Insect predators affect plant resistance via density‐ and trait‐mediated indirect interactions
Bibliographic record
Abstract
Predators can affect herbivores both through direct consumption (density-mediated interactions) and by changing behavioural, physiological or morphological attributes of the prey (trait-mediated interactions). These effects on the herbivore can in turn affect the plant through density- and trait-mediated indirect interactions (DMIIs and TMIIs). While the effects of DMIIs and TMIIs imposed by predators has been shown to influence plant density and plant communities, we know little about the effects on plant quality. In addition, the DMII and TMII components of the predator may influence each other so that the total effect of the predator on the plant is not simply the sum of the DMII and TMII. We examined DMIIs and TMIIs between a stinkbug predator and a caterpillar, and show how these interactions affect plant quality, as measured by damage, resistance to herbivores, and a defence chemical, peroxidase. We used novel methods to estimate the independent and non-additive contribution of DMIIs and TMIIs to the plant phenotype. Both predator-induced DMIIs and TMIIs caused decreases in the amount of caterpillar herbivory on plants; a strong non-additive effect between the two resulted from redundancy in their effects. TMIIs initiated by the predator were primarily responsible for a decrease in induced plant resistance. However, DMIIs predominated for reducing the production of peroxidase. These data demonstrate how DMIIs and TMIIs initiated by predators cascade through tri-trophic interactions to affect plant damage and induced resistance.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".