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Record W2803664782 · doi:10.1111/jeb.13291

Gene flow does not prevent personality and morphological differentiation between two blue tit populations

2018· article· en· W2803664782 on OpenAlexaff
Gabrielle Dubuc‐Messier, Charles Perrier, Kees van Oers, Denis Réale, Anne Charmantier

Bibliographic record

VenueJournal of Evolutionary Biology · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsUniversité du Québec à Montréal
FundersEuropean Research CouncilNederlandse Organisatie voor Wetenschappelijk OnderzoekAgence Nationale de la Recherche
KeywordsBiologyGene flowPhenotypic traitTraitEvolutionary biologyPhenotypic plasticityPopulationGenetic divergenceLocal adaptationCyanistesAdaptation (eye)Directional selectionEcologyPhenotypeGenetic variationGeneticsGeneGenetic diversityDemography

Abstract

fetched live from OpenAlex

Abstract Understanding the causes and consequences of population phenotypic divergence is a central goal in ecology and evolution. Phenotypic divergence among populations can result from genetic divergence, phenotypic plasticity or a combination of the two. However, few studies have deciphered these mechanisms for populations geographically close and connected by gene flow, especially in the case of personality traits. In this study, we used a common garden experiment to explore the genetic basis of the phenotypic divergence observed between two blue tit ( Cyanistes caeruleus ) populations inhabiting contrasting habitats separated by 25 km, for two personality traits (exploration speed and handling aggression), one physiological trait (heart rate during restraint) and two morphological traits (tarsus length and body mass). Blue tit nestlings were removed from their population and raised in a common garden for up to 5 years. We then compared adult phenotypes between the two populations, as well as trait‐specific Q st and F st . Our results revealed differences between populations similar to those found in the wild, suggesting a genetic divergence for all traits. Q st – F st comparisons revealed that the trait divergences likely result from dissimilar selection patterns rather than from genetic drift. Our study is one of the first to report a Q st – F st comparison for personality traits and adds to the growing body of evidence that population genetic divergence is possible at a small scale for a variety of traits including behavioural traits.

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 score0.456
Threshold uncertainty score0.420

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 head0.055
GPT teacher head0.295
Teacher spread0.240 · 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

Citations17
Published2018
Admission routes1
Has abstractyes

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