Forensic Science International: Genetics 23 (2016) 197–209
Contents lists available at ScienceDirect
Forensic Science International: Genetics
journa l homepage: www.elsevier.com/locate/fsig
Forensic timber identification: a case study of a CITES listed species,
Gonystylus bancanus (Thymelaeaceae)
a, a a a
Kevin Kit Siong Ng *, Soon Leong Lee , Lee Hong Tnah , Zakaria Nurul-Farhanah ,
a a b c d e
Chin Hong Ng , Chai Ting Lee , Naoki Tani , Bibian Diway , Pei Sing Lai , Eyen Khoo
a
Genetics Laboratory, Forest Research Institute Malaysia, 52109 Kepong, Selangor Darul Ehsan, Malaysia
b
Forestry Division, Japan International Research Center for Agricultural Sciences, Ohwashi, Ibaraki 305-8686, Japan
c
Sarawak Forestry Corporation, Botanical Research Centre Semenggoh, KM20, Jalan Puncak Borneo, 93250 Kuching, Sarawak, Malaysia
d
Malaysia Pepper Board, Lot 1115, Jalan Utama, Tanah Putih, 93916 Kuching, Sarawak, Malaysia
e
Forest Research Centre, KM 23, Labuk Road, Sepilok, 90715 Sandakan, Sabah, Malaysia
A R T I C L E I N F O A B S T R A C T
Article history:
Received 25 April 2015 Illegal logging and smuggling of Gonystylus bancanus (Thymelaeaceae) poses a serious threat to this
Received in revised form 6 May 2016 fragile valuable peat swamp timber species. Using G. bancanus as a case study, DNA markers were used to
Accepted 7 May 2016 develop identification databases at the species, population and individual level. The species level
Available online 10 May 2016
database for Gonystylus comprised of an rDNA (ITS2) and two cpDNA (trnH-psbA and trnL) markers based
on a 20 Gonystylus species database. When concatenated, taxonomic species recognition was achieved
Keywords:
with a resolution of 90% (18 out of the 20 species). In addition, based on 17 natural populations of
DNA barcoding
G. bancanus throughout West (Peninsular Malaysia) and East (Sabah and Sarawak) Malaysia, population
DNA profiling
and individual identification databases were developed using cpDNA and STR markers respectively. A
Ramin
haplotype distribution map for Malaysia was generated using six cpDNA markers, resulting in 12 unique
Random match probability
multilocus haplotypes, from 24 informative intraspecific variable sites. These unique haplotypes suggest
Short tandem repeats (STRs)
a clear genetic structuring of West and East regions. A simulation procedure based on the composition of
Timber tracking
the samples was used to test whether a suspected sample conformed to a given regional origin. Overall,
the observed type I and II errors of the databases showed good concordance with the predicted 5%
threshold which indicates that the databases were useful in revealing provenance and establishing
conformity of samples from West and East Malaysia. Sixteen STRs were used to develop the DNA profiling
databases for individual identification. Bayesian clustering analyses divided the 17 populations into two
main genetic clusters, corresponding to the regions of West and East Malaysia. Population substructuring
(K = 2) was observed within each region. After removal of bias resulting from sampling effects and
population subdivision, conservativeness tests showed that the West and East Malaysia databases were
conservative. This suggests that both databases can be used independently for random match probability
estimation within respective regions. The reliability of the databases was further determined by
independent self-assignment tests based on the likelihood of each individual’s multilocus genotype
occurring in each identified population, genetic cluster and region with an average percentage of
correctly assigned individuals of 54.80%, 99.60% and 100% respectively. Thus, after appropriate validation,
the genetic identification databases developed for G. bancanus in this study could support forensic
applications and help safeguard this valuable species into the future.
ã 2016 Elsevier Ireland Ltd. All rights reserved.
1. Introduction
In recent years, large areas of forest have been lost due to
conversion for agricultural use or degradation through poor
* Corresponding author.
E-mail addresses: [email protected] (K.K.S. Ng), [email protected] (S.L. Lee), logging practices neglectful of sustainability and biodiversity [1].
[email protected] (L.H. Tnah), [email protected] (Z. Nurul-Farhanah), As tropical forest degradation and deforestation continue, the loss
[email protected] (C.H. Ng), [email protected] (C.T. Lee), [email protected]
of biodiversity in the tropics also occurs in tandem. Illegal logging
(N. Tani), [email protected] (B. Diway), [email protected] (P.S. Lai),
and the international trade in illegally sourced timber are also
[email protected] (E. Khoo).
http://dx.doi.org/10.1016/j.fsigen.2016.05.002
1872-4973/ã 2016 Elsevier Ireland Ltd. All rights reserved.
198 K.K.S. Ng et al. / Forensic Science International: Genetics 23 (2016) 197–209
major contributors to deforestation and biodiversity losses. It has polymorphic nuclear short tandem repeat (STR) markers could be
been estimated that 50% of the timber traded in the market from used to generate DNA profiling databases, in which a suspected log
the Amazonian Basin, Central Africa, Southeast Asia and the could be matched with its original stump [16,17]. To ensure
Russian Federation are from illegal sources [2]. valuable species are not lost forever due to illegal logging, all three
The Convention on International Trade in Endangered Species DNA-based methods require swift development of large compre-
of Wild Fauna and Flora (CITES) is the most prevalent convention in hensive databases to be incorporated into the traceability systems.
relation to rare and threatened species. Currently, there are 181 In principle, the species of unknown wood samples can be
parties in CITES seeking to build the capacity of more effective law identified easily with a set of cpDNA and/or rDNA markers based
enforcement to restrict trade of species listed under the Conven- on sequence variation to a known species database [18].
tion. Listings on CITES are currently the legal mechanism by which Subsequently, the geographical origin of wood samples can then
importing countries can take action to halt shipments of such be examined with a number of cpDNA markers that reveal clear
items. Consuming countries must only allow the import of timber geographical patterns within the species distribution range. A
and timber products with official CITES permits from exporting previous study on Cedrela odorata throughout Mesoamerica using
parties. For example, an exporting country would require a CITES cpDNA markers indicated a strong geographical pattern that can be
export permit and a certificate of origin guaranteeing the product used to determine the geographical origin of C. odorata timbers
did not originate elsewhere. All CITES parties are required to issue [19]. It was also proven possible on European oak [14], where the
CITES certificates for any re-exportation, and must only issue these strong geographical structure and differentiation of the western
if they are satisfied that the wood has been imported legally with versus eastern populations were used to develop an oak wood
CITES documents. Unfortunately, due to fraud and poor gover- traceability system. In addition, a recent study demonstrated that
nance, the implementation of reliable certification methods Neobalanocarpus heimii, a dipterocarp timber species, could be
presents a challenge in many countries [3]. distinguished from the northern and southern regions of
Numerous other initiatives have been implemented to deal Peninsular Malaysia using cpDNA markers [15]. In that study,
with illegally sourced timber and timber products. In 2008, the partitioning of individuals specifically to the source population was
USA amended its Lacey Act to prohibit all trade in timber and rather limited due to the paucity of variable sites in the cpDNA
timber products that are illegally sourced within or outside its used. In another related study, partitioning of N. heimii individuals
borders. In 2003, the European Union (EU) developed the Forest to specific populations was achieved because of the availability of
Law Enforcement, Governance and Trade (FLEGT) Action Plan an individual identification database generated using STR markers,
which provides a number of measures to exclude illegal timber along with the use of individual assignment test based on
from the markets, improve the supply of legal timber and increase multilocus genotypes [16]. An assignment test can provide a
the demand for wood products from legal sources. The two main likelihood that an unknown sample originated in a given
elements of the action plan are the European Union Timber population [20–23].
