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Determinants and consequences of age of primiparity in bighorn ewes

2011· article· en· W2118711165 on OpenAlexafffund
Julien G. A. Martin, Marco Festa‐Bianchet

Bibliographic record

VenueOikos · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicReproductive Physiology in Livestock
Canadian institutionsUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOvis canadensisReproductionLongevityBiologyPopulationReproductive successZoologyEcologyDemography

Abstract

fetched live from OpenAlex

Because variation in age of first reproduction can have major effects on individual fitness and population dynamics, it is important to understand what maintains that variability. Although early primiparity is assumed to be costly, it is sometimes associated with high lifetime reproductive success. We used a long‐term study on bighorn sheep Ovis canadensis to determine what variables affect age at first reproduction, investigate the impact of primiparity on body resources and quantify the reproductive performance of primiparous ewes. We then examined the consequences of delayed primiparity on adult body mass, longevity and lifetime reproductive success. Environmental conditions during early development, body mass as a yearling, genotype and maternal effects affected age of primiparity. Primiparous ewes lost more mass in winter and gained less mass in summer than multiparous ewes. Small yearling ewes that postponed reproduction attained similar adult mass than heavy yearling ewes who reproduced at a younger age. Early primiparity did not reduce longevity and was positively associated with lifetime reproductive success. Starting to reproduce as soon as possible appears to maximize fitness of females. When early life conditions are unfavorable, however, delayed primiparity allows greater body growth and likely maximizes survival. The combination of a conservative reproductive strategy and maternal effects on age of primiparity may partly delay population recovery following density‐dependent declines.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.476
Threshold uncertainty score0.166

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 head0.062
GPT teacher head0.260
Teacher spread0.198 · 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

Citations40
Published2011
Admission routes2
Has abstractyes

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