Conservation priorities for peripheral species: the example of British Columbia
Bibliographic record
Abstract
Most jurisdictions must assign conservation priorities to peripheral species. British Columbia hosts more than 1300 peripheral taxa, about 900 of which appear on the Red and Blue Lists prepared by the province to guide conservation actions. Conversely, fewer than half of the endemic taxa, or taxa for which the province has major global stewardship responsibility, appear on provincial Red and Blue Lists. We examine why we conserve and list species, concluding that the primary scientific or practical reason is to sustain genetic variability. We consider two broad kinds of peripheral species: disjunct (geographically marginal) populations and continuous peripheral populations that straggle irregularly across provincial boundaries. Populations of both groups may be ecologically marginal, with λ < 1. We document the degree to which each group enters provincial Red and Blue Lists. Factors used to modify rankings of risk are correlated in a fashion that artificially biases continuous peripheral populations toward rankings of higher risk. Federal initiatives in recovery plans for most continuous peripheral species appear doomed to failure for sound biological reasons. We note alternative approaches to ranking species for conservation action and recommend that conservation efforts for peripheral species be focused on disjunct peripheral populations, rather than continuous peripheral populations.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".