scholarly journals Species identification of zooplankton resting eggs based on DNA barcode technology: A case study of Lake Liuye (Changde), Lake Dongting Basin

2020 ◽  
Vol 32 (1) ◽  
pp. 154-163
Author(s):  
YU Wenbo ◽  
◽  
WANG Qing ◽  
WEI Nan ◽  
LIANG Diwen ◽  
...  
Keyword(s):  
Species Identification ◽  
Dna Barcode ◽  
Resting Eggs

scholarly journals Using DNA barcodes to assess identity and diversity of Dendropsophus minutus: Failure?

Zootaxa ◽  
2008 ◽  
Vol 1691 (1) ◽  
pp. 67 ◽  
Author(s):  
M. ALEX SMITH
Keyword(s):  
Cytochrome C ◽  
Small Group ◽  
Cytochrome C Oxidase ◽  
Species Identification ◽  
Mitochondrial Gene ◽  
Dna Barcode ◽  
Gene Region ◽  
Dna Barcodes ◽  
Taxonomic Groups

The 5' end (Folmer or Barcode region) of cytochrome c oxidase 1 (CO1) has been proposed as the gene region of choice for a standardized animal DNA barcode (Hebert et al. 2003). Concerns have been raised regarding the decision to utilize this particular mitochondrial gene region as a barcode. Nevertheless, widely divergent taxonomic groups have reported success using CO1 for both species identification and discovery. The utility of CO1 for barcoding amphibians was raised early on (Vences, et al. 2005) and concerns for this group were reported widely (Waugh 2007)—although some considered that the reporting of the concerns outstripped the data that had been analyzed at that point (Smith et al. 2008). Indeed, our analysis of CO1 for a small group of Holarctic amphibians was neither more difficult to generate nor to analyze than for other groups where we have utilized the technique.

Species identification of the medicinal plant Tulipa edulis (Liliaceae) by DNA barcode marker

2014 ◽  
Vol 55 ◽  
pp. 362-368 ◽  
Author(s):  
Hong-liang Ma ◽  
Zai-biao Zhu ◽  
Xiao-ming Zhang ◽  
Yuan-yuan Miao ◽  
Qiao-sheng Guo
Keyword(s):  
Medicinal Plant ◽  
Species Identification ◽  
Dna Barcode

Mini-DNA barcode in identification of the ornamental fish: A case study from Northeast India

Gene ◽  
2017 ◽  
Vol 627 ◽  
pp. 248-254 ◽  
Author(s):  
Bishal Dhar ◽  
Sankar Kumar Ghosh
Keyword(s):  
Dna Barcode ◽  
Northeast India ◽  
Ornamental Fish

scholarly journals Multi-locus DNA barcoding identifies matK as a suitable marker for species identification in Hibiscus L.

Genome ◽  
2016 ◽  
Vol 59 (12) ◽  
pp. 1150-1156 ◽  
Author(s):  
Sundar Poovitha ◽  
Nithaniyal Stalin ◽  
Raju Balaji ◽  
Madasamy Parani
Keyword(s):  
Dna Barcoding ◽  
Species Identification ◽  
Dna Barcode ◽  
Species Differentiation ◽  
Molecular Method ◽  
Peninsular India ◽  
Plant Materials ◽  
Medicinal Value ◽  
Formed Part ◽  
Suitable Marker

The genus Hibiscus L. includes several taxa of medicinal value and species used for the extraction of natural dyes. These applications require the use of authentic plant materials. DNA barcoding is a molecular method for species identification, which helps in reliable authentication by using one or more DNA barcode marker. In this study, we have collected 44 accessions, representing 16 species of Hibiscus, distributed in the southern peninsular India, to evaluate the discriminatory power of the two core barcodes rbcLa and matK together with the suggested additional regions trnH-psbA and ITS2. No intraspecies divergence was observed among the accessions studied. Interspecies divergence was 0%–9.6% with individual markers, which increased to 0%–12.5% and 0.8%–20.3% when using two- and three-marker combinations, respectively. Differentiation of all the species of Hibiscus was possible with the matK DNA barcode marker. Also, in two-marker combinations, only those combinations with matK differentiated all the species. Though all the three-marker combinations showed 100% species differentiation, species resolution was consistently better when the matK marker formed part of the combination. These results clearly showed that matK is more suitable when compared to rbcLa, trnH-psbA, and ITS2 for species identification in Hibiscus.

scholarly journals Species Identification by Mitochondrial DNA: A Case Study of Macaque Remains from Shuri Castle, Okinawa

