (Bloxham 2002). The site also occupies parts of two adjoining grants that were allocated to John Jacques (300 acres) in 1819 and to John Campbell (2000 acres) in 1823 (Cannon 1997). The latter was developed into Bungarribee, which was a very large and successful mixed farming operation (Bloxham 2002). Prospect has the poorest historical documentation of the five sites; the size and location of the grants on which it is established indicate a history of grazing and broad- acre cropping but it is not clear when and where this happened. James (1997) noted however, domestic livestock grazing within the woodland during the 1970s and the site is currently grazed by kangaroos, wallabies, rabbits and hares (pers. obs. 2006).

2.2.5 Scheyville National Park

2.2.5.1 Location

Scheyville National Park (33°36ˈS, 150°53ˈE; Scheyville hereafter) is located in the Hawkesbury LGA in the northwest of the Cumberland Plain, approximately 25 km northwest of Parramatta and 5 km northeast of Windsor (Figure 2.1). Scheyville is situated within a rural-residential landscape that is being subdivided for urban development. The National Park encompasses 954 ha and is dissected by a private road, several public roads and many horse trails and walking tracks (NPWS 2000).

2.2.5.2 Climate and physical geography

Scheyville is underlain by Ashfield Shale (Jones and Clark 1991) and the area is characterised by the Blacktown soil landscape (DECC 2008d). The nearest meteorological station is located at Richmond. This site experiences a pronounced seasonality of rainfall, with a summer/autumn maximum and a winter/spring minimum. The medium annual rainfall for Richmond is 792 mm with the wettest and driest months

JK Fitzgerald Chapter 2 Page 56

being January and August respectively (BOM 2009). The highest mean maximum daily temperature is 29.4°C and this occurs in January, while the lowest mean minimum daily temperature is 3.2°C, which occurs in July (Figure 2.2h).

2.2.5.3 Vegetation

The National Park contains the largest reserved remnant of Cumberland Plain Woodland, which has been most recently classified as Shale Plains Woodland (Tozer 2003). Benson (1992) considered this remnant to be the most important and best remaining example of Grey Box-Ironbark Woodland and Scheyville National Park has been listed on the Register of the National Estate for its exemplary Cumberland Plain Woodland and significant associations of the Hawkesbury River (Australian Heritage Commission 2009b). Scheyville National Park also protects a number of flora and fauna species that are of state and national significance. This includes populations of A. pubescens and D. tenuifolia, which are listed as vulnerable on the TSC Act and EPBC Act, as well as the endangered bush pea P. parviflora (James 1997; NPWS 2000). Approximately one-third of the National Park is covered with grasslands that are dominated by exotic perennial grasses, namely Paspalum and African Love Grass (NPWS 2000). These areas were once used for domestic livestock grazing and broad- acre cropping, as well as fruit and vegetable growing (Thorp 1992).

2.2.5.4 European land use history

Scheyville has a long and varied European history that is dominated by agriculture. All of the past land uses at the site occupied the same parcel of land (Thorp 1992) and Figure 2.9 is the most comprehensive historical land use map for the site. While the precise locations of all agricultural activities carried out at the site are unknown, the general locations for numerous activities have been indicated by the presence of various artefacts, such as the remains of portable water tanks, silos, a sheep-dip, cattle yards, a piggery, an orchard, feeding trolleys and bridges, as well as an abundance of fence lines, fence posts and gates (Dallas and Navin 1990). The past land uses for Scheyville are summarised in Table 2.7, along with information that is indicative of the nature and extent of agricultural activities carried out at the site since 1804.

JK Fitzgerald Chapter 2 Page 57

Figure 2.9 Historical European land use map for Scheyville (EDAW Pty. Ltd (1992) in Thorp 1992).

During the period 1804-1890, the site formed part of the Pitt Town-Nelson Common, which was crown land set aside to be used for cattle and sheep grazing by the local farmers of the Macquarie Towns (Stubbs and Stubbs 1983). In response to the economic downturn of the late 19th Century, the government established three extremely controversial social and agricultural schemes at the site, these being the Pitt Town Co- Operative Labour Settlement, a Casual Labour Farm and a Government Agricultural Training Farm (Thorp 1992).

Substantial tracts of land were cleared for cultivation during the three years of the Labour Settlement, which began in 1893. Income was generated through tree felling; softwoods were felled for firewood while hardwoods, particularly ironbarks, were felled for use as constructions materials (NPWS 2000). The number and complexity of farming activities increased with the advent of the Casual Labour Farm, which was a retraining facility for destitute men (Stubbs and Stubbs 1983).

JK Fitzgerald Chapter 2 Page 58

Table 2.7 Indicators of the extent and nature of agricultural activities on abandoned farmland at Scheyville (compiled from Stubbs and Stubbs (1983), Kinhill Engineers Pty. Ltd. (1990), Graham Edds and Associates (1991), Thorp (1992), Keyes (1997) and Donnelly (2001)). Period of European Time Indicators of Agricultural Development Occupation Frame  Used as pasture only Common 1804 – 1890  Area described as “heavily timbered” and “undulating land with box and ironbark stands”  In 1895 there were 440 people  Clearance and cultivation of substantial tracts of land Pitt Town  200-300 acres of cleared timber Co-Operative Labour 1893 – 1896  Sawmill in operation Settlement  Income generated from firewood supply  Crops included potatoes, pumpkins, melons, lucerne, millet, sorghum, maize, garlic and fruit  Farming site encompassed 2150 acres  180 acres were cleared  125 acres were cultivated 100 acres used for grazing Casual Labour Farm 1896 – 1910   11 horses, 40 cattle, 70 pigs, 87 sheep  A dam in every paddock and one silo at the site  Pigs and firewood sent to the Sydney markets each week  In 1905 there was 20 km of fencing  245 acres under cultivation  Crops included wheat, oats, maize, sorghum, potatoes, turnips, pumpkins, melons, rape, barley, lucerne and millet  An orchard with oranges, plums, peaches, apricots, apples and lemons  Vegetable garden included beetroot, beans, broccoli, carrots, spinach, cabbage, peas, brussel sprouts, leeks, Government lettuce, onions, spinach, strawberries and rhubarb Agricultural Training 1910 - 1940  3 silos and 25 dams Farm  Millet broom factory - produced 200 brooms per season  In 1934 there were 173 cattle, 36 horses, 337 sheep, 50 pigs and 567 head of poultry  Blacksmith, saddler, wheelwright, carpenter and tinsmith shops, abattoir and butcher shop  In 1912 there was 45 km of fencing  By 1929, 4500 boys had been trained  In 1933 and 1940 it was reported that 400-500 boys were trained annually  Used as a military training school for artillery and anti- tank warfare  Occupied by the First Parachute Battalion Military Occupation 1940 – 1945  Maintenance of the farm was undertaken by a limited number of staff  Extent and type of farming activities is unclear  Cattle and horses present Extent of farming activities unclear Migrant Hostel 1945 – 1964   Migrant centre occupied 100 acres of the original estate Officer Training Camp 1965 – 1973  Extent of farming activities unclear Horse riding/agistment Community Use 1977 – 1996   Cattle grazing

JK Fitzgerald Chapter 2 Page 59

The Government Agricultural Training Farm was an experimental farm that provided training in farm operations and management to an average of 400-500 immigrant boys per year (Stubbs and Stubbs 1983). This farm was atypical due to the range of activities carried out in close proximity to each other, with the land being intensively used for a variety of purposes all year round. This period represents the height of agriculture at the site, reflected by the number and type of indicators listed in Table 2.7.

The site was subsequently used as a military training school during the second half of World War II and the First Parachute Battalion was stationed at the site (Keyes 1997). During this time, the army used a number of farm buildings and a small group of workers were kept on for farm maintenance (Thorp 1992). After the end of the war, the site was converted into the largest migrant hostel in Australia (Stubbs and Stubbs 1983). The nature and extent of farming activities throughout this period is unclear.

The site was used as an Officer Training Unit for National Serviceman during the Vietnam War (Donnelly 2001). After 1973 the site was used for a variety of short-term purposes (Kinhill Engineers Pty. Ltd. 1990), for example, the Hawkesbury Agricultural College used the site for accommodation and horse agistment from 1977 to 1983 (Stubbs and Stubbs 1983). Cattle grazing has been a persistent activity of the site since European settlement and the cessation of grazing occurred in 1997 (NPWS 2000).

JK Fitzgerald Chapter 2 Page 60

CHAPTER 3. The soil of abandoned farmland and Cumberland Plain Woodland

3.1 Introduction

The value and importance of understanding the impacts of past land use on the soil for the management and restoration of the pre-disturbance community is widely acknowledged (Flinn and Vellend 2005). This is because prior agricultural land use can have a dramatic and lasting impact on the physical, chemical and biological fertility of the soil, which may prevent or impede natural regeneration and restoration efforts on old fields (Yates and Hobbs 1997; Walker et al. 2004; Flinn and Marks 2007). An understanding of small-scale patch dynamics may also aid in restoration because the spatial distribution of soil resources, along with the composition and cover of the ground layer, may be influenced by individual trees and shrubs (Pickett and White 1985; Belsky and Canham 1994). This could be particularly important for vegetation communities like Cumberland Plain Woodland, which have the bulk of their plant diversity in the ground layer (Benson and Howell 2002; Tozer 2003).

Altered soil properties and processes resulting from past agricultural land use may be constraining the natural regeneration and restoration of Cumberland Plain Woodland on abandoned farmland. This has been largely ignored until now although Hill et al. (2005) did investigate the role of the soil in weed invasions on the Cumberland Plain. Furthermore, the theory of small-scale patch dynamics has been applied to the restoration of Cumberland Plain Woodland on abandoned farmland (Davies and Christie 2001) but with limited success. This is because the planting of native trees and shrubs in these areas has done little to enhance native ground species richness over a 12 year period (Wilkins et al. 2003; Nichols 2005). There is thus a general lack of understanding regarding soil-vegetation relationships in Cumberland Plain Woodland and the impacts of past land use on the soils of abandoned farmland, which have been earmarked for the restoration of this endangered vegetation community, are also unknown.

JK Fitzgerald Chapter 3 Page 61

To address these knowledge gaps and thus contribute to the improved management and restoration of Cumberland Plain Woodland on abandoned farmland, this study aimed to: 1. Identify the impacts of past agricultural land use on the soils of abandoned farmland to highlight potential soil-related barriers for restoration; and 2. Investigate small-scale patch dynamics in Cumberland Plain Woodland by measuring changes in soil properties between a range of canopy and inter- canopy patch types.

3.2 Methodology

3.2.1 Experimental design

The five sites described in Section 2.2 were used for this study and as previously mentioned, they contained remnant Cumberland Plain Woodland and abandoned farmland. Prior to European settlement, these sites were covered with Cumberland Plain Woodland (Benson and Howell 1990a) and they have a very long history of domestic livestock grazing, which can be traced back to the original land grants made during the very early 1800s. The study sites give good spatial coverage of the Cumberland Plain (north, south, east and west) and they‟re characterised by the dominant geology (Wianamatta Shale), soil landscape (Blacktown) and soil types (red and yellow podzolic soils) of the region. The areas of woodland and abandoned farmland that were sampled at each site had the same aspect and very similar slope gradients and elevations. Sampling was restricted to the hillslopes, although some sites were flatter than others.

Sampling was undertaken in areas that were at least 5 years post fire but controlling for differences in long-term fire history was not possible since this information was lacking. Five years was considered to be an appropriate timeframe because the direct effects of fire on the soil environment are generally short-lived (Raison 1979; Humphreys and Craig 1981; Tomkins et al. 1991). Indirect effects could occur in the long-term though, via fire-related changes to the vegetation.

Despite the strong focus on domestic livestock grazing, it is possible that some areas may have been used for other agricultural activities, such as orcharding, viticulture or

JK Fitzgerald Chapter 3 Page 62

market gardening, at some point in the past. To reduce the chance of inadvertently sampling these areas, aerial photographs from the 1940s onwards were examined for signs of cultivation and cropping (Fensham and Fairfax 2002). Geometric patterns, such as evenly spaced trees in parallel rows, or rectangular areas with furrows and mounds, were indicative of cropping and horticulture, while variable textures, shades and colours also signified different land uses (Emery et al. 1986). Site reconnaissance was also carried out to identify furrowed areas and no plough layers were detected when sampling the soil.

Four different patch types were investigated for this study, these being: tree, shrub, open and pasture. The first three represent the most frequently occurring strata in Cumberland Plain Woodland (Tozer 2003) while the last one typifies the abandoned farmland. The tree patch type (Plates 1-3) was occupied by an adult Eucalyptus moluccana, the shrub patch type (Plates 4-6) was covered with Bursaria spinosa and the open and pasture patch types (Plates 7-12) were covered with native and exotic perennial grasses respectively. The tree patch type was free of a shrub layer, the shrub patch type had no overstorey, the open patch type was an inter-canopy area and the pasture patch type was also without a tree or shrub stratum. E. moluccana and B. spinosa were chosen for the tree and shrub patches because they‟re the dominant species of these strata in Cumberland Plain Woodland (Benson 1992; Myerscough 1998; French et al. 2000; Tozer 2003). The patch types were large enough to cover 100 m2 (i.e. a 10 x 10 m quadrat) and the tree patch type had the eucalypt at the centre of the quadrat with the canopy extending to the edges. Beneath each of the four patch types, the soil was sampled at three different depth intervals, as described below. The full sampling design was thus 5 sites x 3 sub-sites x 4 patch types x 3 soil depths to give 180 soil samples.

3.2.2 Field and soil sampling

Soil was sampled from mid April to early May 2006 (Table 3.1). The mean minimum and maximum temperatures and total rainfall during the four week period (28 days) prior to sampling at each site are shown in Table 3.1.

JK Fitzgerald Chapter 3 Page 63

Plate 1 Photo by Jennifer Kit Fitzgerald

Plate 2 Plate 3 Photo from DECCW (2009a) Photo by Jennifer Kit Fitzgerald

Plates 1 and 3: Cumberland Plain Woodland at Hoxton Park showing a range of patch types, including tree patch types dominated by Eucalyptus moluccana individuals. Plate 2: E. moluccana in flower.

JK Fitzgerald Chapter 3 Page 64

Plate 4 Photo from DECCW (2009a)

Plate 5 Plate 6 Photo by Jennifer Kit Fitzgerald Photo from DECCW (2009a)

Plates 4 and 6: Bursaria spinosa in flower. Plate 5: a shrub patch type dominated by B. spinosa, which was used for soil and vegetation sampling at Hoxton Park.

JK Fitzgerald Chapter 3 Page 65

Plate 7 Plate 8 Photo from DECCW (2009a) Photo from DECCW (2009a)

Plate 9 Photo by Jennifer Kit Fitzgerald

Plates 7 and 8: Aristida vagans and Themeda australis respectively, which are common ground layer species in Cumberland Plain Woodland. Plate 9: an open patch type at Mount Annan dominated by

native perennial grasses.

JK Fitzgerald Chapter 3 Page 66

Plate 10 Plate 11 Photo from DECCW (2009a) Photo from DECCW (2009a)

Plate 12 Photo by Jennifer Kit Fitzgerald

Plates 10 and 11: two common exotic perennial pasture species on the Cumberland Plain, Chloris

gayana and Paspalum dilatatum respectively. Plate 12: abandoned farmland at Mount Annan dominated by P. dilatatum.

JK Fitzgerald Chapter 3 Page 67

Table 3.1. Sampling dates for each site, along with the mean minimum and maximum temperatures and total rainfall during the four week period (28 days) prior to sampling*. Hoxton Park Mount Annan Orchard Hills Prospect Scheyville Sampling date 24/04/2006 18/04/2006 17/04/2006 2/05/2006 20/04/2006 Minimum temperature (°C) 11.1 11.0 13.6 11.2 11.1 Maximum temperature (°C) 26.3 25.2 26.3 24.7 26.5 Rainfall (mm) 10.2 29.6** 19.4 3.6*** 11.8 *Data was calculated from BOM (2006) using the following weather stations for each of the study sites: Liverpool (station 067020) for Hoxton Park; Camden Airport (station 068192) for Mount Annan; Penrith Lakes (station 067113) for Orchard Hills; Prospect Dam (station 067019) for Prospect; and Richmond RAAF (station 067105) for Scheyville. **This figure includes 24.6 mm, which fell on 31st March 2006 ***Total rainfall 32 days prior to sampling was 20.0 mm

A transparent grid was placed over a 1:25 000 topographic map of each site and the dimensions of the sampling area for the woodland and abandoned farmland were calculated. The coordinates (x, y) of a sub-site were then determined using a random number generator, with the following procedure being carried out three times for each sampling area at each site. The first number (x) was the distance (in metres) along the edge of the sampling area from which the second coordinate (y) was measured; the position of y was measured at right angles to point x and the position of x was measured, depending on the orientation of a site, from the southern- or western-most point of the sampling area. If the random point y was located in abandoned farmland, then that point became the centre of a 10 x 10 m quadrat. If y lay within a woodland, 10 x 10 m quadrats were established at the centres of the nearest tree, shrub and open patch types, which were clustered together to minimise environmental variability.

The soil was sampled at three intervals, these being 0-5 cm, 18-23 cm and 58-63 cm, for the determination of various chemical properties, as well as gravimetric soil moisture content. These intervals were chosen to achieve good coverage of the soil profile and because salts and certain nutrients, namely nitrate and sulphur (S), may accumulate at depth due to leaching (Lewis 1999; Shaw 1999; Strong and Mason 1999). In Australia, the determination of soil nitrate in agricultural systems has been carried out for a range of depths (typically <100 cm, for example see Strong and Mason 1999; Kemp et al. 2000; Sangha et al. 2005) and Falkengren-Grerup et al. (2006) used the concentration of nitrate at the 50-60 cm soil depth as an estimate of nitrate leaching in forest plantations in Europe. The mid-points of the depth intervals, for example 2.5 cm for the 0-5 cm soil depth, were used to graph and tabulate the data. Bulk density was also measured but

JK Fitzgerald Chapter 3 Page 68

only for the surface soil (0-5 cm). For the tree patch type, the samples were collected mid-way between the trunk and canopy edge at randomly selected compass bearings (0- 360°). For the other three patch types, samples were collected from the mid-canopy region, which was at or near the centre of the quadrat.

A hand auger, which had a bucket that was approximately 10 cm wide and 15 cm deep, was used to collect samples for moisture and nutrient analysis. Within each quadrat, two separate samples were collected from each depth interval. They were subsequently bulked and kept cool (on ice in the field then refrigerated at 4°C in the laboratory) until sample preparation was carried out. This is particularly important for the determination of plant-available N, which can be affected by changes in temperature and moisture conditions (Strong and Mason 1999). The samples were air-dried at 40°C then ground (if necessary) and passed through a 2 mm sieve, with the fine-earth fraction (<2 mm) being used for analysis (Brown 1999). Further grinding was carried out to produce powder-like (<0.425 mm) sub-samples for the determination of total C, total N and total S (Rayment and Higginson 1992).

To collect the bulk density samples, litter was removed from the surface of the soil and a steel core was pushed vertically into the ground until the top of the core was level with the soil surface. The core was then removed using a spade and any excess soil at the bottom of the core was trimmed with a steel ruler. The sample was then pushed through the core into a plastic bag to be transported to the laboratory. The same core was used to collect all of the samples (5.00 cm in height with an internal diameter of 4.75 cm). Two samples were collected per quadrat and the mean was used for statistical analysis. The total number of bulk density samples collected therefore was 120 (5 sites x 3 sub-sites x 4 patch types x 2 replicates).

3.2.3 Soil physical and chemical determinations

The physical and chemical properties measured for this study are outlined below and Australian standard procedures were used in most cases and the alpha-numeric codes (where listed) refer to these (Rayment and Higginson 1992). For all variables, except for bulk density, approximately 10% of the total number of samples were analysed in duplicate for quality control (pers. comm. M. Emmanuel 2006).

JK Fitzgerald Chapter 3 Page 69

3.2.3.1 Bulk density

Bulk density is the mass of solid particles per unit volume of soil and it was measured using the standard intact core method (McKenzie et al. 2002). Bulk density is an indicator of soil structure that is commonly used to measure the degree of soil compaction (Hazelton and Murphy 2007). While other measures, such as field capacity and infiltration, provide more detailed assessments of soil structure than bulk density alone (McKenzie et al. 2002), the intact core method is quick and easy to carry out and provides good baseline data where no other information exists. In addition to this, general guidelines for the interpretation of bulk density values have been developed in Australia (Cass 1999; Hazelton and Murphy 2007).

3.2.3.2 Soil moisture content

Gravimetric soil moisture content is the moisture content of a sample as a percentage of its oven-dry mass and it was measured using the air-dry moisture content procedure (2A1). This procedure does not measure the full extent of the soils capacity to hold water but it does indicate the amount of water held by the soil at a particular point in time. It is also required for some measures of chemical fertility, namely nitrate and ammonium, to convert calculations based on an air-dry basis to an oven-dry basis because the residual water held by a sample after air-drying can „inflate‟ the concentration of the variable being measured. This is particularly important for soils with high clay contents (Rayment and Higginson 1992), such as the podzolic soils of the Cumberland Plain.

3.2.3.3 pH

Soil pH is a major factor influencing the chemical and biological fertility of the soil (Attiwill and Leeper 1987). It can affect processes such as microbial activity and decomposition, as well as nutrient availabilities and the concentration of toxic elements, for example, the availability of Ca, Mg, P and N decreases with increasing acidity, while Al becomes toxic at low pH levels (Slattery et al. 1999). There are two standard methods for measuring pH; one is based on a water suspension (4A1) while the other uses a CaCl2 extract (4B1). The salt suspension is not affected by seasonal variations in

JK Fitzgerald Chapter 3 Page 70

moisture content like the water extract and so it tends to be a better diagnostic tool for farmers and land managers concerned with soil acidity and nutrient availability (Slattery et al. 1999). As such, the 1:5 soil:0.01MCaCl2 extract (4B1) was used for this study.

3.2.3.4 Electrical conductivity

Salinity can affect the productivity and survival of the vegetation (Cullen 2003; Zeppel et al. 2003) and secondary salinity has become a major problem in some areas on the Cumberland Plain due to vegetation clearance and engineering works (DEC 2005). Electrical conductivity (EC) measures the concentration of soluble salts in the soil solution and is commonly used as an indicator of soil salinity (Shaw et al. 1999). Electrical conductivity was measured in this study using the 1:5 soil:water extract method (3A1). Since suspended clay particles may interfere with the conductivity reading for clay-rich soils (Shaw et al. 1999), the samples were centrifuged following extraction (pers. comm. D. Yu 2006).

3.2.3.5 Active C

Active C, which is also referred to as labile C or light fraction C, has received considerable attention over recent years for its potential to be an indicator of soil quality that is much more sensitive to changes in land management than total C (Haynes 2005). This is because active C represents the most readily oxidisable forms of C in the soil; it is closely related to microbial processes and has a much faster turnover rate than total C (Weil et al. 2003; Crow et al. 2007).

Working within cropped and uncropped areas in northern and central NSW, Blair et al. (1995) developed a procedure to measure the concentration of active C within the soil using potassium permanganate as an oxidising agent. It was found however, that the concentration of the potassium permanganate was often too strong, resulting in the oxidation of both labile and recalcitrant forms of C. Weil et al. (2003) addressed this problem by using a very dilute solution and this method has been used successfully applied in Australia (for example see Eldridge and Mensinga 2007). As such, the method of Weil et al. (2003) was used for this study.

JK Fitzgerald Chapter 3 Page 71

3.2.3.6 Extractable P

Phosphorus is an essential plant nutrient (Keith 1997). Australian soils are generally low in P and while native plants are well adapted to this, crops and pastures typically require fertilisation (Handreck 1997; Moody and Bolland 1999). Phosphate minerals are chemically stable, sparingly soluble or insoluble and so very little P is in the soil solution at any one time (Holford 1997). In fact, soil P is the most inaccessible nutrient required by plants (Moody and Bolland 1999).

Procedures that measure soil P determine either total or available concentrations. Total P represents all forms of P in the soil and these are: P ions in solution; microbial phosphates; adsorbed P in micropores on mineral surfaces; and adsorbed P that has become incorporated into mineral structures (Holford 1997). Available P on the other hand, refers to the proportion of total P available for plant uptake. This includes ions in solution and P that is adsorbed onto the surfaces of minerals, namely hydrous oxides of iron and aluminium (Holford 1997). The vast majority of total P exists in forms that plants cannot access and so it is a very poor indicator of the amount of plant-available P (Handreck 1997; Moody and Bolland 1999). In line with this, only available (extractable) P was determined for this study.

The most frequently used tests to measure the concentration of available P in NSW are the Lactate, Bray 1 P and Bray 2 P tests (Holford 1997). The Lactate test has been used extensively on alkaline soils of the wheatbelt to predict crop responses to fertiliser, while the Bray extracts were developed to measure the concentration of adsorbed P (bound primarily to Al, Ca and iron (Fe)) in the soil (Bray and Kurtz 1945). It is thus more appropriate to refer to the latter as extractable P rather than available P. The Bray 1 P and 2 P methods are best used on acidic and alkaline soils respectively (Rayment and Higginson 1992) and so the Bray 1 P method (9E1) was most suitable for this study.

3.2.3.7 Nitrate and ammonium

Nitrogen is an essential plant nutrient and like P, N can be divided into „total‟ and „available‟ pools and the latter is comprised of ammonium, nitrite and nitrate. Nitrite typically occurs in very small quantities and so plant-available N, or mineral N,

JK Fitzgerald Chapter 3 Page 72

generally refers to nitrate and ammonium only (Rayment and Higginson). A lot of research has focused on N and P because these are the primary limiting nutrients in many natural systems (Chapin 1980). Not surprisingly, changes to N (and P) cycling and availabilities have been associated with ecosystem degradation, commonly in the form of exotic species invasions, in a wide range of systems (Ehrenfeld 2003; Kulmatiski et al, 2006). Plant-available N was determined using the standard mineral nitrogen with 2M KCl automated colour technique (7C2).

3.2.3.8 Total C, total N and total S

The concentration of „total‟ nutrient pools can be a useful indicator of the soils long- term ability to supply a particular nutrient (Lewis 1999; Strong and Mason 1999), as such total N, C and S were measured for this study. These variables were measured simultaneously using a dry combustion method (LECO analysis; 6B3), which is currently recognised as the optimal technique for determining total C and total N in Australian soils (Baldock and Skjemstad 1999; Skjemstad et al. 2000).

3.2.4 Statistical analysis

The data was analysed using a split-plot analysis of variance (ANOVA) with the main effects being site, patch type and soil depth. Site was a random between-subjects (main plot) factor, while patch type and soil depth were fixed within-subjects (split-plot) factors. Sub-site was nested in site, while patch type and soil depth were orthogonal to site and sub-site. Sub-site was the between-subjects error term and its interactions with patch type and soil depth formed the within-subjects error terms. The analysis for bulk density was slightly different because there was no depth component. Alpha was set at 0.05 and the expected mean squares for both analyses (i.e. with and without soil depth) are shown in Appendix 1. SPSS Statistics v. 17.0 was used for the analysis.

Post hoc tests were used to investigate significant main effects. Pair-wise comparisons of sites were carried out using Tukey‟s Honestly Significant Different test while differences between patch types and soil depths were examined using estimated marginal means and a Bonferroni adjustment (Sokal and Rohlf 2000). For many factors, effects were significant both in interaction terms and as main effects; a common pattern

JK Fitzgerald Chapter 3 Page 73

was that the interaction terms were weak while the main effects were strong (as judged by the size of the F ratios and associated P values). In such cases, examination of main effects can assist in the interpretation of results by pointing to dominant trends that may vary to a lesser extent in interaction with the other factors (Sokal and Rohlf 2000). This approach was taken in the analyses reported.

The assumptions of normality, homogeneity of variance and sphericity were tested using the Kolmogorv-Smirnov, Levene‟s and Mauchly‟s tests respectively. Variables were natural log- transformed where necessary to meet the first two assumptions and the Greenhouse-Geisser epsilon was used to adjust (i.e. decrease) the degrees of freedom for the F-test when the assumption of sphericity was violated (Quinn and Keogh 2002). Back-transformed variables are presented with their 95% confidence limits (Sokal and Rohlf 2000), as are the arithmetic means (to maintain consistency since most variables needed transformation).

3.3 Results

For each variable, the main effects of site, patch type and soil depth are summarised in Tables 3.1-3.3 and the highest order significant interaction is also presented in this Section but for brevity, these means have not been furnished with their confidence intervals and instead, they are tabulated in Appendix A1. The ANOVA tables and post hoc tests, along with the mean values and 95% confidence intervals for any other significant interactions are also tabulated in Appendix A1.

3.3.1 Bulk density and soil moisture content

The only significant effect on bulk density was site (F4,10=13.750, P=0.000; Table 3.2); mean values ranged from 0.95 g cm-3 at Mount Annan to 1.44 g cm-3 at Orchard Hills

(Figure 3.1). Patch type did not have a significant effect, either alone (F3,12=1.487, P

>0.05; Table 3.3) or in interaction with site (F12,30=1.205, P=0.325).

Soil moisture showed complex patterns of variability across sites, patch types and depth

(site x patch type x depth interaction, F24,60=3.23, P=0.00012). At Hoxton Park, Prospect and Scheyville for example, the pasture had the highest moisture levels to 20.5

JK Fitzgerald Chapter 3 Page 74

cm but at Orchard Hills, the pasture had the lowest moisture content for this soil depth (Figures 3.2a-e). In general terms, soil moisture increased significantly with depth

(Table 3.4) and these changes were affected by site (F8,20=2.96, P=0.023) but not by patch type (F6,24=1.25, P>0.05).

3.3.2 pH and electrical conductivity

Site, patch type and depth combined to significantly affect soil pH (second-order interaction; F10,26=2.38, P<0.05) and the most striking similarity between the five sites was the elevated pH levels within the surface soil (0-5 cm) beneath the woodland trees (Figures 3.3a-e).

Some overriding patterns were evident in other terms in the analysis. The soil became more acidic with depth (F2,8=8.22, P<0.05; Table 3.4) and this was affected by site

(F8,20=7.84, P<0.0001) but not by patch type (F3,10=3.18, P>0.05). At Hoxton Park for example, there was only a small change (from 4.36 to 4.31) in pH to 60.5 cm but at Mount Annan and Scheyville, the pH decreased by more than one unit over the same depth.

The effect of trees in raising pH was detected in the patch term (F3,12=7.82, P=0.01), with the soil beneath trees being significantly less acidic than the soil beneath the shrubs (P=0.029; Table 3.3).

Electrical conductivity differed amongst the patch types (main effect; F2.72,10.89=4.79, P<0.05), with EC under trees being significantly higher than under any other patch type (Table 3.3). This patch-to-patch variability was not affected by interactions with site

(site x patch type interaction: F10.89,27.22=0.95, P>0.05) or soil depth (site x depth interaction; F2.76,11.03=3.16, P>0.05).

EC increased significantly with depth (F1.05,4.18=61.08, P<0.01), with the pattern differing amongst sites (site x depth interaction: F4.18,10.45=5.66, P<0.05); surface levels were similar for all five sites but at 60.5 cm, Hoxton Park and Prospect had noticeably higher EC values than Mount Annan and Scheyville (Figure 3.4).

JK Fitzgerald Chapter 3 Page 75

Table 3.2. Mean concentrations and the upper (L2) and lower (L1) 95% confidence limits for the physical and chemical soil properties, averaged over all patch types and soil depths, at each site. Results are reported to 3 significant figures and back-transformed means are presented where the analysis was performed on transformed data, as indicated by an asterisk. Different superscripts within a row indicate significant differences.

Hoxton Park Mount Annan Orchard Hills Prospect Scheyville Variable Mean L1 L2 Mean L1 L2 Mean L1 L2 Mean L1 L2 Mean L1 L2 Bulk density (g cm-3) 1.09ac 1.01 1.17 0.950a 0.858 1.04 1.44b 1.33 1.54 1.16ac 1.07 1.24 1.24bc 1.13 1.34 Soil moisture content (%) 5.66a 5.06 6.26 6.54a 6.05 7.02 5.56a 5.00 6.12 6.42a 5.55 7.30 3.96b 3.43 4.49 pH 4.34a 4.24 4.44 4.82b 4.56 5.09 4.72bc 4.51 4.92 4.41ac 4.29 4.53 4.40ac 4.18 4.62 Electrical conductivity (dS m-1)* 0.158a 0.0915 0.230 0.131a 0.0891 0.175 0.135a 0.0791 0.193 0.153a 0.093 0.217 0.102a 0.0618 0.145 Active C (mg kg-1) 480a 373 586 450ac 343 558 417bc 334 500 408bc 331 484 408bc 322 493 Bray 1 P (mg kg-1)* 1.02a 0.704 1.40 1.93b 1.29 2.75 0.71c 0.510 0.945 0.69c 0.482 0.929 1.02a 0.791 1.28 Ammonium (mg kg-1)* 6.72a 5.39 8.33 6.11a 4.92 7.55 3.91b 3.19 4.75 5.84a 4.81 7.06 3.30b 2.85 3.82 Nitrate (mg kg-1)* 0.488a 0.262 0.755 1.51b 0.769 2.552 0.417a 0.159 0.732 0.353a 0.0795 0.695 0.683ab 0.357 1.086 Total C (%)* 2.49a 1.80 3.36 2.39a 1.73 3.20 1.69b 1.29 2.16 1.96b 1.50 2.52 1.19c 0.905 1.51 Total N (%)* 0.102ac 0.0730 0.142 0.152a 0.114 0.204 0.101ac 0.0818 0.124 0.0934c 0.0737 0.118 0.271b 0.175 0.420 Total S (%) 0.0285a 0.0231 0.0338 0.0233ab 0.0184 0.0281 0.0232ab 0.0194 0.0270 0.0262ab 0.0225 0.0299 0.0150b 0.0120 0.0180

1.50 Moisture content (%) 0 3 6 9 12

) 0 -3

-10 1.00 Pasture -20 Open -30 Shrub Bulk density (g cm (g density Bulk -40 Tree 0.50 Soil depth (cm) depth Soil -50

-60

Fig 3.1 -70 Fig 3.2a Hoxton Park

Moisture content (%) Moisture content (%) 0 3 6 9 12 0 3 6 9 12 0 0

-10 -10

-20 Pasture -20 Pasture Open Open -30 Shrub -30 Shrub -40 Tree -40 Tree Soil depth (cm) depth Soil Soil depth (cm) depth Soil -50 -50

-60 -60 Fig 3.2b Mount Annan Fig 3.2c Orchard Hills -70 -70

Moisture content (%) Moisture content (%) 0 3 6 9 12 0 3 6 9 12 0 0

-10 -10 Pasture -20 -20 Open -30 -30 Shrub Tree -40 -40 Soil depth (cm) depth Soil

Pasture (cm) depth Soil -50 -50 Open -60 Shrub -60 Tree -70 Fig 3.2d Prospect -70 Fig 3.2e Scheyville

Figures 3.1 and 3.2a-e. Mean surface soil (0-5 cm) bulk density for the study sites and mean moisture content with depth for the patch types at each site. See Appendix 1 for the 95% confidence limits for soil moisture.

Tables 3.3 and 3.4. Mean concentrations and the upper (L2) and lower (L1) 95% confidence limits for the physical and chemical soil properties for the main effects of patch type and soil depth. Back-transformed means are presented for those variables with an asterisk. Different superscripts within a row indicate significant differences. Table 3.3 Pasture Open Shrub Tree Variable Mean L1 L2 Mean L1 L2 Mean L1 L2 Mean L1 L2 Bulk density (g cm-3) 1.16a 1.02 1.29 1.22a 1.09 1.34 1.20a 1.09 1.32 1.11a 1.00 1.23 Soil moisture content (%) 6.54a 5.87 7.21 5.29a 4.73 5.85 5.25a 4.70 5.81 5.43a 4.83 6.03 pH 4.59ab 4.44 4.74 4.43ab 4.28 4.57 4.36a 4.23 4.49 4.78b 4.53 5.02 Electrical conductivity (dS m-1)* 0.110a 0.0722 0.149 0.116a 0.0742 0.160 0.130a 0.0832 0.179 0.188b 0.127 0.253 Active C (mg kg-1) 430a 345 515 384a 317 452 433a 351 516 482a 391 573 Bray 1 P (mg kg-1)* 0.86a 0.60 1.16 0.92a 0.69 1.18 0.99ab 0.73 1.28 1.41b 0.97 1.94 Ammonium (mg kg-1)* 6.00a 4.79 7.45 5.04a 4.21 5.99 4.19a 3.51 4.97 5.02a 4.18 5.98 Nitrate (mg kg-1)* 1.09a 0.55 1.82 0.45bc 0.20 0.76 0.31b 0.15 0.50 0.84ac 0.50 1.25 Total C (%)* 1.81a 1.37 2.33 1.82a 1.41 2.30 1.93a 1.48 2.47 2.05a 1.53 2.68 Total N (%)* 0.193a 0.130 0.286 0.125a 0.0938 0.166 0.101a 0.0808 0.127 0.123a 0.0966 0.156 Total S (%) 0.0262a 0.0218 0.0306 0.0222a 0.0184 0.0259 0.0225a 0.0190 0.0260 0.0220a 0.0178 0.0261

Table 3.4 2.5 cm 20.5 cm 60.5 cm Variable Mean L1 L2 Mean L1 L2 Mean L1 L2 Soil moisture content (%) 4.83a 4.27 5.39 5.10a 4.64 5.56 6.96b 6.58 7.34 pH 4.87a 4.71 5.02 4.54b 4.41 4.67 4.21c 4.08 4.34 Electrical conductivity (dS m-1)* 0.0537a 0.0440 0.0634 0.0461a 0.0371 0.0552 0.329b 0.279 0.381 Active C (mg kg-1) 763a 720 805 365b 341 389 169c 154 185 Bray 1 P (mg kg-1)* 2.38a 2.01 2.81 0.89b 0.75 1.06 0.31c 0.23 0.39 Ammonium (mg kg-1)* 7.92a 6.76 9.26 4.04b 3.57 5.57 3.86b 3.36 4.42 Nitrate (mg kg-1)* 2.11a 1.46 2.93 0.317b 0.191 0.455 0.0874c 0.0435 0.133 Total C (%)* 4.50a 4.02 5.02 1.71b 1.53 1.90 0.643c 0.579 0.710 Total N (%)* 0.295a 0.256 0.340 0.115b 0.0928 0.141 0.0673c 0.0527 0.0861 Total S (% 0.0296a 0.0255 0.0337 0.0145b 0.0127 0.0162 0.0256c 0.0229 0.0283

pH pH 0 5 10 0 5 10 0 0

-10 -10

-20 -20 Pasture Pasture Open -30 Open -30 Shrub Shrub -40 -40 Tree

Soil depth (cm) depth Soil Tree -50 (cm) depth Soil -50

-60 -60

-70 Fig 3.3a Hoxton Park -70 Fig 3.3b Mount Annan

pH pH 0 5 10 0 5 10 0 0

-10 -10

-20 -20 Pasture Pasture -30 Open -30 Open Shrub -40 -40 Shrub Soil depth (cm) depth Soil Tree (cm) depth Soil Tree -50 -50

-60 -60 Fig 3.3c Orchard Hills Fig 3.3d Prospect -70 -70

pH EC (dS m-1) 0 5 10 0 0.2 0.4 0.6 0 0

-10 -10 Hoxton Park -20 -20 Pasture Mount Annan -30 -30 Open Orchard Hills Shrub -40 Prospect -40 (cm) depth Soil Tree

Soil depth (cm) depth Soil -50 -50 -60 -60 -70 Fig 3.3e Scheyville Fig 3.4 -70

Figures 3.3a-e and 3.4. Mean pH with depth for the patch types at each site and back-transformed mean EC values with depth for the study sites. See Appendix 1 for 95% confidence limits.

3.3.3 Active C and total C

Active C did not vary significantly with patch type, either alone (main effect:

F3,12=2.66, P>0.05; Table 3.3) or in interaction with site (site x patch type interaction;

F12,30=1.87, P>0.05). The concentration of active C declined markedly with depth (main effect; F1,5=220.816, P<0.0001; Table 3.4); this decline differed more weakly amongst sites (site x depth interaction; F5,13=4.64, P<0.05). For the surface soil (0-5 cm), the concentrations ranged from 686 mg kg-1 (Prospect) to 871mg kg-1 (Hoxton Park) but the sites had very similar concentrations of this variable (149–184 mg kg-1) at 60.5 cm (Figures 3.5a-e).

Whilst differences amongst sites varied with depth, site differences were also significant in themselves (main effect; F4,10=7.39, P=0.00488). Hoxton Park had the highest (480 mg kg-1) concentration of active C while Prospect and Scheyville had the lowest concentrations (both 408 mg kg-1; P<0.022; Table 3.2).

For total C, differences amongst patch types were evident at some sites (site x patch type interaction; F12,30=4.92, P<0.001). The highest concentration of total C occurred within the pasture at Prospect and Scheyville and beneath the woodland trees at Hoxton Park, Mount Annan and Orchard Hills. Mount Annan had very similar values for the pasture, open and shrub patch types.

The concentration of total C fell markedly with depth (main effect; F2,8=102.348, P<0.0001; Table 3.4) and this trend varied between sites (site x depth interaction:

F8,20=16.698, P<0.0001). Hoxton Park and Mount Annan had very similar values for all soil depths, while the rest of the sites were clearly separated at 2.5 cm and 20.5 cm. Scheyville had the lowest concentrations of total C throughout the soil profile (Figure 3.6).

Site had a significant effect on total C in its own right (main effect; F4,10=72.3, P= 0.000). Hoxton Park and Mount Annan had the highest levels of total C, Prospect and Orchard Hills had intermediate levels and Scheyville had the lowest concentration (Table 3.2). In fact Hoxton Park, with a mean concentration of 2.49% and Mount Annan (2.39%) had twice the concentration of Scheyville (1.19%; P=0.000).

JK Fitzgerald Chapter 3 Page 80

Active C (mg kg-1) Active C (mg kg-1) 0 400 800 1200 0 400 800 1200 0 0

-10 -10

-20 Pasture -20 Pasture Open Open -30 -30 Shrub Shrub -40 Tree -40 Tree

Soil depth (cm) depth Soil -50 -50(cm) depth Soil

-60 Fig 3.5a Hoxton Park -60 Fig 3.5b Mount Annan

-70 -70

Active C (mg kg-1) Active C (mg kg-1) 0 400 800 1200 0 400 800 1200 0 0

-10 -10 Pasture -20 -20 Pasture Open Open -30 -30 Shrub Shrub Soil depth (cm) depth Soil -40 Tree -40 Tree

-50 -50(cm) depth Soil

-60 -60 Fig 3.5c Orchard Hills Fig 3.5d Prospect -70 -70

Active C (mg kg-1) Total C (%) 0 400 800 1200 0 5 10 0 0

-10 -10 Pasture Hoxton -20 -20 Park Open Mount -30 -30 Shrub Annan Orchard Tree -40 (cm) depth Soil -40 Hills

Soil depth (cm) depth Soil Prospect -50 -50 Scheyville -60 -60 Fig 3.5e Scheyville Fig 3.6 -70 -70

Figures 3.5a-e and 3.6. Mean concentration of active C and back-transformed total C levels with depth beneath the patch types at each site (active C) or averaged across the patch types at each site (total C). See Appendix 1 for 95% confidence limits. [Type text] Page 81

3.3.4 Extractable P and total S

Extractable P varied with site, patch type and depth (second-order interaction;

F24,60=1.78, P<0.05). The highest concentration of Bray 1 P within the surface soil (0-5 cm) occurred below the woodland trees (except for Scheyville) and the rate of change with depth was greatest below the tree patch type at Mount Annan. For the surface soil, Scheyville was the only site that had elevated Bray 1 P levels within the pasture (Figures 3.7a-e).

As with other variables, important patterns from the interaction above emerged elsewhere in the analysis. The pattern of elevated concentrations of extractable P under the woodland trees was detected as a significant difference amongst patch types (main effect; F3,12=3.90, P<0.05), with the highest levels occurring beneath the trees (1.41 mg kg-1) and the lowest concentrations occurring in the pasture (0.86 mg kg-1; Table 3.3).

The concentration of Bray 1 P declined significantly with depth (main effect; F2,8=68.7,

P<0.0001; Table 3.4) and while this trend was not influenced by patch type (F6,24=1.78,

P>0.05), it was influenced by site (F8,20=5.85, P<0.001). Mount Annan had the highest concentrations of Bray 1 P at all soil depths and Orchard Hills and Prospect had very similar values throughout the soil profile. For the surface soil, Mount Annan had about two to three times the concentration of Bray 1 P than the other four sites.

The concentration of total S changed significantly with depth (F2,8=8.97, P<0.01; Table 3.4) and while the lowest levels for all of the sites were found at 20.5 cm, the rate of change to 60.5 cm varied between sites (site x depth interaction: F8,20=12.5, P<0.0001). Scheyville, for example, had much smaller changes in total S with depth compared to Hoxton Park and the concentration of total S within the surface soil differed noticeably between sites (Figure 3.8). Differences among patch types were not significant in any term in the analysis.

3.3.5 Nitrate, ammonium and total N

Nitrate levels differed amongst patch types and with soil depth depending on site (site x patch x depth interaction; F11,28=2.45, P<0.05). For the surface soil, nitrate levels were

JK Fitzgerald Chapter 3 Page 82

Bray 1 P (mg kg-1) Bray 1 P (mg kg-1) 0 3 6 9 12 15 18 0 3 6 9 12 15 18 0 0

-10 -10 Pasture Pasture -20 -20 Open Open -30 -30 Shrub Shrub

-40 Tree -40 Tree Soil depth (cm) depth Soil Soil depth (cm) depth Soil -50 -50

-60 -60 Fig 3.7a Hoxton Park Fig 3.7b Mount Annan -70 -70 Bray 1 P (mg kg-1) Bray 1 P (mg kg-1) 0 3 6 9 12 15 18 0 3 6 9 12 15 18 0 0

-10 -10 Pasture Pasture -20 -20 Open Open -30 -30 Shrub Shrub

-40 Tree -40 Tree Soil depth (cm) depth Soil Soil depth (cm) depth Soil -50 -50

-60 -60 Fig 3.7c Orchard Hills Fig 3.7d Prospect -70 -70

Bray 1 P (mg kg-1) Total S (%) 0 3 6 9 12 15 18 0 0.02 0.04 0.06 0 0

-10 -10 Hoxton Park -20 Pasture -20 Mount Annan Open -30 -30 Orchard Hills Shrub Prospect -40 Tree -40 Scheyville Soil depth (cm) depth Soil Soil depth (cm) depth Soil -50 -50

-60 -60 Fig 3.7e Scheyville Fig 3.8 -70 -70

Figures 3.7a-e and 3.8. Back-transformed mean Bray 1 P concentrations with depth for the patch types at each site and the mean concentration of total S with depth at the study sites. See Appendix 1 for 95% confidence limits. JK Fitzgerald Chapter 3 Page 83

either highest within the pasture (Mountt Annan, Orchard Hills and Prospect) or beneath the woodland trees (Hoxton Park and Scheyville) and at these two sites surface nitrate levels for the pasture were well within the woodland range (Figures 3.9a-e). The difference between patch types was also detected as a main effect (F3,12=3.74, P<0.05), with concentrations being highest in the pasture (1.09 mg kg-1) and lowest under the shrubs (0.31 mg kg-1; Table 3.3). Within the woodland, levels were significantly higher under the trees (0.84 mg kg-1) than beneath the shrubs (P=0.02; Table 3.3).

Nitrate levels decreased significantly with depth (main effect: F1,5=22.6, P<0.05; Table

3.4) and this trend varied between sites (site x depth interaction: F5,13=4.95, P<0.05) but not between patch types (patch type x depth interaction: F2.75,11.02=3.03, P>0.05). Mount Annan had the highest concentration of nitrate at all soil depths and the greatest rate of change from 2.5 cm to 20.5 cm. For the surface soil, Mount Annan had about three times the concentration of nitrate than Scheyville and five times the concentration of Hoxton Park, Orchard Hills and Prospect.

Ammonium levels differed with patch type, being greatest either in the pasture or under the woodland trees, depending on site (site x patch type interaction; F7.17,17.92=2.85, P<0.05). The greatest concentrations were found in the pasture at Hoxton Park, Mount Annan and Scheyville, as well as under the trees at Orchard Hills and Prospect (where concentrations in pasture ranked second to trees). The lowest concentrations were found under the open patch type (2 sites) or shrubs (3 sites). Ammonium concentrations also decreased in different ways with depth beneath the patch types (patch x depth interaction; F6,24=2.71, P<0.05); concentrations in the surface soil were highest under pasture (12.5 mg kg-1) and ranged from 5.9–7.28 mg kg-1 under the woodland patch types.

Ammonium decreased markedly with depth (depth main effect: F2,8=30.5, P<0.001;

Table 3.4) with different trends across sites (depth x site interaction; F8,20=3.00, P<0.05). At Hoxton Park and Mount Annan for example, the concentration of ammonium at 2.5 cm was more than double the concentration at 20.5 cm, which itself was very similar to the surface concentration of ammonium at Orchard Hills and Scheyville (Figure 3.10).

