Back to the past: Long-term persistence of bull kelp forests in the Strait of Georgia, Salish Sea, Canada
Bibliographic record
Abstract
The Salish Sea, a dynamic system of straits, fjords, and channels in southwestern British Columbia, is home to ecologically and culturally important bull kelp (Nereocystis luetkeana) forests. Yet the long-term fluctuations in the area and the persistence of this pivotal coastal marine habitat are unknown. Using very high-resolution satellite imagery to map kelp forests over two decades, we present the spatial changes in kelp forest area within the Salish Sea, before (2002 to 2013) and after (2014 to 2022) the ‘Blob,’ an anomalously warm period in the Northeast Pacific. This analysis was spatially constrained by local environmental conditions. Based on nearshore sea surface temperatures (SSTs) from four decades (1984–2022), we found two periods of distinct increases in SST, one starting in 2000 and another in 2014. Further, the highest SST anomalies occurred on warmer coastlines in the enclosed inlets and the Strait of Georgia, while smaller anomalies were found on colder coastlines near the Strait of Juan de Fuca and the Discovery Passage. The total area of bull kelp forests from 2014 to 2022 has decreased compared to 2002 to 2013, particularly in the northern sector of the Salish Sea. Using the satellite-derived kelp data, we also present an analysis of kelp persistence compared with historical distribution of kelp forests depicted on British Admiralty Nautical Charts from 1858 to 1956. This analysis shows that warm, sheltered areas experienced a considerable decrease in persistence of kelp beds when compared to satellite-derived distribution of modern kelp, confirming a century-scale loss. In particular, the presence of kelp forests in the Strait of Georgia and on the warmest coasts has decreased considerably over the century, likely due to warming temperatures. While the coldest coasts to the south have maintained their centennial persistence, the northern Salish Sea requires further research to understand its current dynamics. This research contributes to a wider understanding of temporal and spatial factors for kelp from the regional perspective of the Salish Sea.
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 | 0.003 |
| Science and technology studies | 0.002 | 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".