Priority areas to conserve biodiversity in Canada
Bibliographic record
Abstract
Canada has committed to protecting 30% of its land by 2030, yet existing protected areas cover only 12.4% of Canadian lands, which is insufficient to protect terrestrial biodiversity. In this study, we identified priority areas for biodiversity conservation in Canada, using data on 1506 species across nine taxonomic groups. We first evaluated the effectiveness of existing protected areas in conserving at-risk and other species. Then, we applied optimization algorithms to determine priority areas that could enhance the existing system. Our results reveal that over 90% of the species studied have less than 30% of their spatial distribution currently protected. To meet a constant conservation target of protecting at least 30% of the spatial distribution for all species, Canada would need to expand its protected area system by 16%–17% of its total land area, focusing on regions like Nunavut, Quebec, and the Northwest Territories. Alternatively, when using relative conservation targets based on species’ range sizes, Canada would need to prioritize expanding protected areas by 4.56%–5.46% of its land, with new areas primarily in Ontario, British Columbia, and Quebec. Achieving these goals will require collaborative strategies that respect Indigenous rights and involve agreements with private landowners.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".