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Record W2602459995 · doi:10.1111/evo.13240

Direct benefits of choosing a high-fitness mate can offset the indirect costs associated with intralocus sexual conflict

2017· article· en· W2602459995 on OpenAlexafffund
Alison Pischedda, Adam K. Chippindale

Bibliographic record

VenueEvolution · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsBiologySexual conflictGenetic FitnessOffspringReproductive successSelection (genetic algorithm)Mate choiceSexual selectionEvolutionary biologyDemographyGeneticsBiological evolutionPopulationComputer scienceMating

Abstract

fetched live from OpenAlex

Intralocus sexual conflict generates a cost to mate choice: high-fitness partners transmit genetic variation that confers lower fitness to offspring of the opposite sex. Our earlier work in the fruit fly, Drosophila melanogaster, revealed that these indirect genetic costs were sufficient to reverse potential "good genes" benefits of sexual selection. However, mate choice can also confer direct fitness benefits by inducing larger numbers of progeny. Here, we consider whether direct benefits through enhanced fertility could offset the costs associated with intralocus sexual conflict in D. melanogaster. Using hemiclonal analysis, we found that females mated to high-fitness males produced 11% more offspring compared to those mated to low-fitness males, and high-fitness females produced 34% more offspring than low-fitness females. These direct benefits more than offset the reduction in offspring fitness caused by intralocus sexual conflict, creating a net fitness benefit for each sex to pairing with a high-fitness partner. Our findings highlight the need to consider both direct and indirect effects when investigating the fitness impacts of mate choice. Direct fitness benefits may shelter sexually antagonistic alleles from selection, suggesting a novel mechanism for the maintenance of fitness 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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.455
Threshold uncertainty score1.000

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 studies0.0010.000
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 head0.028
GPT teacher head0.230
Teacher spread0.202 · 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
Published2017
Admission routes2
Has abstractyes

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