The Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) aims at regulating the trade of selected species. Appendix II groups species that are not necessarily threatened with extinction but in which trade is controlled to avoid utilization incompatible with their survival. High- value African timber genera Afzelia spp., Khaya spp. and Pterocarpus spp. entered the appendix recently, joining previously listed e.g., African Dalbergia spp., Pericopsis elata and three Guibourtia species.
Apart from document checks, wood sampling is needed to verify both labelling and origin of African timber genera. Several identification methods are more developed than current provenancing methods. Complementary to the ongoing development of last-mentioned methods, exact botanical identification up to the species level is an often overlooked source of information for provenancing. Currently, different species within genera remain hard to distinguish using only one method. We provide an overview of combined methods that substantially help to identify African CITES genera up to species level (e.g., wood anatomy combined with DART for Afzelia sp.). If certain species are associated with well-defined distribution areas, knowledge at this species level provides valuable information on provenance. Our first objective is to develop sample protocols allowing for multiple analyses and combine results to determine the potential for species level identifications (DART TOF mass spectrometry, wood anatomy, stable isotopes, genetics...).
Knowledge on the CITES species also benefits from analyses of lookalikes. The decision on listing all species of a genus as CITES is often based on the risk of (un)intentionally mislabeling true critical species as lookalikes. Our second objective is to provide a data-based definition of the term ‘lookalike’, by creating indices of similarities based on InsideWood species descriptions. First, a set of macroscopic features is tested on reference wood samples. Then, a combination with more subtle microscopical features is made to determine sound criteria for lookalikes of African CITES species.
Our results confirm the power of joining forces for sound identifications. Hands-on tools are being developed to assist stakeholders without scientific background (e.g., apps using AI, sample protocols) and results are translated into recommendations for future CITES-listings.