The Role of Local Species Abundance in the Evolution of Pollinator Attraction in Flowering Plants
Bibliographic record
Abstract
We present a population genetic model that incorporates aspects of pollinator efficiency and abundance to examine the effect of the local plant community on the evolution of floral trait specialization. Our model predicts that plant species evolve to be pollinator specialists on the most effective and common pollinators when their abundance is low relative to other plant species in the community (i.e., conspecific pollen is relatively rare) and evolve to be pollinator generalists when they are numerically dominant (i.e., conspecific pollen is abundant). Strong flower constancy also favors generalist floral traits. Furthermore, generalist species are predicted to differentiate when there is a concave trade-off in attracting pollinator species with different floral trait preferences. This result implies that populations that evolve toward a generalist strategy may be more prone to speciation. Ours is the first theoretical model to include local species abundance explicitly, despite the fact that it has been previously identified as an important factor in the evolution of plant specialization. Our results add a layer of ecological complexity to previous models of floral evolution and therefore have the potential to improve our power to predict circumstances under which specialized and generalized plant-pollinator interactions should evolve.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".