The relative impacts of recreational activity and landscape protection on a Rocky Mountain mammal community
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".