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Record W1936493094 · doi:10.1890/14-1461

Changes in horn size of Stone's sheep over four decades correlate with trophy hunting pressure

2016· article· en· W1936493094 on OpenAlexafffundabout
Mathieu Douhard, Marco Festa‐Bianchet, Fanie Pelletier, Jean‐Michel Gaillard, Christophe Bonenfant

Bibliographic record

VenueEcological Applications · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and phenotypic traits in livestock
Canadian institutionsUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of CanadaMinistère de l'Education Nationale, de l'Enseignement Superieur et de la Recherche
KeywordsTrophyFrench hornEcologyGeographyBiologyArchaeology

Abstract

fetched live from OpenAlex

Selective harvest may lead to rapid evolutionary change. For large herbivores, trophy hunting removes males with large horns. That artificial selection, operating in opposition to sexual selection, can lead to undesirable consequences for management and conservation. There have been no comparisons of long-term changes in trophy size under contrasting harvest pressures. We analyzed horn measurements of Stone's rams (Ovis dalli stonei) harvested over 37 years in two large regions of British Columbia, Canada, with marked differences in hunting pressure to identify when selective hunting may cause a long-term decrease in horn growth. Under strong selective harvest, horn growth early in life and the number of males harvested declined by 12% and 45%, respectively, over the study period. Horn shape also changed over time: horn length became shorter for a given base circumference, likely because horn base is not a direct target of hunter selection. In contrast, under relatively lower hunting pressure, there were no detectable temporal trends in early horn growth, number of males harvested, or horn length relative to base circumference. Trophy hunting is an important recreational activity and can generate substantial revenues for conservation. By providing a reproductive advantage to males with smaller horns and reducing the availability of desirable trophies, however, excessive harvest may have the undesirable long-term consequences of reducing both the harvest and the horn size of rams. These consequences can be avoided by limiting offtake.

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 score0.730
Threshold uncertainty score0.258

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 head0.009
GPT teacher head0.228
Teacher spread0.219 · 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

Citations54
Published2016
Admission routes3
Has abstractyes

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