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Record W2957310322 · doi:10.1111/nph.16056

Population genomics of the facultatively asexual duckweed <i>Spirodela polyrhiza</i>

2019· article· en· W2957310322 on OpenAlexafffund
Eddie K. H. Ho, Magdalena P. Bartkowska, Stephen Wright, Aneil F. Agrawal

Bibliographic record

VenueNew Phytologist · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiologySelfingPopulationGenetic diversityNegative selectionNonsynonymous substitutionEffective population sizeEvolutionary biologyLinkage disequilibriumGenetic driftPopulation genomicsNatural selectionCoalescent theoryEcologyGenomeGenetic variationGenomicsGeneticsAllelePhylogeneticsGeneHaplotype

Abstract

fetched live from OpenAlex

Clonal propagation allows some plant species to achieve massive population sizes quickly but also reduces the evolutionary independence of different sites in the genome. We examine genome-wide genetic diversity in Spirodela polyrhiza, a duckweed that reproduces primarily asexually. We find that this geographically widespread and numerically abundant species has very low levels of genetic diversity. Diversity at nonsynonymous sites relative to synonymous sites is high, suggesting that purifying selection is weak. A potential explanation for this observation is that a very low frequency of sex renders selection ineffective. However, there is a pronounced decay in linkage disequilibrium over 40 kb, suggesting that though sex may be rare at the individual level it is not too infrequent at the population level. In addition, neutral diversity is affected by the physical proximity of selected sites, which would be unexpected if sex was exceedingly rare at the population level. The amount of genetic mixing as assessed by the decay in linkage disequilibrium is not dissimilar from selfing species such as Arabidopsis thaliana, yet selection appears to be much less effective in duckweed. We discuss alternative explanations for the signature of weak purifying selection.

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 score0.021
Threshold uncertainty score0.696

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)0.0010.000

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 head0.013
GPT teacher head0.233
Teacher spread0.221 · 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

Citations63
Published2019
Admission routes2
Has abstractyes

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