Grizzly bear habitat management in Canada's Rocky Mountain parks: Balancing visitor expectations with bear habitat requirements
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Protected area managers are continually challenged to balance ecological integrity with human recreation needs and expectations. In Banff, Yoho, Kootenay, and Jasper National Parks in Canada’s Rocky Mountains, part of this challenge is centered on providing grizzly bears with adequate access to high quality habitats while ensuring safe and ample recreation opportunities for millions of annual visitors. Using an interdisciplinary approach, I investigated this complexity through biological and social methods to define a series of management recommendations that maintain grizzly bear habitat security and meet trail user expectations. I conducted field work in the spring, summer and fall from August 2013 to August 2015. I used remote cameras on trails of low, medium, and high human use to quantify grizzly bear and human use of randomly selected trails. I used movement and location data generated from GPS collars on 27 grizzly bears to examine habitat use. I employed an intercept survey to assess trail users expectations and support of various management options pertaining to grizzly bears. Remote cameras captured human activity across the study area in all hours of the day and night across the seasons, although human activity was highest during the day and the summer/fall. Grizzly bears were more likely to be detected by camera on trails during the spring; trail human use level was not a significant predictor of grizzly bear presence. Most grizzly bear camera detections occurred at night or before 8 human events occurred on the trail that day. The GPS data showed that grizzly bears consistently selected for high quality habitat across all seasons. Grizzly bears selected habitat closer to roads in the spring, and closer to roads and trails in the summer than in the fall. I used a Step Selection Function (SSF) analysis to examine grizzly bear movement and ii habitat selection in the study area. The results of the SSF showed a high level of individual variation in grizzly bear selected steps in relation to trails of varying levels of human use and roads. Most grizzly bears selected steps close to low human use trails, but only some bears selected steps closer to high human use trails as well. Grizzly bear steps were longer during the day and shorter when in proximity to high use trails during the spring and summer. This suggests that bears were active diurnally and displayed decreased movement rates when near high use trails. The survey showed that trail users were supportive of prioritizing grizzly bear habitat use over their own recreational needs. The most supported management options were to close the trail or put up a warning sign when a bear was in the area; the least supported management options were relocating the bear or applying aversive conditioning. The level of support for management options did differ, however, if it was a lone grizzly bear or a female with cubs in the vicinity of the trail. In the latter scenario, trails users were more support of restrictive management options like closing the trail. Visiting trails users were more supportive of restrictive management options than residents. By integrating biological and social science data, I identified areas of focus in the spring where grizzly bear habitat quality and trail use was high; these areas should have human use restrictions applied during the spring. Resulting management recommendations that combined both biological and social data included: closing the trail when a female grizzly bear with cubs is in the area, implementing trail opening times in high quality grizzly bear habitat during the spring, and improving public education efforts. The interdisciplinary nature of this work helps managers to make decisions founded in biological sciences and to identify when and to what degree those decisions will be supported by trail users.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it