Supporting Information for Struebig et al. Global Change Biology
APPENDIX S1
Further description of source data and bioclimatic modelling for Bornean orang-utan.
Climate projections
The outcomes of bioclimatic models can vary depending on climate data used (e.g. Tuanmu et al., 2013).
Therefore, for future climate we used multiple projections from two global circulation models (GCMs) under
two emission story-lines, chosen to reflect the range of projected values available for the timeframe and
resolution of the study. GCM projections were from either the Commonwealth Scientific and Industrial
Research Organisation Australia (CSIRO-Mk3), or the Hadley Centre for Climate Prediction and Research
UK (HADCM3), and the A2 and B2 storylines were chosen to represent a potential worst-case and best-case
emission scenario under each of the GCM projections. All climatic data were downloaded from
http://www.ccafs-climate.org/data/. Additional GCM projections have since been released under the IPCCAR5 assessment, but were not available at the time of this study. Although these additional GCMs bring
subtle differences in projections, as is to be expected between models, these spatial and temporal patterns are
consistent between the two assessment periods (Kharin 2013), and any differences within the Borneo study
region lie within the bounds of the presence thresholds used in our distribution modelling.
Projecting species distributions into future time periods can become problematic if projection
conditions are entirely novel - i.e. they are poorly represented in the training dataset (Elith et al. 2010).
Therefore, to aid understanding and interpretation of our bioclimatic modelling under these four projection
variants, we constructed vector plots comparing projected data with present-day data, and summarised
variation across the projection timeframe, GCMs and emission scenarios using violin plots (Fig. S1). These
analyses are presented for bioclimatic variables that strongly contributed to the orang-utan MaxEnt model.
While variation across projections and time was evident in the dataset, the vast majority of projected values
(i.e. the interquartile range) lay within the range of baseline values, with the exception of bio1 (annual mean
temperature) for which some values in 2080 projections exceeded the baseline. See additional multivariate
environmental similarity surfaces in Struebig et al. (In Press) for further integrative presentation of these
data.
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Supporting Information for Struebig et al. Global Change Biology
Fig. S1:
CSIRO, A2
20
15
12.0
14.0
16.0
18.0
20.0
22.0
24.0
26.0
28.0
Hadley, A2
10
15
20
25
30 5
10
10.0
5
Annual mean temperature (°C)
25
30
a
curr
2020 2050 2080
CSIRO,A2
11
10
9
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
Hadley, A2
8
9
10
11
12
13
14
7
8
7.0
7
Temperature range (°C)
12
13
14
b
curr
2020 2050 2080
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Supporting Information for Struebig et al. Global Change Biology
3000
4000
CSIRO, A2
2000
2500
3000
10
20
30
3500
4000
4500
5000
Hadley, A2
3000
4000
5000
2000
1500
2000
Annual precipitation (mm)
5000
c
curr
2020 2050 2080
CSIRO, A2
60
50
40
30
50
60
70
Hadley, A2
30
40
50
60
70
80
10
20
40
10
20
Precipitation seasonality (CV)
70
80
d
curr
2020 2050 2080
Fig. S1. Variation in projected annual mean temperature (a), temperature range (b), annual mean
precipitation (c) and for Borneo over space and time. For each panel the main map (right) presents a vector
plot of projected change in temperature by 2080 compared to current baseline levels (inset) according to the
CSIROmk3-A2 climate model. Kernel density plots illustrate the distribution of data according to latitude
and longitude, with high values reflecting areas with large differences. Violin plots (left) present variation
over the projection timeframe for the CSIRO and Hadley model climate projections (both a2 emission
scenario). A violin plot combines information on kernel density with a standard box plot.
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Supporting Information for Struebig et al. Global Change Biology
Diurnal temperature range
Rainfall in driest month
Annual temperature range
95
76
70
78
85
90
105
250
200
100
90
150
10
0
0
20
150
100
100
85
90
82
84
82
95
150
68
72
80
78
86
150
85
90
80
70
25
0
80
0
25
150
95
150
95
100
100
10
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80
82
86
88
105
110
100
100
74
76
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72
85
68
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105
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64
80
80
15
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200
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100
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66
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82
150
150
200
250
68
72
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80
82
84
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86
150
90
90
84
100
95
78
70
66
HADCM3
64
150
200
200
80
92
88
150
25
0
85
74
76
CSIRO-mk3
80
74
72
66
68
0
10
0
25
150
90
74
85
95
78
105
200
150
10
0
110
82
120
84
115
88
100
100
5
12
86
50
highly suitable
suitable
unsuitable
Fig. S2 Differences in climatic suitability for orangutan over Borneo in 2080 between CSIRO-Mk3 and
HADCM3 projections (A2 emission scenario). The three variables chosen contribute the most to the MaxEnt
model. Climate suitability (unsuitable, suitable, highly suitable) is defined by thresholds from MaxEnt
response curves. See Fig. 1 in main text for final habitat suitability models.
