Kelp in the Salish Sea: Spatial Patterns of Persistence, Loss, and Data Gaps Using a Harmonized Dataset
Bibliographic record
Abstract
Kelp forests are ecologically and culturally significant marine habitats that provide critical ecosystem services. Despite increasing concerns over kelp loss, regional-scale analyses of kelp distribution in the Salish Sea remain limited by disparate datasets collected using varying methodologies. This study harmonizes multiple independent kelp datasets through a standardized geospatial data schema, addressing key barriers to data integration. By focusing on essential attributes—observation year, species, and location—this study develops a unified dataset that enables the first comprehensive assessment of kelp distribution trends across the region. Analysis of the harmonized dataset reveals an overall 9.4% regional decline in kelp extent with data representing kelp observations from 1858 - 2024, with localized losses reaching 48% in South Puget Sound. Additionally, results highlight significant survey gaps, particularly in remote regions of the northern Salish Sea, mid-eastern Vancouver Island, and the southern shoreline of Vancouver Island. In contrast, repeat surveys were primarily conducted near Port Townsend and within the San Juan Islands Archipelago. These findings underscore the importance of expanding long-term monitoring efforts to under-surveyed areas. This study highlights the importance of planning surveys that engage volunteers and prioritize a well-structured data schema, ensuring data collection supports comprehensive analysis through to reporting. It emphasizes the need to design monitoring transects based on species distribution modeling (SDM) to capture accurate ecological patterns of absence and presence. Additionally, the research calls for the establishment of a transboundary data repository that adheres to a unified data schema, fostering collaboration and consistency across regions. These steps will be critical for sustaining kelp forests in the Salish Sea and informing long-term conservation and management strategies.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".