Supporting Information
Mapping perceptions of species’ threats and population trends to inform
conservation efforts: the Bornean orangutan case study
Nicola K. Abram; Erik Meijaard; Jessie A. Wells; Marc Ancrenaz; Anne-Sophie Pellier;
Rebecca K. Runting; David Gaveau; Serge Wich; Nardiyono; Albertus Tjiu, Anton
Nurcahyo; Kerrie Mengersen.
Corresponding author: Nicola K. Abram ([email protected])
Appendix S1: Spatial data framework
Orangutan habitat
The known geographic distribution of breeding orangutan populations were derived from
(Wich et al., 2012). Using the orangutan distribution layer we generated two layers: (1)
Euclidian distance from known orangutan breeding populations; and (2) the summed values
of neighbouring cells (at 1 km cell size) within a 10 km radius (using the focal statistics tool
in ArcGIS 10.0).
Land-use and land cover
Eight land cover layers were used (see Table S1) including: mangrove; intact natural forest;
logged forest; severely degraded logged forest; agro-forests/forest re-growth; industrial
timber plantations; oil palm plantations; and 'other' land cover types. Details of the generation
of these layers can be found in (Gaveau et al., 2014). We included a protected area network
layer, for details see (Wich et al., 2012). For these nine layers we calculated Euclidian
distance to that feature (in meters). We developed an above ground carbon stock layer from
LiDAR (Light Detection and Ranging) data from (Baccini et al., 2012) and converted values
to Mg of CO2 per hectare at 30 arc-second resolution (approx 1 km2). For these ten variables
we calculated the summed values of neighbouring cells within a 10 km radius using the focal
statistics tool in ArcGIS 10.0. The 10 km threshold was determined under the assumptions
that this was a reasonable distance for a person travelling on foot and a reasonable area for
defining the local environment of the village surroundings.
Table S1 Classes of Land cover types and degree of forest degradation for forested regions;
along with their descriptions and processing steps.
Class
Description
Processing steps
1. Intact
natural forest
Medium to tall old-growth natural forests that
have never been logged by the timber industry.
Open to closed canopy: closure is probably higher
than 30%. Includes Lowland and montane
dipterocarp forests, riverine forests, heath forests
on plateaux, tall closed-canopy peat forests and
open-canopy pole peat forests. Note, our intact
forest class may include areas where the forest has
been degraded slightly by small-scale logging,
which we could not detect using Landsat imagery.
Step 1: Generated a forest cover map for year 2010
using SarVision 2010 (unpublished) land cover map.
Merged classes: 3 (pole peat forest), 6 (closed
forest), 11 (riparian forest) and 17 (peat-swamp
forest close canopy).
Step 2: transformed the 1990-2000-2010 logging
road network (indicating mechanized logging) into a
road density index (km/km2) of 1x1km grid cell
(search radius 5km).
Step 3: overlaid the road density map onto the forest
cover map generated in Step 1 and recoded forest
into intact in areas where road density = 0. Areas
where road density > 0 were coded as class 9 (see
1
below).
2. Mangroves
Closed canopy Medium Forest with closed canopy
of 10 % to 30% occurring in tidal affected zones.
This class has been taken directly from SarVision
2010 land cover map.
3. Agroforests /
forest regrowth
Medium to tall agro-forests and forest regrowth.
Open to closed canopy: closure is probably equal
or higher than 30%. Includes traditional rubber
agroforests, fruit gardens, and land under fallow,
where forests is regenerating.
Step 1: Generated a broad vegetation cover map
including agroforests/forest regrowth/very degraded
logged forests using SarVision 2007 (Hoekman et
al., 2010) and 2010 land cover map. Merged class 8
(forest mosaic, fragmented or degraded forest) from
SarVision’s 2007 land cover map with class 2
(woodland) and class 13 (open forest) from
SarVision’s 2010 land cover map.
Step 2: Overlaid the logging road network onto this
broad vegetation map and recoded this class into
agroforests/forest regrowth in areas where there were
no logging roads.
4. Non-forest
Includes: 1) Low vegetation of grasses or shrubs
occurring on drained soils, occasionally flooded;
2) dry rice cultivation; 3) Low herbaceous
vegetation with including tall grasslands and
ferns; 4) can include agricultural cropland areas;
5) dry to occasionally flooded terrain; 6) areas of
herbaceous vegetation, 7) shrub lands and young
forest regrowth in fallow lands.
