T h e s o i l o f ab an d o n ed fa rm l an d ,
Cumberland Plain Woodland and restored
v eg et at i o n : i m p l i cat i o n s fo r t h e r es t o rat i o n
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 Sydney,
Australia.
July, 2009.
Cumberland Plain Woodland at Hoxton Park. Photo by Jennifer Kit Fitzgerald
Abandoned farmland at Scheyville National Park. Photo by Jennifer Kit Fitzgerald
ii
Decl ar at i o n
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 Cumberland Plain 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 shrubs 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 shrub) 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
agriculture. This thesis addressed these issues by investigating the soils 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
Penrith Lakes Development Corporation and the Linnaean Society of New South Wales
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
plant 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 woodlands ................................ 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 Orchard 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 Prospect Reservoir ..................................................................................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
L i s t o f t a bl e s
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 Blacktown 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 Shale Hills
Woodland and Shale Plains 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
FIGURE 2.2a
FIGURE 2.2b
FIGURE 2.2c
FIGURE 2.2d
FIGURE 2.2e
FIGURE 2.2f
FIGURE 2.2g
FIGURE 2.2h
FIGURE 2.3
FIGURE 2.4
FIGURE 2.5
FIGURE 2.6
FIGURE 2.7
FIGURE 2.8
FIGURE 2.9
FIGURE 3.1
FIGURE 3.2a-e
FIGURE 3.3a-e
FIGURE 3.4
FIGURE 3.5a-e
FIGURE 3.6
FIGURE 3.7a-e
FIGURE 3.8
FIGURE 3.9a-e
FIGURE 3.10
FIGURE 3.11
Map of the Cumberland Plain and surrounding Hawkesbury
Sandstone plateaux showing the locations of the five study sites
................................................................................................ 25
Long-term climatic data for selected variables for Badgerys Creek
................................................................................................ 27
Long-term climatic data for selected variables for Camden ......... 27
Long-term climatic data for selected variables for Liverpool ....... 28
Long-term climatic data for selected variables for Orchard Hills .28
Long-term climatic data for selected variables for Parramatta .....29
Long-term climatic data for selected variables for Picton ............ 29
Long-term climatic data for selected variables for Prospect ......... 30
Long-term climatic data for selected variables for Richmond ...... 30
Block diagram showing the six physiographic units of the Sydney
region ..................................................................................... 32
Schematic diagram of the Blacktown soil landscape showing
changes in soil types and soil materials along the toposequence
................................................................................................ 35
Land granted on the Cumberland Plain during the period 1788
to1821 ..................................................................................... 38
Crown land on the Cumberland Plain in 1806 ............................. 40
The extent of agricultural land uses in various districts of the
County of Cumberland in 1810, 1815 and 1820 ...................... 41
Contemporary land uses for the Cumberland Plain ...................... 45
Historical European land use map for Scheyville ........................ 58
Mean surface soil (0-5 cm) bulk density for the study sites ......... 77
Mean moisture content with depth for the patch types at each site
................................................................................................ 77
Mean pH with depth for the patch types at each site .................... 79
Back-transformed mean EC values with depth for the study sites 79
Mean concentration of active C with depth beneath the patch types
at each site .............................................................................. 81
Back-transformed total C levels with depth at the study sites ...... 81
Back-transformed mean Bray 1 P concentrations with depth for the
patch types at each site ........................................................... 83
Mean concentration of total S with depth at the study sites .......... 83
Back-transformed mean nitrate concentrations with depth for the
patch types at each site ........................................................... 85
Back-transformed mean ammonium levels with depth for the study
sites ........................................................................................ 85
Back-transformed mean total N concentrations with depth for the
study sites ............................................................................... 86
xii
FIGURE 4.1a
FIGURE 4.1b
FIGURE 4.2a
FIGURE 4.2b
FIGURE 4.2c
FIGURE 4.3
FIGURE 4.4
FIGURE 4.5a
FIGURE 4.5b
FIGURE 4.6a
FIGURE 4.6b
FIGURE 4.7a
FIGURE 4.7b
FIGURE 4.7c
FIGURE 4.7d
FIGURE 4.7e
FIGURE 4.8
Mean native species richness for the ground layer at the study sites
.............................................................................................. 110
Mean native species richness for the ground layer of the four patch
types ..................................................................................... 110
Mean exotic species richness for the ground layer at the study sites
.............................................................................................. 111
Mean exotic species richness for the ground layer of the four patch
types ..................................................................................... 111
Mean exotic species richness for the ground layer of the four patch
types at each of the study sites .............................................. 111
Dendrogram showing the percentage similarity between samples
where ground species composition and cover were measured in
10 x 10 m quadrats ............................................................... 112
nMDS ordination of ground species composition and cover ...... 113
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
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
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
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
nMDS ordination of the samples based on the normalised
Euclidean distance for soil moisture content, nitrate, total N and
exchangeable Na .................................................................. 119
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
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
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
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
Positive feedbacks between plant litter chemistry and N
mineralization ...................................................................... 127
xiii
FIGURE 4.9
FIGURE 5.1
The „microbial-N loop‟ ............................................................. 129
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 yearold 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
L i s t o f pl a t e s
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 Eucalyptus
moluccana individuals ............................................................ 64
PLATE 2
E. moluccana in flower ............................................................ 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
L i s t o f a pp e n di c e s
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
CHAP TER 1. The significance of Cumberland P lain
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,
Woronora 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 20 th 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 ecosystems 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
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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 Eucalyptus
moluccana and Eucalyptus tereticornis 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 herbs 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).
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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 plants; 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 vines 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 Thr ea tened Species Conser va tion Act 1995
The objectives of the TSC Act are (AustLII 2008):

„to conserve biological diversity and promote ecologically sustainable

development, and;

populations and ecological communities, and;

ecological communities that are endangered, and;
to prevent the extinction and promote the recovery of threatened species,
to protect the critical habitat of those threatened species, populations and
to eliminate or manage certain processes that threaten the survival or
evolutionary development of threatened species, populations and ecological

communities, and;

and ecological communities is properly assessed, and
to ensure that the impact of any action affecting threatened species, populations
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;
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


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 threatening processes;

