The effect of experimental pollinator decline on pollinator-mediated selection on floral traits
Bibliographic record
Abstract
Human-mediated environmental change, by reducing mean fitness, is hypothesized to strengthen selection on traits that mediate interactions among species. For example, human-mediated declines in pollinator populations are hypothesized to reduce mean seed production by increasing the magnitude of pollen limitation and thus strengthen pollinator-mediated selection on floral traits that increase pollinator attraction or pollen transfer efficiency. To test this hypothesis, we measured two female fitness components and six floral traits of Lobelia siphilitica plants exposed to supplemental hand-pollination, ambient open-pollination, or reduced open-pollination treatments. The reduced treatment simulated pollinator decline, while the supplemental treatment was used to estimate pollen limitation and pollinator-mediated selection. We found that plants in the reduced pollination treatment were significantly pollen-limited, resulting in pollinator-mediated selection for taller inflorescences and more vibrant petals, both traits that could increase pollinator attraction. This contrasts with plants in the ambient pollination treatment, where reproduction was not pollen-limited and there was no significant pollinator-mediated selection on any floral trait. Our results support the hypothesis that human-mediated environmental change can strengthen the selection on traits of interacting species and suggest that these traits have the potential to evolve in response to changing environments.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".