Genome skimming reveals novel plastid markers for the molecular identification of illegally logged African timber species

PLoS One. 2021 Jun 11;16(6):e0251655. doi: 10.1371/journal.pone.0251655. eCollection 2021.

Abstract

Tropical forests represent vast carbon stocks and continue to be key carbon sinks and buffer climate changes. The international policy constructed several mechanisms aiming at conservation and sustainable use of these forests. Illegal logging is an important threat of forests, especially in the tropics. Several laws and regulations have been set up to combat illegal timber trade. Despite significant enforcement efforts of these regulations, illegal logging continues to be a serious problem and impacts for the functioning of the forest ecosystem and global biodiversity in the tropics. Microscopic analysis of wood samples and the use of conventional plant DNA barcodes often do not allow to distinguish closely-related species. The use of novel molecular technologies could make an important contribution for the identification of tree species. In this study, we used high-throughput sequencing technologies and bioinformatics tools to obtain the complete de-novo chloroplast genome of 62 commercial African timber species using the genome skimming method. Then, we performed a comparative genomic analysis that revealed new candidate genetic regions for the discrimination of closely-related species. We concluded that genome skimming is a promising method for the development of plant genetic markers to combat illegal logging activities supporting CITES, FLEGT and the EU Timber Regulation.

MeSH terms

  • Africa
  • Conservation of Natural Resources / methods
  • DNA Barcoding, Taxonomic / methods
  • Forests
  • Genetic Markers
  • Genome, Chloroplast
  • High-Throughput Nucleotide Sequencing
  • Phylogeny
  • Plastids* / genetics
  • Trees / genetics

Substances

  • Genetic Markers

Grants and funding

This study is supported by the Plant.ID project. Plant.ID has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 765000. In addition, this research received support from the SYNTHESYS Plus project (https://www.synthesys.info/) funded under H2020-EU.1.4.1.2. grant agreement 823827.