Estimating historic sea otter prevalence from archaeological and contemporary California mussel size structure
Bibliographic record
Abstract
Along the northeastern Pacific, the extirpation and subsequent recovery of sea otters generated profound changes in coastal social-ecological systems. Today, most conservation targets for sea otter recovery are formulated on pre-fur trade population estimates reflecting ecosystems devoid of humans. However, evidence suggests that for millennia prior to European contact, complex hunting and management protocols by Indigenous communities limited sea otters at sites of high human occupation in order to enhance local access to shellfish. To make inferences about relative sea otter prevalence in deep time, we compared the size structure of ancient California mussels (Mytilus californianus) from five archaeological sites on the Northwest Coast of North America to modern mussels at locations with and without sea otters. To estimate mussel shell length from archaeological umbo fragments, we established a morphometric regression between modern mussel umbo thickness and maximum shell length. We also quantified modern mussel size distributions from eight locations on the central coast of British Columbia, Canada, varying in sea otter occupation time. Comparisons of modern and ancient mussel size revealed that pre-fur trade mussel size distributions are more similar to modern mussel size distributions in the absence of sea otters, suggesting that sea otters prior to the maritime fur trade were maintained below carrying capacity as a result of human intervention. These findings provide broader insight into the conditions under which humans and sea otters persisted over millennia, and potential solutions for their coexistence in the future.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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".