Implications of enemy escape on chemically mediated interactions with mutualists: wild parsnip pollination in two hemispheres
Bibliographic record
Abstract
When plant species invade new areas, they can escape from specialist enemies and thereby reduce investment in chemical defense. Enemy release may have other impacts on plant chemistry; in the absence of specialists, plants may be able to increase production of volatiles that enhance attractiveness to pollinators. In the United States (US), the introduced Eurasian wild parsnip, Pastinaca sativa, has long been subject to attack by an introduced coevolved florivore, Depressaria pastinacella, the parsnip webworm. In 2004, webworms were found for the first time attacking parsnips in New Zealand (NZ). Relative to US P. sativa, NZ P. sativa produces lower levels of defenses but higher levels of certain floral volatiles, suggesting that escape from its specialist florivore may have resulted in changes in volatile profiles to increase pollinator attraction.In this study, we examined the influence of wild parsnip floral volatiles on pollinator attraction and seed production in NZ and the US. While many insect groups were observed on parsnip umbels, the percentage of flowers that set seed could significantly be predicted by the occurrence of large calyptrate flies and small syrphids in both localities. In the US, β-pinene, γ-terpinene, hexyl butyrate, octyl butyrate, germacrene D and an unknown monoterpene were all positively correlated with visitation by large calyptrates and small syrphids. In NZ, trans- ocimene, carene and octyl butyrate were positively correlated with visitation. Remarkably, most compounds positively associated with visitation are produced in significantly higher proportions in NZ flowers, suggesting that NZ flowers, in the absence of specialized florivores, may be chemically better constituted to attract pollinators.
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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 | 0.000 |
| 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".