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Genomic consequences of genetic rescue in an insular population of bighorn sheep (<i>Ovis canadensis</i>)

2012· article· en· W1929441448 on OpenAlexafffund
Joshua M. Miller, Jocelyn Poissant, John T. Hogg, David W. Coltman

Bibliographic record

VenueMolecular Ecology · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and phenotypic traits in livestock
Canadian institutionsUniversity of Alberta
FundersU.S. Fish and Wildlife ServiceAlberta InnovatesEppley Foundation for ResearchCharles Engelhard FoundationAlberta Conservation AssociationNational Geographic Society
KeywordsBiologyOvis canadensisPopulationOutbreeding depressionInbreedingInbreeding depressionGeneticsMicrosatellitePopulation fragmentationGenetic loadEvolutionary biologyGenetic variationAlleleGene flowGeneDemography

Abstract

fetched live from OpenAlex

Genetic rescue is a management intervention whereby a small population is supplemented with individuals from other populations in an attempt to reverse the effects of inbreeding and increased genetic load. One such rescue was recently documented in the population of bighorn sheep (Ovis canadensis) within the National Bison Range wildlife refuge (Montana, USA). Here, we examine the locus-specific effects of rescue in this population using a newly developed genome-wide set of 195 microsatellite loci and first-generation linkage map. We found that the rate of introgression varied among loci and that 111 loci, 57% of those examined, deviated from patterns of neutral inheritance. The most common deviation was an excess of homozygous genotypes relative to neutral expectations, indicative of directional selection. As in previous study of this rescue, individuals with more introduced alleles had higher reproductive success and longevity. In addition, we found 30 loci, distributed throughout the genome, which seem to have individual effects on these life history traits. Although the potential for outbreeding depression is a major concern when translocating individuals between populations, we found no evidence of such effects in this population.

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 score0.427
Threshold uncertainty score0.650

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 head0.010
GPT teacher head0.238
Teacher spread0.228 · 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

Citations108
Published2012
Admission routes2
Has abstractyes

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