DNA barcoding of vouchered xylarium wood specimens of nine endangered Dalbergia species

Opening the black box: explainable deep-learning classification of wood microscopic image of endangered tree species

BACKGROUND

Traditional method of wood species identification involves the use of hand lens by wood anatomists, which is a time-consuming method that usually identifies only at the genetic level. Computer vision method can achieve "species" level identification but cannot provide an explanation on what features are used for the identification. Thus, in this study, we used computer vision methods coupled with deep learning to reveal interspecific differences between closely related tree species.

RESULT

A total of 850 images were collected from the cross and tangential sections of 15 wood species. These images were used to construct a deep-learning model to discriminate wood species, and a classification accuracy of 99.3% was obtained. The key features between species in machine identification were targeted by feature visualization methods, mainly the axial parenchyma arrangements and vessel in cross section and the wood ray in tangential section. Moreover, the degree of importance of the vessels of different tree species in the cross-section images was determined by the manual feature labeling method. The results showed that vessels play an important role in the identification of Dalbergia, Pterocarpus, Swartzia, Carapa, and Cedrela, but exhibited limited resolutions on discriminating Swietenia species.

CONCLUSION

The research results provide a computer-assisted tool for identifying endangered tree species in laboratory scenarios, which can be used to combat illegal logging and related trade and contribute to the implementation of CITES convention and the conservation of global biodiversity.

Retrieving complete plastid genomes of endangered Guibourtia timber using hybridization capture for forensic identification and phylogenetic analysis

The high economic value and increased demand for timber have led to illegal logging and overexploitation, threatening wild populations. In this context, there is an urgent need to develop effective and accurate forensic tools for identifying endangered Guibourtia timber species to protect forest ecosystem resources and regulate their trade. In this study, a hybridization capture method was developed and applied to explore the feasibility of retrieving complete plastid genomes from Guibourtia sapwood and heartwood specimens stored in a xylarium (wood collection). We then carried out forensic identification and phylogenetic analyses of Guibourtia within the subfamily Detarioideae. This study is the first to successfully retrieve high-quality plastid genomes from xylarium specimens, with 76.95-99.97% coverage. The enrichment efficiency, sequence depth, and coverage of plastid genomes from sapwood were 16.73 times, 70.47 times and 1.14 times higher, respectively, than those from heartwood. Although the DNA capture efficiency of heartwood was lower than that of sapwood, the hybridization capture method used in this study is still suitable for heartwood DNA analysis. Based on the complete plastid genome, we identified six endangered or commonly traded Guibourtia woods at the species level. This technique also has the potential for geographical traceability, especially for Guibourtia demeusei and Guibourtia ehie. Meanwhile, Bayesian phylogenetic analysis suggested that these six Guibourtia species diverged from closely related species within the subfamily Detarioideae ca. 18 Ma during the Miocene. The DNA reference database established based on the xylarium specimens provides admissible evidence for diversity conservation and evolutionary analyses of endangered Guibourtia species.

The DNA barcode identification of Dalbergia odorifera T. Chen and Dalbergia tonkinensis Prain

BACKGROUND

Dalbergia odorifera is a precious tree species with unique economic and medicinal values, which is difficult to distinguish from Dalbergia tonkinensis by traditional identification methods such as morphological characteristics and wood structure characteristics. It has been demonstrated that the identification of tree species can be effectively achieved using DNA barcoding, but there is a lack of study of the combined sequences used as DNA barcodes in the two tree species. In this study, 10 single sequences and 4 combined sequences were selected for analysis, and the identification effect of each sequence was evaluated by the distance-based method, BLAST-based search, character-based method, and tree-based method.

