A predictive modeling and ecocultural study of pine mushrooms (Tricholoma murrillianum) with the Lílwat Nation in British Columbia, Canada
Bibliographic record
Abstract
Although recognized by the United Nations Declaration on the Rights of Indigenous Peoples (UNDRIP), Indigenous rights to traditionally held and managed forestlands and forest resources are only beginning to gain visibility in forest research and management in Canada. This presents challenges to First Nations whose cultural and economic priorities for forest use conflict with those of private and public entities, particularly when evidence is required to support traditional use claims. Knowledge of traditional use is customarily maintained as oral history and is rarely available in formats recognized by Canadian legal and governance institutions. Such is the case with the Líl̓wat First Nation, in British Columbia, Canada, and Tricholoma murrillianum (pine mushroom), an elusive, ectomycorrhizal mushroom species whose value to Líl̓wat people is put at risk by competing timber interests. Rich Líl̓wat Indigenous knowledge (IK) of pine mushrooms signals their importance and is encoded in temporally long and detailed records of their presence on the landscape. I elicit Líl̓wat IK to generate a map of pine mushroom habitat in their traditional territory and demonstrate the multifaceted value of pine mushrooms to Líl̓wat people. I utilize the species distribution modeling (SDM) software Maxent to compare two methods for incorporating Líl̓wat IK to produce pine mushroom occurrence data, yielding two models of suitable habitat. I demonstrate that Líl̓wat IK generates species distribution models with high area under the curve values (0.920, 0.923) and low omission error rates (0.054, 0.062). This study also demonstrates the novel application of IK to fungi SDM. Drawing from semi-structured interviews, document analysis and discourse analysis, I show that harvesting pine mushrooms is an expression of Líl̓wat cultural revitalization and consequently, colonial resistance. Documented traditional Líl̓wat practices show that pine mushrooms have long been managed in relation to other species, such as deer, and as part of broader sociocultural systems founded in reciprocity. Where Western scientists are increasingly interested in working with Indigenous communities and IK, I highlight respectful and reciprocal ways in which ecological and ethnoecological research can be undertaken.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".