Contrasting effects of landscape on nest founding and colony success of bumble bees in a mixed‐crop agroecosystem
Bibliographic record
Abstract
Abstract Nest‐founding bumble bee queens search landscapes broadly for a nest site, whose later‐developed workers are constrained to foraging around the nest. Landscape could therefore have different influences on nest site selection and subsequent colony success. Additionally, the density of bumble bees in a landscape reflects the product of their nest density and nest success. To examine separate landscape effects on nest density and success, we examined nest occupancy (reflecting nest density) and colony size (reflecting nest success) using ground‐installed nest boxes placed adjacent to blueberry ( Vaccinium corymbosum ) fields in the Lower Mainland of British Columbia, Canada. Nest‐searching queens occupied 59% of these boxes. We classified landscapes in a 1.5 km radius of colonies using habitats of a priori relevance to bumble bees: beneficial (i.e. flowering) agriculture, non‐beneficial agriculture (NBA), forest, open semi‐natural, suburban, and their configuration (habitat edge density). Landscape strongly affected nest founding but only weakly affected colony success. Nest founding increased in landscapes with more forest habitat, more open semi‐natural habitat (and little NBA), and more habitat edge (and little NBA). Colony success increased in landscapes with more edge density (and much NBA). Overall, edge habitats enhanced bumble bee populations, but enhancement was conditional: edge increased nest occupation in landscapes with little NBA and nest success in landscapes with a lot of NBA. Populations of crop‐pollinating bumble bees might therefore best be enhanced by locally enhancing nesting: protecting forests, 2D semi‐natural habitats (when flowering crops are uncommon), and edge habitats.
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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 | 0.000 | 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".