Evolutionary ecology of nectar
Bibliographic record
Abstract
Background: Floral nectar is an important determinant of plant-pollinator interactions and an integral component of pollination syndromes, suggesting it is under pollinator-mediated selection. However, compared to floral display traits, we know little about the evolutionary ecology of nectar. Combining a literature review with a meta-analysis approach, we summarize the evidence for heritable variation in nectar traits and link this variation to pollinator response and plant fitness. We further review associations between nectar traits and floral signals and discuss them in the context of honest signalling and targets of selection. Scope: Although nectar is strongly influenced by environmental factors, heritable variation in nectar production rate has been documented in several populations (mean h2 = 0.31). Almost nothing is known about heritability of other nectar traits, such as sugar and amino acid concentrations. Only a handful of studies have quantified selection on nectar traits, and few find statistically significant selection. Pollinator responses to nectar traits indicate they may drive selection, but studies tying pollinator preferences to plant fitness are lacking. So far, only one study conclusively identified pollinators as selective agents on a nectar trait, and the role of microbes, herbivores, nectar robbers and abiotic factors in nectar evolution is largely hypothetical. Finally, there is a trend for positive correlations among floral cues and nectar traits, indicating honest signalling of rewards. Conclusions: Important progress can be made by studies that quantify current selection on nectar in natural populations, as well as experimental approaches that identify the target traits and selective agents involved. Signal-reward associations suggest that correlational selection may shape evolution of nectar traits, and studies exploring these more complex forms of natural selection are needed. Many questions about nectar evolution remain unanswered, making this a field ripe for future research.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".