Regulations (EUTR) and Voluntary Partnership Agreements (VPAs). Herein, DNA-based technology was employed for species,
On 3 March 2013, the new EUTR came into effect to stop the population and individual identification of a CITES listed species,
circulation of illegally logged wood in the EU. Under the EUTR, only Gonystylus bancanus distributed throughout Malaysia. This study
wood carrying a FLEGT license or CITES permit is considered legally aimed to develop a timber tracking tool in the context of illegal
sourced. VPAs are trade agreements with timber exporting logging and chain-of-custody certification, specifically to (1)
countries that help prevent illegal timber from being placed on identify markers suitable for species identification databases;
the European market. There are currently six countries (Cameroon, (2) identify specific chloroplast haplotypes that could be used to
Central African Republic, Ghana, Indonesia, Liberia and Republic of generate a population identification database; (3) generate a DNA
the Congo) that have signed VPAs with the EU. profiling database for individual identification of G. bancanus based
Low technology tools such as paint (conventional and on a subpopulation-cum-inbreeding model; and (4) evaluate the
ultraviolet) have been widely used to mark and track the flow of reliability of the individual identification database as a reference
timber. However, these relatively inexpensive tools are not database using Bayesian assignment approaches.
particularly useful in broader-based or national-level efforts to
control illegal logging or trade as they are easily reproduced by 2. Materials and methods
unscrupulous individuals. More stringent national tracking sys-
tems involving a combination of databases have been imple- 2.1. Species description
mented, such as the physical tagging of logs and spot-checking [4].
Unfortunately, most of these timber tracking systems rely mostly Gonystylus is one of the three genera of plants under the
on tags or certificates of origin issued at source which are Gonystylus subfamily of the Thymelaeaceae family. Genus Gonys-
vulnerable to falsification in many countries [5]. Although, fourier tylus consists of approximately 30 species of tall trees and shrubs
transform near-infra-red spectroscopy and isotopic analyses have [24]. The most common generic name for Gonystylus and also the
been used to reveal provenance [6–9], these are limited by trade name of its timber is ‘Ramin’ [25] with the most important
inconsistent results [6,8]. source being the species Gonystylus bancanus (Miq.) Kurz native to
DNA markers could be applied for forensic timber identification Brunei, Indonesia (Kalimantan and Sumatra) and Malaysia (Malay
in three ways, namely (1) for species identification; ribosomal Peninsula, Sabah and Sarawak). Gonystylus bancanus grows mainly
nuclear DNA (rDNA) markers such as the internal transcribed in peat swamp forests, where it dominates the vegetation and can
spacer (ITS) is a powerful phylogenetic marker at the species level be locally common. Decline in peat swamp vegetation and logging
showing high levels of interspecific divergence [10,11] and have led to concerns about the conservation status of this species;
chloroplast DNA (cpDNA) markers have been widely used for assigned as Vulnerable (VU) and at high risk of extinction in the
DNA barcoding technology due to their highly conserved region wild in the medium-term future (A1 cd) under the IUCN Red List
and relatively low evolutionary rates compared with nuclear Categories and Criteria (version 2.3) [26]. Gonystylus bancanus
markers [12,13]. (2) for population identification; cpDNA markers populations are becoming increasingly fragmented and scattered
that are variable enough to reveal geographical structure could be due to deforestation. Owing to the high demand for its timber in
used to differentiate the origin of one source of timber from the international market, illegal harvesting and smuggling of this
another [14,15], and (3) for individual identification; a set of highly species have become rampant [27]. It is expected that the species
K.K.S. Ng et al. / Forensic Science International: Genetics 23 (2016) 197–209 199
might become threatened in the near future. Presently, it is listed in Specifically, 8 mL of ExoSAP-IT (50X dilution) was added to 2 mL of
CITES Appendix II, which means that exporting G. bancanus wood each PCR product and incubated at 37 C for 30 min to degrade the
has to be accompanied by a CITES export permit indicating it is remaining primers and nucleotides. The mixture was incubated at
legally acquired and its export not detrimental to the survival of 80 C for 20 min to inactivate the ExoSAP-IT reagent.
the species in the wild [27]. The cleaned products were sequenced in both directions using
the BigDye Terminator Sequencing Kit v.3.1 (Applied Biosystems)
2.2. Sample collection and DNA extraction based on the standard dideoxy-mediated chain termination
method. The sequencing thermal profile was 50 cycles at 96 C
For species identification, a total of 20 Gonystylus species were for 10 s, 50 C for 5 s and 60 C for 4 min on a GeneAmp PCR System
used (Table S1). Each species was represented by two to five 9700. Sequencing reactions were purified by ethanol precipitation
samples. Voucher specimens for each of the samples are kept at the and run on the ABI 3130 l Genetic Analyzer (Applied Biosystems).
herbarium of the Botanical Research Centre (BRC), Kuching, Sequencing data were edited and assembled using SEQUENCHER
Sarawak, Malaysia. v.5.0 (Gene Code Corporation). The aligned sequence from each
A total of 747 G. bancanus individuals with more than 1 cm sample was trimmed to the same length.
