Past Floral Resources as a Predictor of Present Bee Visits in Agroecosystems
Bibliographic record
Abstract
Relying on wild bees for pollination services has become necessary as the global demand for crops dependent on animal pollination increases. If wild bee populations are to establish and persist in agricultural landscapes, there must be sufficient floral resources over time and space. This study examines the relationship between bee visits in agroecosystems and the spatiotemporal availability of floral resources over one season. I expected that landscapes with greater floral resources earlier in the season would subsequently experience more bee visits than landscapes with fewer early-season floral resources, and that the spatiotemporal scale of this effect would differ among taxa. I measured bee visitation rate and floral resource density over three spatial scales and during four time-periods spanning one season, in 27 agricultural sites across Ontario and Québec, Canada. The present abundance of floral resources at a local scale positively influenced bee visits across all sampling periods. However, differences in the temporal scale of bees’ response to floral resources were observed at landscape scales. Past and present floral resources were positively or negatively associated with bee visits depending on the time of season and which taxon was examined. The number of visits by Andrenidae, honey bees, and bumble bees increased with floral resource abundance in previous time-periods, while other taxa exhibited a negative association with past floral resources, suggesting possible dilution of bee populations at a landscape scale. Understanding the scales at which bee taxa are influenced by floral resources can allow development of land management strategies that could enhance crop pollination and conserve species threatened by agricultural intensification.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".