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Record W6927275523 · doi:10.25946/13395818

Grizzly bear habitat management in Canada's Rocky Mountain parks: Balancing visitor expectations with bear habitat requirements

2023· dissertation· en· W6927275523 on OpenAlexaboutno aff

Bibliographic record

VenueAcquire (CQUniversity) · 2023
Typedissertation
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsnot available
Fundersnot available
KeywordsGrizzly BearsRecreationHabitatVisitor patternHuman–wildlife conflictWork (physics)Global Positioning SystemHuman use

Abstract

fetched live from OpenAlex

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.099
Threshold uncertainty score0.200

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0040.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.007
GPT teacher head0.201
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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