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Record W2567511664 · doi:10.3354/meps12009

Temporal genetic change in North American Pacific oyster populations suggests caution in seascape genetics analyses of high gene-flow species

2016· article· en· W2567511664 on OpenAlexaboutno aff
Xiujun Sun, Dennis Hedgecock

Bibliographic record

VenueMarine Ecology Progress Series · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine Bivalve and Aquaculture Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSeascapePopulationPacific oysterBiologyPopulation geneticsEcologyGenetic variationGene flowCrassostreaGeographyOysterPhylogeographyFisheryDemographyGeneticsGenePhylogenetic treeHabitat

Abstract

fetched live from OpenAlex

MEPS Marine Ecology Progress Series Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsTheme Sections MEPS 565:79-93 (2017) - DOI: https://doi.org/10.3354/meps12009 Temporal genetic change in North American Pacific oyster populations suggests caution in seascape genetics analyses of high gene-flow species Xiujun Sun1,2, Dennis Hedgecock1,* 1Department of Biological Sciences, University of Southern California, Los Angeles, California 90089-0371, USA 2Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao 266071, PR China *Corresponding author: dhedge@usc.edu ABSTRACT: The Pacific oyster Crassostrea gigas was, for decades, massively introduced to North America from Japan and established large, self-recruiting populations in the Pacific Northwest of the USA and Canada. A previous study of mtDNA variation revealed little population genetic structure among populations from British Columbia and Washington State. Here, we used samples from that study, more recent samples from 2 of the same localities, and 2 additional samples, including 1 from Japan, to investigate spatial and temporal genetic variation at 52 mapped, coding, single-nucleotide polymorphisms (SNPs) assayed by high-resolution melting (HRM). Little variation was detected among North American populations, which, as a group, are distinct, perhaps adaptively so, from oysters in Hiroshima, Japan. However, significant excesses of heterozygotes with respect to random mating expectations and of pairwise linkage disequilibria revealed that North American populations are not in Hardy-Weinberg (random mating) equilibrium. Moreover, genetic changes over 10 to 21 yr in 2 localities are substantial, despite high gene flow, and are as large as spatial variance per generation. These results caution against basing connectivity or seascape genetic analyses on snapshots of spatial population structure in high gene-flow species. Because migration and selection are ruled out as causes of temporal genetic change, random genetic drift is the most parsimonious explanation. This implies effective population sizes (Ne) of hundreds to a few thousands, orders of magnitude smaller than the natural abundance (N) of this oyster. These low Ne:N ratios are compatible with the hypothesis of sweepstakes reproductive success. KEY WORDS: Crassostrea gigas · Single nucleotide polymorphism · High-resolution melting · Linkage disequilibrium · Genetic variance · Effective population size · Sweepstakes reproductive success Full text in pdf format Supplement 1 Supplement 2 PreviousNextCite this article as: Sun X, Hedgecock D (2017) Temporal genetic change in North American Pacific oyster populations suggests caution in seascape genetics analyses of high gene-flow species. Mar Ecol Prog Ser 565:79-93. https://doi.org/10.3354/meps12009 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in MEPS Vol. 565. Online publication date: February 17, 2017 Print ISSN: 0171-8630; Online ISSN: 1616-1599 Copyright © 2017 Inter-Research.

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 score0.220
Threshold uncertainty score0.792

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.032
GPT teacher head0.285
Teacher spread0.253 · 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

Citations23
Published2016
Admission routes1
Has abstractyes

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