Abstract
The genus Dalbergia contains many valuable timber species threatened by illegal logging and deforestation, but knowledge on distributions and threats is often limited and accurate species identification difficult. The aim of this study was to apply DNA barcoding methods to support conservation efforts of Dalbergia species in Indochina. We used the recommended rbcL, matK and ITS barcoding markers on 95 samples covering 31 species of Dalbergia, and tested their discrimination ability with both traditional distance-based as well as different model-based machine learning methods. We specifically tested whether the markers could be used to solve taxonomic confusion concerning the timber species Dalbergia oliveri, and to identify the CITES-listed Dalbergia cochinchinensis. We also applied the barcoding markers to 14 samples of unknown identity. In general, we found that the barcoding markers discriminated among Dalbergia species with high accuracy. We found that ITS yielded the single highest discrimination rate (100%), but due to difficulties in obtaining high-quality sequences from degraded material, the better overall choice for Dalbergia seems to be the standard rbcL+matK barcode, as this yielded discrimination rates close to 90% and amplified well. The distance-based method TaxonDNA showed the highest identification rates overall, although a more complete specimen sampling is needed to conclude on the best analytic method. We found strong support for a monophyletic Dalbergia oliveri and encourage that this name is used consistently in Indochina. The CITES-listed Dalbergia cochinchinensis was successfully identified, and a species-specific assay can be developed from the data generated in this study for the identification of illegally traded timber. We suggest that the use of DNA barcoding is integrated into the work flow during floristic studies and at national herbaria in the region, as this could significantly increase the number of identified specimens and improve knowledge about species distributions.
Highlights
Conservation of threatened species is an essential part of reaching the target of the Convention on Biological Diversity 2020 on improving the status of global biodiversity [1]
For threatened species, whose trade is regulated by the Convention on International Trade of Endangered Species (CITES), correct identification is crucial for the enforcement of the regulations and future conservation of the species
The specific aims of this study are to i) establish a reference library for Dalbergia using the recommended rbcL+matK+ITS barcode, ii) use this barcode to infer on the taxonomy of the sampled Dalbergia species, iii) test the discrimination ability of the chosen markers, using both traditional distance-based methods as well as machine learning-based approaches, iv) apply the method on unidentified Dalbergia samples, including cambium/bark and wood samples, to explore how the method could be used in situations where identification by morphological characters is uncertain or not possible at all
Summary
Conservation of threatened species is an essential part of reaching the target of the Convention on Biological Diversity 2020 on improving the status of global biodiversity [1]. The first crucial step in conserving and managing threatened species is correct identification and delimitation of the target species [2]. Accurate identification in species-rich or taxonomically complex groups typically requires expert knowledge, which is not always available, especially in tropical areas [3, 4]. Correct taxonomical identification of endangered tropical tree species is often difficult. For threatened species, whose trade is regulated by the Convention on International Trade of Endangered Species (CITES), correct identification is crucial for the enforcement of the regulations and future conservation of the species. The identification process can be problematic, especially if similar non-threatened species appear in the trade, and if only parts of the plant are being traded (e.g. wood)
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