Interspecific competition in pollination systems: costs to male fitness via pollen misplacement
Bibliographic record
Abstract
Summary 1. Although competition for pollination is often invoked as a driver of broad‐scale evolutionary and ecological patterns, we still lack a clear understanding of the mechanics of such competition. When flower visitors alternate between two species of flower, heterospecific pollen transfer takes place. The impact of these mixed loads on the female reproductive success of a recipient has received considerable attention, but the concomitant loss of male reproductive success – because of pollens grains being lost to foreign stigmas – has received less. Furthermore, pollen losses are not limited to grains that land on stigmas, but can also include deposition on non‐stigmatic surfaces of the intervening flowers, or loss from the animal’s body through passive detachment or active grooming. We collectively term these losses because of competition ‘pollen misplacement’. 2. Here, we quantify pollen transferred by nectar bats between focal flowers (Aphelandra acanthus) with and without intervening visits to one of two competitor species. One competitor (Centropogon nigricans) places its pollen in the same region of bats’ heads as the focal species, while the other (Burmeistera sodiroana) places its pollen farther forward. 3. We found that (i) any intervening visit caused some reduction in the number of pollen grains transferred, (ii) competitor flowers with similar pollen‐placement locations caused greater reductions in pollen transfer and (iii) of these competitors, those in male phase (dispensing pollen) caused greater pollen loss than those in female phase (without pollen). 4. This study provides rare empirical support for the detrimental effects of competition for pollination on male fitness via pollen misplacement and is the first to show an added cost imposed by male‐phase competitors. Although this competition is especially strong when competitors overlap in pollen placement, diverging in pollen placement will not completely eliminate pollen loss during visits to foreign flowers, simply because pollen sheds or is groomed from pollinator’s bodies at some background rate over time. This suggests that any angiosperms that share pollinators face pervasive selection through male fitness to diverge in floral traits, alleviating competition by attracting different pollinators, altering floral phenology or encouraging floral constancy.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".