Delayed flowering as a potential benefit-decreasing cost of compensatory regrowth
Bibliographic record
Abstract
Some monocarpic plants show surprising variation in response to natural and simulated grazing. In favourable conditions, injured plants may grow larger and produce more fruits and viable seeds compared with their uninjured counterparts. Such overcompensation, however, is associated with potential costs, particularly delays in flowering and fruit maturation. These delays may further increase a risk of frost injury before seed maturation in the early autumn. We tested the effects of artificially advanced night frosts on the compensatory capacity (i.e., the capacity to compensate the lost biomass by regrowth) of the monocarpic herb Erysimum strictum P. Gaertn., B. Mey., and Scherb. in a common garden experiment. Two simulated herbivory treatments were applied: 25% apical clipping in early vs. late June. Frost treatment was applied in a climate chamber in late August – early September, about 3 weeks before the first natural frost period. Apically damaged plants not exposed to frost treatment produced 1.9–2.6 times more total biomass and 2.5 times more fruit than intact plants. Frost treatment reduced plant performance by 35%–48%, but in contrast to our expectation, there was no significant difference between intact and apically damaged plants in response to early frost. In spite of the delay in phenology, compensatory regrowth did not increase the risk of frost injury. We conclude that while early night frosts imply a potential risk to monocarpic herbs recovering from herbivory, possibly other suboptimal conditions, such as drought in late summer, may provide a greater threat for early-flowering meadow and grassland plants recovering from grazing. Possibly multiple selective forces and environmental risk factors operate together in the evolution of grazing tolerance associated with flowering phenology, plant growth strategy, and architecture.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".