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.

Field-Deployable Computer Vision Wood Identification of Peruvian Timbers.

Illegal logging is a major threat to forests in Peru, in the Amazon more broadly, and in the tropics globally. In Peru alone, more than two thirds of logging concessions showed unauthorized tree harvesting in natural protected areas and indigenous territories, and in 2016 more than half of exported lumber was of illegal origin. To help combat illegal logging and support legal timber trade in Peru we trained a convolutional neural network using transfer learning on images obtained from specimens in six xylaria using the open source, field-deployable XyloTron platform, for the classification of 228 Peruvian species into 24 anatomically informed and contextually relevant classes. The trained models achieved accuracies of 97% for five-fold cross validation, and 86.5 and 92.4% for top-1 and top-2 classification, respectively, on unique independent specimens from a xylarium that did not contribute training data. These results are the first multi-site, multi-user, multi-system-instantiation study for a national scale, computer vision wood identification system evaluated on independent scientific wood specimens. We demonstrate system readiness for evaluation in real-world field screening scenarios using this accurate, affordable, and scalable technology for monitoring, incentivizing, and monetizing legal and sustainable wood value chains.

Towards Sustainable North American Wood Product Value Chains, Part I: Computer Vision Identification of Diffuse Porous Hardwoods.

Availability of and access to wood identification expertise or technology is a critical component for the design and implementation of practical, enforceable strategies for effective promotion, monitoring and incentivisation of sustainable practices and conservation efforts in the forest products value chain. To address this need in the context of the multi-billion-dollar North American wood products industry 22-class, image-based, deep learning models for the macroscopic identification of North American diffuse porous hardwoods were trained for deployment on the open-source, field-deployable XyloTron platform using transverse surface images of specimens from three different xylaria and evaluated on specimens from a fourth xylarium that did not contribute training data. Analysis of the model performance, in the context of the anatomy of the woods considered, demonstrates immediate readiness of the technology developed herein for field testing in a human-in-the-loop monitoring scenario. Also proposed are strategies for training, evaluating, and advancing the state-of-the-art for developing an expansive, continental scale model for all the North American hardwoods.

The XyloTron: Flexible, Open-Source, Image-Based Macroscopic Field Identification of Wood Products.

Forests, estimated to contain two thirds of the world's biodiversity, face existential threats due to illegal logging and land conversion. Efforts to combat illegal logging and to support sustainable value chains are hampered by a critical lack of affordable and scalable technologies for field-level inspection of wood and wood products. To meet this need we present the XyloTron, a complete, self-contained, multi-illumination, field-deployable, open-source platform for field imaging and identification of forest products at the macroscopic scale. The XyloTron platform integrates an imaging system built with off-the-shelf components, flexible illumination options with visible and UV light sources, software for camera control, and deep learning models for identification. We demonstrate the capabilities of the XyloTron platform with example applications for automatic wood and charcoal identification using visible light and human-mediated wood identification based on ultra-violet illumination and discuss applications in field imaging, metrology, and material characterization of other substrates.

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.

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

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-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.

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 Barcode Authentication and Library Development for the Wood of Six Commercial Pterocarpus Species: the Critical Role of Xylarium Specimens.

DNA barcoding has been proposed as a useful tool for forensic wood identification and development of a reliable DNA reference library is an essential first step. Xylaria (wood collections) are potentially enormous data repositories if DNA information could be extracted from wood specimens. In this study, 31 xylarium wood specimens and 8 leaf specimens of six important commercial species of Pterocarpus were selected to investigate the reliability of DNA barcodes for authentication at the species level and to determine the feasibility of building wood DNA barcode reference libraries from xylarium specimens. Four DNA barcodes (ITS2, matK, ndhF-rpl32 and rbcL) and their combination were tested to evaluate their discrimination ability for Pterocarpus species with both TaxonDNA and tree-based analytical methods. The results indicated that the combination barcode of matK + ndhF-rpl32 + ITS2 yielded the best discrimination for the Pterocarpus species studied. The mini-barcode ndhF-rpl32 (167-173 bps) performed well distinguishing P. santalinus from its wood anatomically inseparable species P. tinctorius. Results from this study verified not only the feasibility of building DNA barcode libraries using xylarium wood specimens, but the importance of using wood rather than leaves as the source tissue, when wood is the botanical material to be identified.

