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Predicting the Distribution of Sunda Pangolin (Manis javanica Desmarest, 1822) in Way Canguk Research Station, Bukit Barisan Selatan National Park, Lampung

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The distribution of a species can help guide the protection activities in their natural habitat. Conversely, the lack of information on this distribution makes the protection strategy of this species difficult. The research was conducted in Way Canguk Research Station, Bukit Barisan Selatan National Park from January until March 2018. The purposes of this research were to create a distribution prediction map of Sunda pangolin (Manis javanica) and estimating the environment variables that most influenced the probability of the distribution. Fourteen points of camera trap coordinates were used for presence data with nine types of environment variables such as elevation, slope, understorey, canopy cover, distance from roads, distance from rivers, distance from villages, food source, and distance from the threat. The result of maxent showed an Area Under the Curve (AUC) value of 0.909 categorized as very good. The highest probability of Sunda pangolin distributions was in the Pemerihan Resort and Way Haru Resort area, while the dominant environmental variables included the distance from the village, the canopy cover, and the distance from threat with the value 47.7; 25.85; and 15.8%, respectively. Prediction maps and environment variables can help to identify the population of Sunda pangolin in the wild and can provide input for the national parks to prioritize protection areas for Sunda pangolin from the increased poaching.
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ABSTRACT
The distribution of a species can help guide the protection activities in their natural
habitat. Conversely, the lack of information on this distribution makes the
protection strategy of this species difficult. The research was conducted in Way
Canguk Research Station, Bukit Barisan Selatan National Park from January until
March 2018. The purposes of this research were to create a distribution prediction
map of Sunda pangolin (Manis javanica) and estimating the environment variables
that most influenced the probability of the distribution. Fourteen points of camera
trap coordinates were used for presence data with nine types of environment
variables such as elevation, slope, understorey, canopy cover, distance from roads,
distance from rivers, distance from villages, food source, and distance from the
threat. The result of maxent showed an Area Under the Curve (AUC) value of
0.909 categorized as very good. The highest probability of Sunda pangolin
distributions was in the Pemerihan Resort and Way Haru Resort area, while the
dominant environmental variables included the distance from the village, the
canopy cover, and the distance from threat with the value 47.7; 25.85; and 15.8%,
respectively. Prediction maps and environment variables can help to identify the
population of Sunda pangolin in the wild and can provide input for the national
parks to prioritize protection areas for Sunda pangolin from the increased
poaching.
Keywords: Manis javanica, maxent, species distribution, Way Canguk research
station
Research Article
Predicting the Distribution of Sunda Pangolin (
Manis
javanica
Desmarest, 1822) in Way Canguk Research Station,
Bukit Barisan Selatan National Park, Lampung
Silvi Dwi Anasari1*, Wulan Pusparini2, Noviar Andayani1
1) Department of Biology, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Kampus UI Depok, Indonesia,
16424
2) University of Oxford, Oxford OX1 2JD, United Kingdom
* Corresponding author, email: silvidwianasari@gmail.com
Submitted: 14 August 2020; Accepted: 20 March 2021; Published online: 14 April 2021
Journal of Tropical Biodiversity and Biotechnology
Volume 06, Issue 01 (2021): jtbb58612
DOI: 10.22146/jtbb.58612
INTRODUCTION
A pangolin is a type of mammals of the Order of Pholidota having scales
(Payne et al. 1985). There are four types of pangolins distributed in Asia
(Manis javanica, Manis craussicaudata, Manis pentadactyla, and Manis culionensis)
and four in Africa (Manis temminckii, Manis tricuspis, Manis gigantea, and Manis
tetradactyla). Manis javanica or Sunda pangolin are distributed on the islands of
Sumatra, Java, and Kalimantan (Challender 2014). Geographically, Sunda
pangolins can be found at different altitudes: 10-100 masl (TEAM camera
traps photo findings), 350-900 masl (Wirdateti et al. 2013), and 1170 masl
(Manshur 2015).
Copyright: © 2021, J. Tropical Biodiversity Biotechnology (CC BY-SA 4.0)
J. Tropical Biodiversity Biotechnology, vol. 06 (2021), jtbb58612
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Generally, a Sunda pangolin is a shy animal, not aggressive, and
solitary, although on some occasions they found more than one individual.
Sunda pangolins have an important value as a natural controller of the insect
population, such as ants and termites. They can eat up to 70 thousand ants
and termites per year, and they play a role in improving soil quality
(Rodrigues 2011). Sunda pangolins also have high economic values, their
scales are used for traditional medicine and the meat is cooked for exotic
food (Sawitri & Takandjandji 2016). In Malaysia, the scales of Sunda
pangolin used for asthma medication and protection from witchcraft,
whereas, in Indonesia believed to protect from harmful magic (Chong 2020).