diameter at breast height (dbh) were sampled from 17 natural
populations throughout Malaysia of which seven populations 2.3.2. Species identification database
comprising 280 individuals from West Malaysia and ten popula- The informative variable sites, both substitutions and insertions
tions comprising 467 individuals from East Malaysia (Table 1 and and deletions (indels), which distinguish Gonystylus species, were
Fig. 1). extracted (Table S3). The sequences of each region (trnH-psbA, trnL
All samples were collected either in the form of inner bark or and ITS2) and a concatenated sequence comprising all three
leaf tissues. Genomic DNA was extracted using the Cetyl regions (trnH-psbA + trnL + ITS2) were analyzed using three differ-
trimethylammonium bromide (CTAB) procedure [28] with modi- ent approaches to ascertain the resolution of the species
fication (2x CTAB), and further purified using High Pure PCR identification database. First, a nearest-neighbour approach;
Template Preparation Kit (Roche Diagnostics GmbH). sequences were aligned using MUSCLE [29] and a Neighbour-
Joining (NJ) tree was constructed using the Kimura 2-parameter
2.3. Species identification (K2P) model with 1000 bootstrap replicates implemented in
MEGA6 [30]. Secondly, a barcoding gap approach, in which
2.3.1. PCR amplification and sequencing intraspecific variation and inter-specific divergences are quantified
All extracted DNA from the 20 species, comprising 76 samples across Gonystylus species using the generated K2 P distance matrix
underwent PCR amplification using the Type-it Microsatellite-PCR [31]. In this approach, a species is considered to be resolved if the
Kit (Qiagen). One rDNA, internal transcribed spacer 2 (ITS2) and minimum interspecific distance is greater than the maximum
three cpDNA non-coding intergenic regions (trnL and trnH-psbA) intraspecific distance [32]. Finally, the standalone Basic Local
were selected for species identification (Table S2). The PCR Alignment Search Tool (BLAST) approach which uses the tradi-
amplifications were performed in 10 mL reactions consisting of tional BLAST algorithm to compute pairwise alignments between
approximately 10 ng of template DNA, 1X Type-it Mutiplex PCR reference and query sequences, and provides an alignment score
Master Mix (Qiagen), and 0.3 mM of each primer. The reaction for interpreting similarity. Briefly, for this approach, a self database
mixture was subjected to amplification using GeneAmp PCR was prepared consisting of a set of sequences generated from
System 9700 (Applied Biosystems) for an initial denaturing step of different Gonystylus species and then the same set of sequences
94 C for 5 min, 35 cycles of 95 C for 30 s, 50–55 C annealing was assigned as query sequences. The query sequences were
temperature for 90 s, and 72 C for 1 min. This was followed by blasted against the database. Successful species identification
further primer extension at 60 C for 30 min. The PCR products occurred when an alignment score of 100% identity with sequences
were purified using ExoSAP-IT PCR Product Cleanup (Affymetrix). of the same species.
Table 1
Locations of the 17 populations of Gonystylus bancanus and the number of samples used for individual and population identification.
a
Region Locality Population acronyms Latitude Longitude Number of samples used
Individual ID Population ID
0 0
West Malaysia 1. Pekan FR, Pahang PE N03 25 08.1” E103 21 17.8” 189 25
0 0
2. Nenasi FR, Pahang NE N03 03 32.2” E103 22 08.9” 15 15
0 0
3. Resak FR, Pahang RE N03 01 19.2” E103 20 56.3” 10 10
0 0
4. Kedondong FR, Pahang KE N03 16 17.4” E103 12 57.7” 4 4
0 0
5. Air Hitam FR, Johor AH N02 01 55.1” E102 48 33.1” 28 16
0 0
6. Sg. Karang FR, Selangor SK N03 42 20.5” E101 08 13.8” 14 14
0 0
7. Raja Musa FR, Selangor RM N03 26 59.9” E101 16 21.3” 20 16
0 0
East Malaysia 8. Sedilu FR, Sarawak SE N01 22 24.3” E110 49 39.9” 31 16
0 0
9. Lingga FR, Sarawak LI N01 21 13.1” E111 10 29.6” 30 16
0 0
10. Serapau-Lingga FR, Sarawak SL N01 19 08.6” E111 12 00.1” 30 16
0 0
11. Maludam NP, Sarawak MA N01 37 44.5” E111 03 29.2” 36 16
0 0
12. Manggut FR, Sarawak MN N01 28 31.2” E111 15 54.3” 53 16
0 0
13. Betong FR, Sarawak BE N01 25 12.4” E111 23 06.6” 40 16
0 0
14. Naman FR, Sarawak NA N02 11 02.8” E111 50 15.6” 55 16
0 0
15. Loagan Bunut NP, Sarawak LB N03 47 29.9” E114 14 10.6” 101 32
0 0
16. Kayangeran FR, Sarawak KG N04 53 12.7” E115 25 19.7” 29 16
0 0
17. Klias FR, Sabah KL N05 17 12.1” E115 37 44.8” 62 16
a
FR-Forest Reserve and NP-National Park.
200 K.K.S. Ng et al. / Forensic Science International: Genetics 23 (2016) 197–209
Fig. 1. Locations of the 17 Gonystylus bancanus populations and the number of samples collected (n) throughout Malaysia.
2.4. Population identification reference to the populations of origin based on the significant
intraspecific variable sites.
2.4.1. PCR amplification and sequencing A statistical procedure based on either presence or absence of a
A total of 31 cpDNA universal primer pairs for higher plants [33] given haplotype in the sample was used to test whether a sample
were initially screened for 8 individuals from different popula- conformed to its stated geographical origin [14]. The null
tions. Subsequently, a subset of 276 of the 747 G. bancanus samples hypothesis HO: ‘the sample is of the presumed origin’ was defined.
from the 17 populations (Table 1 and Fig. 4) was subjected to For each haplotype i identified in the samples, p(i) indicates its
cpDNA analysis to identify intraspecific variability. Six cpDNA non- frequency in the region of presumed origin, while N denotes the
coding regions were found to be informative in the characteriza- number of samples. The probability P(i) of observing a rare
tion of haplotypes in G. bancanus: the intergenic spacers of haplotype at least once is an indication of non-conformity, but the
trnK-rps16 [34], rpoC1-2 [33], trnE-trnT [35,36], ycf9-trnG [37,33], value would depend on the sample size. By using the classical
psbM-trnD [37,33] and trnG-rps14 [35]. PCR amplifications were threshold of p(i) = 0.05 as the criterion for rarity:
m
performed in 20 L reactions consisting of approximately 10 ng of N
if p(i) < 0.05, then P(i) = 1 [1 p(i)] ,
template DNA, 50 mM of KCl, 20 mM of Tris-HCl (pH 8.0), 1.5 mM of
MgCl2, 0.4 mM of each primer, 0.2 mM of each dNTP and 1 U of
GoTaq Flexi DNA polymerase (Promega). The reaction mixture was
if p(i) 0.05, then P(i) = 1.
subjected to amplification using GeneAmp PCR System 9700
(Applied Biosystems) for an initial denaturing step of 94 C for The probability P to observe a particular configuration of
5 min, 40 cycles of 94 C for 1 min, 50 C annealing temperature for haplotypes in the sample is given by P = PP(i). If P < S, with S being
fi
1 min, and 72 C for 1 min. This was followed by a nal primer the maximum risk accepted and set to 0.05, with a risk a S (a is
extension at 72 C for 8 min. the probability of type I error), the hypothesis HO is rejected and
fi fi
PCR products were puri ed using the MinElute PCR Puri cation the sample is declared as non-conforming.
