Microclimate predicts kelp forest extinction in the face of direct and indirect marine heatwave effects
Bibliographic record
Abstract
Marine heatwaves threaten the persistence of kelp forests globally. However, the observed responses of kelp forests to these events have been highly variable on local scales. Here, we synthesize distribution data from an environmentally diverse region to examine spatial patterns of canopy kelp persistence through an unprecedented marine heatwave. We show that, although often overlooked, temperature variation occurring at fine spatial scales (i.e., a few kilometers or less) can be a critical driver of kelp forest persistence during these events. Specifically, though kelp forests nearly all persisted toward the cool outer coast, inshore areas were >3°C warmer at the surface and experienced extensive kelp loss. Although temperatures remained cool at depths below the thermocline, kelp persistence in these thermal refugia was strongly constrained by biotic interactions, specifically urchin populations that increased during the heatwave and drove transitions to urchin barrens in deeper rocky habitat. Urchins were, however, largely absent from mixed sand and cobble benthos, leading to an unexpected association between bottom substrate and kelp forest persistence at inshore sites with warm surface waters. Our findings demonstrate both that warm microclimates increase the risk of habitat loss during marine heatwaves and that biotic interactions modified by these events will modulate the capacity of cool microclimates to serve as thermal refugia.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".