Timber is an important resource in the modern day society from being used in construction to producing materials for everyday uses such as paper, with little thought of where the timber is sourced. Forests cover 31% of the land area on our planet (1) and produce the vital oxygen that species need to survive whilst removing carbon dioxide from the atmosphere. Mass Deforestation is happening in four regions; South America – Brazil, Russia, and Central Africa – Republic of Congo, and Southeast Asia – Indonesia. The effects of mass deforestation but not limited to are land conflicts, damaging the forest ecosystems, the extinction of species and has an overall effect on health and global warming.
The illegal timber trade consists of harvesting, transporting, processing and being bought or sold without the correct permit. This occurs for reasons such as clearing the forestland for plantations like palm oil and farmland. False documentation for the species and places of origin can be produced to avoid being caught domestically and internationally and causes loss of tax revenue.
The traceability and identification of timber can be an important tool in determining if its illegal sourced and if it is an endangered species. This can disrupt and prevent further illegal logging by preventing the sales of illegally sourced logs and further deterring the timber from being harvested in such a manner. The traditional method of identification of trees were limited to surveying the area at specific times of the season and recording the morphologies of the trees. However this used specialists, contained inaccuracies and could not trace the tree back to the source once illegally logged due to the lack of this led to the development of modern techniques; DNA barcoding (Single nucleotide polymorphisms, chloroplast  and nuclear short tandem repeat analysis) and Chemical analysis (Strontium isotopes ).
DNA barcoding utilises Single nucleotide polymorphisms (SNP), chloroplast and/or short tandem repeat (STR) analysis. This consists of taking samples from tree populations in specific regions at any time of the year and developing and/or targeting markers or looking at SNP differences between the tree species populations. The data is then recorded and processed into a reference database to allow future tests to have a potential match.
The objectives for this review are to 1) examining the different genetic methods available 2) analysis of the genetic markers involved in identifying timber and 3) the inhibiting factors in genetic methods
DNA barcoding uses short tandem repeats from intergenic or intron regions to create libraries of information, with the animal identification and reference library using the cytochrome c oxidase 1 (CO1) gene, due to plants having low variability in this region it’s not suitable (5). An ideal marker should have low mutation rates, high interspecific divergence and low intraspecific variation.
Consortium for the Barcode of Life 2009 suggested that using four coding genes (ribulose–bisphosphate carboxylase (rbcL), maturase K (matk), rpoB and rpoC1) and three non coding (atpF-atpH, trnH-psbA, and psbK-psbL) were the seven ideal candidate DNA loci. matK and rbcl markers were suggested to be a standard 2-locus barcode (6) and have demonstrated its ability in determining species by (8) who was using DNA barcoding to authenticate wood samples of threatened and commercial timber trees within the tropical dry evergreen forest of India. The results reported correctly identifying 136 out of 143 species; rbcl 90.2% and matK 96% but has been otherwise been reported less than 75% (6",7) showing variation in the DNA marker results.
A problem that is present within DNA barcoding is that extraction is usually of poor quality either by low copy number or degrade DNA causing amplification problems (11) which has been evident when (9) was attempting to identify Amazonian trees with ITS (41%) and matK (68%) having a low sequence success even after using two different primer pairs to increase the success. (2) found that when amplifying oak wood, amplification rate isolated from sapwood was 70% whilst it was only 15% from heartwood with no clear relation between fragment length and amplification rate being stated because as suspected the shorter fragment had the highest rate, some were so successful indicating primer efficiencies. This study designed 9 primers targeting cpDNA regions (trnD-trnT and trnL) in an attempt to trace back white oaks to the place of origin. Using leaf tissue would increase amplification success over saw dust samples from either sapwood or heartwood as demonstrated by (4), amplification success was trnH-psbA 100% rcbL 95%, and ITS 88%. (4) also stated that there was no correlation between specimen age and amplification success. However collecting leaf matter from illegal logged timber can be impossible as it is removed by the time of transport, therefore sapwood would be the next region containing the highest quantity and purity of DNA.
Generally shown throughout the studies is that cpDNA has a higher amplification success than nuclear DNA, this could be due to the increased amount of cpDNA that is present compared to nuclear dna, but th
Single nucleotide polymorphisms (SNP) is another method that can be used in the identification and origin of wood. A single nulcleotide polymorphisms is a single base sequence variation between individuals at a specific point in a genome. study by (13) developed a set of 253 single nucleotide polymorphism loci for the larix spp. In Europe and Russia, with the placing of the origin success rate ranging from 74% to 88%, a smaller set of created from this of 76 SNP loci inwhich successfully tested in a blind test of 10 samples. (14) looked at identifying the tropical hardwood ramin Gonystylus spp., after examining 5 different chloroplast gene regions, they discovered that matK has the most numerous numbers of single nucleotide polymorphisms to distinguish between ramin and non ramin wood and four further loci were chosen was analysis for further discrimination. They estimated that with minimal training one person could process 96 samples per day.(14)