From archives to satellites: uncovering loss and resilience in the kelp forests of Haida Gwaii
Bibliographic record
Abstract
Coastal foundation species such as kelps, corals, and seagrasses play vital roles in supporting marine biodiversity and ecosystem services globally, but are increasingly threatened by climate change. In particular, kelp forests are highly dynamic ecosystems experiencing natural fluctuations across seasons and climate cycles, e.g., El Niño Southern Oscillation, Pacific Decadal Oscillation. As climate change increases variability in these cycles and extreme events such as marine heatwaves become more frequent, long term data are essential to understand deviations from the norm and to better estimate trends of change. This study uses a century-long dataset to examine kelp forest responses to regional drivers in Haida Gwaii, British Columbia, by combining remote sensing data from 1973-2021 with a snapshot of kelp distribution derived from historical records from 1867-1945. We reveal complex patterns of change, with kelp losses and resilience varying at different spatial scales. Kelp forests that had likely persisted for over a century exhibited an overall declining trend of 5 ± 2% per decade starting in the 1970s. Throughout the time series kelp area was driven by multi-year impacts of the Pacific Decadal Oscillation, El Nino Southern Oscillation, sea surface temperature anomalies and marine heatwaves, such as the 1998 El Niño and the 2014-2016 marine heatwave known as the ‘Blob’. In the warmest areas, kelp forests completely disappeared during the 1977 Pacific Decadal Oscillation shift. Cooler areas showed greater resilience, buffering the loss at the region wide scale, highlighting the importance of local gradients in understanding areas vulnerable to climate change. Lastly, local in situ surveys showed a lack of urchin barrens, and the presence of turf algae in the study region, further supporting the hypothesis that temperature, not herbivory, drove kelp forest loss in this region.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".