Poachers used snares, traps, or dogs to catch on the Sunda pangolin (Newton
et al. 2008). They are one of the protected and are included in the Appendix
I Convention on International Trade in Endangered Species (CITES) due to
illegal hunting since 2000 (Sawitri et al. 2012). The IUCN Red-List
established Sunda pangolin as critically endangered in 2013.
One of the habitats of Sunda pangolins is Bukit Barisan Selatan
National Park (BBSNP). This research is the first ecological study about
Sunda pangolins in this area since 1997. Wirdateti et al. (2013) was
researched the distribution and population of Sunda pangolin in Tanggamus
and West Lampung, but that was not in the BBSNP area. Studies on Sunda
pangolins are very rarely done in Indonesia, especially in Sumatra. So far,
most of the studies on pangolins were about genetics (Nie et al. 2009;
Wirdateti et al. 2013; Wirdateti & Semiadi 2017), physiology (Cahyono 2008),
behavior in the captivity (Febriyanti 2016), and trades (Takandjanji & Sawitri
2016).
However, the information about the ecological nature of Sunda
pangolins was found to be lacking, including the information about the
distribution of Sunda pangolin in a certain region. The purposes of this
research are to predict the distribution pattern of Sunda pangolins and to
study the environmental variables that influenced the presence of Sunda
pangolin in the BBSNP. By evaluating the distribution of Sunda pangolins,
BBSNP can determine the priority areas for the protection and conservation
of Sunda pangolin.
METHODS
Research location
The research was conducted at Way Canguk Research Station, Bukit Barisan
Selatan National Park (BBSNP) (Figure 1) from January - March 2018. The
range of altitude of this research station was 0-100 masl (Endarwin 2006).
Data collection
Presence data of Sunda pangolin (direct observation)
The presence was used for maxent analysis, and they were recorded not only
by direct encounter with the species (the primary sign of existence) but also
by coordinates point of presence of species from camera traps (the secondary
sign of existence). Several Sunda pangolin photos were caught by camera trap
installed by WCS-IP through the TEAM (Tropical Ecology Assessment and
Monitoring program) from 2010-2017. Fourteen presence data of Sunda
pangolin were recorded from TEAM camera trap coordinate points from
2010-2017. The camera trap installation area included three resorts:
Pemerihan Resort, Way Haru Resort, and Way Nipah Resort with a total of
60. All camera trap locations were evaluated to obtain environment variables,
and camera traps points were recorded using Microsoft Office Excel with
three columns: species, longitude, and latitude in CSV format. Additionally,
UTM (Universal Transverse Mercator) for a geographic coordinate system was
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used.
Figure 1. Research location in Bukit Barisan Selatan National Park.
Environment variables
The data of environment variables were obtained directly and indirectly.
Some parameters, such as tree vegetation survey, food sources, understorey,
canopy cover, and distance from threat were recorded directly. Tree
vegetation survey used a 20 m x 20 m grid, and points of camera trap were
used as a center point. Measurement of DBH (Diameter at the Breast
Height) was carried out 1.3 m above the ground and only the trees having the
DBH > 20 cm were surveyed. Food sources data were obtained from ants
and termite nests in every camera trap point. Understorey data were obtained
using a gridded sheet sized 1 m x 1 m which was divided into 16 squares, and
camera trap points were used as the center point.
The distance from the camera trap point to obtain the data was 10
meters each in four directions following the compass direction. Canopy
cover data were obtained using spherical densiometer model C in four
directions based on compass direction, and camera traps points were used as
the center point. Distances from threat data were obtained from traps
coordinate point directly in the field and human photos of presence
coordinate point at camera trap were also used.
Four data from the environment variables were recorded directly to
Microsoft Office Excel with CSV format, and then ArcGIS was used for the
deterministic interpolation (Arctoolbox interpolation IDW). The
processed data were in raster format, and then they were converted into
ASCII format (Arctoolbox Conversion Tools From Raster Raster to
ASCII). Distances from villages, road, and river data were obtained from
Rupa Bumi Indonesia (RBI) map with a scale of 1:200.000 in point and line
shapes. Slope and elevation data were obtained from USGS Explorer from
DEM (Digital Elevation Model) 30 arc second via https://
earthexplorer.usgs.gov/ website.