plan; and

identify critical habitat and state what must be done to protect it;
identify ways to minimise any adverse socio-economic consequences of the
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 ecologica l
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
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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.
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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
the school land contains EECs.
Medium
Management of EECs to be included in the conditions for Crown land trusts, lease and
licence holders.
Medium
Prepare and implement community awareness, education and involvement strategy.
Medium
Support community conservation by providing nursery or other facilities, for regeneration
activities.
Low
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
conservation is compatible.
Promote best practice management guidelines.
High
Medium
Habitat management: ongoing EIA - advice to consent and planning authorities
Incorporate consideration of EEC protection in regional open space planning.
Encourage planning authorities to address EECs in development of environmental planning
instruments and, where possible, seek biodiversity certification.
High
Medium
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
conservation significance assessment.
High
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
tenures.
High
Ensure the consideration of impacts on EECs when enforcing noxious weed or pest species
control in EECs.
Medium
Habitat Protection (including voluntary conservation agreements etc.)
Develop and implement Cumberland Plain Reservation Strategy and create a protected
bushland network through targeted land acquisition as land becomes available.
High
Public authorities will promote management agreements to landholders through their
ongoing land use planning activities.
Medium
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
of vegetation across the Cumberland Plain.
Recovery Plan Preparation: multi species
Medium
Finalise the multi-EEC recovery plan as a State priority in accordance with contractual
obligations with DEH by July 2007.
Medium
Research
Liaise with institutions to facilitate research relevant to the recovery of the Cumberland
Plain EECs.
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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
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Table 1.2. Previous research on the soils of the Cumberland Plain.
Topic
Author / Date
Henson (1887)
Jensen (1921)
Walker (1960)
Classification
and mapping
Corbett (1972)
Hamilton (1976)
Bannerman and Hazelton
(1990)
DECC (2008d)
Parker and Chartres (1983)
Hollinger et al. (2001)
Erskine et al. (2003)
Land use
impacts
Hill et al. (2005)
Chan and Barchia (2007)
Chan et al. (2007)
Quilty et al. (1976)
Urban and rural
land capabilities
Johnston and Hicks (1984)
Logan and Luscombe
(1984)
Brief Description
- earliest recorded classification of soils in the Sydney
region
- soils grouped according to the origin of their parent
material
- surveyed and mapped the soils of the Hawkesbury
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
- soil physical and chemical properties for characteristic
profiles of each soil series were measured
- discussed major factors controlling soil formation
- mapped the soils of the Cumberland Plain according to
Great Soil Groups and described typical profiles
- dominant factors affecting profile development and soil
formation were discussed
- mapped the soils of the Hawkesbury River Catchment
Area at a scale of 1:250 000
- mapped the soil landscapes of the Penrith 1:100 000
map sheet, which covers most of the Cumberland Plain
- mapped the soil landscapes of the Hawkesbury-Nepean
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
Gum High Forest) on morphological, physical and
chemical properties of some red podzolic soils of the
Cumberland Plain
- measured sediment yields and nutrient losses in runoff
from a market garden in Richmond
- compared soil loss under different land uses
(woodland/forest, pasture, cultivated and urbanised) in
the South Creek Catchment
- examined relationships between anthropogenic
disturbances, soil properties and exotic species in
Cumberland Plain Woodland
- measured earthworm distribution, abundance and
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
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
engineering and construction works for the South
Penrith Housing Project
- mapped and surveyed the soils for constraints to
engineering and construction works in the Camden Park
Development Area
- mapped and surveyed the soils for constraints to urban
and rural development in the north west sector of
Sydney
…continued over
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Table 1.2. Previous research on the soils of the Cumberland Plain (continued).
Topic
Agricultural
assessment
Sustainable
farming
Author / Date
Brief Description
Guthrie (1891)
- general soil fertility was described and recommendations
made for liming
- described the soils most suited for orchards on the
Cumberland Plain
- described methods to reduce soil erosion and improve
(exotic) pasture quality and quantity for grazing and
dairying in and around Camden
- outlined how to prevent mass movement on farms in the
Camden district
- investigated the relationship between mineralogy and
soil colour for the podzolic soils of the Cumberland
Plain
- examined soil development on the floodplain of the
Nepean River
- investigated cadmium levels in vegetables and soils in
Greater Sydney, including some areas of the
Cumberland Plain
- looked at why areas of the floodplain of the Hawkesbury
River had saline soils
- surveyed the soils and vegetation of the outer north west
region of Sydney to identify areas suitable for
recreational and scientific uses
Jensen (1910)
Beirne (1953)
Huston (1953)
Mineralogy
Davey et al. (1975)
Soil
development
Walker and Hawkins
(1957)
Heavy metals
Jinadasa et al. (1997)
Salinity
Collis-George and Evans
(1964)
Nature
conservation
Forster et al. (1977)
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
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Table 1.3. Previous research on the vegetation of the Cumberland Plain.
Topic
Author / Date
Pidgeon (1941)
Burrell (1972)
Benson and Howell
(1990a)
Benson et al. (1990)
Classification
and mapping
Benson (1992)
French et al. (2000a)
French et al. (2000b)
NPWS (2002b)
Tozer (2003)
Benson and Howell
(1990b)
Biodiversity and
conservation
James (1997)
Myerscough (1998)
James et al. (1999)
Brief Description
- classified and compared the vegetation of Hawkesbury
Sandstone and Wianamatta Shale
- Cumberland Plain Woodland was classified as part of
the Eucalyptus hemiphloia - Eucalyptus tereticornis
Association
- attempted to reconstruct the vegetation of the Sydney
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
- mapped the current and pre-European distribution of
each vegetation community
- they were the first to use the term „Cumberland Plain
Woodland‟
- mapped the vegetation of the Bents Basin State
Recreation Area
- revised Benson and Howell (1990a) but the
classification maintained the use of the dominant canopy
species
- mapped the vegetation of the Penrith 1:100 000 map
sheet
- used Cumberland Plain Woodland as a case study for
examining the adequacy of subjective classification
systems for endangered vegetation communities
- 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
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)
- 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
urbanisation for habitat destruction on the Cumberland
Plain
- identified poorly conserved vegetation types and areas of
conservation significance within each suburb
- compiled species list (flora and fauna) for each LGA,
identified core and complimentary biodiversity areas and
identified actual and potential vegetation corridors
- significant species, conservation needs and major threats
to diversity were reported
- reviewed various aspects of the ecology of Myrtaceae in
the Sydney area, with some attention given to the
eucalypts of the Cumberland Plain
- a comprehensive native flora for the Cumberland Plain
with over 500 species listed
…continued over
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Table 1.3. Previous research on the vegetation of the Cumberland Plain (continued).
Topic
Author / Date
Benson and Howell (2002)
Biodiversity and
conservation
(cont.)
Darley (2005)
James (1994)
Thomas (1994)
Disturbance
regimes
Lewis (2001)
Hill and French (2004)
Watson (2005)
McDonald (1996)
Wood (2001)
Seed ecology
Hill and French (2003)
Clarke and French (2005)
Ens (2002)
Willis et al. (2003)
Weed ecology
and
management
Berryman (2005)
Cuneo and Leishman
(2006)
Brief Description
- compared 19th century descriptions of the floristics and
structure of Cumberland Plain Woodland to
contemporary records based on 14 years of monitoring
at Mount Annan Botanic Gardens
- concerned with the debates surrounding the structure
(grassy vs. shrubby) and composition (how many
species have become extinct since 1788?) of
Cumberland Plain Woodland
- studied the ecology, specifically the mycorrhizal
infection sites, of orchids on the Cumberland Plain to aid
in their conservation
- observed the effects of a change from high to low
frequency mowing on species composition and richness
in a regrowth stand of Turpentine- Ironbark Forest
- investigated the effects of hazard reduction burning on
the structure and floristics of Cumberland Plain
Woodland at Prospect Reservoir
- observed the impacts of the cessation of mowing on the
floristics of a stand of Blue Gum High Forest
- investigated the effects of fire and grazing on floristics
and regeneration of shrubs and eucalypts within the
Holsworthy Military Area
- investigated the effects of different fire frequencies on
the floristics and structure of Cumberland Plain
Woodland
- applied fire, tillage and herbicide treatments to a pasture
that was once Cumberland Plain Woodland, to observe
the germination response of the soil seed bank
- investigated the size and species composition of soil
seed banks at Nurragingy Reserve and Clarendon
Paddocks; also looked at germination responses for
individual species
- analysed the soil seed bank at Holsworthy Military Area
and compared it to species richness and abundance of
the standing vegetation
- tested the germination response of grasses to heat and
smoke, with seeds collected from two stands of
Cumberland Plain Woodland
- an honours thesis which examined the distribution of
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
population in Cumberland Plain Woodland) and
Asparagus asparagoides (threatening weed), to see if
fire could be used to promote the former and eliminate
the later
- examined resistance to weed invasions in monocultures
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
contribute to its success as an environmental weed
- described a model for invasion using sites on the
Cumberland Plain as a case study
…continued over
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Table 1.3. Previous research on the vegetation of the Cumberland Plain (continued).
Topic
Author / Date
Wilkins et al. (2003)
Restoration
assessment
Nichols (2005)
Lomov (2006)
Brief Description
- measured and compared the floristic composition and
structure of vegetation along a 9 year chronosequence of
restored sites to assess restoration success
- developed a method to evaluate the success of the
attempted restoration of Cumberland Plain Woodland on
abandoned farmland
- assessed Cumberland Plain Woodland restoration in
terms of plant-insect 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
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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 predisturbance 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 localand ecosystem-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
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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, insects 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
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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
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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 sheep 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
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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 forests. 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 herb 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
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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);
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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
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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 macrofauna (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, nonsporeforming fungi and some bacteria (Raison 1979). In addition to this, the recovery of
microbial communities often results in a very different composition compared to prefire 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,
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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).
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Prober and Thiele (2005) proposed a framework, based on the integration of process
and diversity, to maximise restoration outcomes for temperate woodlands and
grasslands 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).
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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 intercanopy 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
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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 intercanopy 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?
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CHAP TER 2 Description of the Cumberland Plain and
st u d y si t es
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).
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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
Ashfield
Auburn
Bankstown
Baulkham Hills
Blacktown
Burwood
Camden
Campbelltown
Canada Bay
Canterbury
Fairfield
Hawkesbury
Holroyd
Hornsby
Hunters Hill
Hurstville
Kogarah
Ku-Ring-Gai
Lane Cove
Leichhardt
Liverpool
Marrickville
Parramatta
Penrith
Rockdale
Ryde
South Sydney
Strathfield
Sydney
Willoughby
Wollondilly
Size of LGA (ha)
827
3,236
7,744
39,958
23,934
713
20,052
31,037
1,975
3,347
10,132
276,761
4,010
50,537
562
2,460
1,933
8,514
1,039
1,251
30,524
1,650
6,119
40,288
3,003
4,054
1,779
1,385
638
2,216
255,029
Area on the Plain (ha)
683
3,111
7,057
11,959
23,934
713
20,052
14,836
557
3,145
10,132
27,005
4,010
6,661
82
1,580
231
3,395
203
245
25,827
1,331
6,119
38,372
527
2,730
532
1,385
72
1,070
57,056
Portion on the Plain (%)
82.6
96.1
91.1
29.9
100
100
100
47.8
28.2
94.0
100
9.8
100
13.2
14.6
64.2
11.9
39.9
19.5
19.6
84.6
80.7
100
95.2
17.6
67.4
29.9
100
11.2
48.3
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
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120
30
100
25
80
Rainfall (mm)
Temperature (°C)/Number of rain days
35
20
60
15
40
10
5
20
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
35
120
30
100
25
80
20
60
15
40
10
5
20
0
0
Rainfall (mm)
Temperature (°C)/Number of rain days
Figure 2.2a Long-term climatic data for selected variables for Badgerys Creek (BOM 2009).
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).
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120
30
100
25
80
20
60
15
40
10
5
20
0
0
Rainfall (mm)
Temperature (°C)/Number of rain days
35
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
35
120
30
100
25
80
20
60
15
40
10
5
20
0
0
Rainfall (mm)
Temperature (°C)/Number of rain days
Figure 2.2c Long-term climatic data for selected variables for Liverpool (BOM 2009).
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).
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120
30
100
25
80
20
60
15
40
10
5
20
0
0
Rainfall (mm)
Temperature (°C)/Number of rain days
35
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
35
120
30
100
25
80
20
60
15
40
10
5
20
0
0
Rainfall (mm)
Temperature (°C)/Number of rain days
Figure 2.2e Long-term climatic data for selected variables for Parramatta (BOM 2009).
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).
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120
30
100
25
80
20
60
15
40
10
5
20
0
0
Rainfall (mm)
Temperature (°C)/Number of rain days
35
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
35
120
30
100
25
80
20
60
15
40
10
5
20
0
0
Rainfall (mm)
Temperature (°C)/Number of rain days
Figure 2.2g Long-term climatic data for selected variables for Prospect (BOM 2009).
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).
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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 saucershaped tectonic depression that underlies most of western Sydney with a long, narrow
extension from Parramatta to Botany Bay (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
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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 Georges River 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 Shales, 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 Wianamatta Group, which
consists of three formations that were laid down during a single regressive episode
during the Middle Triassic (Herbert 1979). The Wianamatta Group is mainly composed
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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: Ashfield
Shale; Minchinbury Sandstone; and Bringelly Shale. 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, coal, 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
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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 rock 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).
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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).
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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).
Podzolic soil
Leaching of
bases
O horizon
development
Brown
wd
wd
Red
wd
min
Yellow
wd
min
Illuviation
Iron and
aluminium
wd
Clay
wd
wd
wd
wd
wd
wd
Bleicherde
formation
Humus
wd
min
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 socioeconomic 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 Discover y a nd settlement of the Cumber la nd P la in, 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
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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
Texture
Colour
pH (water)
Surface condition
Ped shape
Ped size
Ped fabric
Roots
Charcoal fragments
Iron nodules
A1
loam to clay loam
10YR 2/2, 5YR 3/2, 10YR 3/4
5.5-7.0
friable
sub-angular blocky
2-20 mm
rough faced and porous
common
uncommon
uncommon
bty 2
Horizon
Texture
Colour
pH (water)
Surface condition
Ped shape
A2
hardsetting
clay loam to silty clay loam
low fertility
7.5YR 4/3, 2.5YR 3/3, 10YR 3/3
strongly acidic
5.0-6.5
high Al toxicity
hardsetting & water repellent
weakly developed sub-angular blocky
Ped size
Ped fabric
Roots
Charcoal fragments
Iron nodules
20-50 mm
rough faced and porous
uncommon
uncommon
common
bty 3
Horizon
Texture
Colour
pH (water)
Ped shape
Ped size
Ped fabric
Roots
Charcoal fragments
Mottling
B
light to medium clay
7.5YR 4/6, 2.5YR 4/6, 10YR 4/6
4.5-6.5
polyhedral to sub-angular blocky
5-20 mm
smooth faced & dense
uncommon
uncommon
red, yellow & grey
localised shrink-swell capacity
low wet strength
low permeability
low available water
localised salinity
localised sodicity
very low fertility
very strongly acidic
very high Al toxicity
bty 4
Horizon
Texture
B3 or C
silty clay to heavy clay
localised shrink-swell capacity
low wet strength
Colour
pH (water)
Ped shape
Ped size
Ped fabric
Roots
10YR 7/1, 2.5YR 6/2
4.0-5.5
polyhedral to sub-angular blocky
2-20 mm
smooth faced & dense
uncommon
stoniness
low permeability
low available water
localised salinity
localised sodicity
very low fertility
Charcoal fragments
Iron nodules
Mottling
uncommon
common
red, yellow & grey
very strongly acidic
very high Al toxicity
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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
Parramatta River, around Prospect and on the floodplain of the Hawkesbury River in the
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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 doublecropping 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
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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
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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
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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 Agr icultur a l consolida tion of the Cumber la nd P la in, 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 Great Western
Highway, 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 depressio n
during the 1840s, spurred many large landholders to move the bulk of their operations
to the North Coast and Northern Tablelands 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
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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 Industr ia lisa tion of the Cumber la nd P la in, 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 timbergetting 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 Casuarina, were
also being collected to meet the ever-increasing demand for firewood. In addition to
this, Acacia 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, gravel,
shale and blue metal (Proudfoot 1987).
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2.1.7.4 Ur ba nisa tion of the Cumber la nd P la in, 1880-the pr esent da y
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
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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).
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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
Shale Sandstone Transition
Forest
Cooks River Castlereagh
Ironbark Forest
Castlereagh Swamp Woodland
Castlereagh Scribbly Gum
Woodland
Agnes Banks Woodland
Cumberland Plain Woodland
Sydney Coastal River-flat
Forest
Western Sydney Dry Rainforest
Moist Shale Woodland
Sydney Turpentine-Ironbark
Forest
Freshwater Wetlands
Elderslie Banksia Scrub Forest
Shale/Gravel Transition Forest
Blue Gum High Forest
TOTAL
Pre-European (ha)
Current (ha)
Remaining (%)
TSC Act
43 990.10
9949.80
22.6
11/09/1998
12 185.40
1011.60
8.3
10/05/1998
1006.00
616
61.2
24/12/1999
5852.40
3083.30
52.7
24/12/1999
615.2
97.8
15.9
3/11/2000
125 446.30
11 054.50
8.8
13/06/1997
39 161.80
5446.10
13.9
12/02/1999
1281.80
2033.60
338.2
604.1
26.4
29.7
24/03/2000
19/04/2002
26,516.40
1181.70
4.5
16/10/1998
1552.40
Not modelled
664.2
13.4
42.8
n/a
22/12/2000
9/10/1998
5427.40
3720.10
1721.20
167.8
31.7
4.5
19/04/2002
3/09/1997
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.
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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. Eucalyptus
crebra may become co-dominant towards the edge of the Cumberland Plain and
similarly, Corymbia maculata and Eucalyptus eugenioides 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 acacias 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
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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
Tree
Small Tree
Shrub
Ground


o





o
o
o
o







Eucalyptus moluccana
Eucalyptus tereticornis
Eucalyptus crebra
Acacia implexa
Eucalyptus eugenioides
Eucalyptus amplifolia
Corymbia maculata
Bursaria spinosa
Acacia falcata
Breynia oblongifolia
Indigophera australis
Dodonea viscosa subsp. cuneata
Aristida ramosa
Brunoniella australis
Cheilanthes sieberi spp. sieberi
Desmodium varians
Dichondra repens
Microlaena stipoides var.
stipoides
Themeda australis


o
o
o




Eucalyptus moluccana
Eucalyptus tereticornis
Eucalyptus crebra
Eucalyptus eugenioides
Corymbia maculata
Acacia decurrens
Acacia parramattensis subsp.
parramattensis
Exocarpus cupressiformus
Bursaria spinosa








Aristida vagans
Brunoniella australis
Desmodium varians
Dichelachne micrantha
Dichondra repens
Microlaena stipoides var. stipoides
Opercularia diphylla
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 vascular plant 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 Loca tion
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 Western Sydney Parklands, 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 Clima te a nd physica l geogr a phy
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
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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
Landforms
Local relief
Altitude
Slope gradient
Rock outcrop
Rural Land Capability
Grazing limitations
Cultivation limitations
Urban capability
Occurrence of steep slopes
Mass movement hazard
Occurrence of seasonal waterlogging
Occurrence of permanent waterlogging
Flood hazard
Foundation hazard
Salinity hazard
Blacktown
Luddenham
Rises and low hills
10-50 m
10-20 masl
0-9%
nil
IV (II, VI)
Low
Low-moderate
B(C)
Not observed
Low hills and hills
30-100 m
10-104 masl
5-20%
nil
IV (VI)
Low-moderate
Low-high
B(C)
Localised
Not observed
Localised
Not observed
Not observed
Localised
Localised
Localised
Localised
Not observed
Localised
Widespread
Localised
Localised
Localised
Widespread
Localised
Sheet erosion
Gully erosion
JK Fitzgerald
Chapter 2
Page 50
2.2.1.3 Vegeta tion
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 Eur opea n la nd use histor y
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
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2.2.2 Mount Annan Botanic Garden
2.2.2.1 Loca tion
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 Clima te a nd physica l geogr a phy
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 Vegeta tion
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 rabbit 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
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2.2.2.4 Eur opea n la nd use histor y
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 Loca tion
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 Sydney Water 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 Clima te a nd physica l geogr a phy
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
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2.2.3.3 Vegeta tion
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, Grevillea 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 Eur opea n la nd use histor y
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
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currently grazed by Eastern Grey Kangaroos and wallabies, as well as several feral
animals, namely rabbits and hares (pers. obs. 2006).
2.2.4 Prospect Reservoir
2.2.4.1 Loca tion
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 Clima te a nd physica l geogr a phy
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 Vegeta tion
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
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Plain; it is also core habitat for the threatened species A. pubescens and P. spicata
(James 1997).
2.2.4.4 Eur opea n la nd use histor y
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 broadacre 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 Loca tion
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 Clima te a nd physica l geogr a phy
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
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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 Vegeta tion
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 wetland 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 broadacre cropping, as well as fruit and vegetable growing (Thorp 1992).
2.2.5.4 Eur opea n la nd use histor y
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
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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 CoOperative 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
JK Fitzgerald
facility
for
destitute
men
Chapter 2
(Stubbs
and
Stubbs
1983).
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
Occupation
Time
Frame
Common
1804 – 1890
Pitt Town
Co-Operative Labour
Settlement
Casual Labour Farm
1893 – 1896
1896 – 1910




















Government
Agricultural Training
Farm
1910 - 1940








Military Occupation
1940 – 1945
Migrant Hostel
1945 – 1964
Officer Training Camp
1965 – 1973
Community Use
1977 – 1996
JK Fitzgerald









