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EXTRA-PAIR PATERNITY AND THE VARIANCE IN MALE FITNESS IN SONG SPARROWS (<i>MELOSPIZA MELODIA</i>)

2012· article· en· W1772969502 on OpenAlexaff
Christophe Lebigre, Peter Arcese, Rebecca J. Sardell, Lukas F. Keller, Jane M. Reid

Bibliographic record

VenueEvolution · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsUniversity of British Columbia
FundersRoyal Society
KeywordsBiologySparrowReproductive successVariance (accounting)MatingPopulationMating systemCovarianceGenetic FitnessStatisticsEvolutionary biologyEcologyDemographyGeneticsMathematicsBiological evolution

Abstract

fetched live from OpenAlex

The variance in fitness across population members can influence major evolutionary processes. In socially monogamous but genetically polygynandrous species, extra-pair paternity (EPP) is widely hypothesized to increase the variance in male fitness compared to that arising given the socially monogamous mating system. This hypothesis has not been definitively tested because comprehensive data describing males' apparent (social) and realized (genetic) fitness have been lacking. We used 16 years of comprehensive social and genetic paternity data for an entire free-living song sparrow (Melospiza melodia) population to quantify and compare variances in male apparent and realized fitness, and to quantify the contribution of the variances in within-pair reproductive success (WPRS) and extra-pair reproductive success (EPRS) and their covariance to the variance in realized fitness. Overall, EPP increased the variance in male fitness by only 0-27% across different fitness and variance measures. This relatively small effect reflected the presence of socially unpaired males with zero apparent and low realized fitness, small covariance between WPRS and EPRS, and large variance in WPRS that was relatively unaffected by EPP. Therefore, although EPP altered individual males' contributions to future generations, its impact on population-level parameters such as the opportunity for selection and effective population size was limited.

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)0.001
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 score0.033
Threshold uncertainty score0.261

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.018
GPT teacher head0.211
Teacher spread0.193 · 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

Citations38
Published2012
Admission routes1
Has abstractyes

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