Interspecific variation in plant abundance can contribute to the diversity and evolutionary assembly of floral communities
Bibliographic record
Abstract
Angiosperms show remarkable floral diversity in pollinator use. However, how this diversity evolves in a community context remains poorly understood. In this article I propose that different plant species abundances promote adaptation to different pollinators and different degrees of specialization. In this view, interspecific variation in species abundance can foster floral diversity. 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. I integrate the model to an adaptive dynamic framework to simulate the evolutionary assembly of pollination networks. The model demonstrates that different pollinator attributes and degrees of generalization are favored at different plant relative abundances. Within communities, interspecific variation in plant abundance increased diversity in pollinator use and degree of generalization while also promoting the evolution of pollination networks with higher nestedness and lower connectance, leading to networks more consistent with natural pollination networks. The model helps understand the evolutionary assembly of flower communities and suggests 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".