Combining high-resolution remotely sensed data with local and Indigenous Knowledge to model the landscape suitability of culturally modified trees: biocultural stewardship in Kitasoo/Xai’xais Territory
Bibliographic record
Abstract
Environmental management and monitoring must reconcile social and cultural objectives with biodiversity stewardship to overcome political barriers to conservation. Suitability modelling offers a powerful tool for such “biocultural” approaches, but examples remain rare. Led by the Stewardship Authority of the Kitasoo/Xai’xais First Nation in coastal British Columbia, Canada, we developed a locally informed suitability model for a key biocultural indicator, culturally modified trees (CMTs). CMTs are trees bearing evidence of past cultural use that are valued as tangible markers of Indigenous heritage and protected under provincial law. Using a spatial multi-criteria evaluation framework to predict CMT suitability, we developed two cultural predictor variables informed by Kitasoo/Xai’xais cultural expertise and ethnographic data in addition to six biophysical variables derived from LiDAR and photo interpretation data. Both cultural predictor variables were highly influential in our model, revealing that proximity to known habitation sites and accessibility to harvesters (by canoe and foot) more strongly influenced suitability for CMTs compared with site-level conditions. Applying our model to commercial forestry governance, we found that high CMT suitability areas are 51% greater inside the timber harvesting land base than outside. This work highlights how locally led suitability modelling can improve the social and evidentiary dimensions of environmental management.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".