Ancient dog diets on the Pacific Northwest Coast: zooarchaeological and stable isotope modelling evidence from Tseshaht territory and beyond
Bibliographic record
Abstract
Domestic dogs are frequently encountered in Indigenous archaeological sites on the Northwest Coast of North America. Although dogs depended on human communities for care and provisioning, archaeologists lack information about the specific foods dogs consumed. Previous research has used stable isotope analysis of dog diets for insight into human subsistence ('canine surrogacy' model) and identified considerable use of marine resources. Here, we use zooarchaeological data to develop and apply a Bayesian mixing model (MixSIAR) to estimate dietary composition from 14 domestic dogs and 13 potential prey taxa from four archaeological sites (2,900-300 BP) in Tseshaht First Nation territory on western Vancouver Island, British Columbia, Canada. Two candidate models that best match zooarchaeological data indicate dogs predominantly consumed salmon and forage fish (35-65%), followed by nearshore fish (4-40%), and marine mammals (2-30%). We compared these isotopic data to dogs across the Northwest Coast, which indicated a pronounced marine diet for Tseshaht dogs and, presumably, their human providers. These results are broadly consistent with the canine surrogacy model as well as help illuminate human participation in pre-industrial marine food webs and the long-term role of fisheries in Indigenous economies and lifeways.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".