Bumblebee occupancy responds to complex interactions between local and landscape land use, climatic niche properties and climate change
Bibliographic record
Abstract
Abstract Insect biodiversity is changing rapidly, driven by a complex suite of pressures, foremost among which are human land use, land-use intensification, and increasingly climate change. Bumblebees deliver important pollination services to wild plants and human crops, but we lack large-scale empirical evidence on how land use and climate change interact to drive bumblebee biodiversity changes. We assess bumblebee occupancy responses to interactive effects of land use and climate pressures across North America and Western Europe. Occupancy increases with landscape natural habitat and decreases with the duration of human use of landscapes. Responses to historical climate warming are negative in natural habitats but positive in human land uses, while human land use reduces occupancy most in the centre of species’ temperature niches. We estimate that the combined pressures have reduced bumblebee occupancy by 61% across sampled natural habitats, and 65% across human land uses, suggesting that treating present-day natural habitats as an undisturbed reference is misleading. Our results can inform efforts to conserve bumblebee biodiversity in the face of ongoing land-use changes and accelerating climatic changes. One-sentence summary Land use and climate change interact to drive large declines in bumblebee occupancy in both natural and human-modified 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.005 | 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".