Journal of Threatened Taxa |
www.threatenedtaxa.org | 26 June 2022 | 14(6): 21127–21139
ISSN 0974-7907 (Online) | ISSN 0974-7893
(Print)
https://doi.org/10.11609/jott.7572.14.6.21127-21139
#7572 | Received 15 July 2021 | Final
received 22 March 2022 | Finally accepted 15 May 2022
Identification of confiscated
pangolin for conservation purposes through molecular approach
Wirdateti 1, R. Taufiq P. Nugraha 2, Yulianto
3 & Gono Semiadi 4
1,4 Research Centre for Ecology and
Ethnobiology, 2,3 Research Centre for Applied Zoology,
National Research and Innovation
Agency, Jl. Raya Jakarta-Bogor Km. 46, Cibinong 16911, Indonesia.
1 teti_mzb@yahoo.com (corresponding
author), 2 tragulus@gmail.com, 3 yulianto.mzb@gmail.com, 4
semiadi@gmail.com
Abstract: Over the past decade, the
pangolin has emerged as one of the most prominent illegally traded mammals, and
high extraction rates of Manis javanica from
Indonesia have become a world concern. With the rise of the illegal trade,
tools for uncovering the origins of pangolins for law enforcement are needed.
Use of genetic markers for species and population identification has become a
versatile tool in law enforcement efforts related to illegal wildlife trade and
the management of endangered species. This study aims to uncover the origin of
confiscated pangolins via a molecular approach using COI mtDNA
markers. Forty-eight samples came from confiscated pangolins in Jakarta,
Surabaya, Jember, Pangkalan
Bun, Medan, Lampung, Riau, and Palembang, as well as four samples from the wild
population in Riau, Pangkalan Bun, and East Java.
Grouping using phylogenetic trees showed two groups with a bootstrap value of
90% based on wild samples. The first group consists of Sumatra and Kalimantan
populations, while the second group consists of a Javan population. From a
total of 44 confiscated samples, 12 were identified as Javan, nine from
Kalimantan, and 23 from Sumatra. Genetic distance value (d) among individuals
was d= 0.012 ± 0.002, with haplotype diversity (Hd)
0.864 ± 0.0444. The analysis of molecular variance (AMOVA) shows a clear
genetic difference among populations (75%) and within populations (25%). The
results showed that animals confiscated in one location may come from several
different populations. These results can be used to track the flow of the
pangolin trade in Indonesia, and support conservation management for the
release of confiscated animals.
Keywords: COI, confiscated, illegal
wildlife trade, Manis javanica, Pangolin,
population.
Editor: G. Umapathy,
CSIR-Centre for Cellular and Molecular Biology (CCMB), Hyderabad, India. Date
of publication: 26 June 2022 (online & print)
Citation: Wirdateti, R.T.P. Nugraha, Yulianto & G. Semiadi (2022). Identification of confiscated pangolin for conservation
purposes through molecular approach. Journal of Threatened Taxa 14(6): 21127–21139. https://doi.org/10.11609/jott.7572.14.6.21127-21139
Copyright: © Wirdateti et al. 2022. Creative Commons Attribution 4.0
International License. JoTT allows unrestricted use, reproduction, and
distribution of this article in any medium by providing adequate credit to the
author(s) and the source of publication.
Funding: This study was funded by Southeast Asian Regional Centre for Tropical Biology (SEAMEO BIOTROP)
research grant 2014, contract number: 060.12/PSRP/SPK-PNLT/III/2014.
Competing interests: The authors declare no competing interests.
Author details: Wirdateti is a researcher focusing on molecular genetics and ecology of wildlife
species, she has working closely with multiple law enforcement agency for
molecular identification of wildlife on illegal wildlife crime case. R. Taufiq P. Nugraha
is a researcher working on wildlife physiology and conservation, he also has a
great interest on wildlife management, illegal wildlife crime and zoonotic
diseases. Yulianto
is a laboratory technician specializing on molecular analysis and comparative
wildlife reproduction. Gono Semiadi is a
researcher who has been working on wildlife management, zoos management and biopolicy.
Author contributions: W, RTPN, Y and GS have an equal
contribution to this work.
Acknowledgements: Our gratitude goes to the
Research Center for Biology-LIPI, Southeast Asian
Regional Centre for Tropical Biology (SEAMEO BIOTROP), The Jakarta Natural
Resources Conservation Center (BKSDA), The Pangkalan Bun-Central Kalimantan Natural Resources
Conservation Center (BKSDA), The Jember-East
Java Natural Resources Conservation Center (BKSDA),
The Lampung-South Sumatra Natural Resources Conservation Center
(BKSDA), The Zamrud National Park Siak
Regency, Riau Province, and to Dewi Citra Murniati, Pramesa Narakusomo for assisting data analysis.
INTRODUCTION
There are eight extant pangolin
species (Manis sp.) distributed in Asia and Africa. Four species are
known In Asia: Manis pendactyla in China, M.
crassicaudata in India, and two in southeastern Asia, the Sunda
pangolin (M. javanica) also occurring in
Indonesia apart from other southeastern countries,
and M. culionensis in the Philippines (Feiler
1998; Gaubert & Antunes 2005; Gaubert
et al. 2018; Kumar et al. 2018). In Indonesia, the Sunda
Pangolin is one of several species listed as protected under the Minister of
Environment and Forestry Regulation Number P.106 of 2018 concerning protected
plant and animal. Under the International Union for Conservation of Nature
(IUCN), this species is ‘Critically Endangered’, while CITES (Convention on
International Trade in Endangered Species of Wild Fauna and Flora) list Sunda Pangolin in Appendix I since 2016. In Indonesia,
pangolins can be found in Sumatra, Java, Kalimantan, and other surrounding
islands. Over the past decade, pangolins have emerged as one of the world’s
highest illegally traded mammal species surpassing other iconic species such as
tigers, rhinos, and elephants (Kumar et al. 2018a).
