Ancient DNA reveals phenological diversity of Coast Salish herring harvests over multiple centuries
Bibliographic record
Abstract
Phenological diversity in food resources prolongs foraging opportunities for consumers and buffers them against environmental disturbances. Such diversity is particularly important in forage fish such as Pacific herring (Clupea pallasii), which are foundational to coastal food webs and fisheries. While the importance of phenological diversity is well-known from contemporary studies, the extent to which different populations contribute to fisheries over long time scales is mostly unknown. In this study, we investigated the relative contributions of genetically and phenologically distinct herring populations to Indigenous Peoples' food systems over multiple centuries, using ancient DNA extracted from archaeological herring bones. These bones were excavated from two Coast Salish archaeological sites (Burton Acres Shell Midden and Bay Street Shell Midden) in the Puget Sound region, USA. Using genetic stock identification from seven nuclear DNA markers, we showed that catches at the two sites in central Puget Sound were dominated by January-February and March-April spawners, which are the contemporary spawning groups in the vicinity of the sites. However, May spawners were detected in the older Burton Acres assemblage (dated to 910-685 cal BP), and a mixed stock analysis indicated that catches at this site consisted of multiple populations. These results suggest that Coast Salish ancestors used a portfolio of herring populations and benefited from the ecological resource wave created by different spawning groups of herring. This study of ancient DNA allowed us to glimpse into Indigenous traditional food and management systems, and it enabled us to investigate long-term patterns of biodiversity in an ecologically important forage fish species.
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 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".