Attracting mutualists and antagonists: Plant trait variation explains the distribution of specialist floral herbivores and pollinators on crops and wild gourds
Bibliographic record
Abstract
UNLABELLED: • PREMISE OF THE STUDY: Floral traits play important roles in pollinator attraction and defense against floral herbivory. However, plants may experience trade-offs between conspicuousness to pollinators and herbivore attraction. Comparative studies provide an excellent framework to examine the role of multiple traits shaping mutualist and antagonist interactions.• METHODS: To assess whether putative defensive and attractive traits predict species interactions, we grew 20 different Cucurbitaceae species and varieties in the field to measure interactions with pollinators and herbivores and in the greenhouse to assess trait variation. Cucurbits are characterized by the production of cucurbitacins, bitter nonvolatile terpenoids that are effective against generalist herbivores but can attract specialist beetles. We determined whether plant traits such as cucurbitacins predict herbivore resistance and pollinator attraction using an information-theoretic approach.• KEY RESULTS: Mutualists and floral antagonists were attracted to the same cucurbit varieties once they flowered. However, rather than cucurbitacin concentration, we found that the size of the flower and volatile emissions of floral sesquiterpenoids explained both pollinator and floral herbivore visitation preference across cucurbit taxa. This pattern held across cucurbit taxa and within the Cucurbita genus.• CONCLUSIONS: Surprisingly, floral sesquiterpenoid volatiles, which are associated with direct defense, indirect defense, and attraction, rather than defense traits such as cucurbitacins, appeared to drive interactions with both pollinators and floral herbivores across cucurbit taxa. Identifying the relevant plant traits for attraction and deterrence is important in this economically valuable crop, particularly if pollinators and floral herbivores use the same plant traits as cues.
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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 | 0.001 | 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".