The illegal trade in the eastern
Asian and southeastern Asian markets was primarily
driven by the demand for pangolin scales that were allegedly used by
Traditional Chinese Medicine (TCM) and as accessories/ornaments, for spiritual
and ritualistic purposes (Boakye et al. 2004; Challender
2011; Mahmood et al. 2012; Kumar et al. 2018 Xing et al. 2020). Scales of
pangolins are the most valuable part, followed by meat (Li & Wang 1999; Pantel & Chin 2009; Challender
2011). The decline of the pangolin population in mainland Indo-China region due
to excessive utilization caused traders to expand the range of pangolin search
to all types in southeastern Asia, such as Indonesia,
Malaysia, and India, as well as Africa. Factors responsible for pangolin
population vulnerability are a low reproduction rate, predation, habitat loss,
and poaching.
The level of poaching and illegal
trade of pangolin in Indonesia is in stark contrast to the biological data,
information, and studies on pangolins. Until now, the population, reproduction,
most of the biological information of this species in nature are unknown. In
contrast, the rapid decline in the population will undoubtedly continue every
year, mainly due to hunting and habitat loss. Pangolins are particularly
vulnerable to over exploitation because they are easy to hunt and have a slow
reproductive rate (Yang et al. 2007; Challender
2011). Large-scale commercial harvesting and international trade have been
going on since the early 20th century. Dammerman
(1929) in Vincent (2015) reported the export of several tonnes of Sunda Pangolin scales from Indonesia on the island of Java
to China in the period 1925–1929 involving at least 4,000–10,000 pangolins per
year, even though the species is legally protected. Likewise, for the period
1958–1964, Harrisson & Loh
(1965) in Vincent (2015) documented export licenses of more than 60,000 kg of
pangolin scales which most likely came from Indonesian Borneo (Kalimantan)
through Malaysia from Sarawak to Singapore and Hong Kong. Furthermore, data
obtained from press and law enforcement authorities have shown that around
30,000 pangolins were caught in southeastern Asia
between 2000 and 2007 (Chin & Pantel 2009 in
Mahmood et al. 2012), indicating that M. javanica
was mainly from Indonesia. The high hunting rate for Sunda
Pangolins can be seen from the description of the results of confiscations from
2012 to 2015, where there were 45 confiscations: 12 in 2012, 10 in 2013, 17 in
2014, and seven in 2015. Sumatra is the location where most seizures occurred,
with 21 confiscations totaling 4,046 individuals;
Java had 14 confiscations with 6,736 individuals, and Kalimantan region seven
with 793 pangolins destined for China (Vincent 2015). Data from tirto.id states
that between 1999 and 2017, at least 192,567 pangolins were involved in illegal
trade. Moreover, it estimated that the actual number is much higher due to many
confiscation data not adequately recorded.
One of the main problems for law
enforcement in the illegal wildlife trade of pangolins is the lack of
information regarding the origin of confiscated pangolins (national and
transboundary), since they can only be visually identified as Sunda Pangolin. This data is crucial for surveillance and
conservation management to protect this species from extinction, e.g., choosing
the right location to release confiscated animals. A DNA-based approach to
species and population identification may prove to be a powerful tool for
wildlife law enforcement agencies (Ogden et al. 2009; Zhang et al. 2015;
Rajpoot et al. 2016). Genetic profiling of Indonesian Pangolin using
mitochondria (mtDNA) reveals the genetic structure of
the Sunda Pangolin population based on cytochrome b
gene and control region (D-loop) (Kumar et al. 2018a; Wirdateti
& Semiadi 2013, 2017). Nevertheless, this study
is only conducted on a small part of the mtDNA gene,
while mtDNA using a single marker is prone to bias
(Ballard & Whitclok 2004). Recently, a
whole-genome sequence of Sunda Pangolin originating
from Malaysia provides a genome infrastructure for genetic research related to
conservation and management (Cho et al. 2016), providing broader insight into
genome conservation to reveal possible illegal trade routes and mixing of
pangolin lineages in southeastern Asia (Nash et al.
2017). In the present study, we conducted identification of confiscated Sunda pangolins using COI genes to provide information for
their management and conservation.
MATERIALS
AND METHODS
Sample Collection
A total of 48 samples were taken
from confiscated (44) and wild pangolin (4) in several places (Java, Sumatra,
and Kalimantan; Table 1). DNA materials were collected as tissue from meats,
and scales, and were preserved in absolute ethanol. Wild samples are taken from
scales of dead pangolins found in their natural habitat. Confiscated samples
were collected in 2008 from Medan, from Kalimantan in 2013 (Pangkalan
Bun), from Java in 2010 and 2014 (Jember, Jakarta,
and Surabaya), from Sumatra in 2014 and 2018 (Lampung, Riau, and Palembang;
Figure 1). Wild samples were acquired
from Central Kalimantan, Riau, and East Java. Some of these samples (26 samples
originating from 2013 and 2014 confiscation) had been analyzed
using Cytochrome b (Wirdateti et al. 2013).
