Conservation of Marine Foundation Species: Learning from Native Oyster Restoration from California to British Columbia
Bibliographic record
Abstract
Abstract Marine foundation species are critical to the structure and resilience of coastal ecosystems and provide key ecosystem services. Since many have suffered severe population declines, restoration of foundation species has been undertaken worldwide. The Olympia oyster (Ostrea lurida) is a foundation species, and the restoration of depleted populations is a priority for maintaining ecosystem function of estuaries along the west coast of North America. Here, we synthesize all native oyster restoration projects conducted from California, USA, to British Columbia, Canada, and analyze project goals, methods, and outcomes. Currently, restoration projects are spread unevenly across the species’ range, driven by locally varying goals and implemented with contrasting approaches. We highlight the value of regional strategic planning and decision support tools to evaluate project design and methods for restoration, including the selection of substrates and the targeted use of aquaculture. We recommend future investment in larger projects, which our analysis found were more cost-effective, but which have been relatively rare for this species. We also recommend that funders support monitoring over broader temporal and spatial scales than in the past to better characterize long-term effects of restoration on oyster populations and the services they provide beyond the project footprint. We found that most projects successfully supported native oysters and engaged local communities, and recommend similar efforts to continue to enhance understanding of Olympia oysters, which remain unfamiliar to many coastal residents. We believe that the results of this synthesis are broadly applicable to marine foundation species generally, and can inform restoration and conservation efforts worldwide.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".