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Age‐specific variation in survival, reproductive success and offspring quality in red squirrels: evidence of senescence

2008· article· en· W1968368170 on OpenAlexfundno aff
Sébastien Descamps, Stan Boutin, Dominique Berteaux, Jean‐Michel Gaillard

Bibliographic record

VenueOikos · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsSenescenceOffspringBiologyDemographyReproductive successWeaningLongevityPopulationReproductionPhysiologyZoologyEcologyPregnancyAnimal scienceGenetics

Abstract

fetched live from OpenAlex

Individual performance is expected to decrease with age because of senescence. We analyzed long‐term data collected on a North American red squirrel population to assess the influence of age on body mass, survival and reproductive performance, and to study the effects of sex and of environmental conditions during early life on senescence patterns. Mass of males and females did not decrease at the end of life, possibly because body mass mostly reflects overall size in income breeders such as red squirrels. On the other hand, we found evidence of senescence in survival of both sexes and, to a lesser extent, in female reproductive traits. When compared to females, males had both higher survival and delayed decrease in survival, suggesting a weaker senescence in males. The offspring survival from weaning to one year of age also decreased with increasing mother age. This suggests that older females produce juveniles of lower quality, providing evidence of an intergenerational effect of mother's age on juveniles’ fitness. Finally, our results indicate that variations in food conditions during early life influenced the reproductive tactics of females in the first years of their life, but not senescence patterns.

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 score0.042
Threshold uncertainty score0.387

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 head0.089
GPT teacher head0.322
Teacher spread0.233 · 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

Citations110
Published2008
Admission routes1
Has abstractyes

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