DNA Amplification
Total genome DNA was extracted
using Qiagen Dneasy Blood and Tissue Kit Mini Stool
(Qiagen) for tissue samples. For scale samples, and tissue with low yields, we
extracted DNA using conventional phenol-chloroform (Kocher et al. 1989). This
study used the COI gene mtDNA as a marker to
determine the population origin of the confiscated pangolins by using a
specific primer on Sunda Pangolin as long as 870 bp. The primer was designed as follows COI Treng F: TGGAAACTGACTAGTGCCCC; COI Treng
R: GCTCCCATGGAGAGAACGTA. Primers were designed using a sequence template from
COI Pangolin. The primers were designed using Primers3 (v.0.4.0) and Pick
primers tools.
The amplification uses 30 µl
polymerase chain reactions (PCR) containing 1 µl DNA template, 17 µl PCR mix
reaction (FirstBase, Singapore), 2.5 µl primer F and
R respectively, and distilled water (MQ) up to 30 µl. PCR reaction started with
a 3-min denaturation at 95°C, followed by 35 cycles of denaturation at 94°C for
30 seconds, annealing at 56°C for 45 seconds and extension at 72°C
for 30 seconds. The final incubation was at 72°C for 10 min.
Sequencing
PCR products were sequenced using
the same forward and reverse primer as in amplification at FirstBase,
Singapore using the Sanger method. PCR products were purified using the kit SureClean Plus (Bioline USA Inc.)
according to the manufacturer’s manual and sequenced using BigDye
Terminator v3.1 Cycle Sequencing Kit DNA Analyzer (Applied Biosystems)
following Vendor’s protocol.
Data Analysis
All
nucleotide sequence results were stored in a database using BioEdit
software. The complement sequence between the forward primer and the reverse
was edited with Chromas Pro software. All sequences were compared with the NCBI
Genbank BLAST Database (www.ncbi.nlm.nih.gov/BLAST).
DNA alignment was done using Clustal X (Thompson et
al. 1997), and data analysis was conducted using MEGA 6.0 software (Tamura et
al. 2013) and DNaSP ver. 5.0 (Librado
& Rozas 2009). The MEGA 6.0 calculates the
genetic distance and site variations among 48 samples, and the phylogenetic
trees were used to determine each confiscated pangolins’ position based on the
wild samples data. The analysis of DNA polymorphism includes the calculation of
haplotype (h), haplotype diversity (Hd), and
diversity of the nucleotides (π) using the DNaSP ver 5.0 software. Identification of Sunda
pangolin was conducted using comparisons of Asian pangolin species, M. pendactyla (China), M. crassicaudata
(India), and M. culionensis (Philippines) in GeneBank NCBI (NCBI Reference Sequence: NC_016008.1;
NC_036434.1; and NC_036433.1, respectively). The phylogenetic tree formed was
constructed using ML (Maximum Likehood) methods with
bootstrap precision of 5,000.
For the
selection of the best-fit model of nucleotide substitution using Bayesian
inference (BAY) was conducted with the software IQ-TREE 1.6.12 (Nguyen L et al.
2015). The best-fitting of nucleotide substitution model for gene was
determined with jModelTest v.2.1.6 (Kalyaanamoorth et al. 2017) selected by the Bayesian Information
Criterion (BIC). The nucleotide frequencies for COI: A = 0.2509, C = 0.2915, G
= 0.185, T = 0.2726; proportion of invariable sites I = 0.7542. The result was
shown in FigTree v1.4.4 (Rambaut
2018). Bootstrap percentages (BP) were computed using 5,000 replicates.
The analysis
of molecular variance (AMOVA) (Excoffier et al. 1992)
was conducted to investigate the hierarchical structure of mitochondrial marker
variation to test the significance of the three pangolin populations using
Arlequin v.3.5. 2.2 (Exocoffier & Lisher 2010). The significance of this structure was tested
with 20,000 random permutations to test the significance of the three pangolin
populations (Weir & Cockerham 1984).
AMOVA was performed by grouping samples according to their geographical
location according to our result from
the previous analysis (MEGA). We calculated genetic differentiation among
pangolin populations as pairwise fixation indices (Fst)
in Arlequin. We used the pairwise FST values distances as the input data and
200 permutations were performed to determine the level of significance.
RESULTS
A. Genetic Variations
The COI fragment from all samples
was 866 bp long obtained using COI primer Treg F and COI Treg R designed
from a sequence available on the GenBank NCBI. Only four samples had known
origins: the wild samples obtained from Central Kalimantan (Pangkalan
Bun), East Java (Jember), and Sumatra (Riau), while
the other 44 samples came from the confiscated, market, and private collection
with unknown origin. The use of wild samples is essential as a comparison to
provide information of the unknown sample’s origin. Nucleotide blast in GeneBank NCBI revealed similarities (homology) sequence of
98.75 % to 99.75% for all samples with Sunda Pangolin
(M. javanica). Furthermore, the genetic
variation analysis of several parameters for the identification of confiscated
samples can be seen in Tables 2 and 3.
