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Record W3033811237 · doi:10.1139/gen-2019-0191

The importance of DNA barcode choice in biogeographic analyses — a case study on marine midges of the genus <i>Clunio</i>

2020· article· en· W3033811237 on OpenAlexvenueno aff
Nico Fuhrmann, Tobias S. Kaiser

Bibliographic record

VenueGenome · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLepidoptera: Biology and Taxonomy
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyMitochondrial DNAEvolutionary biologyDNA barcodingGenomeNucleotide diversityEcotypeHaplotypeGeneticsGeneGenotype

Abstract

fetched live from OpenAlex

DNA barcodes are widely used for species identification and biogeographic studies. Here, we compare the use of full mitochondrial genomes versus DNA barcodes and other mitochondrial DNA fragments for biogeographic and ecological analyses. Our dataset comprised 120 mitochondrial genomes from the genus Clunio (Diptera: Chironomidae), comprising five populations from two closely related species (Clunio marinus and Clunio balticus) and three ecotypes. We extracted cytochrome oxidase c subunit I (COI) barcodes and partitioned the mitochondrial genomes into non-overlapping windows of 750 or 1500 bp. Haplotype networks and diversity indices were compared for these windows and full mitochondrial genomes (15.4 kb). Full mitochondrial genomes indicate complete geographic isolation between populations, but do not allow for conclusions on the separation of ecotypes or species. COI barcodes have comparatively few polymorphisms, ideal for species identification, but do not resolve geographic isolation. Many of the similarly sized 750 bp windows have higher nucleotide and haplotype diversity than COI barcodes, but still do not resolve biogeography. Only when increasing the window size to 1500 bp, two windows resolve biogeography reasonably well. Our results suggest that the design and use of DNA barcodes in biogeographic studies must be carefully evaluated for each investigated species.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.090
Threshold uncertainty score0.275

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.031
GPT teacher head0.276
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2020
Admission routes1
Has abstractyes

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