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Record W3003160476 · doi:10.1038/s41467-020-14479-7

Balancing selection via life-history trade-offs maintains an inversion polymorphism in a seaweed fly

2020· article· en· W3003160476 on OpenAlexafffund
Claire Mérot, Violaine Llaurens, Éric Normandeau, Louis Bernatchez, Maren Wellenreuther

Bibliographic record

VenueNature Communications · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal plant biology
Canadian institutionsUniversité Laval
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaFonds de Recherche du Québec - SantéVetenskapsrådet
KeywordsBalancing selectionBiologyPleiotropyEvolutionary biologyNatural selectionOverdominanceGenetic variationLife history theoryGenetic driftGeneticsSelection (genetic algorithm)EcologyLife historyPhenotypeGeneAlleleComputer scienceMachine learning

Abstract

fetched live from OpenAlex

How natural diversity is maintained is an evolutionary puzzle. Genetic variation can be eroded by drift and directional selection but some polymorphisms persist for long time periods, implicating a role for balancing selection. Here, we investigate the maintenance of a chromosomal inversion polymorphism in the seaweed fly Coelopa frigida. Using experimental evolution and quantifying fitness, we show that the inversion underlies a life-history trade-off, whereby each haplotype has opposing effects on larval survival and adult reproduction. Numerical simulations confirm that such antagonistic pleiotropy can maintain polymorphism. Our results also highlight the importance of sex-specific effects, dominance and environmental heterogeneity, whose interaction enhances the maintenance of polymorphism through antagonistic pleiotropy. Overall, our findings directly demonstrate how overdominance and sexual antagonism can emerge from a life-history trade-off, inviting reconsideration of antagonistic pleiotropy as a key part of multi-headed balancing selection processes that enable the persistence of genetic variation.

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 score0.465
Threshold uncertainty score0.996

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 science0.0010.000
Research integrity0.0000.001
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 head0.023
GPT teacher head0.220
Teacher spread0.198 · 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

Citations132
Published2020
Admission routes2
Has abstractyes

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