1
Electronic Appendix 1. Calculation of grazing rates and grazing conversions
2
3
Calculations of contemporary stocking rates were based on detailed unpublished records of
4
stock numbers from the past 20 years obtained from the Department of Lands (Western Lands
5
Commission: Eldridge and Koen, 2003). These figures were adjusted for additional herbivore
6
activity of kangaroos (Macropus spp., 0.005 to 0.1 ha-1), goats (Capris hirtus, 0.3 to 1 ha-1)
7
and European rabbits (Oryctolagus cuniculus 15 to 50 ha-1; Myers and Poole, 1963; Cooke,
8
1983), with exact densities depending on vegetation community (Bayliss, 1985; Cairns and
9
Coombs, 1992). Published and unpublished data were then converted to dry sheep equivalents
10
per hectare (DSE ha-1; e.g. Russell, 2010). The assessment of grazing intensity was supported
11
by information on grazing–induced disturbance such as soil erosion, stock tracks, dung
12
deposits, and the degree of shrub browsing, particularly by goats measured at the sites.
13
14
References
15
16
Bayliss P. 1985. The population Dynamics of Red and Western Grey Kangaroos in Arid New
17
South Wales, Australia. I. Population Trends in Rainfall. The Journal of Animal Ecology 54:
18
111-125.
19
20
Cairns SC, Coombs MT. 1992. The monitoring of the distributions of commercially harvested
21
species of macropod in New South Wales, unpublished report to the Australian National
22
Parks and Wildlife Service.
23
24
Cooke BD.1983. Changes in the age-structure and size of populations of wild rabbits in South
25
Australia following the introduction of European rabbit fleas, Spilopsyllus cuniculi (Dale), as
26
vectors of myxomatosis. Australian Wildlife Reasearch 10: 105-120.
27
28
Eldridge DJ, Koen TB. 2003. Detecting environmental change in eastern Australia: rangeland
29
health in the semi-arid woodlands. Science of the Total Environment 310: 211-219.
30
31
Myers K, Poole WE. 1963. A study of the biology of the wild rabbit, Oryctolagus cuniculus
32
(L.), in confined populations. IV. The effects of rabbit grazing on sown pastures. Journal of
33
Ecology 51: 435-451.
34
35
Russell A. 2010. Using DSEs and carrying capacities to compare sheep enterprises. New
36
South Wales Department of Primary Industries and Agriculture. Sourced from:
37
http://www.dpi.nsw.gov.au/agriculture/farmbusiness/budgets-
38
/livestock/sheep/background/dse
39
40
41
Total current stocking rates (DSE ha-1) based on published and unpublished data on domestic
42
and native animal densities
43
Site name
Grazing rate
Current grazing rates (DSE ha-1)
Sheep Goats Kangaroos Rabbits Total
44
45
Yathong Eastern
Low
0
0.8
0.1
10
2.6
Cobar Common 1
Low
0
1
0.1
10
2.9
Cobar Common 2
Low
0
1
0.1
10
2.9
Cobar Common 3
Low
0
1
0.1
10
2.9
Glenwood
Low
0.5
0.5
0.1
15
3.2
Cobar Common 4
Medium
0
1.2
0.1
15
3.5
Yathong Cobar Road Medium
0
1
0.1
15
3.5
Yathong Marta1
Medium
0
1
0.1
15
3.5
Yathong Marta1
Medium
0
1
0.1
15
3.5
Yathong Back Rd
Medium
0
1
0.2
15
3.6
Nullawarra
High
0.7
1
0.1
15
4.2
Yathong Airstrip
High
0
1.1
0.2
20
4.4
Experimental Area
High
0.7
2.5
0.1
15
6.6
Goat1
High
0
3
0.1
15
6.7
Goat2
High
0
3
0.1
15
6.7
46
Electronic Appendix 2. Description of the 13 soils surface condition attributes recorded at
47
each site (after Tongway, 1995).
48
No. Indicators
1
Description and relevance
Type and method
No of classes
of indicator
of measurement
Range of values
Surface
Surface microtopography.