RESULTS

Among the single sequences and the combined sequences, the interspecies distance of trnH-psbA and ITS2 + trnH-psbA was greater than the intraspecies distance, and there was no overlap in their frequency distribution plots. The results of the Wilcoxon signed-rank test for the interspecies distance of each sequence showed that the interspecies differences of the single sequences except trnL-trnF, trnH-psbA, and ycf3 were significantly smaller than those of the combined sequences. The results of BLAST analysis showed that trnH-psbA could accurately identify D. odorifera and D. tonkinensis at the species level. In the character-based method, single sequences of trnL-trnF, trnH-psbA with all the combined sequences can be used for the identification of D. odorifera and D. tonkinensis. In addition, the neighbor-joining (NJ) trees constructed based on trnH-psbA and ITS2 + trnH-psbA were able to cluster D. odorifera and D. tonkinensis on two clades.

CONCLUSIONS

The results showed that the character-based method with the BLOG algorithm was the most effective among all the evaluation methods, and the combined sequences can improve the ability to identify tree species compared with single sequences. Finally, the trnH-psbA and ITS2 + trnH-psbA were proposed as DNA barcodes to identify D. odorifera and D. tonkinensis.

Screening of potential chemical marker with interspecific differences in Pterocarpus wood and a spatially-resolved approach to visualize the distribution of the characteristic markers

Profiling the spatial distributions and tissue changes of characteristic compounds with interspecific differences is critical to elucidate the complex species identification during tree species traceability, wood anti-counterfeiting verification and timber trade control. In this research, in order to visualize the spatial position of characteristic compounds in two species with similar morphology (Pterocarpus santalinus and Pterocarpus tinctorius), a high coverage MALDI-TOF-MS imaging method was used to found the mass spectra fingerprints of different wood species. 2-Mercaptobenzothiazole matrix was used to spray wood tissue section to enhance the detection effect of metabolic molecules, and the mass spectrometry imaging data were obtained. Based on this technology, the spatial location of fifteen potential chemical markers with remarkable interspecific differences in 2 Pterocarpus timber species were successfully obtained. Distinct chemical signatures obtained from this method can promote rapid identification at the wood species level. Thus, matrix-assisted laser desorption/time-of-flight/ionization mass spectrometry imaging (MALDI-TOF-MSI) provides a spatial-resolved way for traditional wood morphological classification and breaking through the limitations of traditional wood identification technology.

Comparison of DNA extraction methods on CITES-listed timber species and application in species authentication of commercial products using DNA barcoding

Quality and quantity of DNA extracted from wood is important for molecular identification of wood species, which can serve for conservation of wood species and law enforcement to combat illegal wood trading. Rosewood (Dalbergia and Pterocarpus) and agarwood (Aquilaria) are the most commonly found hardwood in timber seizure incidents. To monitor international trade of timber and commercial wood products and to protect these endangered wood species from further population decline, in this study, we have compared three extraction protocols for DNA extraction from 12 samples of rosewood and agarwood timber logs, and later applied the best DNA extraction protocol on 10 commercial wood products claimed to be rosewood and agarwood. We also demonstrated the applicability of DNA mini-barcoding with multi-loci combination with reference library for identifying the species of timber and commercial wood products. We found that a silica column-based method with guanidine thiocyanate-containing binding buffer served the best in DNA extraction from different parts of wood in all three genera with good quality and quantity. Single barcode region ITS2 or multi-loci combinations including ITS2 barcode region generally provide better discriminatory power for species identification for both rosewood and agarwood. All 10 products were identified to species-level using multi-loci combination. In terms of accuracy in labelling, 80% of them were labelled correctly. Our work has shown the feasibility of extracting good quality of DNA from authentic wood samples and processed wood products and identifying them to species level based on DNA barcoding technology.

