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Record W7024340289

The relative impacts of recreational activity and landscape protection on a Rocky Mountain mammal community

2023· dissertation· en· W7024340289 on OpenAlexaboutno aff

Bibliographic record

VenueUVic’s Research and Learning Repository (University of Victoria) · 2023
Typedissertation
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsnot available
Fundersnot available
KeywordsWildlifeRecreationHabitatPopulationDisturbance (geology)Natural landscapeGrizzly BearsWildlife conservationRange (aeronautics)
DOInot available

Abstract

fetched live from OpenAlex

Modern human expansion and landscape development has substantially restructured natural landscapes with cascading impacts on biodiversity, resulting in population declines and range contractions in many North American large mammal species. While conservation efforts through the establishment of protected areas (PA) mitigate stressors to wildlife by preventing further landscape disturbance, mammals are still impacted by high human use within PA, and ongoing landscape development outside PA boundaries. Comprised of a network of PA and unprotected areas, Canada’s Rocky Mountains provide important habitat to a rich mammal community. The Rocky Mountains also support a range of human uses, including industrial development creating ongoing landscape disturbance, and recreational use of landscape features such as trails and roads. The relative importance of PAs in supporting mammal populations, as well as the impacts of recreational landscape use to mammals, are not well understood. In this thesis, I used wildlife camera arrays to investigate the relative impacts of recreation and landscape protection on a Rocky Mountain mammal community, assessing distributions of six species: wolves, grizzly bears, coyote, black bears, white-tailed deer, and mule deer. I chose to assess multiple species as species are expected to respond differently to disturbance, with wolves and grizzly bears being disturbance-sensitive while coyotes and white-tailed deer are more disturbance-tolerant. In my second chapter, within an unprotected region, I investigated whether motorized recreation influenced mammal distributions, weighing its importance against landscape disturbance, and natural landscape features. I found that wolves avoided areas of high motorized use; coyote, white-tailed deer, and grizzly bears were better explained by landscape disturbance features, and black bears and mule deer were best explained by natural landscape features. Recreational use can cause spatial displacement of wildlife, with the effect being constrained to more disturbance-sensitive species such as wolves. These results have important implications in managing habitat for disturbance sensitive species, but also emphasize the importance of minimizing and restoring ongoing landscape disturbance, as disturbance facilitates recreational use, and ultimately has a larger impact on the greater mammal community. In my third chapter, I investigated whether protected areas outweigh natural or anthropogenic landscape features in explaining species occurrence, across a range of PA and unprotected areas in the Rocky Mountains. I found that PAs best explained the occurrence of four out of six species: wolves, grizzly bears, coyote, and mule deer. Wolves, grizzly bears, and mule deer had positive associations with PAs, while coyotes had negative associations. Black bears, white-tailed deer, and mammal diversity were best explained by anthropogenic landscape disturbance. These results underscore the importance of PAs in providing habitat for disturbance-sensitive predators, and demonstrate that anthropogenic landscape management and alteration are driving factors in determining species distributions. This research has important implications for future landscape management. For disturbance-sensitive species, such as wolves, limiting the extent of motorized recreation is important; on a broader scale, the establishment of PAs is important for providing habitat for disturbance-sensitive top predator species, and ultimately reducing ongoing landscape alteration and restoring habitat is essential to mitigate ongoing impacts to mammal communities.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.986
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.024
GPT teacher head0.266
Teacher spread0.242 · 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 designObservational
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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