The changing status of imperiled species in British Columbia over the last 15 years in the absence of a dedicated species at risk law
Bibliographic record
Abstract
ABSTRACT Gaps in biodiversity protection occur in Canada due to limited jurisdiction of federal species-at-risk laws and the absence of dedicated legislation in many provinces, including British Columbia (B.C.). While lacking legal protection, B.C. maintains Red, Blue, and Yellow Lists of threatened, special-concern, and secure species, respectively, using a NatureServe ranking system. We compiled historical data on species status in B.C. between 2008 and 2024 from the B.C. Conservation Data Centre. B.C. is home to 5,485 Yellow, 1,116 Blue, 491 Red-listed species. Changes in status over this time period were reported for 967 animal species, with an even split in uplistings (more imperiled) and downlistings (less imperiled). More status changes were reported for plants (2,902), mainly due to updated methodology leading to a lower risk status. Analysing the accompanying explanation for each status change revealed that most changes were non-genuine (e.g., new information, taxonomy, or methodology) rather than genuine (e.g., true changes in population size, range, or threats). Genuine improvements in the status of species in B.C. have been exceedingly rare. This analysis indicates that current laws and regulations have been insufficient to recover species at risk within B.C. Plain-language summary British Columbia is the most biodiverse province in Canada, yet 1,607 species are imperiled in the province. We analysed trends, finding a 16% rise in the number of species at risk since 2008. By analysing the provided explanations, we found that status improvements were mostly “non-genuine”, caused by changing methodology and/or new data. Strengthening legal protections is essential to prevent biodiversity loss in the province.
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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 | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".