JK Fitzgerald Chapter 3 Page 84

Nitrate (mg kg-1) Nitrate (mg kg-1) 0 5 10 15 20 0 5 10 15 20 0 0

-10 -10

-20 Pasture -20 Pasture Open Open -30 -30 Shrub Shrub -40 Tree -40 Tree Soil depth (cm) depth Soil -50 (cm) depth Soil -50

-60 -60 Fig 3.9a Hoxton Park Fig 3.9b Mount Annan -70 -70

Nitrate (mg kg-1) Nitrate (mg kg-1) 0 5 10 15 20 0 5 10 15 20 0 0

-10 -10

-20 Pasture -20 Pasture Open Open -30 -30 Shrub Shrub -40 Tree -40 Tree Soil depth (cm) depth Soil -50 (cm) depth Soil -50

-60 -60 Fig 3.9c Orchard Hills Fig 3.9d Prospect -70 -70

Nitrate (mg kg-1) Ammonium (mg kg-1) 0 5 10 15 20 0 5 10 15 20 0 0

-10 -10

-20 Pasture -20 Hoxton Park Open -30 -30 Mount Annan Shrub -40 Tree -40 Orchard Hills Soil depth Soil (cm) Soil depth (cm) depth Soil -50 -50 Prospect

-60 -60 Scheyville Fig 3.9e Scheyville Fig 3.10 -70 -70

Figures 3.9a-e and 3.10. Back-transformed mean nitrate concentrations with depth for the patch types at each site and back-transformed mean ammonium levels with depth for the study sites. See Appendix 1 for 95% confidence limits. JK Fitzgerald Chapter 3 Page 85

Site differences were sufficiently strong to be detected in their own right (site main effect; F4,10=27.2, P=0.000). Hoxton Park, Mount Annan and Prospect had significantly higher concentrations of ammonium than Orchard Hills and Scheyville (P<0.005); the concentration at Hoxton Park (6.72 mg kg-1) was double that for Scheyville (3.30mg kg- 1; Table 3.2).

The concentration of total N was either highest in the pasture or under the woodland trees, depending on site (site x patch type interaction: F12,30=10.2, P<0.0001). Values were greatest under the pasture at two sites (Scheyville and Prospect) and beneath the tree patch type at three sites (Hoxton Park, Mount Annan, Orchard Hills). Hoxton Park and Mount Annan had similar values of total N for all of the patch types, while Scheyville had dramatically higher concentrations within the pasture and beneath the open patch type compared to the other four sites.

Total N (%) 0 0.2 0.4 0.6 0

-10 Hoxton -20 Park Mount -30 Annan Orchard Hills -40 Prospect Soil depth (cm) depth Soil -50 Scheyville

-60

-70

Figure 3.11. Back-transformed mean total N concentrations with depth for the study sites. See Appendix 1 for 95% confidence limits.

The concentration of total N decreased significantly with depth (main effect; F1,5=20.8,

P<0.01; Table 3.4) and this trend varied significantly across the five sites (F5,12=14.1, P<0.0001). The changes at Scheyville for example, were very small compared to the other four sites (Figure 3.11).

JK Fitzgerald Chapter 3 Page 86

3.4 Discussion

Overview

Within the woodland, the trees were associated with soil nutrient „hotspots‟ and generally had elevated pH levels and higher concentrations of Bray 1 P, active C, total C and nitrate than the open and shrub patch types. This is consistent with findings from a wide range of vegetation communities, both in Australia and overseas (Rhoades 1997; Prober et al. 2002a; Wilson 2002; Eldridge and Wong 2005; Gnankambary et al. 2008). Much of this research however, has attributed these to the presence of domestic livestock, native animals and (for example see Wilson et al. 2007). In Cumberland Plain Woodland, these patterns occur in the absence of such disturbances and appear to be strongly related to the (small-scale) spatial heterogeneity of the vegetation. It is currently not known if this trend is also associated with particular patterns of ground species diversity but this will be examined in Chapter 4. This is of particular importance for the management of this endangered woodland because it is characterised by an extremely diverse ground layer that has high levels of intra- and inter-site variability (James et al. 1999; French et al. 2000).

There were marked changes in the concentration of the various chemical properties with depth and these changes were often affected by site, patch type, or both of these factors. The greatest difference between patch types within a site however, or between sites in general, was typically related to the surface soil (0-5 cm), which often reflected a greater accumulation of nutrients beneath certain patch types or at certain sites.

Many soil physical and chemical properties differed little between the pasture and woodland. In many cases, the pasture soils had nutrient concentrations that were well within the woodland range and this was most notable for Bray 1 P. This is because elevated soil P levels (relative to the original vegetation) are typically associated with old fields (for example see Standish et al. 2006 and 2007) but this trend was evident for one site only in this study (i.e. Scheyville). The various measures of soil N on the other hand, showed a tendency to be elevated beneath the woodland trees or within the pasture. The concentrations of nitrate and ammonium were, more often than not, higher within the soils of the abandoned farmland, although this varied from site to site. This

JK Fitzgerald Chapter 3 Page 87

suggests however, that the abandoned farmland and Cumberland Plain Woodland may function differently with respect to soil N, as has been shown for many different systems throughout the world (for example see Paschke et al. 2000 and Flinn and Vellend 2005) and this may therefore be an abiotic barrier to the restoration of Cumberland Plain Woodland in these areas.

Since mineral-N varies substantially throughout the year in response to seasonal fluctuations in rainfall and temperature (Strong and Mason 1999), as well as plant growth and decay (Hobbie 1992), one-off measures of mineral-N do not provide an adequate summary of this nutrient pool. Instead, nitrate and ammonium need to be measured through time to gain a more informative picture of whether or not the pasture soils have consistently higher concentrations of these nutrients than the woodland patch types and this is addressed in Chapter 5.

There was great site-to-site variability for many of the soil properties measured; Bray 1 P and nitrate in particular, showed a great deal of inter-site variability. This is likely to be a result of differences in past land use. Even though the study sites share a common history dominated by domestic livestock grazing, very little is known about past land management practices such as grazing regimes, fertiliser use, cropping and long-term fire history. This variability may mean that a region-wide approach to management and restoration may be inappropriate and instead, decisions regarding the development and implementation of restoration techniques may need to be done on a site-by-site basis.

Bulk density

Data on the physical attributes of the soils of the Cumberland Plain is scarce. Bannerman and Hazelton (1990) reported the percentage composition of clay, silt, fine sand, coarse sand and gravel for the soil landscapes of the Penrith map sheet and assessed several indicators of structural stability as well (i.e. dispersion percentage, Emerson Aggregate Test and volume expansion). They also described soil structure and noted the occurrence of structural degradation, which they linked to the low wet strength of the soils (Bannerman and Hazelton 1990). Domestic livestock grazing, cultivation and forestry have long been associated with soil structure decline in many parts of Australia (Greacen and Sands 1980; Braunack and Walker 1985; Graetz and

JK Fitzgerald Chapter 3 Page 88

Tongway 1986; White 1988; Rab 1994; Connolly et al. 1997; Yates et al. 2000; Yates et al. 2000; Spooner et al. 2002; Drewry et al. 2008) and soil compaction from past land use (grazing and cultivation) has been identified as a potential problem for revegetation and restoration activities on the Cumberland Plain (DEC 2005).

Chan and Barchia (2007) recently measured the bulk density of the surface soil (0-7.5 cm) from a dairy farm located at Camden in the southwest of the Cumberland Plain. They found both well structured and compacted soils with values ranging from 1.04- 1.69 g cm-3 (Chan and Barchia 2007). I found no evidence for surface soil compaction in this study since the four different patch types had very similar bulk densities for the 0-5 cm soil depth (i.e. 1.11-1.22 g cm-3). On a general scale of bulk density, these values were quite low and indicate suitable conditions for agriculture (Hazelton and Murphy 2007). It is possible that the podzolic soils of the Cumberland Plain have some degree of structural resilience since they possess certain properties that aid in soil aggregation.

Structural resilience refers to the natural ability of a soil to re-aggregate following compaction, pugging and pulverisation. Soils with a high shrink-swell capacity, such as black, brown and grey clays, usually have a high degree of structural resilience because they contain high levels of clay and a large proportion of 2:1 clay minerals (e.g. illite, smectite and vermiculite), which promote soil aggregation during wet and dry cycles (Geeves et al. 2007). The red and yellow podzolic soils of the Cumberland Plain also have shrink-swell properties (Bannerman and Hazelton 1990) due to increasing clay contents with depth (Walker 1960) and the presence of large amounts of vermiculite (Herbert 1979) and interstratified illite-smectite (Davey et al. 1975). They can also have friable surfaces (Bannerman and Hazelton 1990), which enables them to maintain good aggregation if they‟re cultivated (Geeves et al. 2007).

Soil moisture content

There was a significant increase in soil moisture with depth and this is typical for podzolic soils because of the increase in clay content down the soil profile (Bannerman and Hazelton 1990). The pasture had the highest moisture levels of the four patch types at all sites, except for Orchard Hills, where elevated moisture levels occurred beneath

JK Fitzgerald Chapter 3 Page 89

the woodland trees. The tendency for elevated moisture levels within the pasture might be related to the denser biomass within this patch type (pers. obs. 2006), which could reduce evaporative water loss from the uppermost soil layers (Sangha et al. 2005; Kowaljow and Mazzarino 2007; Yan et al. 2007). pH

The soils of the abandoned farmland and Cumberland Plain Woodland were strongly acidic, especially within the subsoil, which is typical for the podzolic soils of the Cumberland Plain (Walker 1960; Bannerman and Hazelton 1990). These results therefore, indicate the potential for aluminium toxicity because many of the pH (1:5 soil:CaCl2) values were less than 4.7 (Slattery et al. 1999). Bannerman and Hazelton (1990) reported increasing levels of exchangeable aluminium with depth for the Blacktown soil landscape, although the siliceous nature of these soils (Walker 1960) may limit the dissolution of aluminium (Corbett 1969; Attiwill and Leeper 1987).

There was a significant effect of patch type on pH and the pH of the pasture was well within the woodland range. Other studies carried out in the region have found higher pH levels within the surface soil of abandoned Paspalum-dominated pastures compared to the original vegetation of Blue Gum High Forest and Cumberland Plain Woodland (Parker and Chartres 1983; Hill et al. 2005), which supports Corbett‟s (1972) hypothesis that the development of exotic perennial pastures on the Cumberland Plain could increase the soils pH. In contrast, DEC (2005) identified agriculturally induced soil acidity to be a potential problem for the management and restoration of native vegetation on the Cumberland Plain. Importantly, their assessment was not based on research carried out in the region, although it is possible that (current) domestic livestock grazing could decrease the soils pH, especially if stocking rates were high or if certain nitrogenous fertilisers were used (Helyar and Porter 1989; Robinson et al. 1995). The results presented here however, show that abandoned Paspalum-dominated pastures do not have enhanced acidity levels compared to the original woodland.

The soil beneath the woodland trees had reduced acidity levels compared to the other three patch types and many other studies have also found higher pH levels beneath eucalypts compared to adjacent „open‟ patches, such as inter-canopy areas, native

JK Fitzgerald Chapter 3 Page 90

pastures and improved perennial pastures (Wilson 2002; Graham et al. 2004; Eldridge and Wong 2005; Wilson et al. 2007). This alkalinising effect of eucalypts on the soil has been attributed to the flux of basic cations through the soil-tree system by a variety of processes, namely litterfall, throughfall, stemflow and biological pumping.

Litterfall plays a key role in returning some nutrients, most notably Ca and Mg, from the trees to the soil (Guthrie et al. 1978; Keith 1997). Unlike N, P and K for example, Ca and Mg are structurally bound within the cell walls and so are not translocated during leaf senescence (Keith 1997; Bruce 1999; McIvor 2001). Several studies have examined the effect of eucalypt leaf litter on soil pH and found a positive relationship between the level of Ca within the litter, the amount of extractable Ca within the soil and soil pH (Noble et al. 1996; Noble and Randall 1999; Graham et al. 2004), although this effect can vary greatly between species (Noble and Randall 1999; Graham et al. 2004). Throughfall and stemflow also contribute to the return of cations from eucalypts to the soil because rainwater leaches cations from plant tissues and washes aerosols, such as Ca, K, Mg and Na, from the surfaces of leaves and stems and transports them to the soil (Keith 1997). Surface soil acidity may also be reduced beneath individual eucalypts by the „biological pumping‟ of cations from the subsoil to the uppermost soil layers via root uptake, litterfall and decomposition. This may be a plausible mechanism for areas of southern Australia that have acidic topsoils and basic subsoils (Noble et al. 1996) but it‟s unlikely to be an important process on the Cumberland Plain where base depleted soils prevail.

Electrical conductivity

The increase in EC down the soil profile observed in this study is typical for many Australian soils (Shaw 1999) and this trend has previously been reported for the podzolic soils of the Cumberland Plain (Walker 1960; Banner and Hazelton 1990). The surface soils (0-5 cm) had EC values typical of non-saline soils (~0.03 dSm-1; Hazelton and Murphy 2007) and the values at 60.5 cm were well below the value (1.60 dSm-1) used to delineate saline soils (Hazelton and Murphy 2007).

The elevated values beneath the woodland trees has been reported elsewhere in Australia, for example, Facelli and Brock (2000), Prober et al. (2002a) and Eldridge and

JK Fitzgerald Chapter 3 Page 91

Wong (2005) found higher EC values beneath woodland trees than in adjacent open areas. Hill et al (2005) however, found no significant difference in EC between abandoned farmland and Cumberland Plain Woodland.

Active C

Significant site and depth effects were detected in this study for active C and the marked decline with depth is to be expected since soil organic matter and the microbial biomass are concentrated within the upper soil layers (Kennedy and Papendick 1995). Active C hasn‟t been routinely measured in Australia for either agriculture or ecological purposes (but see Bell et al. 1998; Armstrong et al. 1999; Bell et al. 1999; Noble et al. 2003; Dalal et al. 2005; Sangha 2003 and Macdonald et al. 2007) but the importance of this variable for assessing the impacts of land use change and the sustainability of farming practices has gained wide recognition over recent years, both in Australia and overseas (Blair et al. 1995; Bell et al. 1999; Weil et al. 2003; Haynes 2005; Cochran et al. 2007; Jinbo et al. 2007; von Lutzow et al. 2007). Bell et al. (1998) for example, highlighted the importance of using active C as an indicator of the physical (aggregate stability and infiltration) and chemical (effective cation exchange capacity (ECEC)) fertility of Krasnozems and Euchrozems (Ferrosols) used for cropping in northern and south eastern . This is because active C had a much stronger correlation with aggregate stability and ECEC than total measures of C (Bell et al. 1998). They reported values that were an order of magnitude larger than those obtained for this study and this reflects, at the very least, the stark difference in general fertility between Krasnozems and podzolic soils (Murphy et al. 2007).

Eldridge and Mensinga (2007) and James and Eldridge (2007) also measured active C but for various patch types in semi-arid and arid areas of NSW and South Australia. They reported values within the same order of magnitude as this study but the Cumberland Plain had much higher concentrations, which once again reflects a basic difference in soil fertility between regions with different climates and parent materials. Unlike Eldridge and Mensinga (2007) however, this study detected no significant effect of patch type on active C but an increasing trend in concentration from open areas (open patch types) to closed areas (tree patch types) was evident in both studies.

JK Fitzgerald Chapter 3 Page 92

Total C

The mean concentrations of total C for the sites and patch types were similar to the median values reported by Baldock and Skjemstad (1999) for red (2.6%) and yellow (2%) podzolic soils in Australia. They were also comparable to the values obtained by Bannerman and Hazelton (1990) for the Blacktown soil landscape but only after a conversion factor of 1.3 had been applied to their data (see Skjemstad et al. (2000) for a discussion on the comparison of soil carbon data derived from the Walkley-Black method and the LECO procedure). Like active C, the dramatic reduction in total C with depth is characteristic of this soil property (Wolf and Snyder 2003).

Site had a significant effect on total C. Hazelton and Murphy (2007) ranked soil carbon in terms of soil quality, which refers to the ability of a soil to provide nutrients and water to vegetation; maintain good structure; and resist changes to pH. Based on their classification, Hoxton Park and Mount Annan had very high levels of soil carbon while Orchard Hills and Prospect had high levels and Scheyville had a moderate level. This ranking for Scheyville implies a lower degree of structural stability, a reduced buffering capacity, poorer chemical fertility and a smaller water-holding capacity than the other four sites (Hazelton and Murphy 2007). In accordance with this, Scheyville was the least fertile site in terms of active C, ammonium and total S but it had the highest concentration of total N due to higher values within the pasture and open patch types.

Patch effects were evident for total C, with differences across sites; total C was either highest under the woodland trees, as occurred at Hoxton Park, Mount Annan and Orchard Hills, or within the pasture like at Prospect and Scheyville. For any soil type, the concentration of total C can vary greatly within individual horizons or at the same depth within the soil profile. This can be attributed to the impacts of past and present land use and land management practices on total C and this topic has received considerable attention over the past ten years due to issues relating to climate change (for example see Rhoades et al. 2000, Silver et al. 2000, Murty et al. 2002, Young et al. 2005 and Paul et al. 2008). The factors that influence soil C levels are, in order of decreasing importance: management, climate, vegetation and soil biota, topography and finally, soil type (Baldock and Skjemstad 1999). Thus, the significant site x patch type interaction for total C is likely to reflect differences in past land use and this may be

JK Fitzgerald Chapter 3 Page 93

related to factors such as time since agricultural abandonment and fire history. Hill et al. (2005) reported higher levels of total C within the surface soil (0-10 cm) of abandoned pastures compared to the original Cumberland Plain Woodland but they used loss-on- ignition, which can over estimate carbon levels for clay-rich soils due to hygroscopic water loss at high temperatures (Dean 1974; Baldock and Skjemstad 1999).

The occurrence of higher C levels beneath trees compared to open areas has been reported for several different systems in Australia including: a semi-arid woodland in NSW (Eldridge and Mensinga 2007); an arid woodland in South Australia (Facelli and Brock 2000); temperate grazing lands in NSW (Wilson 2002; Graham et al. 2004; Eldridge and Wong 2005; Wilson et al. 2007); ungrazed temperate grassy woodlands in NSW (Prober et al. 2002a); a wet forest in NSW (Ryan and McGarity 1983); and grazed tropical woodlands in north eastern Queensland (Jackson and Ash 1998; Jackson and Ash 2001). Many overseas studies have reported the same trend, for example Belsky et al. (1989), Scholes (1990), Belsky et al. (1993), Ko and Reich (1993) and Burke et al. (1995).

Bray 1 P

The concentrations of Bray 1 P observed in this study were consistent with those reported by Bannerman and Hazelton (1990) for the Blacktown soil landscape. Thomas (1994) also measured plant-available P for regularly burnt and unburnt Cumberland Plain Woodland at Prospect but a direct comparison with her results is not possible because she used the Lactate method, which is unsuitable for acidic soils (Holford 1997).

The mean site, patch and depth concentrations were well below the critical values generally required for crop and pasture production (Moody and Bolland 1999) but they‟re indicative of the low P content of many Australian soils that are derived from very old and highly weathered parent materials naturally deficient in P (Beadle 1966; Polglase et al. 1992; Attiwill and Adams 1993; Handreck 1997; Keith 1997). In the highly productive forests of Victoria for example, an 80 year old stand of Eucalyptus regnans had a Bray 2 P concentration of 1.1 mg kg-1 for the surface soil, while a younger stand (9 years post fire) had a value of 3.5 mg kg-1 for the same soil depth

JK Fitzgerald Chapter 3 Page 94

(Polglase et al. 1992). Importantly, Handreck (1997) highlighted the very poor relationship between agricultural indices of plant-available P and the P requirements and productivity of native vegetation communities throughout Australia.

The sites were separated into three significantly different groups on the basis of extractable P: Mount Annan was the most fertile site; Hoxton Park and Scheyville were moderately fertile; and Orchard Hills and Prospect were the least fertile sites. This trend was clearly evident in the site x depth interaction as well because Mount Annan had the highest concentrations of Bray 1 P to 60.5 cm while Orchard Hills and Prospect had much lower concentrations that varied little throughout the soil profile. This may reflect differences in the intensity of past land use, for example, Mount Annan was part of a very large and successful dairy farm for many years prior to abandonment, while Hoxton Park and Orchard Hills were typical of large estates used primarily for beef production. In general, dairy farms require much higher inputs of fertiliser than other types of pastoral activities due to the more intensive levels of grazing (Havilah et al. 2005) and the need for superphosphate throughout the region was stressed by Allan (1980).

The pasture and tree patch types had the lowest and highest concentrations of Bray 1 P respectively. The mean concentration of Bray 1 P for the pasture was 0.86 mg kg-1, which is extremely low for loam and clay loam soils used for agriculture (Brouwer 1998; Hazelton and Murphy 2007). In the central and southern tablelands of NSW for example, the critical concentration of Bray 1 P for exotic perennial pastures on a range of soil types is 10-12 mg kg-1 for the 0-7.5 cm soil depth (Moody and Bolland 1999). Not surprisingly therefore, Allan (1980) recommended heavy applications of single superphosphate during the first three years of pasture development on the Cumberland Plain. In line with this but in contrast to the results presented here, Hill et al. (2005) found significantly higher concentrations of total P within the soil (0-10 cm) of abandoned pastures compared to Cumberland Plain Woodland. Together though, these results may indicate a run-down pasture (for example, see Sangha et al. 2005) where P is tied up in the plant and microbial biomasses (low extractable P; this study) and in the slowly mineralised pool of soil P (high total P; Hill et al. 2005).

The concentration of Bray 1 P was significantly higher beneath the woodland trees

JK Fitzgerald Chapter 3 Page 95

compared to the open and pasture patch types. Similar trends have been observed in other temperate grazing systems in Australia where eucalypts, existing as either isolated paddock trees or growing in small stands, have elevated nutrient levels, most commonly P, N and C, beneath their canopies compared to the surrounding pasture (Wilson 2002; Eldridge and Wong 2005; Graham et al. 2004; Wilson et al. 2007). This trend has thus been commonly attributed to the effects of livestock grazing and stock camps, which lead to an increased deposition of these nutrients beneath trees via dung and urine (Wilson 2002). Eucalypts can also be associated with soil nutrient „hotspots‟ in relatively undisturbed systems (Prober et al. 2002a) however, as shown here for Cumberland Plain Woodland. This natural pattern of nutrient enrichment has also been reported for a range of other tree and shrub species, both in Australia and overseas (Facelli and Brock 2000; Diemont et al. 2006).

The natural pattern of elevated soil P beneath individual eucalypts is likely to be the result of interactions between cation cycling and soil pH, as well as the affects of mycorrhiza on the uptake and cycling of P. The speciation of P and its concentration in the soil solution is strongly affected by: soil pH, especially in the rhizosphere; the concentration of metals that will bond with P; and the concentration of organic ligands that will complex with minerals containing P (Hinsinger 2001).

Precipitation-dissolution equilibria determine what kind of metal phosphates will form while adsorption-desorption equilibria control reactions between P ions and minerals such as sesquioxides (Al and Fe oxides) and organic ligands (e.g. citrate and oxalate). P is most commonly fixed by Al, Fe and Ca. As the pH (in CaCl2) falls below 4.2, P ions will precipitate with Al and Fe and will adsorb onto Al and Fe oxides, which become increasingly soluble as the soil becomes more acidic (Hinsinger 2001; Hazelton and Murphy 2007). In neutral to alkaline soils on the other hand, Ca phosphates will predominate because the solubility of Ca increases as the pH (in CaCl2) exceeds approximately 7 (Hazelton and Murphy 2007). The availability of P for plant and microbial uptake is therefore highest within the pH range of 4.2-7 and the lower limit was approached by the tree patch type in this study. As such, higher soil P beneath the tree patch type is probably linked to the effects of leaf litter on soil pH, as previously discussed. McColl (1969) drew a similar conclusion in his study of several eucalypt associations on the south coast of NSW.

JK Fitzgerald Chapter 3 Page 96

The trend of elevated Bray 1 P levels beneath the woodland trees did not occur at all sites however, as shown by the significant site x patch x depth interaction. The surface concentration of Bray 1 P was highest beneath the woodland trees for all sites except Scheyville, which had a higher concentration within the pasture. This could be related to differences in past land use and fertiliser application, as well as time since the last fire. The woodland at Scheyville for example, was long unburnt compared to the pasture (pers. comm. J. Sanders 2006).

Mycorrhizal associations, which are common in many eucalypt communities throughout Australia (Keith 1997; Anderson et al. 2007), may also be responsible for increasing the concentration of plant-available P beneath individual eucalypts (Wilson 2002). Mycorrhiza can increase the uptake of nutrients by eucalypts by increasing the surface area of their roots. Mycorrhiza can also increase the availability of certain nutrients by excreting a range of organic acids, such as oxalate, that modify the chemical nature of the rhizosphere (Keith 1997). Ectomycorrhiza for example, can produce large amounts of oxalate (Malajczuk and Cromack 1982; Hinsinger 2001), which can increase the availability of soil P (Hinsinger 2001) and in Australia this type of fungus is commonly associated with mature eucalypts (Keith 1997). The importance of mycorrhiza for eucalypt recruitment in pastures on the Southern Tablelands of NSW has been highlighted by Stol and Trappe (2006) and the importance of mycorrhiza on the Cumberland Plain has been investigated for rare and endangered orchid species (Darley 2005) but not for the eucalypts of the region.

Total S

The mean concentrations of total S for the different sites ranged from 0.0150% to 0.0285% and the mean value for the surface soil was 0.0296%. Low values such as these are typical for Australian soils (Attiwill and Leeper 1987) and S deficiencies have been reported for the Northern Tablelands of NSW (Williams and Andrew 1970; Blair and Nicolson 1975) and in areas of South Australia and Western Australia that have sandy loam soils (Barrow (1974) and Clarke and Lewis (1974) in Lewis (1999)). Like total P and N however, total S is a poor indicator of plant-available S, which can fluctuate widely throughout the year in response to changing moisture and temperature regimes (Lewis 1999).

JK Fitzgerald Chapter 3 Page 97

There were distinct changes in total S to 60.5 cm, which were apparent for the main effect and all of the interactions that involved depth; there were elevated levels at 2.5 cm and 60.5 cm with a drop in concentration at 20.5 cm. This suggests that the vertical distribution of total S was affected by similar processes (e.g. leaching) or conditions regardless of site or patch type. The rate of change and the actual concentrations of total S within the soil profile differed between sites however, as indicated by the significant site x depth interaction.

Nitrate, ammonium and total N

Important differences between the pasture and the woodland emerged in the measures of soil nitrogen. For nitrate, concentrations were greatest under the pasture and least under the shrubs. Notably, values under the pasture were significantly greater than the open and shrub patch types but not the tree patch type, which had the highest concentration of nitrate within the woodland. There is no data for plant-available N on the Cumberland Plain with which to compare these results but in terms of general agricultural standards, all of the patch types had extremely low concentrations of nitrate, even the pasture, which had a mean of 1.09 mg kg-1. In the southern wheat belt of NSW for example, a soil with less than 8 mg kg-1 of nitrate in the top 30 cm of the profile will respond very well to nitrogenous fertilisers while a soil with less than 3 mg kg-1 of nitrate in the 0-15 cm soil depth will require 150-300 kg of urea per hectare to improve crop productivity (Hazelton and Murphy 2007).

Not all sites had elevated nitrate levels in the pasture, as seen from the significant site x patch type interaction. Those that did were Mount Annan, Orchard Hills and Prospect but Hoxton Park and Scheyville had the highest concentration of nitrate beneath the woodland trees. The site x patch type interaction for total N did not show the same trend, that is, Mount Annan and Orchard Hills did not have elevated levels of total N within the pasture and Scheyville did not have higher concentrations beneath the woodland trees. Not surprisingly, total N is a very poor indicator of plant-available N because it is a component of the recalcitrant pool of organic matter that has a very slow turnover rate (Strong and Mason 1999).

Regardless of this, the results showed that the concentration of nitrate can vary

JK Fitzgerald Chapter 3 Page 98

significantly between the pasture and woodland patch types and this suggests that the abandoned farmland and Cumberland Plain Woodland may function differently with respect to nitrogen. This has been shown to affect species composition and abundance during old field succession in a range of different systems (Inouye and Tilman 1998; Paschke et al. 2000; Flinn and Vellend 2005) and reducing nitrate levels on abandoned farmland has been identified as a key requirement for the restoration of native plant species diversity on a range of old fields, especially in the tall grass prairies and short grass steppes of North America (Averett et al. 2004; Corbin and D‟Antonio 2004). As the concentration of nitrate can vary substantially over a range of time scales, one-off measures provide a snap-shot of plant-available N only and measures through time, both within the pasture and Cumberland Plain Woodland are required to gain a better understanding of the trends presented in this study.

At some sites, the highest nitrate levels were found beneath the woodland trees. It is likely that the reduced soil acidity beneath the woodland trees contributed to this because the availability of N is reduced below a pH (in CaCl2) of 4.2 (Landon 1991). In addition to this, the cycling of N and P are often closely related because N-fixing bacteria depend on soil P for nutrition (Beadle 1953; Eisele et al. 1989; Pywell et al. 1994). As such, the high levels of extractable P beneath the woodland trees may also be enhancing soil nitrate levels.

Elevated soil nitrate levels beneath individual woodland trees have been observed elsewhere in Australia (Jackson and Ash 1998; Prober et al. 2002a). In an open woodland in northeast Queensland for example, Jackson and Ash (1998) measured significantly higher concentrations of nitrate beneath eucalypts and corymbias compared to adjacent open areas, which were native perennial pastures used for beef production. They attributed this to higher litterfall beneath the woodland trees and they found a positive effect of increased soil nutrients beneath trees on forage quality (Jackson and Ash 1998). Prober et al. (2002a) found a very different trend in remnant White Box woodlands in NSW because the soil beneath eucalypt canopies and in inter- canopy (open) areas had very similar levels of nitrate and ammonium within the top 10 cm of the profile. The woodland trees however, were associated with significantly higher concentrations of total N than the open areas (Prober et al. 2002a).

JK Fitzgerald Chapter 3 Page 99

In general, ammonium is the dominant form of N in N-limited ecosystems (Davidson et al. 2007) and eucalypt forests usually have a higher concentration of ammonium in their soils than nitrate (Keith 1997) because nitrifying bacteria are not as abundant, or active, in soils with a low (<5.5 measured in water) pH (Attiwill and Leeper 1987; Landon 1991). It is not surprising therefore, that nitrate concentrations across the study sites and patch types were consistently lower than that of ammonium. Site had a significant main effect on ammonium levels within the soil. Hoxton Park, Mount Annan and Prospect were the most fertile sites with an average concentration of 6.22 mg kg-1, while Orchard Hills and Scheyville had much lower concentrations with a mean of 3.61 mg kg-1.

As for nitrate, there was evidence of higher ammonium values in the pasture relative to the woodland patch types. The site x patch type interaction showed that ammonium was highest within the pasture for Hoxton Park, Mount Annan and Scheyville, while the pasture had the second highest concentration at Orchard Hills and Prospect. There was also a significant interaction between patch type and soil depth and the greatest rate of decline occurred within the pasture. The woodland patch types on the other hand, had very similar changes with depth, although the lowest concentrations occurred beneath the shrub patch type.

Many Australian studies have found higher concentrations of total N beneath trees compared to open patch types (Jackson and Ash 1998; Facelli and Brock 2000; Prober et al. 2002a; Wilson 2002; Wilson et al. 2007) and this trend also occurred at Hoxton Park, Mount Annan and Orchard Hills. For these sites, the pasture had a mean concentration of total N that was well within the woodland range. Similarly, Arnold et al. (1999) found no significant difference in total N between abandoned farmland and undisturbed vegetation in the wheatbelt of Western Australia. Many overseas studies however, have found higher concentrations of total N in old fields and in the soils of secondary vegetation growing on abandoned farmland compared to ancient forests and woodlands (Pywell et al. 1994; Koerner et al. 1997; Dupouey et al. 2002; Flinn and Vellend 2005). This was the case for Scheyville however, which had much higher concentrations of total N within the pasture compared to the other three patch types.

Importantly, this study has provided evidence that soil N may be an important soil property for the restoration of Cumberland Plain Woodland on abandoned farmland

JK Fitzgerald Chapter 3 Page 100

since many sites had elevated N levels within the pasture compared to the woodland patch types. That being said, N levels were also noticeably elevated beneath the woodland trees at some sites. Further research on soil N and its dynamics is thus warranted in this system.

JK Fitzgerald Chapter 3 Page 101

CHAPTER 4. The ground flora of abandoned farmland and Cumberland Plain Woodland and its relationship with soil chemical properties

4.1 Introduction

The previous study highlighted some key differences in soil fertility between remnant Cumberland Plain Woodland and abandoned pastures that were once covered by this threatened vegetation community. The first study also revealed great spatial heterogeneity of the woodland soils in relation to tree, shrub and open patch types. Most importantly, the pasture soils tended to have higher concentrations of mineral-N than the woodland soils while the trees were generally associated with soil nutrient „hotspots‟ within the woodland.

Elevated nutrient levels on abandoned farmland (compared to pre-disturbance conditions) are one of the major limiting factors to the natural regeneration and restoration of the pre- disturbance community (Flinn and Vellend 2005). This is because native species are typically less competitive in nutrient-rich environments than agronomic and ruderal species (Chapin 1980; Mack and D‟Antonio 2003). In addition to this, different structural elements of the vegetation, at the scale of individual trees and shrubs, or in relation to inter-canopy areas, can impart spatial variability on plant species composition, cover and productivity within the ground layer (Scanlan and Burrows 1990; Ko and Reich 1993; Treydte et al. 2007). This occurs in response to the influence of the overstorey species (or lack thereof) on abiotic resources, namely, light intensities, temperature regimes and moisture levels, as well as biotic processes such as litterfall and the biogeochemical cycling of nutrients (Collins and Pickett 1998; Belsky et al. 1989; Rhoades et al. 1998).

Ground species composition can vary greatly from open to closed canopy positions and this has been observed in both temperate (Prober et al. 2002a) and arid environments (Belsky et al. 1993). The reasons for this are unclear in many cases, although it seems to be related to competitive interactions between different species as a result of changing soil properties

JK Fitzgerald Chapter 4 Page 102

and microclimates across the various patch types (Pickett and White 1985). Herb layer productivity has been found to both increase and decrease in response to the presence of overstorey canopies (Walker et al. 1986) and this seems to be related to rainfall and tree (or shrub) densities. It seems more likely, for example, for herb layer productivity to increase beneath trees in areas that are moisture-limited due to improved water relations brought about by shading effects and vice versa (Belsky et al. 1993; Jackson and Ash 1998).

As previously discussed in Chapter 1, understanding natural patterns of diversity and the natural operation of ecological processes within native vegetation communities is essential for developing tools and strategies for their effective management and restoration in degraded areas (Tongway 1991; King and Hobbs 2006). In fact Prober and Thiele (2005) recently highlighted the importance of understanding small-scale patch dynamics, in terms of ground species composition and abundance in canopy and inter-canopy areas, along with the soil properties within these patch types (Prober et al. 2002a; 2002b), to enhance restoration outcomes for temperate woodlands and grasslands in Australia.

This approach to understanding the ecology of woodland communities has particular relevance to Cumberland Plain Woodland, which is characterised by high intra- and inter- site floristic variability (French et al. 2000; Benson and Howell 2002), as well as high levels of diversity and endemism that is concentrated within the ground layer (James et al. 1999; Tozer 2003). Understanding small-scale patch dynamics in Cumberland Plain Woodland is also important for current restoration efforts, which are focused on revegetation as a means to facilitate the colonisation of abandoned farmland by native ground layer species (Davies and Christie 2001).

In light of the above, the aims of this study were to examine: 1. How canopy and inter-canopy patch types within Cumberland Plain Woodland and abandoned farmland affect ground species composition, richness and cover; and 2. What underlying relationships exist between soil chemical properties and the ground layer of Cumberland Plain Woodland and abandoned farmland.

JK Fitzgerald Chapter 4 Page 103

4.2 Methodology

4.2.1 Experimental design

The two factors in the experimental design were site and patch type, as previously described in Section 3.2.1 and the sampling quadrats used for the previous study (see Section 3.2.2) were also used for the vegetation survey described herein. The full sampling design was thus 5 sites x 3 sub-sites per site x 4 patch types per sub-site, which resulted in 60 quadrats being sampled for floristic analysis.

4.2.2 Vegetation and soil sampling

Prior to sampling the soils of the abandoned farmland and Cumberland Plain Woodland at the five study sites (Chapter 3), ground species composition and cover were recorded for all vascular species located within the various patch types. The ground flora was defined as herbaceous species and any trees or shrubs that were less than 0.5 m in height (for example, eucalypt seedlings and very small saplings). This vegetation survey was carried out in February and March of 2006. Ideally, a follow-up survey would have been undertaken during the middle of the year when rainfall and temperature are typically at their lowest (for example see Burrows 2004) but this was not possible due to logistical constraints. In each 10 x 10 m quadrat therefore, the percentage cover of each species was visually estimated with the aid of cover estimation charts (McDonald et al. 1990); to maintain consistency, these estimates were carried out by the author only. Species were identified as native or exotic and followed Harden (1990; 1991; 1992; 1993). Values for native and exotic species richness were calculated from this data.

The soil data collected for the first study, as outlined in Section 3.2.3, was also used for this study. In addition, soluble and exchangeable cations were also measured. Ca, Mg, K and Na are the four most commonly measured basic cations because they have a strong bearing on the physical and chemical fertility of the soil (Rengasamy and Churchman 1999). Ca and Mg typically impart structural stability, whereas a high concentration of Na can lead to

JK Fitzgerald Chapter 4 Page 104

dispersion (Rengasamy and Olsson 1991). Ca, Mg and K are essential plant nutrients and Na plays an important role in salinisation (Charman and Wooldridge 2007). For this study, soluble and exchangeable cations (Ca, Mg, K and Na) were measured as per Eldridge and Wong (2005), who used a simplified version of method 15A1 (Rayment and Higginson) to reduce the time taken for analysis. Only the surface soil (0-5 cm) measurements for moisture content and the various chemical properties were used for the multivariate analysis, since the findings of the previous study showed that, in general, the greatest differences between patch types and sites occurred within the surface soil.

4.2.3 Univariate analysis

Native species richness and exotic species richness were analysed using a split-plot ANOVA with the main effects being site and patch type. This analysis was the same as that for bulk density in the previous study and the details of the main-plot and split-plot factors, as well as the error terms, are described in Section 3.2.4. Alpha was set at 0.05.

Significant main effects were once again examined using post-hoc tests. Tukey‟s Honestly Significant Different test was carried out to detect significant differences between sites and estimated marginal means and a Bonferroni adjustment were used to undertake pair-wise comparisons of patch types. The assumptions of normality and homogeneity of variance were tested using the Kolmogorov-Smirnov and Levene‟s tests respectively and no transformations were required. The assumption of sphericity was tested using Mauchly‟s test and the Greenhouse-Geisser epsilon was used to decrease the degrees of freedom for the significance test when the data were non-spherical. As in Chapter 3, where both main effects and interactions were significant, both are reported to assist interpretation of complex patterns in the data.

4.2.4 Multivariate analyses

Multivariate analyses were carried out using the PRIMER statistical package (v.5; Clarke and Gorley 2001) to detect patterns in ground species composition and cover across sites

JK Fitzgerald Chapter 4 Page 105

and patch types. The raw data matrix, which consisted of 178 rows (species) and 60 columns (samples), contained the percentage cover of each species in each sample. The data were not standardised because the samples were the same size (i.e. measured in 10 x 10 m quadrats) but were fourth root transformed to allow all species to contribute to the calculation of the Bray-Curtis similarity coefficients (Clarke and Warwick 2001), which formed the basis for cluster analysis, ordination and analysis of similarity.

4.2.4.1 Examining the floristic similarity of samples using cluster analysis and ordination

Bray-Curtis similarity coefficients were calculated for each pair wise combination of samples and cluster analysis and ordination were then used to investigate the degree of similarity in species composition and cover between samples (Clarke and Warwick 2001). A dendrogram was constructed using hierarchical agglomerate clustering with group- average linking and an ordination plot was made using non-metric multi-dimensional scaling (nMDS) with 10 random restarts.

4.2.4.2 Investigating the effects of site and patch type on ground species composition and cover with analysis of similarity and the SIMPER routine

To test the null hypothesis that site and patch type had no effect on ground species composition and cover, a two-way crossed analysis of similarity (ANOSIM) was carried out with site as the block effect and patch type as the treatment, with both factors being defined prior to analysis. The significance of the test was evaluated by comparing the observed value of the statistic with its permutation distribution (see below).

ANOSIM is based on the rank similarity matrix of the biotic data and just like its univariate counterpart (i.e. analysis of variance), the test statistic, which is referred to as R, is based on within and between group variability and lies between -1 and 1. If the null hypothesis is true (i.e. no significant differences between sites or treatments) then R will be approximately zero because the rank similarities for replicates both within and between sites (or treatments) will be about the same. If R approaches 1 then all of the replicates

JK Fitzgerald Chapter 4 Page 106

within a site (or treatment) are more similar to each other than they are to any other replicate from any other site (or treatment). As such, the numerical value of R is a very useful indicator of the degree of similarity between sites and treatments (Clarke and Warwick 2001).

The significance of R is assessed against its permutation distribution, which is constructed by recalculating R a large number of times (the rule of thumb is at least 999) following random rearrangement of the sample labels (i.e. permutations). The significance level at which the null hypothesis can be rejected is calculated as:

P=(t+1)/(T+1)

Where: t is the number of times that the recalculated values of R were equal to or greater than the observed value of R; and T is total number of random permutations that were carried out.

Since R is a global statistic, pair wise comparison tests need to be carried out to determine where significant differences occur. Multiple comparison tests are often associated with an increased risk of a Type I error (Sokal and Rohlf 2000) but for ANOSIMs, this is offset by the numerical value of R, which is an extremely useful indicator of the similarity between groups regardless of its statistical significance (Clarke and Warwick 2001).

Any significant differences were subsequently examined using the SIMPER routine to identify which species contributed most to the dissimilarity between sites or patch types. Any groups that were not statistically different (in this case, the tree and shrub patch types) were combined for this analysis.

4.2.4.3 Linking the floristic and soil data using the BVSTEP procedure

Assuming that (a) abiotic factors shape biotic patterns and (b) the abiotic factors responsible for this are known, then an ordination of the abiotic data, based on Euclidean JK Fitzgerald Chapter 4 Page 107

distances, along with an ordination of the biotic data, based on Bray-Curtis similarities, will position the samples in a very similar way (Clarke and Warwick 2001). This is the basis for the BIO-ENV procedure, which calculates the Spearman rank correlation coefficient for abiotic and biotic similarity matrices. All possible combinations of the abiotic variables are considered and so the variable, or variables, that best explain the biotic pattern can be determined. When the number of variables is greater than 15 however, this procedure is impractical due to long computation times. To overcome this, only those variables that are good indicators of change in other variables should be included in the analysis. As a rule of thumb, a variable can be safely omitted from the procedure if it is highly correlated with (~0.95) another variable that will be included in the analysis. This is because both variables will make similar contributions to the similarity matrix and retaining both will not improve the ordination (Clarke and Warwick 2001). If the number of variables cannot be reduced then a related procedure called BVSTEP can be used.

BVSTEP is an extension of the BIO-ENV routine which, instead of searching through all possible combinations of the variables, performs a stepwise search with forward selection and backward elimination to find which abiotic variables produce a similarity matrix that best matches (i.e. correlates with) the biotic similarity matrix. Thus, BIO-ENV and BVSTEP are exploratory tools that attempt to uncover any underlying relationships between the biotic and abiotic data (Clarke and Warwick 2001).

The soil data were analysed in conjunction with the floristic data using the BVSTEP procedure to explore which soil variables best explained the observed pattern of species composition and cover across the samples. The BVSTEP procedure was chosen in favour of the BIO-ENV routine because of the large number of soil variables and the low correlations between them (Appendix 2, Tables A2.1a-c). The soil data were transformed where necessary (as per Chapter 3) and normalisation was also carried out so that the variables, which were reported with different units, could be compared on a dimensionless scale (Clarke and Warwick 2001). An MDS ordination based on normalised Euclidean distance was carried out for the variables reported by BVSTEP and „bubbles‟ representing the values of the variables for each sample were superimposed on this plot.

JK Fitzgerald Chapter 4 Page 108

4.3 Results

4.3.1 Univariate analysis

Native species richness differed amongst sites (main effect; F4,10=4.610, P=0.02) with the highest value observed at Scheyville and the lowest at Prospect (Figure 4.1a; P=0.048).

Patch types also differed in native species richness (main effect; F3,12=18.039, P<0.0001); the pasture had less than half the number of native species than the woodland patch types (P=0.000), which did not differ significantly ( 22-26 species per 100 m2; Figure 4.1b). The interaction between site and patch was not significant (F12,30=1.886, P=0.078).

Exotic species richness differed amongst patch types, depending on site (site x patch type interaction; F7.53,18.8=3.976, P=0.007) but differences amongst both sites and patch types were sufficiently strong to be detected as main effects as well (Figures 4.2a and 4.2b). The overall trend was for the pasture to have significantly more exotic species per 100 m2 than the woodland patch types (P≤0.005; Figure 4.2b) and the tree patch type to have nearly twice the number of exotic species than the shrub patch type (P=0.002; Figure 4.2b). Orchard Hills did not display this pattern (Figure 4.2c), resulting in the significant interaction. Comparison of site means showed that Mount Annan had a significantly higher number of exotic species per 100 m2 than Orchard Hills, Prospect and Scheyville (P≤0.041; Figure 4.2a).

4.3.2 Multivariate analyses

4.3.2.1 Cluster analysis and ordination

The pasture and woodland samples formed two distinct groups at approximately 20% similarity (Figure 4.3). Both of these groups displayed similar trends: the samples were clustered most strongly by site; Prospect was clearly separated from the other four sites; and Mount Annan and Orchard Hills were clustered together around the 45% similarity

JK Fitzgerald Chapter 4 Page 109

mark, as were Hoxton Park and Scheyville. Within the woodland, samples from the same patch type were rarely grouped together at the highest level of similarity.

30 -2 25

20

15

10

5 No. native species per 100 m 100 per species native No. 0 Hoxton Park Mount Annan Orchard Hills Prospect Scheyville

Figure 4.1a Mean native species richness for the ground layer at the study sites. Error bars represent standard errors of the means.

30 -2 25

20

15

10

5 No. native species per 100 m 100 per species native No. 0 Pasture Open Shrub Tree

Figure 4.1b Mean native species richness for the ground layer of the four patch types. Error bars represent standard errors of the means.

JK Fitzgerald Chapter 4 Page 110

20 -2

15

10

5 No. exotic species per 100 m 100 per species exotic No. 0 Hoxton Park Mount Annan Orchard Hills Prospect Scheyville

Figure 4.2a Mean exotic species richness for the ground layer at the study sites. Error bars represent standard errors of the means.

20 -2

15

10

5 No. exotic species per 100 m 100 per species exotic No. 0 Pasture Open Shrub Tree

Figure 4.2b Mean exotic species richness for the ground layer of the four patch types. Error bars represent standard errors of the means.

20 -2

15

10

5

0 No. exotic species per 100 m 100 per species exotic No. Hoxton Park Mount Annan Orchard Hills Prospect Scheyville

Pasture Open Shrub Tree

Figure 4.2c Mean exotic species richness for the ground layer of the four patch types at each of the study sites. Error bars represent standard errors of the means.

JK Fitzgerald Chapter 4 Page 111

MAT MAT MAS MAT MAO MAO MAS MAS MAO OHO OHO OHS OHT OHO OHT OHS OHT OHS HPT HPT HPS HPS HPT HPO HPO HPO HPS SNPO SNPS SNPT SNPT SNPS SNPO SNPT SNPO PRS PRT PRO PRT PRT PRO PRS SNPS PRS PRO MAP HPP MAP MAP OHP OHP OHP SNPP SNPP SNPP HPP HPP PRP PRP PRP 0 20 40 60 80 100

Similarity (%)

Figure 4.3 Dendrogram showing the percentage similarity between samples where Similarity (%) ground species composition and cover were measured in 10 x 10 m quadrats. The

samples are labelled by a site code first then a P, O, S or T to signify the pasture, open, shrub and tree patch types respectively. The sites are identified as follows: HP is Hoxton Park; MA is Mount Annan; OH is Orchard Hills; PR is Prospect; and SNP is Scheyville.

JK Fitzgerald Chapter 4 Page 112

The ordination clearly separated the pasture and woodland samples and the within and between site variability is plain to see (Figure 4.4). Several sites had widely spaced pasture samples (Orchard Hills, Hoxton Park and Scheyville), while the pasture samples from Mount Annan and Prospect formed more cohesive (but separate) groups. The woodland samples from Mount Annan, Orchard Hills, Scheyville and Prospect (in particular) formed discrete clusters, while those from Hoxton Park were more scattered. The latter had an affinity for samples from Mount Annan and to a lesser extent, from Orchard Hills and Scheyville. The relative positions of the woodland and pasture samples from each site were very similar, except for those from Mount Annan. The nMDS showed that the tree, shrub and open patch types were grouped most strongly according to site and not by patch type.

Species composition and percentage cover for the first study_10 restarts with...

Stress: 0.17 HPT HPS HPO HPP

MAT MAS MAO MAP

OHT OHS OHO OHP

PRT PRS PRO PRP

SNPT SNPS SNPO SNPP

Figure 4.4 nMDS ordination of ground species composition and cover based on fourth root transformed cover (%) values and Bray-Curtis similarities. Diamonds represent pasture

patches; squares are open patches; downward pointing triangles are shrub patches; and upward pointing triangles are tree patches. The samples from Hoxton Park are coloured green; those from Mount Annan are pink; Orchard Hills samples are dark blue; Prospect is yellow; and Scheyville is light blue.

