Timber DNA release using focused ultrasound extraction (FUSE) for genetic species identification

The use of genetic data for timber species and population assignment is a powerful tool for combating the illegal timber trade, but the challenges of extracting DNA from timber have prevented the routine use of genetics as a supply chain management tool. To overcome these challenges, we explored the feasibility of focused ultrasound extraction (FUSE) for rapid DNA release from timber. Using high-pressure ultrasound pulses, FUSE generates a cavitation bubble cloud that disintegrates samples into acellular debris, resulting in the mechanical release of DNA. In this work, FUSE was applied to white oak (Quercus alba) timber shavings to test the feasibility of using FUSE for timber DNA extraction for the first time. Results showed that FUSE processing disintegrated the tissue samples and released significant quantities of DNA. After five minutes of tissue processing DNA quantities of 0.21 ± 0.02 ng/mg, 0.99 ± 0.32 ng/mg, and 0.14 ± 0.01 ng/mg, were released from medium, coarse, and combination shaving groups, respectively. Amplification and sequencing of regions within the matK and rbcL chloroplast genes confirmed that the quality of DNA prepared with FUSE was suitable for PCR and short-read sequencing applications. Overall, these results show that FUSE can serve as a DNA sample preparation method capable of releasing high-quality DNA from timber in a fraction of the time required by conventional extraction methods. Based on the improved efficiency of DNA release with FUSE, ongoing work aims to develop this technology into portable systems that can be used to rapidly prepare timber samples for genetic species identification.

Artificial intelligence in timber forensics employing DNA barcode database

Extreme difficulties in species identification of illegally sourced wood with conventional tools have accelerated illicit logging activities, leading to the destruction of natural resources in India. In this regard, the study primarily focused on developing a DNA barcode database for 41 commercial timber tree species which are highly vulnerable to adulteration in south India. The developed DNA barcode database was validated using an integrated approach involving wood anatomical features of traded wood samples collected from south India. Traded wood samples were primarily identified using wood anatomical features using IAWA list of microscopic features for hardwood identification. Consortium of Barcode of Life (CBOL) recommended barcode gene regions (rbcL, matK & psbA-trnH) were employed for developing DNA barcode database. Secondly, we employed artificial intelligence (AI) analytical platform, Waikato Environment for Knowledge Analysis (WEKA) for analyzing DNA barcode sequence database which could append precision, speed, and accuracy for the entire identification process. Among the four classification algorithms implemented in the machine learning algorithm (WEKA), best performance was shown by SMO, which could clearly allocate individual samples to their respective sequence database of biological reference materials (BRM) with 100 % accuracy, indicating its efficiency in authenticating the traded timber species. Major advantage of AI is the ability to analyze huge data sets with more precision and also provides a large platform for rapid authentication of species, which subsequently reduces human labor and time.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-023-03604-0.

SmartWoodID-an image collection of large end-grain surfaces to support wood identification systems

Wood identification is a key step in the enforcement of laws and regulations aimed at combatting illegal timber trade. Robust wood identification tools, capable of distinguishing a large number of timbers, depend on a solid database of reference material. Reference material for wood identification is typically curated in botanical collections dedicated to wood consisting of samples of secondary xylem of lignified plants. Specimens from the Tervuren Wood Collection, one of the large institutional wood collections around the world, are used as a source of tree species data with potential application as timber. Here, we present SmartWoodID, a database of high-resolution optical scans of the end-grain surfaces enriched with expert wood anatomical descriptions of macroscopic features. These can serve as annotated training data to develop interactive identification keys and artificial intelligence for computer vision-based wood identification. The first edition of the database consists of images of 1190 taxa, with a focus on potential timber species from the Democratic Republic of the Congo with at least four different specimens per species included. Database URL https://hdl.handle.net/20.500.12624/SmartWoodID_first_edition.

