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Publicly Available Published by De Gruyter June 24, 2017

Tracking the geographical origin of timber by DNA fingerprinting: a study of the endangered species Cinnamomum kanehirae in Taiwan

  • Kuo-Hsiang Hung EMAIL logo , Chia-Hung Lin and Li-Ping Ju EMAIL logo
From the journal Holzforschung

Abstract

Cinnamomum kanehirae Hay. is endemic in Taiwan and is severely threatened due to intensive utilization and illegal logging. To combat illegal logging, suitable identification markers are needed, which are usable in a court of law, such as microsatellite marker for genotyping. In the present paper, a genetic fingerprinting database was generated based on 15 microsatellites, which are suitable to assess the timber’s origin and its population genetic structure. The quality of DNA extractions from C. kanehirae timbers was assessed by comparing cpDNA trnL–trnF sequence lengths. The cumulative probability of identifying unrelated individuals in these microsatellites was 5.151×10−17. The results indicate that the low genetic diversity is a consequence of illegal logging and that there is a significant genetic differentiation among C. kanehirae populations. It was possible to trace back the geographical origin of unknown C. kanehirae timbers based on a genetic reference database, i.e. all blind wood samples were assigned to their true geographical origins. Accordingly, microsatellites are a useful tool to identify the population origins of timbers and can be considered as a tool for combating illegal logging of C. kanehirae.

Introduction

The genus Cinnamomum Schaeff. belongs to the Lauraceae family and consists of approximately 250 species distributed in tropical and subtropical regions (Mabberley 2008). The timbers of the most Cinnamomum species are widely used for furniture, carving, and building construction materials, and thus they are of great economic value, also in Taiwan. The taxonomy, genetic diversity, and population genetic structure of Cinnamomum species have seldom been studied and the taxonomic relationships among species are poorly defined. Cinnamomum research has focused mainly on their phenotypic and chemical characteristics such as their external morphology, microscopic examination of the woods and leaves, and composition of essential oils (Fujita 1967; Ou 1989; Chang 1995, 2005; Ho and Hung 2011).

Cinnamomum kanehirae is endemic in Taiwan, which is distributed at 200–2000m elevation, frequently mixed with other Cinnamomum species. It was abundant in the broadleaf forests of Taiwan early in the 20th century, and always was considered as an economically important timber (Kuo et al. 2010; Hung et al. 2014). Antrodia cinnamomea, an endemic mushroom in Taiwan, is a host-specific saprophytic fungus that causes brown rot in C. kanehirae. The fruiting bodies and mycelia of A. cinnamomea contain substances with anticancer, anti-inflammatory, anticholinergic, blood pressure regulatory and antiserotonergic activities (Chen et al. 1995; Shen et al. 2003; Chang and Wang 2005). Step by step, the slow growing C. kanehirae became endangered due to illegal logging mainly for use as a growth medium for the expensive A. cinnamomea and A. camphorata (US $2500 kg−1 wet weight) (Chen et al. 2007). Cinnamomum kanehirae timbers are even more expensive than the valuable Chamaecyparis formosensis Matsum. and C. taiwanensis Masam. & Suzuki. In the course of combatting illegal logging, it is difficult to verify the geographical origins or individual genetic traits to discriminate between C. kanehirae timbers via chemical analysis or by microscopic observations.