Indicators of Agricultural Development
Used as pasture only
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
200-300 acres of cleared timber
Sawmill in operation
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
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,
lettuce, onions, spinach, strawberries and rhubarb
3 silos and 25 dams
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 antitank warfare
Occupied by the First Parachute Battalion
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 centre occupied 100 acres of the original estate
Extent of farming activities unclear
Horse riding/agistment
Cattle grazing
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
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CHAP TER 3. The soil of abandoned farmland and
Cumberland P lain 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
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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 intercanopy 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
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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.
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Plate 1
Photo by Jennifer Kit Fitzgerald
Plate 2
Photo from DECCW (2009a)
Plate 3
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
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Plate 4
Photo from DECCW (2009a)
Plate 5
Photo by Jennifer Kit Fitzgerald
Plate 6
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
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Plate 7
Photo from DECCW (2009a)
Plate 8
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.
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Plate 10
Photo from DECCW (2009a)
Plate 11
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.
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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
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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 (0360°). 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).
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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 moistur e 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
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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 Electr ica l 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.
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3.2.3.6 Extr a cta ble 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 Nitr a te a nd a mmonium
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,
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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 Tota l C, tota l N a nd tota l S
The concentration of „total‟ nutrient pools can be a useful indicator of the soils longterm 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
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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 (F 4,10=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 (F 3,12=1.487, P
>0.05; Table 3.3) or in interaction with site (F 12,30=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, F 24,60=3.23, P=0.00012). At Hoxton Park,
Prospect and Scheyville for example, the pasture had the highest moisture levels to 20.5
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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 (F 8,20=2.96, P=0.023) but not by
patch type (F 6,24=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; F 10,26=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 (F 2,8=8.22, P<0.05; Table 3.4) and this was affected by site
(F 8,20=7.84, P<0.0001) but not by patch type (F 3,10=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 (F 3,12=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; F 2.72,10.89=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: F 10.89,27.22=0.95, P>0.05) or soil depth (site x depth
interaction; F 2.76,11.03=3.16, P>0.05).
EC increased significantly with depth (F 1.05,4.18=61.08, P<0.01), with the pattern
differing amongst sites (site x depth interaction: F 4.18,10.45=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
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values
than
Mount
Annan
Chapter 3
and
Scheyville
(Figure
3.4).
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
Mean
L1
Variable
-3
Bulk density (g cm )
1.09
Soil moisture content (%)
pH
-1
Electrical conductivity (dS m )*
-1
Bray 1 P (mg kg )*
-1
Ammonium (mg kg )*
Total C (%)*
Total N (%)*
Total S (%)
4.34
a
0.158
a
480
-1
Nitrate (mg kg )*
5.66
a
a
Active C (mg kg )
-1
ac
1.02
a
6.72
a
0.488
a
2.49
a
0.102
ac
0.0285
a
1.01
5.06
4.24
0.0915
373
0.704
5.39
0.262
1.80
0.0730
0.0231
L2
1.17
6.26
4.44
0.230
586
1.40
8.33
0.755
3.36
0.142
0.0338
Mount Annan
Mean
L1
0.950
a
6.54
a
b
4.82
0.131
a
ac
450
b
1.93
6.11
a
b
1.51
2.39
a
0.152
a
0.0233
ab
0.858
6.05
4.56
0.0891
343
1.29
4.92
0.769
1.73
0.114
0.0184
L2
1.04
7.02
5.09
0.175
558
2.75
7.55
2.552
3.20
0.204
0.0281
Orchard Hills
Mean
L1
b
1.44
5.56
a
bc
4.72
0.135
417
a
bc
0.71
c
b
3.91
0.417
a
b
1.69
0.101
0.0232
ac
ab
1.33
5.00
4.51
0.0791
334
0.510
3.19
0.159
1.29
0.0818
0.0194
L2
1.54
6.12
4.92
0.193
500
0.945
4.75
0.732
2.16
0.124
0.0270
Prospect
Mean
L1
1.16
ac
6.42
4.41
a
ac
0.153
a
bc
408
0.69
c
5.84
a
0.353
a
b
1.96
0.0934
0.0262
c
ab
1.07
5.55
4.29
0.093
331
0.482
4.81
0.0795
1.50
0.0737
0.0225
L2
1.24
7.30
4.53
0.217
484
0.929
7.06
0.695
2.52
0.118
0.0299
Scheyville
Mean
L1
L2
bc
1.13
1.34
b
3.43
4.49
ac
4.18
4.62
a
0.0618
0.145
bc
322
493
a
0.791
1.28
b
2.85
3.82
ab
0.357
1.086
c
0.905
1.51
b
0.175
0.420
b
0.0120
0.0180
1.24
3.96
4.40
0.102
408
1.02
3.30
0.683
1.19
0.271
0.0150
Moisture content (%)
1.50
0
3
6
9
12
Bulk density (g cm-3)
0
-10
1.00
Pasture
Soil depth (cm)
-20
0.50
Open
-30
Shrub
-40
Tree
-50
-60
Fig 3.1
Fig 3.2a Hoxton Park
-70
Moisture content (%)
0
3
6
9
12
0
0
0
-10
-10
Soil depth (cm)
Open
-30
Shrub
-40
Tree
Soil depth (cm)
Pasture
-20
Tree
Moisture content (%)
12
0
-10
-20
-20
Soil depth (cm)
-10
-30
-40
-70
Pasture
Open
Shrub
Tree
Fig 3.2c Orchard Hills
-70
0
-60
Pasture
-40
0
-50
12
Shrub
-60
0
9
Open
-60
Moisture content (%)
3
6
9
6
-30
-50
Fig 3.2b Mount Annan
3
-20
-50
-70
Soil depth (cm)
Moisture content (%)
3
6
9
12
Pasture
Open
-30
-40
Shrub
Tree
-50
-60
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
Soil moisture content (%)
6.54
4.59
pH
-1
Electrical conductivity (dS m )*
-1
Active C (mg kg )
ab
0.110
430
-1
a
a
a
1.02
5.87
4.44
0.0722
345
Bray 1 P (mg kg )*
0.86
a
0.60
Ammonium (mg kg-1)*
6.00a
4.79
-1
Nitrate (mg kg )*
Total C (%)*
Total N (%)*
Total S (%)
Table 3.4
Variable
pH
-1
Electrical conductivity (dS m )*
-1
Active C (mg kg )
Bray 1 P (mg kg )*
-1
Ammonium (mg kg )*
-1
Nitrate (mg kg )*
Total S (%
0.193
a
0.0262
a
4.83
a
4.87
a
0.0537
a
763
-1
Total N (%)*
1.81
a
Mean
Soil moisture content (%)
Total C (%)*
1.09
a
a
2.38
a
7.92
a
2.11
a
4.50
a
0.295
a
0.0296
a
0.55
1.37
0.130
1.29
1.22a
7.21
a
4.74
0.149
515
5.29
4.43
ab
0.116
384
a
317
4.02
0.256
0.0255
452
0.69
1.18
4.21
1.82
bc
2.33
0.286
0.45
1.82
a
0.125
a
0.0222
a
5.39
1.46
0.160
5.04a
a
6.76
4.57
0.92
Mean
2.01
5.85
a
7.45
L2
720
1.20a
1.16
2.5 cm
L1
0.0440
0.0742
1.34
a
0.0306
4.71
4.73
4.28
a
0.0218
4.27
1.09
5.02
0.0634
805
2.81
9.26
2.93
5.02
0.340
0.0337
5.10
b
4.54
0.0461
365
a
b
b
0.89
b
4.04
b
0.317
b
1.71
b
0.115
b
0.0145
0.20
1.41
0.0938
0.0184
20.5 cm
L1
4.64
4.41
0.0371
341
0.75
3.57
0.191
1.53
0.0928
0.0127
5.25
4.36
1.09
4.70
a
0.130
4.23
a
0.0832
a
433
351
1.32
1.11a
1.00
1.23
5.81
a
4.83
6.03
b
4.53
5.02
b
0.127
0.253
a
391
573
b
4.49
0.179
516
5.43
4.78
0.188
482
ab
0.73
1.28
1.41
0.97
1.94
5.99
4.19a
3.51
4.97
5.02a
4.18
5.98
0.76
b
0.50
ac
0.50
1.25
2.05
a
1.53
2.68
0.123
a
0.0966
0.156
0.0220
a
0.0178
0.0261
2.30
0.166
0.0259
0.99
0.31
1.93
a
0.101
a
0.0225
a
0.15
1.48
0.0808
0.0190
L2
6.58
7.34
c
4.08
4.34
b
0.279
0.381
c
154
185
c
0.23
0.39
b
3.36
4.42
0.0874
c
0.0435
0.133
0.643
c
0.579
0.710
0.0673
c
0.0527
0.0861
0.0256
c
0.0229
0.0283
5.56
389
1.06
5.57
0.455
1.90
0.141
0.0162
0.0260
b
Mean
0.0552
0.127
60.5 cm
L1
L2
4.67
2.47
6.96
4.21
0.329
169
0.31
3.86
0.84
pH
5
0
10
0
0
-10
-10
Pasture
-30
Open
-40
Shrub
Tree
-50
-60
Pasture
Open
-30
Shrub
-40
Tree
-50
-60
Fig 3.3a Hoxton Park
-70
Fig 3.3b Mount Annan
-70
pH
0
pH
5
0
10
0
-10
-10
Pasture
-30
Open
Soil depth (cm)
-20
Shrub
-40
Tree
Soil depth (cm)
0
Pasture
Open
-40
Shrub
-60
-60
Fig 3.3c Orchard Hills
Tree
Fig 3.3d Prospect
-70
EC (dS m-1)
pH
5
10
0
0
0
-10
-10
-20
Pasture
-30
Open
Shrub
Tree
Soil depth (cm)
Soil depth (cm)
0
10
-30
-50
-70
5
-20
-50
-40
10
-20
-20
Soil depth (cm)
Soil depth (cm)
0
pH
5
0.2
-20
-30
-40
0.4
0.6
Hoxton
Park
Mount
Annan
Orchard
Hills
Prospect
-50
-50
-60
-60
-70
Fig 3.3e Scheyville
-70
Fig 3.4
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;
F 12,30=1.87, P>0.05). The concentration of active C declined markedly with depth (main
effect; F 1,5=220.816, P<0.0001; Table 3.4); this decline differed more weakly amongst
sites (site x depth interaction; F 5,13=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; F 4,10=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; F 12,30=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; F 2,8=102.348,
P<0.0001; Table 3.4) and this trend varied between sites (site x depth interaction:
F 8,20=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; F 4,10=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
JK Fitzgerald
twice
the
concentration
of
Chapter 3
Scheyville
(1.19%;
P=0.000).
Page 80
Active C (mg kg-1)
0
400
800
Active C (mg kg-1)
1200
0
0
0
-10
-10
Soil depth (cm)
Open
-30
Shrub
Tree
-40
-50
800
1200
Pasture
-20
Soil depth (cm)
Pasture
-20
400
Open
-30
Shrub
Tree
-40
-50
-60
Fig 3.5a Hoxton Park
-60
Fig 3.5b Mount Annan
-70
-70
Active C (mg kg-1)
0
400
800
Active C (mg kg-1)
0
1200
0
-10
-10
Pasture
-20
Open
-30
Shrub
-40
Tree
-50
800
1200
Pasture
-20
Open
Soil depth (cm)
Soil depth (cm)
0
400
-30
Shrub
Tree
-40
-50
-60
-60
Fig 3.5c Orchard Hills
-70
Fig 3.5d Prospect
-70
Active C (mg kg-1)
0
400
800
Total C (%)
1200
0
0
-10
-10
Soil depth (cm)
-20
-30
-40
Pasture
Open
Shrub
Tree
Soil depth (cm)
0
-20
-30
-40
-50
-50
-60
-60
Fig 3.5e Scheyville
-70
-70
5
10
Hoxton
Park
Mount
Annan
Orchard
Hills
Prospect
Scheyville
Fig 3.6
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;
F 24,60=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; F 3,12=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; F 2,8=68.7,
P<0.0001; Table 3.4) and while this trend was not influenced by patch type (F 6,24=1.78,
P>0.05), it was influenced by site (F 8,20=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 (F 2,8=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: F 8,20=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; F 11,28=2.45, P<0.05). For the surface soil, nitrate levels were
JK Fitzgerald
Chapter 3
Page 82
Bray 1 P (mg kg-1)
0
3
6
9
12
Bray 1 P (mg kg-1)
15
0
18
0
0
-10
-10
Pasture
Soil depth (cm)
Open
-30
Shrub
-40
Tree
Shrub
Tree
-40
-60
Fig 3.7a Hoxton Park
Fig 3.7b Mount Annan
-70
0
3
6
9
12
Bray 1 P (mg kg-1)
15
0
18
0
0
-10
-10
Pasture
Open
-30
Shrub
-40
Tree
6
9
12
15
18
Pasture
-20
Open
-30
Shrub
Tree
-40
-50
-50
-60
-60
Fig 3.7c Orchard Hills
Fig 3.7d Prospect
-70
-70
Bray 1 P (mg kg-1)
0
3
6
9
12
15
0
18
0
-10
-10
Pasture
Open
Shrub
Tree
Soil depth (cm)
0
Soil depth (cm)
3
Soil depth (cm)
Soil depth (cm)
-20
-20
-30
Total S (%)
0.02
0.04
0.06
Hoxton Park
Mount Annan
Orchard Hills
Prospect
-40
Scheyville
-50
-50
-70
18
Open
Bray 1 P (mg kg-1)
-60
15
Pasture
-30
-70
-40
12
-50
-60
-30
9
-20
-50
-20
6
Soil depth (cm)
-20
3
-60
Fig 3.7e Scheyville
-70
Fig 3.8
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 (F 3,12=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: F 1,5=22.6, P<0.05; Table
3.4) and this trend varied between sites (site x depth interaction: F 5,13=4.95, P<0.05) but
not between patch types (patch type x depth interaction: F 2.75,11.02=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; F 7.17,17.92=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; F 6,24=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: F 2,8=30.5, P<0.001;
Table 3.4) with different trends across sites (depth x site interaction; F 8,20=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)
0
5
10
15
Nitrate (mg kg-1)
20
0
0
0
-10
-10
Pasture
Open
-30
Shrub
Tree
-40
15
20
Pasture
-20
Open
-30
Shrub
Tree
-40
-50
-50
-60
-60
Fig 3.9a Hoxton Park
Fig 3.9b Mount Annan
-70
-70
0
Nitrate (mg kg-1)
5
10
15
20
0
0
0
-10
-10
Pasture
Open
-30
Shrub
Tree
-40
-50
Open
-30
Tree
-60
Fig 3.9c Orchard Hills
Fig 3.9d Prospect
-70
0
Nitrate (mg kg-1)
5
10
15
Ammonium (mg kg-1)
20
0
-10
-10
Pasture
Open
Shrub
Tree
-50
-60
Soil depth (cm)
0
5
10
15
20
-20