Rapid Identification for the Pterocarpus Bracelet by Three-Step Infrared Spectrum Method

In order to explore a rapid identification method for the anti-counterfeit of commercial high value collections, a three-step infrared spectrum method was used for the pterocarpus collection identification to confirm whether a commercial pterocarpus bracelet (PB) was made from the precious species of Pterocarpus santalinus (P. santalinus). In the first step, undertaken by Fourier transform infrared spectroscopy (FTIR) spectrum, the absorption peaks intensity of PB was slightly higher than that of P. santalinus only at 1594 cm⁻¹, 1205 cm⁻¹, 1155 cm⁻¹ and 836 cm⁻¹. In the next step of second derivative IR spectra (SDIR), the FTIR features of the tested samples were further amplified, and the peaks at 1600 cm⁻¹, 1171 cm⁻¹ and 1152 cm⁻¹ become clearly defined in PB. Finally, by means of two-dimensional correlation infrared (2DIR) spectrum, it revealed that the response of holocellulose to thermal perturbation was stronger in P. santalinus than that in PB mainly at 977 cm⁻¹, 1008 cm⁻¹, 1100 cm⁻¹, 1057 cm⁻¹, 1190 cm⁻¹ and 1214 cm⁻¹, while the aromatic functional groups of PB were much more sensitive to the thermal perturbation than those of P. santalinus mainly at 1456 cm⁻¹, 1467 cm⁻¹, 1518 cm⁻¹, 1558 cm⁻¹, 1576 cm⁻¹ and 1605 cm⁻¹. In addition, fluorescence microscopy was used to verify the effectiveness of the above method for wood identification and the results showed good consistency. This study demonstrated that the three-step IR method could provide a rapid and effective way for the anti-counterfeit of pterocarpus collections.

Toehold switch based biosensors for sensing the highly trafficked rosewood Dalbergia maritima

Nucleic acid sensing is a 3 decades old but still challenging area of application for different biological sub-domains, from pathogen detection to single cell transcriptomics analysis. The many applications of nucleic acid detection and identification are mostly carried out by PCR techniques, sequencing, and their derivatives used at large scale. However, these methods’ limitations on speed, cost, complexity and specificity have motivated the development of innovative detection methods among which nucleic acid biosensing technologies seem promising. Toehold switches are a particular class of RNA sensing devices relying on a conformational switch of secondary structure induced by the pairing of the detected trigger RNA with a de novo designed synthetic sensing mRNA molecule. Here we describe a streamlined methodology enabling the development of such a sensor for the RNA-mediated detection of an endangered plant species in a cell-free reaction system. We applied this methodology to help identify the rosewood Dalbergia maritima, a highly trafficked wood, whose protection is limited by the capacity of the authorities to distinguish protected logs from other unprotected but related species. The streamlined pipeline presented in this work is a versatile framework enabling cheap and rapid development of new sensors for custom RNA detection.

Comparative Analysis of Chloroplast Genomes of Dalbergia Species for Identification and Phylogenetic Analysis

Dalbergia L.f. is a pantropical genus consisting of 269 species of trees, shrubs, and woody lianas. This genus is listed in CITES Appendices because of illegal logging and trafficking driven by the high economic value of its heartwood. Some species are also used medicinally. Species identification of Dalbergia timber and herbs is challenging but essential for CITES implementation. Molecular methods had been developed for some timber species, mostly from Madagascar and Southeast Asia, but medicinal species in south China were usually not included in those studies. Here, we sequenced and assembled the chloroplast genomes of five Dalbergia species native to Hong Kong, four of which are medicinal plants. Our aim is to find potential genetic markers for the identification of medicinal Dalbergia species based on divergence hotspots detected in chloroplast genomes after comparative and phylogenetic analysis. Dalbergia chloroplast genomes displayed the typical quadripartite structure, with the 50 kb inversion found in most Papilionoideae lineages. Their sizes and gene content are well conserved. Phylogenetic tree of Dalbergia chloroplast genomes showed an overall topology similar to that of ITS sequences. Four divergence hotspots (trnL(UAA)-trnT(UGU), ndhG-ndhI, ycf1a and ycf1b) were identified and candidate markers for identification of several Dalbergia species were suggested.

