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Record W4402895094 · doi:10.1086/733307

Who Keeps the House after Divorcing? Partner and Nest Fidelity in the Long-Lived Alpine Swift

2024· article· en· W4402895094 on OpenAlexaff
Michela Natalina Dumas, Christoph M. Meier, Pierre Bize, Julien G. A. Martin

Bibliographic record

VenueThe American Naturalist · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsSwiftFidelityNest (protein structural motif)BiologyEcologyZoologyGeographyEvolutionary biologyPhysicsAstrophysicsComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

AbstractDespite the advantages of lasting pair bonds and the prevalence of monogamy, at least in avian species, some individuals switch mates (divorce). Divorce is generally considered to be adaptive (i.e., conferring net fitness benefits), although its causes and consequences often remain unclear, most notably regarding the genetic basis of this behavior. Using more than 30 years of data in a long-lived bird with obligate biparental care, the Alpine swift, we first described the overall patterns of mate and nest site fidelity and investigated the predictors of between-year divorce. We show that 16.6% of pairings ended in divorce, with low reproductive success and young age as predictors of divorce, and that males retained the nest site more often than females. By then studying individual repeatability and heritability of divorce, we show moderate repeatability in females and low repeatability in males and little additive genetic variance in either sex. Finally, we assessed the fitness consequences of divorce and report that an active decision to modify the pair bond (divorce) may be more beneficial than reactionary re-pairing following a partner's death. Overall, divorce may provide some reproductive benefits for Alpine swifts, but no microevolutionary potential of this behavior is evident in this population.

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 score0.513
Threshold uncertainty score0.803

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.019
GPT teacher head0.266
Teacher spread0.247 · 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

Citations4
Published2024
Admission routes1
Has abstractyes

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