Taxonomic identification accuracy from BOLD and GenBank databases using over a thousand insect DNA barcodes from Colombia

Preliminary Development of Global-Local Balanced Vision Transformer Deep Learning with DNA Barcoding for Automated Identification and Validation of Forensic Sarcosaphagous Flies

Morphological classification is the gold standard for identifying necrophilous flies, but its complexity and the scarcity of experts make accurate classification challenging. The development of artificial intelligence for autonomous recognition holds promise as a new approach to improve the efficiency and accuracy of fly morphology identification. In our previous study, we developed a GLB-ViT (Global-Local Balanced Vision Transformer)-based deep learning model for fly species identification, which demonstrated improved identification capabilities. To expand the model's application scope to meet the practical needs of forensic science, we extended the model based on the forensic science practice scenarios, increased the database of identifiable sarcosaphagous fly species, and successfully developed a WeChat Mini Program based on the model. The results show that the model can achieve fast and effective identification of ten common sarcosaphagous flies in Hainan, and the overall correct rate reaches 94.00%. For the few cases of identification difficulties and suspicious results, we have also constructed a rapid molecular species identification system based on DNA Barcoding technology to achieve accurate species identification of the flies under study. As the local fly database continues to be improved, the model is expected to be applicable to local forensic practice.

Freshwater mollusc community screening - Classical and eDNA monitoring methods to detect rare, indicator and invasive species

Freshwater habitats and their quality have always been of utmost importance for human subsistence. Water quality assessment is an important tool, covering biological, chemical and hydromorphological aspects. Bioindicators such as the bivalves can be used as evidence for good water quality, but widespread groups such as species of the family Sphaeriidae Deshayes,1855 (1822) and genus Pisidium/Euglesa/Odhneripidisium also known as 'pea clams' are poorly known and lack taxonomic expertise. The situation is similar for many other benthic macroinvertebrate species used in biomonitoring. In this study, we tested if pea clams can be detected using eDNA metabarcoding methods applied to sediment and plankton samples from 15 lakes and rivers in Sweden. Additionally, we detected benthic macroinvertebrates, so-called indicator species used in freshwater monitoring, as well as rare or red-listed and invasive species. We created a COI reference barcode library of 22 species of Swedish freshwater molluscs, of which one species is new, and five species have less than five records on NCBI and BOLD. From 272 sediment and plankton samples, we detected 497 benthic macroinvertebrate indicator species, 20 mollusc species and 3 invasive species in 15 freshwater environments in Sweden using eDNA metabarcoding. We show that one of the sediment sampling methods (M42) can detect slightly more species in autumn compared to the plankton or sediment kick-net methods, or to collecting samples in spring. A clear advantage is that biological water quality indices formerly calculated using taxa identified to the family level can now be calculated using the species level, giving higher precision. We suggest that future freshwater monitoring efforts can be greatly improved and sped up through large-scale and strategic habitat screening using barcoding and metabarcoding methods to support decision-making and help fulfill the goals of the UN 2030 Agenda.

Environmental DNA barcoding reveals general biodiversity patterns in the large tropical rift Lake Albert

Lake Albert, Africa's seventh-largest lake and a biodiversity hotspot, faces significant environmental challenges, including unregulated anthropogenic pressure and a lack of comprehensive biological studies. To address the scarcity of biodiversity data, we utilized environmental DNA (eDNA) metabarcoding to assess the lake's eukaryotic and metazoan communities. Surface water samples were collected at three distinct locations: close to the southern inflow of the Semliki River, the central part of the lake, and close to the northern inflow of the Victoria Nile and outflow of the Albert Nile. We aimed to study ecological patterns across the lake, focusing on sequence variant richness and community composition, testing for differences among locations and between shoreline and pelagic zones. Consistent with previous morphology-based observations, our results revealed differences in community composition among the three sites, with cyclopoid copepods dominating the communities. Distance from shore was a significant factor influencing community composition, confirming expectations about the effects of nutrient and oxygen availability gradients. However, the lack of comprehensive reference sequence data limited accurate taxonomic assignments. Despite these limitations, our study demonstrates that eDNA metabarcoding is highly useful for assessing biodiversity in underexplored tropical freshwater ecosystems. We advocate for urgent efforts to generate reference sequences from tropical regions to enhance the utility of eDNA for biodiversity monitoring and conservation. Our findings underscore the potential of eDNA in providing insights into ecological patterns of entire communities and emphasize the need for comprehensive studies addressing the full taxonomic spectrum in tropical freshwater ecosystems.

Most soil and litter arthropods are unidentifiable based on current DNA barcode reference libraries

We are far from knowing all species living on the planet. Understanding biodiversity is demanding and requires time and expertise. Most groups are understudied given problems of identifying and delimiting species. DNA barcoding emerged to overcome some of the difficulties in identifying species. Its limitations derive from incomplete taxonomic knowledge and the lack of comprehensive DNA barcode libraries for so many taxonomic groups. Here, we evaluate how useful barcoding is for identifying arthropods from highly diverse leaf litter communities in the southern Appalachian Mountains (USA). We used 3 reference databases and several automated classification methods on a data set including several arthropod groups. Acari, Araneae, Collembola, Coleoptera, Diptera, and Hymenoptera were well represented, showing different performances across methods and databases. Spiders performed the best, with correct identification rates to species and genus levels of ~50% across databases. Springtails performed poorly, no barcodes were identified to species or genus. Other groups showed poor to mediocre performance, from around 3% (mites) to 20% (beetles) correctly identified barcodes to species, but also with some false identifications. In general, BOLD-based identification offered the best identification results but, in all cases except spiders, performance is poor, with less than a fifth of specimens correctly identified to genus or species. Our results indicate that the soil arthropod fauna is still insufficiently documented, with many species unrepresented in DNA barcode libraries. More effort toward integrative taxonomic characterization is needed to complete our reference libraries before we can rely on DNA barcoding as a universally applicable identification method.

DNA barcoding insufficiently identifies European wild bees (Hymenoptera, Anthophila) due to undefined species diversity, genus-specific barcoding gaps and database errors

Recent declines in insect abundances, especially populations of wild pollinators, pose a threat to many natural and agricultural ecosystems. Traditional species monitoring relies on morphological character identification and is inadequate for efficient and standardized surveys. DNA barcoding has become a standard approach for molecular identification of organisms, aiming to overcome the shortcomings of traditional biodiversity monitoring. However, its efficacy depends on the completeness of reference databases. Large DNA barcoding efforts are (almost entirely) lacking in many European countries and such patchy data limit Europe-wide analyses of precisely how to apply DNA barcoding in wild bee identification. Here, we advance towards an effective molecular identification of European wild bees. We conducted a high-effort survey of wild bees at the junction of central and southern Europe and DNA barcoded all collected morphospecies. For global analyses, we complemented our DNA barcode dataset with all relevant European species and conducted global analyses of species delimitation, general and genus-specific barcoding gaps and examined the error rate in DNA data repositories. We found that (i) a sixth of all specimens from Slovenia could not be reliably identified, (ii) species delimitation methods show numerous systematic discrepancies, (iii) there is no general barcoding gap across all bees and (iv) the barcoding gap is genus specific, but only after curating for errors in DNA data repositories. Intense sampling and barcoding efforts in underrepresented regions and strict curation of DNA barcode repositories are needed to enhance the use of DNA barcoding for the identification of wild bees.