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Record W2160643483 · doi:10.2193/2009-335

Compensatory Growth Limits Opportunities for Artificial Selection in Alpine Chamois

2010· article· en· W2160643483 on OpenAlexafffund
ARCO RUGHETTI, Marco Festa‐Bianchet

Bibliographic record

VenueJournal of Wildlife Management · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFrench hornBiologySexual selectionReproductive successZoologyLower bodyPopulationEcologyMatingReproductionDemography

Abstract

fetched live from OpenAlex

ABSTRACT In ungulates, big males with large weapons typically outcompete other males over access to estrous females. In many species, rapid early growth leads to large adult mass and weapon size. We compared males in one hunted and one protected population of Alpine chamois ( Rupicapra rupicapra ) to examine the relationship between horn length and body mass. We assessed whether early development and hunter selectivity affected age‐specific patterns of body and horn size and whether sport hunting could be an artificial selection pressure favoring smaller horns. Adult horn length was mostly independent of body mass. For adult males, the coefficient of variation of horn length (0.06) was <50% of that for body mass (0.16), suggesting that horn length presents a lower potential for selection and may be less important for male mating success than is body mass. Surprisingly, early development did not affect adult mass because of apparent compensatory growth. We found few differences in body and horn size between hunted and protected populations, suggesting the absence of strong effects of hunting on male phenotype. If horn length has a limited role in male reproductive success, hunter selectivity for males with longer horns is unlikely to lead to an artificial selective pressure on horn size. These results imply that the potential evolutionary effects of selective hunting depend on how the characteristics selected by hunters affect individual reproductive success.

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 score0.204
Threshold uncertainty score0.370

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 head0.033
GPT teacher head0.242
Teacher spread0.209 · 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

Citations46
Published2010
Admission routes2
Has abstractyes

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