The results of polymorphic sites
based on variations in nucleotide sites (V), singleton base (difference of one
base) (S), informative sites (P), and genetic distance (d) show differences
from each population of Sumatra, Java, and Kalimantan (Table 2, Table 3).
Overall sequence alignment along 866 nucleotides from 48 samples contained of
54 variation sites (V), 16 singleton variation sites (S), and 38 informative
sites (P). Genetic distance between individuals d = 0.012 ± 0.002 (1.2% ±
0.2%), which is formed from base mutations or site variations in the 866 bp nucleotide sequence.
The use of Cyt b on M. javanica
(Sunda Pangolin) showed higher variations of 83 site
variations, 20 singletone variation sites (S), and
226 conserved sites with a sequence length of 331 bp
nucleotide. (Kumar et al. 2018a).
Results of analysis of 20 sequences along 373 nt
Cyt-b mtDNA showed 32 site
variations, 21 sites informative, and 11 singleton sites from confiscated
samples (Wirdateti et al. 2013). While the
identification of confiscated pangolin species in Africa using the COI gene
showed, the genetic distance (d) was from 0.001 to 0.055 (0.1% to 5%) among all
species with M. javanica and P. tricuspis (Mwale et al.
2017).
DNA polymorphism analysis based
on site variations showed 21 haplotypes (h) from the entire study sample with
haplotype diversity Hd = 0.864 ± 0.0444. Nucleotide
diversity (Pi) was Π = 0.01138 ± 0.00140, with the average nucleotide
difference between individuals (k) = 9,801. This value gives the genetic
distance between confiscated pangolin individuals of about 1.1%, indicating
pangolins are in the same species but different populations.
To strengthen the quality of this
study, we calculated the analysis data using AMOVA and Statistic test (Fst) to get strengthening the quality of the study, we
calculated the analysis data using AMOVA and the statistic test (Fst).
The results of AMOVA for total
populations are shown in Table 3. The AMOVA for three populations shows a
significant Fixation Index Fst value of 0.7525,
indicating that at least the pair-wise
populations reveals significant heterogeneity (p < 0.05) (Table 4).
We found significant genetic
differentiation (pairwise Fst) among population and
within population base on localities pangolin samples. And statistic test with
distance method are show values of pairwise Fst
calculated between populations are genetically distinct (Table 5). The values
of Fst between Java and Sumatra (0.81201); Java and
Kalimantan (0.75713); Kalimantan and Sumatra (0.33619). Comparisons of pairs of
these populations are significant (p = 0.000).
B. Phylogeny
The phylogeny tree was formed using
the MEGA 6.0 program (Kumar et al. 2015) with the ML (Maximum Likelihood)
method with a bootstrap of 5000, as shown in Figure 2. As a comparison, other
species from Asia, M. pendactyla, M. culionensis, and M. crassicaudata
from the NCBI GeneBank sequence (NCBI Reference
Sequence: NC_016008.1; NC_036434.1; and NC_0364333.1) were used. Phylogenetic
analysis was used to identify confiscated pangolins based on samples of
pangolins from the wild (Figure 2). From 44 confiscated pangolins and four wild
samples, two main groups with a bootstrap value of 90% were formed,
representing the Sumatra-Kalimantan population and the Java population.
The phylogeny tree shows that the
first group being represented by, Sumatra and Kalimantan, came from four
populations: Population 1 representing the Sumatra, and populations 2, 3, and 4
representing the Kalimantan. This
grouping is based on the wild sample of each population. Wild sample from Zamrud National Park in Riau (MZBR 1423; 1424) representing
Sumatra, and the Pangkalan Bun wild sample (MZBR
1163) representing the population of Kalimantan. This first group shows no
clear differences between the populations of Sumatra and Kalimantan with a low
genetic distance d = 0.004 ± 0.001 (Table 3), and low bootstrap value (30%). In
contrast, the Javan population is clearly separated from the Sumatran and
Kalimantan groups based on the wild samples from Jember
(MZBR 1179), and it was showed high genetic distance from Sumatra and Java (d =
0.024 ± 0.005 with Kalimantan; d = 0.023 ± 0.004 with Sumatra) than the Sumatra
and Kalimantan (Table 3). Based on this grouping, 44 confiscated samples were
identified as 12 samples from the Java population, 23 samples from Sumatra, and
nine samples estimated to be from Kalimantan. Each population variation site
(polymorphic sites) can be seen in Table 2. The population of Java has a fairly
high diversity compared to Sumatra and Kalimantan. As many as 23 sites varied (different
nucleotides) from 13 samples in the Java population; in the Sumatran
population, from 25 samples, only 14 sites were varied, while in the Kalimantan
population from 10 samples, only seven site variations were found. The result
indicates that populations with high nucleotide differences (site variations)
in the sequence range provides high haplotype diversity on the confiscated
pangolins population being tested in this study. The analysis results show that
the genetic diversity in the Java population is quite high, namely nine
haplotypes from 13 individuals. In comparison, the Sumatran population has
eight haplotypes from 25 individuals, and Kalimantan has four haplotypes from
10 individuals or 21 haplotypes were formed in this confiscated sample (Figure
3). Besides high haplotypes, higher genetic diversity in the Javan population
was also indicated by a higher genetic distance (d = 0.006 ± 0.001) than the
Sumatran and Kalimantan populations (d = 0.003 ± 0.001; d = 0.002 ± 0.001).