Assessing candidate DNA barcodes for Chinese and internationally traded timber species

Accurate identification of species from timber is an essential step to help control illegal logging and forest loss. However, current approaches to timber identification based on morphological and anatomical characteristics have limited species resolution. DNA barcoding is a proven tool for plant species identification, but there is a need to build reliable reference data across broad taxonomic and spatial scales. Here, we construct a species barcoding library consisting of 1550 taxonomically diverse timber species from 656 genera and 124 families, representing a comprehensive genetic reference data set for Chinese timber species and international commercial traded timber species, using four barcodes (rbcL, matK, trnH-psbA, and ITS2). The ITS2 fragment was found to be the most efficient locus for Chinese timber species identification among the four barcodes tested, both at the species and genus level, despite its low recovery rate. Nevertheless, the barcode combination matK+trnH-psbA+ITS2 was required as a complementary barcode to distinguish closely related species in complex data sets involving internationally traded timber species. Comparative analyses of family-level discrimination and species/genus ratios indicated that the inclusion of closely related species is an important factor affecting the resolution ability of barcodes for timber species verification. Our study indicates that although nuclear ITS2 is the most efficient single barcode for timber species authentication in China, complementary combinations like matK+trnH-psbA+ITS2 are required to provide broader discrimination power. These newly-generated sequences enrich the existing publicly available databases, especially for tropical and subtropical evergreen timber trees and this current timber species barcode reference library can serve as an important genetic resource for forestry monitoring, illegal logging prosecution and biodiversity projects.

rbcL, a potential candidate DNA barcode loci for aconites: conservation of himalayan aconites

BACKGROUND

Aconitum heterophyllum Wall. ex Royle and Aconitum balfourii Stapf, are two highly important, threatened medicinal plants of the Indian Himalayan Region. Root-tubers of Aconites have occupied an important place in Indian pharmacopoeia from very ancient times. India is a hub of the wild-collected medicinal herbs industry in Asia and these two aconites are known to have been heavily traded from the region in illicit manner. Prosecution of these illegal trading crimes is hampered by lack of pharma-forensic expertise and tools.

METHODS AND RESULTS

Present study was conducted to evaluate the discriminatory potential of rbcL, a Chloroplast based DNA barcode marker for the authentication of these two Himalayan Aconites. Fresh plant samples were collected from their natural distributional range as well as raw materials were procured from herbal market and a total of 32 sequences were generated for the rbcL region. Analysis demonstrated that rbcL region can successfully be used for authentication and importantly, both the aconites, were successfully discriminated by rbcL locus with high bootstrap support (> 50%).

CONCLUSION

Molecular markers could certainly be relied upon morphological and chemical markers being tissue specific, having a higher discriminatory power and not age dependent. Phylogenetic analysis using Maximum Likelihood Method revealed that the rbcL gene could successfully discriminate Himalayan Aconites to species level and have potential to be used in pharma-forensic applications as well as to curb illicit trade of these invaluable medicinal plants.

Traditional System Versus DNA Barcoding in Identification of Bamboo Species: A Systematic Review

Bamboo, a gramineous plant belonging to the family Poaceae, comprises of 1575 species from 116 genera across the globe. It has the ability to grow and evolve on degraded land and hence, can be utilized in the various applications as an alternative for plastic and wood. DNA barcoding, a long genomic sequence, identifies barcode region which shows species-specific nucleotide differences. This technology is considered as advanced molecular technique utilized for characterization and classification of the various species by applying distinctive molecular markers. Recent investigations revealed the potential application of various barcode regions such as matK, rbcL, rpoB, rpoC1, psbA-trnH, and ITS2, in identification of many bamboo species from different genus. In this review we comprehensively discussed the relevance of DNA barcoding as a tool in classification/identification of various bamboo species. We highlighted the methodology, how this advance technology overcomes the challenges associated with traditional methods along with prospects for future research.