Kit (Qiagen) and sequenced in both directions using the BigDye Type I (false positive) and type II (false negative) errors of the
Terminator Sequencing Kit v.3.1 (Applied Biosystems) based on the population identification database (West and East Malaysia) were
standard dideoxy-mediated chain termination method. The estimated by simulating 10,000 artificial sample origins from
fi
sequencing thermal pro le was 50 cycles at 96 C for 10 s, 50 C either the western or eastern provenance. The artificial samples
for 5 s and 60 C for 4 min on a GeneAmp PCR System 9700. were designed to have random haplotypes that were proportional
fi
Sequencing reactions were puri ed by ethanol precipitation and to the haplotype frequencies determined in the 17 populations of
run on the ABI 3130xl Genetic Analyzer (Applied Biosystems). G. bancanus in Malaysia. The conformity of each artificial sample
Sequence data were edited and assembled using SEQUENCHER that originated from the western or eastern provenance was tested
v.5.0 (Gene Code Corporation). Haplotypes were determined based against the population identification databases. The proportion of
on nucleotide substitutions and indels. samples that were considered as non-conforming was measured.
fi
2.4.2. Characterization of the population identi cation database and 2.5. Individual identification
conformity of origin
As a result of its uniparental inheritance, cpDNA was expected 2.5.1. PCR amplifications and reproducibility
to show pronounced levels of population differentiation with a All 747 G. bancanus DNA samples obtained from the 17
fi
large proportion of population speci c haplotypes. Using the populations were used to generate a comprehensive individual
cpDNA markers, a haplotype distribution map throughout identification database for forensic DNA profiling (Table 1). Sixteen
fi
Malaysia was generated. The population identi cation database STR loci developed specifically for G. bancanus: WGb06, WGb17,
was constructed based on 276 samples, which was assigned as WGb22, WGb24, WGb29, WGb32, WGb37, WGb38 and WGb39 [38];
K.K.S. Ng et al. / Forensic Science International: Genetics 23 (2016) 197–209 201
and Gba028, Gba092, Gba108, Gba129, Gba147, Gba348 and Gba430 of individuals sampled from a population and 2n is the number of
[39], which showed specific amplification, single-locus mode of chromosomes counted because autosomes are in pairs due to
inheritance, absence of mononucleotide repeat motifs and null inheritance of one allele each from the paternal and maternal
alleles were selected for DNA profiling [38–40]. Multiplex-PCR parents [57].
amplification was carried out using the Type-it Microsatellite-PCR
Kit (Qiagen). The multiplex-PCR amplifications were performed in 2.5.4. Conservativeness of the individual identification databases
8 mL reactions, with 5 ng template DNA, 1X Type-it Multiplex PCR The profile frequency or random match probability was
Master Mix and 0.067 mM of each primer. The reaction mixture calculated based on the subpopulation-cum-inbreeding model
was subjected to amplification using a GeneAmp PCR System 9700 [58] for the individuals from West Malaysia and East Malaysia;
(Applied Biosystems), for an initial denaturing step of 5 min at excluding those with missing alleles. The profile frequency was
95 C, followed by 35 cycles of 95 C (30 s), 50 C to 57 C (90 s), and calculated by first considering the genotype frequency for each
72 C (30 s), with a 30 min extension at 60 C. The PCR products locus and then multiplying the frequencies across all loci, while the
were electrophoresed along with GeneScan ROX 400 (Applied random match probability is simply the reciprocal of the profile
Biosystems) internal size standard on an ABI 3130xl Genetic frequency [54,59]. To examine the conservativeness of the West
Analyzer (Applied Biosystems). Allele sizes were assigned against and East Malaysia databases, the full profile frequency was
the internal size standard and individuals were genotyped using calculated based on the cognate database (Porigin) against the
GENEMAPPER v.4.0 (Applied Biosystems). Reproducibility of all profile frequency calculated using the West Malaysia database
STR markers was tested by performing five independent PCR (Pcombined). Similarly, the full profile frequency was calculated for
amplifications on one individual and comparing genotype data East Malaysia database. The differences (d) between the databases
[41]. were defined as: d = log10(Porigin/Pcombined). If the d value was
negative, then Porigin was less than Pcombined suggesting that the
2.5.2. Population structure and genetic clustering database was conservative [60].
A model-based clustering approach, employing a Bayesian For a non-conservative database, this could be ascribed to the
algorithm using STRUCTURE v.2.3.3 [42,43] was applied to infer the effect of sampling or population subdivision [61]. To negate the
genetic structure of the G. bancanus populations in West Malaysia problem of subpopulation that is either part of, or not represented
(7 populations), East Malaysia (10 populations), and combined in the working database, adjusting the profile frequency by the use
Malaysia (17 populations). Three independent runs were per- of u is recommended [62,63]. A series of u were applied to correct
formed by setting the number of groups (K), each run consisting of data comprising different subpopulations, of which Porigin was not
a burn-in period of 250,000 iterations followed by 850,000 Monte adjusted but Pcombined was recalculated. The effectiveness of u
Carlo Markov Chain iterations, assuming an admixture model and adjustment was then measured by calculating mean d, its standard
correlated allele frequencies. For other settings, the default deviation and proportion of samples where d < 0.
program was applied and no prior information was used to define
the groups. The most likely number of groups (K) was chosen based 2.5.5. Individual assignment test
on the Delta K statistic [44] and the data was analyzed by the online To ensure the reliability of the generated G. bancanus databases
version of STRUCTURE HARVESTER [45]. After the relevant K value (West and East Malaysia) in inferring the origin of any individuals,
was verified, individual admixture proportions were sorted and a three independent self-assignment approaches were established:
graphical representation of the clustering was generated using (i) by population (existing designated populations), (ii) by group
DISTRUCT v.1.1 [46]. (groups defined by population genetic structure and genetic
Genetic relatedness among populations was quantified using relatedness analyses) and (iii) by region (West and East Malaysia).
Chord distance, DC [47], and cluster analysis on DC via the The likelihood of each reference individual’s multilocus STR
neighbour-joining method [48]. Chord distance was selected genotype occurring in each identified population, group or region
because it has been shown by simulation analysis to generate was determined by performing independent assignment tests on
correct tree topologies regardless of the STR mutation model [49]. the reference databases using the leave-one-out procedure (self-
The relative strengths of the nodes were determined by boot- assignment) in GENECLASS2 [20], assuming random mating and
strapping analysis (1000 replicates) using the program POWER- independence between loci [21].
MARKER [50].