Clay and soil organic matter drive wood multi-elemental composition of a tropical tree species: Implications for timber tracing

Forensic methods to independently trace timber origin are essential to combat illegal timber trade. Tracing product origin by analysing their multi-element composition has been successfully applied in several commodities, but its potential for timber is not yet known. To evaluate this potential the drivers of wood multi-elemental composition need to be studied. Here we report on the first study relating wood multi-elemental composition of forest trees to soil chemical and physical properties. We studied the reactive soil element pools and the multi-elemental composition in sapwood and heartwood for 37 Azobé (Lophira alata) trees at two forest sites in Cameroon. A total of 46 elements were measured using ICP-MS. We also measured three potential drivers of soil and wood elemental composition: clay content, soil organic matter and pH. We tested associations between soil and wood using multiple regressions and multivariate analyses (Mantel test, db-RDA). Finally, we performed a Random Forest analysis of heartwood elemental composition to check site assignment accuracy. We found elemental compositions of soil, sapwood and heartwood to be significantly associated. Soil clay content and organic matter positively influenced individual element concentrations (for 13 and 9 elements out of 46 respectively) as well as the multi-elemental composition in wood. However, associations between wood and topsoil elemental concentrations were only significant for one element. We found close associations between element concentrations and composition in sapwood and heartwood. Lastly, the Random Forest assignment success was 97.3 %. Our findings indicate that wood elemental composition is associated with that in the topsoil and its variation is related to soil clay and organic matter content. These associations suggests that the multi-elemental composition of wood can yield chemical fingerprints obtained from sites that differ in soil properties. This finding in addition to the high assignment accuracy shows potential of multi-element analysis for tracing wood origin.

Organellar microcapture to extract nuclear and plastid DNA from recalcitrant wood specimens and trace evidence

BACKGROUND

Illegal logging is a global crisis with significant environmental, economic, and social consequences. Efforts to combat it call for forensic methods to determine species identity, provenance, and individual identification of wood specimens throughout the forest products supply chain. DNA-based methodologies are the only tools with the potential to answer all three questions and the only ones that can be calibrated "non-destructively" by using leaves or other plant tissue and take advantage of publicly available DNA sequence databases. Despite the potential that DNA-based methods represent for wood forensics, low DNA yield from wood remains a limiting factor because, when compared to other plant tissues, wood has few living DNA-containing cells at functional maturity, it often has PCR-inhibiting extractives, and industrial processing of wood degrades DNA. To overcome these limitations, we developed a technique-organellar microcapture-to mechanically isolate intact nuclei and plastids from wood for subsequent DNA extraction, amplification, and sequencing.

RESULTS

Here we demonstrate organellar microcapture wherein we remove individual nuclei from parenchyma cells in wood (fresh and aged) and leaves of Carya ovata and Tilia americana, amyloplasts from Carya wood, and chloroplasts from kale (Brassica sp.) leaf midribs. ITS (773 bp), ITS1 (350 bp), ITS2 (450 bp), and rbcL (620 bp) were amplified via polymerase chain reaction, sequenced, and heuristic searches against the NCBI database were used to confirm that recovered DNA corresponded to each taxon.

CONCLUSION

Organellar microcapture, while too labor-intensive for routine extraction of many specimens, successfully recovered intact nuclei from wood samples collected more than sixty-five years ago, plastids from fresh sapwood and leaves, and presents great potential for DNA extraction from recalcitrant plant samples such as tissues rich in secondary metabolites, old specimens (archaeological, herbarium, and xylarium specimens), or trace evidence previously considered too small for analysis.

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.

Rapid identification of wood species using XRF and neural network machine learning

An innovative approach for the rapid identification of wood species is presented. By combining X-ray fluorescence spectrometry with convolutional neural network machine learning, 48 different wood specimens were clearly differentiated and identified with a 99% accuracy. Wood species identification is imperative to assess illegally logged and transported lumber. Alternative options for identification can be time consuming and require some level of sampling. This non-invasive technique offers a viable, cost-effective alternative to rapidly and accurately identify timber in efforts to support environmental protection laws and regulations.

Timber identification of Autranella, Baillonella and Tieghemella in the taxonomically challenging Sapotaceae family

BACKGROUND

To enforce timber import laws and perform timber species identification, the identity of the botanical species must be well-defined. Since the Sapotaceae family is known as a taxonomically challenging family, we focus in this study on the four most valuable Sapotaceae timber species from tropical Africa: Autranella congolensis (De Wild.) A.Chev., Baillonella toxisperma Pierre, Tieghemella africana Pierre and Tieghemella heckelii (A.Chev.) Pierre ex Dubard. The wood anatomical characteristic fiber lumen fraction and Direct Analysis in Real Time-Time of Flight Mass Spectrometry (DART-TOFMS) were used to differentiate the four species and to make inferences on species delineation and taxonomic identity.

