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DNA fingerprinting: an effective tool for taxonomic identification of precious corals in jewelry.

ABSTRACT: Precious coral species have been used to produce jewelry and ornaments since antiquity. Due to the high value and demand for corals, some coral beds have been heavily fished over past centuries. Fishing and international trade regulations were put in place to regulate fishing practices in recent decades. To this date, the control of precious coral exploitation and enforcement of trade rules have been somewhat impaired by the fact that different species of worked coral samples can be extremely difficult to distinguish, even for trained experts. Here, we developed methods to use DNA recovered from precious coral samples worked for jewelry to identify their species. We evaluated purity and quantity of DNA extracted using five different techniques. Then, a minimally invasive sampling protocol was tested, which allowed genetic analysis without compromising the value of the worked coral objects.The best performing DNA extraction technique applies decalcification of the skeletal material with EDTA in the presence of laurylsarcosyl and proteinase, and purification of the DNA with a commercial silica membrane. This method yielded pure DNA in all cases using 100?mg coral material and in over half of the cases when using "quasi non-destructive" sampling with sampled material amounts as low as 2.3?mg. Sequence data of the recovered DNA gave an indication that the range of precious coral species present in the trade is broader than previously anticipated.

SUBMITTER: Lendvay B

PROVIDER: S-EPMC7237452 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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DNA fingerprinting: an effective tool for taxonomic identification of precious corals in jewelry.

Lendvay Bertalan B Cartier Laurent E LE Gysi Mario M Meyer Joana B JB Krzemnicki Michael S MS Kratzer Adelgunde A Morf Nadja V NV

Scientific reports 20200519 1

Precious coral species have been used to produce jewelry and ornaments since antiquity. Due to the high value and demand for corals, some coral beds have been heavily fished over past centuries. Fishing and international trade regulations were put in place to regulate fishing practices in recent decades. To this date, the control of precious coral exploitation and enforcement of trade rules have been somewhat impaired by the fact that different species of worked coral samples can be extremely di ...[more]

PMID: 32427854

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Project description:BackgroundSeamount-associated faunas are often considered highly endemic but isolation and diversification processes leading to such endemism have been poorly documented at those depths. Likewise, species delimitation and phylogenetic studies in deep-sea organisms remain scarce, due to the difficulty in obtaining samples, and sometimes controversial. The phylogenetic relationships within the precious coral family Coralliidae remain largely unexplored and the monophyly of its two constituent genera, Corallium Cuvier and Paracorallium Bayer & Cairns, has not been resolved. As traditionally recognized, the diversity of colonial forms among the various species correlates with the diversity in shape of their supporting axis, but the phylogenetic significance of these characters remains to be tested. We thus used mitochondrial sequence data to evaluate the monophyly of Corallium and Paracorallium and the species boundaries for nearly all named taxa in the family. Species from across the coralliid range, including material from Antarctica, Hawaii, Japan, New Zealand, Taiwan, Tasmania, the eastern Pacific and the western Atlantic were examined.ResultsThe concatenated analysis of five mitochondrial regions (COI, 16S rRNA, ND2, and ND3-ND6) recovered two major coralliid clades. One clade is composed of two subgroups, the first including Corallium rubrum, the type species of the genus, together with a small group of Paracorallium species (P. japonicum and P. tortuosum) and C. medea (clade I-A); the other subgroup includes a poorly-resolved assemblage of six Corallium species (C. abyssale, C. ducale, C. imperiale, C. laauense, C. niobe, and C. sulcatum; clade I-B). The second major clade is well resolved and includes species of Corallium and Paracorallium (C. elatius, C. kishinouyei, C. konojoi, C. niveum, C. secundum, Corallium sp., Paracorallium nix, Paracorallium thrinax and Paracorallium spp.). A traditional taxonomic study of this clade delineated 11 morphospecies that were congruent with the general mixed Yule-coalescent (GMYC) model. A multilocus species-tree approach also identified the same two well-supported clades, being Clade I-B more recent in the species tree (18.0-15.9 mya) than in the gene tree (35.2-15.9 mya). In contrast, the diversification times for Clade II were more ancient in the species tree (136.4-41.7 mya) than in the gene tree (66.3-16.9 mya).ConclusionsOur results provide no support for the taxonomic status of the two currently recognized genera in the family Coralliidae. Given that Paracorallium species were all nested within Corallium, we recognize the coralliid genus Corallium, which includes the type species of the family, and thus consider Paracorallium a junior synonym of Corallium. We propose the use of the genus Hemicorallium Gray for clade I-B (species with long rod sclerites, cylindrical autozooids and smooth axis). Species delimitation in clade I-B remains unclear and the molecular resolution for Coralliidae species is inconsistent in the two main clades. Some species have wide distributions, recent diversification times and low mtDNA divergence whereas other species exhibit narrower allopatric distributions, older diversification times and greater levels of mtDNA resolution.

Project description:Rapid and reliable identification of insects is important in many contexts, from the detection of disease vectors and invasive species to the sorting of material from biodiversity inventories. Because of the shortage of adequate expertise, there has long been an interest in developing automated systems for this task. Previous attempts have been based on laborious and complex handcrafted extraction of image features, but in recent years it has been shown that sophisticated convolutional neural networks (CNNs) can learn to extract relevant features automatically, without human intervention. Unfortunately, reaching expert-level accuracy in CNN identifications requires substantial computational power and huge training data sets, which are often not available for taxonomic tasks. This can be addressed using feature transfer: a CNN that has been pretrained on a generic image classification task is exposed to the taxonomic images of interest, and information about its perception of those images is used in training a simpler, dedicated identification system. Here, we develop an effective method of CNN feature transfer, which achieves expert-level accuracy in taxonomic identification of insects with training sets of 100 images or less per category, depending on the nature of data set. Specifically, we extract rich representations of intermediate to high-level image features from the CNN architecture VGG16 pretrained on the ImageNet data set. This information is submitted to a linear support vector machine classifier, which is trained on the target problem. We tested the performance of our approach on two types of challenging taxonomic tasks: 1) identifying insects to higher groups when they are likely to belong to subgroups that have not been seen previously and 2) identifying visually similar species that are difficult to separate even for experts. For the first task, our approach reached $CDATA[$CDATA[$>$$92% accuracy on one data set (884 face images of 11 families of Diptera, all specimens representing unique species), and $CDATA[$CDATA[$>$$96% accuracy on another (2936 dorsal habitus images of 14 families of Coleoptera, over 90% of specimens belonging to unique species). For the second task, our approach outperformed a leading taxonomic expert on one data set (339 images of three species of the Coleoptera genus Oxythyrea; 97% accuracy), and both humans and traditional automated identification systems on another data set (3845 images of nine species of Plecoptera larvae; 98.6 % accuracy). Reanalyzing several biological image identification tasks studied in the recent literature, we show that our approach is broadly applicable and provides significant improvements over previous methods, whether based on dedicated CNNs, CNN feature transfer, or more traditional techniques. Thus, our method, which is easy to apply, can be highly successful in developing automated taxonomic identification systems even when training data sets are small and computational budgets limited. We conclude by briefly discussing some promising CNN-based research directions in morphological systematics opened up by the success of these techniques in providing accurate diagnostic tools.

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DNA fingerprinting: an effective tool for taxonomic identification of precious corals in jewelry.

Publications

DNA fingerprinting: an effective tool for taxonomic identification of precious corals in jewelry.

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