4.3.2.2 Analysis of similarity and SIMPER analysis

Ground species composition and cover differed significantly amongst sites and across some of the patch types (Tables 4.1a and 4.1b). Site differences were due to small contributions

JK Fitzgerald Chapter 4 Page 113

from many different species (Appendix 2, Tables A2.4a-j) and in general, the top three species rarely contributed to more than 10% of the total dissimilarity between sites (Table 4.2). Prospect was associated with the highest levels of dissimilarity (Table 4.2); Prospect and Mount Annan were the most dissimilar sites (75.74%) while Hoxton Park and Scheyville were the two most similar sites with an average dissimilarity of 65.66%. Most commonly, it was differences in the cover of Aristida ramosa, Aristida vagans, Paspalum dilatatum and Themeda australis that contributed most to the dissimilarity between sites, with Cynodon dactylon, Microlaena stipoides and Setaria gracilis also ranking within the top three species on a number of occasions (Table 4.2). The average cover for those species with a cover ≥2% at any site is shown in Figures 4.5a and 4.5b. These species showed great site-to-site variability, for example the cover of T. australis was less than 1% at Hoxton Park but more than 59% at Prospect and there was a five- increase in the cover of P. dilatatum at Mount Annan compared to Prospect.

Table 4.1a Results of the 2-way crossed ANOSIM for the site factor based on ground species composition and cover. Comparison R P Global test 0.917 0.001 Hoxton Park v Mount Annan 0.880 0.001 Hoxton Park v Orchard Hills 0.824 0.001 Hoxton Park v Prospect 0.944 0.002 Hoxton Park v Scheyville 0.861 0.001 Mount Annan v Orchard Hills 0.898 0.001 Mount Annan v Prospect 0.991 0.001 Mount Annan v Scheyville 0.981 0.001 Orchard Hills v Prospect 1.00 0.001 Orchard Hills v Scheyville 0.972 0.001 Prospect v Scheyville 0.935 0.001

Table 4.1b Results of the 2-way crossed ANOSIM for the patch type factor based on ground species composition and cover. Comparison R P Global test 0.61 0.001 Tree v Shrub 0.111 0.181 Tree v Open 0.304 0.008 Tree v Pasture 0.963 0.001 Shrub v Open 0.296 0.002 Shrub v Pasture 0.978 0.001 Open v Pasture 0.985 0.001

The average dissimilarity between the combined tree and shrub patch type and the open patch type was 59.82% (Table 4.3) and 33 species contributed to half of this (Appendix 2,

JK Fitzgerald Chapter 4 Page 114

Table A2.5a). A far greater cover of T. australis occurred beneath the open patch type (~44%) than it did beneath the combined tree and shrub patch type (~29%), while A. ramosa had a fairly consistent cover (~15%) across both patch types (Figure 4.6a). A. vagans, Chloris ventricosa and M. stipoides also made important contributions to the dissimilarity between these two patch types; there was a greater cover of A. vagans within the open patch type while C. ventricosa and M. stipoides had higher covers beneath the tree and shrub canopies (Figure 4.6a)

Table 4.2 The percentage dissimilarity, based on fourth root transformed data, for all pair wise combinations of sites and the individual and cumulative contributions from the top three species for each comparison. Exotic species are marked with an asterisk. HP stands for Hoxton Park; MA is for Mount Annan; OH is for Orchard Hills; PR is for Prospect; and SNP is for Scheyville. Comparison Dissimilarity Species Contribution Cumulative contribution A. vagans 2.76% 2.76% HP v MA 67.37% P. dilatatum* 2.60% 5.36% T. australis 2.55% 7.91% A. vagans 3.72% 3.72% HP V OH 66.41% T. australis 3.71% 7.43% A. ramosa 2.88% 10.31% T. australis 5.42% 5.42% HP V PR 74.26% A. vagans 3.34% 8.76% A. ramosa 2.80% 11.56% T. australis 4.33% 4.33% HP V SNP 65.66% M. stipoides 2.80% 7.13% A. vagans 2.77% 9.90% P. dilatatum* 3.14% 3.14% MA V OH 65.83% T. australis 2.99% 6.13% A. ramosa 2.89% 9.02% T. australis 3.26% 3.26% MA V PR 75.74% A. ramosa 3.15% 6.41% P. dilatatum* 2.55% 8.96% T. australis 2.94% 2.94% MA V SNP 69.04% P. dilatatum* 2.59% 5.53% A. ramosa 2.55% 8.08% A. ramosa 5.13% 5.13% OH V PR 72.34% T. australis 3.65% 8.78% S. gracilis* 2.85% 11.63% A. ramosa 3.39% 3.39% OH V SNP 70.63% T. australis 3.24% 6.63% P. dilatatum* 3.04% 9.67% T. australis 2.93% 2.93% PR v SNP 70.19% P. dilatatum* 2.75% 5.68% C. dactylon* 2.39% 8.07%

JK Fitzgerald Chapter 4 Page 115

The combined tree and shrub patch type and the pasture patch type had an average dissimilarity of 81.25% and half of this was due to 28 species (Table 4.3; Appendix 2, Table A2.5b). The 5 most common pasture species were, in order of decreasing cover: P. dilatatum; C. dactylon; Chloris gayana; S. gracilis; and Briza subaristata. The most common native grass species within the pasture was M. stipoides, which had an average cover of about 2% (Figure 4.6a). The pasture and open patch types had an average dissimilarity of 79.10% and once again, 28 species contributed to 50% of this (Appendix 2, Table A2.5c). M. stipoides had a very similar cover within both of these patch types (Figure 4.6a).

70 T. australis 60 A. ramosa 50 A. vagans 40 M. stipoides 30 C. ventricosa

Cover (%) Cover 20 E. trigonos P. labillardieri 10 E. brownii 0 E. leptostachya Hoxton Mount Orchard Prospect Scheyville P. distans Park Annan Hills

Figure 4.5a Native species that had a mean cover greater than or equal to 2% at any one site and their average cover (%) at each site (from the SIMPER analysis).

70 P. dilatatum 60 C. dactylon 50 B. subaristata 40 S. gracilis 30 C. gayana Cover (%) Cover 20 A. affinis 10 E. curvula 0 Hoxton Mount Orchard Prospect Scheyville Park Annan Hills

Figure 4.5b Exotic species that had a mean cover greater than or equal to 2% at any one site and their average cover (%) at each site (from the SIMPER analysis).

JK Fitzgerald Chapter 4 Page 116

Table 4.3 The percentage dissimilarity for all pair wise combinations of patch types and the individual and cumulative contributions from the top three species for each comparison. Exotic species are marked with an asterisk. P stands for pasture, O is for open, S is for shrub and T is for tree. Comparison Dissimilarity Species Contribution Cumulative contribution T. australis 3.13% 3.13% T & S v O 59.82% A. ramosa 2.97% 6.1% A. vagans 2.59% 8.69% P. dilatatum* 4.65% 4.65% T & S v P 81.25% C. dactylon* 3.86% 8.51% T. australis 3.68% 12.18% T. australis 4.45% 4.45% O v P 79.10% P. dilatatum* 4.34% 8.79% C. dactylon* 4.25% 13.04%

50 45 40 T. australis 35 A. ramosa 30 A. vagans 25 20 M. stipoides

Cover (%) Cover 15 C. ventricosa 10 P. labillardieri 5 0 Tree and shrub Open Pasture

Figure 4.6a Native species that had a mean cover greater than or equal to 2% within any one patch type and their average cover (%) for each patch type (from the SIMPER analysis).

50 45 40 P. dilatatum 35 C. dactylon 30 B. subaristata 25 20 S. gracilis

Cover (%) Cover 15 C. gayana 10 A. affinis 5 0 Tree and shrub Open Pasture

Figure 4.6b Exotic species that had a mean cover greater than or equal to 2% within any one patch type and their average cover (%) for each patch type (from the SIMPER analysis).

JK Fitzgerald Chapter 4 Page 117

4.3.2.3 BVSTEP analysis

The soil variables that best explained the pattern of species composition and cover across the samples (i.e. Figure 4.4) were moisture content, nitrate, total N and exchangeable Na, although the strength of the correlation was weak (Table 4.4). Figures 4.7a-e show a decreasing trend in these soil variables from left to right, or from the bottom left hand corner of the plot to the top right hand corner of the plot. Therefore, the samples on the left hand side, which include most of the pasture samples and the majority of samples from Mount Annan, tend to have the highest values for these variables.

Table 4.4 The soil variables that best explained the observed biotic pattern, in terms of ground species composition and cover, across the samples analysed using the BVSTEP procedure. Variables Rho Moisture, nitrate, total N and exchangeable Na 0.343 Moisture, nitrate, total N, exchangeable Na, ammonium and exchangeable Ca 0.340 Moisture, nitrate, exchangeable Na, ammonium , exchangeable Ca and C:N ratio 0.336

JK Fitzgerald Chapter 4 Page 118

MDS_BVSTEP BEST results_16-11-08

Stress: 0.11 HPT HPS HPO HPP

MAT MAS MAO MAP

OHT OHS OHO OHP

PRT PRS PRO PRP

SNPT SNPS SNPO SNPP

Figure 4.7a nMDS ordination of the samples based on the normalised Euclidean distance for soil moisture content, nitrate, total N and exchangeable Na; these variables best explained the observed patterns in ground species composition and cover across the sites and patch types. Diamonds represent pasture patches; squares are open patches; downward pointing triangles are shrub patches; and upward pointing triangles are tree patches. The samples from Hoxton Park are coloured green; those from Mount Annan are pink; Orchard Hills samples are dark blue; Prospect is yellow; and Scheyville is light blue.

MDS_BVSTEP BEST results_moisture bubble plots_16-11-08

Stress: 0.11

Figure 4.7b nMDS ordination of the samples based on the normalised Euclidean distance for soil moisture content, nitrate, total N and exchangeable Na with superimposed „bubbles‟ that represent the soil moisture content for each sample. Large bubbles signify high moisture contents and vice versa. The positioning of the samples are the same as Figure 4.7a.

JK Fitzgerald Chapter 4 Page 119

MDS_BVSTEP BEST results_nitrate bubble plots_16-11-08

Stress: 0.11 c

MDS_BVSTEP BEST results_total N bubble plots_16-11-08

Stress: 0.11 d

MDS_BVSTEP BEST results_exch Na bubble plots_16-11-08

Stress: 0.11 e

Figures 4.7c-e nMDS ordination of the samples based on the normalised Euclidean distance for soil moisture content, nitrate, total N and exchangeable Na with superimposed „bubbles‟ that represent nitrate, total N and exchangeable Na, in that order. Large bubbles signify a high concentration of these variables JKand Fitzgerald vice versa. The samples are positioned as per ChapterFigure 4.7a. 4 Page 120

4.4 Discussion

Overview

The ground layer species showed differences between the pasture and woodland samples and trends for native and exotic species richness, as well as species composition and cover, were evident across the three woodland patch types. There was also a great deal of site-to- site variability but in general, the pasture samples were associated with higher concentrations of certain soil chemical properties than the woodland samples, which included nitrate and total N.

The influence of site on ground species richness, composition and cover

There was great site-to-site variability for ground species richness, composition and cover. Scheyville had the highest and lowest number of native and exotic species respectively. This was also observed by French et al. (2000), who reported higher levels of native species diversity at Scheyville compared to a number of other sites on the Cumberland Plain, including Orchard Hills, Mount Annan and Prospect. Mount Annan and Hoxton Park on the other hand, had the highest number of exotic species and as discussed in Chapter 3, they were the most fertile sites in terms of active C, total C, Bray 1 P, ammonium and nitrate. The BVSTEP analysis also showed the generally higher level of soil fertility at Mount Annan compared to the other four sites.

All of the sites were significantly different to each other in terms of ground species composition and cover. The cluster analysis and ordination showed that the different woodland patch types within a site were more similar to each other than they were to the same patch types from other sites. This trend was also evident for the pasture samples from Mount Annan and Prospect. In addition to this, 30% of species were recorded from only one quadrat while more than half (56%) were sampled six times or less. Similarly, Tozer (2003) completed extensive floristic surveys of the region and found that 22% of species were recorded only once, indicating that rarity and ephemeral species are a significant

JK Fitzgerald Chapter 4 Page 121

feature of the native vegetation of the Cumberland Plain. Many other studies have also documented high levels of floristic variability at the landscape scale for Cumberland Plain Woodland (James 1997; French et al. 2000; Benson and Howell 2002; NPWS 2002b; Hill et al. 2005). Benson and Howell (2002) considered this to be the result of land clearance and fragmentation and not an inherent feature of the woodland that existed prior to European settlement. This is because rare (native) species do not seem to be clustered in a predictable way and so the localised occurrence of many species is probably due to dispersal limitations brought about by land use change (Benson and Howell 2002). Similar conclusions have been made for temperate deciduous woodlands and forests in Europe and North America (Bellemare et al. 2002; Hermy and Verheyen 2007).

In general, T. australis, A. ramosa or A. vagans were the dominant ground species within the woodland but their cover varied greatly throughout the region. The mean cover for T. australis ranged from 0.2% at Hoxton Park to 59.1% at Prospect, while the mean cover of A. ramosa ranged from nil at Prospect to 35.5% at Orchard Hills. At Orchard Hills, Prospect and Scheyville, there were only two native ground species that had a mean cover greater than 2% but at Hoxton Park and Mount Annan this number increased to six and seven respectively. The sites with the lowest cover of T. australis and the largest number of co-dominant ground species therefore (i.e. Mount Annan and Hoxton Park), were the most fertile sites. This trend is in line with the typical response of T. australis and A. ramosa to increasing soil fertility, which is a decrease in biomass and cover (Mitchell 1996).

P. dilatatum was the dominant pasture species at Mount Annan, Orchard Hills and Scheyville but like the native grass species, its cover was highly variable from site-to-site. C. gayana was the dominant pasture species at Hoxton Park but it was only a minor component of the abandoned farmland elsewhere. Hill et al. (2005) also found P. dilatatum to be a dominant pasture species on the Cumberland Plain but they reported a predominance of Axonopus affinis as well, which was infrequently sampled in this study. This reflects the variable occurrence and dominance of exotic pasture species throughout the region.

JK Fitzgerald Chapter 4 Page 122

The influence of patch type on ground species richness, composition and cover

The pasture had very similar levels of native and exotic species richness, which was also found by Wilkins et al. (2003) when they sampled the vegetation of abandoned farmland in and around Hoxton Park, Cecil Hills and Abbotsbury. The pasture had about half the number of native species and at least double the number of exotic species than the woodland patch types.

Within Cumberland Plain Woodland, the patch types had very similar numbers of native species and this was also found by Watson (2005). This differed to the findings of Prober et al. (2002a) who found significantly higher levels of native species richness beneath the woodland trees than in the intercanopy (open) areas within the White Box woodlands of NSW. The open patch type within Cumberland Plain Woodland was much richer in native species than the open patch type within the White Box woodlands (22.4 cf. 14.9 species per 100 m2), while the tree patch types had very similar levels of native species richness (25.8 species per 100 m2 for Cumberland Plain Woodland and 23.9 species per 100 m2 for the White Box woodlands).

While patch type did not have a significant effect on native species richness within the woodland, it did have a significant impact on exotic species richness, with the tree patch type having 72% more exotic species than the shrub patch type. The tree and open patch types however, had comparable levels of exotic species richness and this trend has also been observed by Prober et al. (2002a). As highlighted in Chapter 3, the trees tend to be associated with soil nutrient „hotspots‟ because they have elevated pH levels and higher concentrations of Bray 1 P and nitrate beneath their canopies. The shrub patch type on the other hand, was usually associated with low soil fertility and this was most notable for plant-available N. These results suggest that enhanced soil fertility beneath individual eucalypts is not having an appreciable effect on native species richness but it might be affecting (increasing) the richness and spatial distribution of exotic species in the woodland. Despite this, the tree and shrub patch types were not significantly different in terms of ground species composition and cover and this appears to be related to two factors.

JK Fitzgerald Chapter 4 Page 123

Firstly, the cover of exotic species was less than 3% for all woodland patch types across the five sites and secondly, the identity and cover of the dominant ground species were very similar beneath these two patch types.

While there were consistent levels of native species richness within the pasture and between the woodland patch types regardless of site, this was not the case for exotic species richness, as reflected by the significant site x patch type interaction. The mean number of exotic species sampled within the pasture ranged from 6.6-14.3 species per 100 m2, while the highest mean number of exotic species sampled within the woodland at any one site ranged from 3.6-9.3 species per 100 m2. It was either the open or tree patch type that had the greatest number of exotic species within the woodland and in four of the five sites, the shrub patch type had the lowest number.

As previously mentioned, the cover of T. australis and A. ramosa at the various study sites might be related to soil fertility and thus site history, with the more fertile sites having a lower representation of these species and vice versa. Soil fertility at the scale of individual patch types however, does not appear to have a strong influence on the cover of T. australis and Aristida spp. within Cumberland Plain Woodland. This is because the shrub and tree patch types, which were the least fertile and most fertile patches respectively, had similar ground species composition and cover. In general, T. australis dominated the three woodland patch types but its cover was approximately 50% greater in the open patch type than beneath the combined shrub and tree patches. Some sites however, were dominated by A. ramosa or A. vagans, which maintained a similar cover across the three woodland patch types.

Several studies of temperate eucalypt woodlands and forests growing on the Central Tablelands and Western Slopes of NSW have found the spatial distribution and dominance of native grasses to be affected by the presence or absence of trees, with C4 grasses dominating open areas and C3 grasses prevailing beneath eucalypt canopies (Chilcott et al. 1997; Gibbs et al. 1999; Prober et al. 2002a). In these systems, T. australis and A. ramosa were more abundant in the open patch types while M. stipoides and Poa sieberiana had a

JK Fitzgerald Chapter 4 Page 124

greater biomass under the woodland trees. This was attributed to the C3 species being better adapted to lower light levels and higher soil fertility beneath the trees than in the open areas and vice versa (Waters et al. 2000). Differences in root architecture and competition with overstorey species for nutrients and water have also been suggested as possible factors that influence the distribution and cover of the dominant grass species in these systems (Gibbs et al. 1999). Unlike these temperate woodlands however, Cumberland Plain Woodland tends to be dominated by C4 grasses (refer to Table A2.6) and given the similarity in ground species composition and cover beneath the shrub and tree patch types, it seems unlikely that soil fertility is playing a large role in shaping the variable cover of T. australis within the woodland. Other factors, such as microclimate or litter depth, are most likely having a greater affect. In addition to this, the cover of T. australis may also be related to fire history (Watson 2005).

The role of soil factors

In Chapter 3 it was suggested that elevated nitrate levels in the pasture could be a barrier to the natural regeneration and restoration of Cumberland Plain Woodland on abandoned farmland. In line with this, the BVSTEP analysis showed that the pasture samples were typically associated with higher moisture levels and elevated concentrations of nitrate, total N and exchangeable Na than the woodland samples. Enhanced soil N levels play a key role in old field succession in a wide range of vegetation communities in many other countries but in Australia, enhanced soil P levels have generally been of major importance for vegetation development following disturbance (Attiwill 1994; Morgan 1998).

Much attention has been given in Australia to the role of enhanced soil P, as a result of past and present land use and disturbance regimes, as a degrading process in native vegetation communities (Clements 1983; Hill et al. 2005; Standish et al. 2007; Dorrough and Scroggie 2008). This stems from the prevalence of P-deficient soils throughout the country and the role this has played in shaping the composition, structure and distribution of the vegetation (Beadle 1954; Beadle 1966). In the Hawkesbury Sandstone environments in Sydney for example, an increase in the concentration of soil P, largely from urban runoff, is a major

JK Fitzgerald Chapter 4 Page 125

contributing factor to weed invasions (Clements 1983; Leishman 1990; King and Buckney 2000; King and Buckney 2002). In fact, exotic species invasions have been facilitated by an increase in the concentration of soil P in many disturbed communities throughout Australia (Lambert and Turner 1987; Morgan 1998; Dorrough et al. 2006; Fisher et al. 2006), although Hill et al. (2005) concluded that this was not the case for a number of sites on the Cumberland Plain. For the restoration of Cumberland Plain Woodland on abandoned farmland however, the situation is not one of exotic species invasion per se but instead, it is related to the persistence of exotic perennial pastures that were introduced some time ago and the role that soil nutrients may be playing in this. It is pertinent therefore that mineral- N nutrition is of particular importance for temperate native grasslands and improved pastures world-wide (Wedin 1995; Whitehead 1995).

Like this study, Prober et al. (2002b) found that soil nitrate was more important than soil P in shaping the composition and cover of the grassy ground layer in degraded White Box woodlands on the Central Western and South Western Slopes of NSW. Much research has shown that strong feedbacks can establish between the standing vegetation and soil N dynamics, which drive competitive interactions, thus species composition and abundance in many systems, most notably grasslands (van Breemen 1995). Two different mechanisms have been proposed for this, one is centred on the effects of plant tissue chemistry, namely C:N ratio and lignin content on decomposition rates and net N mineralisation, while the other is focused on the impacts of microbial activity, as affected by plant derived C, on the immobilisation of N.

In the first model, differences in plant litter quality drive different rates of decomposition, which result in either high or low levels of net N mineralisation (Figure 4.8). According to this model, a small portion of recently fallen litter is incorporated into a large recalcitrant pool of organic matter that decays slowly and consistently through time. Most of the litter however, becomes part of a small labile pool of soil organic matter, which has a much faster turnover rate that is affected by the quality of the litter itself. If the microbial biomass is N-limited relative to its energy supply (i.e. C), then microbial uptake of N will occur with little or no N being produced for plant assimilation (Wedin 1999). As such, litter with a

JK Fitzgerald Chapter 4 Page 126

Low litter quality

i.e. high C:N ratio

Response of plants Response of microbial i.e. high N use efficiency, decomposers slow growth rates and slow i.e. slow decomposition and tissue turnover N immobilisation

Low N availability

High litter quality

i.e. low C:N ratio

Response of plants Response of microbial i.e. low N use efficiency, decomposers fast growth rates and rapid i.e. fast decomposition and

tissue turnover N mineralisation

High N availability

Figure 4.8 Positive feedbacks between plant litter chemistry and N mineralisation (after Wedin 1999). high C:N ratio and large amounts of lignin will decompose slowly, resulting in low plant- available N. Plant litter containing similar amounts of C and N with low lignin levels on the other hand, will decay rapidly with enough N being mineralised for both microbial and plant uptake. These two extremes are typified by perennial and annual species respectively.

Studies have shown however, that regardless of initial N content and C:N ratio of decomposing litter, N is strongly retained by the litter as it decays and as such, the mass lost through time is largely the result of decreasing C levels. Knops et al. (2002) argued

JK Fitzgerald Chapter 4 Page 127

therefore, that a positive feedback between plant litter quality and net N mineralisation is either very weak or nonexistent and they suggested that other plant traits play a much larger and more important role than litter quality in shaping the N cycle.

In the model proposed by Knops et al. (2002), the driving force behind net N mineralisation is the extent of N immobilisation, which is dependent on microbial activity (Figure 4.9). While C is available to microbes either directly (e.g. via root exudates) or indirectly (e.g. by way of litter inputs) from plants, most plant-derived N is incorporated into a large recalcitrant pool of organic matter, which is the primary source of N for the microbial biomass. The microbes mineralise and subsequently consume N from this pool and when they die most of the N is returned to this reservoir; this has been termed the „microbial N loop‟. A larger energy source (more C) for the microbes means a greater microbial uptake of N and less net N mineralisation and vice versa. Studies have shown that plants with large root biomasses and high root C:N ratios, will be associated with lower rates of net N mineralisation than plants with smaller root systems and low root C:N ratios (Hobbie 1992). This is once again epitomised by differences in perennial and annual species. Most importantly however, this is not due to the C:N ratio of the roots per se, as claimed by the first model but rather, it results from the larger input of C from the roots (i.e. more roots = more C), which means that there is enough C to sustain high rates of N immobilisation by the microbial biomass. Root C:N ratio and root-derived C inputs seem to have a much greater influence over this process than the chemistry of aboveground litter because belowground C inputs are more readily broken down by the microbial biomass (Hart et al. 1994).

These models are not mutually exclusive and both use and support the same generalisations about nutrient use efficiencies, resource allocation patterns and relative growth rates of plants growing in fertile and less fertile environments. That is, species adapted to low-N environments typically use N more efficiently (i.e. produce more biomass per unit of N), grow slower and have more extensive root systems than species that aren‟t affected by soil nutrient deficiencies (Hobbie 1992; Chapin and van Cleve 1989). The former therefore, will produce recalcitrant litter and allocate more C to the belowground biomass, fuelling low

JK Fitzgerald Chapter 4 Page 128

Figure 4.9 The „microbial-N loop‟ from Knops et al. (2002). The arrows represent N fluxes through various components of an ecosystem; the thickness of the arrow indicates the relative size of the flux. rates of net N mineralisation, as predicted by both models, and perpetuating a system for which it is most suited. As previously stated, this is typified by perennial and annual species, with the longer-lived species having higher nutrient use efficiencies, greater belowground biomass and slower growth rates than species with an annual life cycle. The models thus use slightly different mechanisms, which are driven by different plant traits, to predict the same outcome. This is a contentious issue however, which continues to be debated in the literature (for example see Chapman et al. 2005).

JK Fitzgerald Chapter 4 Page 129

Regardless of the actual mechanism involved, or the extent to which both mechanisms operate at the same time, these models (and the research they‟re based on) show that plant traits can directly and indirectly affect ecosystem structure and function through interactions with the soil and their involvement with biogeochemical processes (van Breemen 1995). As such, these differences can be exploited for the restoration of degraded ecosystems. An example of such a restorative approach is carbon addition, which is also referred to as reverse fertilisation because it decreases the N content of the soil. This technique has been most commonly applied in systems where exotic annual species out- compete native perennial species (Corbin and D‟Antonio 2004) and it aims to reinstate the native perennial matrix by immobilising N, which confers a competitive advantage to the perennial species (Torok et al. 2000).

This technique has had varying degrees of success overseas (Jonasson et al. 1996; Hopkins 1998; Reever Morghan and Seastedt 1999; Blumenthal et al. 2003; Averett et al. 2004; Eschen et al. 2006) but it has had promising results in areas of the White Box woodlands that have been degraded by the ingress of exotic annual grasses (Prober et al. 2005; Smallbone et al. 2007). Prober et al. (2004) stated the need for an alternative technique where exotic perennial grasses are degrading temperate eucalypt woodlands in Australia. However, if exotic and native perennial grasses differ sufficiently with respect to nutrient use efficiency, litter quality, root volume, belowground C inputs and root C:N ratio, then this technique, or a variation thereof, such as the use of activated C (for example see Kulmatiski et al. 2006), should be investigated for its value as a restoration tool in these systems. This view is supported by the work of Wedin and Tilman (1990) who found that net N mineralisation differed substantially between monocultures of various perennial grass species. This was strongly correlated with differences in tissue chemistry and belowground biomasses for the different species (Wedin and Tilman 1990).

JK Fitzgerald Chapter 4 Page 130

CHAPTER 5. Soil chemical fertility and biotic processes of abandoned farmland, endangered woodland and restored vegetation at Hoxton Park

5.1 Introduction

The first two studies, as described in Chapters 3 and 4, showed that there were significant differences in various soil chemical properties between reference areas of Cumberland Plain Woodland and abandoned farmland earmarked for the improved management and restoration of this endangered vegetation community. Most notably, mineral-N concentrations, particularly nitrate, were generally elevated within the pasture soils and of the nineteen soil variables measured, four of these best accounted for differences in ground species composition and cover between the pasture and woodland samples; these variables were moisture content, nitrate, total N and exchangeable sodium.

The first study also showed significant variability in the chemical fertility of soils beneath the tree, shrub and open patch types within Cumberland Plain Woodland, with the soils beneath the woodland trees tending to have elevated pH levels and higher concentrations of Bray 1 P and nitrate. Furthermore, the second study highlighted significant differences in native and exotic ground species richness between the various woodland patch types and great variability in ground species composition and cover between the canopy (tree and shrub) and intercanopy (open) areas.

These studies highlight the great potential for mineral-N, namely nitrate, to be an abiotic barrier to the restoration of Cumberland Plain Woodland on abandoned farmland. Importantly though, the first study showed site-to-site variations in the relative concentrations of nitrate within the pasture and beneath the woodland patch types, with some sites having higher levels beneath the woodland trees than within the pasture. In addition to this, mineral-N is highly variable, both spatially and temporally and as such, one-off measures of nitrate and ammonium are likely to be poor indicators of plant- available N for any time except for that at which they were taken (Strong and Mason 1999). There is a need therefore, to study nitrate through time to establish whether there

JK Fitzgerald Chapter 5 Page 131

are persistent differences between the pasture and woodland, as well as between the woodland patch types.

The cycling of N is tightly coupled with that of C and these cycles are dependent, to a large extent, on microbial processes, which are affected by factors such as soil structure, temperature, moisture, pH and plant tissue chemistry (Ritz et al. 1994; Kennedy and Papendick 1995; Huhta 2007). Decomposition of plant litter and soil organic matter is a fundamental process that contributes to and interacts with the cycling of C and other nutrients in terrestrial ecosystems. Processes that result in the decomposition of above- and below-ground organic matter include leaching, mechanical breakdown, digestion by various organisms and chemical degradation by microscopic saprobes (Brussaard et al. 2007). Rates of decomposition are strongly influenced by aspects of the physical environment (temperature, rainfall, soil type etc.) and by the physical or chemical (e.g. C:N ratio and lignin concentration) composition of organic matter (Vitousek et al. 1994; Kirschaum 1995; Barlow et al. 2007; Jin et al. 2008; Sariyildiz 2008). Soil microflora and microfauna play critical roles in the decomposition of organic matter (Elkins and Whitford 1982; Herfitzius 1987) because their activity links decomposition with soil respiration, which is measured as CO2 efflux from the soil (Kirschaum 1995). Soil respiration in turn, is affected by a range of abiotic factors, with temperature and moisture being of particular importance (Jacobson and Jacobson 1998; Kurka et al. 2000).

Soil biology therefore, is extremely important for the functioning of a system, as it can have direct and indirect effects on the physical and biological fertility of the soil, as discussed in Chapter 1, as well as impacting key ecological processes such as decomposition, respiration and nutrient cycling. Importantly, the latter often plays a fundamental role in shaping competitive interactions between plant species during old field succession and this has direct implications for the restoration of native vegetation communities in these areas (Kulmatiski et al. 2006).

Hoxton Park has been a focal point for Cumberland Plain Woodland restoration since the early 1990s and offers great potential for experimental work given the close proximity of remnant Cumberland Plain Woodland, restored vegetation of varying ages and abandoned farmland. While the floristics and vegetation structure of the restored

JK Fitzgerald Chapter 5 Page 132

areas at this site have been studied (Wilkins et al. 2003; Nichols 2005), the soil has not. This is in spite of: altered soil conditions, as a result of past agricultural land use, being acknowledged as potential barriers to native species recruitment; and the hypothesis that revegetation would improve soil conditions and facilitate native species succession on the abandoned farmland at this site (Davies and Christie 2001).

To address this deficit, a study was carried out at Hoxton Park to investigate soil chemical properties that appear to be of particular ecological relevance for the restoration of Cumberland Plain Woodland on abandoned farmland, as suggested by the previous two studies (Chapters 3 and 4). These properties were mineral-N, total N, total C, active C and Bray 1 P. Respiration and decomposition were also studied because they are ecological processes that are likely to have a strong impact on the concentrations and availabilities of these chemical properties. The aim of this study was to compare soil chemical properties and ecological processes through time and across various canopy and inter-canopy patch types in abandoned farmland, restored vegetation and remnant woodland to see how restoration has impacted the soil.

5.2 Methodology

5.2.1 Site description

This study was carried out at Hoxton Park, the characteristics (long term climate averages, geology, soil landscapes, soil types and past land use) of which were described in Section 2.2. The rainfall and temperature data for the sampling period of this study however, are shown in Figure 5.1. The long term average (median value) for rainfall in June is 41.8 mm but during this study it was 305.4 mm, which is approximately one-third of the median annual rainfall for the site. The long term rainfall average (median value) for December is 57.4 mm but for the study period it was 119.6 mm.

JK Fitzgerald Chapter 5 Page 133

Temperature Rainfall (oC) (mm) 40 400 Rainfall 35 350 30 300 Mean Maximum Temperature 25 250 20 200 Highest Temperature 15 150 10 Mean Minimum Temperature 5 100 0 50 Lowest Temperature -5 0

Figure 5.1 Rainfall and temperature data for Liverpool during the 12 month study of soils at Hoxton Park (BOM 2009).

5.2.2 Experimental design

The principal factors in the experimental design were location (which was designed to cover the pasture – revegetated area – remnant woodland range), patch type and time (where variables were sampled more than once over a 12 month period). Sampling was restricted to the surface soil (0-5 cm) layer.

Four locations were selected at Hoxton Park: an abandoned pasture; a 6-year old restored area; a 14-year old restored area; and a remnant stand of Cumberland Plain Woodland. The age of the restored areas refers to the length of time since they‟d been revegetated and the 14-year old area was the oldest restored area at the site. These locations were situated within a 4 km radius of each other and Figure 5.2 shows their proximity and orientation at the site. The experimental design was constrained by certain features of the site: the attempted restoration did not take statistical analysis or rigour into account and so the variously aged restored areas were not replicated; and there was only one stand of remnant woodland at the site. In spite of this, there was good replication within each of the locations (see below) and their proximity to each other helped to minimise environmental (spatial) variability.

In line with the previous two studies, tree, shrub and open patch types were studied within the restored areas and woodland. These patch types varied systematically across

JK Fitzgerald Chapter 5 Page 134

these three locations but they shared some key attributes as well.

Figure 5.2 Geographic spread of the locations and sampling quadrats at Hoxton Park. The pasture is denoted with a „P‟, the 6-year old and 14-year old restored areas are identified by „6‟ and „14‟ respectively and the woodland samples begin with a „W‟. The sub-locations are numbered 1-8 and the tree, shrub and open patch types are identified as „T‟, „S‟ and „O‟ respectively. Source: base image from Google Earth (www.googleearth.com).

Within the restored areas, the tree patch types were identified by a single Eucalyptus moluccana that had been mechanically planted either 6 or 14 years prior to sampling. The shrub patch types were similarly defined by a single Acacia parramattensis (a legume) and the open patch types were typical of the structure and composition of the abandoned farmland, that is, they lacked an overstorey and were covered with exotic perennial grasses. E. moluccana and A. parramattensis were chosen for this study because they represent the most common overstorey species within the restored areas (Nichols 2005).

The woodland patch types were very similar to those for the first study, except that both E. moluccana and Corymbia maculata were sampled for the tree patch types, while Bursaria spinosa and Dillwynia sieberi comprised the shrub patch type. The tree and shrub strata at Hoxton Park are dominated by these species (Benson1992), with the leguminous shrub, D. sieberi, forming thickets not unlike that of B. spinosa. In fact, the

JK Fitzgerald Chapter 5 Page 135

shrubby thickets at Hoxton Park are often comprised of both of these species (pers. obs. 2006) and so the combined B. spinosa and D. sieberi thickets were sampled for this study.

In the restored areas, the patch types were sampled using a 3 x 3 m quadrat, which accommodated the tree and shrub crowns at these locations. These sized quadrats were also used to sample the pasture and woodland patch types, except for the woodland trees, which were sampled using 10 x10 m quadrats because they were far too large for the smaller quadrat. The trees were situated at the centre of the quadrats, as were the A. parramattensis individuals, with their canopies extending to the edges. Within the woodland, the shrub patch types completely covered the quadrat and were characterised by an approximate 50/50 cover of B. spinosa and D. sieberi.

Eight sub-locations were randomly selected within each location using the procedure outlined in Section 3.2.2 but with a slight modification for the pasture. In this location, three quadrats were randomly selected at each sub-location to mimic the sampling structure of the restored areas and woodland, where the three different patch types were sampled in clusters (Section 3.2.2). For each sub-location in the pasture therefore, the randomly selected point became the centre of the first quadrat and the other two quadrats were subsequently located from this point. The centre of the second quadrat was located by walking a metres along a randomly selected compass bearing (0-360°). The distance a was randomly selected as a number between 3 and 42, which was the range of average distances (in metres) that separated the three patch types of a cluster within the other three locations. This process was repeated to locate the position of the final quadrat. The latitude and longitude of each quadrat was recorded using a Global Positioning System device.

A number of soil chemical and biological properties were measured at various times over a 12 month period, which commenced in May 2007 and finished in April 2008; the details of this are described below.

JK Fitzgerald Chapter 5 Page 136

5.2.3 Soil sampling

5.2.3.1 Chemical properties and respiration

The soil was sampled from the 0-5 cm depth interval. This depth was chosen because as seen from the first study, the nutrients derived primarily from the breakdown of litter and soil organic matter (e.g. active C, total C, mineral N and total N) were concentrated within this layer and the greatest differences in soil fertility between patch types and sites were typically related to the surface soil (0-5 cm). Across all locations, the soil from the open patch types was sampled from the mid-canopy region, as was the soil from beneath the shrub patch types within the woodland. For the tree patch types, as well as the shrub patch types within the restored areas, the soil was sampled mid-way between the trunk and canopy edge (as per Section 3.2.2).

A range of soil chemical properties and biological processes were investigated with some variables being measured more than once over the sampling period. Table 5.1 lists the variables measured, when they were measured and the method of analysis. Except for soil respiration and decomposition, the importance and utility of these variables for assessing the impacts of land use change on the soil were previously addressed in Chapter 3 and explanations for the use of the various analytical techniques were also given Section 3.2.3. However, while the 1:5 soil:CaCl2 extract (method number 4B1, Rayment and Higginson 1992) was used to measure soil pH for the first study, the 1:5 soil:water suspension (method number 4A1, Rayment and Higginson 1992) was more appropriate for this study because it reflects seasonal changes in pH due to changes in moisture levels (Slattery et al. 1999).

Bray 1 P was measured once during the sampling period because soil P is, at best, only sparingly soluble (Holford 1997) and so it seemed unlikely that the concentration of this variable would change considerably over a 12 month period, especially in comparison to nutrients such as nitrate and ammonium. It is acknowledged however, that a considerable change in soil pH could lead to a change in the concentration of Bray 1 P (Attiwill and Leeper 1987) and furthermore, measuring soil P through time is seen as an essential part of managing fertiliser use and costs in a range of agricultural systems (for example see Moody and Bolland 1999).

JK Fitzgerald Chapter 5 Page 137

Total C and total N were measured only once because they represent large recalcitrant pools of nutrients that have a slow turnover rate (Baldock and Skjemstad 1999). It was important to measure these variables in conjunction with mineral-N since the cycling of C and N is tightly coupled (Lou and Zhou 2006). In addition to this, the C:N ratio of the soil is an indicator of the soils ability to mineralise or immobilise N and is thus related to decomposition (Lou and Zhou 2006; Traore et al. 2007).

Table 5.1 The soil chemical properties and ecological processes measured across the abandoned farmland, restored vegetation and remnant Cumberland Plain Woodland at Hoxton Park. The alpha-numeric codes refer to Australian standard analytical techniques as per Rayment and Higginson (1992). Frequency of sampling Variable Method extractable P Bray 1 P (9E1) total C high frequency induction furnace (LECO: 6B3) Once in June 2007 total N high frequency induction furnace (LECO) C:N ratio ratio of LECO results (8A1) pH 1:5 soil:water suspension (4A1) Twice: June and active C oxidation with KMnO (Weil et al. 2003) December 2007 4 respiration ex situ soil respiration (Anderson 1982) Four times: June, moisture content gravimetric soil moisture content (2A1) September and nitrate 2MKCl (7C2) December 2007 and March 2008 ammonium 2MKCl (7C2) Five times: June, July, September and decomposition of organic mass loss of a standard material through time December 2007 and matter (Latter and Howson 1977) June 2008

The remaining variables (i.e. pH, active C, respiration, soil moisture, mineral-N and decomposition) can exhibit great temporal variability in response to factors such as rainfall and temperature (Bonde and Rosswall 1987; Slattery et al. 1999; Haynes 2005). They can also fluctuate through time as a result of direct and indirect nutrient additions to the soil due to plant growth and senescence (Hobbie 1992). As such, sampling was carried out during winter, spring, summer and autumn in an attempt to capture some of this variability (Table 5.1). Soil pH, active C and soil respiration were measured on samples collected in June 2007 and December 2007, while soil moisture content, nitrate and ammonium were measured in June, September and December of 2007, as well as in March 2008.

Soil respiration is the production of CO2 by living entities within the soil, which includes microbes, fauna, plant roots and rhizomes (Lou and Zhou 2006). Soil respiration is linked to the biogeochemical cycling of nutrients through the microbial

JK Fitzgerald Chapter 5 Page 138

decomposition of litter and soil organic matter (Lou and Zhou 2006). The method used to measure soil respiration in this study quantified microbial activity since the soil was sieved to remove macroscopic organic material (Anderson 1982). Microbial activity was also measured at Hoxton Park by studying the decomposition of a standard material (calico) over the 12 month sampling period using a method similar to that of Kurka and Starr (1997) and Kurka et al. (2000; 2001).

5.2.3.2 Decomposition

Cotton strips (or calico pieces) have long been used to measure the decomposition of cellulose, which makes up approximately 70% of the carbon compounds found in plants, under field conditions (Latter and Howson 1977). The approach is relatively simple yet it can provide valuable insights into the complex ecological process of decomposition resulting from microbial activity (Kurka and Starr 1997). In general, a piece of C-rich material is placed into the soil for a certain period of time, with the difference between the pre- and post-field mass of the material resulting from decomposition and expressed as mass loss through time. The decomposition of indigenous plant materials and soil organic matter is affected by many different factors and plant tissue chemistry is a key determinant of decay rates (Paschke et al. 2000). By using a standard material however, decomposition under various soil types, or in the one soil type subjected to different treatments, can be compared without the confounding effects of plant tissue chemistry (Latter and Howson 1977; Knacker et al. 2003). The decomposition of calico inserted into the soil therefore, does not measure the potential mass loss through time of indigenous plant material or soil organic matter but instead, it measures decomposition as a result of microbial activity, which can affect the rate of nutrient mineralisation and immobilisation (Kurka and Starr 1997; Gestel et al. 2003; Lou and Zhou 2006).

Due to logistical constraints, only 6 of the 8 sub-locations were used for the decomposition experiment and these were randomly selected for each location using the random number generator on a calculator. Each sample was comprised of a fibreglass mesh envelope, or „litterbag‟, containing a piece of chemical-free (unbleached and undyed) calico. The calico pieces measured 10 x 12 cm and they were less than 1mm thick. Thickness of the material is an important consideration since its placement in the

JK Fitzgerald Chapter 5 Page 139

soil should not alter the microclimate to any great extent, as this could inadvertently affect microbial activity (a thick material could act as a sponge, drawing moisture from surrounding areas; Latter and Howson 1977). The litterbags were made of flyscreen and were slightly larger than the calico pieces at 12 x 14 cm. They had a mesh size of 2 x 2 mm, which was chosen to exclude macro-fauna such as earthworms (Knacker et al. 2003). The calico pieces were weighed prior to being placed in the litterbags and the average mass was 1.6 g. Each sample was individually numbered and labelled using a plastic DYMO label that was placed inside the litterbag before sealing by running a hot iron along the seams.

In accordance with the best practice standards proposed by Knacker et al. (2003) for the collection of data using litterbags, samples were collected five times over the 12 month sampling period. As such, 5 samples were placed in each quadrat in May 2007, with one sample being removed after 1 month, 2 months, 3 months, 6 months and 12 months. For the purpose of this study, one month was equal to 4 weeks and the actual number of days that the samples were in the field for were 29, 56, 84, 171 and 337 respectively. On several occasions, bad weather and problems with site access meant that the samples were retrieved 1-3 days after their scheduled date. In total, 360 samples were placed in the field.

Each sample was positioned within the top 5 cm of the soil profile. To do this, a trowel was pushed into the soil on a 15° angle, the soil was gently lifted and a sample was slid along the trowel and into the soil profile. The trowel was then removed, leaving one short edge of the litterbag level with the soil surface, while the opposite edge extended to 5 cm. The samples were held in place with a 15 cm metal tent peg and were tagged using flagging tape. For the woodland trees, as well as the shrub and tree patch types within the restored areas, the samples were placed on the southern side of the trunk, half-way between the trunk and canopy edge. For all other patch types, the samples were placed at a mid-canopy position. The first sample, which was collected at 1 month, was positioned to the north (i.e. at a 12 o‟clock position) with subsequent samples being placed at regular intervals in a clockwise direction within an area that had a radius of approximately 50 cm.

In many cases, the calico pieces were contaminated with soil and biota, particularly

JK Fitzgerald Chapter 5 Page 140

fungi, when retrieved from the field. This complicated the calculation of mass loss through time because these contaminants added mass to the samples, often resulting in higher post-field than pre-field masses. Potthoff and Loftfield (1998) discussed the problem of litterbag contamination by soil and suggested two ways to deal with this: firstly, sieve or wash the contents of the litterbag to remove the soil; and secondly, correct the final (i.e. post-field) mass of the litterbag contents by using ash residues. For this study, the samples retrieved at 1and 2 months were washed and then dried in a fan- forced oven at 35°C for at least 24 hours prior to being weighed. The samples collected at 3, 6 and 12 months however, were far too fragile for manual cleaning, so a correction involving ash residues was used.

According to Potthoff and Loftfield (1998), the dry mass of soil contamination (DWSC) within litterbags, which can be subtracted from the post-field calico mass to give the corrected mass loss through time, can be calculated as follows:

DWSC = (ARLM-AROM) / ARS

Where:

ARLM is the ash residue of litter bag contents, which in this case, is the ash residue of the post-field calico sample (g);

AROM is the mean ash residue of the litter, which was the mean ash residue of six control (i.e. clean) pieces of oven-dry calico (g) for this study; and

ARS is the ash residue of the soil, which was calculated using soil samples taken from the same quadrats as the decomposition samples (g g-1).

Ash residues were calculated by combusting the samples in a muffle furnace set at 550°C for 2 hours.

It is noted that this procedure does not account for biotic contamination and the calculation of ash residues would have been affected by this, since any organic material would have been combusted in the muffle furnace. In addition to this, the calculation of ash residues for the soil samples may have been affected by hygroscopic water loss, which can occur for clay-rich soils that are subjected to temperatures of 550°C and above (Dean 1974; Baldock and Skjemstad 1999). Furthermore, many calico pieces had

JK Fitzgerald Chapter 5 Page 141

completely decomposed between 6 and 12 months and so meaningful data was not possible for all 360 samples.

5.2.4 Statistical analysis

The sampling program resembled the „after‟ component of a before/after control/impact (BACI) design to detect environmental impacts (Underwood 1993). In the terminology of these designs, location was a random factor, with the pasture as a putatively (unreplicated) „impacted‟ area. The „control‟ or reference locations to which the impacted area could be compared were the three locations with treed vegetation; two of these were revegetated (6- and 14-year old respectively) and one was the remnant woodland. Eight sub-locations were sampled within each location and differences amongst sub-locations formed the error term for tests amongst locations.

Each patch type (fixed factor) was sampled at each sub-location; if sampled more than once, time (random factor) was included in the design. Patch type and time were orthogonal to location and sub-location and because of their spatial proximity in the sub-locations, were regarded as within-subject factors in a split-plot analysis. Whilst sampling effort for patch types was equal across the four locations to maintain a balanced design, the tree, shrub and open patches were dummy entities in the pasture and were only real entities in the „control‟ locations. To account for this, two analyses of the data were conducted; the first analysis was across the 6-year old and 14-year old restored areas and the woodland (the „controls‟), while the second analysis included all four locations. This allowed extraction of terms for the real patch types and their interactions for the „controls‟ in the context of the overall analysis amongst four locations. An asymmetrical ANOVA was constructed by algebraically combining sums of squares and degrees of freedom following the technique described in Underwood (1993). The asymmetrical ANOVA allows a breakdown of the tests for each term into a test amongst all four locations, as well as a separate test restricted to the „controls‟. For terms involving patch therefore, only the test for amongst „controls‟ is reported, since the between locations test of patch type was not valid. The full ANOVA and its expected mean squares are shown in Appendix 3. Tests of some terms in these models rely on pooling of lower-order terms; if these terms are significant and pooling is thus not warranted, tests of the subsequent terms are not possible (Underwood 1993).

JK Fitzgerald Chapter 5 Page 142

Planned comparisons were used to compare amongst location means where these were significant. The pasture versus the „controls‟ comparison is a standard comparison in BACI designs; in this study, it compared the mean for the (tree- and shrub-less) abandoned pasture with the pooled mean for the three locations with trees and shrubs (6-year old and 14-year old restored areas and woodland). Where location was significant in interaction with time, the comparison was made using the interaction means.

The procedures described in Chapter 3 were used to test assumptions of the model; transformations were used where appropriate and the Greenhouse-Geisser epsilon was used to adjust the degrees of freedom of tests of within-subject terms where the assumption of sphericity was not met.

5.3 Results

5.3.1 Variables measured once during the year

5.3.1.1. Bray 1 P

There was significant variability in the concentration of Bray 1 P across the pasture, 6- and 14-year old revegetation sites and woodland (location main effect; F3,28=8.072, P=0.000; Figure 5.3). Concentrations were lowest in the pasture (0.989 mg kg-1) and highest in the 14-year old restored area (1.50 mg kg-1), while the 6-year old restored area and the woodland had very similar concentrations (1.24 mg kg-1 and 1.21 mg kg-1 in that order).

5.3.1.2 Total C

The trend of total C amongst the patch types in the woodland (greatest total C under trees) was not found in the restored locations, where values were greatest under shrubs

(controls: patch x location interaction: F4,42=4.215, P=0.006; Fig. 5.4). Overall, the woodland had the highest concentrations of total C.

