Floral traits and environmental factors regulate insect visits to flowering plants at night
Bibliographic record
Abstract
Flower-visiting insects have co-evolved with flowering plants. While it has been shown that floral traits and environmental factors influence insect visitation during the day, it is still unclear how these factors influence their visitation at night. We sampled a montane meadow located near Jilin in northeastern China in July and August of 2019, for 4 nights each month, and two time periods each night. We sampled 94 flower-visiting insect species in total and documented floral traits and ambient factors. We first allocated all the insects to three functional groups (pollination, predation, and herbivory). Most nocturnal insects exhibited predation behavior, and had the highest species turnover rate. We then focused on environmental factors and found that ambient temperature and relative humidity strongly influenced the diversity of flower-visiting insects. In addition, variation partitioning analysis suggested that ambient temperature had a stronger effect on the flower-visiting insects during the early night hours, whereas relative humidity had a stronger effect on them in the later night hours. Finally, focusing on floral traits, most insects preferred flowers with moderately sized corolla diameters (20 to 30 mm). Furthermore, display size had a strong linear correlation with flower-visiting insect species richness and frequency of presence. In sum, our findings suggest that ambient temperature, relative humidity and floral display size strongly regulate the behavior of nocturnal flower-visiting insects.
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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".