Sea otter recovery buffers century-scale declines in California kelp forests
Bibliographic record
Abstract
The status of kelp forests and their vulnerability to climate change are of global significance. As the foundation for productive and extensive ecosystems, understanding the long-term trends in kelp is critical to coastal ecosystem management, climate resiliency, and restoration programs. In this study, we curate historical US government kelp inventories, develop methods to compare them with contemporary surveys, and use a machine learning framework to evaluate and rank the drivers of change for California kelp forests over the last century. Historical surveys documented Macrocystis and Nereocystis kelp forests covered approximately 120.4 km2 in 1910-1912, which is only slightly above surveys in 2014-2016 (112.0 km2). These statewide comparisons, however, mask dramatic regional changes with increases in Central California (+57.6%, +19.7 km2) and losses along the Northern (-63.0%, -8.1 km2), and Southern (-52.1%, -18.3 km2) mainland coastlines. Random Forest models rank sea otter (Enhydra lutris nereis) population density as the primary driver of kelp changes, with benthic substrate, extreme heat, and high annual variation in primary productivity also significant. This century-scale perspective identifies dramatically different outcomes for California’s kelp forests, providing a blueprint for nature-based solutions that enhance coastal resilience to climate change.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".