JK Fitzgerald Chapter 5 Page 143

1.8 1.6

) 1.4 -1 1.2 1 0.8 0.6

Bray 1 P (mg kg (mg 1 P Bray 0.4 0.2 0 Pasture 6yoR 14yoR Woodland

Figure 5.3 Back-transformed mean concentrations of Bray 1 P within the surface soils (0-5 cm) of the four different locations at Hoxton Park. Error bars represent the 95% confidence intervals.

5.3.1.3 Total N

Total N showed a similar pattern to total C because the differences amongst the patch types within the woodland (i.e. greatest concentrations beneath the trees) were not found in the restored areas (controls: patch x location interaction: F4,42=4.816, P=0.003; Figure 5.5). In the restored areas, total N was greatest under the open and shrub patch types in the younger (6-year old) location and under the shrubs in the older (14-year old) location. Compared to the 6-year old restored area, there was a large drop in the concentration of total N beneath the open and shrub patch types in the woodland.

5.3.1.4 C:N ratio

There was a significant main effect of location on C:N ratio (F3,28=28.923, P=0.000), with the pasture and restored areas having very similar values, which were lower than the woodland (Figure 5.6).

5.3.2 Variables measured twice throughout the year

5.3.2.1 pH

Overall, the pasture and woodland had the highest and lowest pH values respectively, while the restored areas had intermediate levels of acidity, with some differences in this

JK Fitzgerald Chapter 5 Page 144

7 6 5 4 3

Total C (%) Total 2 1 0 6yoR 14yoR Woodland

Open Shrub Tree

Figure 5.4 Back-transformed mean concentrations of total C within the surface soils (0-5 cm) of the various patch types within the restored areas and woodland at Hoxton Park. Error bars represent the 95% confidence intervals.

0.4

0.3

0.2 Total N N (%) Total 0.1

0 6yoR 14yoR Woodland Open Shrub Tree

Figure 5.5 The mean concentration of total N within the surface soils (0-5 cm) of the various patch types within the restored areas and woodland at Hoxton Park. Error bars represent standard errors of the means.

18 16 14 12 10 8 C:N ratio C:N 6 4 2 0 Pasture 6yoR 14yoR Woodland

Figure 5.6 The mean C:N ratio for the surface soils (0-5 cm) of the four different locations at Hoxton Park. Error bars represent standard errors of the means.

JK Fitzgerald Chapter 5 Page 145

6.2 6 5.8 5.6 5.4 pH 5.2 5 4.8 JD J D J D J D 4.6 Pasture 6yoR 14yoR Woodland

Figure 5.7a Back-transformed mean pH values for the surface soils (0-5 cm) of the four locations at Hoxton Park in June (J) and December (D) of 2007. Error bars represent the 95% confidence intervals.

6 a a a 5.8 b 5.6

5.4 pH

5.2

5 JJDD 4.8 Restored areas Woodland

Figure 5.7b Back-transformed mean pH values for the surface soils (0-5 cm) of the restored areas and woodland at Hoxton Park in June (J) and December (D) in 2007. Error bars represent the 95% confidence intervals. Different lower case letters between the locations at a particular sampling time indicate a significant difference.

JK Fitzgerald Chapter 5 Page 146

6.3 6.1 5.9 5.7

pH 5.5 5.3 5.1 J D J D J D 4.9 Open Shrub Tree

Figure 5.7c Back-transformed mean pH values for the surface soils (0-5 cm) beneath the various patch types within the 6 year-old restored area at Hoxton Park in June (J) and December (D) of 2007. Error bars represent the 95% confidence intervals.

6.3 6.1 5.9 5.7

pH 5.5 5.3 5.1 J D J D J D 4.9 Open Shrub Tree

Figure 5.7d Back-transformed mean pH values for the surface soils (0-5 cm) beneath the various patch types within the 14 year-old restored area at Hoxton Park in June (J) and December (D) of 2007. Error bars represent the 95% confidence intervals.

6.3 6.1 5.9 5.7

pH 5.5 5.3 5.1 J D J D J D 4.9 Open Shrub Tree

Figure 5.7e Back-transformed mean pH values for the surface soils (0-5 cm) beneath the various patch types within the woodland at Hoxton Park in June (J) and December (D) of 2007. Error bars represent the 95% confidence intervals.

JK Fitzgerald Chapter 5 Page 147

pattern over time (location x time interaction: F3,28=5.684, P=0.004; Figure 5.7a). There was a noticeable decrease in pH between June and December for all locations except for the 14-year old restored area, which had a slight increase in pH. The magnitude of these changes varied across the four locations, with the greatest change occurring within the woodland (Figures 5.7a and 5.7b).

For the restored areas and woodland, the patch types contributed to this pattern, with the restored areas being different to the woodland (controls: patch x location x time interaction; F4,42=3.243, P=0.021). In the 6-year old and 14-year old restored areas, the open patch type had the highest pH values for both times (Figures 5.7c and 5.7d) but in the woodland, the pH was consistently elevated beneath the trees (Figure 5.7e).

5.3.2.2 Active C

Levels of active C ranged from 595–630 mg kg-1 over the four locations in June. In the pasture, there was an increase of 70 mg kg-1 from June to December while increases in the restored areas and woodland were much lower (location x time interaction;

F3,28=4.920, P=0.007; Figure 5.8a). By December, the concentration of active C within the pasture was significantly higher than that for the combined revegetated and woodland areas (F1,28=15.29, P<0.001; Figure 5.8b).

5.3.2.3 Respiration

Soil respiration showed a similar pattern to that already observed for some other variables: the pattern across the woodland patch types was not apparent for the patch types in the restored areas (controls; location x patch type interaction, F4,42=2.671, P=0.045). The highest soil respiration rate was observed under the trees in woodland -1 -1 (0.842 mg CO2 g soil d ), while values under the open and shrub patches in woodland -1 - ranged down to 0.585 mg CO2 g soil d (Fig. 5.9). In the 14-year old restored area, there was a weak trend for greater respiration rates beneath the trees.

JK Fitzgerald Chapter 5 Page 148

700 680 660 ) -1 640 620 600 580 560 Active C (mg kg (mg C Active 540 520 J D JDD J D J 500 Pasture 6yoR 14yoR Woodland

Figure 5.8a Back-transformed mean concentrations for active C within the surface soils (0-5 cm) of the different locations at Hoxton Park in June (J) and December (D) of 2007. Error bars represent the 95% confidence intervals.

700 680 a

) 660

-1 a b 640 620 a 600 580

Active C (mgkg C Active 560 540 JDJ J D 520 Pasture Controls

Figure 5.8b Back-transformed mean concentrations for active C within the surface soils (0-5 cm) of the pasture and control (6 year-old and 14 year-old restored areas and woodland) locations at Hoxton Park in June (J) and December (D) of 2007. Error bars represent 95% confidence intervals. Different letters (a, b) for the pasture and controls at a particular sampling time indicates a significant difference.

1 ) -1

day 0.8 -1 -1 0.6 gsoil 2 0.4

0.2

0 6yoR 14yoR Woodland Respiration (mg CO (mg Respiration

Open Shrub Tree

Figure 5.9 Mean soil respiration rates for the surface soils (0-5cm) of the various patch types within the restored areas and woodland at Hoxton Park. Error bars represent standard errors of the means.

JK Fitzgerald Chapter 5 Page 149

5.3.3 Variables measured four times throughout the year

5.3.3.1 Soil moisture content

Soil moisture varied strongly with time, with all four locations displaying a similar trend in moisture content; there was a large decline in moisture levels from June to September, with a smaller peak in December before another trough in March (time main effect; F3,9=183.908, P<0.0001; Figure 5.10a). The detail of the trend differed amongst the locations (location x time interaction F3.686,34.402=6.133, P=0.001); the greatest difference occurred in June, with mean values ranging from 12.9% for the 14-year old restored area to 26.1% for the pasture . The pasture had the highest moisture levels at all times, significantly so in June 2007 and March 2008 (Figure 5.10b).

Patch types affected soil moisture in the restored areas and woodland (controls; patch x location interaction; F3.06,32.13=3.545, P=0.025). The highest moisture levels for all three patch types occurred within the 6-year old restored area, which had much higher levels beneath the open and shrub patch types than the 14-year old restored area. While there were fairly consistent moisture levels across the patch types within the 14-year old restored area, there was a noticeable difference in moisture content beneath the patch types in the woodland, with the highest levels occurring beneath the trees (Figure 5.10c).

5.3.3.2 Nitrate

Soil nitrate showed marked temporal variation but the pattern differed across the pasture, restored areas and woodland (time main effect; F3,9=18.401, P<0.001; location x time interaction; F9,84=3.532, P=0.001). The overall time course of nitrate was similar to that already described for soil moisture, with peaks in June and December (Figure 5.11a). In June, nitrate concentration was greatest in the 14-year old restored area and the mean concentration in the pasture was very similar to the woodland (Fig. 5.11a). For subsequent sampling times (September, December and March) the pasture had the highest nitrate concentration, about twice that of the woodland, which had the lowest (Figure 5.11a). On these three occasions, the pasture had significantly higher concentrations of nitrate than the combined restored areas and woodland (pasture vs.

JK Fitzgerald Chapter 5 Page 150

30 25 20 15 10

Moisture content (%) content Moisture 5 0 Jun-07 Jul-07 Aug-07 Sep-07 Oct-07 Nov-07 Dec-07 Jan-08 Feb-08 Mar-08

Pasture 6yoR 14yoR Woodland

Figure 5.10a Back-transformed mean gravimetric soil moisture contents for the surface soils (0-5 cm) of the different locations at Hoxton Park for June, September and December of 2007 and March 2008. Refer to Appendix 3 (Table A3.9d) for the 95% confidence intervals.

30 ** 25 20 15 10 *

Moisture content (%) content Moisture 5 0 Jun-07 Jul-07 Aug-07 Sep-07 Oct-07 Nov-07 Dec-07 Jan-08 Feb-08 Mar-08

Pasture Controls

Figure 5.10b Back-transformed mean gravimetric soil moisture contents for the surface soils (0-5 cm) of the pasture and the controls at Hoxton Park for June, September and December of 2007 and March 2008. Refer to Appendix 3 (Table A3.9e) for the 95% confidence intervals. An asterisk indicates a significant difference between the pasture and controls at a particular sampling time.

9 8 7 6 5 4 3 2 Moisture content (%) content Moisture 1 0 6yoR 14yoR Woodland

Open Shrub Tree

Figure 5.10c Back-transformed mean gravimetric soil moisture contents for the surface soils (0-5 cm) beneath the various patch types within the restored areas and woodland at Hoxton Park. Error bars represent the 95% confidence intervals.

JK Fitzgerald Chapter 5 Page 151

25

) 20 -1 15

10

Nitrate (mg kg (mg Nitrate 5

0 Jun-07 Jul-07 Aug-07 Sep-07 Oct-07 Nov-07 Dec-07 Jan-08 Feb-08 Mar-08

Pasture 6yoR 14yoR Woodland

Figure 5.11a Back-transformed mean nitrate concentrations for the surface soils (0-5 cm) of the different locations at Hoxton Park for June, September and December of 2007 and March 2008. Refer to Appendix 3 (Table A3.10d) for the 95% confidence intervals.

25 *

) 20 -1 15 * * 10

Nitrate (mg kg (mg Nitrate 5

0 Jun-07 Jul-07 Aug-07 Sep-07 Oct-07 Nov-07 Dec-07 Jan-08 Feb-08 Mar-08

Pasture Controls

Figure 5.11b Back-transformed mean nitrate concentrations for the surface soils (0-5 cm) of the pasture and controls at Hoxton Park for June, September and December of 2007 and March 2008. Error bars represent the 95% confidence intervals. An asterisk indicates a significant difference between the pasture and controls at a particular sampling time.

12

10 ) -1 8

6

4 Nitrate (mg kg (mg Nitrate 2

0 Pasture 6yoR 14yoR Woodland

Figure 5.11c Back-transformed mean nitrate concentrations for the surface soils (0-5 cm) of the four locations at Hoxton Park. Error bars represent 95% confidence intervals.

JK Fitzgerald Chapter 5 Page 152

12 10 ) -1 8 6 4 Nitrate (mg kg (mg Nitrate 2 0 6yoR 14yoR Woodland

Open Shrub Tree

Figure 5.11d Back-transformed mean nitrate concentrations for the surface soils (0-5 cm) beneath the various patch types within the restored areas and woodland at Hoxton Park. Error bars represent 95% confidence intervals. controls; P<0.01; Figure 5.9b). Location differences were sufficiently strong to be detected as a main effect (F3,28=3.108, P=0.042) and the rank order of the means was pasture > 14-year old revegetation > 6-year old revegetation > woodland (Figure 5.11c).

Patch differences in nitrate concentration in the revegetated areas and woodland emerged, depending on location (controls; location x patch interaction; F4,42=4.885, P<0.005). Nitrate concentration decreased from the open patches to the trees in the 6- year old revegetated location, but this pattern was absent in the 14-year old revegetation and woodland (Figure 5.11d).

5.3.3.3 Ammonium

The concentration of ammonium, like nitrate, changed through time and the pattern varied across the pasture, restored areas and woodland (location x time interaction;

F5.56,51.90=10.395, P=0.000; Figure 5.12a). The pasture had significantly higher concentrations of ammonium compared to the combined revegetated areas and woodland at three of the four sampling times (P<0.05; Figure 5.12b). In June, the pasture had more than twice the concentration than the controls (55.6 mg kg-1 cf. 20.4 -1 mg kg respectively). The woodland had a significantly higher (F1,52=10.99, P<0.01) concentration of ammonium than the restored areas in June (30.0 mg kg-1 cf. 16.8 mg kg-1 in that order; Figure 5.12c). For the other three sampling times, the restored areas had higher ammonium concentrations than the woodland but they were not significantly

JK Fitzgerald Chapter 5 Page 153

different, although concentrations in the 14-year old revegetated area tracked those in the woodland over the last three sampling times, while concentrations in the 6-year old stand were higher (Figure 5.12a). Location differences were also significant as main effects (F3,28=18.828, P=0.000). The pasture and 6-year old restored area had very similar concentrations, which were double the concentration of ammonium within the 14-year old restored area and 1.5 times the concentration within the woodland (Figure 5.12d).

As for nitrate, patch type effects were present but with differences across the restored areas and woodland through time (location x patch x time interaction: F6.51,68.3=2.879, P=0.012). In the 6-year old restored area, ammonium concentration was lowest under the trees at all sampling times (Figure 5.12e). In the 14-year old restored area on the other hand, the shrubs always had the highest and the open usually had the lowest concentrations of ammonium (Figure 5.12f). For the woodland, the concentration of ammonium was always lower beneath the open patch type, while the shrub patch type had the highest concentrations in June and September, with the tree patch type having the highest concentrations in December and March (Figure 5.12g).

70

) 60 -1 50 40 30 20

Ammonium (mg kg (mg Ammonium 10 0 Jun-07 Jul-07 Aug-07 Sep-07 Oct-07 Nov-07 Dec-07 Jan-08 Feb-08 Mar-08

Pasture 6yoR 14yoR Woodland

Figure 5.12a Back-transformed mean ammonium concentrations for the surface soils (0-5 cm) of the different locations at Hoxton Park for June, September and December of 2007 and March 2008. Refer to Appendix 3 (Table A3.11e) for the 95% confidence intervals.

JK Fitzgerald Chapter 5 Page 154

70 *

) 60 -1 50 40 * 30 * 20

Ammonium (mg kg (mg Ammonium 10 0 Jun-07 Jul-07 Aug-07 Sep-07 Oct-07 Nov-07 Dec-07 Jan-08 Feb-08 Mar-08

Pasture Controls

Figure 5.12b Back-transformed mean ammonium concentrations for the surface soils (0-5 cm) of the pasture and controls at Hoxton Park for June, September and December of 2007 and March 2008. Error bars represent the 95% confidence intervals. An asterisk indicates a significant difference between the pasture and controls at a particular sampling time.

70

) 60 -1 50 * 40 30 20

Ammonium (mg kg (mg Ammonium 10 0 Jun-07 Jul-07 Aug-07 Sep-07 Oct-07 Nov-07 Dec-07 Jan-08 Feb-08 Mar-08

Restored areas Woodland

Figure 5.12c Back-transformed mean ammonium concentrations for the surface soils (0-5 cm) of the restored areas and woodland at Hoxton Park for June, September and December of 2007 and March 2008. Error bars represent the 95% confidence intervals. An asterisk indicates a significant difference between the restored areas and woodland at a particular sampling time.

35 30 ) -1 25 20 15 10 Ammonium (mg kg (mg Ammonium 5 0 Pasture 6yoR 14yoR Woodland

Figure 5.12d Back-transformed mean ammonium concentrations for the different locations at Hoxton Park. Error bars represent 95% confidence intervals.

JK Fitzgerald Chapter 5 Page 155

70

) 60 -1 50 40 30 20

Ammonium (mg kg (mg Ammonium 10 0 Jun-07 Jul-07 Aug-07 Sep-07 Oct-07 Nov-07 Dec-07 Jan-08 Feb-08 Mar-08

6yoR Open 6yoR Shrub 6yoR Tree

Figure 5.12e Back-transformed mean ammonium concentrations for the surface soils (0-5 cm) beneath the various patch types within the 6-year old restored area (6 yoR) at Hoxton Park for June, September and December of 2007 and March 2008. Refer to Appendix 3 (Table A3.11f) for the 95% confidence intervals.

70

) 60 -1 50 40 30 20

Ammonium (mg kg (mg Ammonium 10 0 Jun-07 Jul-07 Aug-07 Sep-07 Oct-07 Nov-07 Dec-07 Jan-08 Feb-08 Mar-08

14yoR Open 14yoR Shrub 14yoR Tree

Figure 5.12f Back-transformed mean ammonium concentrations for the surface soils (0-5 cm) beneath the various patch types within the 14-year old restored area (14yoR) at Hoxton Park for June, September and December of 2007 and March 2008. Refer to Appendix 3 (Table A3.11g) for the 95% confidence intervals.

70

) 60 -1 50 40 30 20

Ammonium (mg kg (mg Ammonium 10 0 Jun-07 Jul-07 Aug-07 Sep-07 Oct-07 Nov-07 Dec-07 Jan-08 Feb-08 Mar-08

W Open W Shrub W Tree

Figure 5.12g Back-transformed mean ammonium concentrations for the surface soils (0-5 cm) beneath the various patch types within the woodland (W) at Hoxton Park for June, September and December of 2007 and March 2008. Refer to Appendix 3 (Table A3.11h) for the 95% confidence intervals.

JK Fitzgerald Chapter 5 Page 156

5.3.4 Decomposition

The mean decomposition of the calico samples for each location and patch type is shown in Figures 5.13a-5.13d as the organic mass remaining through time as a percentage of the original mass. When averaged across all patch types, the woodland had a consistently higher percentage of calico remaining than the other three locations. The trends displayed for the pasture, 6-year old restored area and woodland were very similar and this included a slight increase in the percentage mass remaining (which ranged from 5.5% to 8.2%) from the second to the third month. The 14-year old restored area on the other hand, always showed a decrease in the amount of calico remaining (Figure 5.13a). For the first month of the experiment, between 15.2% and 21.7% of the original mass of calico was lost, with these figures representing the woodland and pasture respectively. By the second month, less than 50% of the original mass remained within the 14-year old restored area, which was markedly lower than that for the other three locations. At 6 months, approximately 94% of the original mass of calico within the 6-year old and 14-year old restored areas had decomposed, while about 91% had decomposed within the pasture and 82% within the woodland (Figure 5.13a). The patch types within the woodland displayed a fairly similar pattern of calico loss through time, as shown in Figure 5.13d. In the restored areas however, there were distinct differences between the various patch types, especially during the second and third months, at which time the open and shrub patch types displayed opposite trends (Figures 5.13b and 5.13c).

100 pasture 90 80 6 yo restored 70 14 yo restored 60 woodland 50 40 30 20 10 0 % Organic Mass Remaining Mass Organic% 0 50 100 150 200 250 300 350 Time (days)

Figure 5.13a The percentage mass of calico remaining for the four locations at Hoxton Park. Error bars represent standard errors of the means.

JK Fitzgerald Chapter 5 Page 157

100 90 80 open 70 60 shrub 50 tree 40 30 20 10 0 % Organic Mass Remaining Mass Organic% 0 50 100 150 200 250 300 350 Time (days)

Figure 5.13b The percentage mass of calico remaining for the tree, shrub and open patch types within the 6-year old restored area at Hoxton Park. Error bars represent standard errors of the means.

100 90 80 open 70 60 shrub 50 tree 40 30 20 10 0 % Organic Mass Remaining Mass Organic% 0 50 100 150 200 250 300 350 Time (days)

Figure 5.13c The percentage mass of calico remaining for the tree, shrub and open patch types within the 14-year old restored area at Hoxton Park. Error bars represent standard errors of the means.

100 90 80 open 70 60 shrub 50 tree 40 30 20 10 0 % Organic Mass Remaining Mass Organic% 0 50 100 150 200 250 300 350 Time (days)

Figure 5.13d The percentage mass of calico remaining for the tree, shrub and open patch types within the woodland at Hoxton Park. Error bars represent standard errors of the means.

JK Fitzgerald Chapter 5 Page 158

5.4 Discussion

Overview

This study of soil properties and ecological processes, examined through time and across the range of pasture, restored areas and woodland at Hoxton Park, confirmed many of the patterns detected in the first study, which focused on a greater number of study sites spread across the Cumberland Plain. The major differences between the pasture and Cumberland Plain Woodland were confirmed; the concentration of soil N was generally higher in the abandoned pasture than the woodland, with some variation on this pattern over time. Within the woodland, the soils were typically more acidic, with higher concentrations of total C and a higher C:N ratio than the pasture. The role of trees in affecting soil properties in the woodland was also confirmed and this study sampled two different canopy species. The restored areas often showed patterns of soil properties amongst patch types that were not the same as the woodland; if the latter is regarded as the target for restoration then the restored areas, in many cases, are yet to reach it.

Bray 1 P

As observed in Chapter 3, the pasture had a lower mean concentration of Bray 1 P than the woodland and in addition to this, the concentration of extractable P had increased as a result of restoration, with the 6-year old restored area and woodland having very similar concentrations while the 14-year old restored area had a somewhat higher concentration than these two locations. Unlike many other systems, where a high concentration of soil P needs to be reduced in degraded areas to aid in the restoration of the original community (for example, see Standish et al. 2007 and Fagan et al. 2008), this is not the case for the restoration of Cumberland Plain Woodland on abandoned farmland. In fact, the planting of native trees and shrubs has helped to increase the concentration of Bray 1 P to a level commensurate with that of the woodland. However, the 14-year old restored area had the highest concentration of this variable and if this was to continue to increase over time, it would surely be to the detriment of the attempted restoration of Cumberland Plain Woodland at this site.

JK Fitzgerald Chapter 5 Page 159

Total C, total N and the C:N ratio

The shrub patch type had very similar concentrations of total C across the restored areas and woodland despite there being inherent differences in the age, identity and structure (architecture) of the dominant shrub species. The concentration of total C beneath the open and tree patch types however, was consistently higher within the woodland; the restored areas differed from woodland in having lower concentrations of total N beneath the trees. This is probably related to the age of the sampled individuals since it is likely that a greater volume and more heterogeneous mix of organic matter is being added to the soil beneath the woodland trees compared to those within the restored areas (for example see Belsky 1994).

The basis for the higher C:N ratio in the woodland compared to the restored areas can be seen in the underlying trends in total C (which increased from the revegetated areas to the woodland) and total N (which showed little change across the various locations). This may result from more recalcitrant litter within the woodland and thus a slower rate of decomposition (Melillo et al. 1982), as reflected by the decomposition of calico samples across the site (see below). The mean C:N ratios for the pasture and woodland were 14.1 and 16.5 respectively, which are consistent with those for similar vegetation types throughout Australia (Snowdon et al. 2005). In a review of soil C:N ratios in pastures, native vegetation and forest plantations throughout the country, Snowdon et al. (2005) reported mean C:N ratios for the surface soil to be 13.7 for pastures (with a minimum and maximum of 11.4 and 19.7 respectively), 13.8 for open woodland communities (minimum 12.9; maximum 14.6), 21.1 for Ironbark woodlands (minimum 15.3; maximum 29.5) and 22.9 for mixed eucalypt woodlands (minimum 15.8; maximum 31.7). As such, the C:N ratio for Cumberland Plain Woodland at Hoxton Park is intermediate between that of open woodlands and Ironbark woodlands and it is clear from the work of Snowdon et al. (2005) that on a broad geographical scale, it appears that Cumberland Plain Woodland may have a relatively low C:N ratio compared to a range of other woodlands and sclerophyll forests in Australia (see Table 16 in Snowdon et al. 2005).

JK Fitzgerald Chapter 5 Page 160

pH

There was a decreasing trend in soil pH across the different locations with pasture > 6- year old restored area > 14-year old restored area > woodland. This supports the findings from the previous study that past land use and subsequent abandonment have not acidified the soil and instead, the opposite has occurred, as hypothesised by Corbett (1972). Corbett (1972) suggested that the conversion of native vegetation on the Cumberland Plain to exotic perennial pastures could eventually transform the (acidic) podzolic soils into (alkaline) prairie soils. This is because the change from forest or woodland to could alter the vertical movement of nutrients, minerals and colloids within the soil profile, thus affecting soil profile development and morphology (Corbett 1969). In temperate regions of North America for example, native grasslands and adjacent forests are associated with different soil types even though they share the same climate, relief and parent material. The forest soils are acidic and have illuvial horizons dominated by clay and sesquioxides (i.e. podzolic soils) while the grassland soils have gradational texture profiles with neutral pH levels throughout (i.e. prairie soils). This difference occurs because the recalcitrant forest litter (high C:N ratio) promotes leaching and podzolisation whereas the labile grassland litter (low C:N ratio) fuels the accumulation of nutrients and colloids at all soil depths (Corbett 1972). These differences are mirrored in the C:N ratio of the soils, with the forest soils having higher C:N ratio than the grassland soils and as previously mentioned, this trend was also evident at Hoxton Park.

These results also show that restoration appears to be reducing soil pH to a level more consistent with that of the original woodland, although there were still marked differences between the open patch types within the restored areas and woodland. The open patch types within the restored areas had a very similar pH to that of the pasture and as suggested in Chapter 3, this may be related to differences in litter quality between the abandoned farmland and woodland but this remains to be tested. The trees had a similar impact on soil pH in the restored areas and woodland and it is interesting to note that while trees are generally associated with less acidic soils within the woodland, as highlighted by the first study, planted individuals on abandoned farmland actually help to initially reduce soil pH. In general, pH values were higher in June than

JK Fitzgerald Chapter 5 Page 161

December across all four locations and this is most likely related to the extreme rainfall event that occurred mid-year.

Active C

The concentration of active C was higher in December than June for each of the locations. Many factors can affect the concentration of active C within the soil, with the most important being moisture and temperature regimes, crop growth and land management practices (Weil et al. 2003; Haynes 2005). In a study focused on soils used for maize growing however, Boone (1994) found that crop growth and litter inputs had much larger impacts of the concentration of active C than moisture levels and temperature. In addition to this, many agricultural studies have found that crop growth over summer tends to equate with larger amounts of root-derived C being added to the soil during this time than in autumn and winter with a concomitant increase in the concentration of active C (Campbell et al. 1999a; Campbell et al. 1999b; Jensen et al. 1997; Franzluebbers et al. 1995; Bonde and Roswall 1987). In line with this, there was a marked increase in the concentration of active C at Hoxton Park from June to December within the pasture, which was dominated by summer growing grasses (see Chapter 4). Compared to the pasture, the more structurally diverse locations (i.e. the restored areas and woodland) had much smaller differences in the concentration of active C between the two sampling times.

Soil respiration

Seasonality is a key factor affecting the rate of soil respiration since microbial activity is often dictated by moisture levels and temperature (for example see Tufekcioglu et al. 2001). However, time did not have a significant effect on respiration rates in this study. The impact of patch type on soil respiration varied across the four locations and the three patch types were clearly differentiated within the woodland, with respiration being markedly higher beneath the trees. This pattern was not present in the restored areas. Bolton et al. (1993) also found higher microbial activities, as well as larger microbial biomasses, beneath shrubs and perennial grasses compared to canopy-free areas covered with cryptogams in a semi-arid shrub-steppe in North America. A similar pattern was observed by Gnankambary et al. (2007) for canopy and canopy-free areas in a tropical

JK Fitzgerald Chapter 5 Page 162

savanna in Sudan but Eldridge and Mensinga (2007) measured very similar rates of respiration from soils beneath tree canopies and in inter-canopy areas within a semi-arid woodland in south eastern Australia.

It was noted in the previous chapter that N is often a limiting nutrient for microbial activity (Luo and Zhou 2006) but soil P limitations can also impose restrictions on the metabolism of microbes (for example see Cleveland et al. 2002) and Gnankambary et al. (2007) attributed the decreasing trend in soil respiration from canopy to canopy-free areas to elevated concentrations of soil P beneath individual tree crowns. This may also occur for Cumberland Plain Woodland since the woodland trees are typically associated with higher Bray 1 P levels than the open and shrub patch types (as discussed in Chapter 3). Alternatively, the higher rate of respiration beneath the woodland trees may indicate the presence of a larger microbial biomass (Pietikainen et al. 2007). Many of the results from this study, along with those from Chapter 3, such as elevated moisture, pH, Bray 1 P and nitrate levels provide evidence for a moderated soil environment beneath the woodland trees that is capable of supporting a larger and more active microbial biomass relative to the open and shrub patch types within the woodland. In addition to this, the soil beneath the woodland trees might support a different assemblage of microbes compared to the other woodland patch types and this could affect microbial functions. For example, Cleveland et al. (2007) carried out a laboratory experiment using soils from a tropical rainforest and found that the composition of the microbial biomass had a large impact on soil respiration and decomposition rates.

Soil moisture

Not surprisingly, soil moisture content varied through time, with up to 6 times the amount of moisture being held by the soil in June compared to September, December and March; this peak coincides with the heavy downpours experienced during June. The other, although much smaller, peak in moisture levels occurred in December, which also experienced above-average rainfall (BOM 2009). The pasture had higher moisture levels than the restored areas and woodland, which is consistent with the trend described in Chapter 3. In June, the mean moisture content within the 6-year old restored area was 50% higher than that within the 14-year old restored area and woodland but there was little difference between the restored areas and woodland for

JK Fitzgerald Chapter 5 Page 163

the remaining times. The open and shrub patch types within the restored areas had noticeably higher moisture levels than those in the woodland, although there was a decrease in moisture content moving from the 6-year old to 14-year old restored area. The tree patch type however, tended to have a more consistent effect on moisture levels across these three locations.

Nitrate

The pasture had the highest mean concentration of nitrate averaged across all sampling times and there was a marked difference between this location and the 6-year old restored area and woodland, which had very similar concentrations. Unlike many of the other variables considered so far, namely moisture content, pH, active C, total N and respiration, the nitrate levels within the 14-year old restored area tended to be higher than those within the 6-year old restored area.

Nitrate levels varied with time and this appears to be related to moisture levels since peaks in concentration occurred in June and December across all four locations. Given the above-average rainfall at these times however, it is difficult to say whether or not these peaks are indicative of the „normal‟ temporal pattern that occurs for nitrate at this site. It is possible that nitrate levels do not fluctuate widely throughout the year unless extreme weather events or other disturbances occur. The results clearly indicate however, that nitrate levels are typically elevated and depressed in the pasture and woodland respectively. The one exception occurred in June, when the pasture and woodland had very similar concentrations. For the remaining times, the pasture had about twice the concentration of nitrate than the woodland. This is supported by the C:N ratio of the soil, as previously discussed, since the lower the ratio the more likely it is that N will be mineralised rather than immobilised by the microbial biomass (Hazelton and Murphy 2007).

In the first study, the pasture at Hoxton Park did not have the highest nitrate levels of the four patch types and instead, the highest levels occurred beneath the woodland trees at this site. The results from these two studies therefore, reflect the need to sample the more labile nutrients, such as nitrate, through time to avoid erroneous, or misleading,

JK Fitzgerald Chapter 5 Page 164

conclusions and this has been repeatedly stressed in soil sampling and analysis handbooks (Rayment and Higginson 1992; Strong and Mason 1999).

In June, the 14-year old restored area had more than 2.5 times the mean concentration of nitrate than the 6-year old restored area and the reasons for this are unclear, especially since the younger restored area had higher moisture contents and pH levels than the 14- year old restored area. Despite the prevalence of Chloris gayana at Hoxton Park (Chapter 4), there is a range of pasture species at this site (for example see Nichols 2005) and the pasture and restored areas can be „patchy‟ with respect to ground species composition and cover on a relatively small (over meters) scale (pers. obs. 2007). Differences in species composition and cover therefore, may account for the variability in nitrate levels between the two restored areas but this remains to be tested. Ideally, this study would have included measures of ground species composition and cover, as occurred for the first two studies but time and financial constraints precluded this.

Alternatively, the differences in soil nitrate concentrations between the 6-year old and 14-year old restored areas may be due to underlying spatial variability at Hoxton Park, since the locations at this site were not able to be replicated (this is a common challenge for research dealing with large-scale restoration projects); furthermore, this trend may the result of restoration. These alternatives could be tested by using a sampling program that achieves replication at the location level, although this is likely to be difficult for the Cumberland Plain.

The findings of this study therefore, concur with the conclusion made in Chapter 3 that nitrate levels are typically elevated within the pasture relative to the woodland patch types. This reflects the utility of interpreting main effects in the presence of significant interactions because they may describe general trends that hold up well in a range of circumstances. These trends may then guide ecologists and restoration practitioners in making decisions regarding research directives, land management practices and restoration techniques, all of which are of particular importance when the long-term persistence of an endemic vegetation community is at stake.

JK Fitzgerald Chapter 5 Page 165

Ammonium

The four locations typically had much higher concentrations of ammonium than nitrate, which tends to occur in N-limited systems (Nadelhoffer et al. 2005). This is largely the result of low (<5.5 in water) pH levels because as previously discussed in Chapter 3, the abundance and activity of nitrifying bacteria are retarded in soils with high acidity levels (Attiwill and Leeper 1987). Averaged over all sampling times, the pasture and 6- year old restored area had distinctly higher concentrations of ammonium than the other two locations. In general, the pasture sustained higher levels of this nutrient than the restored areas and woodland throughout the year, although the 6-year old restored area had a noticeably higher concentration in December. Conversely, the woodland maintained lower levels than the restored areas at all sampling times except for June, when the mean concentration of ammonium in the woodland was nearly double that for the restored areas. These trends therefore, tend to mirror those for nitrate, which is not too surprising since nitrate is produced from the nitrification of ammonium (Attiwill and Leeper 1987). In combination, the nitrate and ammonium results from this study support the hypothesis of a difference in soil nitrogen from higher levels in the pasture to lower levels in woodland; the restored areas are approximately intermediate between these two endpoints.

Decomposition

The decomposition of calico at Hoxton Park occurred most slowly in the woodland, while the pasture and restored areas had quicker rates of mass loss through time and this was particularly evident for the 14-year old restored area during the second and third month of the year-long experiment. Soil respiration, nutrient cycling and decomposition are interrelated processes (Lou and Zhou 2006) and in general, a more active microbial biomass will cycle nutrients more quickly than a less active biomass, resulting in a faster rate of decomposition of organic material. Any factors that affect soil respiration and nutrient levels therefore, can impact decomposition and vice versa. There is thus a wide range of factors that can affect the rate of organic matter decay in the soil, including pH, texture, moisture content, oxygen levels and temperature (Lou and Zhou 2006). The rate of supply and the quality of the litter being added to the soil also has a

JK Fitzgerald Chapter 5 Page 166

bearing on decomposition. These last two factors are related to the productivity of the system (Burke et al. 1998), which can be directly affected by land management and disturbance regimes. It is interesting to note however, that the dependence of the decomposition process on the size, structure and activity of the microbial biomass has recently been questioned by Kemmitt et al. (2008), who proposed an alternative paradigm based on abiotic processes for the decomposition of soil organic matter.

The decomposition of cellulose (e.g. calico) is limited by soil N availability and the rate of decay usually increases with an increasing concentration of mineral N (Lou and Zhou 2006). Similarly, litter samples have also been found to decay more rapidly in high-N environments, which occur either naturally or as a result of N deposition via pollution and fertilisation (Blair et al. 1998; Cortez et al. 2007). Changes to plant species composition, by way of exotic species invasions for example, have also been found to affect the rate of N cycling and decomposition (Hobbie 1992). In light of this, it is not too surprising that the calico samples at Hoxton Park degraded more quickly under the relatively N-rich conditions of the abandoned farmland and restored vegetation. It is notable too, that the 14-year old restored area had higher nitrate and extractable P levels and a generally faster rate of calico loss than the 6-year old restored area.

JK Fitzgerald Chapter 5 Page 167

Chapter 6. The implications of this research for the management and restoration of Cumberland Plain Woodland

Very few old field studies have been carried out in Australia and as such, this thesis forms part of a small body of Australian research that has assessed soil properties in relation to old field succession (Read and Hill 1983; Liangzhong and Whelan 1993; Arnold et al. 1999; Standish et al. 2006). The studies presented herein were the first to extensively examine the soils of Cumberland Plain Woodland and abandoned farmland, along with areas that have undergone the attempted restoration of this threatened vegetation community. Importantly, these studies included measures representing all aspects of soil fertility (physical and chemical properties, as well as biological processes) and these were integrated with vegetation attributes, by way of stratified sampling using patch types and multivariate analysis.

This thesis has identified some key soil chemical properties and ecological processes for the ecology Cumberland Plain Woodland and its restoration on abandoned farmland. The implications of this research for the improved management and restoration of this endangered vegetation community are briefly discussed in terms of the three fundamental questions asked in Chapter 1.

How does the soil and ground layer vegetation of Cumberland Plain Woodland vary in response to canopy and inter-canopy patch types?

The tree, shrub and open patch types within the woodland impart spatial heterogeneity on the soil environment. It was found that soil pH and concentrations of Bray 1 P, active C, total C, nitrate and total N were generally higher beneath individual tree canopies than the shrub or open patch types. Furthermore, soil respiration was markedly higher beneath the trees than under the shrub or open patch types within the remnant woodland at Hoxton Park.

Similarly, exotic species richness within the ground layer of the Cumberland Plain

JK Fitzgerald Chapter 6 Page 168

Woodland also responded to the different patch types, with a greater number of exotic species occurring beneath the tree patch type than the shrub or open patches. Native species richness on the other hand, did not change significantly across the woodland patch types but there was a much greater cover of Themeda australis within inter- canopy (open) areas than beneath the tree and shrub patch types. There was substantial site-to-site variability in the composition and cover of the dominant ground layer species, with some sites being dominated by T. australis while others had a prevalence of Aristida species.

This spatial heterogeneity of soils and ground layer attributes within Cumberland Plain Woodland can inform restoration efforts in terms of setting goals and devising strategies, as well as assessing success. The relatively nutrient-rich soils beneath the woodland trees, which has been reported for many other systems throughout the world (Zinke 1962; Jackson and Ash 1998; Belsky et al. 1989; Ko and Reich 1993; Prober et al. 2002a), may be associated with an increase in the occurrence of exotic species.

How has past agricultural land use affected the soil and vegetation of Cumberland Plain Woodland?

The greatest impact of past agricultural land use on the soil was an increase in the concentration of nitrate, ammonium and total N within the pasture compared to the woodland patch types. This was not a consistent trend across the study sites however, since these variables were sometimes elevated beneath the woodland trees. Patch type was detected as a significant main effect for nitrate and for the highest order interaction (site x patch type x depth), three of the five study sites (Mount Annan, Orchard Hills and Prospect) had higher concentrations of nitrate within the surface soil (0-5 cm) of the pasture compared to the woodland patch types.

This trend also prevailed at three different sampling times (September 2007, December 2007 and March 2008) for Hoxton Park, even though this site was not originally detected as having elevated nitrate levels within the pasture. Similarly, the concentration of ammonium within the surface soil at Hoxton Park was significantly higher than that of the woodland in June 2007, September 2007 and March 2008. In line with this, the

JK Fitzgerald Chapter 6 Page 169

C:N ratio of the soil was markedly lower in the pasture than the woodland at Hoxton Park and the woodland at this site also had a much slower rate of decomposition than the pasture. Thus, it appears that the abandoned pastures and Cumberland Plain Woodland may function differently with respect to the cycling of N and related processes such as decomposition (microbial activity).

In general, the abandoned pastures had double the number of exotic (ground layer) species per 100 m2 than the woodland patch types and vice versa for native species richness. The BVSTEP procedure showed a (weak) correlation of moisture content, nitrate, total N and exchangeable Na with ground species composition and cover across the four patch types (pasture, open, shrub and tree), with the pasture samples being associated with higher levels or concentrations of these variables than the woodland samples. This further supports the hypothesis, initially presented in Chapter 3, that elevated mineral-N concentrations within the soils of abandoned farmland might be impeding the attempted restoration and natural succession of Cumberland Plain Woodland in these areas.

In light of these findings, a much more detailed examination of the N cycle is warranted for abandoned farmland and good quality stands of Cumberland Plain Woodland. This should include measures of: in situ net N and gross N mineralisation and immobilisation rates; litterfall analyses and nutrient concentrations within above- and below-ground plant tissues; and studies of microbial properties, including the size, composition, activity and spatial distribution of the microbial biomass. This research would also need to incorporate measures of ecosystem C, namely soil (including soil organic matter and the microbial biomass), vegetation and litter components, since the cycling of N and C are interrelated (Hart et al. 1994).

What are the impacts of restoration of Cumberland Plain Woodland on the soil of abandoned pastures that were once covered in this vegetation community?

In many cases, the general trend for nutrient levels and rates of microbial activity were in the decreasing order of pasture, 6-year old restored area, 14-year-old restored area

JK Fitzgerald Chapter 6 Page 170

and woodland. This occurred most noticeably for moisture content, pH and active C, while the results for nitrate, ammonium and decomposition showed sometimes higher and sometimes lower concentrations or percentage mass loss in the restored areas compared to the other two locations. Thus, there is evidence to suggest that revegetation has the potential to reinstate some soil properties to a state similar to that of the original vegetation. However, the impact of restoration on mineral-N levels and decomposition (microbial activity), which is potentially a very important consideration for the restoration of Cumberland Plain Woodland on abandoned farmland, is much less clear.

Extrapolating these results over a much longer timeframe however, is not plausible since the oldest available restored area at the site was only 14 years old. It is unknown therefore, what the measured soil properties and processes will be like in the restored areas in 10, 20, 50 or 100 years time. In addition to this, these findings for Hoxton Park may not directly apply to other sites throughout the region but in the absence of any other soil data for restored areas on the Cumberland Plain, they provide an excellent guide for researchers and practitioners concerned with the restoration of this endangered vegetation community. It is pertinent therefore, that many of the trends observed for the pasture and woodland samples at Hoxton Park, mirrored the dominant trends that emerged from the first study (Chapter 3), which was carried out over five disjunct sites. Parallels between the two studies were: the concentration of Bray 1 P was lowest in the pasture; the pasture soils had increased in pH relative to the woodland patch types; and nitrate and ammonium were elevated within the pasture.

JK Fitzgerald Chapter 6 Page 171

References

Adam P (2002) Rarity, rare plant species and the New South Wales Threatened Species Conservation Act - conservation opportunities and challenges. Cunninghamia 7, 651-669. Aide TM, Zimmerman JK, Pascarella JB, Rivera L, Marcano-Vega H (2000) Forest regeneration in a chronosequence of tropical abandoned pastures: implications for restoration ecology. Restoration Ecology 8, 328-338. Allan H (1980) 'Pastures for the County of Cumberland.' (Department of Agriculture, NSW: Mid Coast and ). Anderson IC, Bastias BA, Genney DR, Parkin PI, Cairney JWG (2007) Basidiomycete fungal communities in Australian sclerophyll forest soil are altered by repeated prescribed burning. Mycological Research 111, 482-486. Anderson JPE (1982) Soil respiration. In 'Methods of soil analysis part 2: chemical and microbiological properties'. (Ed. AL Page) pp. 831-872. (American Society of Agronomy Inc. and Soil Science Society of America Inc.: Madison, Wisconsin, USA). Archer S (1995) Harry Stobbs memorial lecture, 1993. Herbivore mediation of grass-woody plant interactions. Tropical Grasslands 29, 218-235. Armstrong RD, Kuskopf BJ, Millar G, Whitbread AM, Standley J (1999) Changes in soil chemical and physical properties following legumes and opportunity cropping on a cracking clay soil. Australian Journal of Experimental Agriculture 39, 445-456. Arnold GW, Abensperg-Traun M, Hobbs RJ, Steven DE, Atkins L, Viveen JJ, Gutter DM (1999) Recovery of communities on abandoned farmland in southwestern Australia: soils, plants, birds and arthropods. Pacific Conservation Biology 5, 163-178. Atkinson G (1993) Soil materials, a layer based approach to soil description and classification. Catena 20, 411-418. Atkinson J (1826) 'An account of the state of agriculture and grazing in New South Wales.' (J. Cross: London). Attiwill PM (1994) Ecological disturbance and the conservative management of eucalypt forests in Australia. Forest Ecology and Management 63, 301-346. Attiwill PM, Adams MA (1993) Tansley review No. 50: Nutrient cycling in forests. New Phytologist 124, 561-582. Attiwill PM, Leeper GW (1987) 'Forest soils and nutrient cycles.' (Melbourne University Press: Victoria). Austlig (1990) Atlas of Australian Resources. Volume 6 Vegetation. AUSMAP, Department of Administrative Services, .

JK Fitzgerald References Page 172

Australasian Legal Information Institute (2008) Threatened Species Conservation Act 1995. http://www.legislation.nsw.gov.au/viewtop/inforce/act+101+1995+FIRST+0+N, accessed 23rd August 2008. Australasian Legal Information Institute (2009) Environment Protection and Biodiversity Conservation Act 1999 http://www.austlii.edu.au/au/legis/cth/consol_act/epabca1999588/ s178.html, accessed 17th January 2009. Australian Gallery Directors Council (1979) 'Converting the wilderness: the art of gardening in colonial Australia.' (Australian Gallery Directors Council: Sydney). Australian Heritage Commission (2009a) Orchard Hills Cumberland Plain Woodland, The Northern Road, Orchard Hills, NSW Australia. http://www.environment.gov.au/cgibin/ ahdb/search.pl?mode=place_detail;search=place_name%3DOrchard%2520Hills%3Bkey word_PD%3Don%3Bkeyword_SS%3Don%3Bkeyword_PH%3Don%3Blatitude_1dir%3 DS%3Blongitude_1dir%3DE%3Blongitude_2dir%3DE%3Blatitude_2dir%3DS%3Bin_r egion%3Dpart;place_id=102211, accessed May 2009. Australian Heritage Commission (2009b) Scheyville Bushland Remnant, Old Pitt Town Road, Oakville,NSW,Australia.http://www.environment.gov.au/cgibin/ahdb/search.pl?mode=pl ace_detail;search=place_name%3DScheyville%3Bstate%3DNSW%3Bkeyword_PD%3D on%3Bkeyword_SS%3Don%3Bkeyword_PH%3Don%3Blatitude_1dir%3DS%3Blongitu de_1dir%3DE%3Blongitude_2dir%3DE%3Blatitude_2dir%3DS%3Bin_region%3Dpart; place_id=19033, accessed May 2009. Averett JM, Klips RA, Nave LE, Frey SD, Curtis PS (2004) Effects of soil carbon amendment on nitrogen availability and plant growth in an experimental tallgrass prairie restoration. Restoration Ecology 12, 568-574. Baldock JA, Skjemstad JO (1999) Soil organic carbon/Soil organic matter. In 'Soil analysis: an interpretation manual'. (Eds KI Peverill, LA Sparrow, DJ Reuter) pp. 159-170. (CSIRO Publishing: Collingwood, Victoria). Bannerman SM, Hazelton PA (1990) 'Soil landscapes of the Penrith 1:100 000 sheet.' (Soil Conservation Service of NSW: Sydney). Barlow J, Gardner TA, Ferreira LV, Peres CA (2007) Litter fall and decomposition in primary, secondary and plantation forests in the Brazilian Amazon. Forest Ecology and Management 247, 91-97. Barnes PW, Archer S (1996) Influence of an overstorey tree (Prosopis glandulosa) on associated shrubs in a savanna parkland: implications for patch dynamics. Oecologia 105, 493-500. Bauhus J, Khanna PK, Raison RJ (1993) The effect of fire on carbon and nitrogen mineralisation and nitrification in an Australian forest soil. Australian Journal of Soil Research 31, 621-639.