2.5.3. Characterization of the individual identification databases 3. Results
The allele frequency of each locus was computed using the
fi
program MICROSATELLITE TOOLKIT [51]. The information content 3.1. Species identi cation
of the STR loci were assessed by calculating the number of alleles
per locus (A), observed heterozygosity (Ho) and expected High quality sequences for all three regions, two cpDNA (trnH-
heterozygosity (He) using the program GDA v.1.0 [52]. The psbA and trnL) and an rDNA (ITS2) were obtained in 93.42% of the
polymorphic information content (PIC), power of discrimination tested samples (71 out of 76 samples). All sequences have been
–
(PD) and match probability (MP) were estimated using the deposited in GenBank under Accession numbers KU990894
program POWERSTATS v.1.2 [53]. The match probability refers to KU991106. In the NJ tree method, the identity of a species is
the probability that two randomly selected individuals will have an considered to be resolved if the accessions under the species form a
identical genotype [54]. The evaluations of Hardy-Weinberg monophyletic group. In this method, based on the individual
equilibrium (HWE) and linkage equilibrium (LE) were performed regions used, the species discrimination was the highest for ITS2 at
using the program GENEPOP v.3.4 [55]. A sequential Bonferroni 80% (16 of the 20 Gonystylus species), however the remaining two
test was then used to compensate for multiple comparisons regions at 15% (3 of the 20 Gonystylus species) each. Higher species
between loci [56]. The coancestry coefficient (u) and inbreeding discrimination was achieved with 90% (18 of the 20 Gonystylus
coefficient (f) were estimated using the program GDA v.1.0 [52] and species) by concatenating the sequences of the three regions (Fig. 2
tested for significance by bootstrapping of 1000 replicates across and Table S4). The two unresolved species were G. augescens and G.
loci. The minimum allele frequency of 5/2n was calculated for the xylocarpus. Failure to achieve a 100% rate of species discrimination
adjustments of random match probability, where n is the number was due to the absence of sequence variation in these two species.
202 K.K.S. Ng et al. / Forensic Science International: Genetics 23 (2016) 197–209
Fig. 2. Neighbor joining tree of 20 Gonystylus species (Kimura-2-parameter, 1000 bootstraps).
Barcoding gap analysis on trnH-psbA and trnL showed 15% (3 of with BLAST E-value parameter. The result showed that 18 out of the
the 20 Gonystylus species) species resolution compared to that of 20 Gonystylus species were correctly identified to the same species
ITS2 with 80% (16 of the 20 Gonystylus species). The concatenated and the remaining two unresolved species were G. augescens and G.
sequences of the three regions showed high sequence variation xylocarpus.
between species (interspecific divergence) and low sequence
variation within species (intraspecific variation) for 18 out of the 20 3.2. Population identification
Gonystylus species (Fig. S1). This showed that 90% of the Gonystylus
species had higher interspecific distance compared to intraspecific 3.2.1. Haplotype distribution
distance. Similar to the NJ method, the two unresolved species In total, 24 intraspecific variable sites were detected within the
were G. augescens and G. xylocarpus (intraspecific = interspecific). six noncoding regions. Among these variable sites, seven sites were
The results for the standalone BLAST methodology (requiring an found in the trnK-rps16 spacer, six in the ycf9-trnG spacer, four in
identity score of 100% to assume species resolution) followed the trnE-trnT spacer, three in both the psbM-trnD and trnG-rps14
closely those of the previous two methods, with both the spacers and one in the rpoC1-2 spacer. In particular, 12 variable
concatenated sequences and ITS2 performing well (90% species sites were caused by base substitutions and the other 12 were the
resolution). Again, as with the previous two methodologies, trnH- result of indels. The sequences have been deposited in GenBank
psbA and trnL performed less well, establishing species resolution with Accession numbers KT964030–KT964054.
in only 15% of cases. Although both the concatenated sequences A total of 12 haplotypes were identified from the 24 variable
and ITS2 produced similar species resolution, the optimum result sites (Table S5). The most common haplotype, P1, was present in
was produced by the concatenated sequences when combined 47.6% of the samples whereas the second most common, P2, was
K.K.S. Ng et al. / Forensic Science International: Genetics 23 (2016) 197–209 203
found in 34.7%. Both haplotypes displayed disjunct distributions. populations was set at K = 2. This suggests that all the individuals
Haplotype P1 was widespread throughout East Malaysia whereas can be clustered into one of the two larger populations (Fig. 4a),
haplotype P2 was solely found in West Malaysia. Of the 12 corresponding to the two regions that are separated by the South
haplotypes, eight (P5-P12) were endemic to specific populations, China Sea; West (Peninsular Malaysia) and East (Sabah and
as shown in Fig. 3. Based on the cpDNA haplotypes, the results Sarawak) Malaysia. Nevertheless, further population substructur-
showed that the West and East Malaysia regions were clearly ing (K = 2) was observed within each of the regions (Fig. 4b); the
distinguishable. East Malaysia region was subdivided into two groups, East
Malaysia I (Sedilu FR, Lingga FR, Serapau-Lingga FR, Maludam
3.2.2. Test of conformity of origin NP, Manggut FR, Betong FR and Naman FR) and East Malaysia II
Type I and II errors were estimated for the population (Klias FR, Kayangeran FR and Loagan Bunut NP). The West Malaysia
identification database of West Malaysia. Type I error was region was similarly subdivided into West Malaysia I (Pekan FR,
estimated by simulating 10,000 samples of western provenance Nenasi FR, Resak FR, Kedondong FR and Air Hitam FR) and West
against the database of West Malaysia showed all samples Malaysia II (Sg. Karang FR and Raja Musa FR). The neighbour-
conformed (Fig. S2a). For the type II error, 100% of those from joining cluster analysis also divided the 17 populations into two
the eastern provenance resulted in non-conformity. Likewise, the main genetic clusters with substructuring within each main cluster
type I error for the population identification database of East (Fig. 4c). Based on the clustering results, West and East Malaysia
Malaysia was estimated by simulating 10,000 samples that databases were established with 280 and 467 individuals
originated from the eastern provenance of Malaysia against the respectively.
database of East Malaysia (Fig. S2b). The number of rejected
samples varied between 0 and 0.77%, which showed non- 3.3.3. Characterization of the individual identification database
conformity at the 5.0% threshold. In order to estimate the type The distribution of the allele frequencies and genetic param-
II error, a similar simulation was performed with samples that eters for West and East Malaysia are shown in Table 2, Tables S6
originated from the western provenance tested against the and S7. A total of 157 and 340 alleles were observed in West and
database of East Malaysia. Those samples that originated from East Malaysia, respectively. Overall, WGb32 was the most
the western provenance showed 100% non-conformity. Overall, the discriminating locus at all levels, the PD values being 0.969 (West
observed types I and II errors resulting from the simulation in Malaysia) and 0.984 (East Malaysia).
population identification database showed good concordance About 18.8% and 75.0% of the STR loci were found to depart
within the predicted 5.0% threshold. significantly from HWE in West and East Malaysia respectively
after employing Bonferroni corrections. To rule out the possibility
3.3. Individual identification that genotyping anomalies or the presence of null alleles were
responsible for the high proportion of STRs that exhibited
3.3.1. Reproducibility of the STR markers deviation from HWE, we tested each subpopulation independently.
All sixteen STR markers gave consistent genotypes for the five The observed deviations from HWE in these subpopulations was
independent PCR amplifications. Base pair size differences substantially lower than for the entire cluster, just six and two loci
between maximum and minimum fragment lengths were less for East Malaysia I and East Malaysia II, respectively. This suggests
than 0.5. significant population substructuring within East Malaysia and
provides some indication that deviation from HWE was due to the
3.3.2. Population structure and genetic clustering Wahlund effect rather than genotyping inconsistencies or STR
The model-based program STRUCTURE, which applied a issues (i.e. null alleles). The LE tests showed that 10.0% (West
Bayesian clustering algorithm without prior population informa- Malaysia) and 94.2% (East Malaysia) of the 120 pairwise loci were
tion, produced the highest likelihood scores when the number of significant after Bonferroni correction for multiple comparisons.