Qualitative
Five classes:
roughness
Rougher surfaces have a
Visual assessment
Small (< 3 mm)
greater ability to retain
to very large (>
abiotic and biotic resources
100 mm)
depressions
2
3
Crust
The ability of the soil to
Quantitative
Five classes:
resistance
resist erosion. More
Penetrometer to
fragile (<3 N
resistance soils are able to
measure penetration
cm-2) to very
withstand erosion by water,
resistance of soil
strong (≥ 80 N
wind or trampling
crust (N cm-2).
cm-2)
Crust
Extent to which the soil
Qualitative
Five classes:
brokenness
crust is broken. Broken
Visual assessment
Nil to intact
crusts are more susceptible
crust
to erosion
4
Crust stability
Ability of soil fragments to
Qualitative
Five classes:
break down in water. Stable
Emerson slake test
Unstable to very
soil fragments will stay
stable
intact with wetting
5
Biocrust cover
The cover of surface
Quantitative
Five classes:
biological crusts. Increased
continuous
Nil to >50%
crust cover indicates greater
Visual assessment
cover
A measure of the degree to
Quantitative
Four classes:
which the surface is eroded.
categorical
< 10 to > 50%
stability and nutrient cycling
6
Erosion cover
Visual assessment
7
Cover of
Deposited material on the
Quantitative
Four classes:
deposited
surface indicates erosion
Visual assessment
< 5% to > 50%
material
from nearby
No. Indicators
8
Description and relevance
Type and method
No of classes
of indicator
of measurement
Range of values
Plant projected Percentage of soil surface
Quantitative
Five classes:
foliage cover
Visual assessment
≤ 1% to > 50%
covered by plant foliage.
Indicates how foliage
protects the soil from
rainsplash
9
Basal plant
Percentage of the surface
Quantitative
Four classes:
cover
covered by plant stems.
Visual assessment
< 1% to > 20%
Percentage and thickness of
Quantitative
Ten classes:
litter cover on soil
Visual assessment
< 10% (< 1 mm
Indicates stability and
potential nutrient cyclings
10
Litter cover
thick) to 100%
(>170 mm thick)
11
Litter origin
Assessment of whether litter
Qualitative
Two classes:
is local or has been
Visual assessment
Local or
transported from elsewhere
12
transported
Litter
The degree to which the
Qualitative
Four classes:
incorporation
litter has become
Visual assessment
Nil to extensive
The percentage of clay in
Qualitative
Four classes:
the surface soil
Bolus technique
Heavy clay to
incorporated into the soil
13
Soil texture
sand
49
50
51
References
52
53
Tongway DJ. 1995. Monitoring soil productive potential. Environmental Monitoring and
54
Assessment 37: 303-318.
55
56
Electronic Appendix 3. Assessment of measures of landscape function and soil surface
57
condition
58
59
The 13 soil surface attributes were assessed within 0.25 m2 quadrats. This soil survey is
60
derived from the Soil Survey Analysis methodology provided by Tongway and Hindley
61
(2004) as a part of the Landscape Function Analysis. Soil surface roughness is a measure of
62
the surface microtopography and is assessed by noting the difference between the lowest and
63
highest points of the soil surface. Soil surface roughness defines the ability of the soil to
64
capture and retain resources such as water and organic matter. Crust resistance is a measure of
65
the ability of the soil to resist erosion and is measured on dry soil. It involves assessing the
66
ease with which the soil can be disturbed, producing material suitable for erosion by wind or
67
water. Crust brokenness is a measure of the extent to which the surface crust is broken. A
68
more detached crust is likely to indicate material prone to movement by erosion, but can also
69
relate to the provision of potential microsites for the collection of seeds. Crust stability, which
70
relates to the stability of soil fragments, is measured using the Emerson slake test (Tongway
71
et al,. 2003). Cover of biocrusts provides a useful measure of surface stability because of the
72
tendency of cryptogams to stabilise surface soils (Eldridge et al., 2011). The cover of erosion,
73
either by wind or water, is assessed by measuring erosional features such as rilling, sheeting,
74
scalding, terracettes and pedestals. Deposited material is material that is deposited by wind or
75
water erosion and derived from elsewhere. Projected foliage cover is the percentage of the
76
soil surface that is located under the canopy of a plant species, assessing the ability of plant
77
species to ameliorate the potentially damaging effects of raindrops on the soil surface. Basal
78
cover is the percentage of perennial vegetation cover that extends into the soil. This indicator
79
assesses the belowground contribution of the vegetation to nutrient cycling and water
80
infiltration processes. The cover of litter is assessed, as well as its origin, whether it is derived
81
from local plants or transported from elsewhere, and its degree of incorporation into the
82
surface, i.e. how well the litter and soil are mixed together. Soil texture is assessed using the
83
bolus technique (Tongway and Hindley, 2004) and relates not only to soil permeability, but its
84
ability to retain water.