Data analysis
Maxent Ver. 3.4.1k at https://biodiversityinformatics.amnh.org/
open_source/maxent/ (free version) was used. The maximum entropy is a
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species distribution model that uses two data sets: presence data and
environment variables (Elith et al. 2006; Phillips et al. 2006). For presence
data in this study, direct presence data and indirect presence data were used.
Direct presence data was finding the species directly in the field, while the
indirect presence data included camera trap coordinates, scratches,
footprints, and feces data. The environment variable data used direct data or
indirect data. Direct data of environment variable were obtained in the field,
for example, food sources (ants and termite nest) of Sunda pangolins, and
indirect data used the GIS layer of RBI maps. Direct data of the environment
variables used the interpolation in the ArcGIS menu for the estimated values.
Interpolation is generally divided into two, deterministic interpolation and
geostatistical methods. Deterministic interpolation is a deterministic
calculation that is used to measure values based on the data obtained from
the field. Deterministic interpolation has various choices of menus at ArcGIS
desktops such as IDW (Inverse Distance Weighting), Natural neighbour,
Trend, and Splind. Geostatistical methods are based on an autocorrelated
statistic model (statistical relationship based on measured points) (ArcGIS
Dekstop 2020). Environment variables were obtained using the GIS layer
and using Euclidean Distance. Euclidean Distance describes each cell-to-
source relationship or set of sources based on straight line distance (ArcGIS
Dekstop 2020).
Maxent estimates environment variables that had important roles in
the prediction model, like an environment variable based on the Jackknife
curve (Phillips et al. 2006). In addition, there was a response curve that had
an important role in showing the presence probability of a species to the
environment variables (Tarjuelo et al. 2014). The prediction was improved
based on AUC (Area Under the Curve) value. AUC is a curve that shows the
probability of a species to maps (Baldwin 2009). A prediction is acceptable if
the AUC value is above 0.75. AUC values according to Baldwin (2009) can
be seen in Table 1.
Table 1. Area Under the Curve (AUC) value classification.
Presence data of the 14 coordinate points containing Sunda pangolin
photos from the TEAM camera trap were saved in CSV format. The
environment variable data were compiled using ASCII (asc) format with the
same extent and cell size (depend on the data) in order running in maxent
software. Cell size and extent are environment settings contained in tools at
ArcGIS software. Cell size refers to raster size and extent refers to a range of
feature data or specified raster (ArcGIS Dekstop 2020). All geographic
coordinate systems used the UTM projection format. Presence data of Sunda
pangolin were recorded in CSV format and environment variable in asc
format. The data result was the prediction distribution of Sunda pangolins
and the prediction of environment variables that most influenced the Sunda
pangolins presence at the research site.
RESULTS AND DISCUSSION
Prediction distribution map of Sunda pangolin
One of the results of the maxent model was AUC values that shaped the
graph, and AUC could easily compare the performance of one model with
AUC value Model performance
0,9 1,0 Very good
0,8 0,9 Good
0,7 0,8 Medium
0,6 0,7 Not good
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another. The black line on the AUC value was a random prediction, while the
red line meant the value of training data. The AUC value of this study
showed that the prediction of Sunda pangolin distribution was 0.909 (Figure
2). Based on the classification of AUC values from Araujo and Gausan
(2006), the AUC value was categorized as very good.
Figure 2. The sensitivity and 1-sensitivity graph of Sunda pangolin.
Figure 3 shows the prediction of the distribution of Sunda pangolin
using the Maxent’s analysis. The color gradations generated in the Maxent
analysis provided separate information for the prediction of the presence of
Sunda pangolins. The green color predicted a low distribution of Sunda
pangolins presence, the orange color showed a moderate prediction
Figure 3. Prediction distribution of Sunda pangolin in BBSNP.
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distribution of Sunda pangolins, and the red color indicated as a high
distribution of Sunda pangolin. The highest prediction of the Sunda pangolin
was found in the Pemerihan Resort and Way Haru Resort area as indicated
with the red color.
The results of the prediction distribution map, especially the line of the
prediction area, were more visible using an overlay. An overlay is an
overlapping thematic map process with different geographical layers to
decide a spatial conclusion. The red color showed the prediction distribution
area of Sunda pangolin with the probability of >0.77 (Figure 4). The highest
prediction for the presence areas of Sunda pangolin was in the Pemerihan
resort and Way Haru resort area. Both resorts were known to be the habitat
for several endangered and protected species such as wild cats, sumatran
tigers, and Sunda pangolins (Putri 2017).
