Compensation through rosette formation: the response of scarlet gilia (<i>Ipomopsis aggregata</i>: Polemoniaceae) to mammalian herbivory
Bibliographic record
Abstract
Plants could potentially compensate for floral herbivory by regrowing flowering stalks and by forming additional vegetative stems. Because scarlet gilia (Ipomopsis aggregata (Pursh) V. Grant) is described as monocarpic, its ability to regrow multiple flowering stalks following the removal of its primary inflorescence has been cited as the species’ primary means of compensating for herbivory. However, ancillary rosette formation could also contribute to compensation in subsequent years. To determine if herbivory induces ancillary rosette formation and whether energy diverted to vegetative regrowth reduces reproductive output, we analyzed the response of scarlet gilia to elk herbivory in the Wenatchee National Forest of Washington State. Control plants were protected from herbivory by wire enclosures; clipped plants were hand-cut to simulate herbivory; and grazed plants were left vulnerable to elk herbivory. Ninety percent of plants that lost inflorescences regrew multiple flowering stalks; these plants produced fewer fruits and seeds than protected plants, indicating that scarlet gilia undercompensated for herbivory despite greater aboveground biomass. The plants that regrew multiple flowering stalks were also more likely to form ancillary rosettes, which could increase compensation over multiple seasons. Although herbivory reduced initial fecundity, grazing generated morphological changes that could enable the plant to achieve a greater degree of compensation over time.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".