2000 ◽  
Vol 16 (2) ◽  
pp. 87-94 ◽  
Author(s):  
Toshio MOURI ◽  
Takeshi AGATSUMA ◽  
Moritoshi IWAGAMI ◽  
Yoshi KAWAMOTO
Keyword(s):  
Mitochondrial Dna ◽  
Species Identification

scholarly journals Machine Learning for Species Identification: The HebelomaProject from database to website

2021 ◽  
Vol 5 ◽  
Author(s):  
Peter Bartlett ◽  
Ursula Eberhardt ◽  
Nicole Schütz ◽  
Henry Beker
Keyword(s):  
Machine Learning ◽  
Species Identification ◽  
Average Length ◽  
Dna Barcode ◽  
Morphological Characters ◽  
Molecular Data ◽  
Learning Phase ◽  
Training Set ◽  
Species Determination ◽  
Almost All

Attempts to use machine learning (ML) for species identification of macrofungi have usually involved the use of image recognition to deduce the species from photographs, sometimes combining this with collection metadata. Our approach is different: we use a set of quantified morphological characters (for example, the average length of the spores) and locality (GPS coordinates). Using this data alone, the machine can learn to differentiate between species. Our case study is the genus Hebeloma, fungi within the order Agaricales, where species determination is renowned as a difficult problem. Whether it is as a result of recent speciation, the plasticity of the species, hybridization or stasis is a difficult question to answer. What is sure is that this has led to difficulties with species delimitation and consequently a controversial taxonomy. The Hebeloma Project—our attempt to solve this problem by rigorously understanding the genus—has been evolving for over 20 years. We began organizing collections in a database in 2003. The database now has over 10,000 collections, from around the world, with not only metadata but also morphological descriptions and photographs, both macroscopic and microscopic, as well as molecular data including at least an internal transcribed spacer (ITS) sequence (generally, but not universally, accepted as a DNA barcode marker for fungi (Schoch et al. 2012)), and in many cases sequences of several loci. Included within this set of collections are almost all type specimens worldwide. The collections on the database have been analysed and compared. The analysis uses both the morphological and molecular data as well as information about habitat and location. In this way, almost all collections are assigned to a species. This development has been enabled and assisted by citizen scientists from around the globe, collecting and recording information about their finds as well as preserving material. From this database, we have built a website, which updates as the database updates. The website (hebeloma.org) is currently undergoing beta testing prior to a public launch. It includes up-to-date species descriptions, which are generated by amalgamating the data from the collections of each species in the database. Additional tools allow the user to explore those species with similar habitat preferences, or those from a particular biogeographic area. The user is also able to compare a range of characters of different species via an interactive plotter. The ML-based species identifier is featured on the website. The standardised storage of the collection data on the database forms the backbone for the identifier. A portion of the collections on the database are (almost) randomly selected as a training set for the learning phase of the algorithm. The learning is “supervised” in the sense that collections in the training set have been pre-assigned to a species by expert analysis. With the learning phase complete, the remainder of the database collections may then be used for testing. To use the species identifier on the website, a user inputs the same small number of morphological characters used to train the tool and it promptly returns the most likely species represented, ranked in order of probability. As well as describing the neural network behind the species identifier tool, we will demonstrate it in action on the website, present the successful results it has had in testing to date and discuss its current limitations and possible generalizations.

scholarly journals Baiting/Luring Improves Detection Probability and Species Identification—A Case Study of Mustelids with Camera Traps

Animals ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 2178
Author(s):  
Christoph Randler ◽  
Tobias Katzmaier ◽  
Jochen Kalb ◽  
Nadine Kalb ◽  
Thomas K. Gottschalk
Keyword(s):  
Species Identification ◽  
Camera Traps ◽  
Biological Research ◽  
Control Group ◽  
Target Species ◽  
Control Group Design ◽  
Species Activity ◽  
Trail Cameras ◽  
Scent Lures

Motion-triggered trail cameras (hereafter camera traps) are powerful tools which are increasingly used in biological research, especially for species inventories or the estimation of species activity. However, camera traps do not always reliably detect animal visits, as a target species might be too fast, too small, or too far away to trigger an image. Therefore, researchers often apply attractants, such as food or glandular scents, to increase the likelihood of capturing animals. Moreover, with attractants, individuals might remain in front of a camera trap for longer periods leading to a higher number of images and enhanced image quality, which in turn might aid in species identification. The current study compared how two commonly used attractants, bait (tuna) and glandular scent (mustelid mix), affected the detection and the number of images taken by camera traps compared to control camera sites with conventional camera traps. We used a before–after control group design, including a baseline. Attractants increased the probability of detecting the target species and number of images. Tuna experiments produced on average 7.25 times as many images per visit than control camera traps, and scent lures produced on average 18.7 times as many images per visit than the control traps.

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