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Supporting Information for Struebig et al. Global Change Biology
Table S1
Variables used to delineate habitat suitability for Bornean orang-utan.
Model stage and variable name
Source
1. Bioclimatic model (MaxEnt)
Climate 2010
(bioclim variables 1-19)
Hijmans et al. (2005)
(http://www.worldclim.org/bioclim)
Future climate: 2020, 2050, 2080
(bioclim variables 1-19; four variants)
IPCC AR4: CSIRO-Mk3 and HADCM3 projections, and
A2 and B2 emission scenarios
(http://www.ccafs-climate.org/data/)
Topographic ruggedness
Kramer-Schadt et al. (2013), based on Riley et al. (1999)
Distance to water
(3 variants - small, medium, or large catchment sizes)
Kramer-Schadt et al. (2013), derived from digital elevation
model, http://srtm.csi.cgiar.org
Distance to wetlands
(includes peatswamp, freshwater swamp, mangroves)
Wetlands International:
www.wetlands.or.id/publications_maps.php
Distance to limestone karst
Digitised from 1: 250,000 national maps:




Kalimantan - Indonesia REPPROT land systems
(fields GBJ-22, KPR-17 and OKI-38).
Sabah - soils, British Government’s Overseas
Administration for Sabah, 1974 (limestone fields).
Sarawak - soils; Sarawak Department of Agriculture,
1968 (limestone fields).
Brunei - no limestone
2. Habitat suitability
2010 land-cover/elevation class, in 17 classes
(average suitability weightings in parentheses):
1. Lowland forest, 0-500 m
2. Upland forest, 501-1000 m
3. Lower montane forest, 1001-1500 m
4. Upper montane forest, >1500 m
5. Forest fragment mosaics, 0-500m
6. Forest fragment mosaics, 501-1000m
7. Forest fragment mosaics, 1001-1500m
8. Forest fragment mosaics, >1500m
9. Freshwater and peatswamp forest
10. Mangroves
11. Old plantations
12. Young plantations and crops
13. Burnt forest
14. Mixed crops
15. Water and fishponds
16. Water
17. No data
(1.00)
(0.75)
(0.63)
(0.25)
(0.83)
(0.58)
(0.50)*
(0.33)*
(1.00)
(0.50)
(0.38)
(0.31)
(0.19)
(0.25)
(0)
(0)
(0)
From Sarvision (for PALSAR methodology see Hoekman
et al., 2009). Reclassified according to Kramer-Schadt et
al. (2013), and suitability weightings assigned by orangutan experts. Classes not represented in orang-utan
analyses are indicated by *.
Future land-cover: 2020, 2050, 2080.
Reclassified according to Appendix S2.
Former land cover (pre-1950s)
Reclassified 2010 land-cover to classes 1,2,3,4,9 and 10.
Human population density (people per km2), in
5 classes (suitability weights in parentheses):
LandScan 2007, http://web.ornl.gov/sci/landscan/





0-3
4-15
16-35
36-63
>64
(1.00)
(0.75)
(0.50)
(0.25)
(0)
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Supporting Information for Struebig et al. Global Change Biology
APPENDIX S2
Further description of the predictive deforestation model for Borneo.
Predictors of deforestation
In tropical landscapes deforestation is frequently explained by measures of agricultural and logging
suitability (e.g. forest types), accessibility (e.g. slope, elevation, distances to forest edge, roads or other
human settlements) and land-use (Mas, 2005; Andam et al., 2008; Gaveau et al., 2014). On Borneo, the main
forest types are determined by elevation, with the highest quality timbers mostly found at low elevations. At
low elevations, two main forest types prevail: forests that grow on peat or on mineral soils. Therefore, the
predictors we selected for our deforestation model were elevation, distance to roads, travel times, soil type
(categorical, peat or mineral soils), and land-use categories (categorical).