The relevance of DNA markers for tracing illegal logging was recognized more than one decade ago (Degen and Fladung 2007), and since that time the development of DNA analysis has fostered the application of this technique. DNA extraction from fresh and dried leaves is a common practice in plant genetic studies. Nevertheless, few studies have reported on DNA extraction from dry wood (Tnah et al. 2012) as it is still the major challenge. Once this obstacle is overcome, DNA fingerprinting technology is potentially an efficient tool to identify the origins of timber and track logs along supply chains. In an ideal case, timber species or their origins could be detected and timber logs could be traced back along the forestry supply chains (Finkeldey et al. 2010; Lowe and Cross 2011). Examples of these kind of research include Intsia palembanica Miq (Lowe et al. 2010), Entandrophragma cylindricum (Sprague) Sprague (Jolivet and Degen 2012), Swietenia macrophylla King (Degen et al. 2013), Populus euphratica Oliv. (Jiao et al. 2015), P. tremula L. (Verbylaitė et al. 2010), Picea abies (L.) H. Karst. (Sandak et al. 2015; Nowakowska et al. 2015), Pinus sylvestris L. (Nowakowska et al. 2015), Abies alba Mill. (Nowakowska et al. 2015), Larix decidua Mill. (Nowakowska et al. 2015), Aquilaria sinensis (Lour.) Spreng. (Jiao et al. 2014), members of the Meliaceae family (Michael et al. 2012), Dalbergia odorifera T.C. Chen (Yu et al. 2016), Robinia pseudoacacia L. (De Filippis and Magel 1998), Quercus L. ssp. (Dumolin-Lapègue et al. 1999; Deguilloux et al. 2002), Gonystylus bancanus (Miq.) Kurz (Asif and Cannon 2005), and various dipterocarps (Tnah et al. 2012; Rachmayanti et al. 2006, 2009; Yoshida et al. 2007). Liepelt et al. (2006) suggested that DNA extracted from ancient Abies Mill., Pinus L., Fagus L., and Quercus wood samples could also be used in genetic studies. Several factors determine the successful isolation and high quality of the DNA extracted from dry timbers. These include mechanical treatment, chemical compounds within woods, and aging. These factors vary significantly among tree species (Finkeldey et al. 2010). In addition, the DNA extracted from timber is often contaminated by microbial DNA.

In terms of C. kanehirae, it should be emphasized that the assessment of its genetic variation or population structure would be necessary for its conservation (Finkeldey et al. 2010; Chiang et al. 2014; Li et al. 2016) as this tree is heavily exploited, as pointed out above. This is the reason why its distribution now is limited and fragmented within its natural area in Taiwan. This also leads to a decrease of genetic variation and population structure. Kuo et al. (2010) examined the genetic patterns of cpDNA in C. kanehirae and found that its genetic variation is smaller than that of other tree species in Taiwan, which is also manifest in its nuclear genes (Liao et al. 2010).

The highly polymorphic microsatellites markers (or simple sequence repeats, SSR) typically consist of repeated 10–60 bps units, and dispose of a unique genetic patterns for each individual, and generate DNA genotype profiling databases for individual discrimination. Sourcing the timbers from its DNA profiles requires the assessment of the genetic variation across entire natural populations (Degen et al. 2013). Genetic variation arises from differences in evolutionary histories, geographical environments, climates, dispersal abilities, and local selection pressures.

In this study, a DNA-based tracking method will be in focus in terms of the question, whether this approach is suitable for discrimination between legally and illegally logged wood, which could work in a similar fashion to that in human forensic applications. The microsatellite technique will be applied. The discrimination power of microsatellite loci of C. kanehirae should be studied and a genetic reference database generated. Genetic variation and differentiation levels of C. kanehirae should also be assessed.

Materials and methods

Sampling and DNA isolation:

Seventy-two wood and 26 leaf samples of C. kanehirae were collected from four populations (ZH, DA, NA, and JH) in northern Taiwan, and 30 leaf samples from southern Taiwan (LI) (Table 1, Figure 1). This material served as a genetic reference database, and 12 wood samples were applied in blind tests to validate the geographical origins. Young leaves and dry woods were collected in the field. Genomic DNA of the samples was extracted based on the cetyltrimethylammonium bromide (CTAB) protocol (Doyle and Doyle 1987) and a modified CTAB technique (Asif and Cannon 2005). The genomic DNA isolation was done from about 60–100 mg of dry wood shavings.

Table 1:

Localities, symbols, sample types, and sample numbers of five sampled populations of Cinnamomum kanehirae in Taiwan.

LocationSymbolLongitude and latitudeTypes of sampleNumber
Zhudong, HsinchuZH121°16′E, 24°40′NWood20
Leaf6
Dahu, MiaoliDA120°57′E, 24°29′NWood30
Leaf10
Nanzhuang, MiaoliNA120°59′E, 24°31′NWood14
Jhuolan, MiaoliJH120°59′E, 24°24′NWood8
Leaf10
Liouguei, KaohsiungLI120°47′E, 23°05′NLeaf30
Figure 1: The distribution of Cinnamomum kanehirae populations collected in Taiwan.The symbols for each population are given in Table 1.
Figure 1:

The distribution of Cinnamomum kanehirae populations collected in Taiwan.