This class has been generated by merging classes 4,
5, 7, 8, 9, 10, 15 from SarVision’s 2010 land cover
map.
5. Water
bodies
Large lakes and large rivers.
As identified up by SarVision’s 2010 land cover
maps
6. Oil palm
plantations in
2010
Planted or recently cleared industrial scale oil
palm plantations as of year 2010.
Industrial Oil palm plantations in 1990-, 2000-, and
2010-eras were manually digitized in ArcGIS 10 by
visual inspection of >150 Landsat satellite images
downloaded from the Global Land Survey database
(http://earthexplorer.usgs.gov/).
Industrial-scale
plantations were readily identified as large
geometrically-shaped
areas
with
distinctive
homogeneous spectral signatures characteristic of
monoculture stands. We digitized any area planted
with or being cleared for oil palm. Imagery acquired
at earlier dates from the main key dates were often
required to verify clearing and planting because
newly cleared plantations (<1yr since planting) is
usually easiest to detect using Landsat imagery.
7. Industrial
timber
plantations
Planted or recently cleared industrial scale timber
plantations as of year 2010.
Industrial Oil palm plantations in 1990-, 2000-, and
2010-eras were manually digitized in ArcGIS 10 by
visual inspection of >150 Landsat satellite images
downloaded from the Global Land Survey database
(http://earthexplorer.usgs.gov/).
Industrial-scale
plantations were readily identified as large
geometrically-shaped
areas
with
distinctive,
homogeneous spectral signatures characteristic of
monoculture stands. We digitized any area planted
with or being cleared for rubber or Acacia
mangium). Imagery acquired at earlier dates from the
main key dates were often required to verify clearing
and planting because newly cleared plantations (<1yr
since planting) is usually easiest to detect using
Landsat imagery.
8. severely
degraded
logged forests
This class includes natural old-growth forests that
have become so severely degraded that they no
longer resemble the spectral signatures of forests
in class 1 or 9. These forests are primarily found
in east Kalimantan, and elsewhere only occur in
Step 1: Similar to the process for generating class 4,
we first generated a broad vegetation cover map
including agroforests/forest regrowth/very degraded
logged forests using the SarVision 2007 and 2010
landcover map. Merged class 8 (forest mosaic,
2
9. Logged
forests
small areas in Sabah, Sarawak and south
Kalimantan. In east Kalimantan these forests have
been burnt severely twice in March-April 1983
and March-April 1998 (i.e. during the two most
intense El-Niño fire pulses on record, also
declared national disaster in Indonesia). This
forest class shows little sign of regenerating
towards tall forest, probably because of invasion
by flammable grasses.
fragmented or degraded forest) from SarVision’s
2007 land cover map with class 2 (woodland) and
class 13 (open forest) from SarVision’s 2010
landcover map.
Medium to tall old-growth natural forests that
have been logged by the timber industry using
heavy machinery and networks of logging trails.
Open to closed canopy: Includes Lowland and
montane dipterocarp forests and tall closedcanopy peat forests.
Step 1: Generated a forest cover map for year 2010
using SarVision 2010 landcover map. Merged
classes: 3 (pole peat forest), 6 (closed forest), 11
(riparian forest) and 17 (peat-swamp forest close
canopy).
Step 2: Overlaid the logging road network onto this
broad vegetation map and recoded this class into
severely degraded logged forests in areas where
there were logging roads.
Step 2: transformed the 1990-2000-2010 logging
road network (indicating mechanized logging) into a
road density index (km/km2) of 1x1km grid cell
(search radius 5km).
Step 3: overlaid the road density map onto the forest
cover map generated in Step 1 and recoded forest
into logged forests in areas where road density > 0.
(If road density = 0, see class 1).
