A Two-Eyed Seeing approach to predicting the distribution of skwenkwínem (Claytonia lanceolata: Pursh), a culturally significant plant
Bibliographic record
Abstract
Colonial practices and policies and a changing climate have threatened culturally significant food plants and the well-being of those who rely on those plants. Skwenkwínem (western spring beauty, Claytonia lanceolata Pursh: Montiaceae) is a culturally significant food plant for the Secwépemc People of Skeetchestn Indian Band. Skwenkwínem is a corm-bearing geophyte that emerges immediately after snowmelt. Many Indigenous Peoples historically used skwenkwínem corms as a significant portion of their diet, and it remains an important traditional food source today. The Skeetchestn community has noticed a decline in the abundance and quality of their skwenkwínem patches. With increasingly unpredictable seasonal climatic changes, accessing skwenkwínem as a food source is under threat. It is important to know where suitable habitat exists now, and where suitable habitat will exist in the future, for informing conservation efforts. This study uses a species distribution model to predict the distribution of skwenkwínem within its known range in western North America. We have made use of the tidysdm R package to predict habitat suitability for skwenkwínem in the present time and under future climate conditions. We have a two-pronged approach to this study: we model habitat suitability based on predictors selected from interviews conducted with Skeetchestn community members (Informed Model). In tandem, we model habitat suitability using the 19 bioclimatic predictors from WorldClim. Our goals are to produce continuous habitat suitability indices for flexible interpretation of suitable habitat, as well as binary predictions with suitable area estimates. We are also contributing to the sparse socio-ecological species distribution literature, with a fully reproducible example. This study is an application of the Two-Eyed Seeing concept, where two different knowledge systems come together to gain a more holistic understanding of an issue (Bartlett et al. 2012). in our case, we use Skeetchestn community Knowledge to inform our predictor selection (Informed Model), and we create habitat suitability predictions using these informed predictors in our SDM. This study braids Indigenous and western Knowledge for insight and predictions we would not achieve with the 19 bioclimatic variables alone. Data and scripts associated with the final publication are accessible at: https://doi.org/10.17605/OSF.IO/M4U8Q
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.009 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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".