Mechanical leaf damage causes localized, but not systemic, changes in leaf movement behavior of the Sensitive Plant, <i>Mimosa pudica</i> (Fabaceae) L.
Bibliographic record
Abstract
A small number of species, including Mimosa pudica, use rapid leaf movement as a presumptive defensive strategy. How movement-based defenses change in response to mechanical damage and whether changes are localized or systemic is unknown. This is in contrast to a substantial literature describing how mechanical leaf damage can cause morphological and chemical responses within a diversity of plant species. Depending on the species and the stimuli, these chemical and morphological responses can be localized to the tissues damaged or systemic throughout the plant body. Here we report the results of a small experiment designed to test the following: (i) whether mechanical leaf damage influences subsequent leaf closure behavior, and (ii) whether changes were systemic or localized. To do this, we scored leaves using a behavioral assay (time-to-reopen leaves following a subsequent touch stimuli) for several days before and following mechanical damage. Leaves above and below the damaged leaf were observed, on damaged and undamaged plants, allowing us to assess whether any change was systemic. We found leaf damage caused strong localized effects, greatly increasing the time-to-reopen of the damaged, but not adjacent, leaves. Neither the physiological cause nor fitness consequences of this behavioral shift are known. Interestingly, this altered behavior resulted in damaged leaves remaining “hidden” longer than undamaged leaves. If leaf closure reduces risk of herbivory, there could be adaptive value, analogous to inducible chemical and morphological defenses.
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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 | 0.001 |
| 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".