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Record W2433641442 · doi:10.1111/1365-2656.12541

Kin effects on energy allocation in group‐living ground squirrels

2016· article· en· W2433641442 on OpenAlexafffund
Vincent A. Viblanc, Claire Saraux, Jan O. Murie, F. Stephen Dobson

Bibliographic record

VenueJournal of Animal Ecology · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsKin recognitionReproductionInclusive fitnessBiologyOffspringNest (protein structural motif)EcologyKin selectionReproductive successCooperative breedingForagingDemographyPopulation

Abstract

fetched live from OpenAlex

The social environment has potent effects on individual phenotype and fitness in group-living species. We asked whether the presence of kin might act on energy allocation, a central aspect of life-history variation. Using a 22-year data set on reproductive and somatic allocations in Columbian ground squirrels (Urocitellus columbianus), we tested the effects of co-breeding and non-breeding kin on the fitness and energy allocation balance between reproduction and personal body condition of individual females. Greater numbers of co-breeding kin had a positive effect on the number of offspring weaned, through the mechanism of altering energy allocation patterns. On average, females with higher numbers of co-breeding kin did not increase energy income but biased energy allocation towards reproduction. Co-breeding female kin ground squirrels maintain close nest burrows, likely providing a social buffer against territorial invasions from non-kin ground squirrels. Lower aggressiveness, lower risks of infanticide from female kin and greater protection of territorial boundaries may allow individual females to derive net fitness benefits via their energy allocation strategies. We demonstrated the importance of kin effects on a fundamental life-history trade-off.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.197
Threshold uncertainty score0.848

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)0.0010.000

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 head0.011
GPT teacher head0.245
Teacher spread0.234 · 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

Citations11
Published2016
Admission routes2
Has abstractyes

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