JK Fitzgerald References Page 173

Bayley WA (1974) 'History of Campbelltown, New South Wales.' (Campbelltown City Council: Campbelltown). Beadle NCW (1953) The edaphic factor in plant ecology with a special note on soil phosphates. Ecology 34, 426-428. Beadle NCW (1954) Soil phosphate and the delimitation of plant communities in eastern Australia. Ecology 35, 370-375. Beadle NCW (1966) Soil phosphate and its role in molding segments of the Australian flora and vegetation with special reference to xeromorphy and sclerophylly. Ecology 47, 992-1007. Beirne KG (1953) Soil conservation in the Camden district. The Journal of the Soil Conservation Service of New South Wales 9, 135-141. Bell MJ, Moody PW, Connolly RD, Bridge BJ (1998) The role of active fractions of soil organic matter in physical and chemical fertility of Ferrosols. Australian Journal of Soil Research 36, 809-819. Bell MJ, Moody PW, Yo SA, Connolly RD (1999) Using active fractions of soil organic matter as indicators of the sustainability of Ferrosol farming systems. Australian Journal of Soil Research 37, 279-288. Bellemare J, Motzkin G, Foster DR (2002) Legacies of the agricultural past in the forested present: an assessment of historical land-use effects on rich mesic forests. Journal of Biogeography 29, 1401-1420. Belsky AJ (1994) Influences of trees on savanna productivity: tests of shade, nutrients, and tree- grass competition. Ecology 75, 922-932. Belsky AJ, Amundson RG, Duxbury JM, Rhia SJ, Ali AR, Mwonga SM (1989) The effects of trees on their physical, chemical and biological environments in a semi-arid savanna in Kenya. Journal of Applied Ecology 26, 1005-1024. Belsky AJ, Canham CD (1994) Forest gaps and isolated savanna trees. BioScience 44, 77-84. Belsky AJ, Mwonga SM, Amundson RG, Duxbury JM, Ali AR (1993) Comparative effects of isolated trees on their undercanopy environments in high- and low-rainfall savannas. The Journal of Applied Ecology 30, 143-155. Bembrick CS, Herbert C, Clark NR (1991) Permo-Triassic stratigraphy. In 'Geology of the Penrith 1:100 000 Sheet 9030'. (Eds DC Jones, NR Clark) pp. 7-28. (New South Wales Geological Survey: Sydney). Benson D, Howell J (1990b) 'Taken for granted: the bushland of Sydney and its suburbs.' (Kangaroo Press, Pty. Ltd.: Kenthurst). Benson D, Howell J (2002) Cumberland Plain Woodland ecology then and now: interpretations and implications from the work of Robert Brown and others. Cunninghamia 7, 631-650. Benson D, McDougall L (1998) plant species Part 6 Dicotyledon Family Myrtaceae. Cunninghamia 5, 809-987.

JK Fitzgerald References Page 174

Benson DH (1992) The natural vegetation of the Penrith 1:100 000 map sheet. Cunninghamia 2, 541-596. Benson DH, Howell J (1990a) Sydney's vegetation 1788-1988: utilisation, degradation and rehabilitation. Proceedings of the Ecological Society of Australia 16, 115-127. Benson DH, Thomas J, Burkitt J (1990) The natural vegetation of Bents Basin State Recreation Area. Cunninghamia 2, 223-262. Berryman T (2005) Competitive relationships between four native Cumberland Plain grassland species in an addition-style experiment on post-mining land at Penrith Lakes. Ecological Management and Restoration 6, 74. Bishop PM, Mitchell PB, Paton TR (1980) The formation of duplex soils on hillslopes in the , Australia. Geoderma 23, 175-189. Blair G, Lefroy R, Lisle L (1995) Soil carbon fractions based on their degree of oxidation, and the development of a carbon management index for agricultural systems. Australian Journal of Agricultural Research 46, 1459-1466. Blair GJ, Nicolson AJ (1975) The occurrence of sulfur deficiency in temperate Australia. In 'Sulfur in Australasian agriculture '. (Ed. KD McLachlan) pp. 137-144. (Sydney University Press: Sydney). Blair JM, Crossley DA, Jr. (1988) Litter decomposition, nitrogen dynamics and litter microarthropods in a Southern Appalachian hardwood forest 8 years following clearcutting. The Journal of Applied Ecology 25, 683-698. Bloxham F (2002) 'A history of Prospect.' Unpublished historic account, State Library of NSW, Sydney. Blumenthal DM, Jordan NR, Russelle MP (2003) Soil carbon addition controls weeds and facilitates prairie restoration. Ecological Applications 13, 605-615. Bolton H, Smith JL, Link SO (1993) Soil microbial biomass and activity of a disturbed and undisturbed shrub-steppe ecosystem. Soil Biology and Biochemistry 25, 545-552. Bonde TA, Rosswall T (1987) Seasonal variation of potentially mineralisable nitrogen in four cropping systems. Soil Science Society of America Journal 51, 1508-1514. Boone RD (1994) Light-fraction soil organic matter: Origin and contribution to net nitrogen mineralization. Soil Biology and Biochemistry 26, 1459-1468.Bradshaw AD (2004) The role of nutrients and the importance of function in the assembly of ecosystems. In 'Assembly rules and restoration ecology: bridging the gap between theory and practice'. (Eds VM Temperton, RJ Hobbs, T Nuttle, S Halle) pp. 325-340. (Island Press: Washington). Braunack MV, Walker J (1985) Recovery of some surface soil properties of ecological interest after sheep grazing in a semi-arid woodland. Australian Journal of Ecology 10, 451-460.

JK Fitzgerald References Page 175

Bray JR, Kurtz LT (1945) Determination of total, organic, and available forms of phosphorus in soils. Soil Science 59, 39-45. Brokaw N (1985) Treefalls, regrowth, and community structure in tropical forests. In 'The ecology of natural disturbance and patch dynamics'. (Eds STA Pickett, PS White) pp. 53- 69. (Academic Press, Inc: Orlando, Florida). Brouwer D (1998) 'Fertilisers for your farm: their role in the environment.' (NSW Agriculture: Orange). Brown AJ (1999) Soil sampling and sample handling for chemical analysis. In 'Soil analysis: an interpretation manual'. (Eds KI Peverill, LA Sparrow, DJ Reuter) pp. 35-54. (CSIRO Publishing: Collingwood, Victoria). Bruce RC (1999) Calcium. In 'Soil analysis: an interpretation manual'. (Eds KI Peverill, LA Sparrow, DJ Reuter) pp. 247-254. (CSIRO Publishing: Collingwood, Victoria). Brussaard L, Pulleman MM, Ouedraogo E, Mando A, Six J (2007) Soil fauna and soil function in the fabric of the food web. Pedobiologia 50, 447-462. Bureau of Meteorology (1991) Sydney, New South Wales climatic survey, December 1991. (Australian Government Publishing Service: Canberra). Bureau of Meterology (2006) Daily weather observations for New South Wales. http://www.bom.gov.au/climate/dwo/IDCJDW0200.shtml. Accessed December 2006. Bureau of Meterology (2009) Climatic Averages. http://www.bom.gov.au/climate/averages. Accessed June 2009. Burke IC, Lauenroth WK, Coffin DP (1995) Soil organic matter recovery in semiarid grasslands: implications for the conservation reserve program. Ecological Applications 5, 793-801. Burke IC, Lauenroth WK, Vinton M.A, Hook P.B., Kelly R.H., Epstein H.E., Aguiar M.R, Robles M.D., Aguilera M.O., Murphy K.L, Gill R.A. (1998) Plant-soil interactions in temperate grasslands. Biogeochemistry 42, 121-143. Burrell JP (1972) Vegetation of the Sydney area: 1788 and 1961. Proceedings of the Ecological Society of Australia 7, 71-78. Burrows GE (2004) The importance of seasonality in the timing of flora surveys in the South and Central Western Slopes of New South Wales. Cunninghamia 8, 514-520. Buttrey R (2006) 'A short history of the life of Gregory Blaxland.' (Brush Farm Historical Society Inc: Eastwood, Sydney). Campbell CA, Biederbeck VO, Wen G, Zentner RP, Schoenau J, Hahn D (1999a) Seasonal trends in selected soil biochemical attributes: effects of crop rotation in the semiarid prairie. Canadian Journal of Soil Science 79, 73-84.

JK Fitzgerald References Page 176

Campbell CA, Lafond GP, Biederbeck O, Wen G, Schoenau J, Hahn D (1999b) Seasonal trends in soil biochemical attributes: effects of crop management on a Black Chernozem. Canadian Journal of Soil Science 79, 85-97. Cannon G (1997) 'The first title holders of land in the County of Cumberland Part 1 List of parishes and titleholders.' Unpublished report, State Library of NSW, Sydney. Carolin R, Tindale M (1993) 'Flora of the Sydney region.' (Reed: Chatswood). Cass A (1999) Interpretation of some soil physical indicators for assessing soil physical fertility. In 'Soil analysis: an interpretation manual'. (Eds KI Peverill, LA Sparrow, DJ Reuter) pp. 95-102. (CSIRO Publishing: Collingwood, Victoria). Chan KY, Barchia I (2007) Soil compaction controls the abundance, biomass and distribution of earthworms in a single dairy farm in south-eastern Australia. Soil and Tillage Research 94, 75-82. Chan KY, Dorahy CG, Tyler S, Wells AT, Milham PP, Barchia I (2007) Phosphorus accumulation and other changes in soil properties as a consequence of vegetable production, Sydney region, Australia. Australian Journal of Soil Research 45, 139-146. Chapin FS, III (1980) The mineral nutrition of wild plants. Annual Review of Ecology and Systematics 11, 233-260. Chapin FS, van Cleve K (1989) Approaches to studying nutrient uptake, use and loss in plants. In 'Plant physiological ecology: field methods and instrumentation'. (Eds RW Pearcy, JR Ehleringer, HA Mooney, PW Rundel) pp. 185-208. (Chapman and Hall: London). Chapman GA, Atkinson G (2007) Soil survey and mapping. In 'Soils: their properties and management'. (Eds PEV Charman, BW Murphy) pp. 109-136. (Oxford University Press: South Melbourne). Chapman GA, Murphy CL (1989) 'Soil landscapes of the Sydney 1:100 000 sheet.' (Soil Conservation Service of NSW: Sydney). Chapman SK, Langley JA, Hart SC, Koch GW (2006) Plants actively control nitrogen cycling: uncorking the microbial bottleneck. New Phytologist 169, 27-34. Charman PEV, Murphy BW (Eds) (2007) 'Soils: their properties and management.' (Oxford University Press: South Melbourne, Victoria). Charman PEV, Roper MM (2007) Soil Organic Matter. In 'Soils: their properties and management'. (Eds PEV Charman, BW Murphy) pp. 276-286. (Oxford University Press: South Melbourne, Victoria). Charman PEV, Wooldridge AC (2007) Soil salinisation. In 'Soils: Their properties and management'. (Eds PEV Charman, BW Murphy). (Oxford University Press: Melbourne). Chilcott C, Reid NCH, King K (1997) Impact of trees on the diversity of pasture species and soil biota in grazed landscapes on the Northern Tablelands, NSW. In 'Conservation

JK Fitzgerald References Page 177

Outside Nature Reserves'. (Eds P Hale, D Lamb) pp. 378-386. (Centre for Conservation Biology: University of Queensland). Chilcott C, Reid NCH, King K (1997) Impact of trees on the diversity of pasture species and soil biota in grazed landscapes on the Northern Tablelands, NSW. In 'Conservation Outside Nature Reserves'. (Eds P Hale, D Lamb) pp. 378-386. (Centre for Conservation Biology: University of Queensland). Clarke S, French K (2005) Germination response to heat and smoke of 22 Poaceae species from grassy woodlands. Australian Journal of Botany 53, 445-454. Clarke, KR, Gorley, RN (2001) PRIMER v5: user manual/tutorial. (PRIMER-E: Plymouth). Clarke, KR, Warwick, RM (2001) Change in marine communities: an approach to statistical analysis and interpretation. 2nd Ed. (PRIMER-E: Plymouth). Clements A (1983) Suburban development and resultant changes in the vegetation of the bushland of the region. Australian Journal of Ecology 8, 307-319. Cleveland CC, Nemergut D, Schmidt SK, Townsend AR (2007) Increases in soil respiration following labile carbon additions linked to rapid shifts in soil microbial community composition. Biogeochemistry 82, 229-240. Cleveland CC, Townsend AR, Schmidt SK (2002) Phosphorus limitation of microbial processes in moist tropical forests: Evidence from short-term laboratory incubations and field studies. Ecosystems 5, 680-691. Cochran RL, Collins HP, Kennedy A, Bezdicek DF (2007) Soil carbon pools and fluxes after land conversion in a semiarid shrub-steppe ecosystem. Biology and Fertility of Soils V43, 479-489. Cole I, Lunt ID (2005) Restoring Kangaroo Grass (Themeda triandra) to grassland and woodland understoreys: a review of establishment requirements and restoration exercises in south-east Australia. Ecological Management and Restoration 6, 28-33. Collins BS, Dunne KP, Pickett STA (1985) Responses of forest herbs to canopy gaps. In 'The ecology of natural disturbance and patch dynamics'. (Eds STA Pickett, PS White) pp. 218-234. (Academic Press, Inc: Orlando, Florida). Collins BS, Pickett STA (1987) Influence of canopy openings on the environment and herb layer in a northern hardwoods forest. Vegetatio 70, 3-10. Collins BS, Pickett STA (1988) Response of Herb Layer Cover to Experimental Canopy Gaps. American Midland Naturalist 119, 282-290. Collis-George N, Evans GN (1964) A hydrologic investigation of salt-affected soils in an alluvial plain of the Hawkesbury River, NSW. Australian Journal of Soil Research 2, 20- 28. Connell JH, Slatyer RO (1977) Mechanisms of succession in natural communities and their role in community stability and organisation. The American Naturalist 111, 1119-1144.

JK Fitzgerald References Page 178

Connolly RD, Freebairn DM, Bridge BJ (1997) Change in infiltration characteristics associated with cultivation history of soils in south-eastern Queensland. Australian Journal of Soil Research 35, 1341-1358. Cooper WS (1926) The fundamentals of vegetational change. Ecology 7, 391-413. Corbett JR (1969) 'The living soil: the processes of soil formation.' (Martindale Press: West Como, Sydney). Corbett JR (1972) Soils of the Sydney area. In 'The city as a life system? Proceedings of the Ecological Society of Australia'. (Ed. HA Nix) pp. 41-70. Corbin JD, D'Antonio CM (2004) Can carbon addition increase competitiveness of native grasses? A case study from California. Restoration Ecology 12, 36-43. Cortez J, Garnier E, Perez-Harguindeguy N, Debussche M, Gillon D (2007) Plant traits, litter quality and decomposition in a Mediterranean old-field succession. Plant and Soil 296, 19-34. Cramer VA (2007) Old fields as complex systems: new concepts for describing the dynamics of abandoned farmland. In 'Old fields: dynamics and restoration of abandoned farmland'. (Eds VA Cramer, RJ Hobbs) pp. 31-46. (Island Press: Washington). Cramer VA, Hobbs RJ (2007) (Eds) 'Old fields: dynamics and restoration of abandoned farmland'. (Island Press: Washington). Crawford DM, Baker TG, Maheswaran J (1995) Changes in soil chemistry associated with changes in soil pH in Victorian pastures. Australian Journal of Soil Research 33, 491- 504. Crow S, Swanston C, Lajtha K, Brooks J, Keirstead H (2007) Density fractionation of forest soils: methodological questions and interpretation of incubation results and turnover time in an ecosystem context. Biogeochemistry 85, 69-90. Cullen P (2003) Salinity. In 'Ecology: an Australian perspective'. (Eds P Attiwill, B Wilson) pp. 474-488. (Oxford University Press: Melbourne). Cuneo P, Leishman MR (2006) African Olive (Olea europaea subsp. cuspidata) as an environmental weed in eastern Australia: a review. Cunninghamia 9, 545-577. Curry JP, Byrne D (1997) Role of earthworms in straw decomposition in a winter cereal field. Soil Biology and Biochemistry 29, 555-558. Dalal RC, Harms BP, Krull E, Wang WJ (2005) Total soil organic matter and its labile pools following mulga (Acacia aneura) clearing for pasture development and cropping 1. Total and labile carbon. Australian Journal of Soil Research 43, 13-20. Dallas M, Navin K (1990) 'Archaeological study of the Scheyville development area at Scheyville, NSW.' (Unpublished report to Hawkesbury City Council).

JK Fitzgerald References Page 179

Darley L (2005) A survey of mycorrhizal infection sites in Cumberland Plains orchids (abstract). The ecology and management of Cumberland Plain habitats: a symposium. University of Western Sydney, 16th February 2005. Davey BG, Russell JD, Wilson MJ (1975) Iron oxide and clay minerals and their relation to colours of red and yellow podzolic soils near Sydney, Australia. Geoderma 14, 125-138. Davidson EA, de Carvalho CJR, Figueira AM, Ishida FY, Ometto JPHB, Nardoto GB, Saba RT, Hayashi SN, Leal EC, Vieira ICG, Martinelli LA (2007) Recuperation of nitrogen cycling in Amazonian forests following agricultural abandonment. 447, 995-998. Davies R, Christie J (2001) Rehabilitating Western Sydney's bushland: processes needed for sustained recovery. Ecological Management and Restoration 2, 167-178. Dean WE Jr (1974) Determination of carbonate and organic matter in calcareous sediments and sedimentary rocks by loss on ignition: comparison with other methods. Journal of Sedimentary Petrology 44, 242-248. Department of Environment and Climate Change (2007) Introducing the NSW Threatened Species Priorities Action Statement (PAS). (DECC (NSW): Sydney).Department of Environment and Climate Change (2008a) Cumberland Plain Woodland Endangered Ecological Community listing: NSW Scientific Committee Final Determination. http://www.environment.nsw.gov.au/determinations/CumberlandPlainWoodlandEndCom Listing.htm, accessed 27th August 2008. Department of Environment and Climate Change (2008b) Cumberland Plain Woodland – Priority actions. http://www.threatenedspecies.environment.nsw.gov.au/tsprofile/ pas_profile.aspx?id=10191, accessed 27th August 2008. Department of Environment and Climate Change (2008c) List of Key Threatening Processes. http://www.environment.nsw.gov.au/threatenedspecies/KeyThreateningprocessesByDoct ype.htm, accessed 23rd August 2008. Department of Environment and Climate Change (2008d) Soil and land resources of the Hawkesbury-Nepean Catchment (DVD-R). (Natural Resource Information Unit: Parramatta). Department of Environment and Climate Change (2009) Cumberland Plain Woodland in the Sydney Basin Bioregion: proposed Critically Endangered Community listing. http://www.environment.nsw.gov.au/determinations/cumberlandplainpd.htm, accessed 19th January 2009. Department of Environment, Climate Change and Water (2009a) Ecology of Cumberland Plain Woodland: woodland plants photo gallery http://www.rbgsyd.nsw.gov.au/science/Evolutionary_Ecology_Research/Ecology_of_Cu mberland_Plain_Woodland/woodland_plant_species/woodland_plants_photo_gallery, accessed May 2009.

JK Fitzgerald References Page 180

Department of Environment, Climate Change and Water (2009b) Ecology of Cumberland Plain Woodland: plant species in the woodland http://www.rbgsyd.nsw.gov.au/science/Research/Ecology_of_Cumberland_Plain_Woodl and/woodland_plant_species, accessed November 2009. Department of Environment and Conservation (2005) 'Recovering bushland on the Cumberland Plain: best practice guidelines for the management and restoration of bushland.' (DEC (NSW): Sydney). Department of Environment, Water, Heritage and the Arts (2008a) EPBC Act fact sheet. http://www.environmnet.gov.au/epbc/publications/pubs/epbc-act-fact-sheet.pdf, accessed 17th January 2009. Department of Environment, Water, Heritage and the Arts (2008b) EPBC Act List of Threatened Ecological Communities. http://www.environment.gov.au/cgi- bin/sprat/public/ publiclookupcommunities.pl, accessed 23rd August 2008. Department of Environment, Water, Heritage and the Arts (2009) Cumberland Plain woodlands ecological community: nomination. http://www.environment.gov.au/biodiversity/ threatened/communities/Cumberland_Plain_woodlands.pdf, accessed 6th May 2009. Department of Infrastructure Planning and Natural Resources (2004) 'The Western Sydney Parklands Management Vision, Summary Report, November 2004.' (Department of Infrastructure, Planning and Natural Resources: Sydney). Diemont SAW, Martin JF, Levy-Tacher SI, Nigh RB, Lopez PR, Golicher D (2006) Lacandon Maya forest management: restoration of soil fertility using native tree species. Ecological Engineering. Donald B (1997) 'Liverpool, the first Macquarie town.' (Liverpool City Council: Liverpool, NSW). Donnelly R (2001) 'The Scheyville experience: the Officer Training Unit Scheyville, 1965- 1973.' (University of Queensland Press: St. Lucia, Queensland). Dormaar JF, Smoliak S, Willms WD (1990) Soil chemical properties during succession from abandoned cropland to native range. Journal of Range Management 43, 260-265. Dorrough J, Moxham C, Turner V, Sutter G (2006) Soil phosphorus and tree cover modify the effects of livestock grazing on plant species richness in Australian grassy woodland. Biological Conservation 130, 394-405. Dorrough J, Scroggie MP (2008) Plant responses to agricultural intensification. Journal of Applied Ecology 45, 1274-1283. Drewry JJ, Cameron KC, Buchan GD (2008) Pasture yield and soil physical property responses to soil compaction from treading and grazing - a review. Australian Journal of Soil Research 46, 237-256.

JK Fitzgerald References Page 181

Dupouey JL, Dambrine E, Laffite JD, Moares C (2002) Irreversible impact of past land use on forest soils and biodiversity. Ecology 83, 2978-2984. Egan D, Howell EA (Eds) (2001) 'The historical ecology handbook: a restorationist's guide to reference ecosystems.' (Island Press: Washington, D.C.). Egler FE (1954) Vegetation science concepts I. Initial floristic composition, a factor in old-field vegetation development. Vegetatio 4, 412-417. Ehrenfeld JG (2003) Effects of exotic plant invasions on soil nutrient cycling processes. Ecosystems 6, 503-523. Ehrenfeld JG, Toth LA (1997) Restoration ecology and the ecosystem perspective. Restoration Ecology 5, 307-317. Eisele KA, Schimel DS, Kapustka LA, Parton WJ (1989) Effects of available P and N:P ratios on non-symbiotic dinitrogen fixation in tallgrass prairie soils. Oecologia 79, 471-474. Eldridge DJ, Mensinga A (2007) Foraging pits of the short-beaked echidna (Tachyglossus aculeatus) as small-scale patches in a semi-arid Australian box woodland. Soil Biology and Biochemistry 39, 1055-1065. Eldridge DJ, Wong VNL (2005) Clumped and isolated trees influence soil nutrient levels in an Australian temperate box woodland. Plant and Soil 270, 331-342. Elkins NZ, Whitford WG (1982) The role of microarthropods and nematodes in decomposition in a semi-arid ecosystem. Oecologia 55, 303-310. Emery KA, Morse RJ, Houghton PD (1986) Aerial photograph interpretation for land resource mapping. Technical Handbook No. 8. Soil Conservation Service, NSW. Ens E-J (2002) Chilean needle grass (Nassella neesiana) on the Cumberland Plain: expose of distribution and impacts on invertebrates. Unpublished Honours thesis, University of New South Wales. Erskine WD, Mahmoudzadeh A, Browning CM, Myers C (2003) Sediment yields and soil loss rates from different land uses on Triassic shales in western Sydney, NSW. Australian Journal of Soil Research 41, 127-140. Eschen R, Muller-Scharer H, Schaffner U (2006) Soil carbon addition affects plant growth in a species-specific way. Journal of Applied Ecology 43, 35-42. Facelli JM, Brock DJ (2000) Patch dynamics in arid lands: localized effects of Acacia papyrocarpa on soils and vegetation of open woodlands of South Australia. Ecography 23, 479-491. Fagan KC, Pywell RF, Bullock JM, Marrs RH (2008) Do restored calcareous grasslands on former arable fields resemble ancient targets? The effect of time, methods and environment on outcomes. Journal of Applied Ecology 45, 1293-1303

JK Fitzgerald References Page 182

Falkengren-Grerup U, ten Brink D, Brunet J (2006) Land use effects on soil N, P, C and pH persist over 40-80 years of forest growth on agricultural soils. Forest Ecology and Management 225, 74-81. Fensham RJ, Fairfax RJ (2002) Aerial photography for assessing vegetation change: a review of applications and the relevance of findings for Australian vegetation history. Australian Journal of Botany 50, 415-429. Fisher JL, Veneklaas EJ, Lambers H, Loneragan WA (2006) Enhanced soil and leaf nutrient status of a Western Australia Banksia woodland community invaded by Ehrharta calycina and Pelargonium capitatum. Plant and Soil 284, 253-264. Flinn KM, Marks PL (2007) Agricultural legacies in forest environments: tree communities, soil properties and light availability. Ecological Applications 17, 452-463. Flinn KM, Vellend M (2005) Recovery of forest plant communities in post-agricultural landscapes. Frontiers in Ecology and the Environment 5, 243-250. Flinn KM, Vellend M, Marks PL (2005) Environmental causes and consequences of forest clearance and agricultural abandonment in central New York, USA. Journal of Biogeography 32, 439-452. Forster GR, Campbell D, Benson D, Moore RM (1977) 'Vegetation and soils of the western region of Sydney: technical memorandum 77/10.' (CSIRO Division of Land Use Research: Canberra). Franzluebbers AJ (2002) Water infiltration and soil structure related to organic matter and its stratification with depth. Soil and Tillage Research 66, 197-205. Franzluebbers AJ, Hons FM, Zuberer DA (1995) Tillage and crop effects on seasonal soil carbon and nitrogen dynamics. Soil Science Society of America Journal 59, 1618-1624. Fraser PM, Beare MH, Butler RC, Harrison-Kirk T, Piercy JE (2003) Interactions between earthworms (Aporrectodea caliginosa), plants and crop residues for restoring properties of a degraded arable soil. Pedobiologia 47, 870-876. Fraterrigo J, Turner M, Pearson S (2006) Interactions between past land use, life-history traits and understory spatial heterogeneity. Landscape Ecology 21, 777-790. Freckleton RP, Watkinson AR (2002) Large-scale spatial dynamics of plants: metapopulations, regional ensembles and patchy populations Journal of Ecology 90, 419-434. French K, Callaghan B, Hill S (2000a) Classifying endangered vegetation communities: a case study of Cumberland Plain Woodlands. Pacific Conservation Biology 6, 120-129. French K, Pellow B, Henderson M (2000b) Vegetation of the Holsworthy Military Area. Cunninghamia 6, 893-940. Gachet S, Leduc A, Bergeron Y, Nguyen-Xuan T, Tremblay F (2007) Understory vegetation of boreal tree plantations: Differences in relation to previous land use and natural forests. Forest Ecology and Management 242(1): 49-57.

JK Fitzgerald References Page 183

Garcia H, Tarrason D, Mayol M, Male-Bascompte N, Riba M (2007) Patterns of variability in soil properties and vegetation cover following abandonment of olive groves in Catalonia (NE Spain). Acta Oecologica 31, 316-324. Garner W, Steinberger Y (1989) A proposed mechanism for the formation of 'Fertile Islands' in the desert ecosystem. Journal of Arid Environments 16, 257-262. Geeves GW, Craze B, Hamilton GJ (2007) Soil physical properties. In 'Soils: their properties and management'. (Eds PEV Charman, BW Murphy) pp. 206-221. (Oxford University Press: South Melbourne). Gestel C, Kruidenier M, Berg MP (2003) Suitability of wheat straw decomposition, cotton strip degradation and bait-lamina feeding tests to determine soil invertebrate activity. Biology and Fertility of Soils 37, 115-123. Gibbs L, Reid N, Whalley RDB (1999) Relationships between tree cover and grass dominance in a grazed temperate stringybark (Eucalyptus laevopinea) open-forest. Australian Journal of Botany 47, 49-60. Gill AM, Groves RH, Noble IR (Eds) (1981) 'Fire and the Australian biota.' (Australian Academy of Science: Canberra). Gnankambary Z, Ilstedt U, Nyberg G, Hien V, Malmer A (2008) Nitrogen and phosphorus limitation of soil microbial respiration in two tropical agroforestry parklands in the south- Sudanese zone of Burkina Faso: The effects of tree canopy and fertilization. Soil Biology and Biochemistry 40, 350-359. Gobert V (1978) Proposed nomenclature for the Cainozic sediments of the Penrith-Windsor area. Quarterly Notes of the Geological Society of New South Wales 32, 1-9. Gough MW, Marrs RH (1990) A comparison of soil fertility between semi-natural and agricultural plant communities: Implications for the creations of species-rich grassland on abandoned agricultural land. Biological Conservation 51, 83-96. Graetz RD, Tongway DJ (1986) Influence of grazing management on vegetation, soil structure and nutrient distribution and the infiltration of applied rainfall in a semi-arid chenopod shrubland. Australian Journal of Ecology 11, 347-360. Graham S, Wilson BR, Reid N, Jones H (2004) Scattered paddock trees, litter chemistry, and surface soil properties in pastures of the New England Tablelands, New South Wales. Australian Journal of Soil Research 42, 905-912. Greacen E, Sands R (1980) Compaction of forest soils. a review. Australian Journal of Soil Research 18, 163-189. Greene RSB, Chartres CJ, Hodgkinson KC (1990) The effects of fire on the soil in a degraded semi-arid woodland. I. Cryptogam cover and physical and micromorphological properties. Australian Journal of Soil Research 28, 755-777.

JK Fitzgerald References Page 184

Greenwood KL, Mundy GN, Kelly KB, Dellow KE, Austin SM (2006) Improved soil and irrigation management for forage production 1. Site establishment and soil physical properties. Australian Journal of Experimental Agriculture 46, 307-317. Gunapala N, Venette RC, Ferris H, Scow KM (1998) Effects of soil management history on the rate of organic matter decomposition. Soil Biology and Biochemistry 30, 1917-1927. Gustavsson E, Lennartsson T, Emanuelsson M (2007) Land use more than 200 years ago explains current grassland plant diversity in a Swedish agricultural landscape. Biological Conservation 138, 47-59. Guthrie FB (1891) Notes on the soils of County Cumberland. The Agricultural Gazette of New South Wales 9, 481-487. Guthrie H, Attiwill P, Leuning R (1978) Nutrient cycling in a Eucalyptus obliqua forest. II. A study in a small catchment. Australian Journal of Botany 26, 189-201. Hamilton GJ (1976) Soil resources of the Hawkesbury River Catchment, New South Wales. Soil Conservation Journal October 1976, 204-229. Handreck KA (1997) Phosphorous requirements of Australian native plants. Australian Journal of Soil Research 35, 241-289. Harden GJ (1990) 'Flora of New South Wales, Volume 1.' (New South Wales University Press: Kensington). Harden GJ (1991) 'Flora of New South Wales, Volume 2.' (New South Wales University Press Kensington). Harden GJ (1992) 'Flora of New South Wales, Volume 3.' (New South Wales University Press: Kensington). Harden GJ (1993) 'Flora of New South Wales, Volume 4.' (New South Wales University Press: Kensington). Hart SC, DeLuca TH, Newman GS, MacKenzie MD, Boyle SI (2005) Post-fire vegetative dynamics as drivers of microbial community structure and function in forest soils. Forest Ecology and Management 220, 166-184. Hart SC, Nason GE, Myrold DD, Perry DA (1994) Dynamics of gross nitrogen transformations in an old-growth forest: the carbon connection. Ecology 75, 880-891. Hatten J, Zabowski D, Scherer G, Dolan E (2005) A comparison of soil properties after contemporary wildfire and fire suppression. Forest Ecology and Management 220, 227- 241. Havilah E, Warren H, Lawrie R, Senn A, Milham P (2005) 'Fertilisers for pastures.' (NSW Department of Primary Industries: Orange). Haworth RJ (2003) The shaping of Sydney by its urban geology. Quaternary International 103, 41-55.

JK Fitzgerald References Page 185

Haynes RJ (2005) Labile organic matter fractions as central components of the quality of agricultural soils: an overview. Advances in Agronomy. (Ed. DL Sparks) pp. 221-268. (Academic Press). Hazelton PA, Murphy BW (2007) 'Interpreting soil test results: what do all the numbers mean?' (CSIRO Publishing: Collingwood, Victoria). Hazelton PA, Tille PJ (1990) 'Soil landscapes of the Wollongong- 1:100 000 sheet.' (Soil Conservation Service of NSW: Sydney). Helyar KR, Porter WM (1989) Soil acidification, its measurement and the processes involved. In 'Soil acidity and plant growth'. (Ed. AD Robson) pp. 61-102. (Academic Press: Sydney). Hendrix PF, Parmelee RW (1985) Decomposition, nutrient loss and microarthropod densities in herbicide-treated grass litter in a Georgia piedmont agroecosystem. Soil Biology and Biochemistry 17, 421-428. Henson JB (1887) Soils and subsoils of Sydney and suburbs. Journal of the Royal Society of New South Wales 21, 220-226. Herbert C (1979) 'The geology and resource potential of the Wianamatta group.' (Department of Mineral Resources and Development, Geological Survey of New South Wales). Herbert C, Clark NR (1991) Structural geology. In 'Geology of the Penrith 1:100 000 Sheet 9030'. (Eds DC Jones, NR Clark) pp. 87-94. (New South Wales Geological Survey: Sydney). Herfitzius, H. (1987) Decomposition in five woodland soils: relationships with some invertebrate populations and with weather. Biology and Fertility of Soils3:85-89. Hermy M, Verheyen K (2007) Legacies of the past in the present-day forest biodiversity: a review of past land-use effects on forest plant species composition and diversity. Ecological Research 22, 361-371. Hill SJ, French K (2003) Response of the soil seed-bank of Cumberland Plain Woodland to heating. Austral Ecology 28, 14-22. Hill SJ, French K (2004) Potential impacts of fire and grazing in an endangered ecological community: plant composition and shrub and eucalypt regeneration in Cumberland Plain Woodland. Australian Journal of Botany 52, 23-29. Hill SJ, Tung PJ, Leishman MR (2005) Relationships between anthropogenic disturbance, soil properties and plant invasion in endangered Cumberland Plain Woodland, Australia. Austral Ecology 30, 775-788. Hinsinger P (2001) Bioavailability of soil inorganic P in the rhizosphere as affected by root- induced chemical changes: a review. Plant and Soil 237, 173-195. Hobbie SE (1992) Effects of plant species on nutrient cycling. Trends in Ecology and Evolution 7, 336-339.

JK Fitzgerald References Page 186

Hobbs RJ (1993) Effects of landscape fragmentation on ecosystem processes in the Western Australian wheatbelt. Biological Conservation 64, 193-201. Hobbs RJ, Harris JA (2001) Restoration ecology: repairing the earth's ecosystems in the new millennium. Restoration Ecology 9, 239-246. Hobbs RJ, Norton DA (2004) Ecological filters, thresholds, and gradients in resistance to ecosystem reassembly. In 'Assembly rules and restoration ecology: bridging the gap between theory and practice'. (Eds VM Temperton, RJ Hobbs, T Nuttle, S Halle) pp. 72- 95. (Island Press: Washington). Hobbs RJ, Yates CJ (2003) Impacts of ecosystem fragmentation on plant populations: generalising the idiosyncratic. Australian Journal of Botany 51, 471-488. Holford ICR (1997) Soil phosphorous: its measurement and its uptake by plants. Australian Journal of Soil Research 35, 227-239. Hollinger E, Cornish PS, Baginska B, Mann R, Kuczera G (2001) Farm-scale stormwater losses of sediment and nutrients from a market garden near Sydney, Australia. Agricultural Water Management 47, 227-241. Hooker TD, Compton JE (2003) Forest ecosystem carbon and nitrogen accumulation during the first century after agricultural abandonment. Ecological Applications 13, 299-313. Hooper E (2008) Factors affecting the species richness and composition of neotropical secondary succession: a case study of abandoned agricultural land in Panama. In 'Post- agricultural succession in the neotropics'. (Ed. RW Myster). (Springer: New York). Hooper E, Legendre P, Condit R (2005) Barriers to forest regeneration of deforested and abandoned land in Panama. Journal of Applied Ecology 42, 1165-1174. Hopkins AA (1998) Reverse fertilisation experiment produces mixed results in semi-arid environment (Colorado). Restoration and Management Notes 16, 84-85. Huhta V (2007) The role of soil fauna in ecosystems: a historical review. Pedobiologia 50, 489- 495. Humphreys FR, Craig FG (1981) Effects of fire on soil chemical, structural and hydrological properties. In 'Fire and the Australian biota'. (Eds AM Gill, RH Groves, IR Noble) pp. 177-202. (Australian Academy of Science: Canberra). Huston JJ (1953) Earth flows cause widespread damage in the Camden district. The Journal of the Soil Conservation Service of New South Wales 9, 149-154. Inouye RS, Huntly NJ, Tilman D, Tester JR, Stillwell M, Zinnel KC (1987) Old-field succession on a Minnesota sand plain. Ecology 68, 12-26. Inouye RS, Tilman D (1988) Convergence and divergence of old-field plant communities along experimental nitrogen gradients. Ecology 69, 995-1004.

JK Fitzgerald References Page 187

Jackson J, Ash AJ (1998) Tree-grass relationships in open eucalypt woodlands of northeastern Australia: influence of trees on pasture productivity, forage quality and species distribution. Agroforestry Systems 40, 159-176. Jackson J, Ash AJ (2001) The role of trees in enhancing soil nutrient availability for native perennial grasses in open eucalypt woodlands of north-east Queensland. Australian Journal of Agricultural Research 52, 377-386. Jacobson KM, Jacobson PJ (1998) Rainfall regulates decomposition of buried cellulose in the Namib Desert. Journal of Arid Environments 38, 571-583. James AI, Eldridge DJ (2007) Reintroduction of fossorial native mammals and potential impacts on ecosystem processes in an Australian desert landscape. Biological Conservation 138, 351-359. James T, McDougall L, Benson D (1999) 'Rare bushland plants of Western Sydney.' (Royal Botanic Gardens: Sydney). James TA (1994) Observations on the effects of mowing on native species in remnant bushland, western Sydney. Cunninghamia 3, 515-519. James, T. (1997). Native flora in Western Sydney. NSW National Parks and Wildlife Service, Urban Bushland Biodiversity Survey. Stage 1: Western Sydney. (NSW NPWS: Hurstville). Jensen HI (1910) Orchard soils of the County of Cumberland. The Agricultural Gazette of New South Wales 21, 461-463. Jensen HI (1921) Soils of the Hawkesbury Agricultural Farm. Department of Agriculture Science Bulletin 5, 3-17. Jensen LS, Mueller T, Magid J, Nielsen NE (1997) Temporal variation of C and N mineralization, microbial biomass and extractable organic pools in soil after oilseed rape straw incorporation in the field. Soil Biology and Biochemistry 29, 1043-1055. Jim CY (2003) Soil recovery from human disturbance in tropical woodlands in Hong Kong. Catena 52, 85-103.

Jin X, Wang S, Zhou Y (2008) Microbial CO2 production from surface and subsurface soil as affected by temperature, moisture and nitrogen fertilisation. Australian Journal of Soil Research 46, 273-280. Jinadasa KBPN, Milham PJ, Hawkins CA, Cornish PS, Williams PA, Kaldor CJ, Conroy JP (1997) Survey of cadmium levels in vegetables and soils of greater Sydney, Australia. Journal of Environmental Quality 26, 924-933. Jinbo Z, Changchun S, Shenmin W (2007) Dynamics of soil organic carbon and its fractions after abandonment of cultivated wetlands in northeast China. Soil and Tillage Research 96, 350-360.

JK Fitzgerald References Page 188

Johnston D, Hicks RW (1984) 'Urban capability study: Camden Park development area.' (Soil Conservation Service of New South Wales: New South Wales). Jonasson S, Vestergaard P, Jensen M, Michelsen A (1996) Effects of carbohydrate amendments on nutrient partitioning, plant and microbial performance of a grassland-shrub ecosystem. Oikos 75, 220-226. Jones DC, Clark NR (Eds) (1991) 'Geology of the Penrith 1:100 000 sheet 9030.' (New South Wales Geological Survey: Sydney). Kass T (2005) 'Western Sydney thematic history.' (NSW Heritage Office). Keating C (1996) 'On the frontier: a social history of Liverpool.' (Hale and Iremonger Pty Ltd: Sydney, NSW). Keith H (1997) Nutrient cycling in eucalypt ecosystems. In 'Eucalypt ecology: individuals to ecosystems'. (Eds JE Williams, JCZ Woinarski) pp. 197-226. (Cambridge University Press: Cambridge). Kemmitt SJ, Lanyon CV, Waite IS, Wen Q, Addiscott TM, NRA, O'Donnell AG, Brookes PC (2008) Mineralization of native soil organic matter is not regulated by the size, activity or composition of the soil microbial biomass-a new perspective. Soil Biology and Biochemistry 40, 61-73. Kemp DR, Dowling PM (2000) Towards sustainable temperate perennial pastures. Australian Journal of Experimental Agriculture 40, 125-132. Kennedy AC, Papendick RI (1995) Microbial characteristics of soil quality. Journal of Soil and Water Conservation 50, 243 Keyes D (1997) 'The Scheyville experience‟ [videorecording]. (Film Australia: Lindfield, NSW). King EG, Hobbs RJ (2006) Identifying linkages among conceptual models of ecosystem degradation and restoration: towards an integrative framework. Restoration Ecology 14, 369-378. King SA, Buckney RT (2000) Urbanisation and exotic plants in northern Sydney streams. Austral Ecology 25, 455-461. King SA, Buckney RT (2002) Invasion of exotic plants in nutrient-enriched urban bushland. Austral Ecology 27, 573-583. Kinhill Engineers Pty. Ltd. (1990) 'Scheyville natural habitat study.' (Unpublished report to the Department of Housing, Hawkesbury City Council). Kirschaum, M, U. F. (1995) The temperature dependence of soil organic matter decompositionand the effect of global warming on soil organic storage. Soil Biology and Biochemistry 27(6), 753-760.

JK Fitzgerald References Page 189

Knacker T, Forster B, Rombke J, Frampton GK (2003) Assessing the effects of plant protection products on organic matter breakdown in arable fields-litter decomposition test systems. Soil Biology and Biochemistry 35, 1269-1287. Knops JMH, Bradley KL, Wedin DA (2002) Mechanisms of plant species impacts on ecosystem nitrogen cycling. Ecology Letters 5, 454-466. Knops JMH, Tilman D (2000) Dynamics of soil nitrogen and carbon accumulation for 61 years after agricultural abandonment. Ecology 81, 88-98. Ko LJ, Reich PB (1993) Oak tree effects on soil and herbaceous vegetation in savannas and pastures in Wisconsin. American Midland Naturalist 130, 31-42. Koerner W, Dupouey JL, Dambrine E, Benoit M (1997) Influence of past land use on the vegetation and soils of present day forest in the Vosges mountains, France. Journal of Ecology 85, 351-358. Kowaljow E, Julia Mazzarino M (2007) Soil restoration in semiarid Patagonia: chemical and biological response to different compost quality. Soil Biology and Biochemistry 39, 1580- 1588. Kulmatiski A, Beard KH, Stark JM (2006) Soil history as a primary control on plant invasion in abandoned agricultural fields. Journal of Applied Ecology 43, 868-876. Kurka A-M, Starr M (1997) Relationship between decomposition of cellulose in the soil and tree stand characteristics in natural boreal forests. Plant and Soil 197, 167-175. Kurka A-M, Starr M, Heikinheimo M, Salkinoja-Salonen M (2000) Decomposition of cellulose strips in relation to climate, litterfall nitrogen, phosphorus and C/N ratio in natural boreal forests. Plant and Soil 219, 91-101. Kurka A-M, Starr M, Karsisto M, Salkinoja-Salonen M (2001) Relationship between decomposition of cellulose strips and chemical properties of humus layer in natural boreal forests. Plant and Soil 229, 137-146. Lambert MJ, Turner J (1987) Suburban development and change in vegetation nutritional status. Australian Journal of Ecology 12, 193-196. Landon JR (1991) 'Booker tropical soil manual: a handbook for soil survey and agricultural land evaluation in the tropics and subtropics'. (Longman Scientific and Technical: Harlow). Latter PM, Howson G (1977) The use of cotton strips to indicate cellulose decomposition in the field. Pedobiologia 17, 145-155. Leishman MR (1990) Suburban development and resultant changes in the phosphorous status of soils in the area of Ku-ring-gai, Sydney. Proceedings of the Linnean Society of New South Wales 112, 15-25. Lewis DC (1999) Sulfur. In 'Soil analysis: an interpretation manual'. (Eds KI Peverill, LA Sparrow, DJ Reuter) pp. 221-228. (CSIRO Publishing: Collingwood, Victoria).

JK Fitzgerald References Page 190

Lewis JA (2001) Regeneration of remnant Blue Gum High Forest vegetation following the cessation of mowing. Cunninghamia 7, 173-182. Liangzhong Z, Whelan RJ (1993) Natural reforestation of abandoned farmland: the role of soils. Australian Geographer 24, 14-25. Liverpool City Council (2007) The history of our suburbs. http://www.liverpool.nsw.gov.au/adetailedhistoryofliverpool.htm#suburbs. Accessed September 2007. Logan P, Luscombe G (1984) 'Urban and rural capability study: north west sector, Sydney.' (Soil Conservation Service of New South Wales: New South Wales). Lomov B (2006) Plant-insect interactions as indicators for restoration ecology. Unpublished PhD Thesis, University of Sydney, Australia. Lou Y, Zhou X (2006) 'Soil respiration and the environment.' (Elservier: California). Loucks O, Plumb-Mentjes ML, Rogers D (1985) Gap processes and large-scale disturbances in sand prairies. In 'The ecology of natural disturbances and patch dynamics'. (Eds STA Pickett, PS White) pp. 72-83. (Academic Press, Inc: Orlando, Florida). Ludwig J, Bartley R, Hawdon A, Abbott B, McJannet D (2007) Patch configuration non- linearly affects sediment loss across scales in a grazed catchment in north-east Australia. Ecosystems 10, 839-845. Ludwig JA, Hodgkinson KC, Macadam RD (1990) Principles, problems, and priorities for restoring degraded rangelands. Australian Rangeland Journal 12, 30-33. Ludwig JA, Tongway D (1995) Spatial organisation of landscapes and its function in semi-arid woodlands, Australia. Landscape Ecology 10, 51-63. Ludwig JA, Tongway DJ, Bastin GN, James CD (2004) Monitoring ecological indicators of rangeland functional integrity and their relation to biodiversity at local to regional scales. Austral Ecology 29, 108-120. Ludwig JA, Tongway DJ, Marsden SG (1994) A flow-filter model for simulating the conservation of limited resources in spatially heterogeneous, semi-arid landscapes. Pacific Conservation Biology 1, 209-213. Lunt ID, Eldridge DJ, Morgan JW, Witt GB (2007) Turner Review No. 13: A framework to predict the effects of livestock grazing and grazing exclusion on conservation values in natural ecosystems in Australia. Australian Journal of Botany 55, 401-415. Macdonald AJ, Murphy DV, Mahieu N, Fillery IRP (2007) Labile soil organic matter pools under a mixed grass/lucerne pasture and adjacent native bush in Western Australia. Australian Journal of Soil Research 45, 333-343. Mack MC, D'Antonio CM (2003) Exotic grasses alter controls over soil nitrogen dynamics in a Hawaiian woodland. Ecological Applications 13, 154-166.

JK Fitzgerald References Page 191

Maestre FT, Cortina J (2004) Insights into ecosystem composition and function in a sequence of degraded semiarid steppes. Restoration Ecology 12, 494-502. Malajczuk N, Cromack K Jr. (1982) Accumulation of calcium oxalate in the mantle of ectomycorrhizal roots of Pinus radiata and Eucalyptus marginata. New Phytologist 92, 527-531. McColl JG (1969) Soil-plant relationships in a Eucalyptus forest on the south coast of New South Wales. Ecology 50, 354-362. McDonald MC (1996) Ecosystem resilience and the restoration of damaged plant communities: a discussion focusing on Australian case studies. Unpublished PhD Thesis, University of Western Sydney, Australia. McDonald RC, Isbell RF, Speight JG, Walker J, Hopkins MS (1990) 'Australian soils and land survey field handbook.' (Inkata Press: Melbourne). McIntyre S, Lavorel S (2007) A conceptual model of land use effects on the structure and function of herbaceous vegetation. Agriculture, Ecosystems and Environment 119, 11-21. McIvor JG (2001) Litterfall from trees in semiarid woodlands of north-east Queensland. Austral Ecology 26, 150-155. McIvor JG, McIntyre S, Saeli I, Hodgkinson JJ (2005) Patch dynamics in grazed subtropical native pastures in south-east Queensland Austral Ecology 30, 445-464. McKenzie N, Coughlan K, Cresswell HP (Eds) (2002) 'Soil physical measurement and interpretation for land evaluation.' (CSIRO Publishing: Canberra). McLauchlan K (2006) The nature and longevity of agricultural impacts on soil carbon and nutrients: a review. Ecosystems 9, 1364-1382. Melillo JM, Aber JD, Muratore JF (1982) Nitrogen and lignin control of hardwood leaf litter decomposition dynamics. Ecology 63, 621-626. Messer J (1997) Foreward. In 'On the brink: your bush, their habitat, our Act, is the Threatened Species Conservation Act working? Proceedings of the conference held at the Mallet Street Campus Camperdown on May 1 and 2 1997'. (Ed. H Webb) (Nature Conservation Council of NSW Inc.: Sydney). Mitchell M (1996) 'Native grasses: identification handbook for temperate Australia.' (Agmedia: East Melbourne). Moody PW, Bolland MDA (1999) Phosphorus. In 'Soil analysis: an interpretation manual'. (Eds KI Peverill, LA Sparrow, DJ Reuter) pp. 187-220. (CSIRO Publishing: Collingwood, Victoria). Morgan JW (1998) Patterns of invasion of an urban remnant of a species-rich grassland in southeastern Australia by non-native plant species. Journal of Vegetation Science 9, 181- 190.