Hoxton Park
-30
Mount Annan
-40
Orchard Hills
-50
Prospect
-60
Scheyville
Fig 3.9e Scheyville
-70
Shrub
-40
0
-40
Pasture
-20
-70
-30
20
-50
-60
-20
Nitrate (mg kg-1)
5
10
15
Soil depth (cm)
Soil depth (cm)
-20
Soil depth (cm)
10
Soil depth (cm)
Soil depth (cm)
-20
5
-70
Fig 3.10
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; F 4,10=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: F 12,30=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
Soil depth (cm)
-10
-40
Hoxton
Park
Mount
Annan
Orchard
Hills
Prospect
-50
Scheyville
-20
-30
-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; F 1,5=20.8,
P<0.01; Table 3.4) and this trend varied significantly across the five sites (F 5,12=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 fro m
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 birds (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 1.041.69 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
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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
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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
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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 Queensland. 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.
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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
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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-onignition, 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 sclerophyll 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
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(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
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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.
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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).
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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
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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 intercanopy (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).
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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
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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.
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CHAP TER 4. The ground flora of abandoned farmland and
Cumberland P lain 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 predisturbance 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
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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 intersite 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
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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 taxonomy 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
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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
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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 Exa mining the flor istic simila r ity of sa mples using cluster a na lysis a nd or dina tion
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 groupaverage linking and an ordination plot was made using non-metric multi-dimensional
scaling (nMDS) with 10 random restarts.
4.2.4.2 Investiga ting the effects of site a nd pa tch type on gr ound species composition a nd
cover with a na lysis of simila r ity a nd the SIMP ER r outine
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
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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 flor istic a nd soil da ta using the BVSTEP pr ocedur e
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
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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.
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Chapter 4
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4.3 Results
4.3.1 Univariate analysis
Native species richness differed amongst sites (main effect; F 4,10=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; F 3,12=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 (F 12,30=1.886, P=0.078).
Exotic species richness differed amongst patch types, depending on site (site x patch type
interaction; F 7.53,18.8=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 a na lysis a nd or dina tion
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
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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.
No. native species per 100 m-2
30
25
20
15
10
5
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.
No. native species per 100 m-2
30
25
20
15
10
5
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
No. exotic species per 100 m-2
20
15
10
5
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.
No. exotic species per 100 m-2
20
15
10
5
0
Pasture
Open
Shrub
Tree
No. exotic species per 100 m-2
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
15
10
5
0
Hoxton Park
Mount Annan
Pasture
Orchard Hills
Open
Shrub
Prospect
Scheyville
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
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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 Ana lysis of simila r ity a nd SIMP ER a na lysis
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
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Chapter 4
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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-fold 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
composition and cover.
Comparison
Global test
Tree
v
Tree
v
Tree
v
Shrub
v
Shrub
v
Open
v
ANOSIM for the patch type factor based on ground species
Shrub
Open
Pasture
Open
Pasture
Pasture
R
0.61
0.111
0.304
0.963
0.296
0.978
0.985
P
0.001
0.181
0.008
0.001
0.002
0.001
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
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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
60
Cover (%)
50
40
30
20
10
0
Hoxton
Park
Mount
Annan
Orchard
Hills
Prospect
Scheyville
T. australis
A. ramosa
A. vagans
M. stipoides
C. ventricosa
E. trigonos
P. labillardieri
E. brownii
E. leptostachya
P. distans
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
Cover (%)
50
B. subaristata
40
S. gracilis
30
C. gayana
20
A. affinis
10
E. curvula
0
Hoxton
Park
Mount
Annan
Orchard
Hills
Prospect
Scheyville
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
Cover (%)
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
35
30
25
20
15
10
5
0
T. australis
A. ramosa
A. vagans
M. stipoides
C. ventricosa
P. labillardieri
Tree and shrub
Open
Pasture
Cover (%)
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
35
30
25
20
15
10
5
0
P. dilatatum
C. dactylon
B. subaristata
S. gracilis
C. gayana
A. affinis
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 a na lysis
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
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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
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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
vice versa. The samples are positioned as per Chapter
Figure 4.7a.
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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-tosite 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
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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.
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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.
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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
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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
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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 mineralN 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
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Low litter quality
i.e. high C:N ratio
Response of plants
i.e. high N use efficiency,
slow growth rates and slow
tissue turnover
Response of microbial
decomposers
i.e. slow decomposition and
N immobilisation
Low N availability
High litter quality
i.e. low C:N ratio
Response of plants
i.e. low N use efficiency,
fast growth rates and rapid
tissue turnover
Response of microbial
decomposers
i.e. fast decomposition and
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 plantavailable 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
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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
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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).
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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 outcompete 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).
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CHAP TER 5. Soil chemical fertility and biotic processes
of abandoned farmland, endangered woodland and
restored vegetation at Hoxton P ark
5 . 1 In t r o d u c t i o n
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 plantavailable 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
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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 aboveand 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
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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 M e t h o d o l o gy
5 . 2 . 1 Si t e d e s c r i p t i o n
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.
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Rainfall
Temperature
(mm)
(oC)
40
35
30
25
20
15
10
5
0
-5
400
350
300
250
200
150
100
50
0
Rainfall
Mean Maximum Temperature
Highest Temperature
Mean Minimum Temperature
Lowest Temperature
Figure 5.1 Rainfall and temperature data for Liverpool during the 12 month study of soils at Hoxton Park
(BOM 2009).
5 . 2 . 2 E x p e r i me n t a l d e s i g n
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
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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
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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.
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5 . 2 . 3 So i l s a m p l i n g
5.2.3.1 Chemica l pr oper ties a nd r espir a tion
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).
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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
Once in June 2007
Twice: June and
December 2007
Four times: June,
September and
December 2007 and
March 2008
Five times: June, July,
September and
December 2007 and
June 2008
extractable P
Bray 1 P (9E1)
total C
high frequency induction furnace (LECO: 6B3)
total N
high frequency induction furnace (LECO)
C:N ratio
ratio of LECO results (8A1)
pH
1:5 soil:water suspension (4A1)
active C
oxidation with KMnO4 (Weil et al. 2003)
respiration
ex situ soil respiration (Anderson 1982)
moisture content
gravimetric soil moisture content (2A1)
nitrate
2MKCl (7C2)
ammonium
2MKCl (7C2)
decomposition of organic
matter
mass loss of a standard material through time
(Latter and Howson 1977)
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
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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
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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
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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 fanforced 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
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completely decomposed between 6 and 12 months and so meaningful data was not
possible for all 360 samples.
5 . 2 . 4 St a t i s t i c a l a n a l y s i s
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).
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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 V a r i a b l e s me a s u r e d o n c e d u r i n g t h e y e a r
5.3.1.1. Br a y 1 P
There was significant variability in the concentration of Bray 1 P across the pasture, 6and 14-year old revegetation sites and woodland (location main effect; F 3,28=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 Tota l 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: F 4,42=4.215, P=0.006; Fig. 5.4). Overall, the
