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Record W4221012194 · doi:10.1038/s42003-022-03159-5

Population genomics of Sitka black-tailed deer supports invasive species management and ecological restoration on islands

2022· article· en· W4221012194 on OpenAlexafffundabout
Brock T. Burgess, Robyn L. Irvine, Michael A. Russello

Bibliographic record

VenueCommunications Biology · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsParks CanadaOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of CanadaParks CanadaCompute Canada
KeywordsPopulation genomicsEcologyGeographyPopulationInvasive speciesGenomicsBiologyFisheryMedicineGenomeGene

Abstract

fetched live from OpenAlex

Abstract Invasive mammals represent a critical threat to island biodiversity; eradications can result in ecological restoration yet may fail in the absence of key population parameters. Over-browsing by invasive Sitka black-tailed deer ( Odocoileus hemionus sitkensis ) is causing severe ecological and cultural impacts across the Haida Gwaii archipelago (Canada). Previous eradication attempts demonstrate forest regeneration upon deer removal, but reinvasion reverses conservation gains. Here we use restriction-site associated DNA sequencing (12,947 SNPs) to investigate connectivity and gene flow of invasive deer ( n = 181) across 15 islands, revealing little structure throughout Haida Gwaii and identifying the large, central island of Moresby (>2600 km 2 ) as the greatest source of migrants. As a result, the archipelago itself should be considered the primary eradication unit, with the exception of geographically isolated islands like S G ang Gwaay. Thus, limiting eradications to isolated islands combined with controlled culling and enhanced biosecurity may be the most effective strategies for achieving ecological restoration goals.

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 score0.007
Threshold uncertainty score0.870

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 science0.0000.001
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 head0.027
GPT teacher head0.245
Teacher spread0.218 · 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

Citations10
Published2022
Admission routes3
Has abstractyes

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