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8 results for “taxonomic expertise”
Data from: The role of taxonomic expertise in interpretation of metabarcoding studies
<p>The performance of DNA metabarcoding approaches for characterizing biodiversity can be influenced by multiple factors. Here we used morphological assessment of taxa in zooplankton samples to develop a large barcode database and to assess the congruence of taxonomic identification with metabarcoding under different conditions. We analyzed taxonomic assignment of metabarcoded samples using two genetic markers (COI, 18S V1-2), two types of clustering into molecular operational taxonomic units (OTUs, ZOTUs), and three methods for taxonomic assignment (RDP Classifier, BLASTn to GenBank, BLASTn to a local barcode database). The local database includes 1042 COI and 1108 18S (SSU) barcode sequences, and we added new high-quality sequences to GenBank for both markers, including 109 contributions at the species level. The number of phyla detected and the number of taxa identified to phylum varied between genetic marker and among the three methods used for taxonomic assignments. Blasting the metabarcodes to the local database generated multiple unique contributions to identify OTUs and ZOTUs. We argue that a multi-marker approach combined with taxonomic expertise to develop a curated, vouchered, local barcode database increases taxon detection with metabarcoding, and its potential as a tool for zooplankton biodiversity surveys.</p>
Data from: The role of taxonomic expertise in interpretation of metabarcoding studies
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Figure 2 from: Greeff M, Caspers M, Kalkman V, Willemse L, Sunderland BD, Bánki O, Hogeweg L (2022) Sharing taxonomic expertise between natural history collections using image recognition. Research Ideas and Outcomes 8: e79187. https://doi.org/10.3897/rio.8.e79187
Figure 2 In the Central Library of Datasets, natural history collection staff will find correctly identified images of their target organisms and download the data for training of an individually customized classifier (photos: Lepidoptera by Entomological Collection of ETH Zürich; Orthoptera by Naturalis Biodiversity Center; Brassicaceae by United Herbaria Z+ZT, ZT-00164967, ZT-00167494, ZT-00171530, CC BY-SA 4.0). The current figure shows a mock-up.
Figure 3 from: Greeff M, Caspers M, Kalkman V, Willemse L, Sunderland BD, Bánki O, Hogeweg L (2022) Sharing taxonomic expertise between natural history collections using image recognition. Research Ideas and Outcomes 8: e79187. https://doi.org/10.3897/rio.8.e79187
Figure 3 Sharing of taxonomic knowledge between institutes. (1) Each algorithm contains two basic components: the feature extractor and the classifier. (2) The Central Library of Datasets allows the user to browse through all available images of collection objects; (3) based on all available images, a regularly updated central feature extractor is created and published; (4) custom made algorithms can relatively easily be created by building a classifier based on a selection of taxa from the central library and combining this with the central feature extractor; (5) newly created algorithms together with their metadata (probability & information on content) are published through a web service in the Central Library of Algorithms (6) and can be used through the Identification web services (API) either for batch processing of images or through a mobile app. Models can be easily extended by other institutions by combining data sources (7).
Figure 1 from: Greeff M, Caspers M, Kalkman V, Willemse L, Sunderland BD, Bánki O, Hogeweg L (2022) Sharing taxonomic expertise between natural history collections using image recognition. Research Ideas and Outcomes 8: e79187. https://doi.org/10.3897/rio.8.e79187
Figure 1 In the Central Library of Algorithms, natural history collection staff will select algorithms (feature extractors, models, etc.) that are most appropriate for the identification of their target organisms and add them to the workbench. The current figure shows a mock-up.
Figure 6 from: Greeff M, Caspers M, Kalkman V, Willemse L, Sunderland BD, Bánki O, Hogeweg L (2022) Sharing taxonomic expertise between natural history collections using image recognition. Research Ideas and Outcomes 8: e79187. https://doi.org/10.3897/rio.8.e79187
Figure 6 Mock-up of an interface for automated taxon identification. Naturalis holds over 500.000 specimens of unmounted, unsorted and often unidentified, papered butterflies and moths that were collected mostly in Europe and Asia over the past 200 years. In early 2016, Naturalis embarked on a 10-year-project to digitally identify all these specimens with the help of dedicated volunteers (Caspers et al. 2019). Specimens are unpacked, photographed, had their label data registered and then repacked, still unmounted, for long-term storage. Specimen images were then dragged and dropped into a web-based interface to get a near-instant response with multiple predictions about the taxonomic identity including probability values.
Figure 5 from: Greeff M, Caspers M, Kalkman V, Willemse L, Sunderland BD, Bánki O, Hogeweg L (2022) Sharing taxonomic expertise between natural history collections using image recognition. Research Ideas and Outcomes 8: e79187. https://doi.org/10.3897/rio.8.e79187
Figure 5 Non-expert collection staff easily find and afterwards sort specimens by taxon (line color) and by accuracy of the identification (line type). The insect drawer is from the Oxford University Museum of Natural History. The current figure shows a mock-up.
Figure 4 from: Greeff M, Caspers M, Kalkman V, Willemse L, Sunderland BD, Bánki O, Hogeweg L (2022) Sharing taxonomic expertise between natural history collections using image recognition. Research Ideas and Outcomes 8: e79187. https://doi.org/10.3897/rio.8.e79187
Figure 4 Algorithms recognize and number individual specimens in a drawer of unsorted items. The insect drawer is from the Oxford University Museum of Natural History. The current figure shows a mock-up.
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