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A MODEL OF THE EVOLUTION OF DICHOGAMY INCORPORATING SEX‐RATIO SELECTION, ANTHER‐STIGMA INTERFERENCE, AND INBREEDING DEPRESSION

2006· article· en· W2176746381 on OpenAlexaff
Risa D. Sargent, Mohammad A. Mandegar, Sarah P. Otto

Bibliographic record

VenueEvolution · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBiologyInbreeding depressionPollenBiological dispersalStamenOutbreeding depressionPopulationInbreedingEvolutionary biologyBotanyDemography

Abstract

fetched live from OpenAlex

Historically, explanations for the evolution of floral traits that reduce self-fertilization have tended to focus on selection to avoid inbreeding depression. However, there is growing support for the hypothesis that such traits also play a role in promoting efficient pollen dispersal by reducing anther-stigma interference. The relative importance of these two selective pressures is currently a popular topic of investigation. To date, there has been no theoretical exploration of the relative contributions of selection to avoid the genetic costs of self-fertilization and selection to promote efficient pollen dispersal on the evolution of floral traits. We developed a population genetic model to examine the influence of these factors on the evolution of dichogamy: the temporal separation of anther maturation and stigma receptivity. Our analysis indicates that anther-stigma interference can favor dichogamy even in the absence of in-breeding depression. Although anther-stigma interference and inbreeding depression are the key forces driving the initial evolution of dichogamy, selection to match the timing of pollen dispersal to the availability of ovules at the population level becomes a more potent force opposing the further evolution of dichogamy as the extent of temporal separation increases. This result may help to explain otherwise puzzling phenomena such as why dichogamy is rarely complete in nature and why dichogamy tends to be associated with asynchronous flower presentation.

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 score0.600
Threshold uncertainty score0.233

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 head0.028
GPT teacher head0.195
Teacher spread0.167 · 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

Citations49
Published2006
Admission routes1
Has abstractyes

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