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Song and Sperm in Crickets: A Trade-off between Pre- and Post-copulatory Traits or Phenotype-Linked Fertility?

2010· article· en· W2150809205 on OpenAlexaff
Samantha P. Klaus, Lauren P. Fitzsimmons, Trevor E. Pitcher, Susan M. Bertram

Bibliographic record

VenueEthology · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of WindsorCarleton University
Fundersnot available
KeywordsWindsorLibrary scienceBiologyArtEcologyComputer science

Abstract

fetched live from OpenAlex

When females mate multiply (polyandry) both pre- and post-copulatory sexual selection can occur. Sperm competition theory predicts there should be a trade-off between investment in attracting mates and investment in ejaculate quality. In contrast, the phenotype-linked fertility hypothesis predicts a positive relationship should exist between investment in attracting mates and investment in ejaculate quality. Given the need to understand how pre- and post-copulatory sexual selection interacts, we investigated the relationship between secondary sexual traits and ejaculate quality using the European house cricket, Acheta domesticus. Although we found no direct relationship between cricket secondary sexual signals and ejaculate quality, variation in ejaculate quality was dependent on male body weight and mating latency: the lightest males produced twice as many sperm as the heaviest males but took longer to mate with females. Our findings are consistent with current theoretical models of sperm competition. Given light males may have lower mating success than heavy males because females take longer to mate with them in no-choice tests, light males may be exhibiting an alternative reproductive tactic by providing females with more living sperm. Together, our findings suggest that the fitness of heavy males may depend on pre-copulatory sexual selection, while the fitness of light males may depend on post-copulatory fertilization success.

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 score0.581
Threshold uncertainty score0.827

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 head0.044
GPT teacher head0.261
Teacher spread0.217 · 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

Citations23
Published2010
Admission routes1
Has abstractyes

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