The symbols for each population are given in Table 1.

Verification of DNA isolation:

PCR amplification was conducted to determine the success of DNA isolation for dry wood samples, while the cpDNA region was amplified. The intergenic spacer between trnL (UAA) 3′exon and trnF (GAA) (Taberlet et al. 1998; Kuo et al. 2010) was amplified and sequenced according to Kuo et al. (2010). Three randomly chosen PCR products from wood samples were purified and sequenced by means of an automated sequencer (Applied Biosystems Model 377A). A Blast search against the NCBI database indicated that these sequences belong to the expected genomic regions.

Microsatellite PCR amplification and genotyping:

All wood and leaf samples were genotyped at 15 previously developed, polymorphic microsatellites (Hung et al. 2014). Microsatellite PCR amplification was performed in a 25-μl reaction volume containing 10 ng genomic DNA, 0.2 mM dNTP, and 5 pmol of each primer according to Hung et al. (2014). The genotyping of PCR product was performed on an ABI 3100 automated sequencer (Applied Biosystems, Foster City, CA, USA). GENEMAPPER software v. 3.7 (Applied Biosystems, Foster City, CA, USA) was available for fragment size determination.

Discrimination ability of the microsatellites:

The power of discrimination (PD) was assessed for each microsatellite: PD=1−ΣPi2, where: Pi is the frequency of genotype i (Tessier et al. 1999). We also calculated cumulative power of discrimination (cPD) for 15 microsatellites as cPD=1–[(1-PD1)(1-PD2)…(1-PD15)] (Fisher 1951). The polymorphic information content (PIC) and the probability of identity (PID) for each marker (triplicate determination) were also calculated, which show the identification ability. PowerMarker v. 3.25 (Liu and Muse 2005), and Cervus v. 3.0 7 (Kalinowski et al. 2007) were available to this purpose. PID is the probability that two randomly chosen individuals within a population have the same genotype on a set of markers.

Genetic variation and population structure:

Parameters of genetic variation within species/populations were examined by means of GenAlEx v. 6.5 (Peakall and Smouse 2012), including the number of alleles per locus (A), the observed and expected heterozygosity levels (Ho and He), the inbreeding coefficient (FIS), and the levels of genetic differentiation (FST). Population genetic structure was examined via STRUCTURE v. 2.3.4 with Bayesian simulation (Pritchard et al. 2000; Falush et al. 2003, 2007). The number of clusters (K) can be inferred without existing sample population data. The program was run for K=1 to 7 clusters with 10 independent runs to assess simulation stability. Each simulation was run for an initial 100,000 burn-in period followed by 1,000,000 replications based on the Markov Chain Monte Carlo (MCMC) method and an ancestry admixture model (Hubisz et al. 2009). STRUCTURE HARVESTER v. 0.6.8 (Earl and vonHoldt 2012) served for determination of the optimal number of clusters with ΔK values (Evanno et al. 2005).

Genotype assignment for blind test:

The predefined genetic reference populations to unknown (blind) samples was assigned by GENECLASS v. 2.0 (Piry et al. 2004). This test calculates the probability of finding individuals belonging to their own populations (Rannala and Mountain 1997) via probability computation and a Monte Carlo re-sampling algorithm (Paetkau et al. 2004). The probability values for each sample were obtained with a statistical support value (>1.0) and a threshold value (<0.05) chosen for population assignment. Unknown individuals are assigned to a specific population, if their probability exceeds the chosen threshold.

