Applying Behavioral-Ecological Theory to Plant Defense: Light-Dependent Movement in <i>Mimosa pudica</i> Suggests a Trade-Off between Predation Risk and Energetic Reward
Bibliographic record
Abstract
Many animal species tolerate different amounts of predation risk based on environmental conditions and the individual's own condition, often accepting greater risk when energetically stressed. We studied the sensitive plant Mimosa pudica to see whether it too accepts greater risk of predation when less light energy is available. This plant displays a defensive behavior of rapidly folding its leaves when stimulated by touch, thereby decreasing visibility to herbivores. Averting herbivory involves a trade-off because leaf closure results in a reduction in light foraging. We manipulated the light environment of individual M. pudica plants and recorded the time it took a plant to reopen its leaves following stimulation as a measure of tolerance of predation risk. As predicted by theory, avoidance behavior was sustained longer under high light conditions than under more light-limited conditions. These findings suggest this species balances the risk and reward of antiherbivore behavior in relation to current environmental conditions and that behavioral-ecological theory is a useful framework for understanding plant responses to predators.
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 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".