An ecological cost of plant defence: attractiveness of bitter cucumber plants to natural enemies of herbivores
Bibliographic record
Abstract
Abstract Plants produce defences that act directly on herbivores and indirectly via the attraction of natural enemies of herbivores. We examined the pleiotropic effects of direct chemical defence production on indirect defence employing near‐isogenic varieties of cucumber plants (Cucumis sativus) that differ qualitatively in the production of terpenoid cucurbitacins, the most bitter compounds known. In release–recapture experiments conducted in greenhouse common gardens, blind predatory mites were attracted to plants infested by herbivorous mites. Infested sweet plants (lacking cucurbitacins), however, attracted 37% more predatory mites than infested bitter plants (that produce constitutive and inducible cucurbitacins). Analysis of the headspace of plants revealed that production of cucurbitacins was genetically correlated with large increases in the qualitative and quantitative spectrum of volatile compounds produced by plants, including induced production of (E )‐β‐ocimene (3E )‐4,8‐dimethyl‐1,3,7‐nonatriene, (E,E)‐α‐farnesene, and methyl salicylate, all known to be attractants of predators. Nevertheless, plants that produced cucurbitacins were less attractive to predatory mites than plants that lacked cucurbitacins and predators were also half as fecund on these bitter plants. Thus, we provide novel evidence for an ecological trade‐off between direct and indirect plant defence. This cost of defence is mediated by the effects of cucurbitacins on predator fecundity and potentially by the production of volatile compounds that may be repellent to predators.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".