Roads (including logging roads) were digitised for the whole of Borneo from a composite of 269
Landsat TM satellite images (Gaveau et al., 2014), and the localities of cities were derived from a
constructed surface area density map obtained from nighttime satellite imagery (Sutton et al., 2010). Travel
times were derived by least-cost modelling, using friction maps based on slope data and walking speeds
based on those calculated for agricultural areas in the Philippines by Verburg et al. (2004), assuming that
travel times in forests would be similar. Travel time maps were produced to roads, plantation edges and
forest edges, and combined into a single dataset.
In nearby Sumatra agents of deforestation tend to avoid clearing protected areas (e.g. national parks
and hydrological reserves), production forests (and gazetted logging concessions), and first target conversion
forests including those gazetted for agriculture and timber plantations (Gaveau et al., 2009b; Gaveau et al.,
2009a). Therefore, we used the following land-use classes as predictors: 1) protected areas, 2) logging
concessions, 3) limited production areas with no logging concession, 4) production areas with no logging
concession, 5) conversion areas with no concessions, 6) industrial timber concessions, and 7) industrial oil
palm concessions. These categories were based on those used in Indonesia's spatial plans for years 2000 to
2010, but derived and reclassified from multiple state-level planning agencies for the other Borneo states
(see Wich et al. 2012). The protected area category was kept as a reference, against which the other
categories could be compared.
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Supporting Information for Struebig et al. Global Change Biology
Deforestation model
The deforestation base-map was produced by fitting a generalised linear model with binomial error
distribution and logit link function (i.e. a logistic regression) to predict forest presence or absence as a binary
response using the explanatory variables outlined above. Forest loss was determined within a sample of 1
km2 training cells that were randomly generated for the year 2000 map within homogeneous forest stands in
Kalimantan. Cells that were placed within 2 km of a previously chosen cell were rejected, to reduce spatial
autocorrelation in the dataset, leaving up to 6,234 cells available for analysis. From this random sample, only
those that were fully forested (≥0.95 km2) in the year 2000 were retained (including n= 3,391 cells with
100% forest cover). Deforestation by 2010 was defined as ≥ 0.2 km2 forest loss within a 1 km2 cell, and all
such cells were re-coded as such (n=451 cells). We then randomly selected an equal number of cells that
experienced no deforestation (i.e. forest presence) to build a binary model, and assessed spatial
autocorrelation in the model residuals using Moran's I.
Land-cover reclassification
For each future land-coverage we overlaid the 2010 land-use map and reclassified deforested cells reflecting
likely timeframes and agents of land development as follows. For 2020 all deforested cells were reclassified
as young plantations and crops, given the limited potential for crop establishment and reforestation in this
timeframe (class 12 in Table S1). Additionally for 2050 and 2080 all deforested cells in production forests
and/or logging concessions were reclassified to fragmented forest mosaics of the appropriate elevation class
(classes 5-6). For deforested land within conversion forest and/or allocated to plantation we reclassified cells
to old plantation (class 11), assuming these areas would be planted. Deforested cells within protected areas
were reclassified to mixed crops (class 14) as forest loss in these areas typically results from small-holder
encroachment. In 2080 deforestation also affected protected areas since no unprotected forest was left for
conversion.
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Supporting Information for Struebig et al. Global Change Biology
Table S2
Parameter estimates of spatial deforestation model for Borneo. Predictors are presented in
decreasing order of contribution to the model. Land classification as protected areas (PAs, categorical) was
set as a dummy variable against which other land-use classifications could be compared. Variables
significant at 0.05 are indicated by *. Spatial autocorrelation in model residuals was weak and nonsignificant (Moran’s I = 0.035; p > 0.2).
Variables
Coefficients
SE
Wald
P-value
Exp(B)
2.2
0.355
38.433
0.0001
9.027
Conversion forest vs PAs*
1.465
0.43
11.592
0.0001
4.327
Paper/pulp concession vs PAs*
1.226
0.467
6.898
0.009
3.408
Production forests vs PAs*
0.924
0.416
4.922
0.027
2.518
Limited production vs PAs
0.866
0.515
2.829
0.093
2.378
Peatland *
0.846
0.278
9.229
0.002
2.329
Logging concessions vs PAs
0.121
0.37
0.107
0.743
1.129
Elevation *
-0.01
0.002
36.959
0.0001
0.99
-0.032
0.011
8.975
0.003
0.968
1.708
0.211
65.756
0.0001
5.517
Oil palm concession vs PAs*
Travel time to cities *
Constant
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Supporting Information for Struebig et al. Global Change Biology
APPENDIX S3
Mapping suitable land for oil palm cultivation
The expansion of oil palm agriculture on Borneo is a leading threat to orangutans (Swarna-Nantha & Tisdell,
2009; Meijaard et al., 2011), and so it is important to anticipate whether land deemed suitable or unsuitable
for oil palm in the future overlaps with suitable orangutan habitat.
