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Record W2154884318 · doi:10.1086/674130

Extrapair Paternity Rates Vary with Latitude and Elevation in Emberizid Sparrows

2013· article· en· W2154884318 on OpenAlexaff
Frances Bonier, Cas Eikenaar, Paul R. Martin, Ignacio T. Moore

Bibliographic record

VenueThe American Naturalist · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsQueen's University
Fundersnot available
KeywordsLatitudeBiologyElevation (ballistics)EcologyMating systemPopulationMatingPaternal careDemographyOffspringZoologyGeographyPregnancy

Abstract

fetched live from OpenAlex

Mating systems can vary among species and populations and thus influence evolutionary trajectories, ecological traits, and population demography. The siring of offspring by an extrapair male, or extrapair paternity (EPP), is a widespread and varied phenomenon in all vertebrate classes. However, we do not understand all of the factors associated with variation in EPP rates. The breeding synchrony hypothesis suggests that EPP rates should increase with latitude and elevation, whereas the paternal care hypothesis predicts that EPP rates should decrease with elevation. To address these hypotheses, we investigated how population EPP rates vary over elevation and latitude in emberizid sparrows. In comparative analyses including the effects of phylogeny, the relationship between EPP rates and elevation depended on latitude. EPP rates were greater in higher-latitude populations. But within higher-latitude populations, EPP rates decreased with increasing elevation. These findings provide support for both the breeding synchrony and paternal care hypotheses, suggesting that in lower-latitude, higher-elevation populations, the need for male parental care does not outweigh the benefits of seeking extrapair fertilizations in populations with relatively synchronous breeding. In contrast, at higher-latitude, higher-elevation sites, the need for male parental care is greater and might drive lower rates of extrapair mating despite highly synchronous breeding.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score1.000

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.224
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.

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

Citations28
Published2013
Admission routes1
Has abstractyes

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