Pollination ecology of lowbush blueberry (<i>Vaccinium angustifolium</i> Aiton) in an island ecosystem
Bibliographic record
Abstract
Plant pollination is influenced by plant traits, pollinator community, plant community, and abiotic conditions. Island ecosystems, which often have reduced pollinator communities, provide unique insights into pollination ecology. Lowbush blueberry, Vaccinium angustifolium Aiton, has predominantly been studied in managed agricultural areas with introduced honeybees. We examined diurnal and nocturnal pollination of unmanaged lowbush blueberry patches on an 80 ha island in New Brunswick without honeybees. We restricted pollinator access to blueberry stems in five different treatments. Using mesh bags, we made stems accessible to pollinators 24 h a day (positive control), only during the day, only at night, or never (negative control), with an additional sham control. Blueberry stems accessible only to diurnal visitors had 70.55% fruit set and a mean 7.33 viable seeds per fruit. Stems accessible only to nocturnal visitors had 63.76% fruit set and 5.87 viable seeds, significantly higher than the continuously bagged negative control stems. The most common diurnal insects observed visiting flowers were bumblebees and two wasp species. Insects collected from plants at night were beetles and flies. There was substantial variation between blueberry patches in fruit set and fruit size. We examined whether flower color could be driving patch differences in pollination, and quantified flower color using spectrophotometry. We found no effect of flower color on metrics of pollination. As well as demonstrating substantial nocturnal pollination, we record unusually high fruit set, especially for an island without managed bees. We discuss some of the potential drivers of this high fruit set.
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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 | 0.000 |
| 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".