Assessing the impacts of heli-skiing on the behaviour and spatial distribution of Mountain Cariboo (Rangifer tarandus caribou)
Bibliographic record
Abstract
Mountain caribou (Rangifer tarandus caribou) are listed as endangered in Canada, with isolated subherds only remaining in British Columbia and parts of northern Idaho. A loss of old-growth forest habitat has caused a decline in their range, making them more likely to be disturbed by backcountry recreational activities such as heli-skiing. This study investigated whether an interaction between heli-skiing and Mountain Caribou could be detected. The detectability of caribou from helicopters indicated that caribou are often not detected when within close proximity to active skiing. Data on the behavioural responses of caribou that were recorded by Mike Wiegele heli-skiing personnel between 1996 and 2010 were analysed. The relationship between the type of responses and the frequency of ski run usage was examined. Responses were not higher in areas subject to more frequent skiing, but overt behavioural responses to heliskiing were documented. Three GIS analyses were performed on GPS data from 25 caribou collared between 1996 and 2007 to determine any spatial effects of skiing activity on how animals use their range. The first test examined habitat use near ski runs. Actual numbers of GPS locations within suitable habitat near ski runs were more than expected. The second test determined the distance established by caribou between themselves and ski runs with different intensities of use. More than an expected number of locations were found close to frequently skied runs; while fewer than expected locations were found close to runs not skied. The third test compared caribou’s rate of movement within zones skied frequently and less often. There was no significant difference in the degree of movement in areas skied heavily or not. Results of these analyses suggest that caribou in the area were not directly displaced by heli-skiing activities during the years studied, but avoidance at finer scales than I measured is possible. It appears as if ‘Best Management Practices’ that enforce closing areas to skiing upon detection of caribou may be helpful in reducing conflicts with caribou; however, mountain caribou in this study area have likely habituated to more than 40 years of skiing. Continued avoidance management, more specific research on short-term reactions to heliskiing and with herds in other areas are suggested to ensure a coexistence of caribou and heli-skiing.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".