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

MAIN CONCLUSION: 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.

Force of habit: shrubs, trees and contingent evolution of wood anatomical diversity using Croton (Euphorbiaceae) as a model system.

Background and Aims: Wood is a major innovation of land plants, and is usually a central component of the body plan for two major plant habits: shrubs and trees. Wood anatomical syndromes vary between shrubs and trees, but no prior work has explicitly evaluated the contingent evolution of wood anatomical diversity in the context of these plant habits. Methods: Phylogenetic comparative methods were used to test for contingent evolution of habit, habitat and wood anatomy in the mega-diverse genus Croton (Euphorbiaceae), across the largest and most complete molecular phylogeny of the genus to date. Key Results: Plant habit and habitat are highly correlated, but most wood anatomical features correlate more strongly with habit. The ancestral Croton was reconstructed as a tree, the wood of which is inferred to have absent or indistinct growth rings, confluent-like axial parenchyma, procumbent ray cells and disjunctive ray parenchyma cell walls. The taxa sampled showed multiple independent origins of the shrub habit in Croton , and this habit shift is contingent on several wood anatomical features (e.g. similar vessel-ray pits, thick fibre walls, perforated ray cells). The only wood anatomical trait correlated with habitat and not habit was the presence of helical thickenings in the vessel elements of mesic Croton . Conclusions: Plant functional traits, individually or in suites, are responses to multiple and often confounding contexts in evolution. By establishing an explicit contingent evolutionary framework, the interplay between habit, habitat and wood anatomical diversity was dissected in the genus Croton . Both habit and habitat influence the evolution of wood anatomical characters, and conversely, the wood anatomy of lineages can affect shifts in plant habit and habitat. This study hypothesizes novel putatively functional trait associations in woody plant structure that could be further tested in a variety of other taxa.

Force-displacement measurements of earlywood bordered pits using a mesomechanical tester.

The elastic properties of pit membranes are reported to have important implications in understanding air-seeding phenomena in gymnosperms, and pit aspiration plays a large role in wood technological applications such as wood drying and preservative treatment. Here we present force-displacement measurements for pit membranes of circular bordered pits, collected on a mesomechanical testing system. The system consists of a quartz microprobe attached to a microforce sensor that is positioned and advanced with a micromanipulator mounted on an inverted microscope. Membrane displacement is measured from digital image analysis. Unaspirated pits from earlywood of never-dried wood of Larix and Pinus and aspirated pits from earlywood of dried wood of Larix were tested to generate force-displacement curves up to the point of membrane failure. Two failure modes were observed: rupture or tearing of the pit membrane by the microprobe tip, and the stretching of the pit membrane until the torus was forced out of the pit chamber through the pit aperture without rupture, a condition we refer to as torus prolapse.

Incidence of bark- and wood-boring insects in firewood: a survey at Michigan's Mackinac Bridge.

Firewood is a major pathway for the inadvertent movement of bark- and wood-infesting insects. After discovery of Agrilus planipennis Fairmaire (Coleoptera: Buprestidae) in southeastern Michigan in 2002, quarantines were enacted including prohibition of transporting firewood across the Mackinac Bridge between Michigan's Lower and Upper peninsulas. Drivers are required to surrender firewood before crossing the bridge. We surveyed recently surrendered firewood in April, July, and September 2008 and categorized it by genus, cross-sectional shape (whole, half, or quarter), approximate age (years since it was a live tree), presence of bark, and evidence of bark- and wood-boring insects. The 1045 pieces of firewood examined represented 21 tree genera: primarily Acer (30%), Quercus (18%), Fraxinus (15%), Ulmus (12%), Betula (5%), and Prunus (5%). Live borers (Bostrichoidea, Brentidae, Buprestidae, Cerambycidae, Cossidae, Curculionidae [Scolytinae and non-Scolytinae], and Siricidae) were found in 23% of the pieces and another 41% had evidence of previous borer infestation. Of the 152 Fraxinus firewood pieces, 13% had evidence of past A. planipennis infestation, but we found no live A. planipennis. We discuss national "don't move firewood" campaigns and U.S. imports of fuelwood. During 1996-2009, the United States imported fuelwood valued at > dollars U.S. 98 million from 34 countries.