Nest density of orange-belted bumble bees (Bombus ternarius) in Atlantic Canadian agroecosystems
Bibliographic record
Abstract
Pollination services are essential for global biodiversity and agricultural production. Global pollinator declines have been well documented, putting delivery of these services at risk. Despite global importance of wild pollinators such as bumble bees (Bombus spp.), little research has investigated their nesting preferences. As a ubiquitous species in Atlantic Canada, orange-belted bumble bees (hereafter, bumble bees), B. ternarius, provide important pollination services, making them an ideal candidate for this study. Objectives of this study were (1) to use microsatellites to estimate sibling relationships (thus, indirectly, nest density) in worker bumble bees in agroecosystems and (2) to evaluate the relationships between bumble bee nest density and land cover distribution. Six microsatellites were amplified in bumble bee worker DNA and relatedness (nest density) was estimated using COLONY software. This was the first study to use ArcGIS and FRAGSTATS to evaluate the relationship between nest densities and land cover distribution. Of three selected sites, highest bumble bee nest density was found around a lowbush blueberry agroecosystem. Limitations of having only three sites prevent firm deductions but it is clear that bumble bees have complex interactions at both floral and landscape scales. Results strongly encourage future research that includes more sites as well as a detailed evaluation of bumble bee floral resource relationships. This is valuable information that would allow one to give recommendations to farmers on what or how they should supplement their crops to attract more wild pollinators to their fields, which will increase crop yields
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".