The Viability of Preferred Large Ungulates as Traditional Foods, and Implications on Indigenous Food Systems in Yukon Territory
Bibliographic record
Abstract
Northern Canada’s boreal ecosystems are undergoing rapid, cumulative change driven by climate warming, habitat disturbance, and intensified human activity. These ecological and colonial transformations have altered the distribution and abundance of wildlife species that underpin Indigenous food systems, disrupting access to culturally significant foods for First Nations across northern Canada. Guided by systems ecology and the FAO’s Indigenous food systems frameworks, this dissertation examines how ecological and sociocultural factors interact to shape the resilience of Indigenous food systems in the Kluane Region of Yukon Territory. It evaluates how changes in wildlife population viability affect the long-term sustainability of Indigenous food systems among the Kluane First Nation, Champagne and Aishihik First Nations, and White River First Nation. A mixed-methods design was employed across two core studies. The first involved a scoping review and qualitative content analysis of sixty-six peer-reviewed and grey literature sources to characterize Indigenous food systems in northern Canada. Findings reveal that most studies emphasize consumption and climate change while giving limited attention to harvesting, sharing, preparation, and storage practices central to Indigenous food systems. The analysis identified five overarching categories - food system activities, endogenous inputs, sociocultural elements, food systems outcomes, and food systems drivers - and highlighted persistent gaps linking ecological processes with cultural and governance dimensions. The second component applied the VORTEX Population Viability Analysis (PVA) model to assess the long-term viability of moose, woodland caribou, and wood bison under multiple stressors. Twenty-eight scenarios simulated population trajectories over a 100-year period under varying harvest intensities, predation, disease, vehicle collisions, and climate change. When all stressors were combined with current harvest rates, projections indicated severe population declines and potential collapse for all three species. Harvest intensity and female mortality emerged as the most decisive factors influencing viability. This dissertation positions PVA as a demonstrative tool to visualize population trajectories and foster dialogue among First Nations, co-management bodies, and governments regarding adaptive harvest strategies, habitat stewardship, and food system resilience. Academically, it advances an integrative framework linking systems ecology with Indigenous food systems scholarship. Practically, it provides an evidence-informed foundation for co-management approaches aligning wildlife conservation with Indigenous governance and food sovereignty.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".