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Record W4402593351 · doi:10.1126/sciadv.adm8596

The genetic legacy of the Quaternary ice ages for West Palearctic butterflies

2024· article· en· W4402593351 on OpenAlexaff
Leonardo Dapporto, Mattia Menchetti, Vlad Dincă, Gerard Talavera, Aurora García‐Berro, Jacopo D’Ercole, Paul D. N. Hebert, Roger Vila

Bibliographic record

VenueScience Advances · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsQuaternaryBiological dispersalEcologyGlacial periodGenetic variationIce ageGeographySpecies richnessPhylogeographyGenetic structureLast Glacial MaximumBiologyEvolutionary biologyPhylogeneticsPaleontologyDemography

Abstract

fetched live from OpenAlex

The interplay between geographic barriers and climatic oscillations over the past 2.6 million years structured genetic variation at the continental scale. The genetic legacy of the Quaternary ice ages (GLQ) hypothesis outlines this phenomenon for Europe, but a comprehensive data-driven assessment is lacking. Using innovative genetic landscape methods, we model the GLQ in the West Palearctic based on 31,653 Cytochrome c oxidase subunit 1 (COI) sequences from 494 butterfly species and three functional traits. Seven distinct bioregions with varying levels of genetic endemicity emerge, revealing a latitudinal gradient in variation that confirms the "southern richness, northern purity" hypothesis. Through shift from case studies to a comparative approach, we objectively identify the main glacial refugia, colonization routes, and barriers to dispersal. Our findings offer a quantitative model of the GLQ across Europe, North Africa, and neighboring Asia, with broader applicability to other taxa and potentially scalable to encompass life on Earth.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.769
Threshold uncertainty score0.687

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.257
Teacher spread0.225 · 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

Citations22
Published2024
Admission routes1
Has abstractyes

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