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Record W2141662354 · doi:10.1525/cond.2012.110007

Temporal Analysis of mtDNA Variation Reveals Decreased Genetic Diversity in Least Terns

2012· article· en· W2141662354 on OpenAlexaff
Hope M. Draheim, Patricia A. Baird, Susan M. Haig

Bibliographic record

VenueOrnithological Applications · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic diversity and population structure
Canadian institutionsSimon Fraser University
FundersMuseum of Comparative Zoology, Harvard UniversityFlorida Museum of Natural HistoryHarvard UniversityBoettcher Foundation
KeywordsSubspeciesPhylogeographyTernGenetic diversityRange (aeronautics)BiologyGenetic variationEcologyEvolutionary biologyPopulationMitochondrial DNAConservation geneticsZoologyGeographyPhylogeneticsDemographyGeneticsMicrosatelliteGene

Abstract

fetched live from OpenAlex

The Least Tern (Sternula antillarum) has undergone large population declines over the last century as a result of direct and indirect anthropogenic factors. The genetic implications of these declines are unknown. We used historical museum specimens (pre-1960) and contemporary (2001–2005) samples to examine range-wide phylogeographic patterns and investigate potential loss in the species' genetic variation. We obtained sequences (522 bp) of the mitochondrial gene for NADH dehydrogenase subunit 6 (ND6) from 268 individuals from across the species' range. Phylogeographic analysis revealed no association with geography or traditional subspecies designations. However, we detected potential reductions in genetic diversity in contemporary samples from California and the Atlantic coast Least Tern from that in historical samples, suggesting that current genetic diversity in Least Tern populations is lower than in their pre-1960 counterparts. Our results offer unique insights into changes in the Least Tern's genetic diversity over the past century and highlight the importance and utility of museum specimens in studies of conservation genetics.

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 score0.032
Threshold uncertainty score0.326

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 head0.028
GPT teacher head0.267
Teacher spread0.239 · 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

Citations16
Published2012
Admission routes1
Has abstractyes

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