85
86
For a particular quadrat, the value of each attribute was given a score, usually from 1–5, but
87
sometimes from 1–4, such that a larger score equated with a healthier surface. For example,
88
the soil microtopography classes of <5 mm, 5–8 mm, 8–15 mm, 15–25 mm and >25 mm were
89
assigned the scores of 1, 2, 3, 4 and 5 respectively. Thus a rougher surface implies a better
90
soil condition and therefore receives a higher score. The quadrat’s index of stability was
91
derived as the sum of the seven scores for crust resistance, crust stability, cover of biocrusts,
92
cover of erosion, cover of deposited material, projected foliage cover, and litter cover
93
expressed as a percentage of 40, the maximum possible score. Four of the surface attributes
94
described above (surface roughness, biocrust cover, basal plant cover, and a combined litter
95
index derived from the product of its origin, cover and degree of incorporation) were used to
96
derive a score for the nutrient status of the soil, based on its ability to cycle and retain
97
nutrients. Finally, values for surface roughness, crust resistance, crust stability, basal cover,
98
soil texture and the combined product of litter cover–origin–incorporation were used to derive
99
an index of infiltration, i.e., how well the soil accepts water. These indices have been used
100
widely to assess different landscapes worldwide and are known to be related with laboratory
101
and field measurements of their related processes (e.g. Tongway, 1995; Tongway and
102
Hindley, 2004; Maestre and Puche, 2009).
103
104
References
105
106
Eldridge DJ, Val J, James AI. 2011. Abiotic effects predominate under prolonged livestock–
107
induced disturbance. Austral Ecology 36: 367-377.
108
109
Maestre FT, Puche MD. 2009. Indices based on surface indicators predict soil functioning in
110
Mediterranean semi-arid steppes. Applied Soil Ecology 41: 342-350.
111
112
Mills AJ, Fey MV, Grongroft A, Petersen A, Medinski TV. 2006. Unraveling the effects of
113
soil properties on water infiltration: segmented quantile regression on a large data set from
114
arid south-west Africa. Australian Journal of Soil Research 44: 783-797.
115
116
Tongway DJ. 1995. Monitoring soil productive potential. Environmental Monitoring and
117
Assessment 37: 303-318.
118
119
Tongway, DJ, Hindley N. 2004. Indicators of ecosystem rehabilitation success. Unpublished
120
report. Available from
121
http://www.cse.csiro.au/publications/2003/IndicatorsOfMinesiteRehabilitationSuccessStage2-
122
100703.pdf.
123
124
Tongway DJ, Sparrow AD, Friedel MH. 2003. Degradation and recovery processes in arid
125
grazing lands in central Australia: Part 1. Soil and land resources. Journal of Arid
126
Environments 55: 310-326.
127
128
Electronic Appendix 4. Structural Equation Model procedures
129
130
The process of constructing structural equation models typically involves model specification,
131
model identification, parameter estimation, testing model fit, and, model re-specification
132
(Malaeb et al., 2000, Iriondo et al., 2003). This requires the use of theoretical knowledge to
133
develop a hypothetical systems model. Then, numerical data that represents variables within
134
the model is gathered, the value of unknown parameters estimated (Iriondo et al., 2003), and,
135
model fit tested. The testing of model fit is then repeated under the final process of re-
136
specification.
