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Record W7090783415 · doi:10.5061/dryad.w0vt4b959

Haplodiploidy accelerates mitogenome evolution in insects

2025· dataset· en· W7090783415 on OpenAlexaff

Bibliographic record

VenueDRYAD · 2025
Typedataset
Languageen
FieldAgricultural and Biological Sciences
TopicChromosomal and Genetic Variations
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsIndelMitochondrial DNAHaplodiploidyGeneGenomePhylogenetic treeNuclear geneMutationPhylogenetics

Abstract

fetched live from OpenAlex

Rates of mitogenome evolution differ among animal lineages, and this variation has been linked to life history, ecological traits, and— potentially—to sex-determination system. Insects are a strong model for examining the latter factor because haplodiploidy (HD) has evolved on multiple occasions from a diplodiploid (DD) ancestral state. We tested for rate differences between DD and HD taxa by examining sequence change in a sentinel segment of the mitogenome, the 658 bp barcode region of the cytochrome c oxidase I (COI) gene. Specifically, we investigated if amino acid substitutions and indels are more frequent in HD than DD lineages by inspecting COI sequences from over 86,000 BINs (a species proxy) representing 783 insect families and 26 orders. Among them, ten lineages, varying in rank from tribe to order, are HD. Our analysis, which accounts for phylogeny, indicates that HD lineages have higher rates (1.7×) of amino acid substitution, higher Ka/Ks (3.5×), and far more indels than DD taxa. While our results demonstrate that HD accelerates mitogenome evolution, future work needs to clarify its mechanistic basis. We hypothesize that HD facilitates positive selection for mitochondrial mutations which encode proteins that interact with nuclear gene products. Such coevolutionary interactions should be facilitated because recessive mutations in the nuclear genome are fully exposed to selection in males of HD but not DD lineages.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.072
Threshold uncertainty score0.889

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.227
Teacher spread0.212 · 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 designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2025
Admission routes1
Has abstractyes

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