Indigenous‐led research on traditional territories highlights the impacts of forestry harvest practices on culturally important plants
Bibliographic record
Abstract
Abstract Indigenous knowledge and governance are critical to successful conservation and Indigenous Peoples' ability to live sustainably on their lands. However, various industrial land use practices impact the conservation value and traditional resources these lands provide. Here, we evaluated the effects of harvest, glyphosate application, and fire on 51 edible and medicinal plant species identified by traditional knowledge of Indigenous Peoples in the western boreal forest of Canada, a landscape of rapid industrialized landscape change. We collected vegetation data between 2007 and 2020 and used linear models and machine learning to model the richness and abundance of edible and medicinal plant species. Glyphosate application and harvest best explained the richness and abundance of species. Despite our models' indication that species richness and abundance were higher in harvested and treated study sites, detailed qualitative data based on local Indigenous knowledge suggest these forestry practices negatively impacted Indigenous Peoples' ability to use traditional plants. Importantly, plants in areas treated with glyphosate were unsuitable for human consumption and exhibited abnormal color and flavor presentations. Concerns over access to traditional resources are increasingly important as industrial impacts continue to expand globally. Thus, we hope that this Indigenous‐led study design leveraging both quantitative and qualitative data can result in successful partnerships that better reflect the environmental concerns of Indigenous Peoples.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| 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".