Climate change-driven range losses among bumblebee species are poised to accelerate
Bibliographic record
Abstract
Climate change has shaped bee distributions over the past century. Here, we conducted the first species-specific assessment of future climate change impacts on North American bumblebee distributions, using the most recent global change scenarios developed in the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC). We assessed potential shifts in bumblebee species distributions with models generated using Maxent. We tested different assumptions about bumblebee species' dispersal capacities, drawing on observed patterns of range shifts to date, dispersal rates observed for bumblebee queens, and, lastly, assuming unlimited dispersal. Models show significant contractions of current ranges even under scenarios in which dispersal rates were high. Results suggest that dispersal rates may not suffice for bumblebees to track climate change as rapidly as required under any IPCC scenario for future climate change. Areas where species losses are projected overlap for many species and climate scenarios, and are concentrated in eastern parts of the continent. Models also show overlap for range expansions across many species, suggesting the presence of "hotspots" where management activities could benefit many species, across all climate scenarios. Broad-scale strategies are likely to be necessary to improve bumblebee conservation prospects under climate change.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".