Assessing biodiversity hotspots below the species-level in Canada using designatable units
Bibliographic record
Abstract
The biodiversity hotspot approach is commonly used to identify key regions of conservation priority based on species richness and uniqueness. Like other countries, Canada uses below species-level conservation units, called Designatable Units (DUs), for assessing extinction risk on a case-by-case basis. Researchers have yet to investigate conservation unit hotspots below the species level, despite the potential benefits to optimize the impact of conservation strategies. Across taxa, we: (i) identified at-risk DU hotspots, eco-geographic regions in Canada with a disproportionate number of at-risk DUs relative to species richness; and (ii) compared at-risk DU hotspots using two sets of eco-geographic maps adopted by the Committee on the Status of Endangered Wildlife in Canada (COSEWIC). DU richness counts were based on COSEWIC assessed species and hence biased towards at-risk DUs; species richness values were obtained from the International Union for Conservation of Nature (IUCN) Red List. At-risk DU hotspots were consistently found in the Pacific and Great Lakes regions. A positive correlation was observed between at-risk DUs and species richness using both eco-geographic maps, but important regional nuances were also detected, i.e., hotspots were not always found in regions of high species richness. Moreover, there were 3.45 at-risk DUs for every at-risk species across all taxa, providing greater resolution for refining conservation prioritization across regions. For Canada, the at-risk DU hotspot approach permits the identification of regions with a high number of at-risk DUs relative to species richness, enabling the targeting of multiple DUs and taxa in one management plan. More generally, these results emphasize the importance of incorporating below species-level metrics into conservation decisions to better account for different components of biodiversity and extinction 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".