Climate and topographical variables
Four least correlated climatic variables were used: temperature seasonality; temperature
annual range; annual precipitation; and precipitation seasonality. These variables, as well as
elevation were downloaded (at 30 arc-second resolution) from WorldClim, ver. 1.4 dataset
(http://www.worldclim.org). A ruggosity layer was generated from the elevation data using
the surface area and ratio tool from Jenness (2012). Two river layers were generated: Firstly,
river density was calculated using kernel density tool in ArcGIS 10 with data sourced from
HydroSHEDS (http://hydrosheds.cr.usgs.gov/index.php) (Lehner et al., 2006). Secondly, a
major river vector file digitised from landsat images was used to calculated the euclidian
distance from a major river.
Accessibility
Two accessibility layers were calculated using least-cost functions (path distance in Arc GIS
10.0); using population data as the source, ‘time distance’ data as the cost/impedance (based
on time given to cross 1km via boat/road or walking) and elevation information as the surface
(as this varies time to cross 1km). The first accessibility layer was calculated with a single
threshold population density (10 or more people). The second layer used weighted sums, i.e.
a 'weighted' accessibility layer calculating and summing path distance for cells with 1 or
more person, 2 or more people, 3 or more people and so on up to 6,000; meaning cells that
were accessible more easily by more people get a higher score.
Socio-economic and Cultural factors
Socio-economic and infrastructure data layers consisted of: constructed impervious surface
2010 density layer (Elvidge et al., 2009); poverty index layer (Elvidge et al., 2009); and
human population 2007 density layer (estimated number of people per 1km2) from LandScan
2007TM (Bright et al., 2008). From the LandScan 2007 TM, we calculated settlement density
for cells with 10 or more people per grid (using kernel density function). We generated a road
density index from digisited 1999 to 2002 road data (using a line density function) see (Wich
et al., 2012) for more details.
3
Cultural factors consisted of ethnic group identity, and representation of major religions as a
proportion of the population. To incorporate cultural variation we used a digitized broad
ethnic group map for Borneo (Sellato, 1989). Groups included: central-northern groups;
Dusun and north-eastern groups; Iban and Ibanic groups; Kayan and Kenyah groups; land
Dayak and western groups; Malay groups; Ngaju and Barito groups; Nomadic groups and an
unknown category. Finally, as religion was a dominant factor influencing forest use and
perceptions in a concurrent study (Meijaard et al., 2013) we used percentage data of
population at provincial- or district-level resisted as Christian and Muslim. We obtained
provincial- or district-level proportions of the population that were registered as Christian and
Muslim, from Government Statistical departments from online sources (e.g.,
http://kalteng.bps.go.id/GIS.html) and hard documents (BPS-KalSel, 2009; BPS-KalBar,
2011; BPS-KalTim, 2011) which were then imported into ArcGIS 10.0.
Appendix S2: Observed orangutan killing localities across Kalimantan
Fig. S1 Location of the 512 villages surveyed within Kalimantan, Indonesia (in 2008-2012),
indicating those with one or more reported orangutan killings around village (red dot; n =
116) and those with no reported cases (blue dot; n = 396), shown on a base map of 2010
Landover classes and protected areas (cross hatch).
4
Appendix S3: Subset data for conflict and killing models
Fig. S2 Location of the 245 sampled villages (in 2008-2012) in the subset data in
Kalimantan, Indonesia whose respondents had seen an orangutan around the village in the
year prior to being surveyed, and those villages that had reported orangutan killings (red dot).
Subset data were extracted for those villages that had reported orangutan sightings around
their village in the year prior to the survey. Of this subset, eighty (33%) villages had reported
incidences of human-orangutan conflict. The human-orangutan conflict model with the subset
data (n=245 villages; Fig. S2) performed very well between the predicted and observed
responses (Fig. S3a); performing slightly higher than that of the same model based on the full
(principal) dataset (Fig. 2b). High conflict likelihood was predicted for villages that: were
nearer to severely logged forest; that had very low or high road densities in regions and with
greater temperature seasonality (Fig. S4a). These variables were the top three most important
for this model (Table S2; Fig. S4a) and for the full dataset model (Table 2; Fig. 3b).
Settlement density was seemingly important for the subset data model (Table S2; Fig. S4)
with lower densities contributing towards higher conflict likelihood (Fig. S4a). Similar to the
full dataset model, the mapped output of the subset data model predicted higher likelihoods
of conflict in regions 1, 6 and 4; but with moderate conflict risk probability in and around
region 5, rather than high risk as per predicted for the full model predicted (Fig. S5a).