Sunda pangolin tends to be found on difficult (or steep) lanes and
slopes, and in this study, there were 14 points of Sunda pangolin presence
found using TEAM camera traps which were located in this difficult
condition. Manshur (2015) stated that slopes had an important role as an
environmental variable affecting the presence of Sunda pangolin for as much
as 72%. The slope was used by Sunda pangolins as an anti-predator strategy
which was to roll away to protection. The slope used by Sunda pangolins
ranged from 0-70 degrees (as seen from the result of the Maxent analysis),
and the speed at which a Sunda pangolin roll itself could reach 15 km/
minute (Manshur 2015).
A direct encounter with Sunda pangolins did not occur in this study.
However, scratches on trees and one scale of Sunda pangolin near a
branching river. The scratches of Sunda pangolin that were found in the field
could not use as presence data, because they were difficult to distinguish
from those of the sun bear (Figure 5). The Pangolin Specialist Group (PSG)
team in 2017, which conducted observations for two years in Thailand, was
also still unsure whether the scratch came from Sunda pangolin. They
assumed that the Sunda pangolin’s scratches had 2 to 3 lines that stuck to
Figure 4. The overlay of distribution of Sunda pangolin in BBSNP.
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trees or soil. Sunda pangolin scratches seen in Ragunan Zoo also had 2 to 3
lines.
Figure 5. The scratches form of Sunda pangolin in Ragunan zoo.
Contribution of the environmental variables
Maxent provides the metric to determine the importance of environment
variables in contribution percentages. The results showed that the distance
from the village had the highest percentage of 47.8%, followed by canopy
cover and distance from the threat having a percentage of 25.8% and 15.8%
respectively (Table 2).
Table 2. Contribution percentage of environment variables of Sunda pangolin.
Maxent model estimates that environmental variables have an
important role in the resulting prediction model, i.e, environmental variables
based on the contribution percentages of the jackknife test results. Based on
the percentage of contribution, three environmental variables were most
influential in the presence of Sunda pangolins: distance from the village,
canopy cover, and distance from the threat.
Based on the distance response curve from the village (Figure 6), the
probability of the presence of Sunda pangolin increased the further the
distance from the village was. The probability of the presence of Sunda
pangolin continued to increase until the distance from the road reached 9500
Variable Contribution percentage
Village 47,4
Canopy cover 25,8
Threat 15,8
River 5,9
Slope 4,3
Food source 0,7
Road 0,2
Understorey 0
Elevation 0
Total 100%
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m (red line). Sunda pangolins tend to avoid the center of the crowd caused
by other animals and humans in order to protect themselves from predators,
such as big cats (Wang 2016). Additionally, Sunda pangolins are a solitary
animal which does not like to appear in groups. Sunda pangolins use the
distance of village as a strategy to avoid humans (Manshur 2015).
Figure 6. Environment variable response curve of the village distance of Sunda
pangolin.
Besides the distance of the village, the contribution percentage of
the environment variables showed that canopy cover was the second most
important environment variable affecting the probability of Sunda pangolin’s
existence. Based on the canopy cover response curve, the probability of the
presence of Sunda pangolin increased the higher the canopy cover was
(Figure 7), shown by the consistent increase of the red line. The presence of
Sunda pangolins increased when canopy cover values ranged from 70-90%.
The high level of canopy cover density is a special habitat type used by Sunda
pangolins to obtain food resources and to use as security strategies from
both competitions and predators (Manshur 2015).
At the time of the study, the family of Dipterocarpaceae dominated
the area where Sunda pangolins were present at the TEAM camera trap
coordinates. This family grows at 0-800 masl with a wet climate and high
humidity (Fajri 2008). One type of burrow made by Sunda pangolin was
located under a tree trunk that had a hole near the ground. Pangolins make
burrows in trees and use wood from this family because this type of wood is
resistant to cold temperatures; thus, the temperature in the wood used as the
burrow is still warm.
The third environment variable affecting the probability of Sunda pangolin
was the distance from threats. Based on the distance from the threat
response curve, the probability of the Sunda pangolin decreased the farther
the distance from the threat was (Figure 8), shown by the downward red line.
The Sunda pangolin is an animal that does not have many activities during
the day and spends the afternoon resting and remaining silent. Sunda
pangolin are difficult to find if there is no threat from natural or human
predators. In addition, if the intensity of threat is too high the pangolin will
go out of its burrow (based on the maxent analysis). In Indonesia, the
population of Sunda pangolin in nature is still unknown. Researchers were
difficult to meet Sunda pangolin, thus they used poachers to help them to
research Sunda pangolin. Poachers used their steps or threats to find Sunda
pangolin. The threats such as poacher using dog, traps, or used fire smoke.