Result and discussion

Verification of the DNA extractions data

As mentioned above, wood and leaf DNA extracts were amplified using the chloroplast intergenic spacer between trnL–trnF and then sequenced to evaluate the efficiency of DNA isolation. The PCR results showed that all trnL–trnF sequences of wood and leaf samples have the expected length of ~350 bps (Figure 2). The trnL–trnF sequence amplification was 94.5% and 100% successful in wood and dried leaf samples, respectively. Three PCR products were randomly chosen from the wood samples for sequencing to verify C. kanehirae DNA. After DNA sequencing, a Blast search was performed against the NCBI database, which indicated that these sequences are located in the cpDNA trnL–trnF region and are highly similar to each other within C. kanehirae (EU338495). As a result, C. kanehirae wood is suitable for DNA extraction and future genetic analyses. The average success rate of the PCR microsatellite amplification was 94.7% for wood samples across all microsatellite loci. The range was 80.6–100%. The Cin04 and Cin11 loci had the highest success rates and the Cin02 locus the lowest ones (Table 2). In contrast, PCR amplification was successful for 100% of the leaf samples across all microsatellite loci.

Figure 2: The results of gel electrophoresis of the PCR products obtained using trnL (UAA)3′ exon–trnF (GAA) primers.M, Molecular size markers; W1–W7, wood samples; L1–L4, leaf samples.
Figure 2:

The results of gel electrophoresis of the PCR products obtained using trnL (UAA)3′ exon–trnF (GAA) primers.

M, Molecular size markers; W1–W7, wood samples; L1–L4, leaf samples.

Table 2:

Detailed genetic diversity parameters, discrimination power and probability of identity of Cinnamomum kanehirae determined using 15 microsatellite markers.

LocusAHoHePICHWEPDPIDOrders of PD and PIDSuccessful amplification rate (wood) (%)
Cin0111.0000.2440.7770.785<0.00010.8160.059691.67
Cin025.0000.0790.6780.659<0.00010.7120.1331180.56
Cin0313.0000.3040.7930.783<0.00010.8150.060797.22
Cin0412.0000.9300.8330.8300.00640.8570.0412100.00
Cin055.0000.2750.6440.621<0.00010.6780.1581394.44
Cin069.0000.9400.8140.7930.00650.8270.058597.22
Cin077.0000.5610.8310.802<0.00010.8370.054491.67
Cin0812.0000.9200.8010.777<0.00010.8120.064898.61
Cin0913.0000.5510.8570.843<0.00010.8800.035181.94
Cin105.0000.1160.6680.598<0.00010.6750.1801497.22
Cin115.0000.9320.7660.716<0.00010.7670.10010100.00
Cin126.0000.0950.6870.640<0.00010.7070.1501294.44
Cin138.0000.5060.5970.5730.05850.6110.1881597.22
Cin148.0000.7210.7580.7290.09130.7700.0899100.00
Cin1512.0000.3100.8220.801<0.00010.8330.053398.61
Average8.7330.4990.7550.7300.77394.72
  1. A, Observed average allele number; Ho, observed heterozygosity; He, expected heterozygosity; PIC, polymorphic information content; PD, power of discrimination; PID, probability of identity.

In this study, the trnL–trnF sequence and microsatellite loci amplifications are more successful in leaf than wood samples, possibly because the wood samples have more degraded DNA and PCR inhibitors than do leaf samples (Rachmayanti et al. 2009; Finkeldey et al. 2010). The size of amplified fragment is also a parameter that influences the PCR amplification success rates. Several studies suggest that long DNA fragments are difficult to amplify (Rachmayanti et al. 2006, 2009; Finkeldey et al. 2010). Higher PCR amplification success rates of microsattelite loci were also obtained for C. kanehirae than dipterocarp species, and I. palembanica (Rachmayanti et al. 2009; Lowe et al. 2010). The present study demonstrated that the C. kanehirae wood is a good source of DNA for future genetic studies.

Polymorphisms of 15 microsatellites and population genetic structure analysis

A panel of 128 C. kanehirae samples was genotyped with 15 polymorphic microsatellites. A total of 131 alleles and an average allele number of 8.73 per locus were identified (Table 2). The highest allele number (A=13.00) was detected for Cin03 and Cin09 loci, whereas Cin02, Cin05, Cin10, and Cin11 had the lowest number (A=5.00). The PIC values of the 15 polymorphic microsatellite markers ranged from 0.57–0.84. The Cin09 locus had the highest PIC value and Cin13 the lowest. All microsatellite markers exhibited high polymorphism (PIC>0.50) (Table 2). The observed and expected heterozygosities (Ho and He) ranged from 0.079–0.940 and 0.597–0.857, with means of 0.499 and 0.755, respectively. Heterozygote deficiency was found at all loci except Cin04, Cin06, Cin08, and Cin11 (Table 2). Significant deviations from the Hardy-Weinberg equilibrium (Table 2) were identified in 13 of the 15 microsatellite loci (the exceptions were Cin13 and Cin14).

