Arbuscular mycorrhizal interactions and nutrient supply mediate floral trait variation and pollinator visitation
Bibliographic record
Abstract
Floral traits, including floral display and nutritional rewards from pollen and nectar, drive pollinator visitation. Even within a single plant species, environmental factors can influence the quality and quantity of floral resources. Yet, the ecological interactions driving this variation in floral resources, especially those belowground, remain unknown. Here, we investigate how soil microbial community composition and nutrient availability, specifically distinct arbuscular mycorrhizal fungi (AMF) species and phosphorus (P) supply, affect plant growth, AMF traits, floral traits, and how that, in turn, affects bee visitation. We found that increased AMF richness of functional diversity enhanced floral display (flower size and number) and rewards (nectar volume and pollen protein) and increased bee visitation. Using structural equation modeling, we found that AMF associations could boost bee visitation by enhancing flower size. However, trade-offs occur; flower size correlates negatively with root colonization but positively with hyphal length, suggesting that AMF traits drive the effects of AMF on flower growth. Overall, the effect of AMF on floral traits and bee visitation was not homogenous; instead, AMF trait differences interact with P supply, resulting in varying effects on floral traits and subsequently bee foraging dynamics. These results highlight that focusing on beneficial belowground interactions could provide an opportunity to bolster bee visitation.
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 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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 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".