Bumble Bee Probability of Occurrence Responds to Interactions Between Local and Landscape Land Use, Climatic Niche Properties and Climate Change
Bibliographic record
Abstract
Insect biodiversity is changing rapidly, driven by a suite of pressures, notably land use, land-use intensification and increasingly climate change. We lack large-scale evidence on how land use and climate change interact to drive insect biodiversity changes. We assess bumble bee responses to interactive effects of land use and climate pressures across North America and Europe. The probability of occurrence increases in landscapes with a higher proportion of natural habitat and a shorter history of human disturbance. Responses to climate warming relative to historical conditions are weakly negative in natural habitats but positive in human land uses, while human land use reduces the probability of occurrence most in the centre of species' temperature niches. We estimate that the combined pressures have reduced bumble bee probability of occurrence by 44% across sampled natural habitats and 55% across human land uses, highlighting the pervasive influence that human pressures have had on biodiversity across 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 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".