Combining data from field surveys and archaeological records to predict the distribution of culturally important trees
Bibliographic record
Abstract
Abstract Aim Indigenous communities involved in conservation planning require spatial datasets depicting the distribution of culturally important species. However, accessing datasets on the location of these species can be challenging, particularly when the current distribution no longer reflects areas with the full range of suitable growing conditions because of past logging. We test whether using occurrence data from community‐based field surveys and archaeological records in species distribution models can help predict the distribution of monumental western redcedar trees (Thuja plicata)—large, high‐quality trees suitable for cultural purposes such as carving dug‐out canoes, totem poles and traditional houses. This species is critically important to indigenous people of the Pacific Northwest of North America, but trees suitable for traditional carving and building are diminishing in abundance due to logging. Location Our analysis covers the spatial extent of the traditional territory of the Heiltsuk First Nation, which encompasses a portion of the Great Bear Rainforest in British Columbia, Canada. Methods We built and compared species distribution models using the machine learning program, Maxent, based on occurrence data from field surveys and archaeological records of culturally modified trees. Results Our findings highlight similarities and differences between the predictions from these species distribution models. When validating these models against occurrences from an independent dataset, the archaeological record model performs better than the field survey model. These findings may arise because the independent dataset was collected on an unlogged island—an environment that aligns more closely with the historic forest conditions revealed by the archaeological records than the current distribution revealed by the field surveys. Main conclusions We demonstrate and discuss the utility of using archaeological data in species distribution modelling and conservation planning when the target species is associated with shifting environmental baselines, data limitations and an important cultural resource.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".