However, the haplotype diversity of the confiscated samples was still quite
high (Hd = 0.864 ± 0.0444). Haplotypes can identify
the origin of the population from confiscated samples based on on-site
variations and groupings in the phylogeny tree. Individuals who share the same
haplotype can provide information on the origin of confiscated pangolins, trade
routes, assist in controlling and monitoring hunting sites, and policymaking on
conservation directions.
The phylogeny using Bayesian
(BAY):
These three populations are
clustered into two distinct groups; the first group includes the Java population;
the second one includes the other two populations. The second group seemingly
came from either Sumatra population or Kalimantan (Borneo) population.
We included the posterior
probabilities obtained by BAY in the tree obtained by ML and supported by bootstrap
values (Figure 4). The TN+F+I (Tamura Nei, parameter
F: Nucleotide Frequencies; I: Invariance Sites) model nucleotide substitution
was selected as the best-fit model according to BIC (Bayesian Information
Criterion scores and weights) of evolution for all gene fragments using JModeltest (Kalyaanamoorthy et
al. 2017). The nucleotide frequencies
for COI: A = 0.2509, C = 0.2915, G = 0.185, T = 0.2726; proportion of
invariable sites I = 0.7542. This topology is almost similar to the NJ tree in
the MEGA Program, where Kalimantan and Sumatra are in one group (Figure 5).
DISCUSSION
To see the position of the Sunda Pangolin species, other Asian pangolin species M. pendactyla, M. culionensis
and M. crassicaudata were used as a
comparison. The results showed that all samples used in this research belonged
to the M. javanica species group. The results
of the analysis showed that M. javanica was
separated from M. culionensis (Philippines) by
a genetic distance (d) of 3.6%, and from M. crassicaudata
(India) by a genetic distance (d) of 14.4%, the separation between these two
species had a high bootstrap value (93%). The genetic distance (d = 3.6%)
between M. javanica and M. culionensis indicates that the two species are closely
related. This result is also supported by the results of previous studies,
which stated that the Palawan Pangolin M. culionensis
(Philippines) is often considered a subspecies of M. javanica;
the species was later raised to the species level based on morphological
differences with M. javanica (Feiler 1998; dan Antunes 2005). Likewise, Gaudin et al. 2009 (Gaubert et al. 2018) stated that the thick-tailed pangolin,
the Sunda and Palawan pangolins (M. javanica and M. culionensis)
are sister species. The Chinese Pangolin species are located in population 4 or
one clade with the Sunda Pangolin in the phylogenetic
tree (Figure 2). Meanwhile, another sample in population 4 came from
confiscated in Medan, Jakarta, Lampung, and Palembang. The other studies on
both species using the same COI marker showed the separation between M. pentadactyla and M. javanica
in the phylogenetic tree (dan Antunes 2015; Hassanin et al. 2015). So, the presence of M. pendactyla in subgroup 4 (Kalimantan), possibly
indicates that the sample from NCBI is not M. pendactyla,
or the confiscated sample is of unknown origin.
The confiscated pangolins are
identified as Sunda Pangolin with a genetic distance
of d = 0.012 ± 0.002 (1.2%), and nucleotide diversity Π = 0.01138 ± 0.00140,
which indicated the value of differences within one species. However, the use
of the mtDNA COI gene in this species has not shown a
clear separation between the Sumatran and Kalimantan populations, as shown in
the phylogenetic tree, while the Javan population is clearly genetically
separated (Figure 2). Based on the group formed, the location of the
confiscation does not always indicate the origin place of the pangolin. It can be seen that several confiscated
samples from central Kalimantan (Pangkalan Bun) were
in the same group as Sumatra (1165, 1164, 1157, etc. Table 1.), while
confiscated samples from Sumatra (Medan 270, Lampung 057) and Jakarta (1034,
1030) were in the same group as Kalimantan (wild). The same result can also be seen in the one
Kalimantan confiscated sample (1166) clustered in the Javan group. Previous
research using mtDNA (mitochondria) levels also
showed that some samples from Medan, Kalimantan were clustered with the Javan
population, then, confiscated samples from Sumatra and Java were clustered in
the Kalimantan population (Nash et al. 2017). The grouping of each individual
also gave the same results as the haplotype phylogeny, which gave a clear
difference in the Java population, with nine haplotypes from 13 individuals
with a bootstrap value of 99% (Figure 3, Table 2). Zhang et al. (2015) revealed
that the analysis of confiscated scales using multiple levels of mitochondrial
DNA also gave an unclear separation in the population of M. javanica species. The results above can illustrate that
the illegal trade of pangolin in Indonesia is run through several routes,
namely Sumatra, Java, and Kalimantan.
AMOVA analysis with genetic
structure testing showed significant genetic differentiation (pairwise Fst) among pangolin populations. Although the phylogenetic
tree shows several genealogical branches or geographic clusters, the results of
cluster analysis, sequence statistics, and AMOVA indicate a significant
division between these three populations. The cluster analysis suggests that
these three populations can be clustered into two groups, one includes Java
populations, and the second population includes Kalimantan and Sumatra. Fst values between the Java population and Sumatra, between
the Java and Kalimantan populations, and between the Sumatra and Kalimantan
populations show significant genetic differences (Table 4), indicating that at
least two populations exist of pangolins in Indonesia. The AMOVA results show
that among the population percentage of variance is 75.25%, and within a
population is 24.75%, and the Fixation index (Fst) value
is 0.7525 which indicates a significance (p< 0.005) (Table 4). A
considerable Fst value indicates a genetic structure
with a high degree of variation between populations, and each population is
geographically separated where the allele frequencies are different. While
within the population shows a small diversity value in the genetic structure,
the possibility of mating or breeding is high among the population due to the
low effective population size (Ne). If Fst is small,
it means that allele frequencies in each population are the same; if it is
large, the allele frequencies are different (Hosinger
& Weir 2019).
