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Record W2952715890 · doi:10.1098/rsbl.2018.0061

Individual repeatability and heritability of divorce in a wild population

2018· article· en· W2952715890 on OpenAlexfundno aff
Ryan R. Germain, Matthew E. Wolak, Jane M. Reid

Bibliographic record

VenueBiology Letters · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of British Columbia
KeywordsHeritabilityBiologySelection (genetic algorithm)TraitRepeatabilityPopulationAdaptation (eye)Genetic correlationEvolutionary biologyGenetic variationDemographyGeneticsStatistics

Abstract

fetched live from OpenAlex

Understanding micro-evolutionary responses of mating systems to contemporary selection requires estimating sex-specific additive genetic variances and cross-sex genetic covariances in key reproductive strategy traits. One key trait comprises the occurrence of divorce versus mate fidelity across sequential reproductive attempts. If divorce represents an evolving behavioural strategy that responds to selection it must have non-zero individual repeatability and heritability, but quantitative estimates from wild populations are scarce. We used 39 years of individual breeding records and pedigree data from free-living song sparrows ( Melospiza melodia ) to quantify sex-specific permanent individual and additive genetic variances, and hence estimate repeatability and heritability, in liability for divorce. We estimated moderate repeatability among females, but little repeatability among males. Estimates of additive genetic variance were small in both sexes, and the cross-sex genetic covariance was close to zero. Consequently, the total heritability was small but likely non-zero, indicating low potential for micro-evolutionary response to selection. Rapid micro-evolutionary change of divorce rate, therefore, appears unlikely, even if there were substantial fitness benefits of divorce and resulting 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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.325
Threshold uncertainty score0.236

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 head0.024
GPT teacher head0.248
Teacher spread0.224 · 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

Citations8
Published2018
Admission routes1
Has abstractyes

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