Resource availability and repeated defoliation mediate compensatory growth in trembling aspen (<i>Populus tremuloides</i>) seedlings
Bibliographic record
Abstract
Plant ecologists have debated the mechanisms used by plants to cope with the impact of herbivore damage. While plant resistance mechanisms have received much attention, plant compensatory growth as a type of plant tolerance mechanisms has been less studied. We conducted a greenhouse experiment to evaluate compensatory growth for trembling aspen (Populus tremuloides) seedlings under varying intensities and frequencies of simulated defoliation, with or without nutrient enriched media. For the purpose of this study, changes in biomass production and non-structural carbohydrate concentrations (NSC) of roots and leaves were considered compensatory responses. All defoliated seedlings showed biomass accumulation under low defoliation intensity and frequency, regardless of resource availability; however, as defoliation intensity and frequency increased, compensatory growth of seedlings was altered depending on resource availability. Seedlings in a resource-rich environment showed complete compensation, in contrast responses ranged from undercompensation to complete compensation in a resource-limited environment. Furthermore, at the highest defoliation intensity and frequency, NSC concentrations in leaves and roots were similar between defoliated and non-defoliated seedlings in a resource-rich environment; in contrast, defoliated seedlings with limited resources sustained the most biomass loss, had lower amounts of stored NSC. Using these results, we developed a new predictive framework incorporating the interactions between frequency and intensity of defoliation and resource availability as modulators of plant compensatory responses.
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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".