The sample size that is not large
enough or irregular or small will affect the genetic structure. The FSt statistic test showed significant results both between
populations and within populations. Based on the comparison of pairs of
population sample test, the Java and Sumatra populations gave a higher value (Fst = 0.812, p <0.001) than Java and Kalimantan (Fst = 0.757, p <0.001), and the lowest values were
Sumatra and Kalimantan (Fst = 0.336, p <0.001)
(Table 5). The FSt values above indicate a robust
genetic structure for the Javan population, with high differentiation with
Sumatra and Kalimantan. The amount of genetic differentiation among populations
has a predictable relationship with the rate of evolutionary processes
(migration, mutation, and drift). Large populations with a lot of migration
tend to show little differentiation, whereas small populations with little
migration tend to be highly differentiated (Wright 1931). The results of other
studies also showed that the intraspecific p-distance in M. javanica and P. tricupis
was higher (COI: 0.037 to 0.030) than African pangolins, which averaged between
0.001–0.055. It has a higher maximum intraspecific divergence indicating a
geographic sub-structure (Mwale et al. 2016).
Like the previous analysis, the
results of the statistical distance test through Alerquin,
showed that the populations of Sumatra and Kalimantan were closer and also
shown in the BI phylogram tree. Bootstrapping does not support the separation
of the two populations, namely node 82, following the Kalimantan population to
be a sub-population, and this result is also shown from different haplotypes or
no shared or nested haplotypes. In contrast, the genetic structure of the Javan
population shows that the population is separated from the other two
populations through Bayesian analysis with nodes 100 bootstrap, AMOVA and Fst statistical tests, with high distance values. The sample
size that is not large enough also affects gene flow (Nm) in genetic structure
(not shown), these results indicate a significant difference in the Java
population (7,025; p <0.001) than in Sumatra (2,220, p <0.001) and
Kalimantan (2,911. P <0.001). Test Differentiation Based on Haplotype
Frequencies (Raymond & Rousset 1995) was
significant between populations (p <0.05). The strong and significant
genetic structure indicates substantial limitations on genetic and demographic
connectivity (Hedgecock et al. 2007) among pangolin
populations in Indonesia.
The Bayesian inference
phylogenetic analysis results can be seen from the phylogram (Figure 4) using
the IQ Tree program. The value at each branch point node is the result of the
bootstrap support value in supporting topological credibility. Some results of
bootstrap on several nodes/branch points have unsupported values with
indistinguishable branches (polytomy) so that the position of external nodes or
individuals may be incorrect. The Bayesian Inference (BI) phylogenetic results
are not much different from the previous analysis, namely MEGA in terms of
population divergence on valid bootstrap support (Hoang et al. 2017). The Java
population still represents a separate group from Sumatra and Kalimantan with valid
bootstrap support of 100 and the position of the Kalimantan population from
Sumatra. However, the sample numbers MZBR 1417 and 1424 were separated from
Sumatra and Kalimantan with a bootstrap of 100, while the Kalimantan population
was separated from Sumatra by a bootstrap of 82. The bootstrap node value of 82
did not support the phylogenetic tree in BI. A phylogenetic tree has supporting
nodes with a bootstrap value of 95 for Bayesian values (Huelsenback
& Hilis 1993). The branching or divergence of
each individual in the population seems to show better resolution and
description, although a very valid bootstrap value has not supported it for
several nodes. Although it doesn’t produce a valid bootstrap support value, the
topology with a better resolution may be due to the Effective population size
(Ne), which is analyzed heuristically to minimize
polytomy. The advantages of Bayesian Inference (BI) resolution over MEGA can be
caused by complex parameters in BI, the use of the MCMC (Monte Carlo Markov
Chain) numerical algorithm, and prior and posterior distributions.
The Java population represents a
monophyletic group with the same common ancestor and lineages and forms a
natural group with a valid bootstrap support value of 100. Although the AMOVA
data clearly shows the population structure, this result cannot be clearly
explained by the separation of the Sumatran and Kalimantan populations.
The results above show that
mitochondrial COI markers in this study have not provided sensitive information
for each population or intra-species. But a DNA-based approach to species and
population identification may prove to be a powerful tool for wildlife law
enforcement agencies (Ogden et al. 2009; Zhang et al. 2015; Rajpoot et al.
2016). Several experts have used mitochondrial markers as validation for
species identification, including cytochrome b (Cyt
b), 12S ribosomal RNA (12S rRNA), 16S ribosomal RNA (16S rRNA), and Cytochrome
oxidase subunit I (COI) genes which are routinely used for species
identification in wildlife forensics (DeSalle et al.
