Shifting baselines in Puget Sound: population abundance of Pacific herring and its use by Native Americans over the millennia
Bibliographic record
Abstract
Healthy marine ecosystems have become a top priority for management and conservation bodies. However, the definition of ecosystem health is usually based on data from populations that have already been degraded by recent human impacts such as commercial resource extraction, climate change and habitat destruction. Unfortunately, this incremental degradation of natural ecosystems is linked directly to the erosion of social systems, especially among Indigenous peoples. Pacific herring (Clupea pallasi) might be an example of ‘shifting baselines’ in the marine environment, as intense commercial fishing in both Canada and the US predate recent biomass estimates. Furthermore, the predominance of herring bones in archaeological remains and the importance of herring in local oral history are often not matched by the limited number of tribal herring fisheries today. Here, we present the rationale of a Washington Sea Grant funded project to reconstruct pre-industrial levels of population diversity of Pacific herring in Puget Sound, and to gather traditional local knowledge on its past abundance and cultural importance to local tribes. The project draws from several disciplines including anthropology, archaeology, and genetics, and is nested within two larger programs, the Herring School (SFU, Canada) and the NSF IGERT Program on Ocean Change (UW, USA). Specifically, we will (i) synthesize traditional local knowledge about herring in Puget Sound, (ii) quantify extant genetic population diversity, (iii) compare pre-industrial genetic population diversity estimated from archaeological bones with that of extant herring, and (iv) carry out outreach activities with our tribal partners. We expect that the project will lead to a re-evaluation of recovery goals of Puget Sound herring and foster discussions about achievable and desirable management goals between tribal and other stakeholder groups.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".