RESULTS

We observed differences in the fiber lumen fraction measurements and discerned two groups: (1) A. congolensis and B. toxisperma, and (2) T. africana and T. heckelii. In addition, all Mann-Whitney U comparisons and differences in distributions (Kolmogorov-Smirnov) for the fiber lumen fraction measurements were significant between all species. When permutating the data between species within those two groups, significant differences were still found between the species within those groups. This could indicate that the fiber lumen fraction is not diagnostic to discern the species. DART-TOFMS analysis showed that A. congolensis and B. toxisperma have distinct chemotypes, while T. heckelii and T. africana have remarkably similar chemotypes.

CONCLUSIONS

Based on our observations of similar chemotype and weakly differentiated fiber lumen ratio, we support an alternative taxonomic hypothesis that considers Tieghemella monotypic, because of the strong resemblance between T. heckelii and T. africana. Larger sample sizes and further research is required to develop methodology for the identification of these species. A taxonomic study utilizing molecular genetics would be beneficial to assess the status of the genus and the species limits. This could have implications towards their potential inclusion on CITES appendices if there is ever need for them to be listed. If Tieghemella africana and T. heckelii remain two distinct species, they should both be listed. Screening agents should be aware that the morphological and chemical differences between T. africana and T. heckelii are minimal.

An interactive identification key for Atlantic Forest tree species based on macroscopic wood anatomy

Abstract The Atlantic Forest is considered a biodiversity hotspot because of its exceptional species richness, endemism, and habitat losses. Commercial logging, industrial forestry, and agriculture represent threats to the Atlantic Forest, and even though it has been protected by law since 2006, forest suppression continues and large volumes of Atlantic Forest wood are traded every year. To promote environmental conservation and prevent illegal logging, the verification of wood species’ identifications is fundamental throughout several stages of the wood supply chain by supervisory bodies, traders, and even consumers. Macroscopic wood anatomy analysis has been shown to be an efficient method for screening, although tools to streamline the efficiency of that process are necessary. We introduce here an interactive identification key for Atlantic Forest tree species, based on standard wood macroscopic features that is now available online at http://gbg.sites.uff.br/lamad/.

SNP Markers as a Successful Molecular Tool for Assessing Species Identity and Geographic Origin of Trees in the Economically Important South American Legume Genus Dipteryx

Dipteryx timber has been heavily exploited in South America since 2000s due to the increasing international demand for hardwood. Developing tools for the genetic identification of Dipteryx species and their geographical origin can help to promote legal trading of timber. A collection of 800 individual trees, belonging to 6 different Dipteryx species, was genotyped based on 171 molecular markers. After the exclusion of markers out of Hardy-Weinberg equilibrium or with no polymorphism or low amplification, 83 nuclear, 29 chloroplast, 13 mitochondrial single nucleotide polymorphisms (SNPs), and 2 chloroplast and 5 mitochondrial INDELS remained. Six genetic groups were identified using Bayesian Structure analyses of the nuclear SNPs, which corresponded to the different Dipteryx species collected in the field. Seventeen highly informative markers were identified as suitable for species identification and obtained self-assignment success rates to species level of 78-96%. An additional set of 15 molecular markers was selected to determine the different genetic clusters found in Dipteryx odorata and Dipteryx ferrea, obtaining self-assignment success rates of 91-100%. The success to assign samples to the correct country of origin using all or only the informative markers improved when using the nearest neighbor approach (69-92%) compared to the Bayesian approach (33-80%). While nuclear and chloroplast SNPs were more suitable for differentiating the different Dipteryx species, mitochondrial SNPs were ideal for determining the genetic clusters of D. odorata and D. ferrea. These 32 selected SNPs will be invaluable genetic tools for the accurate identification of species and country of origin of Dipteryx timber.

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.

