Plant genotype, nutrients, and G × E interactions structure floral visitor communities
Bibliographic record
Abstract
Intraspecific variation in plants is driven by both genetic and environmental factors and has been shown to play an important role in determining assemblages of herbivores, predators, and pathogens. Yet, the consequences of genetic (G) and environmental (E) factors, as well as potential (G × E) interactions, for floral visitor communities remains poorly explored. In a common garden experiment, we compared the relative effects of host‐plant genotype and genotypic diversity as well as soil nutrient enrichment on floral resource abundance and insect floral visitors associated with tall goldenrod,Solidago altissima. We found that the floral visitor community varied considerably among genotypes, driven predominantly by variation in floral phenology amongS. altissimaclones. Floral visitors also varied among nutrient treatments, though this response was much weaker than to different plant genotypes, and was likely driven by effects of floral rewards rather than of floral phenology. Importantly, we also detected several G × E interactions for both flowering and floral visitors. Taken together, our results suggest that the effects of host‐plant genetic variation, and to a lesser extent G × E interactions, are key agents in structuring the diversity and composition of floral visitors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".