While economic, legal and environmental considerations have a large role in deciding where oil
palm can and cannot be planted (Smit et al., 2013), factors concerning crop productivity are also important
(Corley & Tinker, 2003). Crop productivity, and importantly, yield, is a function of soil quality, topography
and climate. Of these attributes, those that are related to climate are expected to change over time, yet recent
efforts to map oil palm suitability (e.g. Mantel et al., 2007; Arshad et al., 2012; Gingold et al., 2012) have
kept these attributes fixed to current values. While changes in temperature will not particularly affect the oil
palm, the alterations in rainfall patterns predicted by climate models will in part determine whether land is
appropriate for establishing this crop.
The World Resources Institute (WRI) and Sekala recently created a simple online mapping tool to
identify land suitable for oil palm cultivation in Indonesia (Gingold et al., 2012). For land to be considered
suitable certain environmental, legal, and socio-economic factors were considered. The main delimiting
criteria included factors concerning crop productivity. Productivity was defined by elevation and slope; soil
depth, type drainage and acidity; as well as mean annual rainfall (Table S3). We expanded the analysis to the
whole of Borneo using the same or comparable datasets as Gingold et al. (2012), but replaced the rainfall
layer for future appraisals with values used in our modelling. We used the same criteria as Gingold et al.
(2012), except we adjusted rainfall criteria to those used by the Malaysian Oil Palm Board, which are
intermediate between other values reported in the literature.
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Supporting Information for Struebig et al. Global Change Biology
Table S3
Criteria used to designate land as suitable or unsuitable for oil palm cultivation following the
approach promoted by the World Resources Institute and Sekala Indonesia.
Variable
Unsuitable
Suitable
Spatial data
Elevation
>1000m
0-1000m
SRTM, 2008
Slope
>30%
0-30%
SRTM, 2008
Mean annual rainfall
<1700, >3000
1700-3000
Bio 12, WorldClim for time-slice
Soil depth
<50cm
>50cm
Soil type
Histosol
Inceptisol, oxisol, alfisol,
ultisol, spodosal, entisol
Soil drainage
Very poor
Poor/imperfect,
well/moderately well,
excessive/slightly excessive
Reclassified from soil and landsystems maps, Table S1
(numerical categories)
Reclassified from soil and landsystems maps, Table S1
(FAO categories)
Reclassified from soil and landsystems maps, Table S1
(descriptive categories)
Soil acidity
pH <3.5, >7
pH 3.5-7
Reclassified from soil and landsystems maps, Table S1
(pH categories)
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Supporting Information for Struebig et al. Global Change Biology
APPENDIX S4
Potential refuges from future environmental change for the Bornean orang-utan
Table S4
List of ten protected refuge areas by subspecies and state that are identified under both 10%and 25%-error suitability thresholds (see Fig. 3 in main text for locations). Reserves are located both within
and outside the currently recognised core range.
Refuge reserves
Subspecies
State(s)
P. p. pygmaeus
1. Gunung Nyuit Wildlife Reserve
2. Danau Sentarum National Park and surrounded peatland
3. Lanjak Entimau/Betung Kerihun National Parks
WK
WK
Sa, WK
P. p. wurmbii
4.
5.
6.
7.
WK
WK
WK, CK
WK, CK
P. p. morio
8. Sangkulirang-Medang protection forest complex
9. Crocker Range park
10. Yayasan Sabah Conservation Areas
(incl. Maliau Basin, Danum Valley)
Gunung Palung National Park and surrounding reserves
Hulu Kerian protection forest
Protection forest complex
Bukit Baka National Park and surrounding reserves
EK
Sb
Sb
States: Sarawak (Sa), Sabah (Sb), West Kalimantan (WK), Central Kalimantan (CK), East Kalimantan (EK)
and North Kalimantan (NK).
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