A computer-aided method for identifying the presence of softwood growth ring boundaries

The objective of this study was to develop a computer-aided method to quantify the obvious degree of growth ring boundaries of softwood species, based on data analysis with some image processing technologies. For this purpose, a 5× magnified cross-section color micro-image of softwood was cropped into 20 sub-images, and then every image was binarized as a gray image according to an automatic threshold value. After that, the number of black pixels in the gray image was counted row by row and the number of black pixels was binarized to 0 or 100. Finally, a transition band from earlywood to latewood on the sub-image was identified. If everything goes as planned, the growth ring boundaries of the sub-image would be distinct. Otherwise would be indistinct or absent. If more than 50% sub-images are distinct, with the majority voting method, the growth ring boundaries of softwood would be distinct, otherwise would be indistinct or absent. The proposed method has been visualized as a growth-ring-boundary detecting system based on the .NET Framework. A sample of 100 micro-images (see S1 Fig via https://github.com/senly2019/Lin-Qizhao/) of softwood cross-sections were selected for evaluation purposes. In short, this detecting system computes the obvious degree of growth ring boundaries of softwood species by image processing involving image importing, image cropping, image reading, image grayscale, image binarization, data analysis. The results showed that the method used avoided mistakes made by the manual comparison method of identifying the presence of growth ring boundaries, and it has a high accuracy of 98%.

Changes in Non-Structural Carbohydrates, Wood Properties and Essential Oil During Chemically-Induced Heartwood Formation in Dalbergia odorifera

The highly valuable heartwood of Dalbergia odorifera T. Chen, known as Jiang Xiang in traditional Chinese medicine, is formed very slowly, and there is a need to better understand the process and promote heartwood formation. Chemical induction is considered to be one of the promising methods to induce heartwood formation. However, to date no method has been proved effective for D. odorifera as little is known about biochemical and physiological changes during heartwood development. Three potential heartwood induction substances viz. acetic acid, sodium chloride, and hydrogen peroxide solutions were injected into the trunk of D. odorifera to determine the effect on heartwood formation and physiological activity. Non-structural carbohydrates, lipids, wood properties, and essential oil were assessed in the post-treatment period. As also observed in the formation of natural heartwood, chemical-induced Jiang Xiang production was accompanied by sapwood dehydration, non-structural carbohydrates consumption, and synthesis of heartwood substances. As the heartwood substances accumulated, basic density and essential oil content increased gradually, thereby Jiang Xiang was finally produced. In this process, physiological parameters of discolored sapwood gradually evolved to resemble those of natural heartwood. Hydrogen peroxide-induced Jiang Xiang was closest to natural heartwood, and the essential oil components met the standards for high-quality Jiang Xiang, while the induction effects of acetic acid and sodium chloride were unsatisfactory. Thus, this study indicates that hydrogen peroxide has the potential to induce Jiang Xiang production in Dalbergia odorifera.

DNA barcodes for the identification of Stephania (Menispermaceae) species

Stephania is a medicinal plants-rich genus of Menispermaceae. However, the identification of morphologically-similar species in Stephania is difficult using the currently reported methods. The indiscriminate overexploitation of Stephania plants has resulted in clinical misuse and endangerment of many species, which necessitates the development of an efficient and reliable method for species authentication. Therefore, six candidate DNA barcode sequences (ITS, ITS2, psbA-trnH, matK, rbcL, and trnL-F) were tested for their capacity to identify Stephania species. The barcodes were analyzed either as a single region or in combination by tree-based [neighbor-joining (NJ) and Bayesian inference (BI)], distance-based (PWG-distance), and sequence similarity-based (TaxonDNA) methods. Amplification and sequencing success rates were 100% for all six candidate barcodes. A comparison of six barcode regions showed that ITS exhibited the highest number of variable and informative sites (182/179), followed by psbA-trnH (173/162). DNA barcoding gap assessment showed that interspecific distances of the six barcodes were greater than intraspecific distances. The identification results showed that species discrimination rates of combination barcodes were higher than those of single-region barcodes. Based on best match and best close match methods, the ITS+psbA-trnH combination exhibited the highest discrimination power (93.93%). Further, all Stephania species could be resolved in the phylogenetic trees based on ITS+psbA-trnH (NJ, BI). This study demonstrates that DNA barcoding is an efficient method to identify Stephania species and recommends that the ITS+psbA-trnH combination is the best DNA barcode for the identification of Stephania species.

