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Climate‐induced changes to the ancestral population size of two Patagonian galaxiids: the influence of glacial cycling

2011· article· en· W2139922128 on OpenAlexafffund
Tyler Zemlak, Sandra J. Walde, Evelyn Habit, Daniel E. Ruzzante

Bibliographic record

VenueMolecular Ecology · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic diversity and population structure
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaKillam TrustsDalhousie UniversityNational Geographic Society
KeywordsGlacial periodQuaternaryInterglacialBiologyPopulationEcologyLast Glacial MaximumRange (aeronautics)PleistoceneGlacierPaleontologyDemography

Abstract

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Patagonia is one of the few areas in the Southern Hemisphere to have been directly influenced by Quaternary glaciers. In this study, we evaluate the influence that Quaternary glacial ice had on the genetic diversity of two congeneric fish species, the diadromous Galaxias maculatus and the nondiadromous Galaxias platei, using multilocus estimates of effective population size through time. Mid-Quaternary glaciations had far-reaching consequences for both species. Galaxias maculatus and G. platei each experienced severe genetic bottlenecks during the period when Patagonia ice sheet advance reached its maximum positions c. 1.1-0.6 Ma. Concordant drops in effective size during this time suggest that range sizes were under similar constraints. It is therefore unlikely that coastal (brackish/marine) environments served as a significant refuge for G. maculatus during glacial periods. An earlier onset of population declines for G. platei suggests that this species was vulnerable to modest glacial advances. Declines in effective sizes were continuous for both species and lasted into the late-Pleistocene. However, G. maculatus exhibited a strong population recovery during the late-Quaternary (c. 400,000 bp). Unusually long and warm interglacials associated with the late-Quaternary may have helped to facilitate a strong population rebound in this primarily coastal 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 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.537
Threshold uncertainty score0.276

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 head0.018
GPT teacher head0.257
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

Citations53
Published2011
Admission routes2
Has abstractyes

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