Mapping nationally and globally at-risk species to identify hotspots for (and gaps in) conservation
Bibliographic record
Abstract
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 (e.g. species that only occur in a jurisdiction at the edge of their geographic range) 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. Here, we compile 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 occurred in Canada at their northern range edge (median=4% of range in Canada), nationally-at-risk species were not significantly more peripheral in Canada than globally-at-risk species. Further, 56% of hotspots of nationally-at-risk taxa were also hotspots of globally-at-risk taxa in Canada, undercutting the perceived trade-off in their protection. Hotspots of nationally-at-risk taxa also strongly overlapped with hotspots of individual taxonomic groups, though less so for mammals. While strong spatial overlap across threat levels and taxa should facilitate efficient habitat protection, <7% of the area in Canada’s at-risk hotspots is protected, and more than 70% of nationally and globally-at-risk species in Canada have <10% of their Canadian range protected. Our results counter the perception that protecting nationally vs. globally at-risk species are at odds, and identify critical areas to target as Canada strives to increase its protected areas and promote species-at-risk recovery.
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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 | 0.005 | 0.004 |
| 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) | 0.003 | 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".