Mapping nationally and globally at-risk species to identify hotspots for (and gaps in) conservation
Bibliographic record
Abstract
Protecting habitat of species at risk is critical to their recovery, but can be contentious. For example, protecting species that are locally imperilled but globally common is often thought to distract from protecting globally imperilled species. However, such perceived trade-offs are based on the assumption that threatened groups have little spatial overlap, which is rarely quantified. We compiled range maps of terrestrial species at risk in Canada to assess the geographic overlap of nationally and globally at-risk species with each other, among taxonomic groups, and with protected areas. While many nationally at-risk taxa only occur in Canada at their northern range edge, they are not significantly more peripheral in Canada than globally at-risk species. Further, 56% of hotspots of nationally at-risk taxa are also hotspots of globally at-risk species, undercutting the perceived trade-off in their protection. While strong spatial overlap across threat levels and taxa should facilitate efficient habitat protection, less than 7% of the area in Canada's at-risk hotspots is protected, and two-thirds of nationally and globally at-risk species in Canada have less than 10% of their Canadian range protected. Our results counter the perception that protecting nationally versus globally at-risk species are at odds, and identify critical areas to target as Canada strives to increase its protected areas and promote recovery of species at risk.
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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 | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".