Fig. 3. Geographic distributions of the 12 chloroplast DNA haplotypes of Gonystylus bancanus shown in different colours. The total pie chart size reflects sample size (n) at each location.
204 K.K.S. Ng et al. / Forensic Science International: Genetics 23 (2016) 197–209
Fig. 4. Bayesian inferences of the number of clusters, K estimated by STRUCTURE among individuals for Gonystylus bancanus in Malaysia. (A) Two clusters (K = 2) of genotypes
were identified. (B) Population substructuring (K = 2) was observed within each of the larger regions (West and East Malaysia). (C) A neighbour-joining tree indicating the
relationship between the 17 populations of G. bancanus in Malaysia. For population abbreviations, see Table 1.
This might indicate that the genotypes were not independent and values, the highest value was observed in East Malaysia (0.091),
some of the loci might be linked. However, deviations from the followed by East Malaysia II (0.067). The estimated u and f values
independence test could also be due to population substructuring were subsequently used to correct the calculations of the random
and inbreeding. match probability, as most of the STR loci in the databases showed
The u and f values calculated for each of the hierarchical levels deviation from HWE and LE.
are listed in Table 3. Bootstrapping analysis showed that all the
calculated u values were significantly greater than zero, as the 95% 3.3.4. Conservativeness of the individual identification databases
confidence interval did not overlap with zero. Similarly, all f values The conservativeness of the West and East Malaysia databases
were also significantly greater than zero except for West Malaysia from the full profile frequency for each of the 245 (out of 246) and
II ( 0.019), which was mainly due to the smaller sample size 33 (out of 34) individuals from West Malaysia I and West Malaysia
compared to the other groups. The highest value of u was observed II respectively was calculated based on the cognate database
for Malaysia (0.121), followed by West Malaysia (0.067). For f (Porigin) against the profile frequency calculated using the West
K.K.S. Ng et al. / Forensic Science International: Genetics 23 (2016) 197–209 205
Table 2
a
Genetic parameters of Gonystylus bancanus databases in West and East Malaysia for the 16 STR loci.
Gba028 Gba092 Gba108 Gba129 Gba147 Gba348 Gba430 WGb06 WGb17 WGb22 WGb24 WGb29 WGb32 WGb37 WGb38 WGb39
West Malaysia
A 14 10 15 7 22 10 12 2 13 9 9 5 15 4 3 7
Ho 0.779 0.757 0.721 0.625 0.621 0.757 0.667 0.004 0.796 0.646 0.696 0.525 0.792 0.521 0.479 0.404
He 0.789 0.798 0.758 0.651 0.634 0.776 0.696 0.004 0.827 0.732 0.728 0.560 0.852 0.528 0.462 0.450
PIC 0.761 0.772 0.727 0.582 0.604 0.742 0.672 0.004 0.805 0.691 0.684 0.502 0.835 0.452 0.392 0.410
b b b
HWE 0.016 0.799 0.058 0.650 0.708 0.047 0.206 1.000 0.510 0.000 0.644 0.422 0.000 0.157 0.313 0.002
MP 0.076 0.067 0.089 0.121 0.166 0.082 0.117 0.993 0.051 0.112 0.113 0.247 0.031 0.306 0.356 0.347
PD 0.924 0.933 0.911 0.879 0.834 0.918 0.883 0.007 0.949 0.888 0.887 0.753 0.969 0.694 0.644 0.653
East Malaysia
A 35 14 35 26 38 17 20 12 23 15 18 27 28 10 11 11
Ho 0.852 0.680 0.839 0.819 0.857 0.748 0.789 0.503 0.769 0.579 0.763 0.737 0.855 0.738 0.662 0.680
He 0.914 0.734 0.910 0.882 0.900 0.844 0.884 0.545 0.869 0.717 0.844 0.857 0.914 0.770 0.757 0.724
PIC 0.907 0.699 0.903 0.871 0.892 0.824 0.872 0.502 0.855 0.688 0.827 0.846 0.907 0.732 0.719 0.676
b b b b b b b b b b b b
HWE 0.000 0.036 0.000 0.000 0.005 0.000 0.000 0.028 0.000 0.000 0.000 0.000 0.000 0.131 0.000 0.000
MP 0.016 0.103 0.019 0.026 0.020 0.045 0.027 0.249 0.032 0.124 0.041 0.042 0.016 0.089 0.100 0.124
PD 0.984 0.897 0.981 0.974 0.980 0.955 0.973 0.751 0.968 0.876 0.959 0.958 0.984 0.911 0.900 0.876
a
A: Total number of alleles; Ho: Observed heterozygosity; He: Expected heterozygosity; PIC: Polymorphic information content; HWE: Hardy-Weinberg equilibrium; MP:
Match probability; PD: Power of discrimination.
b
Deviation from Hardy-Weinberg equilibrium (probability test) was tested after Bonferroni correction (P < 0.05/16 = 0.003).
Table 3
Coancestry coefficients (u) and inbreeding coefficients (f) of Gonystylus bancanus
calculated according to hierarchical levels. Probabilities of the mean u and f were
determined using bootstrap analysis (1000 replications) with a 95% confidence
interval.
