Lines on a map: conservation units, meta‐population dynamics, and recovery of woodland caribou in Canada
Bibliographic record
Abstract
Abstract Delineating conservation units is a fundamental step in recovery planning for endangered species. Yet, challenges remain in the application and validation of scientifically evaluated conservation units in management practice. The Canadian government makes use of Designatable Units ( DU s) as the primary conservation unit under their Species‐at‐Risk Act. DU s must be ecologically discrete and have demonstrated evolutionary significance, which, in the case of woodland caribou ( Rangifer tarandus caribou ), has led to the definition of multiple DU s across Canada. Simultaneously, Environment and Climate Change Canada has released two recovery strategies affecting four DU s, wherein DU s are subdivided into smaller conservation units. However, the two recovery strategies adopt different definitions for the conservation unit. For the Boreal DU , the Local Population is considered the conservation unit for recovery management, whereas for Southern Mountain DU , the conservation unit for recovery is the subpopulation, which may or may not be comprised of several Local Populations. The scientific rationale for the difference between recovery strategies is unclear, not necessarily supported by genetic or demographic evidence, and highlights a policy challenge facing caribou conservation. We argue that the current emphasis on protecting subpopulations within a DU might be inconsistent and unviable for recovery planning. Instead, the recognition and emphasis on maintaining meta‐population dynamics within DU s is essential and currently underutilized in the long‐term recovery of woodland caribou in Canada.
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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 | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".