Evidence map›Paper›PMID 36553540›Full record

ArticleGenes2022

Comparing the Efficiency of Single-Locus Species Delimitation Methods within Trochoidea (Gastropoda: Vetigastropoda).

Bingyu Guo, Lingfeng Kong

Open access · goldAbstract read
In one paragraph

Article in Genes, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
4.5field-weighted citation impact, top 5% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 26 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors at 2 institutions in 1 country.

Bingyu GuoKey Laboratory of Mariculture, Ministry of Education, Ocean University of China, Qingdao 266003, China.
Lingfeng KongKey Laboratory of Mariculture, Ministry of Education, Ocean University of China, Qingdao 266003, China.ORCID 0000-0001-5263-1697
Ocean University of China · CNQingdao National Laboratory for Marine Science and Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the context of diminishing global biodiversity, the validity and practicality of species delimitation methods for the identification of many neglected and undescribed biodiverse species have been paid increasing attention. DNA sequence-based species delimitation methods are mainly classified into two categories, namely, distance-based and tree-based methods, and have been widely adopted in many studies. In the present study, we performed three distance-based (ad hoc threshold, ABGD, and ASAP) and four tree-based (sGMYC, mGMYC, PTP, and mPTP) analyses based on Trochoidea COI data and analyzed the discordance between them. Moreover, we also observed the performance of these methods at different taxonomic ranks (the genus, subfamily, and family ranks). The results suggested that the distance-based approach is generally superior to the tree-based approach, with the ASAP method being the most efficient. In terms of phylogenetic methods, the single threshold version performed better than the multiple threshold version of GMYC, and PTP showed higher efficiency than mPTP in delimiting species. Additionally, GMYC was found to be significantly influenced by taxonomic rank, showing poorer efficiency in datasets at the genus level than at higher levels. Finally, our results highlighted that cryptic diversity within Trochoidea (Mollusca: Vetigastropoda) might be underestimated, which provides quantitative evidence for excavating the cryptic lineages of these species.

Indexed as

GastropodaAnimalsBase SequenceBiodiversityDNA Barcoding, TaxonomicPhylogenyABGDASAPGMYCPTPspecies delimitationTrochoidea

Identifiers

PMID36553540
PMCPMC9778293
OpenAlexW4311519598

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.