Seasonality of floral resources in relation to bee activity in agroecosystems
Bibliographic record
Abstract
The contribution of wild insects to crop pollination is becoming increasingly important as global demand for crops dependent on animal pollination increases. If wild insect populations are to persist in agricultural landscapes, there must be sufficient floral resources (FR) over time and space. The temporal, within-season component of FR availability has rarely been investigated, despite growing recognition of its likely importance for pollinator populations. Here, we examined the visitation rates of common bee genera and the spatiotemporal availability of FR in agroecosystems over one season to determine whether local bee activity was limited by the abundance of landscape FR, and if so, whether it was limited by the present or past abundance of landscape FR. Visitation rates and landscape FR were measured in 27 agricultural sites in Ontario and Québec, Canada, across four time periods and three spatial scales. Landscape FR at varying spatial scales predicted visits for the seven most commonly observed bee genera. Bombus visitation rates were higher in landscapes that had greater cumulative seasonal abundance of FR, suggesting the importance of early-season FR for this taxon. Visits from Halictus and Lasioglossum were higher in landscapes that provided either a stable or increasing amount of FR over the season and were lower in landscapes that experienced a decrease in FR over the course of a season. Andrena , Augochlorella , Megachile , and Peponapis visits were not measurably influenced by FR in previous months but were lower in landscapes that had a higher present abundance of FR, perhaps reflecting pollinator movement or dilution. Our research provides insight into how seasonal fluctuations in floral resources affect bee activity, and by examining each bee genus separately, we could observe how differences in foraging periods, foraging ranges, and the number of broods per season influence how bee taxa respond to food availability within agroecosystems.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".