Characterization of the complete chloroplast genome sequence of Dalbergia species and its phylogenetic implications

The pantropical plant genus Dalbergia comprises approximately 250 species, most of which have a high economic and ecological value. However, these species are among the most threatened due to illegal logging and the timber trade. To enforce protective legislation and ensure effective conservation of Dalbergia species, the identity of wood being traded must be accurately validated. For the rapid and accurate identification of Dalbergia species and assessment of phylogenetic relationships, it would be highly desirable to develop more effective DNA barcodes for these species. In this study, we sequenced and compared the chloroplast genomes of nine species of Dalbergia. We found that these chloroplast genomes were conserved with respect to genome size, structure, and gene content and showed low sequence divergence. We identified eight mutation hotspots, namely, six intergenic spacer regions (trnL-trnT, atpA-trnG, rps16-accD, petG-psaJ, ndhF-trnL, and ndhG-ndhI) and two coding regions (ycf1a and ycf1b), as candidate DNA barcodes for Dalbergia. Phylogenetic analyses based on whole chloroplast genome data provided the best resolution of Dalbergia, and phylogenetic analysis of the Fabaceae showed that Dalbergia was sister to Arachis. Based on comparison of chloroplast genomes, we identified a set of highly variable markers that can be developed as specific DNA barcodes.

Machine Learning Models with Quantitative Wood Anatomy Data Can Discriminate between Swietenia macrophylla and Swietenia mahagoni

Illegal logging and associated trade aggravate the over-exploitation of Swietenia species, of which S. macrophylla King, S. mahagoni (L.) Jacq, and S. humilis Zucc. have been listed in Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) Appendix Ⅱ. Implementation of CITES necessitates the development of efficient forensic tools to identify wood species accurately, and ideally ones readily deployable in wood anatomy laboratories across the world. Herein, a method using quantitative wood anatomy data in combination with machine learning models to discriminate between three Swietenia species is presented, in addition to a second model focusing only on the two historically more important species S. mahagoni and S. macrophylla. The intra- and inter-specific variations in nine quantitative wood anatomical characters were measured and calculated based on 278 wood specimens, and four machine learning classifiers—Decision Tree C5.0, Naïve Bayes (NB), Support Vector Machine (SVM), and Artificial Neural Network (ANN)—were used to discriminate between the species. Among these species, S. macrophylla exhibited the largest intraspecific variation, and all three species showed at least partly overlapping values for all nine characters. SVM performed the best of all the classifiers, with an overall accuracy of 91.4% and a per-species correct identification rate of 66.7%, 95.0%, and 80.0% for S. humilis, S. macrophylla, and S. mahagoni, respectively. The two-species model discriminated between S. macrophylla and S. mahagoni with accuracies of over 90.0% using SVM. These accuracies are lower than perfect forensic certainty but nonetheless demonstrate that quantitative wood anatomy data in combination with machine learning models can be applied as an efficient tool to discriminate anatomically between similar species in the wood anatomy laboratory. It is probable that a range of previously anatomically inseparable species may become identifiable by incorporating in-depth analysis of quantitative characters and appropriate statistical classifiers.

Fraud and misrepresentation in retail forest products exceeds U.S. forensic wood science capacity

Fraud and misrepresentation in forest products supply chains is often associated with illegal logging, but the extent of fraud in the U.S. forest products market, and the availability of forensic expertise to detect it, is unknown. We used forensic wood anatomy to test 183 specimens from 73 consumer products acquired from major U.S. retailers, surveyed U.S. experts regarding their forensic wood anatomy capacity, and conducted a proficiency-testing program of those experts. 62% of tested products (45 of 73) had one or more type of fraudulent or misrepresented claim. Survey respondents reported a total capacity of 830 wood specimens per year, and participants' identification accuracy ranged from 6% to 92%. Given the extent of fraud and misrepresentation, U.S. wood forensic wood anatomy capacity does not scale with the need for such expertise. We call for increased training in forensic wood anatomy and its broader application in forest products supply chains to eliminate fraud and combat illegal logging.

