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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.

Made available in DSpace on 2019-10-04T12:13:41Z (GMT). No. of bitstreams: 0 Previous issue date: 2019-05-01 National Natural Science Foundation of China National High-level Talent for Special Support Program of China China Scholarship Council Main conclusion 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-rp132, 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-rp132. 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. Chinese Acad Forestry, Chinese Res Inst Wood Ind, Dept Wood Anat & Utilizat, Beijing 100091, Peoples R China Chinese Acad Forestry, Wood Collect WOODPEDIA, Beijing 100091, Peoples R China US Forest Serv, Forest Prod Lab, Ctr Wood Anat Res, USDA, Madison, WI 53726 USA Univ Wisconsin, Dept Bot, Madison, WI 53706 USA Purdue Univ, Dept Forestry & Natl Resources, W Lafayette, IN 47907 USA Univ Estadual Paulista, Ciencias Biol Bot, Botucatu, SP, Brazil Univ Estadual Paulista, Ciencias Biol Bot, Botucatu, SP, Brazil National Natural Science Foundation of China: 31600451 National High-level Talent for Special Support Program of China: W02020331 China Scholarship Council: 2017-3109

Made available in DSpace on 2018-11-26T15:45:37Z (GMT). No. of bitstreams: 0 Previous issue date: 2017-12-01 China Postdoctoral Science Foundation National Natural Science Foundation of China Fundamental Research Funds of Chinese Academy of Forestry China Scholarship Council U.S. State Department Interagency Agreement ITS2+ trnH - psbA was the best combination of DNA barcode to resolve the Dalbergia wood species studied. We demonstrate the feasibility of building a DNA barcode reference database using xylarium wood specimens. The increase in illegal logging and timber trade of CITES-listed tropical species necessitates the development of unambiguous identification methods at the species level. For these methods to be fully functional and deployable for law enforcement, they must work using wood or wood products. DNA barcoding of wood has been promoted as a promising tool for species identification; however, the main barrier to extensive application of DNA barcoding to wood is the lack of a comprehensive and reliable DNA reference library of barcodes from wood. In this study, xylarium wood specimens of nine Dalbergia species were selected from the Wood Collection of the Chinese Academy of Forestry and DNA was then extracted from them for further PCR amplification of eight potential DNA barcode sequences (ITS2, matK, trnL, trnH-psbA, trnV-trnM1, trnV-trnM2, trnC-petN, and trnS-trnG). The barcodes were tested singly and in combination for species-level discrimination ability by tree-based [neighbor-joining (NJ)] and distance-based (TaxonDNA) methods. We found that the discrimination ability of DNA barcodes in combination was higher than any single DNA marker among the Dalbergia species studied, with the best two-marker combination of ITS2+trnH-psbA analyzed with NJ trees performing the best (100% accuracy). These barcodes are relatively short regions (< 350 bp) and amplification reactions were performed with high success (ae90%) using wood as the source material, a necessary factor to apply DNA barcoding to timber trade. The present results demonstrate the feasibility of using vouchered xylarium specimens to build DNA barcoding reference databases. Chinese Acad Forestry, Chinese Res Inst Wood Ind, Dept Wood Anat & Utilizat, Beijing 100091, Peoples R China Chinese Acad Forestry, Wood Collect WOODPEDIA, Beijing 100091, Peoples R China US Forest Serv, Ctr Wood Anat Res, USDA, Forest Prod Lab, Madison, WI 53726 USA Univ Wisconsin, Dept Bot, Madison, WI 53706 USA Purdue Univ, Dept Forestry & Nat Resources, W Lafayette, IN 47907 USA Univ Estadual Paulista, Ciencias Biol Bot, Sao Paulo, Brazil Univ Estadual Paulista, Ciencias Biol Bot, Sao Paulo, Brazil China Postdoctoral Science Foundation: 2016M590152 National Natural Science Foundation of China: 31600451 Fundamental Research Funds of Chinese Academy of Forestry: CAFYBB2017ZE003 China Scholarship Council: 2016-3035 U.S. State Department Interagency Agreement: 19318814Y0010-140001-0001/P00001