woodland had the highest concentrations of total C.
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1.8
Bray 1 P (mg kg-1)
1.6
1.4
1.2
1
0.8
0.6
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 Tota l 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: F 4,42=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 r a tio
There was a significant main effect of location on C:N ratio (F 3,28=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 V a r i a b l e s me a s u r e d t w i c e t h r o u gh o u t t h e y e a r
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
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7
Total C (%)
6
5
4
3
2
1
0
6yoR
14yoR
Open
Shrub
Woodland
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
Total N (%)
0.3
0.2
0.1
0
6yoR
14yoR
Open
Shrub
Woodland
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
C:N ratio
14
12
10
8
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.
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Chapter 5
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6.2
6
5.8
pH
5.6
5.4
5.2
5
4.8
J
D
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
pH
5.6
5.4
5.2
5
J
D
J
D
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.
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6.3
6.1
pH
5.9
5.7
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
pH
5.9
5.7
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
pH
5.9
5.7
5.5
5.3
5.1
4.9
J
D
Open
J
D
Shrub
J
D
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
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pattern over time (location x time interaction: F 3,28=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; F 4,42=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;
F 3,28=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 (F 1,28=15.29, P<0.001; Figure 5.8b).
5.3.2.3 Respir a tion
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, F 4,42=2.671,
P=0.045). The highest soil respiration rate was observed under the trees in woodland
(0.842 mg CO2 g-1soil d-1), while values under the open and shrub patches in woodland
ranged down to 0.585 mg CO2 g-1soil d- (Fig. 5.9). In the 14-year old restored area, there
was a weak trend for greater respiration rates beneath the trees.
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Active C (mg kg-1)
700
680
660
640
620
600
580
560
540
520
500
J
D
J
Pasture
D
J
6yoR
D
J
14yoR
D
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
a
Active C (mgkg-1)
680
660
640
620
a
b
J
D
a
600
580
560
540
J
D
520
Pasture
Controls
Respiration (mg CO2 gsoil-1 day-1)
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
0.8
0.6
0.4
0.2
0
6yoR
14yoR
Open
Shrub
Woodland
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.
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5 . 3 . 3 V a r i a b l e s me a s u r e d f o u r t i me s t h r o u g h o u t t h e y e a r
5.3.3.1 Soil moistur e 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; F 3,9=183.908, P<0.0001; Figure 5.10a). The detail of the trend differed amongst
the locations (location x time interaction F 3.686,34.402=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; F 3.06,32.13=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 Nitr a te
Soil nitrate showed marked temporal variation but the pattern differed across the
pasture, restored areas and woodland (time main effect; F 3,9=18.401, P<0.001; location
x time interaction; F 9,84=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.
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Chapter 5
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Moisture content (%)
30
25
20
15
10
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.
Moisture content (%)
30
*
25
20
15
10
*
5
0
Jun-07 Jul-07 Aug-07 Sep-07 Oct-07 Nov-07 Dec-07 Jan-08 Feb-08 Mar-08
Pasture
Controls
Moisture content (%)
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
1
0
6yoR
14yoR
Open
Shrub
Woodland
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.
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Nitrate (mg kg-1)
25
20
15
10
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.
Nitrate (mg kg-1)
25
*
20
15
*
*
10
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
Nitrate (mg kg-1)
10
8
6
4
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.
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Nitrate (mg kg-1)
12
10
8
6
4
2
0
6yoR
14yoR
Open
Shrub
Woodland
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 (F 3,28=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; F 4,42=4.885,
P<0.005). Nitrate concentration decreased from the open patches to the trees in the 6year 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;
F 5.56,51.90=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
mg kg-1 respectively). The woodland had a significantly higher (F 1,52=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
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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 (F 3,28=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: F 6.51,68.3=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).
Ammonium (mg kg-1)
70
60
50
40
30
20
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.
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Ammonium (mg kg-1)
70
*
60
50
*
40
*
30
20
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.
Ammonium (mg kg-1)
70
60
50
40
*
30
20
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
Ammonium (mg kg-1)
30
25
20
15
10
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.
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Ammonium (mg kg-1)
70
60
50
40
30
20
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.
Ammonium (mg kg-1)
70
60
50
40
30
20
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.
Ammonium (mg kg-1)
70
60
50
40
30
20
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.
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5 . 3 . 4 D e c o mp o s i t i o n
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
% Organic Mass Remaining
(Figures 5.13b and 5.13c).
100
90
80
70
60
50
40
30
20
10
0
pasture
6 yo restored
14 yo restored
woodland
0
50
100
150
200
Time (days)
250
300
350
Figure 5.13a The percentage mass of calico remaining for the four locations at Hoxton Park. Error bars
represent standard errors of the means.
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% Organic Mass Remaining
100
90
80
70
60
50
40
30
20
10
0
open
shrub
tree
0
50
100
150
200
Time (days)
250
300
350
% Organic Mass Remaining
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
70
60
50
40
30
20
10
0
open
shrub
tree
0
50
100
150
200
Time (days)
250
300
350
% Organic Mass Remaining
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
70
60
50
40
30
20
10
0
open
shrub
tree
0
50
100
150
Time (days)
200
250
300
350
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.
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5 . 4 D is c us s io n
O v e r vi e w
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.
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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).
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pH
There was a decreasing trend in soil pH across the different locations with pasture > 6year 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 grassland 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
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December across all four locations and this is most likely related to the extreme rainfall
event that occurred mid-year.
Ac t i ve 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.
So i l r e s p i r a t i o n
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
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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.
So i l mo i s t u r e
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
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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,
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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 14year 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.
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Ammoni u m
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 6year 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.
D e c o mp o s i t i o n
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
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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.
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Chapter 6. The implications of this research for the
management and restoration of Cumberland P lain
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
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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 intercanopy (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 o f
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
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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
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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
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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).
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References
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Zimmerman JK (2000) Barriers to forest regeneration in an abandoned pasture in Puerto Rico.
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Zinke PJ (1962) The pattern of influence of individual forest trees on soil properties. Ecology
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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
Site Ai
4
1
b
c
d
n
Sub-site B(A)j(i)
10
1
1
c
d
n
2 + cdn2B(A)
Patch type Ck
3
a
b
0
d
n
2
 + dn2B(A)C + bdn2AC + abdn2C
Site x patch type ACik
12
1
b
0
d
n
Sub-site x patch type B(A)Cj(i)k
30
1
1
0
d
n
Depth Dl
2
a
b
c
0
n
Site x depth ADil
8
1
b
c
0
n
Sub-site x depth B(A)Dj(i)l
20
1
1
c
0
n
Patch type x depth CDkl
6
a
b
0
0
n
Site x patch type x depth ACDikl
24
1
b
0
0
n
Sub-site x patch type x depth B(A)CDj(i)kl
60
1
1
0
0
n
Residual
1
1
1
1
1
1
Total
180
Expected MS
2 + cdn2B(A)+ bcdn2A
2+ dn2B(A)C + bdn2AC
2+ dn2B(A)C
2+ cn2B(A)D+ bcn2AD+ abcn2D
Tested against
B(A)
C
AC
B(A)C
AD
2+ cn2B(A)D+ bcn2AD
B(A)D
2+ n2B(A)CD + bn2ACD+ abn2CD
ACD
2+ cn2B(A)D
2 +n2B(A)CD + bn2ACD
2 +n2B(A)CD
2
B(A)CD
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
Site Ai
4
1
b
c
Sub-site B(A)j(i)
10
1
1
c
2 + cdn2B(A)
Patch type Ck
3
a
b
0
2
 + dn2B(A)C + bdn2AC + abdn2C
Site x patch type ACik
12
1
b
0
Sub-site x patch type B(A)Cj(i)k
30
1
1
0
Residual
1
1
1
1
Total
60
2 + cdn2B(A)+ bcdn2A
2+ dn2B(A)C + bdn2AC
2+ dn2B(A)C
2
Tested against
B(A)
C
AC
B(A)C
Table A1.1c Tukey‟s HSD test for the main effect of site on bulk density
The following acronyms have been used in this appendix:
95% Confidence Interval
GG=Greenhouse-Geisser
HP=Hoxton Park
MA=Mount Annan
OH=Orchard Hills
PR=Prospect
SNP=Scheyville
Site
Site
Mean difference
SE
P
Lower bound
Upper bound
HP
MA
.1400254
.06868238
.315
-.0860139
.3660647
OH
-.3453395*
.06868238
.004
-.5713789
-.1193002
PR
-.0651712
.06868238
.871
-.2912105
.1608682
SNP
-.1458757
.06868238
.282
-.3719150
.0801637
OH
-.4853649*
.06868238
.000
-.7114043
-.2593256
PR
-.2051966
.06868238
.080
-.4312359
.0208428
SNP
-.2859011*
.06868238
.013
-.5119404
-.0598617
PR
.2801684*
.06868238
.015
.0541290
.5062077
SNP
.1994639
.06868238
.091
-.0265755
.4255032
SNP
-.0807045
.06868238
.765
-.3067438
.1453349
Figures in bold highlight significant main effects, interactions or post hoc tests
MA
Table A1.1a Mauchly‟s test of sphericity for bulk density
Epsilon
Within subjects effect
Mauchly's W
Approx. Chi-Square
df
P
GG
.574
4.835
5
.439
.727
Patch
OH
PR
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
Mauchly's
Approx. ChiWithin subjects effect
W
Square
df
P
Epsilon
GG
Patch
.422
7.526
5
.187
.673
Depth
.579
4.917
2
.086
.704
.665
Patch x Depth
.152
14.652
20
.819
Table A1.2c Tukey‟s HSD test for the main effect of site on soil moisture content
95% Confidence Interval
Site
Site Mean difference
SE
P
Lower bound Upper bound
HP
MA
Table A1.2b Split-plot ANOVA for soil moisture content
Source of variation
df
SS
MS
F
P
Site
Subsite
4
10
152.8118
30.808
38.20296
3.081
12.40015
0.000686
Patch type
3
50.58119
16.8604
2.193517
NS
Site x Patch type
Subsite x Patch type
Depth
Site x Depth
Subsite x Depth
Patch type x Depth
Site x Patch type x Depth
Subsite x Patch type x Depth
Residual
12
30
2
8
20
6
24
60
1
92.23761
61.080
161.39
28.65349
24.166
22.25309
71.46869
55.374
7.686467
2.036
80.69499
3.581687
1.208
3.708848
2.977862
.923
3.775303
0.001535
22.52989
2.964246
