Spatial coverage of protection for terrestrial species under the Canadian Species at Risk Act
Bibliographic record
Abstract
Canada’s Species at Risk Act (SARA) has been critiqued for only protecting species on federal lands. However, this shortcoming has never been quantitatively assessed in terms of species’ ranges. We assessed the proportion of ranges of federally-listed terrestrial species at risk (SAR) receiving protection via SARA, excluding birds protected by the Migratory Birds Convention Act. Additionally, we assessed species protection provided by provincial and territorial protected areas within the ranges of SARA-listed species. We show that federal land provides protection within only 8.1% of species’ Canadian ranges on average, and 63.1% of 252 terrestrial SAR are protected within less than 5% of their range. The addition of provincial and territorial protected areas increases this average to 14.6% and reduces the percent with less than 5% protection to 34.9% of species. Eighteen species receive 0% protection within their Canadian ranges. We found no significant difference in average protection among taxonomic groups. Canada’s capacity to protect SAR via SARA could be improved by greater coordination among national, provincial and Indigenous governments, the creation of a more effective protected area network, exercising SARA’s provision for emergency protection orders where applicable, and facilitating greater SAR protection on public and private lands.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".