1993; Hsieh et al. 2001; Guha & Kashyap 2006; Alacs
et al.2009; Kumar et al. 2016, 2018). Likewise, for the identification of
confiscated pangolins, the use of several mitochondrial COI, cyt b genes, and D-loop can distinguish several confiscated
species, namely P. tricuspis, P. tetradactyla, S. gigantea, S. temminckii, M. javanica,
and M. pentadactyla with high bootstrap values
>70%, and the distance between all species was around 0.100–0.188 for COI
and 0.10–0.20 for Cyt b, and 0.048–0.125 for the
D-loop (Mwale et al. 2017). Thus, COI, Cyt b, and D-loop markers were more effectively used for
identification or as inter-species markers.
Reports of high extraction rate
of pangolins from Indonesia have become a concern to the world. However,
counter measures and origin of these pangolins is not clearly understood. One
of the main problems of pangolin confiscation in Indonesia is identifying the
source and distribution of these confiscated pangolins; there is no data on the
genetic distribution map of Sunda Pangolin in
Indonesia. A distribution map will help the conservation of pangolin by
allowing stakeholders to monitor the population and prevent its illegal trade.
The latest report states that about 30% of the proportion of pangolin
confiscated in Sumatra came from Kalimantan (Nash et al. 2017). Identification
using one or two genes certainly cannot reveal the origin of the pangolin in
the same species (intraspecies). This study is only conducted on a small
fraction of mtDNA genes, mtDNA
as a single marker, is prone to bias (Ballard & Whitclok
2004). With this argument, it is necessary to reveal the whole genome mtDNA and approximately 15,000 bp
nucleotides as genetic markers for identification at the population level,
especially for Indonesian pangolin. The data can be used as a baseline for
mapping Indonesian pangolin genetic diversity to assist the conservation and
handling of confiscated animals. The main problem with confiscated pangolin is
that life confiscated animals will be released back into the wild as soon as
possible; in many cases, these animals have been released back to the nearby
confiscation area or region the pangolin while the pangolin itself might not
come from the same population. This will undoubtedly affect each population’s
gene pool, as the results of this study show that there are pretty clear
differences between Sumatra, Kalimantan, and Java populations. In this regard,
the information provided by this research is essential for policymakers and
stakeholders to better understand the management and conservation of pangolins.
CONCLUSION
The use of COI gene markers in
this study has not been able to provide effective information on confiscated
samples based on population origin, especially between Sumatran and Kalimantan,
owing to low genetic distances. However, it can provide a clear separation
between Sumatran and Kalimantan populations with Java populations based on
phylogenetic trees and a higher genetic distance values, and the Javan
population had a stronger genetic structure than the other two populations.
Based on the distribution of haplotypes from confiscated samples can identify
the origin of confiscated pangolin from Java, Sumatra, and Kalimantan
populations. Even though genetic distances and nucleotide differences between
Sumatra and Kalimantan are very low, they can be distinguished from the
haplotype type. This study’s findings showed that the seized material came from
several organized hunting locations from illegal traders in the range of
pangolin distribution areas, as shown that samples from one confiscation
location originated from more than one population. Further analysis is required
with the addition of wild samples with known geographical origin as a
comparison reference. Policymakers can apply this information to release live
pangolin, manage and supervise wild pangolins, and carry out effective law
enforcement.
Table 1. List of samples used in
this study.
|
|
Catalog number |
Year |
Type |
Sample location |
|
1 |
MZBR. T01 (1352) |
2018 |
Confiscated |
KSDA Lampung |
|
2 |
MZBR. T02 (1355) |
2018 |
Confiscated |
KSDA Lampung |
|
3 |
MZBR. T03 (1353) |
2018 |
Confiscated |
KSDA Lampung |
|
4 |
MZBR. T04 (1354) |
2018 |
Confiscated |
KSDA Lampung |
|
5 |
MZBR. T05 (1359) |
2018 |
Confiscated |
KSDA Lampung |
|
6 |
MZBR. T06 (1360) |
2018 |
Confiscated |
KSDA Lampung |
|
7 |
MZBR. T07 (1361) |
2018 |
Confiscated |
KSDA Lampung |
|
8 |