A geographical traceability system for Merbau (Intsia palembanica Miq.), an important timber species from peninsular Malaysia

To inform product users about the origin of timber, the implementation of a traceability system is necessary for the forestry industry. In this study, we developed a comprehensive genetic database for the important tropical timber species Merbau, Intsia palembanica, to trace its geographic origin within peninsular Malaysia. A total of 1373 individual trees representing 39 geographically distinct populations of I. palembanica were sampled throughout peninsular Malaysia. We analyzed the samples using a combination of four chloroplast DNA (cpDNA) markers and 14 short tandem repeat (STR) markers to establish both cpDNA haplotype and STR allele frequency databases. A haplotype map was generated through cpDNA sequencing for population identification, resulting in six unique haplotypes based on 10 informative intraspecifically variable sites. Subsequently, an STR allele frequency database was developed from 14 STRs allowing individual identification. Bayesian cluster analysis divided the individuals into two genetic clusters corresponding to the northern and southern regions of peninsular Malaysia. Tests of conservativeness showed that the databases were conservative after the adjustment of the θ values to 0.2000 and 0.2900 for the northern (f = 0.0163) and southern (f = 0.0285) regions, respectively. Using self-assignment tests, we observed that individuals were correctly assigned to populations at rates of 40.54-94.12% and to the identified regions at rates of 79.80-80.62%. Both the cpDNA and STR markers appear to be useful for tracking Merbau timber originating from peninsular Malaysia. The use of these forensic tools in addition to the existing paper-based timber tracking system will help to verify the legality of the origin of I. palembanica and to combat illegal logging issues associated with the species.

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.

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.

Assessing the Ability of Chloroplast and Nuclear DNA Gene Markers to Verify the Geographic Origin of Jatoba (Hymenaea courbaril L.) Timber

Deforestation-reinforced by illegal logging-is a serious problem in many tropical regions and causes pervasive environmental and economic damage. Existing laws that intend to reduce illegal logging need efficient, fraud resistant control methods. We developed a genetic reference database for Jatoba (Hymenaea courbaril), an important, high value timber species from the Neotropics. The data set can be used for controls on declarations of wood origin. Samples from 308 Hymenaea trees from 12 locations in Brazil, Bolivia, Peru, and French Guiana have been collected and genotyped on 10 nuclear microsatellites (nSSRs), 13 chloroplast SNPs (cpSNP), and 1 chloroplast indel marker. The chloroplast gene markers have been developed using Illumina DNA sequencing. Bayesian cluster analysis divided the individuals based on the nSSRs into 8 genetic groups. Using self-assignment tests, the power of the genetic reference database to judge on declarations on the location has been tested for 3 different assignment methods. We observed a strong genetic differentiation among locations leading to high and reliable self-assignment rates for the locations between 50% to 100% (average of 88%). Although all 3 assignment methods came up with similar mean self-assignment rates, there were differences for some locations linked to the level of genetic diversity, differentiation, and heterozygosity. Our results show that the nuclear and chloroplast gene markers are effective to be used for a genetic certification system and can provide national and international authorities with a robust tool to confirm legality of timber.

Classification of CITES-listed and other neotropical Meliaceae wood images using convolutional neural networks

BACKGROUND

The current state-of-the-art for field wood identification to combat illegal logging relies on experienced practitioners using hand lenses, specialized identification keys, atlases of woods, and field manuals. Accumulation of this expertise is time-consuming and access to training is relatively rare compared to the international demand for field wood identification. A reliable, consistent and cost effective field screening method is necessary for effective global scale enforcement of international treaties such as the Convention on the International Trade in Endagered Species (CITES) or national laws (e.g. the US Lacey Act) governing timber trade and imports.

RESULTS

We present highly effective computer vision classification models, based on deep convolutional neural networks, trained via transfer learning, to identify the woods of 10 neotropical species in the family Meliaceae, including CITES-listed Swietenia macrophylla, Swietenia mahagoni, Cedrela fissilis, and Cedrela odorata. We build and evaluate models to classify the 10 woods at the species and genus levels, with image-level model accuracy ranging from 87.4 to 97.5%, with the strongest performance by the genus-level model. Misclassified images are attributed to classes consistent with traditional wood anatomical results, and our species-level accuracy greatly exceeds the resolution of traditional wood identification.

CONCLUSION

The end-to-end trained image classifiers that we present discriminate the woods based on digital images of the transverse surface of solid wood blocks, which are surfaces and images that can be prepared and captured in the field. Hence this work represents a strong proof-of-concept for using computer vision and convolutional neural networks to develop practical models for field screening timber and wood products to combat illegal logging.

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

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 (≥90%) 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.