<0.001
0.023201
1.245473
3.226609
NS
Total
180
0.000124
OH
PR
MA
-.87465217
.413712634
.286
-2.23621443
.48691010
OH
.09898627
.413712634
.999
-1.26257599
1.46054853
PR
-.76257027
.413712634
.403
-2.12413254
.59899199
SNP
1.70110267*
.413712634
.014
.33954040
3.06266493
OH
.97363843
.413712634
.206
-.38792383
2.33520070
PR
.11208189
.413712634
.999
-1.24948037
1.47364415
SNP
2.57575483*
.413712634
.001
1.21419257
3.93731710
PR
-.86155654
.413712634
.298
-2.22311880
.50000572
SNP
1.60211640
*
.413712634
.020
.24055414
2.96367866
SNP
2.46367294*
.413712634
.001
1.10211068
3.82523520
Table A1.2d. Post hoc test# for the main effect of soil depth on soil moisture content
Difference
Depth
Depth
Mean difference SE
P
Lower bound Upper bound
2.5cm
20.5cm
-.270
.179
.489
-.785
.245
60.5cm
-2.130*
.151
.000
-2.564
-1.696
20.5cm 60.5cm
-1.860*
.257 .000
-2.597
-1.124
based on estimated marginal means and a Bonferroni adjustment for multiple
comparisons
#
Means and 95% confidence intervals for soil moisture content (%)
Site x patch type interaction
Pasture
Mean L1
Hoxton Park
Mount Annan
Orchard Hills
Prospect
Scheyville
7.53
6.98
4.57
8.71
4.91
6.48
5.88
2.75
7.31
4.24
L2
Open
Mean L1
8.58
8.08
6.39
10.10
5.58
4.83
6.62
6.10
5.28
3.63
3.84
5.48
5.31
3.40
2.45
L2
Shrub
Mean L1
5.82
7.76
6.89
7.15
4.80
4.72
6.16
5.35
6.11
3.93
3.43
5.01
4.22
4.51
2.59
L2
6.02
7.32
6.48
7.70
5.26
Tree
Mean L1
5.56
6.38
6.23
5.60
3.37
4.53
5.31
5.43
3.47
2.03
Site x depth interaction
2.5cm
20.5cm
60.5cm
Mean
L1
L2
Mean
L1
L2
Mean
L1
L2
Hoxton Park
Mount Annan
Orchard Hills
Prospect
5.53
6.15
4.24
5.47
4.42
5.50
3.51
3.43
6.65
6.80
4.98
7.51
5.17
5.60
5.46
5.57
3.99
4.91
4.44
4.15
6.34
6.28
6.48
6.99
6.28
7.86
6.98
8.23
5.21
7.16
6.49
7.67
7.35
8.55
7.47
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
20.5cm
7.28
6.94
4.25
1.91
10.3
12.0
4.61
3.75
3.45
1.49
5.78
6.02
4.25
5.13
-0.69
-0.47
9.19
10.73
5.99
4.85
1.92
2.44
10.1
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
20.5cm
7.43
5.56
5.26
2.23
9.60
8.88
5.86
5.78
4.34
1.74
7.37
9.83
5.57
5.43
3.19
3.76
7.95
7.10
5.75
5.61
4.32
2.68
7.18
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
60.5cm
3.42
7.40
-1.01
5.52
7.85
9.28
6.43
7.00
5.08
6.32
7.77
7.67
5.48
6.59
3.39
3.45
7.57
9.74
6.51
6.94
4.46
5.10
8.57
8.78
L2
6.58
7.45
7.03
7.74
4.71
Prospect
Pasture
Open
Shrub
Tree
Mean
L1
L2
Mean
L1
L2
Mean
L1
L2
Mean
L1
L2
10.5
7.25
8.32
6.90
3.59
6.86
14.20
10.9
9.78
3.79
4.17
7.87
0.72
-2.20
6.44
6.86
10.5
9.30
3.92
6.56
7.84
2.06
1.50
5.91
5.78
11.6
9.77
3.63
4.30
8.89
1.18
-0.28
5.46
6.07
8.87
12.3
2.5cm
20.5cm
60.5cm
Scheyville
Pasture
2.5cm
20.5cm
60.5cm
Open
Shrub
Tree
Mean
L1
L2
Mean
L1
L2
Mean
L1
L2
Mean
L1
L2
4.25
4.73
5.76
2.94
3.90
3.45
5.56
5.57
8.07
2.06
3.88
4.94
-0.12
0.64
3.10
4.24
7.11
6.78
2.54
3.69
5.55
0.31
-1.39
4.44
4.77
8.77
6.66
2.12
2.49
5.51
1.41
0.67
2.82
2.82
4.30
8.21
Table A1.3a Mauchly‟s test of sphericity for pH
Within subjects effect
Epsilon
GG
Mauchly's W
Approx. Chi-Square
df
P
.569
.692
.011
4.912
3.316
34.884
5
2
20
.429
.191
Patch
Depth
Patch x Depth
.791
.764
.429
.029
Table A1.3b Split-plot ANOVA for pH
Source of variation
df
SS
MS
F
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
Site x Patch type
12
2.33656
0.19471
0.59765
Subsite x Patch type
30
9.774
.326
Depth
2
12.8512
6.42558
8.21832
Site x Depth
8
6.25488
0.78186
7.84022
Subsite x Depth
20
1.994
.100
Patch type x Depth
6
4.45901
0.74317
3.18474
Site x Patch type x Depth
24
5.60047
0.23335
2.38202
Subsite x Patch type x Depth
60
5.878
.098
Residual
1
62.8476
Total
180
GG df
GG SS
GG MS
GG P
0.01
2.374
4.57061
1.925
<0.05
NS
9.497
2.33656
.246
NS
23.743
9.774
.412
<0.05
1.529
12.8512
8.406
<0.05
<0.0001
6.115
6.25488
1.023
<0.001
15.288
1.994
.130
<0.05
2.571
4.45901
1.734
NS
<0.05
10.285
5.60047
.545
<0.05
25.714
5.878
.229
Table A1.3c Tukey‟s HSD test for the main effect of site on pH
95% Confidence Interval
Site Site Mean difference
SE
P
Lower bound Upper bound
HP
MA
-.48805556*
OH
PR
*
-.38305556
-.07416667
.112763683
.112763683
.112763683
.010
.042
.961
-.85917010
-.75417010
-.44528121
-.01194101
.29694788
-.06694444
.10500000
.112763683
.112763683
.973
.878
-.43805899
-.26611454
.30417010
.47611454
PR
.41388889*
.112763683
.028
.04277435
.78500343
OH
SNP
PR
*
.42111111
.30888889
.112763683
.112763683
.025
.117
.04999657
-.06222565
.79222565
.68000343
PR
SNP
SNP
.31611111
.00722222
.112763683
.112763683
.106
1.000
-.05500343
-.36389232
.68722565
.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
Pasture
Shrub
Tree
Shrub
Tree
-.162
.062
-.349
.224
-.187
.142
.072
.127
.125
.129
1.000
1.000
.124
.625
1.000
-.626
-.174
-.765
-.187
-.611
.302
.299
.068
.635
.237
Shrub
Tree
-.411*
.114 .029
-.785
-.038
based on estimated marginal means and a Bonferroni adjustment for multiple
comparisons
#
2.5 cm
-.11694101
SNP
OH
MA
Table A1.3e Post hoc test# for the main effect of soil depth on pH
Difference
Depth
Depth
Mean difference SE
P
Lower bound Upper bound
20.5 cm
.326*
.038
.000
.216
.437
60.5 cm
*
.065
.000
.467
.842
.654
*
20.5 cm 60.5 cm
.328
.065 .001
.142
.515
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
Mount Annan
Orchard Hills
Prospect
4.36
5.38
4.88
4.68
4.11
4.91
4.61
4.41
4.61
5.84
5.16
4.95
4.34
4.91
4.84
4.35
4.23
4.56
4.55
4.19
4.45
5.25
5.13
4.51
4.31
4.19
4.43
4.20
4.11
3.86
3.92
4.09
4.50
4.52
4.94
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
2.5 cm
20.5 cm
60.5 cm
Open
Shrub
Tree
Mean
L1
L2
Mean
L1
L2
Mean
L1
L2
Mean
L1
L2
4.59
4.45
4.02
3.75
4.13
3.93
5.43
4.77
4.12
4.07
4.34
4.26
3.63
4.21
4.11
4.52
4.47
4.41
4.11
4.26
4.33
2.98
3.45
3.55
5.23
5.07
5.11
4.68
4.30
4.61
4.28
4.05
3.68
5.08
4.56
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
60.5 cm
4.79
4.72
4.12
2.76
5.45
6.68
4.85
3.96
4.61
3.74
5.09
4.17
4.54
3.93
3.59
3.59
5.48
4.27
5.45
4.15
3.28
2.88
7.62
5.42
Orchard Hills
Pasture
2.5 cm
20.5 cm
60.5 cm
Open
Shrub
Tree
Mean
L1
L2
Mean
L1
L2
Mean
L1
L2
Mean
L1
L2
4.68
4.89
4.91
4.47
3.88
0.88
4.88
5.89
8.94
4.90
4.94
4.44
4.27
3.34
3.05
5.53
6.55
5.83
4.55
4.66
4.22
3.94
3.73
3.77
5.16
5.60
4.66
5.41
4.87
4.16
4.18
3.43
3.82
6.65
6.31
4.50
Prospect
Pasture
2.5 cm
20.5 cm
60.5 cm
Open
Shrub
Tree
Mean
L1
L2
Mean
L1
L2
Mean
L1
L2
Mean
L1
L2
4.87
4.54
4.11
4.00
4.06
3.82
5.74
5.03
4.41
4.33
4.41
4.35
3.59
3.41
3.63
5.07
5.41
5.06
4.60
4.16
4.17
3.65
4.13
4.07
5.55
4.20
4.27
4.94
4.28
4.16
3.62
3.82
3.90
6.26
4.74
4.42
Scheyville
Pasture
2.5 cm
20.5 cm
60.5 cm
Open
Shrub
Tree
Mean
L1
L2
Mean
L1
L2
Mean
L1
L2
Mean
L1
L2
4.55
4.62
4.31
4.41
3.83
3.62
4.70
5.41
5.00
4.81
3.88
3.74
4.06
3.31
3.51
5.56
4.46
3.96
5.00
3.97
3.83
4.24
3.39
3.70
5.76
4.56
3.96
5.72
4.56
3.83
4.63
2.36
3.21
6.81
6.77
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
.846
.087
.000
1.456
22.005
72.978
5
2
20
.919
.907
.523
.460
Patch
Depth
Patch x Depth
.000
.000
Table A1.4b Split-plot ANOVA for EC
Source of variation
df
SS
MS
F
P
Site
4
0.05496
0.01374
2.11579
0.15343
Subsite
10
.065
.006
Patch type
3
0.13078
0.04359
4.78592
Site x Patch type
12
0.10931
0.00911
0.95424
Subsite x Patch type
30
.286
.010
Depth
2
2.22889
1.11445
61.0834
Site x Depth
8
0.14596
0.01824
5.65864
Subsite x Depth
20
.064
.003
Patch type x Depth
6
0.10153
0.01692
3.16224
Site x Patch type x Depth
24
0.12843
0.00535
0.89472
Subsite x Patch type x Depth
60
.359
.006
Residual
1
3.67453
Total
180
GG df
GG SS
GG MS
GG P
<0.05
2.722
0.13078
.048
<0.05
NS
10.890
0.10931
.010
NS
27.224
.286
.011
<0.0001
1.045
2.22889
2.132
<0.01
<0.001
4.181
0.14596
.035
<0.05
10.453
.064
.006
<0.05
2.757
0.10153
.037
NS
NS
11.029
0.12843
.012
NS
27.572
.359
.013
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
Shrub
.005
-.012
.022
.018
1.000
1.000
-.066
-.071
.077
.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
60.5 cm
.007
-.232
*
.003
.180
-.003
.017
.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
Mauchly's W
Approx. Chi-Square
df
P
Epsilon
GG
Patch
.622
4.148
5
.531
.758
Depth
.435
7.493
2
.024
.639
Patch x Depth
.044
24.361
20
.263
.573
Within subjects effect
Table A1.5b Split-plot ANOVA for active C
Source of variation
df
SS
MS
F
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
GG MS
GG P
2.275
214876.39
94448.784
NS
9.100
323351.74
35532.266
NS
22.751
431171.10
18952.100
12
323351.74
26945.98
Subsite x Patch type
30
431171.10
14372.37
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
180
NS
GG SS
Site x Patch type
Total
1.87485
GG df
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
71.78403818
*
16.450863089
.010
17.64289647
125.92517989
SNP
71.91319659
*
16.450863089
126.05433830
33.34791319
42.45008090
16.450863089
16.450863089
.010
.320
.148
17.77205488
OH
PR
-2.07932285E+01
-1.16910608E+01
87.48905490
96.59122261
SNP
PR
SNP
SNP
42.57923931
9.10216771
9.23132612
.12915841
16.450863089
16.450863089
16.450863089
16.450863089
.146
.979
.978
1.000
-1.15619024E+01
-4.50389740E+01
-4.49098156E+01
-5.40119833E+01
96.72038102
63.24330942
63.37246783
54.27030012
PR
MA
OH
PR
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
*
17.683
.000
542.842
644.346
593.594
*
#
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
20.5 cm
649
426
40.4
88.7
1258
764
543
280
476
210
611
350
1023
334
892
210
1154
459
1015
536
886
444
1144
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
20.5 cm
676
285
564
242
789
328
737
418
405
144
1070
692
677
359
470
256
883
462
780
423
594
152
966
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
20.5 cm
826
397
599
206
1052
587
630
331
529
161
732
501
626
343
466
193
785
492
664
359
369
288
958
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
20.5 cm
762
352
597
291
926
414
620
299
152
278
1088
321
739
301
381
87
1097
514
804
279
684
230
925
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
Within subjects effect
Mauchly's W
Approx. Chi-Square
df
P
Epsilon
GG
.355
.899
.013
9.028
.956
33.615
5
2
20
.110
.620
.040
.667
.908
.401
Patch
Depth
Patch x Depth
Table A1.6b Split-plot ANOVA for total C
Source of variation
df
SS
MS
F
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
Site x Patch type
12
2.2788234
0.1899
4.92018
Subsite x Patch type
30
1.158
.039
Depth
2
44.157014
22.0785
102.348
Site x Depth
8
1.7257603
0.21572
16.6981
Subsite x Depth
20
.258
.013
Patch type x Depth
6
0.2028145
0.0338
1.12541
Site x Patch type x Depth
24
0.7208552
0.03004
2.04085
Subsite x Patch type x Depth
60
.883
.015
Residual
1
5.696E+01
Total
180
GG df
GG SS
GG MS
GG P
NS
2.000
0.2063501
.103
NS
<0.001
8.000
2.2788234
.285
<0.01
20.001
1.158
.058
<0.0001
1.817
44.157014
24.303
<0.0001
<0.0001
7.268
1.7257603
.237
<0.0001
18.169
.258
.014
NS
2.408
0.2028145
.084
NS
<0.05
9.630
0.7208552
.075
NS
24.075
.883
.037
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
.16405657
*
.031571156
.003
.06015331
.26795984
.46728067
*
.031571156
.000
.36337740
.57118393
.23020069
*
.031571156
.000
.12629742
.33410396
PR
.13408885
*
.031571156
.011
.03018558
.23799212
SNP
.43731294*
.031571156
.33340967
.54121621
PR
-.09611184
.031571156
.000
.073
-.20001511
.00779143
.20711225
*
.031571156
.000
.10320898
.31101552
.30322409
*
.031571156
.000
.19932082
.40712736
PR
SNP
MA
OH
OH
SNP
PR
SNP
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
.707*
20.5 cm
60.5 cm
1.207
*
.021
.000
.648
.766
.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
Mean
Open
L1
L2
Mean
Shrub
L1
L2
Mean
L1
Tree
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
Hoxton Park
Mount Annan
Orchard Hills
Prospect
Scheyville
Mean
2.5 cm
L1
L2
Mean
20.5 cm
L1
L2
Mean
60.5 cm
L1
L2
6.68
6.44
3.51
4.42
2.58
5.69
5.74
2.84
3.76
2.23
7.82
7.23
4.30
5.18
2.98
2.12
2.13
1.63
1.79
1.04
1.58
1.92
1.25
1.45
0.832
2.77
2.35
2.08
2.18
1.28
0.775
0.670
0.642
0.719
0.431
0.549
0.563
0.478
0.648
0.352
1.04
0.784
0.824
0.793
0.514
Table A1.7a Mauchly‟s test of sphericity for Bray 1 P
Within subjects effect
Mauchly's W
Approx. Chi-Square
df
P
Epsilon
GG
.619
4.187
5
.525
.747
Patch
Depth
Patch x Depth
.690
3.334
.049
2
23.465
20
.189
.305
Table A1.7c Tukey‟s HSD test for the main effect of site on Bray 1 P
95% Confidence Interval
Site Site Mean difference
SE
P
Lower bound Upper bound
HP
.764
MA
-.36858633*
.044727590
.000
-.51578850
-.22138415
OH
.16644420
*
.044727590
.026
.01924202
.31364637
PR
.17986624
*
.044727590
.03266407
.32706841
SNP
.00278206
.044727590
.016
1.000
-.14442011
.14998423
OH
.53503052
*
.044727590
.000
.38782835
.68223269
.54845257
*
.044727590
.000
.40125039
.69565474
SNP
PR
*
.37136838
.01342204
.044727590
.044727590
.000
.998
.22416621
-.13378013
.51857056
.16062422
SNP
-.16366214*
.044727590
.028
-.31086431
-.01645997
SNP
*
.044727590
.018
-.32428635
-.02988201
.490
MA
Table A1.7b Split-plot ANOVA for Bray 1 P
PR
Source of variation
df
SS
MS
F
P
Site
4
7.050353
1.762588
48.94714
1.5E-06
Subsite
10
.360
.036
OH
PR
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