For the 15 polymorphic microsatellites, genetic diversity parameters including the number of alleles per locus and the observed and expected heterozygosities of five C. kanehirae populations were evaluated (Table 3). Overall, the number of alleles per locus was in the range of 3.333–4.400. The Ho and He levels were in the range of 0.427–0.544 and 0.469–0.650, respectively. The DA population has the highest number of alleles per locus and the NA population the lowest. The ZH population had the highest expected heterozygosity. All populations except LI showed a positive inbreeding coefficient, which indicates a significant deficiency of heterozygosity in these northern Taiwan populations (ZH, DA, NA, and JH). A higher level of genetic differentiation was detected between the LI population in southern Taiwan and the others in northern Taiwan (FST=0.272–0.339, Table 4). Genetic differentiation among the ZH, DA, NA, and JH populations in northern Taiwan was moderate (FST=0.108–0.177). The ZH population showed higher levels of genetic differentiation than the other northern Taiwan populations (DA, NA, and JH; FST=0.145–0.177, Table 4). The population genetic structure of C. kanehirae was revealed by the STRUCTURE software (Figure 3). The ΔK values were computed to obtain the best grouping fit number and there were strong signals for K=2 and 3 (ΔK=24.3333 and 2.2246, Figure 3a). All the individuals analyzed were assigned to two clusters (K=2). The C. kanehirae samples consist most likely of two subpopulations: LI (southern Taiwan) and ZH, DA, NA, and JH (northern Taiwan) (Figure 3b). This result is consistent with that of the genetic differentiation analysis (Table 4). Similarly, at K=3, the southern and northern Taiwan populations were separated from each other. Nevertheless, there is a genetic admixture of components 1 (red segment) and 2 (green segment) involving the ZH, DA, and JH populations. The NA population has a unique genetic composition (red segment) (Figure 3b).

Table 3:

Detailed genetic diversity parameters identified at 15 microsatellite loci in five Cinnamomum kanehirae populations.

PopulationAHoHeFIS
ZH4.0000.5440.5730.043
DA4.4000.4870.6500.308
NA3.3330.4270.4870.196
JH3.8000.4760.5820.221
LI3.4000.4980.469−0.114
Average3.7870.4920.546
  1. A, Observed average allele number; Ho, observed heterozygosity; He, expected heterozygosity; FIS, inbreeding coefficient.

Table 4:

Pairwise FST between Cinnamomum kanehirae populations using 15 microsatellites.

ZHDANAJHLI
ZH0.000
DA0.1450.000
NA0.1770.1190.000
JH0.1680.1080.1560.000
LI0.3240.2720.3390.2900.000
Figure 3: Genetic composition of Cinnamomum kanehirae.(a) The scatter plots of ΔK. (b) The two and three clusters obtained from STRUCTURE analyses with the highest ΔK value.
Figure 3:

Genetic composition of Cinnamomum kanehirae.

(a) The scatter plots of ΔK. (b) The two and three clusters obtained from STRUCTURE analyses with the highest ΔK value.

The number of alleles is an indicator of the level of genetic diversity and is lower than that for other Taiwanese tree species like Fatsia polycarpa Hay. (Chiang et al. 2014), Amentotaxus formosana H.L. Li (Ge et al. 2015), and Pinus massoniana Lamb. (Ge et al. 2012). The lower level of genetic diversity in C. kanehirae is the result of the stochastic losses of genetic polymorphisms. This observation is consistent with the results of Kuo et al. (2010) and Liao et al. (2010) studies. Positive FIS were found in all populations except LI, which is interpreted that inbreeding may have also contributed to the decrement of genetic diversity of C. kanehirae. The negative effect of inbreeding to a reduction in genetic diversity within the population was already pointed out by Charlesworth (2003) and Frankham et al. (2010).

