Spatial distribution and hotspots of mammals in Canada
Bibliographic record
Abstract
Abstract High-latitude countries often contain the polar range edge of species that are common farther south. The more peripherally a species occurs in a country, the smaller its national range will be and the more its national range will consist of range-edge populations, which are often predicted to be relatively small, isolated, and unproductive. Together, this may focus national conservation efforts toward peripheral species whose global conservation value is controversial. However, if range-edge taxa occur where overall diversity is also high, there would be fewer trade-offs in protecting them. Using 153 of the 158 terrestrial mammal species in Canada, we tested how species’ distributions relate to their national conservation status and total mammal richness. Half of ‘Canadian’ mammals had <20% of their global range in Canada. Range area in Canada was strongly associated with national threat status; mammals considered ‘at-risk’ in Canada had 42% smaller Canadian ranges than mammals considered secure. However, after accounting for range area, being more peripheral (smaller proportion of global range in Canada) did not increase the likelihood that a taxon was considered at-risk. We overlaid the 153 maps to calculate mammal diversity across Canada, divided into 100×100 km grid cells. We found that hotspots of at-risk mammals (cells with >4 at-risk taxa) and hotspots of range-edge mammals (cells with >12 taxa with ≤20% of their range in Canada) were about twice as species rich as non-hotspot cells, containing up to 44% of Canadian mammal diversity per grid-cell. Our results suggest that protecting areas with the most at-risk or range-edge mammals would simultaneously protect habitat for many species currently deemed secure.
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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 | 0.003 | 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) | 0.002 | 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".