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Record W4362715639 · doi:10.1093/beheco/arad017

Mating assortment and the strength of sexual selection in a polyandrous population of Cook Strait giant weta

2023· article· en· W4362715639 on OpenAlexaff
Clint D. Kelly, Darryl Gwynne

Bibliographic record

VenueBehavioral Ecology · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsUniversity of TorontoUniversité du Québec à Montréal
Fundersnot available
KeywordsBiologySexual selectionMatingPolygynySperm competitionMate choiceZoologyMating systemReproductive successPopulationSelection (genetic algorithm)EcologyDemography

Abstract

fetched live from OpenAlex

Abstract Polyandry can profoundly affect the strength of pre-copulatory sexual selection acting on males because each additional mate acquired by a female means that, all else being equal, a male’s paternity share declines. However, accruing additional mates could benefit male fitness if males with greater mating success also tend to have less promiscuous females as partners. If this is indeed the case, then males should experience strong sexual selection to acquire more mates. We tested these predictions by collecting detailed mating data on male and female Cook Strait giant weta (Deinacrida rugosa) in the wild via daily radio tracking. Our sexual network-based approach indeed revealed that the most polygynous male D. rugosa mated the least polyandrous females. This finding therefore suggests that the most successful males likely face lower intensities of sperm competition and so should be selected to accrue more mates. Further, our selection analysis revealed significant pre-copulatory sexual selection on males with those having relatively smaller body size, lighter body mass, and longer legs accruing more mates than otherwise. Thus, it appears that both pre- and post-copulatory episodes of sexual selection reinforce the same male phenotype.

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 score0.344
Threshold uncertainty score0.533

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 head0.030
GPT teacher head0.271
Teacher spread0.241 · 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
Published2023
Admission routes1
Has abstractyes

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