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SPATIOTEMPORAL VARIATION IN LINEAR NATURAL SELECTION ON BODY COLOR IN WILD GUPPIES (POECILIA RETICULATA)

2010· article· en· W2118885991 on OpenAlexaff
Dylan J. Weese, Swanne P. Gordon, Andrew P. Hendry, Michael T. Kinnison

Bibliographic record

VenueEvolution · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsMcGill University
FundersMaine Agricultural and Forest Experiment StationNational Science Foundation
KeywordsBiologyPredationPoeciliaSexual selectionNatural selectionSelection (genetic algorithm)ZoologyEcologyTraitGuppyPoeciliidaePredatorEvolutionary biologyFish <Actinopterygii>

Abstract

fetched live from OpenAlex

We conducted 10 mark-recapture experiments in natural populations of Trinidadian guppies to test hypotheses concerning the role of viability selection in geographic patterns of male color variation. Previous work has reported that male guppies are more colorful in low-predation sites than in high-predation sites. This pattern of phenotypic variation has been theorized to reflect differences in the balance between natural (viability) selection that disfavors bright male color (owing to predation) and sexual selection that favors bright color (owing to female choice). Our results support the prediction that male color is disfavored by viability selection in both predation regimes. However, it does not support the prediction that viability selection against male color is weaker in low-predation experiments. Instead, some of the most intense bouts of selection against color occurred in low-predation experiments. Our results illustrate considerable spatiotemporal variation in selection among experiments, but such variation was not generally correlated with local patterns of color diversity. More complex selective interactions, possibly including the indirect effects of predators on variation in mating behavior, as well as other environmental factors, might be required to more fully explain patterns of secondary sexual trait variation in this system.

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 score0.830
Threshold uncertainty score0.588

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 head0.009
GPT teacher head0.227
Teacher spread0.218 · 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

Citations61
Published2010
Admission routes1
Has abstractyes

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