Land use changes associated with declining honey bee health across temperate North America
Bibliographic record
Abstract
Abstract Urbanization and agricultural intensification continue to reshape landscapes, altering the habitat available to wildlife and threatening species of both economic and conservation concern. The honey bee, Apis mellifera , is a pollinator of economic importance to North American agriculture yet managed colonies are burdened by poor health and high annual mortality. Understanding the factors influencing this species is critical for improving colony health and supporting crop production. We used a nationwide cohort of 638 managed Canadian colonies to study the dominant drivers of colony health and overwintering mortality. We found that fall colony weight—a major predictor of overwintering survival—was strongly associated with landscape composition. Among four broadly defined land cover types, we discovered that urban and forested land covers were the least valuable sources of habitat for colonies, as inferred from fall colony weight measurements. Agricultural land appeared to provide habitat quality of slightly greater value, while herbaceous land cover was most strongly positively associated with fall colony weight. Herbaceous land cover also exhibited an associational effect size which was strongly statistically distinguishable from those of urban and forested land. Our research indicates that recent and ongoing land-use changes exacerbate modern apicultural challenges, and suggests variation in nutrition or floral resource availability plays a major role in modulating honey bee health. Our work highlights the need for additional research investigating whether land use change-associated alterations in floral resource availability increase the potential for resource competition between pollinator species.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".