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Based on camera traps data, there were human illegal activities on nine
camera traps coordinate.
The area of the Sunda pangolins prediction distribution map had a
high level of hunting. This was evidenced by the discovery of traps made of
rope, nylon straps, and iron strings. In addition, traces of poachers' tents,
weapons, traps for birds, rifles or firecrackers, and animals were found. The
research from Pangolin Specialist Group (PSG) team in 2017 showed that
every 5 minutes, there was one pangolin take from nature. Sunda pangolin is
one of the animals that most traded because it has higher economic value.
Sunda pangolin tends to roll up the body when they feel threatened, and
human was easy to catch. The knowing of the situation can encourage the
national park and SMART patrol team to give more attention to the
patrolling of the resort, thus the danger of Sunda pangolin hunting can be
minimized.
Figure 7. Environment variable response curve of canopy cover of Sunda pangolin.
Figure 8. Environment variable response curve of the threat distance of Sunda
pangolin.
CONCLUSION
Sunda pangolins tended to like with tight canopy cover of trees, found in this
study. As solitary animals, Sunda pangolins tended to avoid the crowd. They
would leave the burrow to look for food, and threats also made Sunda
pangolins go out of their burrow and this made it was for them to be caught.
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The tree environmental variables that most influence the distribution of
Sunda pangolin are the distance of village (47.8%), canopy cover (25.8%),
and distance from threat (15.8%). Poachers used smokes or dogs to achieve
this condition. Pemerihan and Way Haru resorts were the highest prediction
distribution of Sunda pangolins but they also had too much hunting and
poaching. Both resorts were still the site of frequent illegal logging and trees
falling naturally.
AUTHOR CONTRIBUTION
S.D.A designed the research and analyzed the data. All authors wrote the
manuscript.
ACKNOWLEDGMENTS
The authors would like to thank the people that help in Way Canguk
Research Station especially Mas Janji, Bray, Agung, Mas Seti, Mas Rahman,
Mas Laji, and also collage friend Anggun who struggle together to collected
data.
CONFLICT OF INTEREST
The authors don’t have conflict of interest.
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... However, it was not in miningaffected sites (MIN) and eruption-affected sites less than 20 years old (ERE). This result is in line with those of Lim and Ng (2008) and Anasari et al. (2021), where M. javanica was discovered in several types of habitats such as near settlements, plantations/agriculture, and secondary forests. This is because the sites were close to food sources such as ants, termites, and other small insects. ...
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The Chinese pangolin (Manis pentadactyla), a representative species of the order Pholidota, has been enlisted in the mammalian whole-genome sequencing project mainly because of its phylogenetic importance. Previous studies showed that the diploid number of M. pentadactyla could vary from 2n = 36 to 42. To further characterize the genome organization of M. pentadactyla and to elucidate chromosomal mechanism underlying the karyotype diversity of Pholidota, we flow-sorted the chromosomes of 2n = 40 M. pentadactyla, and generated a set of chromosome-specific probes by DOP-PCR amplification of flow-sorted chromosomes. A comparative chromosome map between M. pentadactyla and the Malayan pangolin (Manis javanica, 2n = 38), as well as between human and M. pentadactyla, was established by chromosome painting for the first time. Our results demonstrate that seven Robertsonian rearrangements, together with considerable variations in the quantity of heterochromatin and in the number of nucleolar organizer regions (NORs) differentiate the karyotypes of 2n = 38 M. javanica and 2n = 40 M. pentadactyla. Moreover, we confirm that the M. javanica Y chromosome bears one NOR. Comparison of human homologous segment associations found in the genomes of M. javanica and M. pentadactyla revealed seven shared associations (HSA 1q/11, 2p/5, 2q/10q, 4p+q/20, 5/13, 6/19p and 8q/10p) that could constitute the potential Pholidota-specific signature rearrangements.
Five challenges for species distribution modelling
  • M B Araujo
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Kajian anatomi skelet trenggiling jawa (Manis javanica
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Cahyono, E., 2008, Kajian anatomi skelet trenggiling jawa (Manis javanica), Tesis, Fakultas Kedokteran Hewan, Institut Pertanian Bogor, Bogor. pp.40.
The IUCN Red List of Threatened Species
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Challender, D., 2014, Manis javanica, The IUCN Red List of Threatened Species 2014: e.T12763A45222303. http://dx.doi.org/10.2305/ IUCN.UK.2014-2. RLTS.T12763A45222303.en, accessed on 15 Sept 2020.