JK Fitzgerald References Page 192

Motzkin G, Foster DR, Allen A, Harrod J, Boone R (1996) Controlling site to evaluate history: vegetation patterns of a New England sand plain. Ecological Monographs 66, 345-365. Murphy BW, Eldridge D, Chapman GA, McKane D (2007) Soils of New South Wales. In 'Soils: their properties and management'. (Eds PEV Charman, BW Murphy) pp. 137-152. (Oxford University Press: South Melbourne). Murray R, White K (1988) 'Dharug and Dungaree: the history of Penrith and St Marys to 1860.' (Hargreen Publishing Company: North Melbourne, Victoria). Murty D, Kirschbaum MUF, McMurtrie RE, McGilvray H (2002) Does conversion of forest to agricultural land change soil carbon and nitrogen? A review of the literature. Global Change Biology 8, 105-123. Myerscough PJ (1998) Ecology of Myrtaceae with special reference to the Sydney region. Cunninghamia 5, 787-807. Myster RW (2008) Conclusion, synthesis, and future directions. In 'Post-agricultural succession in the neotropics'. (Ed. RW Myster). (Springer: New York). Nadelhoffer KJ, Aber JD, Melillo JM (2005) Seasonal patterns of ammonium and nitrate uptake in nine temperate forest ecosystems. Plant and Soil 80, 321-335. National Parks and Wildlife Service (1997) 'Overview and recommendations of the Western Sydney Urban Bushland Biodiversity Survey. ' (NPWS (NSW): Hurstville). National Parks and Wildlife Service (2000) 'Scheyville National Park and Pitt Town : plan of management.' (NPWS (NSW): Hurstville). National Parks and Wildlife Service (2002a) 'Biodiversity strategy case study, Cumberland Plain subregion, Sydney Basin bioregion, New South Wales, SB8: Cumberland.' (NPWS (NSW): Hurstville). National Parks and Wildlife Service (2002b) 'Interpretation guidelines for the native vegetation of the Cumberland Plain, Western Sydney, final edition.' (NPWS (NSW): Hurstville). Nichols P (2005) 'Evaluation of restoration: a grassy woodland.' Unpublished PhD thesis, University of Western Sydney, Australia. Nicolaidis G (2000) 'Eastern Creek and land settlers.' Unpublished report, State Library of NSW, Sydney. Nielsen SE, Bayne EM, Schieck J, Herbers J, Boutin S (2007) A new method to estimate species and biodiversity intactness using empirically derived reference conditions. Biological Conservation 137, 403-414. Noble A, Randall P (1999) Alkalinity effects of different tree litters incubated in an acid soil of NSW, Australia. Agroforestry Systems 46, 147-160. Noble A, Zenneck I, Randall P (1996) Leaf litter ash alkalinity and neutralisation of soil acidity. Plant and Soil 179, 293-302.

JK Fitzgerald References Page 193

Noble AD, Moody PW, Berthelsen S (2003) Influence of changed management of surgarcane on some soil chemical properties in the humid wet tropics of north Queensland. Australian Journal of Soil Research 41, 1133-1144. Noble IR, Slatyer RO (1980) The use of vital attributes to predict successional changes in plant communities subject to recurrent disturbances. Plant Ecology 43, 5-21. Obot EA (1988) Estimating the optimum tree density for maximum herbaceous production in the Guinea Savanna of Nigeria. Journal of Arid Environments 14, 267-273. Odum EP (1969) The strategy of ecosystem development. Science 164, 262-270. Oliver I, Smith PL, Lunt I, Parkes D (2002) Pre-1750 vegetation, naturalness and vegetation condition: what are the implications for biodiversity conservation? Ecological Management and Restoration 3, 176-178. Onans J, Parsons R (1980) Regeneration of native plants on abandoned mallee farmland in south-eastern Australia. Australian Journal of Botany 28, 479-493. Panetta FD, Hopkins AJM (1991) Weeds in corridors: invasion and management. In 'Nature conservation 2: the role of corridors'. (Eds DA Saunders, RJ Hobbs) pp. 341-351. (Surrey Beatty and Sons: Chipping Norton). Parker CJ, Chartres CJ (1983) The effects of recent land use changes on red podzolic soils near Sydney, NSW, Australia. Catena 10, 61-76. Parsons Brinckerhoff (2002) 'Environmental impact assessment report defence estate Orchard Hills.' (Parsons Brinckerhoff Australia Pty Ltd: Sydney). Paschke MW, McLendon T, Redente EF (2000) Nitrogen availability and old-field succession in a shortgrass steppe. Ecosystems 3, 144-158. Passioura JB (1991) Soil structure and plant growth. Australian Journal of Soil Research 29, 717-728. Paul Davies Pty. Ltd. Architects Heritage Consultants (2007) Penrith heritage study volume 3: locality assessment. Unpublished report to Penrith City Council. Paul S, Flessa H, Veldkamp E, Lapez-Ulloa M (2008) Stabilization of recent soil carbon in the humid tropics following land use changes: evidence from aggregate fractionation and stable isotope analyses. Biogeochemistry 87, 247-263. Pellow B, French K (2003) 'Flora study of the defence establishment Orchard Hills.' A report for Parsons Brinckerhoff and the Department of Defence. Perkins I (1997) 'Land and vegetation management plan: Hoxton Park Corridor (north).' (Ian Perkins Consultancy Services: Sydney). Peterken G, Game M (1984) Historical factors affecting the number and distribution of vascular plant species in the woodlands of central Lincolnshire. Journal of Ecology 72, 155-182. Peterson CJ, Carson WP, McCarthy BC, Pickett STA (1990) Microsite variation and soil dynamics within newly created treefall pits and mounds. Oikos 58, 39-46.

JK Fitzgerald References Page 194

Pettit NE, Froend RH, Ladd PG (1995) Grazing in remnant woodland vegetation: changes in species composition and life form groups. Journal of Vegetation Science 6, 121-130. Pickett STA, White PS (1985) Patch dynamics: a synthesis. In 'The Ecology of Natural Disturbances and Patch Dynamics'. (Eds STA Pickett, PS White) pp. 371-384. (Academic Press, Inc: Orlando, Florida). Pidgeon IM (1941) The ecology of the central coastal areas of New South Wales IV. Forest types on soils from Hawkesbury Sandstone and Wianamatta Shale. Proceedings of the Linnaean Society of NSW 66, 113-137. Pietikainen J, Tikka PJ, Valkonen S, Isomaki A, Fritze H (2007) Is the soil microbial community related to the basal area of trees in a Scots pine stand? Soil Biology and Biochemistry 39, 1832-1834. Polglase PJ, Attiwill P, Adams M (1992) Nitrogen and phosphorus cycling in relation to stand age in Eucalyptus regnans F.Muell. III. Labile inorganic and organic P, phosphatase activity and P availability. Plant and Soil 142, 177-185. Potthoff M, Loftfield N (1998) How to quantify contamination of organic litter bag material with soil? Pedobiologia 42, 147-153. Prober SM, Lunt ID, Thiele KR (2002a) Determining reference conditions for management and restoration of temperate grassy woodlands: relationships among trees, topsoils and understorey flora in little-grazed remnants. Australian Journal of Botany 50, 687-697. Prober SM, Thiele KR (2005) Restoring Australia's temperate grasslands and grassy woodlands: integrating function and diversity. Ecological Management and Restoration 6, 16-27. Prober SM, Thiele KR, Lunt I (2004) A sweet recipe for understorey restoration in grassy woodlands - add sugar, seed and burn in spring! Australasian Plant Conservation 13, 4-6. Prober SM, Thiele KR, Lunt ID (2002b) Identifying ecological barriers to restoration in temperate grassy woodlands: soil changes associated with different degradation states. Australian Journal of Botany 50, 699-712. Prober SM, Thiele KR, lunt ID, Koen TB (2005) Restoring ecological function in temperate grassy woodlands: manipulating soil nutrients, exotic annuals and native perennial grasses through carbon supplements and spring burns. Journal of Applied Ecology 42, 1073-1085. Proudfoot H (1987) 'Exploring Sydney‟s west.' (Kangaroo Press: Kenthurst). Pywell RF, Webb NR, Putwain PD (1994) Soil fertility and its implications for the restoration of heathland on farmland in southern Britain. Biological Conservation 70, 169-181. Quilty JA, Craze B, Styles KA (1976) 'Urban capability study: South Penrith housing project.' Unpublished Report, University of Western Sydney library. Quinn GP, Keough MJ (2002) 'Experimental design and data analysis for biologists.' (Cambridge University Press: Cambridge).

JK Fitzgerald References Page 195

Rab MA (1994) Changes in physical properties of a soil associated with logging of Eucalyptus regnans forest in southeastern Australia. Forest Ecology and Management 70, 215-229. Raison RJ (1979) Modification of the soil environment by vegetation fires, with particular reference to nitrogen transformations: a review. Plant and Soil 51, 73-108. Raison RJ (1980) A review of the role of fire in nutrient cycling in Australian native forests, and of methodology for studying the fire-nutrient interaction. Australian Journal of Ecology 5, 15-21.Rayment GE, Higginson FR (1992) 'Australian laboratory handbook of soil and water chemical methods.' (Inkata Press: Melbourne). Read J, Hill RS (1983) Rainforest invasion onto Tasmanian old-fields. Australian Journal of Ecology 8, 149-161. Reever Morghan KJ, Seastedt TR (1999) Effects of soil nitrogen reduction on non-native plants in restored grasslands. Restoration Ecology 7, 51-55. Reiners WA, Bouwman AF, Parsons WFJ, Keller M (1994) Tropical rain forest conversion to pasture: changes in vegetation and soil properties. Ecological Applications 4, 363-377. Rengasamy P, Churchman GJ (1999) Cation exchange capacity, exchangeable cations and sodicity. In 'Soil analysis: an interpretation manual'. (Eds KI Peverill, LA Sparrow, DJ Reuter) pp. 147-158. (CSIRO Publishing: Collingwood, Victoria). Rengasamy P, Olsson K (1991) Sodicity and soil structure. Australian Journal of Soil Research 29, 935-952. Rhoades C (1997) Single-tree influences on soil properties in agroforestry: lessons from natural forest and savanna ecosystems. Agroforestry Systems 35, 71-94. Rhoades CC, Eckert GE, Coleman DC (2000) Soil carbon differences among forest, agriculture and secondary vegetation in lower montane Ecuador. Ecological Applications 10, 497- 505. Ritz K, Dighton J, Giller KE (Eds) (1994) 'Beyond the biomass: compositional and functional analysis of soil microbial communities.' (John Wily and Sons: Chichester, UK). Robinson JB, Helyar KR, Hochman Z (1995) A model for understanding the importance of various chemical, physical and biological processes in the development of soil profile acidity. In 'Plant-soil Interactions at low pH: principles and management'. (Eds RA Date, NJ Grundon, GE Rayment, ME Probert) pp. 93-98. (Kluwer Academic Publishers: The ). Robinson KW (1953) Population and land use in the Sydney district: 1788-1820. New Zealand Geographer 9, 144-160. Runkle JR (1985) Disturbance regimes in temperate forests. In 'The ecology of natural disturbances and patch dynamics'. (Eds STA Pickett, PS White) pp. 17-33. (Academic Press, Inc.: Orlando, Florida).

JK Fitzgerald References Page 196

Ryan PJ, McGarity JW (1983) The nature and spatial variability of soil properties adjacent to large forest eucalypts. Soil Science Society of America Journal 47, 286-293. Sangha KK (2003) Evaluation of the effects of tree clearing over time on soil properties, pasture composition and productivity. Unpublished PhD thesis, Central Queensland University, Australia. Sangha KK, Jalota RK, Midmore DJ (2005) Impact of tree clearing on soil pH and nutrient availability in grazing systems of central Queensland, Australia. Australian Journal of Soil Research 43, 51-60. Saunders DA, Hobbs RJ, Margules CR (1991) Biological consequences of ecosystem fragmentation: a review. Conservation Biology 5, 18-32. Scanlan JC, Burrows WH (1990) Woody overstorey impact on herbaceous understorey in Eucalyptus spp. communities in central Queensland. Australian Journal of Ecology 15, 191-197. Scholes RJ, Archer SR (1997) Tree-grass interactions in savannas. Annual Review of Ecology and Systematics 28, 517-544. Scougall SA, Majer JD, Hobbs RJ (1993) Edge effects in grazed and ungrazed Western Australian wheatbelt remnants in relation to ecosystem reconstruction. In 'Nature conservation 3: reconstruction of fragmented ecosystems'. (Eds DA Saunders, RJ Hobbs, PR Ehrlich) pp. 163-178. (Surrey Beatty and Sons: Chipping Norton). Shaw RJ (1999) Soil salinity - electrical conductivity and chloride. In 'Soil analysis: an interpretation manual'. (Eds KI Peverill, LA Sparrow, DJ Reuter) pp. 129-146. (CSIRO Publishing: Collingwood, Victoria). Silver WL, Ostertag R, Lugo AE (2000) The potential for carbon sequestration through reforestation of abandoned tropical agricultural and pasture lands. Restoration Ecology 8, 394-407. Skjemstad JO, Spouncer LR, Beech A (2000) Carbon conversion factors for historical soil carbon data. National carbon accounting system technical report No. 15. Australia's Greenhouse Office, Canberra. Slattery WJ, Conyers MK, Aitken RL (1999) Soil pH, aluminium, manganese and lime requirement. In 'Soil analysis: an interpretation manual'. (Eds KI Peverill, LA Sparrow, DJ Reuter) pp. 103-128. (CSIRO Publishing: Collingwood, Victoria). Slocum MG (2000) Logs and fern patches as recruitment sites in a tropical pasture. Restoration Ecology 8, 408-413. Smallbone LT, Prober SM, Lunt ID (2007) Restoration treatments enhance early establishment of native forbs in a degraded temperate grassy woodland. Australian Journal of Botany 55, 818-830.

JK Fitzgerald References Page 197

Snowdon P, Ryan P, Raison J (2005) Review of C:N ratios in vegetation, litter and soil under Australian native forests and plantations. Technical report No. 45 Department of Environment and Heritage Australian Greenhouse Office. Sokal RR, Rohlf FJ (2000) Biometry: the principles and practice of statistics in biological research. 3rd Ed. (W.H. Freeman and Company: New York). Spackman and Mossop (2000) 'Mount Annan Botanic Garden site master plan: volume 1 site management plan.' (Spackman and Mossop Landscape Architects and Planners: Paddington). Specht RL, Specht A, Whelan MB, Hegarty EE (1995) 'Conservation atlas of plant communities in Australia.' (Centre for Coastal Management, Lismore, in association with Southern Cross University Press). Spooner P, Lunt ID, Robinson WA (2002) Is fencing enough? The short-term effects of stock exclusion in remnant grassy woodlands in southern NSW. Ecological Management and Restoration 3, 117-126. Stace HCT, Hubble DG, Brewer R, Northcote KH, Sleeman JR, Mulcahy MJ, Hallsworth EG (1968) 'A handbook of Australian soils.' (Rellim Technical Publications: Glenside, South Australia). Standish RJ, Cramer VA, Hobbs RJ, Kobryn HT (2006) Legacy of land-use evident in soils of Western Australia's wheatbelt. Plant and Soil 280, 189-207. Standish RJ, Stokes BA, Tibbett M, Hobbs RJ (2007) Seedling response to phosphate addition and inoculation with arbuscular mycorrhizas and the implications for old-field restoration in Western Australia. Environmental and Experimental Botany 61, 58-65. Stem C, Margoluis R, Salafsky N, Brown M (2005) Monitoring and evaluation in conservation: a review of trends and approaches. Conservation Biology 19, 295-309. Stol J, Trappe JM (2006) Fungi in agricultural landscapes: implications for eucalypt woodland revegetation. Australasian Plant Conservation 15, 15-19. Strong WM, Mason MG (1999) Nitrogen. In 'Soil analysis: an interpretation manual'. (Eds KI Peverill, LA Sparrow, DJ Reuter) pp. 171-186. (CSIRO Publishing: Collingwood, Victoria). Stubbs R, Stubbs L (1983) 'A history of Scheyville.' (Ladan: Windsor). Tansley AG (1916) The development of vegetation. a review of Clement's ‟Plant Succession„, 1916. The Journal of Ecology 4, 198-204. Temperton VM, Hobbs RJ, Nuttle T, Halle S (Eds) (2004) 'Assembly rules and restoration ecology: bridging the gap between theory and science.' (Island Press: Washington). Thomas D (1993) 'Prospect reservoir land use and environmental management plan.' Unpublished report to the Sydney Water Board.

JK Fitzgerald References Page 198

Thomas J (1994) Effects of hazard reduction burning on a grassy woodland remnant in western Sydney. Unpublished MSc thesis, University of New South Wales, Australia. Thorp W (1992) 'Historical context and discussion paper, Scheyville, NSW: a report prepared for Hawkesbury City Council.' (Unpublished report to Hawkesbury City Council). Tilman D (1985) The resource-ratio hypothesis of plant succession. The American Naturalist 125, 827-852. Toh I, Gillespie M, Lamb D (1999) The role of isolated trees in facilitating tree seedling recruitment at a degraded sub-tropical rainforest site. Restoration Ecology 7, 288-297. Tomkins IB, Kellas JD, Tolhurst KG, Oswin DA (1991) Effects of fire intensity on soil chemistry in a eucalypt forest. Australian Journal of Soil Research 29, 25-47. Tongway DJ (1991) Functional analysis of degraded rangelands as a means of defining appropriate restoration techniques. In 'Proceedings of the fourth international rangeland congress, Montpellier, France, April 22-26'. (Association Francaise de Pastoralisme). Tongway DJ, Hodgkinson KC (1992) The effects of fire on the soil in a degraded semi-arid woodland. III. Nutrient pool sizes, biological activity and herbage response. Australian Journal of Soil Research 30, 17-26.Tongway DJ, Ludwig JA (1990) Vegetation and soil patterning in semi-arid of eastern Australia. Australian Journal of Ecology 15, 23-34. Tongway DJ, Ludwig JA (1996) Rehabilitation of semiarid landscapes in Australia: I. Restoring productive soil patches. Restoration Ecology 4, 388-397. Torok K, Szili-Kovacs T, Halassy M, Toth T, Hayek Z, Paschke MW, Wardell LJ (2000) Immobilisation of soil nitrogen as a possible method for the restoration of sandy grassland. Applied Vegetation Science 3, 7-14. Tozer M (2003) The native vegetation of the Cumberland Plain, western Sydney: a systematic classification and field identification of communities. Cunninghamia 8, 1-75. Traore S, Thiombiano L, Millogo JR, Guinko S (2007) Carbon and nitrogen enhancement in cambisols and vertisols by Acacia spp. in eastern Burkina Faso: relation to soil respiration and microbial biomass. Applied Soil Ecology 35, 660-669. Treydte AC, Heitkonig IMA, Prins HHT, Ludwig F (2007) Trees improve grass quality for herbivores in African savannas. Perspectives in Plant Ecology, Evolution and Systematics 8, 197-205. Tufekcioglu A, Raich JW, Isenhart TM, Schultz RC (2001) Soil respiration within riparian buffers and adjacent crop fields. Plant and Soil 229, 117-124. Turner MG (1989) Landscape ecology: the effect of pattern on process. Annual Review of Ecology and Systematics 20, 171-197. Underwood, AJ (1993) The mechanics of spatially replicated sampling programmes to detect environmental impacts in a variable world. Austral Ecology 18, 99-116.

JK Fitzgerald References Page 199

Vallauri DR, Aronson J, Barbero M (2002) An analysis of forest restoration 120 years after reforestation on badlands in the southwestern alps. Restoration Ecology 10, 16-26. van Breemen N (1995) Nutrient cycling strategies. Plant and Soil 168-169, 321-326. Vetaas OR (1992) Micro-site effects of trees and shrubs in dry savannas. Journal of Vegetation Science 3, 337-344. Vinton MA, Burke IC (1995) Interactions between individual plant species and soil nutrient status in shortgrass steppe. Ecology 76, 1116-1133. Vitousek P, Matson P, Cleve K (1989) Nitrogen availability and nitrification during succession: primary, secondary, and old-field seres. Plant and Soil 115, 229-239. Vitousek PM, Turner DR, Parton WJ, Sanford RL (1994) Litter decomposition on the Mauna Loa environmental matrix, Hawai‟i: patterns, mechanisms and models. Ecology 75: 418- 429. von Lutzow M, Kogel-Knabner I, Ekschmitt K, Flessa H, Guggenberger G, Matzner E, Marschner B (2007) SOM fractionation methods: relevance to functional pools and to stabilization mechanisms. Soil Biology and Biochemistry 39, 2183-2207. Walker J, Robertson JA, Penridge LK (1986) Herbage response to tree thinning in a Eucalyptus crebra woodland. Australian Journal of Ecology 11, 135-140. Walker KJ, Pywell RF, Warman EA, Fowbert JA, Bhogal A, Chambers BJ (2004) The importance of former land use in determining successful re-creation of lowland heath in southern England. Biological Conservation 116, 289-303. Walker LR, Del Moral R (2003) 'Primary succession and ecosystem rehabilitation.' (Cambridge University Press: Cambridge). Walker PH (1960) 'A soil survey of the County of Cumberland, Sydney region, New South Wales. Soil Survey Unit Bulletin No.2.' (New South Wales Department of Agriculture). Walker PH, Hawkins CA (1957) A study of river terraces and soil development on the Nepean River, NSW. Journal of the Royal Society of New South Wales 91, 67-84. Wall A, Hytonen J (2005) Soil fertility of afforested arable land compared to continuously forested sites. Plant and Soil 275, 247-260. Waters C, Whalley W, Huxtable C (2000) 'Grassed up: guidelines for revegetating with Australian native grasses.' (NSW Agriculture: Dubbo). Watson P (2005) 'Fire frequencies for Western Sydney‟s woodlands: indications from vegetation dynamics.' Unpublished PhD thesis, University of Western Sydney, Australia. Watt AS (1947) Pattern and process in the plant community. The Journal of Ecology 35, 1-22. Wedin DA (1995) Species, nitrogen, and grassland dynamics: the constraints of stuff. In 'Linking species and ecosystems'. (Eds CG Jones, JH Lawton) pp. 253-262. (Chapman and Hall: London).

JK Fitzgerald References Page 200

Wedin DA (1999) Nitrogen availability, plant-soil feedbacks and grassland stability. In 'People and rangelands: building the future'. , Queensland, Australia. (Eds D Eldridge, D Freudenberger) pp. 193-197. (VI international Rangeland Congress, Inc.). Wedin DA, Tilman D (1990) Species effects on nitrogen cycling: a test with perennial grasses. Oecologia 84, 433-441. Weil RR, Islam KR, Stine MA, Gruver JB, Samson-Liebig SE (2003) Estimating active carbon for soil quality assessment: a simplified method for laboratory and field use. American Journal of Alternative Agriculture 18, 3-17. Western Sydney Regional Organisation of Councils (2005) ' regional planning and management framework: final report ' (WSROC: Sydney). Wheeler DJB, Jacobs SWL, Whalley RDB (2002) 'Grasses of New South Wales.' (The University of New England: Armidale). White I (1988) Tillage practices and soil hydraulic properties: why quantify the obvious? In 'National soil conference review papers.'. (Ed J Loveday) pp. 87-126. (Australian Society of Soil Science: Canberra, ACT). White PS (1979) Pattern, process and natural disturbance in vegetation. The Botanical Review 45, 229-299. White PS, Pickett STA (1985) Natural disturbance and patch dynamics: an introduction. In 'The ecology of natural disturbances and patch dynamics'. (Eds STA Pickett, PS White) pp. 3- 13. (Academic Press, Inc: Orlando, Florida). Whitehead DC (1995) 'Grassland nitrogen.' (CAB International: Wallingford). Wilkins S, Keith DA, Adam P (2003) Measuring success: evaluating the restoration of a grassy eucalypt woodland on the Cumberland Plain, Sydney, Australia. Restoration Ecology 11, 489-503. Williams CH, Andrew CS (1970) Mineral nutrition of pastures. In 'Australian grasslands'. (Ed RM Moore) pp. 321-338. (Australian National University Press: Canberra). Willis AJ, McKay R, Vranjic JA, Kilby MJ, Groves RH (2003) Comparative seed ecology of the endangered shrub, Pimelea spicata and a threatening weed, Bridal Creeper: smoke, heat and other fire-related germination cues. Ecological Management and Restoration 4, 55-65. Wilson AD (1990) The effect of grazing on Australian ecosystems. Proceedings of the Ecological Society of Australia 16, 235-244. Wilson B (2002) Influence of scattered paddock trees on surface soil properties: a study of the Northern Tablelands of NSW. Ecological Management and Restoration 3, 211-219. Wilson BR, Growns I, Lemon J (2007) Scattered native trees and soil patterns in grazing land on the Northern Tablelands of New South Wales, Australia. Australian Journal of Soil Research 45, 199-205.

JK Fitzgerald References Page 201

Wilson G (1997) Overview of the Threatened Species Conservation Act. In 'On the brink: your bush, their habitat, our Act, is the Threatened Species Conservation Act working? Proceedings of the conference held at the Mallet Street Campus Camperdown on May 1 and 2 1997'. (Ed H Webb) pp. 13-18. (Nature Conservation Council of NSW Inc.: Sydney). Wolf B, Snyder GH (2003) 'Sustainable soils: the place of organic matter in sustaining soils and their productivity.' (Food Products Press: Binghamton, NY). Wood, P. (2001). The soil seed bank of Cumberland Plain Woodland. Unpublished Honours thesis, University of Western Sydney, Australia. Wu J, Loucks OL (1995) From balance of nature to hierarchical patch dynamics: a paradigm shift in ecology. The Quarterly Review of Biology 70, 439-466. Yan JH, Zhou GY, Zhang DQ, Chu GW (2007) Changes of soil water, organic matter, and exchangeable cations along a forest successional gradient in southern China. Pedosphere 17, 397-405. Yates CJ, Hobbs RJ (1997) Temperate eucalypt woodlands: a review of their status, processes threatening their persistence and techniques for restoration. Australian Journal of Botany 45, 949-973. Yates CJ, Hobbs RJ (1999) Temperate eucalypt woodlands in Australia - an overview. In 'Temperate eucalypt woodlands in Australia: biology, conservation, management and restoration'. (Eds RJ Hobbs, CJ Yates) pp. 1-5. (Surrey Beatty and Sons: Chipping Norton). Yates CJ, Hobbs RJ, Atkins L (2000a) Establishment of perennial shrub and tree species in degraded Eucalyptus salmonophloia (Salmon Gum) remnant woodlands: effects of restoration treatments. Restoration Ecology 8, 135-143. Yates CJ, Hobbs RJ, Bell RW (1994) Landscape-scale disturbances and regeneration in semi- arid woodlands of southwestern Australia. Pacific Conservation Biology 1, 214-221. Yates CJ, Norton DA, Hobbs RJ (2000b) Grazing effects on plant cover, soil and microclimate in fragmented woodlands in south-western Australia: implications for restoration. Austral Ecology 25, 36-47. Young R, Wilson BR, McLeod M, Alston C (2005) Carbon storage in the soils and vegetation of contrasting land uses in northern New South Wales, Australia. Australian Journal of Soil Research 43, 21-31. Young RW (1991) Geomorphology. In 'Geology of the Penrith 1:100 000 Sheet 9030'. (Eds DC Jones, NR Clark) pp. 103-108. (New South Wales Geological Survey: Sydney). Zeppel MJB, Murray BR, Eamus D (2003) The potential impact of dryland salinity on the threatened flora and fauna of New South Wales. Ecological Management and Restoration 4, S53-S59.

JK Fitzgerald References Page 202

Zimmerman JK (2000) Barriers to forest regeneration in an abandoned pasture in Puerto Rico. Restoration Ecology 8, 350-360. Zinke PJ (1962) The pattern of influence of individual forest trees on soil properties. Ecology 43,130-133.

JK Fitzgerald References Page 203

Appendix 1. Summary statistics for the soil analyses presented in Chapter 3

Split-plot ANOVA for the soil moisture and chemical data across five sites and four patch types, as analysed in Chapter 3.

Split-plot ANOVA for the bulk density and species data across five sites and four patch types, as analysed in Chapters 3 and 4.

Table A1.1a Mauchly‟s test of sphericity for bulk density TableA 1.1b Split-plot ANOVA for bulk density Table A1.1c Tukey‟s HSD test for the main effect of site on bulk density

Table A1.2a Mauchly‟s test of sphericity for soil moisture content Table A1.2b Split-plot ANOVA for soil moisture content Table A1.2c Tukey‟s HSD test for the main effect of site on soil moisture content Table A1.2d. Post hoc test for the main effect of soil depth on soil moisture content Means and 95% confidence intervals for soil moisture content (%): -Site x patch type interaction -Site x depth interaction -Site x patch type x depth interaction

Table A1.3a Mauchly‟s test of sphericity for pH Table A1.3b Split-plot ANOVA for pH Table A1.3c Tukey‟s HSD test for the main effect of site on pH Table A1.3d Post hoc test for the main effect of patch type on pH Table A1.3e Post hoc test for the main effect of soil depth on pH Means and 95% confidence intervals for pH: -Site x depth interaction - Site x patch type x depth interaction

Table A1.4a Mauchly‟s test of sphericity for EC Table A1.4b Split-plot ANOVA for EC Table A1.4c Post hoc test for the main effect of patch type on EC Table A1.4d Post hoc test for the main effect of soil depth on EC Back transformed means and 95% confidence intervals for EC (dS m-1): -Site x depth interaction

Table A1.5a Mauchly‟s test of sphericity for active C Table A1.5b Split-plot ANOVA for active C Table A1.5c Tukey‟s HSD test for the main effect of site on active C

Table A1.5d Post hoc test for the main effect of soil depth on active C Means and 95% confidence intervals for Active C (mg kg-1): -Site x depth interaction -Site x patch type x depth interaction

Table A1.6a Mauchly‟s test of sphericity for total C Table A1.6b Split-plot ANOVA for total C Table A1.6c Tukey‟s HSD test for the main effect of site on total C Table A1.6d Post hoc test for the main effect of soil depth on total C Back transformed means and 95% confidence intervals for total C (%):

-Site x patch type interaction -Site x depth interaction

Table A1.7a Mauchly‟s test of sphericity for Bray 1 P Table A1.7b Split-plot ANOVA for Bray 1 P Table A1.7c Tukey‟s HSD test for the main effect of site on Bray 1 P Table A1.7d Post hoc test for the main effect of patch type on Bray 1 P Table A1.7e Post hoc test for the main effect of soil depth on Bray 1 P Back transformed means and 95% confidence intervals for Bray 1 P (mg kg-1): -Site x depth interaction -Site x patch type x depth interaction

Table A1.8a Mauchly‟s test of sphericity for total S Table A1.8b Split-plot ANOVA for total S Table A1.8c Tukey‟s HSD test for the main effect of site on total S Table A1.8d Post hoc test for the main effect of soil depth on total S Means and 95% confidence intervals for total S (%): -Site x depth interaction

Table A1.9a Mauchly‟s test of sphericity for nitrate Table A1.9b Split-plot ANOVA for nitrate Table A1.9c Tukey‟s HSD test for the main effect of site on nitrate Table A1.9d Post hoc test for the main effect of patch type on nitrate Table A1.9e Post hoc test for the main effect of soil depth on nitrate Back transformed means and 95% confidence intervals for soil nitrate (mg kg-1): -Site x patch type interaction -Site x depth interaction -Site x patch type x depth interaction

Table A1.10a Mauchly‟s test of sphericity for ammonium

Table A1.10b Split-plot ANOVA for ammonium Table A1.10c Tukey‟s HSD test for the main effect of site on ammonium Table A1.10d Post hoc test for the main effect of soil depth on ammonium

Table A1.11a Mauchly‟s test of sphericity for total N Table A1.11b Split-plot ANOVA for total N Table A1.11c Tukey‟s HSD test for the main effect of site on total N Table A1.11d Post hoc test for the main effect of soil depth on total N Back transformed means and 95% confidence intervals for total N (%): -Site x patch type interaction -Site x depth interaction

Split-plot ANOVA for the soil moisture and chemical data across five sites and four patch types, as analysed in Chapter 3:

A = Site, a = 5, random factor B(A) = Sub-site nested in site, b = 3, random factor C = Patch type, c = 4, fixed factor, orthogonal to site and sub-site D = Soil depth, d = 3, fixed factor, orthogonal to site and sub-site n = 1 Reading per patch type x soil depth x sub-site x site combination

Source of variation df i j k l m Expected MS Tested against

2 2 2 Site Ai 4 1 b c d n  + cdn B(A)+ bcdn A B(A) 2 2 Sub-site B(A)j(i) 10 1 1 c d n  + cdn B(A) C

 2 2 2 2 Patch type Ck 3 a b 0 d n  + dn B(A)C + bdn AC +abdn C AC 2 2 2 Site x patch type ACik 12 1 b 0 d n  + dn B(A)C +bdn AC B(A)C 2 2 Sub-site x patch type B(A)Cj(i)k 30 1 1 0 d n  + dn B(A)C

2 2 2 2 Depth Dl 2 a b c 0 n  + cn B(A)D+bcn AD+abcn D AD 2 2 2 Site x depth ADil 8 1 b c 0 n  + cn B(A)D+bcn AD B(A)D 2 2 Sub-site x depth B(A)Dj(i)l 20 1 1 c 0 n  + cn B(A)D

2 2 2 2 Patch type x depth CDkl 6 a b 0 0 n  + n B(A)CD + bn ACD+abn CD ACD 2 2 2 Site x patch type x depth ACDikl 24 1 b 0 0 n  +n B(A)CD + bn ACD B(A)CD 2 2 Sub-site x patch type x depth B(A)CDj(i)kl 60 1 1 0 0 n  +n B(A)CD 2 Residual 1 1 1 1 1 1   Total 180

Split-plot ANOVA for the bulk density and species data across five sites and four patch types, as analysed in Chapters 3 and 4:

A = Site, a = 5, random factor B(A) = Sub-site nested in site, b = 3, random factor C = Patch type, c = 4, fixed factor, orthogonal to site and sub-site n = 1 Reading per patch type x sub-site x site combination

Source of variation df i j k Expected MS Tested against

2 2 2 Site Ai 4 1 b c  + cdn B(A)+ bcdn A B(A) 2 2 Sub-site B(A)j(i) 10 1 1 c  + cdn B(A) C

 2 2 2 2 Patch type Ck 3 a b 0  + dn B(A)C + bdn AC +abdn C AC 2 2 2 Site x patch type ACik 12 1 b 0  + dn B(A)C +bdn AC B(A)C 2 2 Sub-site x patch type B(A)Cj(i)k 30 1 1 0  + dn B(A)C 2 Residual 1 1 1 1   Total 60

The following acronyms have been used in this appendix: Table A1.1c Tukey‟s HSD test for the main effect of site on bulk density 95% Confidence Interval GG=Greenhouse-Geisser HP=Hoxton Park Site Site Mean difference SE P Lower bound Upper bound MA=Mount Annan HP MA .1400254 .06868238 .315 -.0860139 .3660647 OH=Orchard Hills * PR=Prospect OH -.3453395 .06868238 .004 -.5713789 -.1193002 SNP=Scheyville PR -.0651712 .06868238 .871 -.2912105 .1608682

Figures in bold highlight significant main effects, interactions or post hoc tests SNP -.1458757 .06868238 .282 -.3719150 .0801637

MA OH -.4853649* .06868238 .000 -.7114043 -.2593256 Table A1.1a Mauchly‟s test of sphericity for bulk density PR -.2051966 .06868238 .080 -.4312359 .0208428

Epsilon SNP -.2859011* .06868238 .013 -.5119404 -.0598617

Within subjects effect Mauchly's W Approx. Chi-Square df P GG OH PR .2801684* .06868238 .015 .0541290 .5062077 Patch .574 4.835 5 .439 .727 SNP .1994639 .06868238 .091 -.0265755 .4255032 PR SNP -.0807045 .06868238 .765 -.3067438 .1453349

TableA 1.1b Split-plot ANOVA for bulk density Source df SS MS F P Site 4 1.557 .389 13.750 .000 Subsite 10 .283 .028

Patch type 3 .102 .034 1.487 NS Site x Patch type 12 .273 .023 1.205 .325 Subsite x Patch type 30 .567 .019 Residual 1

Total 60

Table A1.2a Mauchly‟s test of sphericity for soil moisture content Table A1.2c Tukey‟s HSD test for the main effect of site on soil moisture content Mauchly's Approx. Chi- Epsilon 95% Confidence Interval Within subjects effect W Square df P GG Site Site Mean difference SE P Lower bound Upper bound Patch .422 7.526 5 .187 .673 HP MA -.87465217 .413712634 .286 -2.23621443 .48691010 Depth .579 4.917 2 .086 .704 OH .09898627 .413712634 .999 -1.26257599 1.46054853

Patch x Depth .152 14.652 20 .819 .665 PR -.76257027 .413712634 .403 -2.12413254 .59899199

SNP 1.70110267* .413712634 .014 .33954040 3.06266493 MA OH .97363843 .413712634 .206 -.38792383 2.33520070 Table A1.2b Split-plot ANOVA for soil moisture content PR .11208189 .413712634 .999 -1.24948037 1.47364415 Source of variation df SS MS F P SNP 2.57575483* .413712634 .001 1.21419257 3.93731710 Site 4 152.8118 38.20296 12.40015 0.000686 OH PR -.86155654 .413712634 .298 -2.22311880 .50000572 Subsite 10 30.808 3.081 SNP 1.60211640* .413712634 .020 .24055414 2.96367866

Patch type 3 50.58119 16.8604 2.193517 NS PR SNP 2.46367294* .413712634 .001 1.10211068 3.82523520 Site x Patch type 12 92.23761 7.686467 3.775303 0.001535 Subsite x Patch type 30 61.080 2.036 Depth 2 161.39 80.69499 22.52989 <0.001 Table A1.2d. Post hoc test# for the main effect of soil depth on soil moisture content Site x Depth 8 28.65349 3.581687 2.964246 0.023201 Difference Depth Depth Mean difference SE P Lower bound Upper bound Subsite x Depth 20 24.166 1.208 2.5cm 20.5cm -.270 .179 .489 -.785 .245 Patch type x Depth 6 22.25309 3.708848 1.245473 NS * Site x Patch type x Depth 24 71.46869 2.977862 3.226609 0.000124 60.5cm -2.130 .151 .000 -2.564 -1.696 20.5cm 60.5cm -1.860* .257 .000 -2.597 -1.124 Subsite x Patch type x Depth 60 55.374 .923 # based on estimated marginal means and a Bonferroni adjustment for multiple Residual 1 comparisons Total 180

Means and 95% confidence intervals for soil moisture content (%)

Site x patch type interaction Pasture Open Shrub Tree Mean L1 L2 Mean L1 L2 Mean L1 L2 Mean L1 L2 Hoxton Park 7.53 6.48 8.58 4.83 3.84 5.82 4.72 3.43 6.02 5.56 4.53 6.58 Mount Annan 6.98 5.88 8.08 6.62 5.48 7.76 6.16 5.01 7.32 6.38 5.31 7.45 Orchard Hills 4.57 2.75 6.39 6.10 5.31 6.89 5.35 4.22 6.48 6.23 5.43 7.03 Prospect 8.71 7.31 10.10 5.28 3.40 7.15 6.11 4.51 7.70 5.60 3.47 7.74 Scheyville 4.91 4.24 5.58 3.63 2.45 4.80 3.93 2.59 5.26 3.37 2.03 4.71

Site x depth interaction 2.5cm 20.5cm 60.5cm Mean L1 L2 Mean L1 L2 Mean L1 L2 Hoxton Park 5.53 4.42 6.65 5.17 3.99 6.34 6.28 5.21 7.35 Mount Annan 6.15 5.50 6.80 5.60 4.91 6.28 7.86 7.16 8.55 Orchard Hills 4.24 3.51 4.98 5.46 4.44 6.48 6.98 6.49 7.47 Prospect 5.47 3.43 7.51 5.57 4.15 6.99 8.23 7.67 8.79 Scheyville 2.74 2.04 3.44 3.70 2.82 4.57 5.44 4.94 5.94

Site x patch type x depth interaction:

Hoxton Park Pasture Open Shrub Tree Mean L1 L2 Mean L1 L2 Mean L1 L2 Mean L1 L2 2.5cm 7.28 4.25 10.3 4.61 3.45 5.78 4.25 -0.69 9.19 5.99 1.92 10.1 20.5cm 6.94 1.91 12.0 3.75 1.49 6.02 5.13 -0.47 10.73 4.85 2.44 7.25 60.5cm 8.37 7.37 9.36 6.12 3.34 8.90 4.79 1.47 8.11 5.84 2.05 9.63

Mount Annan Pasture Open Shrub Tree Mean L1 L2 Mean L1 L2 Mean L1 L2 Mean L1 L2 2.5cm 7.43 5.26 9.60 5.86 4.34 7.37 5.57 3.19 7.95 5.75 4.32 7.18 20.5cm 5.56 2.23 8.88 5.78 1.74 9.83 5.43 3.76 7.10 5.61 2.68 8.54 60.5cm 7.95 5.59 10.3 8.21 7.77 8.66 7.50 2.74 12.25 7.78 4.65 10.9

Orchard Hills Pasture Open Shrub Tree Mean L1 L2 Mean L1 L2 Mean L1 L2 Mean L1 L2 2.5cm 2.88 1.39 4.37 4.88 3.66 6.10 3.98 1.36 6.59 5.24 3.12 7.35 20.5cm 3.42 -1.01 7.85 6.43 5.08 7.77 5.48 3.39 7.57 6.51 4.46 8.57 60.5cm 7.40 5.52 9.28 7.00 6.32 7.67 6.59 3.45 9.74 6.94 5.10 8.78

Prospect Pasture Open Shrub Tree Mean L1 L2 Mean L1 L2 Mean L1 L2 Mean L1 L2 2.5cm 10.5 6.90 14.20 3.79 0.72 6.86 3.92 2.06 5.78 3.63 1.18 6.07 20.5cm 7.25 3.59 10.9 4.17 -2.20 10.5 6.56 1.50 11.6 4.30 -0.28 8.87 60.5cm 8.32 6.86 9.78 7.87 6.44 9.30 7.84 5.91 9.77 8.89 5.46 12.3

Scheyville Pasture Open Shrub Tree Mean L1 L2 Mean L1 L2 Mean L1 L2 Mean L1 L2 2.5cm 4.25 2.94 5.56 2.06 -0.12 4.24 2.54 0.31 4.77 2.12 1.41 2.82 20.5cm 4.73 3.90 5.57 3.88 0.64 7.11 3.69 -1.39 8.77 2.49 0.67 4.30 60.5cm 5.76 3.45 8.07 4.94 3.10 6.78 5.55 4.44 6.66 5.51 2.82 8.21

Table A1.3a Mauchly‟s test of sphericity for pH Epsilon Within subjects effect Mauchly's W Approx. Chi-Square df P GG Patch .569 4.912 5 .429 .791 Depth .692 3.316 2 .191 .764 Patch x Depth .011 34.884 20 .029 .429

Table A1.3b Split-plot ANOVA for pH Source of variation df SS MS F P GG df GG SS GG MS GG P Site 4 6.83976 1.70994 7.47084 0.0047 Subsite 10 2.289 .229

Patch type 3 4.57061 1.52354 7.82451 0.01 2.374 4.57061 1.925 <0.05 Site x Patch type 12 2.33656 0.19471 0.59765 NS 9.497 2.33656 .246 NS Subsite x Patch type 30 9.774 .326 23.743 9.774 .412 Depth 2 12.8512 6.42558 8.21832 <0.05 1.529 12.8512 8.406 <0.05 Site x Depth 8 6.25488 0.78186 7.84022 <0.0001 6.115 6.25488 1.023 <0.001 Subsite x Depth 20 1.994 .100 15.288 1.994 .130 Patch type x Depth 6 4.45901 0.74317 3.18474 <0.05 2.571 4.45901 1.734 NS Site x Patch type x Depth 24 5.60047 0.23335 2.38202 <0.05 10.285 5.60047 .545 <0.05 Subsite x Patch type x Depth 60 5.878 .098 25.714 5.878 .229 Residual 1 62.8476 Total 180

Table A1.3c Tukey‟s HSD test for the main effect of site on pH Table A1.3e Post hoc test# for the main effect of soil depth on pH 95% Confidence Interval Difference Site Site Mean difference SE P Lower bound Upper bound Depth Depth Mean difference SE P Lower bound Upper bound HP MA -.48805556* .112763683 .010 -.85917010 -.11694101 2.5 cm 20.5 cm .326* .038 .000 .216 .437 OH -.38305556* .112763683 .042 -.75417010 -.01194101 60.5 cm .654* .065 .000 .467 .842 PR -.07416667 .112763683 .961 -.44528121 .29694788 20.5 cm 60.5 cm .328* .065 .001 .142 .515 # SNP -.06694444 .112763683 .973 -.43805899 .30417010 based on estimated marginal means and a Bonferroni adjustment for multiple comparisons MA OH .10500000 .112763683 .878 -.26611454 .47611454 * PR .41388889 .112763683 .028 .04277435 .78500343 SNP .42111111* .112763683 .025 .04999657 .79222565 OH PR .30888889 .112763683 .117 -.06222565 .68000343 SNP .31611111 .112763683 .106 -.05500343 .68722565 PR SNP .00722222 .112763683 1.000 -.36389232 .37833676

Table A1.3d Post hoc test# for the main effect of patch type on pH Difference Patch Patch Mean difference SE P Lower bound Upper bound Open Pasture -.162 .142 1.000 -.626 .302 Shrub .062 .072 1.000 -.174 .299 Tree -.349 .127 .124 -.765 .068 Pasture Shrub .224 .125 .625 -.187 .635 Tree -.187 .129 1.000 -.611 .237 Shrub Tree -.411* .114 .029 -.785 -.038 #based on estimated marginal means and a Bonferroni adjustment for multiple comparisons

Means and 95% confidence intervals for pH

Site x depth interaction 2.5cm 20.5cm 60.5cm Mean L1 L2 Mean L1 L2 Mean L1 L2 Hoxton Park 4.36 4.11 4.61 4.34 4.23 4.45 4.31 4.11 4.50 Mount Annan 5.38 4.91 5.84 4.91 4.56 5.25 4.19 3.86 4.52 Orchard Hills 4.88 4.61 5.16 4.84 4.55 5.13 4.43 3.92 4.94 Prospect 4.68 4.41 4.95 4.35 4.19 4.51 4.20 4.09 4.30 Scheyville 5.02 4.69 5.35 4.26 3.91 4.61 3.93 3.75 4.11

Site x patch type x depth interaction:

Hoxton Park Pasture Open Shrub Tree Mean L1 L2 Mean L1 L2 Mean L1 L2 Mean L1 L2 2.5 cm 4.59 3.75 5.43 4.07 3.63 4.52 4.11 2.98 5.23 4.68 4.28 5.08 20.5 cm 4.45 4.13 4.77 4.34 4.21 4.47 4.26 3.45 5.07 4.30 4.05 4.56 60.5 cm 4.02 3.93 4.12 4.26 4.11 4.41 4.33 3.55 5.11 4.61 3.68 5.55

Mount Annan Pasture Open Shrub Tree Mean L1 L2 Mean L1 L2 Mean L1 L2 Mean L1 L2 2.5 cm 4.77 4.23 5.31 5.12 4.76 5.47 5.13 4.69 5.57 6.49 5.21 7.77 20.5 cm 4.79 4.12 5.45 4.85 4.61 5.09 4.54 3.59 5.48 5.45 3.28 7.62 60.5 cm 4.72 2.76 6.68 3.96 3.74 4.17 3.93 3.59 4.27 4.15 2.88 5.42

Orchard Hills Pasture Open Shrub Tree Mean L1 L2 Mean L1 L2 Mean L1 L2 Mean L1 L2 2.5 cm 4.68 4.47 4.88 4.90 4.27 5.53 4.55 3.94 5.16 5.41 4.18 6.65 20.5 cm 4.89 3.88 5.89 4.94 3.34 6.55 4.66 3.73 5.60 4.87 3.43 6.31 60.5 cm 4.91 0.88 8.94 4.44 3.05 5.83 4.22 3.77 4.66 4.16 3.82 4.50

Prospect Pasture Open Shrub Tree Mean L1 L2 Mean L1 L2 Mean L1 L2 Mean L1 L2 2.5 cm 4.87 4.00 5.74 4.33 3.59 5.07 4.60 3.65 5.55 4.94 3.62 6.26 20.5 cm 4.54 4.06 5.03 4.41 3.41 5.41 4.16 4.13 4.20 4.28 3.82 4.74 60.5 cm 4.11 3.82 4.41 4.35 3.63 5.06 4.17 4.07 4.27 4.16 3.90 4.42

Scheyville Pasture Open Shrub Tree Mean L1 L2 Mean L1 L2 Mean L1 L2 Mean L1 L2 2.5 cm 4.55 4.41 4.70 4.81 4.06 5.56 5.00 4.24 5.76 5.72 4.63 6.81 20.5 cm 4.62 3.83 5.41 3.88 3.31 4.46 3.97 3.39 4.56 4.56 2.36 6.77 60.5 cm 4.31 3.62 5.00 3.74 3.51 3.96 3.83 3.70 3.96 3.83 3.21 4.46

Table A1.4a Mauchly‟s test of sphericity for EC Epsilon Within subjects effect Mauchly's W Approx. Chi-Square df P GG Patch .846 1.456 5 .919 .907 Depth .087 22.005 2 .000 .523 Patch x Depth .000 72.978 20 .000 .460

Table A1.4b Split-plot ANOVA for EC Source of variation df SS MS F P GG df GG SS GG MS GG P Site 4 0.05496 0.01374 2.11579 0.15343 Subsite 10 .065 .006

Patch type 3 0.13078 0.04359 4.78592 <0.05 2.722 0.13078 .048 <0.05 Site x Patch type 12 0.10931 0.00911 0.95424 NS 10.890 0.10931 .010 NS Subsite x Patch type 30 .286 .010 27.224 .286 .011 Depth 2 2.22889 1.11445 61.0834 <0.0001 1.045 2.22889 2.132 <0.01 Site x Depth 8 0.14596 0.01824 5.65864 <0.001 4.181 0.14596 .035 <0.05 Subsite x Depth 20 .064 .003 10.453 .064 .006 Patch type x Depth 6 0.10153 0.01692 3.16224 <0.05 2.757 0.10153 .037 NS Site x Patch type x Depth 24 0.12843 0.00535 0.89472 NS 11.029 0.12843 .012 NS Subsite x Patch type x Depth 60 .359 .006 27.572 .359 .013 Residual 1 3.67453 Total 180

