The effect of fire on spatial separation between wolves and caribou
Bibliographic record
Abstract
Fire management is an important conservation tool in Canada’s national parks. Fires can benefit some species, while others may be negatively impacted. We used GPS and VHF collar data for 47 wolves from 12 separate packs and 153 caribou from 5 separate herds, and resource selection analysis to model the effects of fire on these species’ habitat and potential interactions. Resource selection modeling showed that wolves select for burned areas and areas close to burns, presumably due to the presence of primary prey (i.e., elk and moose), while caribou avoid burns. Fire reduced the amount of high quality caribou habitat (a direct effect), but also increased the probability of wolf-caribou overlap (an indirect effect). We delineated a spatial index of caribou “safe zones” (areas of low overlap with wolves), and found a positive relationship between the proportion of a herd’s home range represented by “safe zone” in winter and population size (P = 0.10, n=4). While currently-planned prescribed fires in Banff and Jasper reduced the amount of quality caribou habitat by up to 4%, they reduced the area of “safe zones” by up to 7%, varying by herd, location, and season. We suggest that conservation managers should account for the indirect, predator-mediated impacts of fire on caribou in addition to direct effects of habitat loss.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".