1
Supplementary Information
2
Part 1 Spatial datasets
3
4
5
1.1 Current
land cover
(LCDB)
6
1.2 Land
tenure and
legal
protection
1.3 Forest plot
data
1.4
Environmental
data (soil,
climate, slope)
7
8
9
2.1 Land
environments
(LENZ)
1.5 Other
socioeconomic
data
2.2 Potential
vegetation
10
1.6 Grassland
conversion
polygons from
remote
sensing
data
1.7
Conservation
land added in
grasslands
1992-2008
11
12
13
3.1 TEC
categories
3.2 VSA
priorities
3.3 Validated
vulnerability
3.4 Realised
protection
14
15
Input into classification
16
Untransformed criterion
17
Transformed surrogate
18
Representativeness framework
19
Response variable
20
Predictor variable
21
22
Fig. S1.1 Spatial data and relationships among data sources used directly and indirectly in our
23
analyses. The suffix 1 indicates primary datasets, the suffix 2 indicates datasets derived from
24
those primary sources, and the suffix 3 identifies the four datasets compared in our analyses
25
1
26
1.1 Current land cover (LCDB)
27
Current land cover provides an estimate of current biodiversity and vegetation patterns, for
28
which we used New Zealand’s Landcover Database 2 (LCDB2) (Thompson 2003). The Land
29
Cover Database version 2 (LCDB2, based on imagery from 2001/02 (Thompson et al. 2003),
30
contains the most up-to-date, nationally comprehensive, spatial land-cover datasets available
31
in New Zealand. LCDB1 was developed in 1997, with improvements made in 2002/03 to
32
create Land Cover Database 2 (LCDB 2) (Thompson et al. 2004). LCDB2 used Landsat 7
33
ETM+ satellite imagery as the primary data for the thematic classification. Each LCDB map
34
consisted of a vector-based thematic classification of 43 land cover/uses, including three
35
indigenous grassland cover classifications: low producing grassland, tall tussock grassland,
36
and depleted grasslands.
37
38
1.2 Land tenure and legal protection
39
Spatial data depicting land tenure and legal protection were compiled from the most up-to-
40
date digital spatial data government agencies could supply. Three categories of land tenure
41
were recognised. Protected land (Category 1, also used as the binary legal protection layer
42
used in the threatened environment classification or TEC) included public protected land
43
administered for natural heritage purposes by the Department of Conservation, regional parks
44
administered by regional territorial authorities, and private land covenants administered by
45
the Department of Conservation, Nga Whenua Rahui, or the QEII National Trust. Crown
46
grazing land (Category 2) was land under a perpetually renewable pastoral lease or fixed-
47
term pastoral occupation licence and owned by the Crown. Remaining land (predominantly
48
privately owned land) was categorised as private land (Category 3). The land tenure surrogate
49
assumed that vulnerability to conversion increased across tenure categories from Category 1
50
to Category 3.
2
51
52
2.1 Land Environments (LENZ)
53
Land environments are defined by a combination of abiotic (soil, climate and topographic)
54
characteristics (1.4 in Fig. S1.1). The 500 Level IV environments from the Land
55
Environments of New Zealand (LENZ; Leathwick et al. 2003) are commonly used as a
56
surrogate to represent units of the potential full range of terrestrial biodiversity. No
57
assumption is made that environments were completely distinct or are equally different. This
58
treatment of environments resembles conservation planning approaches for land systems and
59
land classes (e.g., Cowling and Heijnis 2001).
60
61
3.1 TEC Categories
62
The Threatened Environment Classification groups the 500 land environments at Level IV of
63
LENZ into six categories (Table S1.1). The categories combine land environments, land
64
cover and legal protection. The system assumes that vulnerability to future habitat loss is
65
indicated by both proportion of remaining land cover and proportion of environment
66
protected (Walker et al. 2008). Habitat loss is assumed to cause a non-linear increase in risk
67
to remaining biodiversity, and to be correlated with risk of future loss because extent of
68
ecosystem clearance is often related to the inherent capability of land for intensive human
69
use. Poor legal protection also contributes to future habitat loss in New Zealand, because
70
indigenous ecosystems are more likely to be cleared or degraded if not legally protected
71
(Walker et al. 2006, 2008). The TEC categories have been incorporated into New Zealand
72
non-statutory guidance to territorial land authorities. Indigenous vegetation on land in the
73
first two categories (environments with less than 20% remaining indigenous cover) is
74
designated among ‘national priorities’ for protection on private land. In this paper, we
3
75
assumed that the TEC categories represent a trend from highest to lowest vulnerability to
76
indigenous vegetation clearance in New Zealand (Table S1.1).
