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Record W1980273288 · doi:10.1086/650719

Temporally Variable Multivariate Sexual Selection on Sexually Dimorphic Traits in a Wild Insect Population

2010· article· en· W1980273288 on OpenAlexafffund
David Punzalan, F. Helen Rodd, Locke Rowe

Bibliographic record

VenueThe American Naturalist · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsUniversity of Toronto
FundersUniversity of Toronto
KeywordsSexual dimorphismBiologySelection (genetic algorithm)TraitSexual selectionMultivariate statisticsNatural selectionPopulationEcological selectionMultivariate analysisEvolutionary biologyDirectional selectionEcologyZoologyStatisticsDemographyMathematicsMachine learning

Abstract

fetched live from OpenAlex

A widely held view is that the strength and form of natural selection varies in time and space in response to varying ecological forces; however, adequate quantitative evaluations of this are relatively scarce. In this study, we measured the strength and form of sexual selection acting on a suite of male morphological traits in a wild ambush bug (Phymata americana) population at 10 sampling dates over 2 years. We tested the prediction that the strength and direction of sexual selection would be associated with one or more important ecological variables. We found that patterns of multivariate selection varied considerably over time, and even within a season. Yet, for this population, a sexually dimorphic color pattern trait was consistently a target of directional selection. The strength of sexual selection on this trait was related to both sex ratio and density, which is consistent with the idea that ecological factors can play an important role in generating patterns of sexual selection. We also demonstrate that the median strength of linear selection obtained from replicated cross-sectional methods was qualitatively similar to the estimates obtained from longitudinal methods, providing multiple lines of evidence that the evolution of sexual color dimorphism in this species is attributable to sexual selection.

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 score0.955
Threshold uncertainty score0.965

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.021
GPT teacher head0.253
Teacher spread0.232 · 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

Citations77
Published2010
Admission routes2
Has abstractyes

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