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Record W2116978523 · doi:10.1111/jbi.12404

The influence of biogeographical barriers on the population genetic structure and gene flow in a coastal Pacific seabird

2014· article· en· W2116978523 on OpenAlexafffund
Sarah J. Wallace, Shaye G. Wolf, Russell W. Bradley, A. Laurie Harvey, Vicki L. Friesen

Bibliographic record

VenueJournal of Biogeography · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic diversity and population structure
Canadian institutionsQueen's University
FundersEnvironment Canada
KeywordsGene flowSubspeciesPopulationCoalescent theoryGenetic structureRange (aeronautics)GeographyEcologyBiologyGenetic variationZoologyGeneGeneticsDemography

Abstract

fetched live from OpenAlex

Abstract Aim Our aim was to investigate the influence of biogeographical barriers along the Pacific coast of North America on population genetic structure and gene flow using Cassin's auklet ( Ptychoramphus aleuticus ) as a test case. Location We collected samples from 287 Cassin's auklets breeding along the Pacific coast of North America from the Aleutian Islands, Alaska, USA , to Baja California, Mexico. Methods We amplified a 706 base pair fragment of the mitochondrial control region and 11 microsatellites to obtain independent estimates of population genetic structure and gene flow among colonies using programs based on coalescent and Bayesian theory. We tested whether genetic differentiation was related to geographical distance between sampling sites, and whether gene flow has occurred between differentiated groups. Results We found two distinct genetic groups along the Cassin's auklet breeding range. These clusters matched the current subspecies designations, except that individuals breeding in the Channel Islands, California, were traditionally classified with the northern subspecies but were more genetically similar to the Baja California subspecies. Population genetic differentiation was not evident within either of the two genetic groups, despite large geographical distances between sampling locations. Evidence suggests that gene flow has occurred from the northern genetic group (Aleutian Islands to Southeast Farallon Islands) into the southern genetic group (Channel Islands to San Benito Island) since divergence, but gene flow may not have occurred in the opposite direction. These results suggest that a barrier to gene flow from south to north may occur at Point Conception. Main conclusions Although a relatively short geographical distance occurs between sampling sites of Cassin's auklets across Point Conception, individuals breeding north of Point Conception are genetically differentiated from individuals breeding in southern California and Baja California. Population genetic differentiation of the southern genetic group provides support for a role of a barrier to gene flow around Point Conception in generating biodiversity in this area.

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 score0.046
Threshold uncertainty score0.282

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 head0.003
GPT teacher head0.191
Teacher spread0.188 · 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

Citations17
Published2014
Admission routes2
Has abstractyes

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