Depth
2
27.55555
13.77777
68.69564
<0.0001
Site x Depth
8
1.604501
0.200562
5.854687
<0.001
Subsite x Depth
20
.685
.034
Patch type x Depth
6
0.896226
0.149371
1.762295
NS
Site x Patch type x Depth
Subsite x Patch type x
Depth
24
2.034225
0.084759
1.779953
<0.05
60
2.857
.048
Residual
1
Total
180
-.17708418
Table A1.7d Post hoc test# for the main effect of patch type on Bray 1 P
Difference
Patch
Patch
Mean difference SE
P
Lower bound Upper bound
Open
Pasture
Pasture
Shrub
.033
-.034
.076
.045
1.000
1.000
-.217
-.182
.283
.114
Tree
Shrub
-.225*
-.067
.055
.075
.013
1.000
-.406
-.313
-.045
.179
Tree
-.258*
.059 .008
-.451
-.066
Shrub
Tree
-.191
.058 .050
-.383
.000
#
based on estimated marginal means and a Bonferroni adjustment for multiple
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
*
.024
.000
.881
1.020
.951
*
#
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
20.5 cm
2.19
0.550
1.05
0.303
3.96
0.844
2.54
0.470
1.03
-0.316
5.20
2.16
2.64
1.22
1.67
-0.234
3.95
5.42
3.28
1.06
1.64
-0.180
5.93
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
20.5 cm
3.78
1.15
2.11
-0.029
6.36
3.78
2.68
1.30
0.462
-0.111
8.25
4.93
3.48
2.02
3.27
-0.107
3.70
9.20
15.3
2.25
4.17
0.778
50.3
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
20.5 cm
2.07
0.480
1.13
0.0276
3.43
1.13
1.48
0.502
0.534
0.242
3.01
0.816
1.13
0.517
0.415
0.133
2.19
1.03
2.11
0.681
0.602
0.123
5.05
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
20.5 cm
1.26
0.282
0.324
-0.0244
2.85
0.685
1.50
0.958
0.608
-0.244
2.88
4.07
1.62
0.682
1.11
0.382
2.24
1.05
1.92
0.867
0.768
0.243
3.81
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
20.5 cm
2.48
0.750
1.75
-0.0705
3.40
2.29
1.62
1.11
1.33
0.341
1.95
2.32
1.90
1.06
1.36
0.525
2.56
1.80
2.01
1.07
1.33
0.328
2.90
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
Mauchly's W
Approx. Chi-Square
df
P
Epsilon
GG
Patch
.502
6.013
5
.308
.742
Depth
.742
2.687
2
.261
.795
Patch x Depth
.013
33.522
20
.040
.572
Within subjects effect
Table A1.8b Split-plot ANOVA for total S
Source of variation
df
SS
MS
F
P
Site
4
0.0037479
0.00094
4.21624
0.02958
Subsite
10
.002
.000
Patch type
3
0.0005471
0.00018
0.93019
Site x Patch type
12
0.0023526
0.0002
1.83526
Subsite x Patch type
30
.003
.000
Depth
2
0.0073918
0.0037
8.9671
Site x Depth
8
0.0032973
0.00041
12.461
Subsite x Depth
20
.001
.000
Patch type x Depth
6
0.0011324
0.00019
2.6299
Site x Patch type x Depth
24
0.0017223
7.2E-05
0.88585
Subsite x Patch type x Depth
60
.005
.000
Residual
1
3.114E-02
Total
180
GG df
GG SS
GG MS
GG P
NS
2.226
0.0005471
.000
NS
NS
8.905
0.0023526
.000
NS
22.262
.003
.000
<0.01
1.590
0.0073918
.005
<0.05
<0.0001
6.359
0.0032973
.001
<0.0001
15.897
.001
.000
<0.05
3.432
0.0011324
.000
NS
NS
13.728
0.0017223
.000
NS
34.320
.005
.000
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
OH
PR
MA
.00515654
.003513713
.603
-.00640738
.01672045
OH
PR
.00528814
.00225751
.003513713
.003513713
.582
.964
-.00627578
-.00930641
.01685206
.01382143
SNP
OH
PR
SNP
PR
SNP
SNP
.01347085*
.00013160
-.00289903
.00831432
-.00303063
.00818271
.01121334
.003513713
.003513713
.003513713
.003513713
.003513713
.003513713
.003513713
.022
1.000
.917
.202
.904
.213
.058
.00190694
-.01143231
-.01446294
-.00324960
-.01459455
-.00338120
-.00035057
.02503477
.01169552
.00866489
.01987823
.00853329
.01974663
.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
*
.001
.026
.000
.008
.004
*
#
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
Mean
L1
L2
Mean
20.5 cm
L1
L2
Mean
60.5 cm
L1
L2
HP
MA
OH
PR
0.0410
0.0374
0.0244
0.0304
0.0306
0.0282
0.0184
0.0216
0.0515
0.0465
0.0305
0.0392
0.0173
0.0116
0.0145
0.0185
0.0127
0.00706
0.0114
0.0156
0.0220
0.0162
0.0175
0.0215
0.0270
0.0209
0.0306
0.0296
0.0186
0.0177
0.0230
0.0243
0.0353
0.0242
0.0383
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
Within subjects effect
Mauchly's W
Approx. Chi-Square
df
P
.426
.445
.005
7.450
7.280
40.520
5
2
20
.192
Patch
Depth
Patch x Depth
.026
.007
Epsilon
GG
.630
.643
.459
Table A1.9b Split-plot ANOVA for nitrate
Source of variation
df
SS
MS
F
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
Site x Patch type
12
6.4845239
0.54038
2.51853
Subsite x Patch type
30
6.437
.215
Depth
2
37.528236
18.7641
22.5868
Site x Depth
8
6.6460566
0.83076
4.94653
Subsite x Depth
20
3.359
.168
Patch type x Depth
6
4.6904925
0.78175
3.03159
Site x Patch type x Depth
24
6.1888294
0.25787
2.44619
Subsite x Patch type x Depth
60
6.325
.105
Residual
1
9.643E+01
Total
180
GG df
GG SS
GG MS
GG P
<0.05
1.890
6.0615086
3.208
NS
<0.05
7.558
6.4845239
.858
<0.05
18.895
6.437
.341
<0.001
1.286
37.528236
29.172
<0.05
<0.05
5.146
6.6460566
1.292
<0.05
12.865
3.359
.261
<0.05
2.754
4.6904925
1.703
NS
<0.05
11.016
6.1888294
.562
<0.05
27.540
6.325
.230
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
OH
PR
SNP
-.52138976*
.04910176
.09550468
-.12273611
.144530401
.144530401
.144530401
.144530401
.031
.997
.961
.909
-.99705118
-.42655966
-.38015674
-.59839754
-.04572834
.52476319
.57116610
.35292531
MA
OH
.57049152*
.144530401
.018
.09483010
1.04615295
PR
.61689444
*
.144530401
.14123302
1.09255586
SNP
PR
SNP
SNP
.39865365
.04640292
-.17183788
-.21824079
.144530401
.144530401
.144530401
.144530401
.011
.113
.997
.758
.579
-.07700777
-.42925851
-.64749930
-.69390221
.87431507
.52206434
.30382355
.25742063
OH
PR
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
Shrub
Tree
-.361*
.103
-.232
.104
.072
.079
.035
1.000
.088
-.700
-.133
-.490
-.022
.339
.026
Pasture
Shrub
Tree
.464*
.129
.119
.131
.018
1.000
.074
-.300
.854
.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
60.5 cm
.859*
1.050
*
*
#
.084
.000
.618
1.100
.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
20.5 cm
1.08
0.00
-0.182
0.00
4.27
0.00
1.21
0.245
-0.197
-0.0298
5.07
0.598
0.681
0.474
-0.05
-0.624
1.98
4.79
2.81
0.885
0.09
-0.520
12.3
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
20.5 cm
17.4
2.01
-0.165
-0.734
406
33.1
5.59
0.396
-0.480
0.0071
82.5
0.936
3.61
0.229
1.36
-0.06
8.02
0.607
5.21
1.04
1.42
-0.229
14.9
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
Mean
Open
L1
L2
Mean
L1
Shrub
L2
Mean
Tree
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
Mauchly's W
Approx. Chi-Square
df
P
Epsilon
GG
Patch
.230
12.826
5
.026
.597
Depth
.969
.281
2
.869
.970
Patch x Depth
.101
17.867
20
.633
.670
Within subjects effect
Table A1.10b Split-plot ANOVA for ammonium
Source of variation
df
SS
MS
F
P
Site
4
9.364803
2.3412
27.1733
2.4E-05
Subsite
10
.862
.086
Patch type
3
2.0075527
0.66918
1.57237
NS
1.792
Site x Patch type
12
5.1070607
0.42559
2.84993
<0.01
Subsite x Patch type
30
4.480
.149
Depth
2
13.904631
6.95232
30.5327
Site x Depth
8
1.8216036
0.2277
2.99648
Subsite x Depth
20
1.520
.076
Patch type x Depth
6
2.2444454
0.37407
2.71146
Site x Patch type x Depth
24
3.3110504
0.13796
1.43955
Subsite x Patch type x Depth
60
5.750
.096
Residual
1
5.037E+01
Total
180
GG df
GG SS
GG MS
GG P
2.0075527
1.120
NS
7.168
5.1070607
.712
<0.05
17.920
4.480
.250
<0.001
1.940
13.904631
7.166
<0.001
<0.05
7.762
1.8216036
.235
<0.05
19.404
1.520
.078
<0.05
4.022
2.2444454
.558
NS
NS
16.087
3.3110504
.206
NS
40.218
5.750
.143
9.364803
.862
5.037E+01
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
PR
*
.45273024
.12108079
.069185045
.069185045
.000
.449
.22503658
-.10661287
.68042391
.34877445
SNP
.58425130*
.069185045
.000
.35655764
.81194497
OH
PR
*
.37068663
.03903717
.069185045
.069185045
.002
.977
.14299296
-.18865649
.59838029
.26673083
SNP
.50220768*
.069185045
.000
.27451402
.72990135
OH
PR
SNP
-.33164945
.13152106
*
.069185045
.069185045
.005
.375
-.55934312
-.09617260
-.10395579
.35921472
PR
SNP
.46317051*
.069185045
.000
.23547685
.69086418
MA
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
Within subjects effect
Mauchly's W
Approx. Chi-Square
df
P
.379
.394
.012
8.454
8.377
34.576
5
2
20
.135
Patch
Depth
Patch x Depth
.015
.031
Epsilon
GG
.714
.623
.402
Table A1.11b Split-plot ANOVA for total N
Source of variation
df
SS
MS
F
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
Site x Patch type
12
32.85658521
2.73805
10.1938
Subsite x Patch type
30
8.058
.269
Depth
2
67.33588271
33.6679
20.7503
Site x Depth
8
12.98021081
1.62253
Subsite x Depth
20
2.301
.115
Patch type x Depth
6
1.340072398
Site x Patch type x Depth
24
Subsite x Patch type x Depth
Residual
Total
GG df
GG SS
GG MS
GG P
NS
2.143
10.02781389
4.680
NS
<0.0001
8.570
21
.425
32.85658521
3.834
<0.0001
8.058
.376
<0.001
1.246
67.33588271
54.062
<0.01
14.1057
<0.0001
4.982
12.98021081
2.605
<0.0001
12.455
2.301
.185
0.22335
1.09925
NS
2.410
1.340072398
.556
NS
4.87633165
0.20318
2.06227
<0.05
9.638
4.87633165
.506
NS
60
5.911
.099
24.096
5.911
.245
1
1.777E+02
180
1.777E+02
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
OH
PR
MA
-.40257353
.135359462
.082
-.84805264
.04290558
OH
PR
.00967949
.08563341
.135359462
.135359462
1.000
.966
-.43579962
-.35984570
.45515860
.53111252
SNP
OH
-.97913491*
.41225302
.135359462
.135359462
.000
.073
-1.42461402
-.03322609
-.53365580
.85773213
PR
.48820694*
.135359462
.031
.04272783
.93368605
SNP
PR
-.57656138
.07595392
*
.135359462
.135359462
.011
.978
-1.02204049
-.36952519
-.13108227
.52143303
SNP
-.98881440*
.135359462
.000
-1.43429351
-.54333529
.135359462
.000
-1.51024744
-.61928922
SNP
-1.06476833
*
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
60.5 cm
.948*
1.479
*
*
#
.063
.000
.768
1.127
.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.
Moisture
pH
EC Bray 1 P Active C Ammonium Nitrate
a
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
c
Exch Ca
Exch K
Exch Mg
Exch Na
C:N ratio
Bulk density
Correlation
P
Correlation
P
Correlation
P
Correlation
P
Correlation
P
Correlation
P
Correlation
P
Correlation
P
Correlation
P
Correlation
P
Correlation
P
Correlation
P
Correlation
P
Total C
1
Total S
0.691
0.000
0.432
0.001
-0.355
0.005
-0.190
0.145
-0.145
0.268
-0.080
0.542
1
Total N
Sol Ca
0.306
0.017
-0.151
0.248
0.054
0.683
0.020
0.880
-0.028
0.834
-0.137
0.296
-0.057
0.663
-0.172
0.190
-0.239
0.066
Exch Ca
1
Exch K
Exch Mg
0.706
0.000
0.438
0.000
0.319
0.013
-0.149
0.256
-0.402
0.001
1
0.659
0.000
0.392
0.002
-0.077
0.558
-0.453
0.000
Sol K
Sol Mg
Sol Na
1
1
0.496
0.000
0.319
0.013
0.177
0.175
1
0.803
0.000
0.503
0.000
Exch Na
1
0.800
0.000
C:N ratio
1
Bulk density
1
0.733
0.000
0.160
0.222
-0.325
0.011
1
0.318
0.013
-0.351
0.006
1
0.062
0.638
1
Table A2.2a Mauchly's test of sphericity for native species richness
Within Subjects Effect
patch
Mauchly's W
Approx. Chi-Square
df
P
Epsilon
GG
.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
OH
PR
SNP
OH
PR
.7500000
4.3333333
4.8333333
-.8333333
3.5833333
4.0833333
1.70782513
1.70782513
1.70782513
1.70782513
1.70782513
1.70782513
.991
.158
.102
.987
.292
.195
-4.8705928
-1.2872594
-.7872594
-6.4539261
-2.0372594
-1.5372594
6.3705928
9.9539261
10.4539261
4.7872594
9.2039261
9.7039261
OH
SNP
PR
-1.5833333
.5000000
1.70782513
1.70782513
.880
.998
-7.2039261
-5.1205928
4.0372594
6.1205928
P
SNP
SNP
-5.1666667
-5.67
1.70782513
1.70782513
.075
.048
-10.7872594
-11.2872594
.4539261
-.0460739
MA
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
O
P
S
T
S
T
T
P
S
Mean difference
SE
P
Lower bound
Upper bound
12.933
-1.667
-3.400
-14.600
-16.333
-1.733
1.809
1.439
1.480
1.987
2.348
1.593
.000
1.000
.267
.000
.000
1.000
7.007
-6.382
-8.251
-21.110
-24.026
-6.953
18.860
3.049
1.451
-8.090
-8.641
3.487
Table A2.3a Mauchly's test of sphericity for exotic species richness
Within Subjects Effect
patch
Mauchly's W
Approx. Chi-Square
df
P
Epsilon
GG
.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
Sub-site x Patch type (Error Patch type) 30 136.333 4.544
Residual
1
Total
.007
18.830317 7.2400978
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
OH
PR
SNP
OH
-1.9166667
1.0000000
1.4166667
1.7500000
2.92
.85309893
.85309893
.85309893
.85309893
.85309893
.239
.766
.496
.310
.041
-4.7242853
-1.8076186
-1.3909520
-1.0576186
.1090480
.8909520
3.8076186
4.2242853
4.5576186
5.7242853
PR
SNP
3.33
3.67
.85309893
.85309893
.5257147
.8590480
6.1409520
6.4742853
OH
PR
SNP
.4166667
.7500000
.85309893
.85309893
.019
.011
.987
.898
-2.3909520
-2.0576186
3.2242853
3.5576186
P
SNP
.3333333
.85309893
.994
-2.4742853
3.1409520
MA
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
O
P
S
T
S
T
T
P
S
Mean difference
SE
P
Lower bound
Upper bound
-4.533
2.067
-.467
6.600
4.067
-2.533
.959
.751
.432
1.033
.827
.462
.005
.123
1.000
-7.676
-.395
-1.882
3.216
1.356
-4.047
-1.390
4.529
.949
9.984
6.778
-1.020
.000
.004
.002
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.
0.08
0.58
1.26
38.4
solanderi
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
Mean cover
(%)
OH
Mean cover
(%)
Contribution
(%)
Cumulative contribution
(%)
Aristida vagans
Themeda australis
Aristida ramosa
Microlaena stipoides
18.00
0.17
5.17
11.83
0.08