Table A1.4c Post hoc test# for the main effect of patch type on EC Difference Patch Patch Mean difference SE P Lower bound Upper bound Open Pasture .005 .022 1.000 -.066 .077 Shrub -.012 .018 1.000 -.071 .046 Tree -.063* .017 .029 -.120 -.006 Pasture Shrub -.018 .023 1.000 -.095 .059 Tree -.068 .021 .051 -.137 .000 Shrub Tree -.050 .022 .249 -.121 .020 #based on estimated marginal means and a Bonferroni adjustment for multiple comparisons

Table A1.4d Post hoc test# for the main effect of soil depth on EC Difference Depth Depth Mean difference SE P Lower bound Upper bound 2.5 cm 20.5 cm .007 .003 .180 -.003 .017 60.5 cm -.232* .014 .000 -.272 -.193 20.5 cm 60.5 cm -.240* .011 .000 -.271 -.208 #based on estimated marginal means and a Bonferroni adjustment for multiple comparisons

Back transformed means and 95% confidence intervals for EC (dS m-1)

Site x depth interaction 2.5 cm 20.5 cm 60.5 cm Mean L1 L2 Mean L1 L2 Mean L1 L2 Hoxton Park 0.0414 0.0298 0.0530 0.0585 0.0234 0.0948 0.410 0.259 0.580 Mount Annan 0.103 0.0662 0.140 0.0482 0.0246 0.0723 0.252 0.151 0.362 Orchard Hills 0.0427 0.0320 0.0536 0.0379 0.0264 0.0495 0.350 0.233 0.479 Prospect 0.0433 0.0315 0.0553 0.0529 0.0344 0.0717 0.395 0.275 0.527 Scheyville 0.0397 0.0287 0.0508 0.0333 0.0213 0.0454 0.247 0.158 0.343

Table A1.5a Mauchly‟s test of sphericity for active C Epsilon Within subjects effect Mauchly's W Approx. Chi-Square df P GG Patch .622 4.148 5 .531 .758 Depth .435 7.493 2 .024 .639 Patch x Depth .044 24.361 20 .263 .573

Table A1.5b Split-plot ANOVA for active C Source of variation df SS MS F P GG df GG SS GG MS GG P Site 4 144065.68 36016.42 7.39351 0.00488 Subsite 10 48713.56 4871.36

Patch type 3 214876.39 71625.46 2.65811 NS 2.275 214876.39 94448.784 NS Site x Patch type 12 323351.74 26945.98 1.87485 NS 9.100 323351.74 35532.266 NS Subsite x Patch type 30 431171.10 14372.37 22.751 431171.10 18952.100 Depth 2 10977993 5488996.43 220.816 <0.0001 1.278 10977993 8.591E+06 <0.0001 Site x Depth 8 198861.87 24857.73 4.6417 <0.01 5.112 198861.87 38904.262 <0.05 Subsite x Depth 20 107105.52 5355.28 12.779 107105.52 8381.418 Patch type x Depth 6 141008.17 23501.36 1.2805 NS 3.439 141008.17 41000.906 NS Site x Patch type x Depth 24 440475.4 18353.14 4.11141 <0.001 13.757 440475.4 32019.227 <0.001 Subsite x Patch type x Depth 60 267837.242 4463.95 34.391 267837.242 7787.896 Residual 1 1.330E+07 Total 180

Table A1.5c Tukey‟s HSD test for the main effect of site on active C 95% Confidence Interval Site Site Mean difference SE P Lower bound Upper bound HP MA 29.33395728 16.450863089 .432 -2.48071844E+01 83.47509898 OH 62.68187047* 16.450863089 .022 8.54072876 116.82301218 PR 71.78403818* 16.450863089 .010 17.64289647 125.92517989 SNP 71.91319659* 16.450863089 .010 17.77205488 126.05433830 MA OH 33.34791319 16.450863089 .320 -2.07932285E+01 87.48905490 PR 42.45008090 16.450863089 .148 -1.16910608E+01 96.59122261 SNP 42.57923931 16.450863089 .146 -1.15619024E+01 96.72038102 OH PR 9.10216771 16.450863089 .979 -4.50389740E+01 63.24330942 SNP 9.23132612 16.450863089 .978 -4.49098156E+01 63.37246783 PR SNP .12915841 16.450863089 1.000 -5.40119833E+01 54.27030012

Table A1.5d Post hoc test# for the main effect of soil depth on active C Difference Depth Depth Mean difference SE P Lower bound Upper bound 2.5 cm 20.5 cm 397.715* 10.564 .000 367.396 428.035 60.5 cm 593.594* 17.683 .000 542.842 644.346 20.5 cm 60.5 cm 195.879* 10.547 .000 165.609 226.148 #based on estimated marginal means and a Bonferroni adjustment for multiple comparisons

Means and 95% confidence intervals for Active C (mg kg-1)

Site x depth interaction 2.5 20.5 60.5 Mean L1 L2 Mean L1 L2 Mean L1 L2 Hoxton Park 871 776 966 395 329 462 172 121 224 Mount Annan 808 649 966 394 318 471 149 119 178 Orchard Hills 718 661 774 371 314 428 162 123 200 Prospect 686 616 757 357 320 394 180 151 208 Scheyville 731 650 813 308 277 339 184 142 226

Site x patch type x depth interaction:

Hoxton Park Pasture Open Shrub Tree Mean L1 L2 Mean L1 L2 Mean L1 L2 Mean L1 L2 2.5 cm 960 702 1219 752 412 1092 779 402 1156 993 891 1096 20.5 cm 371 218 525 309 150 467 436 181 690 464 112 817 60.5 cm 135 -38.8 309 165 15.2 316 240 -23.5 503 149 -45.6 344

Mount Annan Pasture Open Shrub Tree Mean L1 L2 Mean L1 L2 Mean L1 L2 Mean L1 L2 2.5 cm 649 40.4 1258 543 476 611 1023 892 1154 1015 886 1144 20.5 cm 426 88.7 764 280 210 350 334 210 459 536 444 628 60.5 cm 162 3.08 321 136 44.7 228 136 -24.2 296 160 78.9 242

Orchard Hills Pasture Open Shrub Tree Mean L1 L2 Mean L1 L2 Mean L1 L2 Mean L1 L2 2.5 cm 676 564 789 737 405 1070 677 470 883 780 594 966 20.5 cm 285 242 328 418 144 692 359 256 462 423 152 693 60.5 cm 123 -33.5 280 224 215 232 116 22.5 209 184 48.7 320

Prospect Pasture Open Shrub Tree Mean L1 L2 Mean L1 L2 Mean L1 L2 Mean L1 L2 2.5 cm 826 599 1052 630 529 732 626 466 785 664 369 958 20.5 cm 397 206 587 331 161 501 343 193 492 359 288 429 60.5 cm 176 146 206 162 90 234 188 56 320 359 288 429

Scheyville Pasture Open Shrub Tree Mean L1 L2 Mean L1 L2 Mean L1 L2 Mean L1 L2 2.5 cm 762 597 926 620 152 1088 739 381 1097 804 684 925 20.5 cm 352 291 414 299 278 321 301 87 514 279 230 327 60.5 cm 143 52.9 232 159 130 188 205 -4.10 415 228 1.79 454

Table A1.6a Mauchly‟s test of sphericity for total C Epsilon Within subjects effect Mauchly's W Approx. Chi-Square df P GG Patch .355 9.028 5 .110 .667 Depth .899 .956 2 .620 .908 Patch x Depth .013 33.615 20 .040 .401

Table A1.6b Split-plot ANOVA for total C Source of variation df SS MS F P GG df GG SS GG MS GG P Site 4 5.1850372 1.29626 72.2501 2.4E-07 Subsite 10 .179 .018

Patch type 3 0.2063501 0.06878 0.3622 NS 2.000 0.2063501 .103 NS Site x Patch type 12 2.2788234 0.1899 4.92018 <0.001 8.000 2.2788234 .285 <0.01 Subsite x Patch type 30 1.158 .039 20.001 1.158 .058 Depth 2 44.157014 22.0785 102.348 <0.0001 1.817 44.157014 24.303 <0.0001 Site x Depth 8 1.7257603 0.21572 16.6981 <0.0001 7.268 1.7257603 .237 <0.0001 Subsite x Depth 20 .258 .013 18.169 .258 .014 Patch type x Depth 6 0.2028145 0.0338 1.12541 NS 2.408 0.2028145 .084 NS Site x Patch type x Depth 24 0.7208552 0.03004 2.04085 <0.05 9.630 0.7208552 .075 NS Subsite x Patch type x Depth 60 .883 .015 24.075 .883 .037 Residual 1 5.696E+01 Total 180

Table A1.6c Tukey‟s HSD test for the main effect of site on total C 95% Confidence Interval Site Site Mean difference SE P Lower bound Upper bound HP MA .02996773 .031571156 .871 -.07393554 .13387099 OH .26016842* .031571156 .000 .15626515 .36407168 PR .16405657* .031571156 .003 .06015331 .26795984 SNP .46728067* .031571156 .000 .36337740 .57118393 MA OH .23020069* .031571156 .000 .12629742 .33410396 PR .13408885* .031571156 .011 .03018558 .23799212 SNP .43731294* .031571156 .000 .33340967 .54121621 OH PR -.09611184 .031571156 .073 -.20001511 .00779143 SNP .20711225* .031571156 .000 .10320898 .31101552 PR SNP .30322409* .031571156 .000 .19932082 .40712736

Table A1.6d Post hoc test# for the main effect of soil depth on total C Difference Depth Depth Mean difference SE P Lower bound Upper bound 2.5 cm 20.5 cm .707* .021 .000 .648 .766 60.5 cm 1.207* .018 .000 1.156 1.258 20.5 cm 60.5 cm .501* .023 .000 .433 .568 #based on estimated marginal means and a Bonferroni adjustment for multiple comparisons

Back transformed means and 95% confidence intervals for total C (%)

Site x patch type interaction Pasture Open Shrub Tree Mean L1 L2 Mean L1 L2 Mean L1 L2 Mean L1 L2 HP 2.02 0.896 3.80 2.17 0.939 4.19 2.96 1.49 5.30 2.92 1.08 6.39 MA 2.30 1.09 4.21 2.32 1.08 4.31 2.27 0.930 4.55 2.67 1.07 5.49 OH 0.970 0.472 1.63 1.97 1.13 3.12 1.82 0.937 3.12 2.18 1.16 3.69 PR 2.58 1.24 4.73 1.78 0.929 3.00 1.78 0.930 2.99 1.79 0.903 3.08 SNP 1.49 0.675 2.69 1.06 0.509 1.81 1.14 0.582 1.89 1.09 0.597 1.73

Site x depth interaction 2.5 cm 20.5 cm 60.5 cm Mean L1 L2 Mean L1 L2 Mean L1 L2 Hoxton Park 6.68 5.69 7.82 2.12 1.58 2.77 0.775 0.549 1.04 Mount Annan 6.44 5.74 7.23 2.13 1.92 2.35 0.670 0.563 0.784 Orchard Hills 3.51 2.84 4.30 1.63 1.25 2.08 0.642 0.478 0.824 Prospect 4.42 3.76 5.18 1.79 1.45 2.18 0.719 0.648 0.793 Scheyville 2.58 2.23 2.98 1.04 0.832 1.28 0.431 0.352 0.514

Table A1.7a Mauchly‟s test of sphericity for Bray 1 P Table A1.7c Tukey‟s HSD test for the main effect of site on Bray 1 P Epsilon 95% Confidence Interval Within subjects effect Mauchly's W Approx. Chi-Square df P GG Site Site Mean difference SE P Lower bound Upper bound Patch .619 4.187 5 .525 .747 HP MA -.36858633* .044727590 .000 -.51578850 -.22138415 Depth .690 3.334 2 .189 .764 OH .16644420* .044727590 .026 .01924202 .31364637 Patch x Depth .049 23.465 20 .305 .490 PR .17986624* .044727590 .016 .03266407 .32706841 SNP .00278206 .044727590 1.000 -.14442011 .14998423 MA OH .53503052* .044727590 .000 .38782835 .68223269 Table A1.7b Split-plot ANOVA for Bray 1 P PR .54845257* .044727590 .000 .40125039 .69565474 Source of variation df SS MS F P SNP .37136838* .044727590 .000 .22416621 .51857056

Site 4 7.050353 1.762588 48.94714 1.5E-06 OH PR .01342204 .044727590 .998 -.13378013 .16062422 Subsite 10 .360 .036 SNP -.16366214* .044727590 .028 -.31086431 -.01645997 PR SNP -.17708418* .044727590 .018 -.32428635 -.02988201

Patch type 3 1.808496 0.602832 3.896349 <0.05 Site x Patch type 12 1.856605 0.154717 1.763977 NS # Subsite x Patch type 30 2.631 .088 Table A1.7d Post hoc test for the main effect of patch type on Bray 1 P Difference Depth 2 27.55555 13.77777 68.69564 <0.0001 Patch Patch Mean difference SE P Lower bound Upper bound Site x Depth 8 1.604501 0.200562 5.854687 <0.001 Open Pasture .033 .076 1.000 -.217 .283 Subsite x Depth 20 .685 .034 Shrub -.034 .045 1.000 -.182 .114

Patch type x Depth 6 0.896226 0.149371 1.762295 NS * Tree -.225 .055 .013 -.406 -.045 Site x Patch type x Depth 24 2.034225 0.084759 1.779953 <0.05 Pasture Shrub -.067 .075 1.000 -.313 .179 Subsite x Patch type x * Depth 60 2.857 .048 Tree -.258 .059 .008 -.451 -.066

Residual 1 Shrub Tree -.191 .058 .050 -.383 .000 #based on estimated marginal means and a Bonferroni adjustment for multiple Total 180 comparisons

Table A1.7e Post hoc test# for the main effect of soil depth on Bray 1 P Difference Depth Depth Mean difference SE P Lower bound Upper bound 2.5 cm 20.5 cm .580* .041 .000 .462 .698 60.5 cm .951* .024 .000 .881 1.020 20.5 cm 60.5 cm .371* .034 .000 .273 .468 #based on estimated marginal means and a Bonferroni adjustment for multiple comparisons

Back transformed means and 95% confidence intervals for Bray 1 P (mg kg-1)

Site x depth interaction 2.5cm 20.5cm 60.5cm Mean L1 L2 Mean L1 L2 Mean L1 L2 Hoxton Park 2.64 2.22 3.11 0.795 0.454 1.22 0.268 0.108 0.451 Mount Annan 4.98 2.92 8.14 1.64 1.09 2.33 0.586 0.252 1.01 Orchard Hills 1.66 1.29 2.10 0.543 0.427 0.668 0.225 0.101 0.362 Prospect 1.56 1.29 1.87 0.676 0.426 0.968 0.126 0.036 0.225 Scheyville 1.99 1.75 2.24 0.992 0.777 1.23 0.381 0.250 0.525

Site x patch type x depth interaction: Hoxton Park Pasture Open Shrub Tree Mean L1 L2 Mean L1 L2 Mean L1 L2 Mean L1 L2 2.5 cm 2.19 1.05 3.96 2.54 1.03 5.20 2.64 1.67 3.95 3.28 1.64 5.93 20.5 cm 0.550 0.303 0.844 0.470 -0.316 2.16 1.22 -0.234 5.42 1.06 -0.180 4.16 60.5 cm 0.180 -0.325 1.065 0.199 -0.092 0.584 0.33 -0.177 1.14 0.377 -0.421 2.28

Mount Annan Pasture Open Shrub Tree Mean L1 L2 Mean L1 L2 Mean L1 L2 Mean L1 L2 2.5 cm 3.78 2.11 6.36 2.68 0.462 8.25 3.48 3.27 3.70 15.3 4.17 50.3 20.5 cm 1.15 -0.029 3.78 1.30 -0.111 4.93 2.02 -0.107 9.20 2.25 0.778 4.96 60.5 cm 0.557 -0.674 6.44 0.653 -0.162 2.26 0.296 0.071 0.567 0.895 -0.321 4.29

Orchard Hills Pasture Open Shrub Tree Mean L1 L2 Mean L1 L2 Mean L1 L2 Mean L1 L2 2.5 cm 2.07 1.13 3.43 1.48 0.534 3.01 1.13 0.415 2.19 2.11 0.602 5.05 20.5 cm 0.480 0.0276 1.13 0.502 0.242 0.816 0.517 0.133 1.03 0.681 0.123 1.52 60.5 cm 0.0596 0.011 0.110 0.291 -0.0656 0.784 0.0862 0.0581 0.115 0.513 0.148 0.995

Prospect Pasture Open Shrub Tree Mean L1 L2 Mean L1 L2 Mean L1 L2 Mean L1 L2 2.5 cm 1.26 0.324 2.85 1.50 0.608 2.88 1.62 1.11 2.24 1.92 0.768 3.81 20.5 cm 0.282 -0.0244 0.685 0.958 -0.244 4.07 0.682 0.382 1.05 0.867 0.243 1.81 60.5 cm 0.0503 -0.0495 0.161 0.0397 -0.00859 0.0903 0.294 -0.180 1.04 0.138 -0.151 0.527

Scheyville Pasture Open Shrub Tree Mean L1 L2 Mean L1 L2 Mean L1 L2 Mean L1 L2 2.5 cm 2.48 1.75 3.40 1.62 1.33 1.95 1.90 1.36 2.56 2.01 1.33 2.90 20.5 cm 0.750 -0.0705 2.29 1.11 0.341 2.32 1.06 0.525 1.80 1.07 0.328 2.21 60.5 cm 0.304 -0.169 1.05 0.503 0.318 0.713 0.324 -0.0404 0.827 0.401 -0.249 1.62

Table A1.8a Mauchly‟s test of sphericity for total S Epsilon Within subjects effect Mauchly's W Approx. Chi-Square df P GG Patch .502 6.013 5 .308 .742 Depth .742 2.687 2 .261 .795 Patch x Depth .013 33.522 20 .040 .572

Table A1.8b Split-plot ANOVA for total S Source of variation df SS MS F P GG df GG SS GG MS GG P Site 4 0.0037479 0.00094 4.21624 0.02958 Subsite 10 .002 .000

Patch type 3 0.0005471 0.00018 0.93019 NS 2.226 0.0005471 .000 NS Site x Patch type 12 0.0023526 0.0002 1.83526 NS 8.905 0.0023526 .000 NS Subsite x Patch type 30 .003 .000 22.262 .003 .000 Depth 2 0.0073918 0.0037 8.9671 <0.01 1.590 0.0073918 .005 <0.05 Site x Depth 8 0.0032973 0.00041 12.461 <0.0001 6.359 0.0032973 .001 <0.0001 Subsite x Depth 20 .001 .000 15.897 .001 .000 Patch type x Depth 6 0.0011324 0.00019 2.6299 <0.05 3.432 0.0011324 .000 NS Site x Patch type x Depth 24 0.0017223 7.2E-05 0.88585 NS 13.728 0.0017223 .000 NS Subsite x Patch type x Depth 60 .005 .000 34.320 .005 .000 Residual 1 3.114E-02 Total 180

Table A1.8c Tukey‟s HSD test for the main effect of site on total S 95% Confidence Interval Site Site Mean difference SE P Lower bound Upper bound HP MA .00515654 .003513713 .603 -.00640738 .01672045 OH .00528814 .003513713 .582 -.00627578 .01685206 PR .00225751 .003513713 .964 -.00930641 .01382143 SNP .01347085* .003513713 .022 .00190694 .02503477 MA OH .00013160 .003513713 1.000 -.01143231 .01169552 PR -.00289903 .003513713 .917 -.01446294 .00866489 SNP .00831432 .003513713 .202 -.00324960 .01987823 OH PR -.00303063 .003513713 .904 -.01459455 .00853329 SNP .00818271 .003513713 .213 -.00338120 .01974663 PR SNP .01121334 .003513713 .058 -.00035057 .02277726

Table A1.8d Post hoc test# for the main effect of soil depth on total S Difference Depth Depth Mean difference SE P Lower bound Upper bound 2.5 cm 20.5 cm .015* .001 .000 .012 .018 60.5 cm .004* .001 .026 .000 .008 20.5 cm 60.5 cm -.011* .001 .000 -.013 -.009 #based on estimated marginal means and a Bonferroni adjustment for multiple comparisons

Means and 95% confidence intervals for total S (%)

Site x depth interaction 2.5 cm 20.5 cm 60.5 cm Mean L1 L2 Mean L1 L2 Mean L1 L2 HP 0.0410 0.0306 0.0515 0.0173 0.0127 0.0220 0.0270 0.0186 0.0353 MA 0.0374 0.0282 0.0465 0.0116 0.00706 0.0162 0.0209 0.0177 0.0242 OH 0.0244 0.0184 0.0305 0.0145 0.0114 0.0175 0.0306 0.0230 0.0383 PR 0.0304 0.0216 0.0392 0.0185 0.0156 0.0215 0.0296 0.0243 0.0350 SNP 0.0148 0.0083 0.0212 0.0103 0.00687 0.0138 0.0199 0.0148 0.0249

Table A1.9a Mauchly‟s test of sphericity for nitrate Epsilon Within subjects effect Mauchly's W Approx. Chi-Square df P GG Patch .426 7.450 5 .192 .630 Depth .445 7.280 2 .026 .643 Patch x Depth .005 40.520 20 .007 .459

Table A1.9b Split-plot ANOVA for nitrate Source of variation df SS MS F P GG df GG SS GG MS GG P Site 4 8.9474267 2.23686 5.94904 0.01025 Subsite 10 3.760 .376

Patch type 3 6.0615086 2.0205 3.73906 <0.05 1.890 6.0615086 3.208 NS Site x Patch type 12 6.4845239 0.54038 2.51853 <0.05 7.558 6.4845239 .858 <0.05 Subsite x Patch type 30 6.437 .215 18.895 6.437 .341 Depth 2 37.528236 18.7641 22.5868 <0.001 1.286 37.528236 29.172 <0.05 Site x Depth 8 6.6460566 0.83076 4.94653 <0.05 5.146 6.6460566 1.292 <0.05 Subsite x Depth 20 3.359 .168 12.865 3.359 .261 Patch type x Depth 6 4.6904925 0.78175 3.03159 <0.05 2.754 4.6904925 1.703 NS Site x Patch type x Depth 24 6.1888294 0.25787 2.44619 <0.05 11.016 6.1888294 .562 <0.05 Subsite x Patch type x Depth 60 6.325 .105 27.540 6.325 .230 Residual 1 9.643E+01 Total 180

Table A1.9c Tukey‟s HSD test for the main effect of site on nitrate 95% Confidence Interval Site Site Mean difference SE P Lower bound Upper bound HP MA -.52138976* .144530401 .031 -.99705118 -.04572834 OH .04910176 .144530401 .997 -.42655966 .52476319 PR .09550468 .144530401 .961 -.38015674 .57116610 SNP -.12273611 .144530401 .909 -.59839754 .35292531 MA OH .57049152* .144530401 .018 .09483010 1.04615295 PR .61689444* .144530401 .011 .14123302 1.09255586 SNP .39865365 .144530401 .113 -.07700777 .87431507 OH PR .04640292 .144530401 .997 -.42925851 .52206434 SNP -.17183788 .144530401 .758 -.64749930 .30382355 PR SNP -.21824079 .144530401 .579 -.69390221 .25742063

Table A1.9d Post hoc test# for the main effect of patch type on nitrate Difference Patch Patch Mean difference SE P Lower bound Upper bound Open Pasture -.361* .104 .035 -.700 -.022 Shrub .103 .072 1.000 -.133 .339 Tree -.232 .079 .088 -.490 .026 Pasture Shrub .464* .119 .018 .074 .854 Tree .129 .131 1.000 -.300 .558 Shrub Tree -.335* .061 .002 -.536 -.133 #based on estimated marginal means and a Bonferroni adjustment for multiple comparisons

Table A1.9e Post hoc test# for the main effect of soil depth on nitrate Difference Depth Depth Mean difference SE P Lower bound Upper bound 2.5 cm 20.5 cm .859* .084 .000 .618 1.100 60.5 cm 1.050* .091 .000 .789 1.311 20.5 cm 60.5 cm .191* .039 .002 .081 .302 #based on estimated marginal means and a Bonferroni adjustment for multiple comparisons

Back transformed means and 95% confidence intervals for soil nitrate (mg kg-1)

Site x patch type interaction Pasture Open Shrub Tree Mean L1 L2 Mean L1 L2 Mean L1 L2 Mean L1 L2 HP 0.276 -0.0696 0.750 0.401 0.021 0.922 0.390 0.0523 0.836 0.975 0.182 2.30 MA 3.31 0.469 11.7 1.20 0.0366 3.66 0.789 0.0207 2.14 1.33 0.220 3.45 OH 1.05 0.178 2.58 0.428 -0.208 1.57 0.00715 -0.00330 0.0177 0.365 0.0207 0.825 PR 1.23 -0.0945 4.51 0.0757 0.00524 0.151 0.0690 -0.0131 0.158 0.304 -0.0243 0.743 SNP 0.571 -0.0693 1.65 0.380 -0.0127 0.928 0.456 0.0394 1.04 1.54 0.365 3.73

Site x depth interaction 2.5 cm 20.5 cm 60.5 cm Mean L1 L2 Mean L1 L2 Mean L1 L2 HP 1.33 0.739 2.12 0.364 0.0489 0.773 0.0383 -0.0151 0.0945 MA 6.68 3.33 12.6 0.802 0.243 1.61 0.139 -0.0278 0.333 OH 1.24 0.313 2.81 0.190 0.0190 0.390 0.0688 0.00383 0.138 PR 1.19 0.164 3.14 0.0582 -0.0143 0.136 0.0660 -0.0319 0.173 SNP 2.30 1.15 4.06 0.278 0.109 0.473 0.129 0.0144 0.257

Site x patch type x depth interaction:

Hoxton Park Pasture Open Shrub Tree Mean L1 L2 Mean L1 L2 Mean L1 L2 Mean L1 L2 2.5 cm 1.08 -0.182 4.27 1.21 -0.197 5.07 0.681 -0.05 1.98 2.81 0.09 12.3 20.5 cm 0.00 0.00 0.00 0.245 -0.0298 0.598 0.474 -0.624 4.79 0.885 -0.520 6.40 60.5 cm 0.00 0.00 0.00 0.00 0.00 0.00 0.0833 -0.203 0.473 0.0728 -0.202 0.441

Mount Annan Pasture Open Shrub Tree Mean L1 L2 Mean L1 L2 Mean L1 L2 Mean L1 L2 2.5 cm 17.4 -0.165 406 5.59 -0.480 82.5 3.61 1.36 8.02 5.21 1.42 14.9 20.5 cm 2.01 -0.734 33.1 0.396 0.0071 0.936 0.229 -0.06 0.607 1.04 -0.229 4.38 60.5 cm 0.443 -0.515 3.30 0.153 -0.156 0.573 0.0105 -0.0338 0.0567 0.00 0.00 0.00

Orchard Hills Pasture Open Shrub Tree Mean L1 L2 Mean L1 L2 Mean L1 L2 Mean L1 L2 2.5 cm 3.77 -0.0621 23.3 1.67 -0.868 52.9 0.00793 -0.010 0.0263 0.952 -0.0729 3.11 20.5 cm 0.477 -0.0452 1.29 0.0281 -0.0834 0.153 0.0136 -0.044 0.0740 0.302 -0.488 2.31 60.5 cm 0.230 -0.0133 0.533 0.0609 -0.0689 0.209 0.00 0.00 0.00 0.00 0.00 0.00

Prospect Pasture Open Shrub Tree Mean L1 L2 Mean L1 L2 Mean L1 L2 Mean L1 L2 2.5 cm 8.99 0.937 50.56 0.153 -0.0331 0.375 0.196 -0.099 0.589 0.682 -0.568 5.55 20.5 cm 0.0923 -0.253 0.597 0.0112 -0.0242 0.0480 0.0211 -0.0668 0.117 0.112 -0.295 0.753 60.5 cm 0.0201 -0.0265 0.0689 0.0673 -0.194 0.412 0.00 0.00 0.00 0.186 -0.431 1.47

Scheyville Pasture Open Shrub Tree Mean L1 L2 Mean L1 L2 Mean L1 L2 Mean L1 L2 2.5 cm 1.80 -0.781 34.9 1.29 -0.130 5.03 1.58 0.741 2.83 6.17 3.81 9.67 20.5 cm 0.280 -0.137 0.899 0.0924 -0.253 0.598 0.125 0.0586 0.195 0.697 0.0165 1.83 60.5 cm 0.0810 -0.0209 0.193 0.0488 -0.0548 0.164 0.0624 -0.0393 0.175 0.349 -0.352 1.81

Table A1.10a Mauchly‟s test of sphericity for ammonium Epsilon Within subjects effect Mauchly's W Approx. Chi-Square df P GG Patch .230 12.826 5 .026 .597 Depth .969 .281 2 .869 .970 Patch x Depth .101 17.867 20 .633 .670

Table A1.10b Split-plot ANOVA for ammonium Source of variation df SS MS F P GG df GG SS GG MS GG P Site 4 9.364803 2.3412 27.1733 2.4E-05 9.364803 Subsite 10 .862 .086 .862

Patch type 3 2.0075527 0.66918 1.57237 NS 1.792 2.0075527 1.120 NS Site x Patch type 12 5.1070607 0.42559 2.84993 <0.01 7.168 5.1070607 .712 <0.05 Subsite x Patch type 30 4.480 .149 17.920 4.480 .250 Depth 2 13.904631 6.95232 30.5327 <0.001 1.940 13.904631 7.166 <0.001 Site x Depth 8 1.8216036 0.2277 2.99648 <0.05 7.762 1.8216036 .235 <0.05 Subsite x Depth 20 1.520 .076 19.404 1.520 .078 Patch type x Depth 6 2.2444454 0.37407 2.71146 <0.05 4.022 2.2444454 .558 NS Site x Patch type x Depth 24 3.3110504 0.13796 1.43955 NS 16.087 3.3110504 .206 NS Subsite x Patch type x Depth 60 5.750 .096 40.218 5.750 .143 Residual 1 5.037E+01 5.037E+01 Total 180

Table A1.10c Tukey‟s HSD test for the main effect of site on ammonium 95% Confidence Interval Site Site Mean difference SE P Lower bound Upper bound HP MA .08204362 .069185045 .759 -.14565004 .30973728 OH .45273024* .069185045 .000 .22503658 .68042391 PR .12108079 .069185045 .449 -.10661287 .34877445 SNP .58425130* .069185045 .000 .35655764 .81194497 MA OH .37068663* .069185045 .002 .14299296 .59838029 PR .03903717 .069185045 .977 -.18865649 .26673083 SNP .50220768* .069185045 .000 .27451402 .72990135 OH PR -.33164945* .069185045 .005 -.55934312 -.10395579 SNP .13152106 .069185045 .375 -.09617260 .35921472 PR SNP .46317051* .069185045 .000 .23547685 .69086418

Table A1.10d Post hoc test# for the main effect of soil depth on ammonium Difference Depth Depth Mean difference SE P Lower bound Upper bound 2.5 cm 20.5 cm .570* .053 .000 .418 .722 60.5 cm .607* .046 .000 .476 .739 20.5 cm 60.5 cm .037 .052 1.000 -.112 .186 #based on estimated marginal means and a Bonferroni adjustment for multiple comparisons

Table A1.11a Mauchly‟s test of sphericity for total N Epsilon Within subjects effect Mauchly's W Approx. Chi-Square df P GG Patch .379 8.454 5 .135 .714 Depth .394 8.377 2 .015 .623 Patch x Depth .012 34.576 20 .031 .402

Table A1.11b Split-plot ANOVA for total N Source of variation df SS MS F P GG df GG SS GG MS GG P Site 4 28.70044933 7.17511 21.756 6.4E-05 Subsite 10 3.298 .330

Patch type 3 10.02781389 3.3426 1.2208 NS 2.143 10.02781389 4.680 NS Site x Patch type 12 32.85658521 2.73805 10.1938 <0.0001 8.570 32.85658521 3.834 <0.0001 21 Subsite x Patch type 30 8.058 .269 .425 8.058 .376 Depth 2 67.33588271 33.6679 20.7503 <0.001 1.246 67.33588271 54.062 <0.01 Site x Depth 8 12.98021081 1.62253 14.1057 <0.0001 4.982 12.98021081 2.605 <0.0001 Subsite x Depth 20 2.301 .115 12.455 2.301 .185 Patch type x Depth 6 1.340072398 0.22335 1.09925 NS 2.410 1.340072398 .556 NS Site x Patch type x Depth 24 4.87633165 0.20318 2.06227 <0.05 9.638 4.87633165 .506 NS Subsite x Patch type x Depth 60 5.911 .099 24.096 5.911 .245 Residual 1 1.777E+02 1.777E+02 Total 180

Table A1.11c Tukey‟s HSD test for the main effect of site on total N 95% Confidence Interval Site Site Mean difference SE P Lower bound Upper bound HP MA -.40257353 .135359462 .082 -.84805264 .04290558 OH .00967949 .135359462 1.000 -.43579962 .45515860 PR .08563341 .135359462 .966 -.35984570 .53111252 SNP -.97913491* .135359462 .000 -1.42461402 -.53365580 MA OH .41225302 .135359462 .073 -.03322609 .85773213 PR .48820694* .135359462 .031 .04272783 .93368605 SNP -.57656138* .135359462 .011 -1.02204049 -.13108227 OH PR .07595392 .135359462 .978 -.36952519 .52143303 SNP -.98881440* .135359462 .000 -1.43429351 -.54333529 PR SNP -1.06476833* .135359462 .000 -1.51024744 -.61928922

Table A1.11d Post hoc test# for the main effect of soil depth on total N Difference Depth Depth Mean difference SE P Lower bound Upper bound 2.5 cm 20.5 cm .948* .063 .000 .768 1.127 60.5 cm 1.479* .080 .000 1.250 1.708 20.5 cm 60.5 cm .531* .035 .000 .431 .631 #based on estimated marginal means and a Bonferroni adjustment for multiple comparisons

Back transformed means and 95% confidence intervals for total N (%)

Site x patch type interaction Pasture Open Shrub Tree Mean L1 L2 Mean L1 L2 Mean L1 L2 Mean L1 L2 HP 0.107 0.0543 0.211 0.0843 0.0348 0.204 0.102 0.0510 0.203 0.117 0.0496 0.275 MA 0.158 0.0831 0.299 0.140 0.0719 0.274 0.140 0.0694 0.284 0.173 0.0829 0.361 OH 0.0848 0.0479 0.150 0.110 0.0738 0.163 0.0877 0.0528 0.146 0.126 0.0841 0.190 PR 0.128 0.0761 0.215 0.0803 0.0472 0.137 0.0863 0.0492 0.151 0.0858 0.0491 0.150 SNP 1.47 0.718 3.006 0.288 0.141 0.589 0.0992 0.0633 0.155 0.128 0.0844 0.194

Site x depth interaction 2.5 cm 20.5 cm 60.5 cm Mean L1 L2 Mean L1 L2 Mean L1 L2 HP 0.333 0.293 0.378 0.0851 0.0666 0.109 0.0372 0.0278 0.0498 MA 0.456 0.408 0.509 0.127 0.113 0.143 0.0607 0.0550 0.0671 OH 0.201 0.182 0.224 0.0929 0.0790 0.109 0.0547 0.0419 0.0713 PR 0.224 0.195 0.258 0.0749 0.0636 0.0881 0.0486 0.0400 0.0589 SNP 0.329 0.175 0.620 0.262 0.103 0.667 0.231 0.0915 0.582

Appendix 2. Supporting materials for the analysis of the soil and ground layer attributes presented in Chapter 4

Table A2.1a-c. Pearsons correlation coefficients and significance levels for the physical and chemical soil properties of the surface soil (0-5 cm)

Table A2.2a Mauchly's test of sphericity for native species richness

Table A2.2b Split-plot ANOVA for native species richness

Table A2.2c Tukey‟s HSD Test for native species richness between sites

Table A2.2d Post hoc test for native species richness between patch types

Table A2.3a Mauchly's test of sphericity for exotic species richness

Table A2.3b Split-plot ANOVA for exotic species richness

Table A2.3c Tukey‟s HSD Test for exotic species richness between sites

Table A2.3d Post hoc test for exotic species richness between patch types

Table A2.4a Species that contributed up to 50% of the dissimilarity between Hoxton Park and Mount Annan in terms of ground species composition and cover

Table A2.4b Species that contributed up to 50% of the dissimilarity between Hoxton Park and Orchard Hills in terms of ground species composition and cover

Table A2.4c Species that contributed up to 50% of the dissimilarity between Hoxton Park and Prospect in terms of ground species composition and cover

Table A2.4d Species that contributed up to 50% of the dissimilarity between Hoxton Park and Scheyville in terms of ground species composition and cover

Table A2.4e Species that contributed up to 50% of the dissimilarity between Mount Annan and Orchard Hills in terms of ground species composition and cover

Table A2.4f Species that contributed up to 50% of the dissimilarity between Mount Annan and Prospect in terms of ground species composition and cover

Table A2.4g Species that contributed up to 50% of the dissimilarity between Mount Annan and Scheyville in terms of ground species composition and cover

Table A2.4h Species that contributed up to 50% of the dissimilarity between Orchard Hills and Prospect in terms of ground species composition and cover

Table A2.4i Species that contributed up to 50% of the dissimilarity between Orchard Hills and Scheyville in terms of ground species composition and cover

Table A2.4j Species that contributed up to 50% of the dissimilarity between Prospect and Scheyville in terms of ground species composition and cover

Table A2.5a Species that contributed up to 50% of the dissimilarity between the combined tree and shrub patch type and the open patch type in terms of ground species composition and cover

Table A2.5b Species that contributed up to 50% of the dissimilarity between the combined tree and shrub patch type and the pasture patch type in terms of ground species composition and cover

Table A2.5c Species that contributed up to 50% of the dissimilarity between the open and pasture patch types in terms of ground species composition and cover

Table A2.6 Summary of life cycle characteristics and metabolic pathways for those grass species with a mean cover greater than or equal to 2% at any one site (from the SIMPER analysis)

The following acronyms have been used in this appendix:

GG=Greenhouse-Geisser HP=Hoxton Park MA=Mount Annan OH=Orchard Hills PR=Prospect SNP=Scheyville

Figures in bold highlight significant main effects, interactions or post hoc tests

Table A2.1a-c. Pearsons correlation coefficients (Correlation) and significance levels (P: 2-tailed tests) for the physical and chemical soil properties of the surface soil (0-5 cm). Analysis was carried out on transformed variables where necessary (as per Chapter 3) using SPSS v. 17.0. a Moisture pH EC Bray 1 P Active C Ammonium Nitrate Moisture Correlation 1 P pH Correlation 0.101 1 P 0.443 EC Correlation -0.101 -0.075 1 P 0.445 0.568 Bray 1 P Correlation 0.141 0.531 -0.110 1 P 0.282 0.000 0.402 Active C Correlation 0.350 0.410 -0.030 0.416 1 P 0.006 0.001 0.819 0.001 Ammonium Correlation 0.667 -0.164 0.030 0.257 0.179 1 P 0.000 0.211 0.820 0.048 0.171 Nitrate Correlation 0.431 0.316 0.065 0.356 0.094 0.472 1 P 0.001 0.014 0.621 0.005 0.475 0.000

b Total C Total S Total N Sol Ca Sol K Sol Mg Sol Na Total C Correlation 1 P Total S Correlation 0.691 1 P 0.000 Total N Correlation 0.432 0.306 1 P 0.001 0.017 Sol Ca Correlation -0.355 -0.151 -0.137 1 P 0.005 0.248 0.296 Sol K Correlation -0.190 0.054 -0.057 0.496 1 P 0.145 0.683 0.663 0.000 Sol Mg Correlation -0.145 0.020 -0.172 0.319 0.803 1 P 0.268 0.880 0.190 0.013 0.000 Sol Na Correlation -0.080 -0.028 -0.239 0.177 0.503 0.800 1 P 0.542 0.834 0.066 0.175 0.000 0.000

c Exch Ca Exch K Exch Mg Exch Na C:N ratio Bulk density Exch Ca Correlation 1 P Exch K Correlation 0.706 1 P 0.000 Exch Mg Correlation 0.438 0.659 1 P 0.000 0.000 Exch Na Correlation 0.319 0.392 0.733 1 P 0.013 0.002 0.000 C:N ratio Correlation -0.149 -0.077 0.160 0.318 1 P 0.256 0.558 0.222 0.013 Bulk density Correlation -0.402 -0.453 -0.325 -0.351 0.062 1 P 0.001 0.000 0.011 0.006 0.638

Table A2.2a Mauchly's test of sphericity for native species richness Epsilon Within Subjects Effect Mauchly's W Approx. Chi-Square df P GG patch .656 3.673 5 .599 .770

Table A2.2b Split-plot ANOVA for native species richness Source df SS MS F P Site 4 322.733 80.683 4.610 .023 Sub-site 10 175.000 17.500

Patch type 3 2492.067 830.689 18.039 <0.0001 Site x Patch type 12 552.600 46.050 1.886 .078 Sub-site x Patch type (Error Patch type) 30 732.333 24.411 Residual 1 Total 60

Table A2.2c Tukey‟s HSD Test for native species richness between sites. HP stands for Hoxton Park; MA is for Mount Annan; OH is for Orchard Hills; PR is for Prospect; and SNP is for Scheyville. 95% Confidence Interval Site Site Mean difference SE P Lower bound Upper bound HP MA .7500000 1.70782513 .991 -4.8705928 6.3705928 OH 4.3333333 1.70782513 .158 -1.2872594 9.9539261 PR 4.8333333 1.70782513 .102 -.7872594 10.4539261 SNP -.8333333 1.70782513 .987 -6.4539261 4.7872594 MA OH 3.5833333 1.70782513 .292 -2.0372594 9.2039261 PR 4.0833333 1.70782513 .195 -1.5372594 9.7039261 SNP -1.5833333 1.70782513 .880 -7.2039261 4.0372594 OH PR .5000000 1.70782513 .998 -5.1205928 6.1205928 SNP -5.1666667 1.70782513 .075 -10.7872594 .4539261 P SNP -5.67 1.70782513 .048 -11.2872594 -.0460739

Table A2.2d Post hoc test for native species richness (based on estimated marginal means and a Bonferroni correction) between patch types. P stands for pasture; O is for open; S is for shrub; and T is for tree. Difference Patch Patch Mean difference SE P Lower bound Upper bound O P 12.933 1.809 .000 7.007 18.860 S -1.667 1.439 1.000 -6.382 3.049 T -3.400 1.480 .267 -8.251 1.451 P S -14.600 1.987 .000 -21.110 -8.090 T -16.333 2.348 .000 -24.026 -8.641 S T -1.733 1.593 1.000 -6.953 3.487

Table A2.3a Mauchly's test of sphericity for exotic species richness Epsilon Within Subjects Effect Mauchly's W Approx. Chi-Square df P GG patch .187 14.611 5 .013 .628

Table A2.3b Split-plot ANOVA for exotic species richness Source df SS MS F P GG df GG MS GG P

Site 4 104.767 26.192 5.998 .010

Sub-site 10 43.667 4.367

Patch type 3 343.333 114.444 6.334 <0.01 1.883 182.330 <0.05 Site x Patch type 12 216.833 18.069 3.976 <0.05 7.532 28.788 .007 Sub-site x Patch type (Error Patch type) 30 136.333 4.544 18.830317 7.2400978 Residual 1

Total 60

Table A2.3c Tukey‟s HSD Test for exotic species richness between sites. HP stands for Hoxton Park; MA is for Mount Annan; OH is for Orchard Hills; PR is for Prospect; and SNP is for Scheyville. 95% Confidence Interval Site Site Mean difference SE P Lower bound Upper bound HP MA -1.9166667 .85309893 .239 -4.7242853 .8909520 OH 1.0000000 .85309893 .766 -1.8076186 3.8076186 PR 1.4166667 .85309893 .496 -1.3909520 4.2242853 SNP 1.7500000 .85309893 .310 -1.0576186 4.5576186 MA OH 2.92 .85309893 .041 .1090480 5.7242853 PR 3.33 .85309893 .019 .5257147 6.1409520 SNP 3.67 .85309893 .011 .8590480 6.4742853 OH PR .4166667 .85309893 .987 -2.3909520 3.2242853 SNP .7500000 .85309893 .898 -2.0576186 3.5576186 P SNP .3333333 .85309893 .994 -2.4742853 3.1409520

Table A2.3d Post hoc test for exotic species richness (based on estimated marginal means and a Bonferroni correction) between patch types. P stands for pasture; O is for open; S is for shrub; and T is for tree. Difference Patch Patch Mean difference SE P Lower bound Upper bound O P -4.533 .959 .005 -7.676 -1.390 S 2.067 .751 .123 -.395 4.529 T -.467 .432 1.000 -1.882 .949 P S 6.600 1.033 .000 3.216 9.984 T 4.067 .827 .004 1.356 6.778 S T -2.533 .462 .002 -4.047 -1.020

Table A2.4a Species that contributed up to 50% of the dissimilarity between Hoxton Park (HP) and Mount Annan (MA) in terms of ground species composition and cover. The mean cover of each species is shown, along with the individual and cumulative contributions of these species to the dissimilarity between sites. Exotic species are marked with an asterisk. HP MA Mean cover Mean cover Contribution Cumulative Species (%) (%) (%) contribution (%) Aristida vagans 18 6.08 2.76 2.76 Paspalum dilatatum* 5.67 15.08 2.6 5.36 Themeda australis 0.17 9.58 2.55 7.92 Chloris ventricosa 1.58 11 2.39 10.31 Aristida ramosa 5.17 16.33 2.28 12.59 Paspalidium distans 2.25 0 2 14.59 Microlaena stipoides 11.83 2 1.88 16.47 Chloris gayana* 10.42 1.33 1.81 18.28 Eragrostis brownii 3.08 0 1.7 19.98 Panicum simile 1.08 0 1.56 21.54 Cynodon dactylon * 0.75 2.92 1.53 23.08 Cymbopogon refractus 1.17 0.33 1.5 24.57 Lomandra spp. 1.42 0 1.5 26.07 Briza subaristata* 6.67 0.08 1.48 27.55 Bothriochloa macra 1.42 0.17 1.45 29 Asperula conferta 0.25 0.75 1.44 30.44 Fimbristylis dichotoma 0.67 0.08 1.41 31.85 Cyperus gracilis 0.33 0.92 1.36 33.21 Eragrostis leptostachya 2.17 0.5 1.35 34.56 Echinopogon ovatus 0.92 0.17 1.3 35.86 Viola hederacea 0.58 0 1.28 37.14 Geranium solanderi var. solanderi 0.08 0.58 1.26 38.4 Oxalis perennans 0.5 1 1.22 39.62 Cheilanthes sieberi subsp. sieberi 0.75 1.33 1.21 40.83 Desmodium varians 0.08 0.58 1.2 42.03 Tricoryne elatior 0.58 0.17 1.2 43.23 Plantago gaudichaudii 0.17 0.58 1.17 44.4 Bursaria spinosa 0.42 0.67 1.17 45.57 Sporobolus indicus var. capensis* 0.5 0.67 1.14 46.72 Einadia trigonos 0 2.83 1.14 47.85 Sporobolus creber 0.42 0.58 1.14 48.99 Dichondra repens 0.67 0.83 1.13 50.12

Table A2.4b Species that contributed up to 50% of the dissimilarity between Hoxton Park (HP) and Orchard Hills (OH) in terms of ground species composition and cover. The mean cover of each species is shown, along with the individual and cumulative contributions of these species to the dissimilarity between sites. Exotic species are marked with an asterisk. HP OH Mean cover Mean cover Contribution Cumulative contribution Species (%) (%) (%) (%) Aristida vagans 18.00 0.08 3.72 3.72 Themeda australis 0.17 27.25 3.71 7.43 Aristida ramosa 5.17 35.50 2.88 10.31 Microlaena stipoides 11.83 0.33 2.72 13.03 Paspalum dilatatum* 5.67 9.25 2.70 15.73 Briza subaristata* 6.67 1.67 2.55 18.28 Paspalidium distans 2.25 0.17 2.08 20.36 Chloris gayana* 10.42 1.25 1.95 22.30 Cymbopogon refractus 1.17 0.17 1.92 24.22 Cynodon dactylon* 0.75 3.75 1.88 26.10 Eragrostis brownii 3.08 0.50 1.79 27.88 Cheilanthes sieberi subsp. sieberi 0.75 0.08 1.76 29.65 Sida rhombifolia* 0.75 0.17 1.75 31.39 Panicum simile 1.08 0.08 1.71 33.10 Desmodium varians 0.08 0.75 1.70 34.80 Desmodium brachypodum 0.00 0.67 1.57 36.37 Lomandra spp. 1.42 0.42 1.54 37.91 Echinopogon ovatus 0.92 0.00 1.50 39.41 Chloris ventricosa 1.58 0.67 1.48 40.90 Plantago gaudichaudii 0.17 0.67 1.48 42.38 Viola hederacea 0.58 0.00 1.46 43.84 Eragrostis leptostachya 2.17 0.33 1.38 45.22 Brunoniella australis 0.75 0.83 1.38 46.59 Setaria gracilis* 2.67 7.83 1.36 47.96 Plantago lanceolata* 0.58 0.33 1.34 49.30 Asperula conferta 0.25 0.58 1.33 50.63

