Effects of Human Activities and Natural Processes on Wolverine Populations
Bibliographic record
Abstract
Understanding the dynamics of large-carnivore populations is critical in light of expanding human activities that may alter their natural habitats. In this thesis, I examine the effects of both human-induced disturbances and natural environmental factors, on the population density and habitat use of wolverines (Gulo gulo) in the Canadian Rocky Mountains and the Columbia Mountains of British Columbia and Alberta, Canada. Carnivores, such as wolverines, are sensitive to human-caused mortality and large-scale habitat changes as they roam widely and have low reproductive rates and densities. Human impacts, including overharvest, ecosystem changes due to resource extraction and additional decreases in habitat quality because of recreation, may pose growing challenges to wolverine conservation. I employed non-invasive genetic sampling, remote camera surveys and spatial capture-recapture techniques to assess population trends and offer critical insights into how wolverines respond to both human-caused pressures and natural environmental factors. Sampling at a population scale, my research provides evidence that recreational activities not only impact habitat use, but can have detrimental impacts on wolverine population density, even within protected areas. While protected areas appear to be vital for maintaining wolverine populations, especially if they are harvested on unprotected lands, edge effects and high disturbance levels from recreation may compromise their effectiveness. My work thus suggests that human disturbances have both direct and indirect effects on wolverine density, with female wolverines being particularly impacted. Areas with higher road density and recreational activity exhibit lower population densities, highlighting the need to manage human access if protection is a societal objective. I also investigated the role of bottom-up factors, such as persistent spring snow cover, which is associated with wolverine ecology and reproduction. The interaction between natural and anthropogenic factors creates complex patterns in habitat use, but by identifying key drivers of population decline, I offer important conservation implications, emphasizing the necessity for integrated management strategies that balance human land use with the needs of wildlife species. These findings underscore the broader applicability of my research to other carnivores facing similar threats, providing a foundation for future conservation efforts.
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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 | 0.001 |
| Scholarly communication | 0.001 | 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".