A strategy for developing high-resolution DNA barcodes for species discrimination of wood specimens using the complete chloroplast genome of three Pterocarpus species

A method for extraction of wood DNA and a strategy for designing high-resolution barcodes for wood were developed. Ycf1b was the prioritized barcode to resolve the Pterocarpus wood species studied. DNA barcoding, an effective tool for wood species identification, mainly focuses on universal barcodes and often lacks high resolution to differentiate species, especially for closely related taxa within the same genus. Therefore, more highly informative DNA barcodes need to be identified. This study is the first to report a strategy for developing specific DNA barcodes of wood tissues. The complete chloroplast genomes of leaf samples of three Pterocarpus species, i.e., P. indicus, P. santalinus, and P. tinctorius, were sequenced, and thereafter, the most variable DNA regions were identified on the scale of the complete chloroplast genomes. Finally, wood DNA was extracted from 30 wood specimens of the three Pterocarpus species, and DNA recovery rates of the selected regions were tested for applicability to verification on the wood specimens studied. The seven regions with the most variation (rpl32-ccsA, rpl20-clpP, trnC-rpoB, ycf1b, accD-ycf4, ycf1a, and psbK-accD) were identified from the chloroplast genome by quantifying nucleotide diversity (Pi > 0.02), which was remarkably higher than that of the plant universal barcodes (rbcL, matK, and trnH-psbA) and the previously reported barcodes (ndhF-rpl32 and trnL-F) used for phylogenetic analysis in Pterocarpus. After comprehensive evaluation of species discrimination ability and applicability, the ycf1b region performed well in terms of the recovery success rate (76.7%) and species identification (100%) for wood specimens of the three Pterocarpus species, and was identified as the preferred high-resolution chloroplast barcode for selected Pterocarpus species. It will offer technical support for curbing illegal timber harvesting activities and for conserving endangered and valuable wood species.

Machine learning approaches outperform distance- and tree-based methods for DNA barcoding of Pterocarpus wood

Machine-learning approaches (MLAs) for DNA barcoding outperform distance- and tree-based methods on identification accuracy and cost-effectiveness to arrive at species-level identification of wood. DNA barcoding is a promising tool to combat illegal logging and associated trade, and the development of reliable and efficient analytical methods is essential for its extensive application in the trade of wood and in the forensics of natural materials more broadly. In this study, 120 DNA sequences of four barcodes (ITS2, matK, ndhF-rpl32, and rbcL) generated in our previous study and 85 downloaded from National Center for Biotechnology Information (NCBI) were collected to establish a reference data set for six commercial Pterocarpus woods. MLAs (BLOG, BP-neural network, SMO and J48) were compared with distance- (TaxonDNA) and tree-based (NJ tree) methods based on identification accuracy and cost-effectiveness across these six species, and also were applied to discriminate the CITES-listed species Pterocarpus santalinus from its anatomically similar species P. tinctorius for forensic identification. MLAs provided higher identification accuracy (30.8-100%) than distance- (15.1-97.4%) and tree-based methods (11.1-87.5%), with SMO performing the best among the machine learning classifiers. The two-locus combination ITS2 + matK when using SMO classifier exhibited the highest resolution (100%) with the fewest barcodes for discriminating the six Pterocarpus species. The CITES-listed species P. santalinus was discriminated successfully from P. tinctorius using MLAs with a single barcode, ndhF-rpl32. This study shows that MLAs provided higher identification accuracy and cost-effectiveness for forensic application over other analytical methods in DNA barcoding of Pterocarpus wood.