27.25
35.50
0.33
3.72
3.71
2.88
2.72
3.72
7.43
10.31
13.03
Paspalum dilatatum*
Briza subaristata*
Paspalidium distans
Chloris gayana*
Cymbopogon refractus
Cynodon dactylon*
Eragrostis brownii
Cheilanthes sieberi subsp.
sieberi
Sida rhombifolia*
Panicum simile
Desmodium varians
Desmodium brachypodum
Lomandra spp.
Echinopogon ovatus
Chloris ventricosa
Plantago gaudichaudii
Viola hederacea
Eragrostis leptostachya
Brunoniella australis
Setaria gracilis*
Plantago lanceolata*
Asperula conferta
5.67
6.67
2.25
10.42
1.17
0.75
3.08
9.25
1.67
0.17
1.25
0.17
3.75
0.50
2.70
2.55
2.08
1.95
1.92
1.88
1.79
15.73
18.28
20.36
22.30
24.22
26.10
27.88
0.75
0.75
1.08
0.08
0.00
1.42
0.92
1.58
0.17
0.58
2.17
0.75
2.67
0.58
0.25
0.08
0.17
0.08
0.75
0.67
0.42
0.00
0.67
0.67
0.00
0.33
0.83
7.83
0.33
0.58
1.76
1.75
1.71
1.70
1.57
1.54
1.50
1.48
1.48
1.46
1.38
1.38
1.36
1.34
1.33
29.65
31.39
33.10
34.80
36.37
37.91
39.41
40.90
42.38
43.84
45.22
46.59
47.96
49.30
50.63
Species
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.
0.58
0.50
1.26
43.06
solanderi
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.
0.67
0.25
1.26
38.15
Capensis*
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
Stackhousia 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
Aristida vagans
Axonopus affinis*
Perennial
Perennial
Perennial
Summer
Summer
November - June
October - June
C4
C4
2-25 years
2-25 years
Briza subaristata*
Chloris gayana*
Perennial
Perennial
Spring
Summer
February - June
C3
C4
2-5 years
Indefinite
Chloris ventricosa
Cynodon dactylon*
Perennial
Perennial
Summer
Summer
C4
C4
5-25 years
Indefinite
Eragrostis brownii
Eragrostis curvula*
Eragrostis leptostachya
Microlaena stipoides
Perennial
Perennial
Perennial
Perennial
Summer - Autumn
Summer
Anytime
C4
C3
2-5 years
Paspalidium distans
Paspalum dilatatum*
Poa labillardieri
Setaria gracilis*
Perennial
Perennial
Perennial
Perennial
October - April
Summer - Autumn
Spring
Summer
C4
C4
C3
C4
2-5 years
Indefinite
5-25 years
2-5 years
Themeda australis
Perennial
Summer
C4
Indefinite
February - April
December - April
December - March
Appendix 3. Statistics for the Hoxton P ark 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
Location Ai
3
1
b
c
d
n
Location B(A)j(i)
28
1
1
c
d
n
σs +cnσ B(A)D+bcnσ2AD+cdnσ2B(A)+bcdnσ2A
σ2+cnσ2B(A)D+cdnσ2B(A)
Patch type Ck
Location x patch ACik
Sub-location x patch B(A)Cj(i)k
Time Dl
2
6
56
3
a
1
1
a
b
b
1
b
0
0
0
c
d
d
d
1
n
n
n
n
σ2+nσ2B(A)CD+ bnσ2ACD+abnσ2CD+ dnσ2B(A)C+bdnσ2AC+abdnσ2C
σ2+nσ2B(A)CD+ bnσ2ACD+dnσ2B(A)C+bdnσ2AC
σ2+nσ2B(A)CD+dnσ2B(A)C
σ2+cnσ2B(A)D+bcnσ2AD+abcnσ2D
Site x time ADil
Sub-location x time B(A)Dj(i)l
Patch type x time CDkl
Site x patch x time ACDikl
Sub-location x patch x time B(A)CDj(i)kl
Residual
9
84
6
18
168
1
1
1
a
1
1
1
b
1
b
b
1
1
c
c
0
0
0
1
1
1
1
1
1
1
n
n
n
n
n
1
σ2+cnσ2B(A)D+bcnσ2AD
σ2+cnσ2B(A)D
σ2+ nσ2B(A)CD+bnσ2ACD+abnσ2CD
σ2+ nσ2B(A)CD+bnσ2ACD
σ2+ nσ2B(A)CD
σ2 e
Total
384
2
2
F vs.
df of test
B(A)
if AD NS
B(A)D
AC
B(A)C
B(A)CD
AD
B(A)D
error term
ACD
B(A)CD
error term
if ACD & CD NS
if ACD NS
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
.667
10.935
2
.004
.750
Patch
Table A3.2b Split-plot ANOVA for Bray 1 P
Source of variation
df
SS
MS
F
P
8.072
.000
Location
All locations
3
2.124
.708
Sub-location
All locations
28
2.456
.088
Patch type
All locations
2
.008
NA
NA
Controls
2
.027
.014
.239
All locations
6
.280
NA
NA
Controls
4
.229
.057
1.326
All locations
56
1.999
NA
NA
Controls
42
1.811
.043
Location x Patch type
Sub-location x Patch type
Residual
Total
1
All locations
96
6.867
GG df
GG MS
GG F
4.501
NA
NA
2.948
.078
1.326
42.009
NA
NA
30.953
.059
GG P
NS
.276
.284
Table A3.3aMauchly‟s test of sphericity for Total C
Within subjects effect
Mauchly's W
Approx. Chi-Square
df
P
Epsilon
GG
.897
2.930
2
.231
.907
Patch
Table A3.3b Split-plot ANOVA for Total C
Source of variation
df
SS
MS
F
P
1.888
.154
Loc
All locations
3
.449
.150
Sub-Location
All locations
28
2.221
.079
Patch type
All locations
2
.157
NA
NA
Controls
2
.122
.061
.547
All locations
6
.450
NA
NA
Controls
4
.447
.112
4.215
All locations
56
1.221
NA
Controls
42
1.114
.027
Location x Patch type
Sub-Location x Patch type
Residual
NS
.006
1
Total
All
96
4.498
Table A3.4a Mauchly‟s test of sphericity for Total N
Within subjects effect
Mauchly's W
Approx. Chi-Square
df
P
Epsilon
GG
.819
5.401
2
.067
.847
Patch
Table A3.4b Split-plot ANOVA for Total N
Source of variation
df
SS
MS
F
P
.572
.638
Loc
All locations
3
.011
.004
Sub-Location
All locations
28
.175
.006
Patch type
All locations
2
.016
NA
NA
Controls
2
.010
.005
.312
All locations
6
.062
NA
NA
Controls
4
.062
.015
4.816
All locations
56
.149
NA
Controls
42
.135
.003
Location x Patch type
Sub-Location x Patch type
Residual
Total
1
All
96
.413
NS
.003
Table A3.5a Mauchly‟s test of sphericity for C:N ratio
Within subjects effect
Mauchly's W
Approx. Chi-Square
df
P
Epsilon
GG
.976
.666
2
.717
.976
Patch
Table A3.5b Split-plot ANOVA for C:N ratio
Source of variation
df
SS
MS
F
P
28.923
.000
Loc
All locations
3
118.648
39.549
Sub-Location
All locations
28
38.288
1.367
Patch type
All locations
2
.811
NA
NA
Controls
2
2.326
1.163
.910
All locations
6
7.348
NA
NA
Controls
4
5.110
1.278
1.959
All locations
56
37.185
NA
NA
Controls
42
27.398
.652
Location x Patch type
Sub-Location x Patch type
Residual
Total
1
All
96
202.280
NS
.119
Table A3.6a Mauchly‟s test of sphericity for pH
Mauchly's W
Approx. Chi-Square
df
P
Epsilon
GG
Patch
.850
4.393
2
.111
.869
Time
1.000
.000
0
.
1.000
Patch x Time
.904
2.733
2
.255
.912
Within subjects effect
TableA3.6b Split-plot ANOVA for pH
Source of variation
df
SS
MS
F
No test
P
Location
All locations
3
.098
.033
Sub-Location
All locations
28
.198
.007
Patch type
All locations
2
.020
NA
NA
Controls
2
.017
.009
No test
All locations
6
.045
NA
NA
Controls
4
.044
.011
No test
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
All locations
28
.045
.002
Controls
21
.040
.002
All locations
2
.001
NA
NA
Controls
2
.001
.000
.158
All locations
6
.012
NA
NA
Controls
4
.012
.003
3.243
All locations
56
.044
NA
Controls
42
.038
.001
Location x Patch type
Sub-Location x Patch type
Sub-Location x Time
Patch type x Time
Location x Patch type x Time
Sub-Locationx Patch type x Time
Residual
Total
1
All locations
192
.609
NS
.021
Table A3.7a Mauchly‟s test of sphericity for active C
Mauchly's W
Approx. Chi-Square
df
P
Epsilon
GG
Patch
.924
2.137
2
.343
.929
Times
Patch x Time
1.000
.955
.000
1.248
0
2
.
.536
1.000
.957
Within subjects effect
Table A3.7b Split-plot ANOVA for active C
Source of variation
df
SS
MS
F
No test
P
Location
All locations
3
.145
.048
Sub-Location
All locations
28
.278
.010
Patch type
All locations
2
.004
NA
NA
Controls
2
.004
.002
.243
All locations
6
.032
NA
NA
Controls
4
.032
.008
.643
.635
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
All locations
28
.147
.005
Controls
21
.117
.006
All locations
2
.003
NA
NA
Controls
2
.005
.002
.719
All locations
6
.016
NA
NA
Controls
4
.014
.003
.388
All locations
56
.408
NA
Controls
42
.368
.009
Location x Patch type
Sub-Location x Patch type
Sub-Location x Time
Patch type x Time
Location x Patch type x Time
Sub-Locationx Patch type x Time
Residual
Total
1
All
192
1.748
NS
NS
.816
Table A3.8a Mauchly‟s test of sphericity for respiration
Mauchly's W
Approx. Chi-Square
df
P
Epsilon
GG
Patch
.800
6.026
2
.049
.833
Time
Patch x Time
1.000
.954
.000
1.285
0
2
.
.526
1.000
.956
Within subjects effect
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
.687
.567
Loc
All
3
.185
.062
Sub-Location
All
28
2.516
.090
Patch type
All
2
.109
NA
NA
C
2
.185
.092
.647
All
6
.709
NA
NA
C
4
.571
.143
2.671
All
56
3.226
NA
Loc x Patch type
Sub-Loc x Patch type
42
2.246
.053
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
All
6
.738
NA
NA
C
4
.619
.155
1.946
All
56
4.754
NA
C
42
3.339
.080
Residual
Total
1
All
192
11.88
GG F
5.0
NA
NA
46.6
NA
.045
C
Sub-Loc x Patch type x Time
GG MS
NS
Time
Loc x Patch type x Time
GG df
NS
.121
GG P
Table A3.9a Mauchly‟s test of sphericity for moisture content
Mauchly's W
Approx. Chi-Square
df
P
Epsilon
GG
Patch
.810
5.675
2
.059
.841
Time
Patch x Time
.040
.005
86.075
135.194
5
20
.000
.000
.410
.442
Within subjects effect
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
4
test
Loc
All
3
10.69
Sub-Loc
All
28
8.60
.307
Patch type
All
2
.28
NA
NA
NS
C
2
.13
.069
.214
NS
All
6
1.35
NA
NA
C
4
1.29
.324
3.54
All
56
4.43
C
42
Time
All
Loc x Time
Loc x Patch type
Sub-Loc x Patch type
Sub-Loc x Time
Patch type x Time
Loc x Patch type x
Time
Sub-Loc x Patch type
x Time
5.04
NA
NA
3.06
.424
3.54
.025
NA
47.0
NA
3.84
.092
32.1
.120
3
178.
59.5
183.
<.0001
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
All
84
4.43
.053
34.4
.129
C
63
4.15
.066
25.2
4.156
All
6
.22
.037
1.09
NS
C
6
.22
.038
.845
NS
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
All
168
4.43
.026
74.1
.060
C
126
3.91
56.0
.070
Residual
.031
.014
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
Jun-07
Sep-07
Dec-07
Mar-08
Mean
L1
L2
17.2
3.61
4.82
3.01
15.7
3.45
4.61
2.83
18.9
3.78
5.04
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
Mean L1
Pasture
6yoR
14yoR
Woodland
26.1
19.3
12.9
13.5
23.7
15.8
11.4
11.4
L2
28.7
23.6
14.5
16.1
Sep-07
Mean L1
3.99
3.93
3.60
3.02
3.72
3.52
3.36
2.79
L2
4.27
4.38
3.85
3.28
Dec-07
Mean L1
5.38
5.32
4.51
4.19
5.05
4.82
4.16
3.87
L2
5.73
5.88
4.89
4.54
Mar-08
Mean L1
3.86
3.31
2.75
2.33
3.61
2.93
2.57
2.04
L2
4.13
3.74
2.95
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
Mean L1
L2
Sep-07
Mean L1
L2
Dec-07
Mean L1
L2
Mar-08
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
.042
Loc
Sub-Loc
All
All
3
28
17.763
53.347
5.921
1.905
3.108
Patch type
All
C
All
2
2
6
.823
1.170
9.348
NA
.585
NA
NA
no test
NA
C
All
C
All
All
C
4
56
42
3
9
6
8.227
22.747
18.683
68.785
11.210
6.175
2.057
NA
.445
22.928
1.246
1.029
4.885
NA
<0.005
18.401
3.532
2.686
<0.001
.001
.022
Pasture
All
C
All
C
All
C
All
C
3
84
63
6
6
18
12
168
126
1
5.035
29.624
24.142
4.857
4.934
4.994
4.052
43.230
32.756
1.678
.353
.383
NA
.822
NA
.338
NA
.260
4.759
<0.005
All
384
266.728
Loc x Patch type
Sub-Loc x Patch type
Time
Loc x Time
Sub-Loc x Time
Patch type x Time
Loc x Patch type x Time
Sub-Loc x Patch type x Time
Residual
Total
NA
2.435
NA
1.299
NA
NS
.227
Planned comparisons for nitrate:
Table A3.10b Pasture vs. Controls
Time
df
SS
MS
F(1,84)
P
Jun-07
Sep-07
1
1
0.0975347
6.9316056
0.0975347
6.9316056
0.2765642
19.65489
>0.25
<0.001
Dec-07
Mar-08
1
1
4.663967
2.894017
4.663967
2.894017
13.224895
8.2061199
<0.001
<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
Jun-07
Sep-07
Dec-07
Mar-08
Mean
L1
L2
11.7
3.98
10.2
4.32
10.0
3.26
8.82
3.56
13.7
4.82
11.7
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
Pasture
6yoR
14yoR
Woodland
Sep-07
Dec-07
Mar-08
Mean
L1
L2
Mean
L1
L2
Mean
L1
L2
Mean
L1
L2
11.0
7.37
20.2
11.3
8.56
4.75
16.4
8.46
14.2
11.2
24.7
15.0
6.92
3.24
3.42
3.14
4.67
2.13
2.29
2.05
10.0
4.74
4.93
4.61
15.3
8.95
10.6
7.20
12.3
6.79
7.80
5.37
19.0
11.7
14.3
9.57
6.18
3.31
5.24
3.14
3.93
2.11
4.29
1.98
9.47
4.96
6.36
4.75
Table A3.11a Mauchly‟s test of sphericity for ammonium
Mauchly's W
Approx. Chi-Square
df
P
Epsilon
GG
Patch
.994
.151
2
.927
.994
Time
Patch x Time
.378
.197
26.032
41.868
5
20
.000
.003
.618
.703
Within subjects effect
Table A3.11b Split-plot ANOVA for ammonium
Source of variation
df
SS
MS
F
P
18.
.000
NA
no
test
ACD
sig
ACD
sig
GG
df
GG
MS
GG F
GG P
Loc
All
3
36.98
12.3
Sub-Loc
All
28
18.33
.655
Patch type
All
2
5.15
NA
C
2
6.66
3.3
All
6
10.49
NA
C
4
7.621
1.90
NA
no
test
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
All
18
6.64
NA
C
12
5.76
.481
6.50
.88
2.87
0.012
All
168
27.58
NA
C
126
21.02
.167
68.3
.30
Loc x Patch type
Loc x Patch type x
Time
Sub-Loc x Patch
type x Time
Residual
.939
NS
2.879
.002
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
Mean L1
L2
Sep-07
Mean L1
L2
Dec-07
Mean L1
L2
Mar-08
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
14yoR
Woodland
33.6
8.17
30.0
23.5
4.82
21.9
47.9
13.4
40.9
27.2
14.2
15.7
22.7
12.1
12.9
32.6
16.6
19.1
23.4
14.3
12.8
20.2
10.8
10.6
27.1
18.8
15.5
19.8
10.5
13.5
16.8
9.05
10.5
23.3
12.1
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
Mean L1
L2
Sep-07
Mean L1
L2
Dec-07
Mean L1
L2
Mar-08
Mean L1
L2
Open
Shrub
55.3
27.7
37.8
15.7
80.8
48.4
32.7
26.8
23.0
20.5
46.4
34.9
22.9
25.8
15.6
22.1
33.6
30.1
19.1
23.8
13.6
17.4
26.9
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
Tree
19.2
7.0
6.6
4.3
52.5
11.0
15.4
13.5
10.8
9.9
21.9
18.3
21.2
14.6
11.1
9.7
39.8
21.6
14.1
9.7
10.8
8.4
18.2
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
Mean L1
Open
Shrub
Tree
24.7
42.3
25.7
17.1
31.5
9.7
L2
35.5
56.6
65.7
Sep-07
Mean L1
13.6
19.9
14.4
10.4
12.8
9.6
L2
17.6
30.7
21.4
Dec-07
Mean L1
9.1
14.3
16.2
6.9
9.8
11.8
L2
12.0
20.5
21.9
Mar-08
Mean L1
L2
7.3
17.7
18.8
4.7
12.8
12.8
11.1
24.2
27.4