Floral traits and environmental factors regulate insects 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 insects visitations during the day, it is yet unclear how these factors influence insects visitations at night. We sampled a montane meadow located near Jilin in northeastern China in July and August in 2019, 4 nights each month, and two time periods each night. We sampled 94 flower-visiting insect species in total and documented the floral traits and ambient factors. First, focusing on the insects functions, we allocated all insects into three functional groups (pollination, predation, and feeding). We found that most nocturnal insects exhibited predation behavior, and they had the highest species turnover rate. Second, focusing on the environmental factors, we found that ambient temperature and relative humidity strongly influence the diversity of flower-visiting insects. Variation partitioning analysis further suggested that ambient temperature has a stronger effect on the flowering-visiting insects at early night, while the relative humidity has a stronger effect on the flowering-visiting insects at late night. Third, focusing on floral traits, we found that most insects have a preference for flowers with moderately-sized corolla diameters (20 to 30 mm). Furthermore, display size had a strong linear correlation with flowering-visiting insect species richness and frequency of presence. In sum, our findings suggest that ambient temperature, relative humidity, and floral display size strongly regulate 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".