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Record W2732849912 · doi:10.1111/ele.12806

The role of ecology, neutral processes and antagonistic coevolution in an apparent sexual arms race

2017· letter· en· W2732849912 on OpenAlexafffund
Jennifer C. Perry, Colin J. Garroway, Locke Rowe

Bibliographic record

VenueEcology Letters · 2017
Typeletter
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of TorontoUniversity of Manitoba
FundersJesus College, University of OxfordJesus College, University of CambridgeNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsEntomological Society of Canada
KeywordsCoevolutionBiologyAntagonistic CoevolutionTraitRace (biology)EcologyPopulationSexual conflictArms raceContext (archaeology)Sexual selectionEvolutionary biologyDemography

Abstract

fetched live from OpenAlex

Some of the strongest examples of a sexual 'arms race' come from observations of correlated evolution in sexually antagonistic traits among populations. However, it remains unclear whether these cases truly represent sexually antagonistic coevolution; alternatively, ecological or neutral processes might also drive correlated evolution. To investigate these alternatives, we evaluated the contributions of intersex genetic correlations, ecological context, neutral genetic divergence and sexual coevolution in the correlated evolution of antagonistic traits among populations of Gerris incognitus water striders. We could not detect intersex genetic correlations for these sexually antagonistic traits. Ecological variation was related to population variation in the key female antagonistic trait (spine length, a defence against males), as well as body size. Nevertheless, population covariation between sexually antagonistic traits remained substantial and significant even after accounting for all of these processes. Our results therefore provide strong evidence for a contemporary sexual arms race.

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 score0.218
Threshold uncertainty score0.964

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.024
GPT teacher head0.221
Teacher spread0.197 · 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

Citations43
Published2017
Admission routes2
Has abstractyes

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