137
138
In the present study, the fit of data to the model was evaluated using a maximum likelihood χ2
139
goodness-of-fit test, Joreskog’s goodness of fit index (GFI), and the normed-fit index (NFI).
140
Under the χ2 test, a good model should have a P value >0.05, the better the data fits the model
141
the greater the P value resulting from the χ2 goodness-of-fit test. The GFI value provides
142
additional assessment of the results obtained from the χ2 goodness-of-fit test through
143
examining the variances and covariances accounted for in the model, thereby showing how
144
closely the model replicates the data. The GFI statistic ranges from 0–1 and typically does not
145
exceed 0.9. The NFI assesses the model by comparing the χ2 value of the model to the χ2 value
146
of the null uncorrelated model. The NFI ranges from 0–1 with values exceeding 0.90 being
147
indicative of a good model fit.
148
149
Consistent with SEM procedure, we used expert knowledge to develop a priori models to
150
present the predicted causal relationships among the variables operating in our system. Our a
151
priori models show the pathways hypothesized to explain the manner in which observed
152
variables influence infiltration (Figure 1) and soil nutrients (Figure 2). Ovals, boxes and
153
hexagons represent different types of conceptual variables, with no regard for how they would
154
be specified in statistical models (Chaudhary et al., 2009) and arrows represent hypothesized
155
causal relationships anticipated to exist between variables. Table 2 details the hypothesized
156
relationships occurring between grazing, shrub cover and our response variables, infiltration
157
(through matrix pores and all pores) and soil nutrients.
158
159
References
160
Chauhary, V.B., Bowker, M.A., O’Dell, T.E. Grace, J.B. Redman, A.E., Rillig, M.C.,
161
Johnson, N.C., 2009. Untangling the biological contributions to soil stability in semiarid
162
shrublands. Ecological Applications 19: 110-122.
163
164
Iriondo, J.M., Albert, M.J., Escudero, A. 2003. Structural equation modelling: an alternative
165
for assessing causal relationships in threatened plant populations. Biological Conservation
166
113: 367-77.
167
168
Malaeb, Z.A., Summers, J.K. Pugesek, B.H. 2000. Using structural equation modeling to
169
investigate relationships among ecological variables. Environmental and Ecological Statistics
170
7: 93-111.
171
172
173
174
175
176
177
Electronic Appendix 5. Median values of the soil surface and nutrient response variables in
178
relation to grazing and microsite.
179
Attribute
Low grazing
Shrub
Surface roughness (mm)
Open
Shrub
Open
High grazing
Shrub
Open
3-8
<3
<3
<3
<3
<3
70.7
75.7
72.6
77.8
84.3
83.1
Slight
Slight
Slight
Slight
Slight
Slight
Moderate
Moderate
Unstable
Moderate
Unstable
Unstable
Biocrust cover (%)
41.1
56.2
32.2
48.5
20.3
17.4
Erosion cover (%)
<10
10-25
<10
<10
<10
<10
Deposited materials (%)
5-20
5-20
5-20
5-20
5-20
5-20
Foliage cover (%)
1-10
1-10
1-10
1-10
1-10
1-10
Basal cover (%)
1-10
1-10
1-10
1-10
1-10
1-10
Litter cover (%)
50-75
10-25
25-50
10-25
25-50
<10
Litter origin
Local
Local
Local
Local
Local
Local
Nil
Nil
Nil
Nil
Nil
Nil
Clay
Clay
Clay
Clay
Clay
Clay
Crust resistance (N)
Crust brokenness
Crust stability
Litter incorporation
Soil texture
180
181
Moderate grazing
182
50 word précis
183
184
In drylands, shrubs are often regarded as indicative of degraded systems. Our work challenges
185
this view and suggests that shrubs are associated with functional soils. Rather than reducing
186
function, we show that shrubs can moderate any negative effects of overgrazing on infiltration
187
and soils.
188
189