Physical defenses and herbivory vary more within plants than among plants in the tropical understory shrub <i>Piper polytrichum</i>
Bibliographic record
Abstract
There is a growing appreciation that much of the trait variation within plant species is represented within individuals, for example, occurring among leaves within a plant. Subindividual variation is predicted to have key ecological consequences, but empirical understanding of how subindividual variation relates to species interactions, such as herbivory, is limited. We measured two physical defenses and herbivore damage on multiple leaves within individual plants of Piper polytrichum C.DC. (Piperaceae), a tropical understory shrub. We partitioned variance among- and within-plants and quantified patterns of trait and damage variation vis-à-vis leaf size and architectural position. We found that variance was considerably higher within plants than among plants for toughness (97%) and trichome density (57%), and that herbivore damage also varied most within plants (74%). Surprisingly, leaf position and size explained only small amounts of variance in traits (2.5%–16.5%) and herbivory (≤4%), indicating subindividual variability had low spatial predictability. The data suggest that individual P. polytrichum plants represent heterogeneous and spatially unpredictable landscapes of physical traits, and that interactions with herbivores are similarly variable. Our results are consistent with the hypothesis that subindividual variability defends plants against herbivores by increasing the difficulty of foraging for high-quality tissue.
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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) | 0.000 | 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 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".