The genetic consequences of population fragmentations depend on the level of gene flow. Both genetic drift and limited gene flows significantly lower the level of genetic diversity within populations, and significant genetic differentiation between populations (Frankham et al. 2010). Higher levels of genetic differentiation were indicated for the five populations of C. kanehirae at the 15 microsatellite loci (FST=0.108–0.339). The highest level of genetic differentiation occurred between the southern LI population and other northern populations (FST=0.272–0.339), whereas the lowest level was detected among northern populations (FST=0.108–0.177) (Table 4). Bayesian clustering of the microsatellite loci suggested group optima for K=2 and 3. These values correspond to the analysis of the genetic differentiation between northern and southern populations (ZH+DA+NA+JH vs. LI). According to the output from STRUCTURE (K=3), the ZH, DA, and JH populations have genetic composition admixtures, in contrary to the other groups. Therefore, the higher gene flow level among the ZH, DA and JH populations resulted in the genetic admixture of individuals (Figure 3). In contrast, it was found that the genetic differentiation was lower in the cpDNA and nrDNA markers than the microsatellite loci, if the southern and northern regions are compared. Microsatellite markers with high mutation rates are highly polymorphic. Their resolution power reveals genetic structure and differentiation caused by recent events within or among populations (Waples 1998; Borrell et al. 2012). In the future, a large number of C. kanehirae populations throughout its whole geographical range in Taiwan will be required to identify their genetic variation and population structure accurately.

Discrimination ability of polymorphic microsatellite markers and blind tests

In this study, all 15 microsatellite loci examined were highly polymorphic. Their mean allele number and discrimination power were 8.73 and 0.77, respectively (Table 2). In all microsatellite loci, the number of alleles per locus ranged from 5.00 to 13.00 and the power of discrimination ranged from 0.61 to 0.88, respectively. Loci with the same allele number may have different discrimination powers, e.g. Cin02, Cin05, Cin10, and Cin11. On the other hand, loci with different allele numbers can have similar discrimination powers, e.g. Cin11 and Cin14. Differences in the frequency of the allele numbers within these loci account for this phenomenon. The PID value is useful and necessary to discriminate and identify the geographical origin of unidentified individuals. PID was evaluated for each locus and it ranged from 0.035 to 0.188. Cin09 had the lowest PID and Cin13 the highest. The cumulative PD, and PID based on all 15 polymorphic microsatellite loci were 0.999 and 5.151×10−17. The order of discrimination power and the optimal combination of microsatellites were estimated from PD and PID (Table 2). Cin09 had the highest discrimination power and Cin13 the lowest. The cumulative PID values were calculated from the optimal combination of microsatellites. A combination of 5 polymorphic microsatellite loci (Cin09+Cin04+Cin15+ Cin07+Cin06) was necessary to reach a PID level of <1×10−6 (Figure 4a). These loci also had the highest allele numbers (A=7.00–13.00), and polymorphisms (PIC=0.793–0.843). If only Cin09 locus were considered, 63 individuals were still non-distinguishable because they shared the same genotype (unique genotypes ratio=50.8%, Figure 4b). Nevertheless, these twelve polymorphic microsatellite loci discriminated all individuals of C. kanehirae (unique genotypes ratio=100%, Figure 4b).

Figure 4: The discrimination power in locus combination.(a) Probability of identity of unrelated individuals (PID). (b) Unique genotypes ratio.
Figure 4:

The discrimination power in locus combination.

(a) Probability of identity of unrelated individuals (PID). (b) Unique genotypes ratio.

The mean polymorphic information content and discrimination power were 0.730 and 0.773, respectively. PID ranged from 0.035–0.188 (Table 2). The cumulative PD and PID indicate that their discrimination ability is high. The microsatellite loci profile used as a reference database in this study is highly efficient for discrimination of individuals, and verification the geographical origin of a C. kanehirae timber. The minimum number of combinations of the 15 microsatellite loci was determined to obtain the numbers of distinguishable genotypes within all samples. The numbers of distinguishable genotypes gradually increased with the number of microsatellite loci combinations evaluated. Maximizing identification sensitivity by consulting the data of more microsatellite loci increases the resolution of individual discrimination (Tessier et al. 1999; Galli et al. 2005; Poetsch et al. 2012). Nevertheless, based on the combinations of nine microsatellite loci, only two individuals could not be distinguished from the others (Figure 4b). By combinations of 10 and 11 microsatellite loci, it was found that Cin11 and Cin02 were the tenth and eleventh most distinguishable and polymorphic loci. The two individuals were distinguished by Cin12. Despite its lower discrimination power, Cin12 (PD=0.707) was preferred and replaced Cin11 (PD=0.767) at step 10. It can be concluded that the ability to discriminate all individuals several microsatellite loci should be combined (Tessier et al. 1999; Galli et al. 2005). In this study, the combinations of 10 microsatellite loci, including Cin12, were able to discriminate effectively among all C. kanehirae individuals.

