DNA barcode library of Portuguese water mites, with the descriptions of two new species (Acari, Hydrachnidia)

This study presents the first results from the analysis of water mites collected in Portugal as part of the Biodiversity Genomics Europe project. 307 COI DNA barcodes clustered into 75 BINs are provided, with 38 BINs being unique and deposited for the first time in the Barcode of Life Data Systems (BOLD). 65 species have been identified, of which 36 are new to the water mite fauna of Portugal. Two species, <i>Torrenticolasoniae</i> Pešić, <b>sp. nov.</b> and <i>T.elisabethae</i> Pešić, <b>sp. nov.</b> (Torrenticolidae), are described as new to science. 47% of the water mite species currently known from Portugal now have reference barcodes in BOLD. High intraspecific distances were recorded for some species, suggesting the presence of cryptic diversity and species complexes that needs further study. Our results improve the DNA barcode reference database for Portuguese water mites, enhancing species identification accuracy and stimulating future investigation.

Authors

Institutions

Publication Details

Journal
ZooKeys
Published
2024-10-31
DOI
https://doi.org/10.3897/zookeys.1217.131730
Citations
6
Primary Topic
Study of Mite Species
Type
article
Field-Weighted Citation Impact
6.17

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

DNA barcode library of Portuguese water mites, with the descriptions of two new species (Acari, Hydrachnidia)

Claúdio Ciofi, Aline Costa Padilha, Sónia Ferreira, Andrzej Zawal et al.
6 citations
ZooKeys
Study of Mite Species
6.17
article

DNA barcode library of Portuguese water mites, with the descriptions of two new species (Acari, Hydrachnidia)

Claúdio Ciofi, Aline Costa Padilha, Sónia Ferreira, Andrzej Zawal, Torbjørn Ekrem, Lorenzo Ballini, Alessio Iannucci, Sara Fratini, Elisabeth Stur, Vladimir Pešić, Laura Benítez, Giorgia Staffoni, Ana Cruz-Oliveira, Dinis Girão, Paolo Turaccio, Samantha Rossini
article en
6 citations

Abstract

This study presents the first results from the analysis of water mites collected in Portugal as part of the Biodiversity Genomics Europe project. 307 COI DNA barcodes clustered into 75 BINs are provided, with 38 BINs being unique and deposited for the first time in the Barcode of Life Data Systems (BOLD). 65 species have been identified, of which 36 are new to the water mite fauna of Portugal. Two species, <i>Torrenticolasoniae</i> Pešić, <b>sp. nov.</b> and <i>T.elisabethae</i> Pešić, <b>sp. nov.</b> (Torrenticolidae), are described as new to science. 47% of the water mite species currently known from Portugal now have reference barcodes in BOLD. High intraspecific distances were recorded for some species, suggesting the presence of cryptic diversity and species complexes that needs further study. Our results improve the DNA barcode reference database for Portuguese water mites, enhancing species identification accuracy and stimulating future investigation.

ZooKeysVol. 1217
University of Szczecin (PL), Universidade do Porto (PT), Norwegian University of Science and Technology (NO), Institut thématique Génétique, génomique et bioinformatique (FR), Campo Arqueologico de Mertola (PT), University of Florence (IT), University of Montenegro (ME)
UK Research and Innovation, Universidade do Porto, Directorate for Computer and Information Science and Engineering, HORIZON EUROPE Framework Programme, Fundação para a Ciência e a Tecnologia, Staatssekretariat für Bildung, Forschung und Innovation, European Regional Development Fund, Erasmus+
Life in Land
Openalex Percentile: Top 4%
Study of Mite Species
6.17
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.