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HOW WELL DO MATING FREQUENCY AND DURATION PREDICT PATERNITY SUCCESS IN THE POLYGYNANDROUS WATER STRIDER AQUARIUS REMIGIS?

2002· article· en· W2044654586 on OpenAlexafffund
Richard Vermette, Daphne J. Fairbairn

Bibliographic record

VenueEvolution · 2002
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiologyMatingEcologyZoologyEvolutionary biology

Abstract

fetched live from OpenAlex

The relationship between mating success and paternity success is a key component of sexual selection but has seldom been estimated for species in which both sexes mate with many partners (polygynandry). We used a modification of Parker's sterile male technique to measure this relationship for the water strider Aquarius remigis in 47 laboratory populations simulating natural conditions of polygynandry. We also tested the hypothesis that prolonged copulation, a characteristic of this species, enhances paternity success. Mating behavior and paternity success were assayed for four days while males and females freely interacted. Paternity success was also assayed for an additional 7 days when females were isolated from males. Mating success significantly predicted paternity success and accounted for < or = 36% of the variance. Copulation duration was negatively related to both mating success and paternity success and did not explain any of the residual variance in paternity success. Thus, we found no evidence that prolonged copulation functions as a paternity assurance strategy in this species. Comparisons of sterile and fertile males suggested that paternity success is directly influenced by the quantity of sperm transferred. Our results support previous studies that have used mating success to estimate sexual selection, but also highlight the potential importance of sperm competition and other postinsemination processes.

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 score0.384
Threshold uncertainty score0.167

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 head0.018
GPT teacher head0.193
Teacher spread0.175 · 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

Citations29
Published2002
Admission routes2
Has abstractyes

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