MZBR. T08 (1363) |
2018 |
Confiscated |
KSDA Lampung |
|
9 |
MZBR. T09 (1356) |
2018 |
Confiscated |
KSDA Lampung |
|
10 |
MZBR. T10 (1367) |
2018 |
Confiscated |
KSDA Lampung |
|
11 |
MZBR. T11 (1322) |
2018 |
Confiscated |
KSDA Lampung |
|
12 |
MZBR. T12 (1340) |
2018 |
Confiscated |
KSDA Lampung |
|
13 |
MZBR. T13 (1421) |
2018 |
Confiscated |
Palembang Market |
|
14 |
MZBR. T14 (1334) |
2018 |
Confiscated |
KSDA Lampung |
|
15 |
MZBR.1038 |
2012 |
Confiscated |
KSDA Bogor 1 |
|
16 |
MZBR. T15 (1341) |
2018 |
Confiscated |
KSDA Lampung |
|
17 |
MZBR. T16 (1418) |
2018 |
Confiscated |
Palembang Market |
|
18 |
MZBR.17 (1420) |
2018 |
Confiscated |
Palembang Market |
|
19 |
MZBR.18 (1416) |
2018 |
Confiscated |
Palembang Market |
|
20 |
MZBR.19 (1422) |
2018 |
Confiscated |
Palembang Market |
|
21 |
MZBR.1034 |
2012 |
Confiscated |
KSDA Bogor 1 |
|
22 |
MZBR.20 (1423) |
2018 |
Wild |
Zamrud National Park |
|
23 |
MZBR.1036 |
2012 |
Confiscated |
KSDA Bogor 1 |
|
24 |
MZBR.21 (1424) |
2018 |
Wild |
Zamrud National Park |
|
25 |
MZBR.22 (1417) |
2018 |
Confiscated |
Palembang Market |
|
26 |
MZBR.1040 |
2012 |
Confiscated |
KSDA Bogor 1 |
|
27 |
MZBR.1165 |
2013 |
Confiscated |
Pangkalanbun, Central
Kalimantan |
|
28 |
MZBR.0270 |
2008 |
Confiscated |
Sukabumi, West Java |
|
29 |
MZBR.1180 |
2014 |
Confiscated |
Jember, East Java |
|
30 |
MZBR.0273 |
2008 |
Confiscated |
Medan, North Sumatra |
|
31 |
MZBR.1181 |
2014 |
Confiscated |
Jember, East Java |
|
32 |
MZBR.1030 |
2012 |
Confiscated |
Tegal Alur, Jakarta |
|
33 |
MZBR.0272 |
2008 |
Confiscated |
Medan, North Sumatra |
|
34 |
MZBR.1182 |
2014 |
Confiscated |
Jember, East Java |
|
35 |
MZBR.1183 |
2014 |
Confiscated |
Jember, East Java |
|
36 |
MZBR.1179 |
2014 |
Wild |
Jember, East Java wild |
|
37 |
MZBR.0276 |
2008 |
Confiscated |
Medan, North Sumatra |
|
38 |
MZBR.1166 |
2013 |
Confiscated |
Pangkalanbun, Central
Kalimantan |
|
39 |
MZBR.1057 |
2012 |
Confiscated |
Tanggamus, Lampung |
|
40 |
MZBR.1069 |
2012 |
Confiscated |
Surabaya, East Java |
|
41 |
MZBR.1070 |
2012 |
Confiscated |
Surabaya, East Java |
|
42 |
MZBR.1071 |
2012 |
Confiscated |
Surabaya, East Java |
|
43 |
MZBR.1072 |
2012 |
Confiscated |
Surabaya, East Java |
|
44 |
MZBR.1157 |
2013 |
Confiscated |
Pangkalanbun, Central
Kalimantan |
|
45 |
MZBR.1163 |
2013 |
wild |
Pangkalanbun, Central
Kalimantan |
|
46 |
MZBR.1164 |
2013 |
Confiscated |
Pangkalanbun, Central
Kalimantan |
|
47 |
MZBR.0275 |
2008 |
Confiscated |
Medan, North Sumatra |
|
48 |
MZBR.1162 |
2013 |
Confiscated |
Pangkalan Bun, Central
Kalimantan |
Table 2. Genetic variation using mtDNA COI markers in 48 pangolin samples.
|
Parameter |
Total samples (Confiscated, Wild) |
Java |
Sumatra |
Kalimantan |
|
|
n = 48 |
N = 13 |
n = 25 |
n = 10 |
|
Polymorphic sites |
|
|
|
|
|
Variable (polymorphic) sites Singleton variable sites Parsimony informative sites Genetic distance (d) |
54 16 38 d = 0.012 ± 0.002 |
23 8 15 0.006± 0.001 |
14 9 5 0.002 ± 0.001 |
7 7 - 0.003 ± 0.001 |
|
DNA Polymorphism |
|
|
|
|
|
Haplotype (h): Haplotype diversity Hd Variance of Hd Nucleotide diversity, Pi: Average of nucleotide
differences, k |
h = 21 Hd = 0.864 ± 0.0444 V = 0.00195 Π = 0.01138 ± 0.00140 K = 9.801 |
9 0.00812± +/-
0.00458 11.647268 |
8 0.00256± +/- 0.00163 2.869185 |
4 0.00336± +/- 0.00217 3.301497 |
Table 3. Genetic distance between
and within population.
|
|
Population |
Between population (d ± SE) |
Within population ( d ± SE ) |
|
1 2 3 |
|||
|
1. |
Java |
0.005 0.004 |
0.006 ± 0.001 |
|
2. |
Kalimantan |
0.024 0.001 |
0.003 ± 0.001 |
|
3. |
Sumatra |
0.023
0.004 |
0.002 ± 0.001 |
Table 4. Analysis of molecular
variance (AMOVA) based on sequences of cytochrome c oxidase subunit I (COI) of
pangolin populations from Java, Sumatra, and Kalimantan.
|
Source of variation |
Sum of squares |
d.f. |
Variance components |
Percentage of variation |
Fixation index (FST) |
|
Among populations |
2 |
166.210 |
5.53432 Va |
75.25 |
0.75254* (p <0.05) |
|
Within populations |
45 |
81.894 |
1.81986 Vb |
24.75 |
|
|
Total |
47 |
248.104 |
7.35418 |
|
|
Table 5. Pairwise Fst calculated among pairs of pangolin population from
Java, Sumatra, and Kalimantan based on sequences of cytochrome c oxidase
subunit I (COI).
|
|
1 |
2 |
3 |
|
1. Java |
0.00000 |
|
|
|
2. Sumatra |
0.81201 |
0.00000 |
|
|
3. KALIMANTAN |
0.75713 |
0.33619 |
0.00000 |
For figures - -
click here for full PDF
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