Pollinator experience, neophobia and the evolution of flowering time
Bibliographic record
Abstract
Environmental changes, such as current climate warming, can exert directional selection on reproductive phenology. In plants, evolution of earlier flowering requires that the individuals bearing genes for early flowering successfully reproduce; for non-selfing, zoophilous species, this means that early flowering individuals must be visited by pollinators. In a laboratory experiment with artificial flowers, we presented captive bumble-bees (Bombus impatiens) with flower arrays representing stages in the phenological progression of a two-species plant community: Bees that had been foraging on flowers of one colour were confronted with increasing numbers of flowers of a second colour. Early flowering individuals of the second 'species' were significantly under-visited, because bees avoided unfamiliar flowers, particularly when these were rare. We incorporated these aspects of bee foraging behaviour (neophobia and positive frequency dependence) in a simulation model of flowering-time evolution for a plant population experiencing selection against late flowering. Unlike simple frequency dependence, a lag in pollinator visitation prevented the plant population from responding to selection and led to declines in population size. Pollinator behaviour thus has the potential to constrain evolutionary adjustments of flowering phenology.
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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 | 0.000 |
| Research integrity | 0.001 | 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".