Identifying conservation priority areas for <scp>North American</scp> bumble bee species in <scp>Canada</scp> under current and future climate scenarios
Bibliographic record
Abstract
Abstract Many bumble bee species are declining globally from multiple threats including climate change. Identifying conservation priority areas with a changing climate will be important for conserving bumble bee species. Using systematic conservation planning, we identified priority areas for 44 bumble bee species in Canada under current and projected climates (year 2050). Conservation priority areas were identified as those that contained targeted amounts of each species predicted occurrence through climate envelope models, while minimizing the area cost of conserving the identified conservation priority areas. Conservation priority areas in the two periods were compared to established protected areas and land cover types to determine the area of current and future priority sites that are protected and the types of landscapes within priority areas. Notably, conservation priority areas were rarely within established protected areas. Priority areas were most often in croplands and grasslands, mainly within the mountain west, central and Southern Ontario, Northern Quebec, and Atlantic Canada under all climate scenarios. Conservation priority areas are predicted to increase in elevation and latitude with climate change. Our findings identify the most important regions in Canada for conserving bumble bee species under current and future climates including consistently selected future sites.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".