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Record W2291278863 · doi:10.1111/evo.12900

Antiherbivore defenses alter natural selection on plant reproductive traits

2016· article· en· W2291278863 on OpenAlexaff
Ken Thompson, Marc T. J. Johnson

Bibliographic record

VenueEvolution · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBiologyHerbivoreNatural selectionPollinatorSelection (genetic algorithm)EcologyEvolutionary biologyPollination

Abstract

fetched live from OpenAlex

While many studies demonstrate that herbivores alter selection on plant reproductive traits, little is known about whether antiherbivore defenses affect selection on these traits. We hypothesized that antiherbivore defenses could alter selection on reproductive traits by altering trait expression through allocation trade-offs, or by altering interactions with mutualists and/or antagonists. To test our hypothesis, we used white clover, Trifolium repens, which has a Mendelian polymorphism for the production of hydrogen cyanide-a potent antiherbivore defense. We conducted a common garden experiment with 185 clonal families of T. repens that included cyanogenic and acyanogenic genotypes. We quantified resistance to herbivores, and selection on six floral traits and phenology via male and female fitness. Cyanogenesis reduced herbivory but did not alter the expression of reproductive traits through allocation trade-offs. However, the presence of cyanogenic defenses altered natural selection on petal morphology and the number of flowers within inflorescences via female fitness. Herbivory influenced selection on flowers and phenology via female fitness independently of cyanogenesis. Our results demonstrate that both herbivory and antiherbivore defenses alter natural selection on plant reproductive traits. We discuss the significance of these results for understanding how antiherbivore defenses interact with herbivores and pollinators to shape floral evolution.

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 score0.964
Threshold uncertainty score0.150

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 head0.026
GPT teacher head0.197
Teacher spread0.171 · 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

Citations32
Published2016
Admission routes1
Has abstractyes

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