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Record W4400959723 · doi:10.1038/s41598-024-67595-5

Museum genomics provide insight into the extinction of a specialist North American warbler species

2024· article· en· W4400959723 on OpenAlexaff
Paige Byerly, Anna M. Kearns, Andreanna J. Welch, Margad-Erdene Ochirbat, Peter P. Marra, Amy Wilson, Michael G. Campana, Robert C. Fleischer

Bibliographic record

VenueScientific Reports · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic diversity and population structure
Canadian institutionsUniversity of British Columbia
FundersSmithsonian Institution
KeywordsWarblerEcologyHabitatHabitat fragmentationPopulationBiologyExtinction (optical mineralogy)Population genomicsRange (aeronautics)Evolutionary biologyZoologyGenomicsGenomeGeneticsDemography

Abstract

fetched live from OpenAlex

Museum genomics provide an opportunity to investigate population demographics of extinct species, especially valuable when research prior to extinction was minimal. The Bachman's warbler (Vermivora bachmanii) is hypothesized to have gone extinct due to loss of its specialized habitat. However, little is known about other potential contributing factors such as natural rarity or changes to connectivity following habitat fragmentation. We examined mitochondrial DNA (mtDNA) and genome-wide SNPs using specimens collected from breeding and migration sites across the range of the Bachman's warbler. We found no signals of strong population structuring across the breeding range of Bachman's warblers in both mtDNA and genome-wide SNPs. Thus, long-term population isolation did not appear to be a significant contributor to the extinction of the Bachman's warbler. Instead, our findings support the theory that Bachman's warblers underwent a rapid decline likely driven by habitat destruction, which may have been exacerbated by the natural rarity, habitat specificity and low genetic diversity of the species.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.993
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.012
GPT teacher head0.224
Teacher spread0.213 · 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 source (direct Gemma or distilled Codex), 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

Citations6
Published2024
Admission routes1
Has abstractyes

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