77
78
Table S1.1 Threatened Environment Classification (Walker et al. 2006)
79
Category Criterion(?)
Assumed level of vulnerability to
conversion in this paper
1
<10% indigenous cover left
Highest
2
10–20% left
High
3
20–30% left
Moderately high
4
>30% left and <10% protected
Moderate
5
>30% left and 10–20% protected
Low
6
>30% left and >20% protected
Lowest
80
81
2.2 Potential vegetation
82
Potential vegetation is an estimate of natural land cover under undisturbed conditions
83
(Leathwick et al. 2003), and is a classification of predicted national spatial distributions of
84
forty common canopy trees (1.3 in Fig. S1.1).
85
86
3.2 VSA priorities
87
Potential vegetation (2.2 above) is used in conjunction with a classification of current land
88
cover (1.1 above) and a classification of land environments (2.1 above) to assess naturalness
89
in VSA (See also Supplementary Information 2 below). A current-potential naturalness table
90
assigns a proportion naturalness value to each unique combination of current land cover and
91
potential vegetation classes, based on expert judgement. A spatial layer called ‘naturalness’ is
92
then output, in which each pixel is given the naturalness value of its current land cover and
93
potential vegetation combination. The naturalness layer is combined with the environmental
4
94
classification in a calculation of ‘environmental representation’, which is the proportion of
95
remaining indigenous land cover in each land environment.
96
In the original run of VSA, a vulnerability surrogate was derived by experts assigning
97
subjective estimates of probability that remaining indigenous grassland would be converted
98
in the coming decade to the three principal types of land tenure (1.2 above): private land,
99
public conservation land, and Crown land under a long-term grazing licence known a
100
'pastoral lease' (Overton et al. 2010). Supplementary Information 2 illustrates the derivation
101
of VSA priorities from vulnerability (‘expected loss’) and environmental representation.
102
3.3 Validated Vulnerability
103
Validated vulnerability predicted using Generalized Additive Models (GAMs) to fit
104
relationships between the dependent (response) variable (presence or absence of conversion)
105
and potential explanatory variables (Weeks et al., in review). We used Generalized
106
Regression Analysis and Spatial Prediction (GRASP) set of functions (Lehmann et al. 2002)
107
in S-PLUS software (MathSoft 1997) for these models. Three recent maps of grassland
108
conversion served as data for conversion from 1990 to 2008 (derived from manually mapped
109
polygons; 1.6 in Fig. S1.1). Each 25-m-grid cell in a map represented grassland that had not
110
been converted or grassland that had been converted for forestry, agriculture (cropland or
111
exotic pasture), or urban development. A comprehensive set of the environmental and socio-
112
economic variables currently available in New Zealand were used as potential predictors of
113
conversion (primary sources 1.4 and 1.5 in Fig. S1.1; see also Supplementary information 3
114
for descriptions of each predictor, including those represented by abbreviations).
115
Probability of conversion was modelled as a binomial variable, and a starting model
116
including all continuous and categorical predictors smoothed with 3 degrees of freedom was
117
fitted first, significant predictor variables were then selected by backward and forwards
118
stepwise procedure using the Bayesian Information Criterion (BIC) for variable selection.
5
119
The chosen predictors for each final model were used to map predictions in geographic space.
120
Models were calibrated using a single set of data, and validated using the Receiver Operating
121
Characteristic (ROC) curves, which measured how well the model could distinguish between
122
predicted and observed conversion in the same time period.