5
(b) 0.50
0.6
0.5
0.1
0.0
0.2
0.5
0.3
0.4
predicted
1.5
1.0
predicted
2.0
0.7
2.5
0.8
(a) 0.98
0.0
0.5
1.0
1.5
2.0
2.5
3.0
0.0
0.5
1.0
1.5
2.0
2.5
3.0
observed
observed
Fig. S3 Correlations of goodness of fit and plots between observed and predicted responses
for the two Boosted Regression Tree models that used the subset data of 245 villages for: (a)
likelihood of human-orangutan conflict risk; and, (b) likelihood of orangutan killings
occurring within one year prior to the surveys, by anyone in the village.
Of the subset data (n=245 villages), 87 (36%) of the villages had reports of someone killing
orangutan around their village within the year prior to the survey. The model for orangutan
killings for the subset data had moderate performance, similar to the full dataset model (Fig.
S3b; Fig. 2c). Paralleling the full dataset model, killing occurrence likelihood increased with
distance from village to oil palm plantations (at around 6 km or more from plantations),
which was by far the most important variable, along with other land cover types (at around 2
km or more) and in areas with higher surrounding orangutan habitat (Table S2; Fig. S4b).
The mapped output of this model infers extensive high killing likelihood in region 5 (Fig.
S5b). High killing likelihood was however, less extensive in this model than in that of the
model of the full dataset (Fig. 4c) with many areas (e.g. parts of regions 1, 4, 5 and 6)
predicted as moderate instead of high likelihood of killings around villages within these
areas.
Table S2 Boosted Regression Tree models top ten most influential spatial variables (for
models based on the 245 village subset data), their percentage contribution (%) and the
summed total percentage of these top ten variables.
Likelihood of humanorangutan conflict
%
svlogged_m
12
road_d
11
temp_season
9
settlemt_d
7
indtim_m
5
pa_m
3
oilpalm_m
3
access_sum
3
elevation
3
intact_s
3
Total
59
Likelihood of orangutan
killings around villages
%
oilpalm_m
52
ou_s
14
otherlc_m
13
elevation
4
logged_s
3
prec_annra
2
pa_m
2
access_sum
1
christian
1
svlogged_m
1
Total
93
For explanation of codes see Table 1.
6
(a) Likelihood of human-orangutan conflict
(b) Likelihood of orangutan killings around the villages
Fig. S4 Plots for the top four most influential spatial predictor variables within the two subset
data Boosted Regression Tree (BRT) models that relate to perceptions of human-orangutan
interactions. Plots show the effect of spatial predictors on the respondent’s response variable
with relative importance values for each variable shown in parentheses on the x-axis.
Fig. S5 Mapped outputs from the two subset data Boosted Regression Tree models (based on
interviews from 245 villages) overlaid with protected areas (hatched) and provinces (black
line). Figure shows the predicted likelihood of a given response mapped as tertiles for the
likelihood of: (a) human-orangutan conflict risk; and, (b) orangutan killings occurring in the
year prior to the surveys by anyone in the village.
Table S3 Correlation matrix between the observed responses of the 6 boosted regression
tree models for the full dataset (n=512 villages)
Orangutan sighting
Human-orangutan conflict
Orangutan killing around village
Orangutan killing by respondent
Orangutan population change over last ten years
Population change in next ten years
Orangutan
sighting
1
Humanorangutan
conflict
0.371
1
Orangutan
killing
around
village
0.456
0.150
1
Orangutan
killing by
respondent
0.111
0.148
0.232
1
Population
change over
last ten years
-0.205
-0.112
-0.131
-0.025
1
Population
change in
next ten years
-0.148
-0.064
-0.076
-0.050
0.793
1
7
Appendix S4: Correlations between the spatial predictor variables
To understand the relationships between variables, correlation coefficients were calculated for each pair of spatial variables using Pearson’s
correlation coefficient (see Table S4). Several of the spatial variables were strongly correlated including a very strong negative correlation
between the proportion of the population that is Christian and those who are Muslim. Strong positive relationships occurred between: the
'weighted' accessibility layer with summed cover of intact forest, distance to mangrove, and distance to oil palm; ruggedness and summed carbon
values suggesting as ruggedness increases so does carbon stock; and, between distance to mangrove with distance to oil palm. A strong negative
relationship was found between summed carbon values and summed other land cover types, suggesting that with increasing carbon stock there is
a decrease in the extent of other land cover types. Sixty moderate correlations were found among other variables and can be seen in Table S4.