The assignment method applied in the present study considers highly variable microsatellites, which are known since a long time to be effective in terms of classifying unknown samples according to their true sources (Cornuet et al. 1999; Boitard et al. 2010; Li et al. 2014). Microsatellite allele numbers, polymorphisms, and genetic differentiation levels affect assignment method accuracy (Bjørnstad and Røed 2002; Guinand et al. 2006; Li et al. 2014). Generally, genotype assignment is very accurate when the genetic structure is strong and the migration rate between populations is low. Nevertheless, genotype assignment to track true origin increases in accuracy as genetic differentiation among populations is high (Cornuet et al. 1999; Jones and Wang 2012; Degen et al. 2013). The highly polymorphic microsatellite loci and clear genetic structures among the populations/species in this study are powerful and effective for future populations/species assignments (Table 5). This method successfully traced the true geographical origins of blind wood samples 1–10. The probabilities that samples 1 and 6 were assigned to their true origins, however, were lower than those for the others (Table 5). In fact, the southern LI population and certain northern populations were better differentiated genetically than the other northern populations (Table 4). Therefore, the matching probabilities for samples 1 and 2 are higher with their true source population (LI). The probabilities were zero for other populations. Blind wood samples 3–10 from the northern populations had the highest match probabilities for their true source population and lower probabilities for the others (Table 5) due to lower genetic differentiation among them. Blind wood samples 11 and 12, though, are both C. camphora (L.) J. Presl and are closely related to C. kanehirae. The zero match probabilities demonstrate that these 15 microsatellites could also be used for distinguishing C. kanehirae and C. camphora. They could be applied in forensic applications in terms of distinguishing illegal and legal logged C. kanehirae timbers. In the future, more microsatellites or individuals of populations will be added to the genetic reference database to improve assignment accuracy.

Table 5:

Results of the assignment test for 12 blind wood samples.

Assigned sampleOriginConclusionTRUE origin
ZH ProbabilityDA ProbabilityNA ProbabilityJH ProbabilityLI Probability
10.0000.0000.0000.0000.654LILI
20.0000.0000.0000.0000.904LILI
30.0050.9580.0420.0660.000DADA
40.0120.8340.0020.0140.000DADA
50.0070.1950.7990.0600.000NANA
60.0000.1240.5810.1790.000NANA
70.0000.0000.0000.9200.000JHJH
80.0120.0010.0000.9970.000JHJH
90.9840.0170.0010.0180.000ZHZH
100.9840.0150.0020.0000.000ZHZH
110.0000.0000.0000.0000.000C. camphora
120.0000.0000.0000.0000.000C. camphora
  1. The five populations of Cinnamomum kanehirae studied were treated as a genetic reference database.

Conclusions

The 15 microsatellites in this study with high polymorphism and discrimination power were shown to be effective markers with high potential for population genetics and forensic applications of C. kanehirae timber. The tested approach was able to discriminate individuals and to verify their geographical origins. Low genetic variation and high genetic differentiation was found in case of C. kanehirae. To conserve C. kanehirae and prevent loss of its genetic diversity, its natural habitats must be protected. At least 10 microsatellite markers were required to maximize the discrimination of all individuals. With this approach, all unknown wood samples could be assigned in blind tests to their true population origin.

Acknowledgments

This work was supported by the Hsinchu Forest District Office, Forestry Bureau, Taiwan (101B098-E9).

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Received: 2017-2-13
Accepted: 2017-5-23
Published Online: 2017-6-24
Published in Print: 2017-10-26

©2017 Walter de Gruyter GmbH, Berlin/Boston

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