Pollinator selection against toxic nectar as a key facilitator of a plant invasion
Bibliographic record
Abstract
nectar contains grayanotoxin I (GTX I) at concentrations that are lethal or sublethal to honeybees and a solitary bee in the plant's non-native range in Ireland. Here we further examined this conflict and tested the hypotheses that nectar GTX I is subject to negative pollinator-mediated selection in the non-native range, but that phenotypic linkage between GTX I levels in nectar and leaves acts as a constraint on independent evolution. We found that nectar GTX I experienced negative directional selection in the non-native range, in contrast to the native Iberian range, and that the magnitude and frequency of pollinator limitation indicated that selection was pollinator-mediated. Surprisingly, nectar GTX I levels were decoupled from those of leaves in the non-native range, which may have assisted post-invasion evolution of nectar without compromising the anti-herbivore function of GTX I (here demonstrated in bioassays with an ecologically relevant herbivore). Our study emphasizes the centrality of pollinator health as a concept linked to the invasion process, and how post-invasion evolution can be targeted toward minimizing lethal or sub-lethal effects on pollinators. This article is part of the theme issue 'Natural processes influencing pollinator health: from chemistry to landscapes'.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".