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Record W2177329370 · doi:10.1086/652473

Fur versus Feathers: Pollen Delivery by Bats and Hummingbirds and Consequences for Pollen Production

2010· article· en· W2177329370 on OpenAlexaff
Nathan Muchhala, James D. Thomson

Bibliographic record

VenueThe American Naturalist · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPollenHummingbirdBiologyPollinationBotanyPollinatorFeatherStamenEcologyZoology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.887
Threshold uncertainty score0.359

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.030
GPT teacher head0.238
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations100
Published2010
Admission routes1
Has abstractyes

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