Table S4: Correlation matrix between the 39 spatial predictor (explanatory) variables
8
REFERENCES
Baccini, A., Goetz, S.J., Walker, W.S., Laporte, N.T., Sun, M., Sulla-Menashe, D., Hackler,
J., Beck, P.S.A., Dubayah, R., Friedl, M.A., Samanta, S. & Houghton, R.A. (2012)
Estimated carbon dioxide emissions from tropical deforestation improved by carbondensity maps. Nature Climate Change, 2, 182-185.
BPS-KalBar (2011) Kalimantan Barat Dalam Angka. Kalimantan Barat in Figures 2011.
Badan Pusat Statistik Provinsi Kalimantan Barat, Pontianak, Indonesia.
BPS-KalSel (2009) Kalimantan Selatan Dalam Angka. Kalimantan Selatan in Figures 2009.
Badan Pusat Statistik Provinsi Kalimantan Selatan,Banjermasin , Indonesia.
BPS-KalTim (2011) Kalimantan Timur Dalam Angka. Kalimantan Timur in Figures 2011.
Badan Pusat Statistik Provinsi Kalimantan Timur, Samarinda, Indonesia.
Bright, E.A., Coleman, P.R., King, A.L. & Rose, A.N. (2008) LandScan 2007. Oak Ridge
National Laboratory, Oak Ridge, TN.
Elvidge, C.D., Sutton, P.C., Ghosh, T., Tuttle, B.T., Baugh, K.E., Bhaduri, B. & Bright, E.
(2009) A global poverty map derived from satellite data. Computers & Geosciences,
35, 1652-1660.
Gaveau, D.L.A., Sloan, S., Molidena, E., Yaen, H., Sheil, D., Abram, N.K., Ancrenaz, M.,
Nasi, R., Quinones, M., Wielaard, N. & Meijaard, E. (2014) Four Decades of Forest
Persistence, Clearance and Logging on Borneo. PLoS ONE, 9, e101654.
Hoekman, D.H., Vissers, M.A.M. & Wielaard, N. (2010) PALSAR Wide-Area Mapping of
Borneo: Methodology and Map Validation. IEEE Journal of Selected Topics in
Applied Earth Observations and Remote Sensing, 3, 605-617.
Jenness, J. (2012) DEM Surface tools. Jenness Enterprises. Flagstaff, AZ,USA.
http://www.jennessent.com/arcgis/surface_area.htm>.
Lehner, B., Verdin, K. & Jarvis, A. (2006) HydroSHEDS. World Wildlife Fund US,
Washington, DC.
Meijaard, E., Abram, N.K., Wells, J.A., Pellier, A.-S., Ancrenaz, M., Gaveau, D.L.A.,
Runting, R.K. & Mengersen, K. (2013) People’s Perceptions about the Importance of
Forests on Borneo. PLoS ONE, 8, e73008.
Sellato, B. (1989) Hornbill and Dragon, arts and culture of Borneo, Ragabooks, Palo Alto,
CA, USA.
Wich, S.A., Gaveau, D.L.A., Abram, N.K., Ancrenaz, M., Baccini, A., Brend, S., Curran, L.,
Delgado, R.A., Erman, A., Fredriksson, G.M., Goossens, B., Husson, S.J., Lackman,
I., Marshall, A.J., Naomi, A., Molidena, E., Nardiyono, Nurcahyo, A., Odom, K.,
Panda, A., Purnomo, Rafiastanto, A., Ratnasari, D., Santana, A.H., Sapari, I., van
Schaik, C.P., Sihite, J., Spehar, S., Santoso, E., Suyoko, A., Tiju, A., Usher, G.,
Atmoko, S.S.U., Willems, E.P. & Meijaard, E. (2012) Understanding the Impacts of
Land-Use Policies on a Threatened Species: Is There a Future for the Bornean Orangutan? PLoS ONE, 7, e49142.
9