Variability in floral abundance can drive flower diversification and specialization
Bibliographic record
Abstract
The angiosperms show remarkable floral diversity. However, the ecological processes involved in flower diversification remain poorly understood. In this article I propose that different species' floral abundances drive adaptation to different pollinators and promote different degrees of specialization. In this view, interspecific variability in abundance can foster floral diversification. I develop a mathematical model of pollen transfer considering the interaction of several pollination processes---pollen removal and carryover, intra- and interspecific competition for pollinator visitation, and interspecific pollen transfer---that are linked to floral abundance. To assess if and how floral abundance can generate floral diversity, I use the model to assemble plant-pollinator networks from simulated plant and pollinator communities. The model shows that evolution on highly specialized pollinators and pollinators with high pollen carryover capacity is favoured at low floral abundance, while evolution on more abundant pollinators is favoured at high floral abundance. Furthermore, floral specialization is favoured at low floral abundance, while generalization is favoured at high abundance. In simulated plant communities of variable floral abundance, different pollinator systems evolve among the different plant species. The model demonstrates a new mechanism by which floral diversity can be generated, contributing to our understanding of floral evolution and diversification.
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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 | 0.002 |
| 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) | 0.001 | 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".