A. Conserva tiveness of West Malaysia Database
Hierarchical level Coancestry coefficient (u) Inbreeding coefficient (f) 6
Mean u lower upper Mean f lower upper
4
Malaysia 0.121 0.106 0.137 0.027 0.020 0.034
(N = 747)
2
West Malaysia 0.067 0.050 0.090 0.044 0.029 0.062
(N = 280)
0
East Malaysia 0.065 0.055 0.077 0.091 0.074 0.110
-20 -15 -10 -5 0
(N = 467)
d -2
West Malaysia II group 0.037 0.003 0.079 0.046 0.019 0.120
(N = 34)
West Malaysia I group 0.036 0.018 0.055 0.020 0.005 0.036 -4
(N = 246)
East Malaysia II group 0.073 0.056 0.091 0.067 0.050 0.086 -6
(N = 192)
East Malaysia I group 0.018 0.015 0.021 0.058 0.044 0.074 -8
(N = 275)
N is the number of samples Log (Profile frequency of West Malaysia I and II) -10
West Malaysia I West Malaysia II
Malaysia database (Pcombined). Likewise, the full profile frequency
for each of the 256 (out of 275) and 183 (out of 192) individuals
from East Malaysia I and East Malaysia II respectively was
B. Conserva tiveness of East Malaysia Database
calculated based on the cognate database (Porigin) against the 4
profile frequency calculated using the East Malaysia database
(Pcombined). The conservativeness of the West and East Malaysia 2
databases was tested by estimating the parameter d for every DNA
fi
pro le in the database using the subpopulation-cum-inbreeding 0
u
model (Fig. 5). When the was set as 0.067, 245 tests performed on -30 -25 -20 -15 -10 -5 0
West Malaysia I showed negative d values (mean d = 1.032
d -2
0.329), indicating that the West Malaysia database was
conservative. Yet, 33 tests performed on West Malaysia II showed
-4
non-conservativeness, with positive d values (mean
d = 2.675 0.779). Accordingly, when the u was set as 0.065 for
the East Malaysia database, all d values were negative (mean -6
d = 3.382 0.876) and this implied that the database was
conservative when tested on East Malaysia I. However, 183 tests Log (Profile frequency of East Malaysia I and II) -8
performed on East Malaysia II showed non-conservativeness, with East Malaysia I East Malaysia II
positive d values (mean d = 2.456 0.517). Notably, the conserva-
Fig. 5. The conservativeness of (A) West and (B) East Malaysia databases tested by
tiveness tests showed that the West and East Malaysia databases
estimating the parameter d for every DNA profile frequency in the database using
were non-conservative when they were used on West Malaysia II
the subpopulation-cum-inbreeding model. X-axis: log (Profile frequency) and Y-
and East Malaysia II.
axis: mean number of differences between origin and combined database (d).
206 K.K.S. Ng et al. / Forensic Science International: Genetics 23 (2016) 197–209
For the series of u adjustments, the estimated mean profile Malaysia (ranging from 99.27 to 99.95%) with an average of 99.60%.
frequency based on the subpopulation-cum-inbreeding model, At the regional scale, both West and East Malaysia regions could
mean differences between origin and combined database (d) and achieve 100% correct individual assignments (Table 5), confirming
proportion of d < 0 are shown in Table 4 and Fig. S3. Generally, in coherence to the STRUCTURE clusters (see Fig. 4a).
cases of non-conservativeness, profile frequencies derived from
the combined databases tend to be lower than those derived from 4. Discussion
cognate population database. For example, for West Malaysia II,
12 9
mean Pcombined was 2.878 10 and Porigin was 1.465 10 . On For forensic timber identification or authenticity testing, the
the other hand, for West Malaysia I, mean Pcombined was establishment of a comprehensive genetic identification database
12 13
1.343 10 and Porigin was 2.362 10 , implying that the is necessary to facilitate the identification of a suspicious sample to
Pcombined was conservative. reveal its species, geographic origin and individual level identifi-
As the value of u used in the estimation increased, the mean cation. Hence, this study strongly demonstrates how DNA-based
profile frequency increased and d decreased. For example, in East techniques can serve as an important authenticity tool to identify
Malaysia II, when u was iterated from 0.000 to 0.200, the mean species, trace the source of origin and verify the legality of
21 12
Pcombined increased from 1.675 10 to 1.159 10 and mean d unknown samples.
decreased gradually from 7.626 0.960 to 2.282 0.723. This An ideal species barcode should hold adequate variation among
adjustment of u was observed to improve the conservativeness of the sequences to discriminate species; yet, it also needs to be
the database. To obtain 100% negative d value in both databases, adequately conserved so that there is less variability within a
the u readjustment needed to be increased to 0.30 and 0.20 in the species than between species [18]. In the present study, a species
West and East Malaysia databases respectively. It is standard identification database was established using 20 out of the 30
practice to adjust profile frequency to take into account both known Gonystylus species. The markers trnH-psbA, trnL and ITS2,
sampling error and population substructuring effects as the can all be sequenced easily from leaf or inner bark (cambium)
adjustments will increase the profile frequency with the intention tissue for the 20 Gonystylus species. Using the 20 Gonystylus species
of under, rather than overstating the weight of the DNA evidence dataset, with concatenated sequences of the three markers, correct
against the defendant [60]. In this case, with closer inspection of matching between barcodes and taxonomic species was achieved
these u adjustments, the mean profile frequencies for West with a resolution of 90% (18 out of the 20 species). However, the
12
Malaysia II and East Malaysia II increased from 2.878 10 to ITS2 region had a higher success rate for discriminating between
8 17 12
6.737 10 and from 2.536 10 to 1.159 10 , respectively, closely related species compared to trnH-psbA and trnL. Recently,
and was therefore demonstrably conservative. several researchers have demonstrated the potential of using ITS2
for taxonomic classification and phylogenetic reconstruction at
3.3.5. Individual assignment test both the genus and species levels for eukaryotes due to its variation
In order to correctly assign a source of origin to each G. bancanus in primary sequence and secondary structures in a way that
individual within the population, group or region reference correlates highly with taxonomic classification [64–66]. The ITS2
databases, independent self-assignment (leave-one-out) tests sequence length for Gonystylus species is about 341 bp, suggesting
were performed on all individuals in the reference database using that it is sufficiently short to allow PCR amplification of even
all available loci. The average percentage of correctly assigned degraded DNA (unpublished data). This thus provides useful
individuals at each population was 54.80% (Table 5), with 0% at biological information for alignment and can be considered as a
Resak and Kedondong due to their limited sample sizes of 10 and 4, molecular character for Gonystylus species identification.
respectively. The percentage of correctly assigned individuals was Ideally, a population identification database should be able to
higher for self-assignment of individuals belonging to the groups specifically partition unknown samples from any population or
within West Malaysia (ranging from 99.19 to 100%) and East forest reserve to the level of specific group. In this study, the
Table 4
Series of coancestry coefficients (u) adjustments applied on the West and East Malaysia databases of Gonystylus bancanus. Mean profile frequency values, mean differences d
and P (d < 0) were calculated for individuals derived from West Malaysia I and II as well as East Malaysia I and II using respective databases. Values in parentheses denote
standard deviations.
