Habitat loss accelerates for the endangered woodland caribou in western Canada
Bibliographic record
Abstract
Abstract Habitat loss is often the ultimate cause of species endangerment and is also a leading factor inhibiting species recovery. For this reason, species‐at‐risk legislation, policies and plans typically focus on habitat conservation and restoration as mechanisms for recovery. To assess the effectiveness of these instruments in decelerating habitat loss, we evaluated spatiotemporal habitat changes for an iconic endangered species, woodland caribou ( Rangifer tarandus caribou ). We quantified changes in forest cover, a key proxy of caribou habitat, for all caribou subpopulations in Alberta and British Columbia, Canada. Despite efforts under federal and provincial recovery plans, and requirements listed under Canada's Species at Risk Act, caribou subpopulations lost twice as much habitat as they gained during a 12‐year period (2000–2012). Drivers of habitat loss varied by ecotype, with Boreal and Northern Mountain caribou affected most by forest fire and Southern Mountain caribou affected more by forest harvest. Our case study emphasizes critical gaps between recovery planning and habitat management actions, which are a core expectation under most species‐at‐risk legislation. Loss of caribou habitat from 2000 to 2018 has accelerated. Linear features within caribou ranges have also increased over time, particularly seismic lines within Boreal caribou ranges, and we estimated that only 5% of seismic lines have functionally regenerated. Our findings support the idea that short‐term recovery actions such as predator reductions and translocations will likely just delay caribou extinction in the absence of well‐considered habitat management. Given the magnitude of ongoing habitat change, it is clear that unless the cumulative impacts of land‐uses are effectively addressed through planning and management actions that consider anthropogenic and natural disturbances, we will fail to achieve self‐sustaining woodland caribou populations across much of North America.
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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 | 0.001 | 0.001 |
| 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".