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Female reproductive success and the evolution of mating‐type frequencies in tristylous populations

2006· article· en· W2158721342 on OpenAlexafffund
Kathryn A. Hodgins, Spencer C. H. Barrett

Bibliographic record

VenueNew Phytologist · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiologyMatingPollenReproductive successFrequency-dependent selectionMating typeFertilityZoologyNatural selectionSelection (genetic algorithm)Evolutionary biologyEcologyPopulationDemographyGenetics

Abstract

fetched live from OpenAlex

In tristylous populations, mating-type frequencies are governed by negative frequency-dependent selection typically resulting in equal morph ratios at equilibrium. However, Narcissus triandrus generally exhibits long-styled (L)-biased populations with a deficiency of the mid-styled (M)-morph. Here we used a pollen-transfer model and measurements of female fertility in natural populations to investigate whether these uneven morph ratios were associated with variation in female reproductive success. Our theoretical analysis demonstrated that morph ratio bias can result from maternal fitness differences among the morphs, and that these effects were magnified by asymmetrical mating. In nine out of 15 populations of N. triandrus, seed set differed significantly among the morphs, but pollen limitation occurred in only two of 11 populations investigated. Average seed set of the M-morph was positively associated with its frequency in populations. Flower size was negatively correlated with the seed set of the M-morph. Our results suggest that interactions between mating patterns and female fertility are responsible for variation in morph frequencies and loss of the M-morph from tristylous populations of N. triandrus.

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 score0.969

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 head0.057
GPT teacher head0.248
Teacher spread0.192 · 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

Citations38
Published2006
Admission routes2
Has abstractyes

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