Cool ocean temperatures offer limited protection to a heat-stressed keystone predator during atmospheric heatwaves
Bibliographic record
Abstract
The increasing frequency and intensity of extreme climatic events demands a better understanding of how organisms respond to temperature shifts and how these responses shape species interactions. Temperature-related disruptions in individual behaviour and physiology can signal broader community change. This is particularly true for keystone species, whose impact on their ecosystem is disproportionately large. We tested how periodic exposure to high air temperatures affects mortality, feeding, and metabolism in juvenile Pisaster ochraceus (ochre sea star), a keystone intertidal predator, by manipulating air and seawater temperatures representing typical and heatwave conditions in Barkley Sound, British Columbia, Canada. To contextualize these findings, we also quantified local environmental temperatures paired with P. ochraceus body temperatures and physical condition in the field. We found the highest mortality (42%) in treatments exposed to cool seawater (~15°C) with high air temperatures (~30°C), which corresponded with ~50% reductions in both mussel consumption and metabolic rate. In contrast, warmer seawater (~20°C) mitigated these effects, supporting greater feeding and metabolic rates, even under high air temperatures (~30°C). These findings refute the assumption that a combination of warm seawater and high air temperatures would lead to greater cumulative heat damage. Thus, we predict that P. ochraceus will be vulnerable to heatwaves in spring and early summer when seawater temperatures remain cool. The timing of extreme heat events therefore plays a critical role in predicting species responses, particularly as warming air temperatures actively alter community dynamics by changing rates of keystone predation.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 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".