123
124
Fig. S1.2 Vulnerability of remaining indigenous grasslands to future conversion based on
125
observed patterns of conversion between 1990 and 2008, and expressed as a probability of
126
conversion. Areas of conversion before 1990, and areas that are not indigenous grasslands are
127
labelled ‘non-indigenous’ and shown in white
6
128
3.4 Realised protection
129
Realised protection was modelled as a binomial variable using the same process and set of
130
predictor variables as validated vulnerability. The dependent (response) variable was the
131
presence or absence of protection derived from a map of all lands added to public
132
conservation land or protected a covenant within the study area between 1992 and 2008 (1.7
133
in Fig. S1.1).
134
135
136
Fig. S1.3 Probability of protection of remaining indigenous grasslands within the study area
137
based on a model of recent patterns of protection
7
138
References for spatial data
139
Cowling RM, Heijnis CE (2001) The identification of broad habitat units as biodiversity
140
entities for systematic conservation planning in the Cape Floristic Region. S Afr J Bot
141
67:15–38
142
Leathwick JR, Overton JM, McLeod M (2003) An environmental domain classification of
143
New Zealand and its use as a tool for biodiversity management. Conserv Biol
144
17:1612–1623
145
146
Lehmann A, Overton JM, Leathwick JR (2002) GRASP: generalized regression analysis and
spatial prediction. Ecol Modell 157:189–207
147
Margules CR, Pressey RL (2000) Systematic conservation planning. Nature 405:243–253.
148
Mathsoft (1998-1999) S-PLUS 2000 Professional Release 2. Mathsoft Inc
149
Overton JMcC, Price R, Stephens T, Cook S, Earl R, Wright E, Walker S (2010)
150
Conservation planning and reporting using Vital Sites Model. Landcare Research
151
contract report to the Department of Conservation
152
153
154
155
156
157
158
Thompson S, Gruner I, Gapare N (2003) New Zealand Land Cover Database Version 2.
Wellington, Ministry for the Environment
Walker S, Price R, Rutledge D, Stephens RTT, Lee WG (2006) Recent loss of indigenous
cover in New Zealand. New Zeal J Ecol 30:169–177
Walker S, Price R, Stephens RTT (2008b) An index of risk as a measure of biodiversity
conservation achieved through land reform. Conserv Biol 22:48–59
Weeks ES, Overton JMc, Walker S (in review). Estimating patterns of vulnerability in a
159
changing landscape: a case study of New Zealand’s indigenous grasslands. Environ
160
Conserv
8
Part 2. VSA procedure
Original indigenous cover in environment
Remaining indigenous cover in environment
Expected loss
a
Original indigenous
cover in environment
Contribution of pixel to
environmental representation
Vwpi
Vwopi
Significance
pixel
Environmental representation
b
Expected loss of environmental
representation with expected loss at pixel
Original indigenous
cover in environment
Vwol
Vwl
Priority
Expected loss
pixel
Environmental representation
Fig. S2.1 Calculations of a. significance (upper row) and b. priority (lower row), shown here
for one pixel. The vertical axis in each graph on the right represents the value (V) of the
original indigenous cover in the environment. The red line is a power curve with an exponent
of 0.25 used as a value function. In a, Vwpiis the current significance of all the remaining
indigenous cover in the environment, including the pixel, Vwopi is the value without the pixel,
and the difference between Vwpi and Vwopi is the marginal value (significance) of the pixel. In
b,Vwol is the value of all the remaining indigenous cover in the environment without the
expected loss at the pixel, and Vwl is the value with the expected loss at the pixel. The
difference between Vwol and Vwl is the priority of the pixel (i.e. the expected loss of value
averted by protecting the pixel)
Original indigenous cover in environment
Remaining indigenous cover in environment
Expected loss (vulnerability)
a
Expected loss of environmental
representation with expected loss at pixel
Original indigenous
cover in environment
Vwol
Vwl
Expected loss
Priority
pixel
Environmental representation
Expected loss of environmental
representation with expected loss at pixel
b
Vwol
Vwl
Priority
Expected loss
pixel
Environmental representation
Expected loss of environmental
representation with expected loss at pixel
c
Vwol
Vwl
Priority
Expected loss
pixel
Environmental representation
Fig. S2.2 Calculations of priority for three different scenarios with the same vulnerability
(expected loss). In a and b, the naturalness at the pixel = 1.0 and environmental representation
is high in a and low in b. Therefore the priority of the pixel in b is greater than the pixel in a.
