Fur versus Feathers: Pollen Delivery by Bats and Hummingbirds and Consequences for Pollen Production
Bibliographic record
Abstract
One floral characteristic associated with bat pollination (chiropterophily) is copious pollen production, a pattern we confirmed in a local comparison of hummingbird- and bat-adapted flowers from a cloud forest site in Ecuador. Previous authors have suggested that wasteful pollen transfer by bats accounted for the pattern. Here we propose and test a new hypothesis: bats select for increased pollen production because they can efficiently transfer larger amounts of pollen, which leads to a more linear male fitness gain curve for bat-pollinated plants. Flight cage experiments with artificial flowers and flowers of Aphelandra acanthus provide support for this hypothesis; in both instances, the amount of pollen delivered to stigmas by birds is not related to the amount of pollen removed from anthers on the previous visit, while the same function for bats increases linearly. Thus, increased pollen production will be linearly related to increased male reproductive success for bat flowers, while for bird flowers, increased pollen production leads to rapidly diminishing fitness returns. We speculate that fur takes up and holds more pollen than feathers, which seem to readily shed excess grains. Our gain-curve hypothesis may also explain why evolutionary shifts from bird to bat pollination seem more common than shifts in the opposite direction.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".