The newly developed single nucleotide polymorphism (SNP) markers for a potentially medicinal plant, Crepidiastrum denticulatum (Asteraceae), inferred from complete chloroplast genome data

Medicinal effects of Crepidiastrum denticulatum have been previously reported. However, the genomic resources of this species and its applications have not been studied. In this study, based on the next generation sequencing method (Miseq sequencing system), we characterize the chloroplast genome of C. denticulatum which contains a large single copy (84,112 bp) and a small single copy (18,519 bp), separated by two inverted repeat regions (25,074 bp). This genome consists of 80 protein-coding gene, 30 tRNAs, and four rRNAs. Notably, the trnT_GGU is pseudogenized because of a small insertion within the coding region. Comparative genomic analysis reveals a high similarity among Asteraceae taxa. However, the junctions between LSC, SSC, and IRs locate in different positions within rps19 and ycf1 among examined species. Also, we describe a newly developed single nucleotide polymorphism (SNP) marker for C. denticulatum based on amplification-refractory mutation system (ARMS) technique. The markers, inferred from SNP in rbcL and matK genes, show effectiveness to recognize C. denticulatum from other related taxa through simple PCR protocol. The chloroplast genome-based molecular markers are effective to distinguish a potentially medicinal species, C. denticulatum, from other related taxa. Additionally, the complete chloroplast genome of C. denticulatum provides initial genomic data for further studies on phylogenomics, population genetics, and evolutionary history of Crepidiastrum as well as other taxa in Asteraceae.

A GC-MS Protocol for Separating Endangered and Non-endangered Pterocarpus Wood Species

Pterocarpus santalinus and Pterocarpus tincorius are commonly used traded timber species of the genus Pterocarpus. P. santalinus has been listed in Appendix II of the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES). As a non-CITES species, P. tincorius is also indiscriminately labeled as P. santalinus due to the similar macroscopic and microscopic features with P. santalinus. In order to understand the molecular discrimination between these easily confused species, xylarium heartwoods of these two species were extracted by three different kinds of solvents and analyzed using gas chromatography–mass spectrometry (GC-MS). Multivariate analyses were also applied for the selection of marker compounds that are distinctive between P. santalinus and P. tincorius. A total of twenty volatile compounds were detected and tentatively identified in three kinds of extracts, and these compounds included alcohols, stilbenoids, esters, aromatic hydrocarbons, ketones, miscellaneous, phenols, and flavonoids. GC-MS analyses also revealed that extraction solvents including ethanol and water (EW), ethyl acetate (EA), and benzene–ethanol (BE) gave the best chemotaxonomical discrimination in the chemical components and relative contents of the two Pterocarpus species. After chemometric analyses, EW displayed higher predictive accuracy (100%) than those of EA extract (83.33%) and BE extract (83.33%). Furthermore, spathulenol (17.58 min) and pterostilbene (23.65 min) were elucidated as the critical compounds for the separation of the EW extracts of P. santalinus and P. tinctorius. Thus, a protocol of GC-MS and multivariate analyses was developed to use for successfully distinguishing P. santalinus from P. tinctorius.

Alignment-free genome comparison enables accurate geographic sourcing of white oak DNA

Background

The application of genomic data and bioinformatics for the identification of restricted or illegally-sourced natural products is urgently needed. The taxonomic identity and geographic provenance of raw and processed materials have implications in sustainable-use commercial practices, and relevance to the enforcement of laws that regulate or restrict illegally harvested materials, such as timber. Improvements in genomics make it possible to capture and sequence partial-to-complete genomes from challenging tissues, such as wood and wood products.

Results

In this paper, we report the success of an alignment-free genome comparison method, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {d}_2^{\ast }, $$\end{document}d2∗, that differentiates different geographic sources of white oak (Quercus) species with a high level of accuracy with very small amount of genomic data. The method is robust to sequencing errors, different sequencing laboratories and sequencing platforms.

Conclusions

This method offers an approach based on genome-scale data, rather than panels of pre-selected markers for specific taxa. The method provides a generalizable platform for the identification and sourcing of materials using a unified next generation sequencing and analysis framework.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-5253-1) contains supplementary material, which is available to authorized users.