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Record W2143099614 · doi:10.1086/677295

Plant Size, Sexual Selection, and the Evolution of Protandry in Dioecious Plants

2014· article· en· W2143099614 on OpenAlexaff
Jessica R. K. Forrest

Bibliographic record

VenueThe American Naturalist · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsBiologyPhenologyDioecyOvuleReproductionNatural selectionSexual selectionPopulationSexual reproductionEcologyDemography

Abstract

fetched live from OpenAlex

It is frequently observed that males of dioecious plant species flower earlier in the season than females, although the generality of this pattern has not been quantified. One hypothesis for earlier male flowering is that females require more time for resource acquisition before reproduction; another is that selection for access to unfertilized ovules favors early-flowering males. Here I show that protandry is indeed the usual pattern in dioecious plants--males typically initiate flowering before females--and I propose a new hypothesis to explain this pattern. In many natural plant populations, individuals that begin flowering early are larger and--in the case of females or hermaphrodites--therefore more fecund. When this population-level seasonal decline in size is included in simulations of flowering time evolution in a dioecious plant, males evolve earlier flowering onset than females. Correlations between size (or condition) and reproductive phenology are widespread and likely contribute to the prevalence of protandry in both plants and animals, but their importance seems to have been overlooked by botanists. I suggest that sexual selection (specifically, male-male competition for access to high-quality ovules) may play a more important role in the evolution of flowering phenology than has previously been recognized.

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 score0.415
Threshold uncertainty score0.998

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 head0.011
GPT teacher head0.197
Teacher spread0.186 · 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

Citations75
Published2014
Admission routes1
Has abstractyes

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