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Record W1590524838 · doi:10.1002/ece3.61

Intense harvesting of eastern wolves facilitated hybridization with coyotes

2011· article· en· W1590524838 on OpenAlexafffundabout
Linda Y. Rutledge, Bradley N. White, Jeffrey R. Row, Brent R. Patterson

Bibliographic record

VenueEcology and Evolution · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsMinistry of Natural Resources and ForestryTrent University
FundersMinistry of Natural Resources
KeywordsIntrogressionCullingWildlifePopulationCanisGeographyMitochondrial DNAWildlife conservationNational parkWildlife managementEcologyBiologyZoologyDemography

Abstract

fetched live from OpenAlex

Despite ethical arguments against lethal control of wildlife populations, culling is routinely used for the management of predators, invasive or pest species, and infectious diseases. Here, we demonstrate that culling of wildlife can have unforeseen impacts that can be detrimental to future conservation efforts. Specifically, we analyzed genetic data from eastern wolves (Canis lycaon) sampled in Algonquin Provincial Park (APP), Ontario, Canada from 1964 to 2007. Research culls in 1964 and 1965 killed the majority of wolves within a study region of APP, accounting for approximately 36% of the park's wolf population at a time when coyotes were colonizing the region. The culls were followed by a significant decrease in an eastern wolf mitochondrial DNA (mtDNA) haplotype (C1) in the Park's wolf population, as well as an increase in coyote mitochondrial and nuclear DNA. The introgression of nuclear DNA from coyotes, however, appears to have been curtailed by legislation that extended wolf protection outside park boundaries in 2001, although eastern wolf mtDNA haplotype C1 continued to decline and is now rare within the park population. We conclude that the wolf culls transformed the genetic composition of this unique eastern wolf population by facilitating coyote introgression. These results demonstrate that intense localized harvest of a seemingly abundant species can lead to unexpected hybridization events that encumber future conservation efforts. Ultimately, researchers need to contemplate not only the ethics of research methods, but also that future implications may be obscured by gaps in our current scientific understanding.

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 score0.002
Threshold uncertainty score0.347

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 head0.016
GPT teacher head0.184
Teacher spread0.168 · 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

Citations74
Published2011
Admission routes3
Has abstractyes

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