In c., environmental representation is low, and naturalness at the pixel is 0.5. Therefore, the
magnitude of expected loss at the pixel in c is less than in b (it is represented by the area to
the right of the vertical dotted line through the pixel). The expected loss of environmental
representation, and the priority of the pixel, aresmaller than in b
1
Part 3 List of environmental and socio-economic variables used in models
2
Table S3.1 Environmental and socio-economic variables used to predict probability of conversion (validated vulnerability) and probability of
3
protection (realised protection)
4
Name of variable
Mean annual temperature
Mean annual solar radiation
Evapo-transpiration
Vapour pressure deficit
Annual water deficit
Rainfall
Substrate age
Soil calcium
Acid soluble phosphorous
Elevation
Slope
Catchment
Regional Council
Abbreviation
mat
mas
r2pet
vpd
deficit
rain
age
calcium
acidp
elevation
slope
catchgroup
region
Land tenure
land tenure
Distance to water
Distance to roads
Distance to irrigators
Distance to roads
Distance to towns
Distance to power
Proximity to agriculture
water
roads
irrigators
roads
towns
power
pad
Land use capabilities
luc
Pasture productivity index
pastprod
Definition
Mean annual temperature
Mean annual solar radiation
Ratio to the annual potential evapo-transpiration
The annual vapour pressure deficit
The annual water deficit
Mean annual rainfall
Estimated age class of substrate
Estimated class of soil calcium
Estimated class of acid soluble phosphorous
Elevation above sea level
Slope estimated from DEM
River catchment
Regional council, where 1 = Otago, 2 = Southland, 3 = Marlborough , 4 =
Canterbury
Land tenure based on seven categories: 1. Former Crown pastoral lease
(FCPL) conservation land, 2. FCPL conservation with grazing licence, 3.
FCPL privatised, 4. FCPL private covenant, 5. Other conservation land, 6.
Other private land, 7. Current Crown pastoral lease
The distance of each pixel to a pixel of water
The distance of each pixel to a pixel of roads
The distance of each pixel to a pixel of irrigators
The distance of each pixel to a pixel of roads
The distance of each pixel to a pixel of towns
The distance of each pixel to a pixel of power lines
The proportion of pixels that are within 2km of land cleared for agriculture by
1990
Land are classified according to their capability to sustain continuous
production, where class 1 has the highest capability, and class 8 the lowest.
Net primary productivity
Units
C
MJ/m2/d
ratio
kPa
mm
mm
class
class
class
m
degrees
class
class
Category
Climate
Climate
Climate
Climate
Climate
Climate
Substrate
Substrate
Substrate
Landform
Landform
Governance
Governance
Source
Land Environments of New Zealand (Leathwick et al. 2003)
Land Environments of New Zealand (Leathwick et al. 2003)
Land Environments of New Zealand (Leathwick et al. 2003)
Land Environments of New Zealand (Leathwick et al. 2003)
Land Environments of New Zealand (Leathwick et al. 2003)
Land Environments of New Zealand (Leathwick et al. 2003)
Land Environments of New Zealand (Leathwick et al. 2003)
Land Environments of New Zealand (Leathwick et al. 2003)
Land Environments of New Zealand (Leathwick et al. 2003)
New Zealand Digital Elevation Model (Barringer et al. 2002)
New Zealand Digital Elevation Model (Barringer et al. 2002)
NZMS260 series topographic map (Land Information New Zealand)
NZMS260 series topographic map (Land Information New Zealand)
class
Land tenure
Department of Conservation
m
m
m
m
m
m
m
Infrastructure
Infrastructure
Infrastructure
Infrastructure
Infrastructure
Infrastructure
Infrastructure
NZMS260 series topographic map (Land Information New Zealand)
NZMS260 series topographic map (Land Information New Zealand)
NZMS260 series topographic map (Land Information New Zealand)
NZMS260 series topographic map (Land Information New Zealand)
NZMS260 series topographic map (Land Information New Zealand)
NZMS260 series topographic map (Land Information New Zealand)
N/A
class
Productivity
NZ Land Resource Information System (Newsome 2000)
g m-2
Productivity
Pasture Productivity Index (Baisden 2006)