Table A2.4c Species that contributed up to 50% of the dissimilarity between Hoxton Park (HP) and Prospect (PR) in terms of ground species composition and cover. The mean cover of each species is shown, along with the individual and cumulative contributions of these species to the dissimilarity between sites. Exotic species are marked with an asterisk. HP PR Mean cover Mean cover Contribution Cumulative contribution Species (%) (%) (%) (%) Themeda australis 0.17 59.08 5.42 5.42 Aristida vagans 18.00 0.08 3.34 8.76 Aristida ramosa 5.17 0.00 2.80 11.56 Microlaena stipoides 11.83 4.00 2.08 13.64 Paspalum dilatatum* 5.67 3.33 2.08 15.72 Briza subaristata* 6.67 2.50 2.04 17.75 Setaria gracilis* 2.67 0.75 2.01 19.76 Cymbopogon refractus 1.17 0.00 1.95 21.70 Cynodon dactylon* 0.75 12.08 1.90 23.60 Paspalidium distans 2.25 0.25 1.77 25.37 Hardenbergia violacea 0.00 0.75 1.76 27.13 Eragrostis brownii 3.08 0.17 1.72 28.85 Sida rhombifolia* 0.75 0.08 1.67 30.52 Pultanaea parviflora 0.00 1.00 1.66 32.18 Bothriochloa macra 1.42 0.17 1.56 33.74 Chloris gayana* 10.42 0.00 1.55 35.29 Panicum simile 1.08 0.17 1.48 36.78 Fimbristylis dichotoma 0.67 0.08 1.44 38.21 Echinopogon ovatus 0.92 0.00 1.35 39.57 Lomandra spp. 1.42 0.67 1.34 40.91 Viola hederacea 0.58 0.58 1.33 42.24 Opercularia diphylla 0.33 0.67 1.30 43.54 Lomandra multiflora 0.25 0.58 1.25 44.78 Axonopus affinis* 3.00 0.33 1.24 46.03 Eucalyptus moluccana 0.33 0.58 1.23 47.26 Plantago lanceolata* 0.58 0.25 1.22 48.48 Dichondra repens 0.67 0.75 1.22 49.70 Eragrostis leptostachya 2.17 0.25 1.20 50.90

Table A2.4d Species that contributed up to 50% of the dissimilarity between Hoxton Park (HP) and Scheyville (SNP) in terms of ground species composition and cover. The mean cover of each species is shown, along with the individual and cumulative contributions of these species to the dissimilarity between sites. Exotic species are marked with an asterisk. HP SNP Mean cover Mean cover Contribution Cumulative contribution Species (%) (%) (%) (%) Themeda australis 0.17 34.58 4.33 4.33 Microlaena stipoides 11.83 0.17 2.80 7.14 Aristida vagans 18.00 1.17 2.77 9.91 Paspalum dilatatum* 5.67 12.50 2.62 12.53 Senecio madagascariensis* 1.08 0.08 2.10 14.63 Cynodon dactylon* 0.75 8.33 1.98 16.61 Chloris ventricosa 1.58 6.67 1.94 18.55 Eremophila debilis 0.00 0.75 1.77 20.32 Eragrostis brownii 3.08 0.17 1.76 22.09 Eragrostis leptostachya 2.17 1.33 1.73 23.82 Sida rhombifolia* 0.75 0.17 1.67 25.49 Chloris gayana* 10.42 0.00 1.62 27.10 Briza subaristata* 6.67 0.08 1.59 28.69 Setaria gracilis* 2.67 1.33 1.57 30.26 Bothriochloa macra 1.42 0.17 1.56 31.82 Aristida ramosa 5.17 1.25 1.55 33.36 Lomandra spp. 1.42 0.33 1.48 34.84 Echinopogon ovatus 0.92 0.00 1.42 36.26 Panicum simile 1.08 1.42 1.41 37.67 Viola hederacea 0.58 0.00 1.38 39.05 Opercularia diphylla 0.33 0.67 1.34 40.39 Paspalidium distans 2.25 0.83 1.33 41.72 Oxalis perennans 0.50 0.92 1.29 43.01 Plantago lanceolata* 0.58 0.25 1.28 44.29 Tricoryne elatior 0.58 0.25 1.26 45.55 Pratia purpurascens 0.50 0.67 1.23 46.78 Dichondra repens 0.67 0.58 1.22 48.00 Lomandra multiflora 0.25 0.67 1.22 49.22 Bursaria spinosa 0.42 0.50 1.21 50.43

Table A2.4e Species that contributed up to 50% of the dissimilarity between Mount Annan (MA) and Orchard Hills (OH) in terms of ground species composition and cover. The mean cover of each species is shown, along with the individual and cumulative contributions of these species to the dissimilarity between sites. Exotic species are marked with an asterisk. MA OH Mean cover Mean cover Contribution Cumulative Species (%) (%) (%) contribution (%) Paspalum dilatatum* 15.08 9.25 3.14 3.14 Themeda australis 9.58 27.25 2.99 6.13 Aristida ramosa 16.33 35.50 2.89 9.02 Chloris ventricosa 11.00 0.67 2.56 11.58 Cynodon dactylon* 2.92 3.75 2.01 13.59 Briza subaristata* 0.08 1.67 2.00 15.58 Aristida vagans 6.08 0.08 1.98 17.56 Oxalis perennans 1.00 0.25 1.96 19.52 Fimbristylis dichotoma 0.08 0.83 1.88 21.40 Microlaena stipoides 2.00 0.33 1.75 23.16 Bothriochloa macra 0.17 0.75 1.68 24.84 Cyperus gracilis 0.92 0.17 1.66 26.49 Setaria gracilis* 5.42 7.83 1.65 28.14 Sida rhombifolia* 0.67 0.17 1.56 29.70 Cheilanthes sieberi subsp. sieberi 1.33 0.08 1.52 31.22 Sporobolus indicus var. Capensis* 0.67 0.25 1.43 32.66 Plantago lanceolata* 0.58 0.33 1.38 34.04 Bursaria spinosa 0.67 0.33 1.36 35.39 Brunoniella australis 0.75 0.83 1.32 36.71 Eucalyptus moluccana 0.33 0.58 1.28 38.00 Desmodium brachypodum 0.50 0.67 1.27 39.27 Sporobolus creber 0.58 0.42 1.27 40.53 Einadia trigonos 2.83 0.00 1.27 41.80 Geranium solanderi var. solanderi 0.58 0.50 1.26 43.06 Eragrostis leptostachya 0.50 0.33 1.24 44.30 Plantago gaudichaudii 0.58 0.67 1.22 45.52 Phyllanthus virgatus 0.08 0.50 1.20 46.73 Desmodium varians 0.58 0.75 1.20 47.93 Asperula conferta 0.75 0.58 1.20 49.13 Sida corrugata* 0.50 0.00 1.16 50.29

Table A2.4f Species that contributed up to 50% of the dissimilarity between Mount Annan (MA) and Prospect (PR) in terms of ground species composition and cover. The mean cover of each species is shown, along with the individual and cumulative contributions of these species to the dissimilarity between sites. Exotic species are marked with an asterisk. MA PR Mean cover Mean cover Contribution Cumulative Species (%) (%) (%) contribution (%) Themeda australis 9.58 59.08 3.26 3.26 Aristida ramosa 16.33 0.00 3.15 6.41 Paspalum dilatatum* 15.08 3.33 2.55 8.96 Chloris ventricosa 11.00 0.00 2.53 11.49 Setaria gracilis* 5.42 0.75 2.18 13.67 Oxalis perennans 1.00 0.08 2.03 15.70 Cynodon dactylon* 2.92 12.08 1.99 17.69 Aristida vagans 6.08 0.08 1.73 19.42 Hardenbergia violacea 0.00 0.75 1.68 21.11 Microlaena stipoides 2.00 4.00 1.61 22.72 Pultanaea parviflora 0.00 1.00 1.59 24.31 Cyperus gracilis 0.92 0.00 1.59 25.90 Opercularia diphylla 0.00 0.67 1.49 27.38 Tricoryne elatior 0.17 0.75 1.49 28.87 Sida rhombifolia* 0.67 0.08 1.42 30.30 Asperula conferta 0.75 0.25 1.40 31.70 Briza subaristata* 0.08 2.50 1.32 33.02 Lomandra spp. 0.00 0.67 1.32 34.33 Lomandra multiflora 0.00 0.58 1.29 35.62 Geranium solanderi var. solanderi 0.58 0.00 1.27 36.89 Sporobolus indicus var. Capensis* 0.67 0.25 1.26 38.15 Plantago lanceolata* 0.58 0.25 1.23 39.38 Cheilanthes sieberi subsp. sieberi 1.33 0.67 1.20 40.58 Sporobolus creber 0.58 0.00 1.18 41.75 Bursaria spinosa 0.67 0.33 1.18 42.93 Eucalyptus moluccana 0.33 0.58 1.17 44.10 Plantago gaudichaudii 0.58 0.00 1.17 45.27 Dichondra repens 0.83 0.75 1.12 46.39 Desmodium varians 0.58 0.42 1.11 47.50 Einadia trigonos 2.83 0.00 1.11 48.61 Poa labillardieri 4.50 0.17 1.08 49.69 Arthropodium milleflorum 0.25 0.50 1.08 50.77

Table A2.4g Species that contributed up to 50% of the dissimilarity between Mount Annan (MA) and Scheyville (SNP) in terms of ground species composition and cover. The mean cover of each species is shown, along with the individual and cumulative contributions of these species to the dissimilarity between sites. Exotic species are marked with an asterisk. MA SNP Mean cover Mean cover Contribution Cumulative Species (%) (%) (%) contribution (%) Themeda australis 9.58 34.58 2.94 2.94 Paspalum dilatatum* 15.08 12.50 2.59 5.53 Aristida ramosa 16.33 1.25 2.55 8.08 Chloris ventricosa 11.00 6.67 2.46 10.54 Cynodon dactylon* 2.92 8.33 1.98 12.52 Aristida vagans 6.08 1.17 1.89 14.41 Setaria gracilis* 5.42 1.33 1.80 16.21 Microlaena stipoides 2.00 0.17 1.72 17.93 Panicum simile 0.00 1.42 1.71 19.63 Asperula conferta 0.75 0.00 1.70 21.33 Paspalidium distans 0.00 0.83 1.68 23.02 Cyperus gracilis 0.92 0.08 1.56 24.58 Senecio madagascariensis* 0.75 0.08 1.49 26.06 Opercularia diphylla 0.00 0.67 1.48 27.54 Pratia purpurascens 0.00 0.67 1.46 29.00 Fimbristylis dichotoma 0.08 0.67 1.40 30.40 Sida rhombifolia* 0.67 0.17 1.40 31.80 Eragrostis leptostachya 0.50 1.33 1.38 33.18 Cymbopogon refractus 0.33 0.83 1.37 34.55 Eremophila debilis 0.33 0.75 1.31 35.86 Geranium solanderi var. solanderi 0.58 0.00 1.29 37.16 Plantago lanceolata* 0.58 0.25 1.23 38.39 Cheilanthes sieberi subsp. sieberi 1.33 0.75 1.21 39.60 Plantago gaudichaudii 0.58 0.00 1.20 40.79 Sporobolus indicus var. capensis* 0.67 0.42 1.19 41.99 Desmodium varians 0.58 0.17 1.18 43.17 Sporobolus creber 0.58 0.17 1.18 44.35 Hypericum gramineum 0.25 0.50 1.14 45.49 Einadia trigonos 2.83 0.00 1.13 46.62 Bursaria spinosa 0.67 0.50 1.12 47.74 Eucalyptus moluccana 0.33 0.50 1.12 48.85 Phyllanthus virgatus 0.08 0.50 1.11 49.96 Olea europaea subsp. cuspidata* 0.50 0.42 1.10 51.06

Table A2.4h Species that contributed up to 50% of the dissimilarity between Orchard Hills (OH) and Prospect (PR) in terms of ground species composition and cover. The mean cover of each species is shown, along with the individual and cumulative contributions of these species to the dissimilarity between sites. Exotic species are marked with an asterisk. OH PR Mean cover Mean cover Contribution Cumulative Species (%) (%) (%) contribution (%) Aristida ramosa 35.50 0.00 5.13 5.13 Themeda australis 27.25 59.08 3.65 8.79 Setaria gracilis* 7.83 0.75 2.85 11.63 Paspalum dilatatum* 9.25 3.33 2.50 14.14 Cynodon dactylon* 3.75 12.08 2.43 16.57 Microlaena stipoides 0.33 4.00 2.18 18.75 Briza subaristata* 1.67 2.50 2.03 20.77 Hardenbergia violacea 0.00 0.75 2.00 22.77 Fimbristylis dichotoma 0.83 0.08 1.93 24.71 Bothriochloa macra 0.75 0.17 1.92 26.63 Pultanaea parviflora 0.00 1.00 1.88 28.51 Opercularia diphylla 0.08 0.67 1.70 30.21 Cheilanthes sieberi subsp. sieberi 0.08 0.67 1.70 31.91 Plantago gaudichaudii 0.67 0.00 1.59 33.49 Lomandra multiflora 0.00 0.58 1.53 35.03 Tricoryne elatior 0.42 0.75 1.47 36.50 Desmodium brachypodum 0.67 0.25 1.45 37.94 Brunoniella australis 0.83 0.67 1.43 39.38 Chloris ventricosa 0.67 0.00 1.43 40.81 Lomandra spp. 0.42 0.67 1.40 42.20 Desmodium varians 0.75 0.42 1.38 43.58 Asperula conferta 0.58 0.25 1.35 44.93 Dichondra repens 0.83 0.75 1.33 46.26 Eucalyptus moluccana 0.58 0.58 1.29 47.55 Phyllanthus virgatus 0.50 0.42 1.29 48.84 Arthropodium milleflorum 0.33 0.50 1.28 50.12

Table A2.4i Species that contributed up to 50% of the dissimilarity between Orchard Hills (OH) and Scheyville (SNP) in terms of ground species composition and cover. The mean cover of each species is shown, along with the individual and cumulative contributions of these species to the dissimilarity between sites. Exotic species are marked with an asterisk. OH SNP Mean cover Mean cover Contribution Cumulative Species (%) (%) (%) contribution (%) Aristida ramosa 35.50 1.25 3.39 3.39 Themeda australis 27.25 34.58 3.24 6.62 Paspalum dilatatum* 9.25 12.50 3.04 9.66 Cynodon dactylon* 3.75 8.33 2.24 11.90 Setaria gracilis* 7.83 1.33 2.19 14.09 Senecio madagascariensis* 0.92 0.08 2.03 16.12 Briza subaristata* 1.67 0.08 1.97 18.09 Chloris ventricosa 0.67 6.67 1.92 20.01 Aristida vagans 0.08 1.17 1.89 21.91 Panicum simile 0.08 1.42 1.81 23.72 Eremophila debilis 0.00 0.75 1.80 25.52 Oxalis perennans 0.25 0.92 1.78 27.29 Cheilanthes sieberi subsp. sieberi 0.08 0.75 1.71 29.00 Eragrostis leptostachya 0.33 1.33 1.70 30.71 Cymbopogon refractus 0.17 0.83 1.67 32.37 Paspalidium distans 0.17 0.83 1.66 34.04 Bothriochloa macra 0.75 0.17 1.65 35.69 Opercularia diphylla 0.08 0.67 1.56 37.25 Desmodium varians 0.75 0.17 1.54 38.79 Pratia purpurascens 0.08 0.67 1.54 40.33 Plantago gaudichaudii 0.67 0.00 1.51 41.84 Asperula conferta 0.58 0.00 1.34 43.17 Desmodium brachypodum 0.67 0.33 1.33 44.51 Brunoniella australis 0.83 0.58 1.32 45.82 Bursaria spinosa 0.33 0.50 1.24 47.07 Eucalyptus moluccana 0.58 0.50 1.23 48.30 Phyllanthus virgatus 0.50 0.50 1.23 49.52 Richardia stellaris* 0.42 0.50 1.20 50.73

Table A2.4j Species that contributed up to 50% of the dissimilarity between Prospect (PR) and Scheyville (SNP) in terms of ground species composition and cover. The mean cover of each species is shown, along with the individual and cumulative contributions of these species to the dissimilarity between sites. Exotic species are marked with an asterisk. PR SNP Mean cover Mean cover Contribution Cumulative contribution Species (%) (%) (%) (%) Themeda australis 59.08 34.58 2.93 2.93 Paspalum dilatatum* 3.33 12.50 2.75 5.68 Cynodon dactylon* 12.08 8.33 2.39 8.06 Microlaena stipoides 4.00 0.17 2.22 10.28 Oxalis perennans 0.08 0.92 2.08 12.36 Aristida ramosa 0.00 1.25 2.00 14.36 Aristida vagans 0.08 1.17 1.92 16.28 Cymbopogon refractus 0.00 0.83 1.87 18.15 Chloris ventricosa 0.00 6.67 1.84 19.99 Eragrostis leptostachya 0.25 1.33 1.78 21.77 Panicum simile 0.17 1.42 1.76 23.54 Hardenbergia violacea 0.75 0.17 1.70 25.24 Setaria gracilis* 0.75 1.33 1.63 26.87 Eremophila debilis 0.17 0.75 1.63 28.49 Pratia purpurascens 0.00 0.67 1.61 30.10 Pultanaea parviflora 1.00 0.25 1.61 31.71 Paspalidium distans 0.25 0.83 1.57 33.28 Tricoryne elatior 0.75 0.25 1.56 34.84 Fimbristylis dichotoma 0.08 0.67 1.55 36.39 Briza subaristata* 2.50 0.08 1.49 37.88 Senecio madagascariensis* 0.58 0.08 1.40 39.27 Lomandra spp. 0.67 0.33 1.36 40.63 Lomandra multiflora 0.58 0.67 1.33 41.96 Dichondra repens 0.75 0.58 1.28 43.24 Bossiaea prostrata 0.50 0.50 1.27 44.51 Brunoniella australis 0.67 0.58 1.26 45.77 Bursaria spinosa 0.33 0.50 1.25 47.02 Eucalyptus moluccana 0.58 0.50 1.25 48.27 Phyllanthus virgatus 0.42 0.50 1.24 49.51 Arthropodium milleflorum 0.50 0.08 1.22 50.73

Table A2.5a Species that contributed up to 50% of the dissimilarity between the combined tree and shrub patch type (T and S) and the open patch type (O) in terms of ground species composition and cover. The mean cover of each species is shown, along with the individual and cumulative contributions of these species to the dissimilarity between patch types. Exotic species are marked with an asterisk. T and S O Mean cover Mean cover Contribution Cumulative Species (%) (%) (%) contribution (%) Themeda australis 29.87 44.47 3.13 3.13 Aristida ramosa 15.13 14.87 2.97 6.10 Aristida vagans 5.77 8.47 2.59 8.69 Chloris ventricosa 6.27 3.40 2.27 10.96 Microlaena stipoides 5.17 2.07 2.05 13.01 Setaria gracilis* 1.47 2.00 1.64 14.66 Paspalidium distans 0.80 1.07 1.57 16.23 Lomandra spp. 0.60 1.07 1.56 17.78 Panicum simile 0.97 0.27 1.48 19.26 Eragrostis leptostachya 1.40 0.27 1.44 20.70 Richardia stellaris* 0.37 0.67 1.43 22.13 Tricoryne elatior 0.47 0.60 1.34 23.47 Cymbopogon refractus 0.63 0.33 1.34 24.81 Desmodium varians 0.53 0.40 1.34 26.15 Fimbristylis dichotoma 0.47 0.53 1.33 27.47 Opercularia diphylla 0.47 0.47 1.31 28.79 Sporobolus creber 0.33 0.53 1.31 30.10 Desmodium brachypodum 0.50 0.40 1.31 31.41 Paspalum dilatatum* 0.27 0.47 1.31 32.72 Oxalis perennans 0.57 0.53 1.31 34.03 Eucalyptus moluccana 0.63 0.53 1.30 35.33 Bursaria spinosa 0.63 0.53 1.30 36.63 Eragrostis brownii 0.93 1.00 1.30 37.93 Sporobolus indicus var. capensis* 0.47 0.40 1.27 39.21 Sida rhombifolia* 0.43 0.40 1.27 40.48 Lomandra multiflora 0.40 0.40 1.26 41.74 Phyllanthus virgatus 0.37 0.40 1.25 42.99 Plantago gaudichaudii 0.37 0.40 1.21 44.20 viminea 0.30 0.40 1.21 45.40 Vernonia cinerea 0.40 0.27 1.18 46.59 Arthropodium milleflorum 0.43 0.13 1.17 47.75 Bothriochloa macra 0.57 0.33 1.16 48.92 Cyperus gracilis 0.47 0.27 1.16 50.08

Table A2.5b Species that contributed up to 50% of the dissimilarity between the combined tree and shrub patch type (T and S) and the pasture patch type (P) in terms of ground species composition and cover. The mean cover of each species is shown, along with the individual and cumulative contributions of these species to the dissimilarity between patch types. Exotic species are marked with an asterisk. T and S P Mean cover Mean cover Contribution Cumulative Species (%) (%) (%) contribution (%) Paspalum dilatatum* 0.27 35.67 4.65 4.65 Cynodon dactylon* 0.13 22.00 3.86 8.51 Themeda australis 29.87 0.33 3.68 12.18 Briza subaristata* 0.13 8.27 2.47 14.65 Aristida ramosa 15.13 1.47 2.35 17.00 Setaria gracilis* 1.47 9.47 2.05 19.05 Dichondra repens 1.07 0.07 1.92 20.97 Brunoniella australis 1.00 0.07 1.81 22.78 Chloris ventricosa 6.27 0.00 1.79 24.57 Chloris gayana* 0.00 10.40 1.78 26.35 Microlaena stipoides 5.17 2.27 1.72 28.07 Aristida vagans 5.77 0.33 1.70 29.77 Cheilanthes sieberi subsp. sieberi 1.03 0.00 1.65 31.42 Digitaria spp. 0.00 1.33 1.44 32.86 Bursaria spinosa 0.63 0.00 1.39 34.25 Plantago lanceolata* 0.27 0.80 1.38 35.63 Eucalyptus moluccana 0.63 0.07 1.37 37.00 Lomandra spp. 0.60 0.00 1.30 38.30 Bothriochloa macra 0.57 0.67 1.27 39.58 Carex inversa 0.13 0.60 1.26 40.84 Eragrostis leptostachya 1.40 0.60 1.26 42.09 Hypochoeris radicata* 0.03 0.60 1.22 43.32 Panicum simile 0.97 0.00 1.21 44.53 Paspalidium distans 0.80 0.13 1.18 45.71 Desmodium varians 0.53 0.13 1.14 46.85 Senecio madagascariensis* 0.67 0.67 1.12 47.97 Cymbopogon refractus 0.63 0.40 1.12 49.09 Oxalis perennans 0.57 0.53 1.08 50.17

Table A2.5c Species that contributed up to 50% of the dissimilarity between the open (O) and pasture patch types (P) in terms of ground species composition and cover. The mean cover of each species is shown, along with the individual and cumulative contributions of these species to the dissimilarity between patch types. Exotic species are marked with an asterisk. O P Mean cover Mean cover Contribution Cumulative Species (%) (%) (%) contribution (%) Themeda australis 44.47 0.33 4.45 4.45 Paspalum dilatatum* 0.47 35.67 4.34 8.79 Cynodon dactylon* 0.00 22.00 4.25 13.04 Aristida ramosa 14.87 1.47 2.58 15.61 Briza subaristata* 0.27 8.27 2.53 18.15 Chloris gayana* 0.00 10.40 1.88 20.03 Aristida vagans 8.47 0.33 1.88 21.90 Cheilanthes sieberi subsp. sieberi 0.80 0.00 1.84 23.74 Microlaena stipoides 2.07 2.27 1.78 25.52 Setaria gracili*s 2.00 9.47 1.74 27.26 Brunoniella australis 0.80 0.07 1.62 28.88 Dichondra repens 0.73 0.07 1.57 30.45 Chloris ventricosa 3.40 0.00 1.53 31.97 Digitaria spp. 0.00 1.33 1.52 33.50 Plantago lanceolata* 0.27 0.80 1.47 34.97 Richardia stellaris* 0.67 0.00 1.45 36.42 Carex inversa 0.07 0.60 1.36 37.79 Tricoryne elatior 0.60 0.20 1.33 39.12 Hypochoeris radicata* 0.07 0.60 1.28 40.40 Bothriochloa macra 0.33 0.67 1.25 41.65 Bursaria spinosa 0.53 0.00 1.19 42.84 Senecio madagascariensis* 0.73 0.67 1.18 44.02 Eucalyptus moluccana 0.53 0.07 1.18 45.20 Fimbristylis dichotoma 0.53 0.40 1.17 46.37 Oxalis perennans 0.53 0.53 1.14 47.51 Sporobolus creber 0.53 0.07 1.14 48.65 Opercularia diphylla 0.47 0.00 1.11 49.76 Paspalidium distans 1.07 0.13 1.11 50.88

Table A2.6 Summary of life cycle characteristics and metabolic pathways for those grass species with a mean cover greater than or equal to 2% at any one site (from the SIMPER analysis). Adapted from Wheeler et al. (2002) and DECCW (2009b). Blank spaces indicate the information was unavailable and asterisks denote exotic species. Species Life cycle Flowering time Mature seeds Metabolic pathway Longevity Aristida ramosa Perennial Summer November - June C4 2-25 years Aristida vagans Perennial Summer October - June C4 2-25 years Axonopus affinis* Perennial

Briza subaristata* Perennial Spring C3 2-5 years

Chloris gayana* Perennial Summer February - June C4 Indefinite Chloris ventricosa Perennial Summer February - April C4 5-25 years Cynodon dactylon* Perennial Summer C4 Indefinite

Eragrostis brownii Perennial

Eragrostis curvula* Perennial Summer - Autumn

Eragrostis leptostachya Perennial Summer C4

Microlaena stipoides Perennial Anytime December - April C3 2-5 years Paspalidium distans Perennial October - April C4 2-5 years

Paspalum dilatatum* Perennial Summer - Autumn C4 Indefinite

Poa labillardieri Perennial Spring C3 5-25 years

Setaria gracilis* Perennial Summer C4 2-5 years

Themeda australis Perennial Summer December - March C4 Indefinite

Appendix 3. Statistics for the Hoxton Park study

Table A3.1 ANOVA of soil data across four locations, three patch types and four sampling times for the Hoxton Park study

Table A3.2a Mauchly‟s test of sphericity for Bray 1 P

Table A3.2b Split-plot ANOVA for Bray 1 P

Table A3.3a Mauchly‟s test of sphericity for Total C

Table A3.3b Split-plot ANOVA for Total C

Table A3.4a Mauchly‟s test of sphericity for Total N

Table A3.4b Split-plot ANOVA for Total N

Table A3.5a Mauchly‟s test of sphericity for C:N ratio

Table A3.5b Split-plot ANOVA for C:N ratio

Table A3.6a Mauchly‟s test of sphericity for pH

TableA3.6b Split-plot ANOVA for pH

Table A3.7a Mauchly‟s test of sphericity for active C

Table A3.7b Split-plot ANOVA for active C

Table A3.8a Mauchly‟s test of sphericity for respiration

Table A3.8b Split-plot ANOVA for respiration

Table A3.9a Mauchly‟s test of sphericity for moisture content

Table A3.9b Split-plot ANOVA for moisture content

Table A3.9c Back-transformed mean moisture contents and the upper and lower 95% confidence limits for the various sampling times

Table A3.9d Back-transformed mean moisture contents and the upper and lower 95% confidence limits for the four locations in June, September, December 2007 and March 2008

Table A3.9e Back-transformed mean moisture contents and the upper and lower 95% confidence limits for the pasture and controls in June, September, December 2007 and March 2008

Table A3.10a Split-plot ANOVA for nitrate

Table A3.10b Planned comparisons for nitrate: pasture vs. controls

Table A3.10c Back-transformed mean nitrate concentrations and the upper and lower 95% confidence limits for the various sampling times

Table A3.10d Back-transformed mean nitrate concentrations and the upper and lower 95% confidence limits for the four locations in June, September, December 2007 and March 2008

Table A3.11a Mauchly‟s test of sphericity for ammonium

Table A3.11b Split-plot ANOVA for ammonium

Table A3.11c Planned comparisons for ammonium: pasture vs. controls

Table A3.11d Planned comparison for ammonium: restored areas vs. woodland

Table A3.11e Back-transformed mean ammonium concentrations and the upper and lower 95% confidence limits for the four locations in June, September, December 2007 and March 2008

Table A3.11f Back-transformed mean ammonium concentrations and the upper and lower 95% confidence limits for the open, shrub and tree patch types within the 6-year old restored area in June, September, December 2007 and March 2008

Table A3.11g Back-transformed mean ammonium concentrations and the upper and lower 95% confidence limits for the open, shrub and tree patch types within the 14-year old restored area in June, September, December 2007 and March 2008

Table A3.11h Back-transformed mean ammonium concentrations and the upper and lower 95% confidence limits for the open, shrub and tree patch types within the woodland in June, September, December 2007 and March 2008

Table A3.1 ANOVA of soil data across four locations, three patch types and four sampling times for the Hoxton Park study A=location, a=4 (pasture, 6-year old restored area, 14-year old restored area and woodland), random factor

B(A)=sub-location nested in location, b=8, random factor C=patch type, c=3 (tree, shrub and open), fixed factor, orthogonal to location and sub-location D=time, d=4, random factor, orthogonal to location and sub-location n=1 measurement per time x patch type x sub-location x location combination

Source of variation df i j k l m F vs. df of test 2 2 2 2 2 Location Ai 3 1 b c d n σs +cnσ B(A)D+bcnσ AD+cdnσ B(A)+bcdnσ A B(A) if AD NS 2 2 2 Location B(A)j(i) 28 1 1 c d n σ +cnσ B(A)D+cdnσ B(A) B(A)D

2 2 2 2 2 2 2 Patch type Ck 2 a b 0 d n σ +nσ B(A)CD+ bnσ ACD+abnσ CD+ dnσ B(A)C+bdnσ AC+abdnσ C AC if ACD & CD NS 2 2 2 2 2 Location x patch ACik 6 1 b 0 d n σ +nσ B(A)CD+ bnσ ACD+dnσ B(A)C+bdnσ AC B(A)C if ACD NS 2 2 2 Sub-location x patch B(A)Cj(i)k 56 1 1 0 d n σ +nσ B(A)CD+dnσ B(A)C B(A)CD 2 2 2 2 Time Dl 3 a b c 1 n σ +cnσ B(A)D+bcnσ AD+abcnσ D AD 2 2 2 Site x time ADil 9 1 b c 1 n σ +cnσ B(A)D+bcnσ AD B(A)D 2 2 Sub-location x time B(A)Dj(i)l 84 1 1 c 1 n σ +cnσ B(A)D error term 2 2 2 2 Patch type x time CDkl 6 a b 0 1 n σ + nσ B(A)CD+bnσ ACD+abnσ CD ACD 2 2 2 Site x patch x time ACDikl 18 1 b 0 1 n σ + nσ B(A)CD+bnσ ACD B(A)CD 2 2 Sub-location x patch x time B(A)CDj(i)kl 168 1 1 0 1 n σ + nσ B(A)CD error term 2 Residual 1 1 1 1 1 1 σ e Total 384

The following acronyms have been used in this appendix:

GG=Greenhouse-Geisser HP=Hoxton Park MA=Mount Annan OH=Orchard Hills PR=Prospect SNP=Scheyville

Figures in bold highlight significant main effects, interactions or post hoc tests

Table A3.2a Mauchly‟s test of sphericity for Bray 1 P Epsilon Within subjects effect Mauchly's W Approx. Chi-Square df P GG Patch .667 10.935 2 .004 .750

Table A3.2b Split-plot ANOVA for Bray 1 P Source of variation df SS MS F P GG df GG MS GG F GG P Location All locations 3 2.124 .708 8.072 .000 Sub-location All locations 28 2.456 .088

Patch type All locations 2 .008 NA NA Controls 2 .027 .014 .239 NS Location x Patch type All locations 6 .280 NA NA 4.501 NA NA Controls 4 .229 .057 1.326 .276 2.948 .078 1.326 .284 Sub-location x Patch type All locations 56 1.999 NA NA 42.009 NA NA Controls 42 1.811 .043 30.953 .059 Residual 1 Total All locations 96 6.867

Table A3.3aMauchly‟s test of sphericity for Total C Epsilon Within subjects effect Mauchly's W Approx. Chi-Square df P GG Patch .897 2.930 2 .231 .907

Table A3.3b Split-plot ANOVA for Total C Source of variation df SS MS F P Loc All locations 3 .449 .150 1.888 .154 Sub-Location All locations 28 2.221 .079

Patch type All locations 2 .157 NA NA Controls 2 .122 .061 .547 NS Location x Patch type All locations 6 .450 NA NA Controls 4 .447 .112 4.215 .006 Sub-Location x Patch type All locations 56 1.221 NA Controls 42 1.114 .027 Residual 1 Total All 96 4.498

Table A3.4a Mauchly‟s test of sphericity for Total N Epsilon Within subjects effect Mauchly's W Approx. Chi-Square df P GG Patch .819 5.401 2 .067 .847

Table A3.4b Split-plot ANOVA for Total N Source of variation df SS MS F P Loc All locations 3 .011 .004 .572 .638 Sub-Location All locations 28 .175 .006

Patch type All locations 2 .016 NA NA Controls 2 .010 .005 .312 NS Location x Patch type All locations 6 .062 NA NA Controls 4 .062 .015 4.816 .003 Sub-Location x Patch type All locations 56 .149 NA Controls 42 .135 .003 Residual 1 Total All 96 .413

Table A3.5a Mauchly‟s test of sphericity for C:N ratio Epsilon Within subjects effect Mauchly's W Approx. Chi-Square df P GG Patch .976 .666 2 .717 .976

Table A3.5b Split-plot ANOVA for C:N ratio Source of variation df SS MS F P Loc All locations 3 118.648 39.549 28.923 .000 Sub-Location All locations 28 38.288 1.367

Patch type All locations 2 .811 NA NA Controls 2 2.326 1.163 .910 NS Location x Patch type All locations 6 7.348 NA NA Controls 4 5.110 1.278 1.959 .119 Sub-Location x Patch type All locations 56 37.185 NA NA Controls 42 27.398 .652 Residual 1 Total All 96 202.280

Table A3.6a Mauchly‟s test of sphericity for pH Epsilon Within subjects effect Mauchly's W Approx. Chi-Square df P GG Patch .850 4.393 2 .111 .869 Time 1.000 .000 0 . 1.000 Patch x Time .904 2.733 2 .255 .912

TableA3.6b Split-plot ANOVA for pH Source of variation df SS MS F P Location All locations 3 .098 .033 No test Sub-Location All locations 28 .198 .007

Patch type All locations 2 .020 NA NA Controls 2 .017 .009 No test Location x Patch type All locations 6 .045 NA NA Controls 4 .044 .011 No test Sub-Location x Patch type All locations 56 .085 NA Controls 42 .071 .002 Time All locations 1 .034 .034 3.725 NS Location x Time All locations 3 .027 .009 5.684 .004 Controls 2 .027 .014 7.053 .005 Pasture 1 .001 .001 .254 NS Sub-Location x Time All locations 28 .045 .002 Controls 21 .040 .002 Patch type x Time All locations 2 .001 NA NA Controls 2 .001 .000 .158 NS Location x Patch type x Time All locations 6 .012 NA NA Controls 4 .012 .003 3.243 .021 Sub-Locationx Patch type x Time All locations 56 .044 NA Controls 42 .038 .001 Residual 1 Total All locations 192 .609

Table A3.7a Mauchly‟s test of sphericity for active C Epsilon Within subjects effect Mauchly's W Approx. Chi-Square df P GG Patch .924 2.137 2 .343 .929 Times 1.000 .000 0 . 1.000 Patch x Time .955 1.248 2 .536 .957

Table A3.7b Split-plot ANOVA for active C Source of variation df SS MS F P Location All locations 3 .145 .048 No test Sub-Location All locations 28 .278 .010

Patch type All locations 2 .004 NA NA Controls 2 .004 .002 .243 NS Location x Patch type All locations 6 .032 NA NA Controls 4 .032 .008 .643 .635 Sub-Location x Patch type All locations 56 .550 NA Controls 42 .521 .012 Time All locations 1 .087 .087 3.346 NS Location x Time All locations 3 .078 .026 4.920 .007 Controls 2 .004 .002 .375 .692 Pasture 1 .073 .073 13.960 <0.001 Sub-Location x Time All locations 28 .147 .005 Controls 21 .117 .006 Patch type x Time All locations 2 .003 NA NA Controls 2 .005 .002 .719 NS Location x Patch type x Time All locations 6 .016 NA NA Controls 4 .014 .003 .388 .816 Sub-Locationx Patch type x Time All locations 56 .408 NA Controls 42 .368 .009 Residual 1 Total All 192 1.748

Table A3.8a Mauchly‟s test of sphericity for respiration Epsilon Within subjects effect Mauchly's W Approx. Chi-Square df P GG Patch .800 6.026 2 .049 .833 Time 1.000 .000 0 . 1.000 Patch x Time .954 1.285 2 .526 .956

Table A3.8b Split-plot ANOVA for respiration („All‟ refers to „All locations‟, „C‟ stands for „Controls‟ and „Loc‟ and „Sub-Loc‟ identify the „Location‟ and „Sub-locations‟ respectively). Source of variation df SS MS F P GG df GG MS GG F GG P Loc All 3 .185 .062 .687 .567 Sub-Location All 28 2.516 .090

Patch type All 2 .109 NA NA C 2 .185 .092 .647 NS Loc x Patch type All 6 .709 NA NA 5.0 NA NA C 4 .571 .143 2.671 .045 Sub-Loc x Patch type All 56 3.226 NA 46.6 NA C 42 2.246 .053 Time All 1 .016 .016 .385 NS Loc x Time All 3 .121 .040 .528 .667 C 2 .029 .014 .157 .856 Sub-Loc x Time All 28 2.141 .076 Patch type x Time All 2 .072 NA NA C 2 .141 .071 .456 NS Loc x Patch type x Time All 6 .738 NA NA C 4 .619 .155 1.946 .121 Sub-Loc x Patch type x Time All 56 4.754 NA C 42 3.339 .080 Residual 1 Total All 192 11.88

Table A3.9a Mauchly‟s test of sphericity for moisture content Epsilon Within subjects effect Mauchly's W Approx. Chi-Square df P GG Patch .810 5.675 2 .059 .841 Time .040 86.075 5 .000 .410 Patch x Time .005 135.194 20 .000 .442

Table A3.9b Split-plot ANOVA for moisture content („All‟ refers to „All locations‟, „C‟ stands for „Controls‟ and „Loc‟ and „Sub-Loc‟ identify the „Location‟ and „Sub-locations‟ respectively). GG GG GG GG Source of variation df SS MS F P df MS F P 3.56 no Loc All 3 10.69 4 test Sub-Loc All 28 8.60 .307

Patch type All 2 .28 NA NA NS C 2 .13 .069 .214 NS Loc x Patch type All 6 1.35 NA NA 5.04 NA NA C 4 1.29 .324 3.54 .014 3.06 .424 3.54 .025 Sub-Loc x Patch type All 56 4.43 NA 47.0 NA C 42 3.84 .092 32.1 .120 Time All 3 178. 59.5 183. <.0001 Loc x Time All 9 2.91 .324 6.13 .000 3.68 .791 6.13 .001 C 6 .79 .132 2.00 .078 2.40 .329 2.00 .148 P 3 2.12 .707 13.3 <.0001 1.27 1.661 12.8 <.05 Sub-Loc x Time All 84 4.43 .053 34.4 .129 C 63 4.15 .066 25.2 4.156 Patch type x Time All 6 .22 .037 1.09 NS C 6 .22 .038 .845 NS Loc x Patch type x Time All 18 .61 .034 1.29 .196 7.94 .078 1.29 .259 C 12 .54 .045 1.44 .152 5.33 .101 1.44 .218 Sub-Loc x Patch type x Time All 168 4.43 .026 74.1 .060 C 126 3.91 .031 56.0 .070 Residual 1 Total All 384 216.6

Table A3.9c Back-transformed mean moisture contents and the upper and lower 95% confidence limits for the various sampling times Mean L1 L2 Jun-07 17.2 15.7 18.9 Sep-07 3.61 3.45 3.78 Dec-07 4.82 4.61 5.04 Mar-08 3.01 2.83 3.20

Table A3.9d Back-transformed mean moisture contents and the upper and lower 95% confidence limits for the four locations in June, September, December 2007 and March 2008 Jun-07 Sep-07 Dec-07 Mar-08 Mean L1 L2 Mean L1 L2 Mean L1 L2 Mean L1 L2 Pasture 26.1 23.7 28.7 3.99 3.72 4.27 5.38 5.05 5.73 3.86 3.61 4.13 6yoR 19.3 15.8 23.6 3.93 3.52 4.38 5.32 4.82 5.88 3.31 2.93 3.74 14yoR 12.9 11.4 14.5 3.60 3.36 3.85 4.51 4.16 4.89 2.75 2.57 2.95 Woodland 13.5 11.4 16.1 3.02 2.79 3.28 4.19 3.87 4.54 2.33 2.04 2.65

Table A3.9e Back-transformed mean moisture contents and the upper and lower 95% confidence limits for the pasture and controls in June, September, December 2007 and March 2008 Jun-07 Sep-07 Dec-07 Mar-08 Mean L1 L2 Mean L1 L2 Mean L1 L2 Mean L1 L2 Pasture 26.1 23.7 28.7 3.99 3.72 4.27 5.38 5.05 5.73 3.86 3.61 4.13 Controls 15.0 13.5 16.6 3.50 3.31 3.69 4.65 4.41 4.91 2.77 2.58 2.97

Table A3.10a Split-plot ANOVA for nitrate („All‟ refers to „All locations‟, „C‟ stands for „Controls‟ and „Loc‟ and „Sub-Loc‟ identify the „Location‟ and „Sub-locations‟ respectively). Source of variation df SS MS F P Loc All 3 17.763 5.921 3.108 .042 Sub-Loc All 28 53.347 1.905

Patch type All 2 .823 NA NA C 2 1.170 .585 no test Loc x Patch type All 6 9.348 NA NA C 4 8.227 2.057 4.885 <0.005 Sub-Loc x Patch type All 56 22.747 NA NA C 42 18.683 .445 Time All 3 68.785 22.928 18.401 <0.001 Loc x Time All 9 11.210 1.246 3.532 .001 C 6 6.175 1.029 2.686 .022 Pasture 3 5.035 1.678 4.759 <0.005 Sub-Loc x Time All 84 29.624 .353 C 63 24.142 .383 Patch type x Time All 6 4.857 NA NA C 6 4.934 .822 2.435 NS Loc x Patch type x Time All 18 4.994 NA NA C 12 4.052 .338 1.299 .227 Sub-Loc x Patch type x Time All 168 43.230 NA NA C 126 32.756 .260 Residual 1 Total All 384 266.728

Planned comparisons for nitrate:

Table A3.10b Pasture vs. Controls

Time df SS MS F(1,84) P Jun-07 1 0.0975347 0.0975347 0.2765642 >0.25 Sep-07 1 6.9316056 6.9316056 19.65489 <0.001 Dec-07 1 4.663967 4.663967 13.224895 <0.001 Mar-08 1 2.894017 2.894017 8.2061199 <0.01 4 14.587124

Table A3.10c Back-transformed mean nitrate concentrations and the upper and lower 95% confidence limits for the various sampling times Mean L1 L2 Jun-07 11.7 10.0 13.7 Sep-07 3.98 3.26 4.82 Dec-07 10.2 8.82 11.7 Mar-08 4.32 3.56 5.20

Table A3.10d Back-transformed mean nitrate concentrations and the upper and lower 95% confidence limits for the four locations in June, September, December 2007 and March 2008 Jun-07 Sep-07 Dec-07 Mar-08 Mean L1 L2 Mean L1 L2 Mean L1 L2 Mean L1 L2 Pasture 11.0 8.56 14.2 6.92 4.67 10.0 15.3 12.3 19.0 6.18 3.93 9.47 6yoR 7.37 4.75 11.2 3.24 2.13 4.74 8.95 6.79 11.7 3.31 2.11 4.96 14yoR 20.2 16.4 24.7 3.42 2.29 4.93 10.6 7.80 14.3 5.24 4.29 6.36 Woodland 11.3 8.46 15.0 3.14 2.05 4.61 7.20 5.37 9.57 3.14 1.98 4.75

Table A3.11a Mauchly‟s test of sphericity for ammonium Epsilon Within subjects effect Mauchly's W Approx. Chi-Square df P GG Patch .994 .151 2 .927 .994 Time .378 26.032 5 .000 .618 Patch x Time .197 41.868 20 .003 .703

Table A3.11b Split-plot ANOVA for ammonium GG GG Source of variation df SS MS F P df MS GG F GG P Loc All 3 36.98 12.3 18. .000 Sub-Loc All 28 18.33 .655

Patch type All 2 5.15 NA NA no ACD C 2 6.66 3.3 test sig Loc x Patch type All 6 10.49 NA NA no ACD C 4 7.621 1.90 test sig Sub-Loc x Patch type All 56 17.74 NA NA NA C 42 14.24 .339 Time All 3 16.97 5.65 1.982 NS Loc x Time All 9 25.68 2.85 10.39 .000 5.56 4.62 10.3 .000 C 6 12.90 2.15 7.834 .000 3.70 3.48 7.84 <0.01 P 3 12.7 4.26 15.51 0 1.85 6.89 15.5 <0.001 Sub-Loc x Time All 84 23.06 .275 51.8 .444 Patch type x Time All 6 2.89 NA C 6 2.70 .451 .939 NS Loc x Patch type x Time All 18 6.64 NA C 12 5.76 .481 2.879 .002 6.50 .88 2.87 0.012 Sub-Loc x Patch type x Time All 168 27.58 NA C 126 21.02 .167 68.3 .30 Residual 1 Total All 383 191.5

Planned comparisons for ammonium:

Table A3.11c Pasture vs. Controls

Time df SS MS F1,52 P Jun-07 1 17.0077 17.0077 38.2704 <0.001 Sep-07 1 2.47284 2.47284 5.56432 <0.05 Dec-07 1 0.86624 0.86624 1.94919 NS Mar-08 1 2.2345 2.2345 5.02802 <0.05 4 22.58

Planned comparison for ammonium:

Table A3.11d Restored areas vs. woodland

Time df SS MS F1,52 P Jun-07 1 4.88305 4.88305 10.9877 <0.01 Sep-07 1 0.71934 0.71934 1.61863 NS Dec-07 1 1.77907 1.77907 4.00322 NS Mar-08 1 0.05975 0.05975 0.13445 NS 4 7.44

Table A3.11e Back-transformed mean ammonium concentrations and the upper and lower 95% confidence limits for the four locations in June, September, December 2007 and March 2008 Jun-07 Sep-07 Dec-07 Mar-08 Mean L1 L2 Mean L1 L2 Mean L1 L2 Mean L1 L2 Pasture 55.6 47.7 64.8 26.9 22.5 32.3 12.9 9.62 17.1 20.6 17.5 24.1 6yoR 33.6 23.5 47.9 27.2 22.7 32.6 23.4 20.2 27.1 19.8 16.8 23.3 14yoR 8.17 4.82 13.4 14.2 12.1 16.6 14.3 10.8 18.8 10.5 9.05 12.1 Woodland 30.0 21.9 40.9 15.7 12.9 19.1 12.8 10.6 15.5 13.5 10.5 17.5

Table A3.11f Back-transformed mean ammonium concentrations and the upper and lower 95% confidence limits for the open, shrub and tree patch types within the 6-year old restored area in June, September, December 2007 and March 2008 Jun-07 Sep-07 Dec-07 Mar-08 Mean L1 L2 Mean L1 L2 Mean L1 L2 Mean L1 L2 Open 55.3 37.8 80.8 32.7 23.0 46.4 22.9 15.6 33.6 19.1 13.6 26.9 Shrub 27.7 15.7 48.4 26.8 20.5 34.9 25.8 22.1 30.1 23.8 17.4 32.5 Tree 24.7 9.4 62.5 22.9 14.9 35.0 21.6 15.8 29.3 17.1 12.8 22.8

Table A3.11g Back-transformed mean ammonium concentrations and the upper and lower 95% confidence limits for the open, shrub and tree patch types within the 14-year old restored area in June, September, December 2007 and March 2008 Jun-07 Sep-07 Dec-07 Mar-08 Mean L1 L2 Mean L1 L2 Mean L1 L2 Mean L1 L2 Open 3.8 1.1 10.0 13.8 10.2 18.5 9.3 6.2 13.8 8.3 6.4 10.8 Shrub 19.2 6.6 52.5 15.4 10.8 21.9 21.2 11.1 39.8 14.1 10.8 18.2 Tree 7.0 4.3 11.0 13.5 9.9 18.3 14.6 9.7 21.6 9.7 8.4 11.3

Table A3.11h Back-transformed mean ammonium concentrations and the upper and lower 95% confidence limits for the open, shrub and tree patch types within the woodland in June, September, December 2007 and March 2008 Jun-07 Sep-07 Dec-07 Mar-08 Mean L1 L2 Mean L1 L2 Mean L1 L2 Mean L1 L2 Open 24.7 17.1 35.5 13.6 10.4 17.6 9.1 6.9 12.0 7.3 4.7 11.1 Shrub 42.3 31.5 56.6 19.9 12.8 30.7 14.3 9.8 20.5 17.7 12.8 24.2 Tree 25.7 9.7 65.7 14.4 9.6 21.4 16.2 11.8 21.9 18.8 12.8 27.4