West Malaysia Database
West Malaysia I West Malaysia II
u Mean profile frequency Mean d P (d < 0) Mean profile frequency Mean d P (d < 0)
15 15
0.000 6.916 10 2.266 (0.605) 0.00 9.248 10 5.211 (1.179) 0.00
12 12
0.067 1.343 10 1.032 (0.329) 1.00 2.878 10 2.675 (0.779) 0.00
12 11
0.100 9.095 10 1.997 (0.461) 1.00 2.343 10 1.595 (0.764) 0.00
11 10
0.150 9.869 10 3.124 (0.615) 1.00 3.161 10 0.334 (0.780) 0.36
10 9
0.200 6.858 10 3.995 (0.728) 1.00 2.608 10 0.638 (0.810) 0.85
9 8
0.250 3.448 10 4.696 (0.817) 1.00 1.513 10 1.419 (0.843) 0.91
8 8
0.300 1.357 10 5.276 (0.888) 1.00 6.737 10 2.066 (0.875) 1.00
East Malaysia Database
East Malaysia I East Malaysia II
u Mean profile frequency Mean d P (d < 0) Mean profile frequency Mean d P (d < 0)
24 21
0.000 7.058 10 3.825 (1.221) 0.00 1.675 10 7.626 (0.960) 0.00
19 17
0.065 2.877 10 3.382 (0.876) 1.00 2.536 10 2.456 (0.517) 0.00
17 16
0.100 1.161 10 5.346 (0.974) 1.00 8.940 10 0.830 (0.566) 0.09
16 14
0.150 7.249 10 7.400 (1.090) 1.00 4.936 10 0.931 (0.651) 0.93
14 12
0.200 1.863 10 8.936 (1.177) 1.00 1.159 10 2.282 (0.723) 1.00
K.K.S. Ng et al. / Forensic Science International: Genetics 23 (2016) 197–209 207
Table 5
Self-assignment (leave-one-out) test on Gonystylus bancanus individuals based on population, genetic cluster and region implemented in GENECLASS2.
Region Group Population Correctly assigned individuals
By population By group By region
West Malaysia West Malaysia I Pekan 86.24% 99.19% 100.00%
Nenasi 33.34%
Resak 0.00%
Kedondong 0.00%
Air Hitam 78.57%
West Malaysia II Sg. Karang 50.00% 100.00%
Raja Musa 70.00%
East Malaysia East Malaysia I Sedilu 54.83% 99.27% 100.00%
Lingga 23.34%
Serapau-Lingga 36.67%
Maludam 44.45%
Manggut 56.60%
Betong 32.50%
Naman 83.63%
East Malaysia II Loagan Bunut 98.02% 99.95%
Kayangeran 93.10%
Klias 90.32%
Mean 54.80% 99.60% 100.00%
Type I error (alpha) = 0.05.
presence of a single prevalent chloroplast haplotype in all the suggests that the West and East Malaysia databases can be used
seven populations from West Malaysia was sufficient to partition independently for random match probability estimation within
West (Peninsular Malaysia) and East (Sabah and Sarawak) Malaysia their respective regions. For example, if a confiscated G. bancanus
into two regions. This clearly divided the 17 populations into two log is found to have originated from Pekan in West Malaysia, the
distinct clusters, corresponding to the two regions that are random match probability can be estimated using the West
separated by the South China Sea. The finding therefore clearly Malaysia database for evidence interpretation. Thus, with the
demonstrates that the population identification database of G. availability of the West and East Malaysia databases that cover the
bancanus could be used to identify individuals with unknown whole distribution range of G. bancanus, it is feasible to estimate
origin at the regional level. the random match probability of a suspected log to its original
Similarly, partitioning into West and East Malaysia at the stump.
highest hierarchical level of population structuring was observed The self-assignment tests showed a lower percentage of
based on a Bayesian model approach by STRUCTURE and the correctly assigned individuals to their population of origin. This
neighbour-joining cluster analysis. Nonetheless, the larger pop- suggests that the existing population designations by humans do
ulations were split further into two subpopulations each at K = 2. not reflect true population genetic boundaries. In contrast, self-
The STRUCTURE results did therefore hint at the presence of two assignment of individuals by group showed a higher percentage of
subclusters in each region. Limited pollen and seed dispersal correctly assigned individuals, suggesting that the grouping of
mechanisms might provide a reason for the observed within individuals by population genetic structure and genetic related-
population substructuring. Although the coancestry coefficient (u) ness analyses are more realistic, with the assumption of correlated
values calculated for West (0.065) and East (0.067) Malaysia were alleles among populations being due to gene flow.
significantly greater than zero, they were generally lower than the Recently, an ongoing investigation in Malaysia by the Selangor
u according to the 17 populations (0.121). This suggests that State Forest Department in collaboration with the Forest Research
populations within each region may have shared a common gene Institute Malaysia was able to identify the species of seized logs
pool or have experienced extensive gene flow in the past. and link them back to the stumps of illegally felled trees, with
Consequently, individual identification databases were established evidence obtained through DNA barcoding and an individual
separately according to these regions in order to ascertain accurate identification database developed for Neobalanocarpus heimii [16].
estimates of random match probabilities. Regions with inbreeding With this initial breakthrough, the use of forensic timber
coefficient (f) value significantly greater than zero indicate the identification approach to curb illegal logging in Malaysia is
presence of inbreeding. Empirical evidence reveals that indepen- beginning to gain confidence among local enforcement authorities.
dence within and among loci is violated in plants because most Consequently, with appropriate validation, the generated G.
plant populations tend to have substantial population substruc- bancanus databases from the current study could be used for
turing and inbreeding among individuals [16,67,68]. Similarly, in forensic timber identification of confiscated or unknown logs in at
the present study, most populations of G. bancanus had signifi- least three scenarios: (1) to identify the species of the unknown
cantly departed from the independence test. Hence, to evaluate log, (2) to authenticate the source of origin of an unknown sample,
DNA evidence that accounts for subpopulation and inbreeding and (3) to match an unknown log to its original stump. In brief, the
effects, the ‘subpopulation-cum-inbreeding model’ [58] was Gonystylus species identification database will be useful for
employed to correct for these effects although its use in forensic authenticating the species of the unknown logs. Subsequently,
casework may not always be conservative [41]. the population identification database would be useful to reveal
The conservativeness tests showed that the East and West the source of origin of an unknown G. bancanus sample from West
Malaysia databases were conservative when used on West or East Malaysia. Once the source region is determined, the
Malaysia I and II, as well as East Malaysia I and II. This further individual identification database can be applied with an
208 K.K.S. Ng et al. / Forensic Science International: Genetics 23 (2016) 197–209
assignment test using multilocus STR genotypes to assign the listings. Donors to the collaborative program include the EU, the
unknown sample to its source of origin (population, group or USA, Japan, Norway, New Zealand and Switzerland.
region).
An intact form of log or unprocessed wood is usually seized Appendix A. Supplementary data
from logging sites, while lumber that is supplied in the trading
market is processed in the form of sawn and/or wood products. Supplementary data associated with this article can be found, in
Thus, the fundamental challenge for forensic timber identification the online version, at http://dx.doi.org/10.1016/j.
applications is the possibility of extracting DNA from dry wood. fsigen.2016.05.002.
Previously, several researchers have developed different DNA
extraction protocols for unprocessed and processed wood from G. References
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