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Record W4403389991 · doi:10.1093/evolut/qpae148

Optimal polyandry in fruit flies

2024· article· en· W4403389991 on OpenAlexafffund
Janice L. Yan, J. Rosenbaum, Yang Dan, Reuven Dukas

Bibliographic record

VenueEvolution · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsMcMaster University
FundersOntario Ministry of Research and InnovationNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsBiologyMatingSexual conflictSperm competitionHarassmentSexual selectionCompetition (biology)Experimental evolutionEcologyReproductive successZoologyDrosophila melanogasterGenetic FitnessMating systemDemographyBiological evolutionGeneticsPopulation

Abstract

fetched live from OpenAlex

The study of polyandry has received increasing scientific attention with an emphasis on the fitness benefits and costs that females derive from mating with multiple males. There are still gaps in our understanding of how polyandry affects female fitness, however, as many previous studies compared the fitness outcomes of a single mating vs. 2 or 3 matings and did not separate the consequences of multiple mating from the costs of sexual harassment. We, therefore, conducted controlled mating trials with female fruit flies (Drosophila melanogaster) that could mate at either low (every 8 days), medium (every 4 days), or high (every other day) rates while controlling for exposure to harassment from males. We found that female lifetime fitness was highest under the high mating-rate followed by the medium mating-rate conditions. Moreover, we did not detect reductions in lifespan as a consequence of higher rates of polyandry. Our results demonstrate that even at realistically high rates, polyandry can lead to net fitness benefits for females, which can have major implications for sexual selection. Specifically, we discuss the significance of our findings as they relate to competition and the evolution of secondary sex characteristics in females, and sperm competition among males.

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 score0.794
Threshold uncertainty score0.182

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 head0.029
GPT teacher head0.210
Teacher spread0.181 · 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

Citations6
Published2024
Admission routes2
Has abstractyes

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