Weaving Indigenous and Western knowledge systems to discern drivers of <i>mooz</i> (moose) population decline
Bibliographic record
Abstract
Abstract Understanding and addressing biodiversity declines across the globe will require interdisciplinary practices that embrace multiple worldviews and weave knowledge systems. Here, we used a Two‐Eyed Seeing approach to weave Anishinaabe ecological knowledge with peer‐reviewed Western scientific literature to provide a comprehensive understanding of the drivers of a declining moose ( mooz ; Alces alces ) population in Ontario, Canada. We interviewed 66 participants from three Anishinabek communities on the causes of moose decline and conducted a literature review of 52 Western‐science studies that focused on factors that affect moose in Ontario. Our study revealed that there was agreement among knowledge systems on the importance of climate change and disease and parasites in explaining moose population decline in Ontario. Unique perspectives were provided on the mechanisms describing climatic impacts on calf recruitment, with an emphasis on spring onset and green‐up by Western science, and winter onset and timing of the rut by Anishinaabe knowledge. Western science also focused on the effects of habitat disturbance and predation on moose, and Anishinaabe knowledge emphasised harvest pressure. Other factors identified by both knowledge systems included the impacts of roads and railways. Distinctive information offered by Anishinaabe knowledge holders included the displacement of moose from areas in response to the range expansion of white‐tailed deer ( waawaashkeshi ; Odocoileus virginianus ) and the negative effects of contaminants introduced into the environment by mining and forestry activity. Overall, weaving knowledge systems offered a nuanced and wholistic understanding of factors affecting moose and provided different perspectives to explain interacting and cumulative effects. Our study showcases the value in weaving knowledge systems to improve understanding of ecological problems and find wholistic strategies for conservation. Read the free Plain Language Summary for this article on the Journal blog.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".