Genetic characterization of a group of commercial African timber species: From genomics to barcoding

PLoS One. 2023 Apr 20;18(4):e0284732. doi: 10.1371/journal.pone.0284732. eCollection 2023.

Abstract

In the last decades, illegal logging has posed a serious threat for the integrity of forest ecosystems and for biodiversity conservation in tropical Africa. Although international treaties and regulatory plans have been implemented to reduce illegal logging, much of the total timber volume is harvested and traded illegally from tropical African forest regions. As a result, the development and the application of analytical tools to enhance the traceability and the identification of wood and related products is critical to enforce international regulations. Among available techniques, DNA barcoding is a promising approach for the molecular identification of plant species. However, although it has been used successfully for the discrimination of animal species, no set of genetic markers is available for the universal identification of plant species. In this work, we firstly characterized the genetic diversity of 17 highly-valuable African timber species from five genera (Afzelia, Guibourtia, Leplea, Milicia, Tieghemella) across their distribution ranges in West and Central Africa using the genome skimming approach in order to reconstruct their chloroplast genomes and nuclear ribosomal DNA. Next, we identified single-nucleotide polymorphisms (SNPs) for the discrimination of closely-related species. In this way, we successfully developed and tested novel species-specific genetic barcodes for species identification.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Africa, Central
  • Animals
  • DNA Barcoding, Taxonomic* / methods
  • Ecosystem
  • Fabaceae*
  • Forests
  • Genomics

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.