Warming and temperature variability determine the performance of two invertebrate predators
Bibliographic record
Abstract
In a warming ocean, temperature variability imposes intensified peak stress, but offers periods of stress release. While field observations on organismic responses to heatwaves are emerging, experimental evidence is rare and almost lacking for shorter-scale environmental variability. For two major invertebrate predators, we simulated sinusoidal temperature variability (±3 °C) around todays' warm summer temperatures and around a future warming scenario (+4 °C) over two months, based on high-resolution 15-year temperature data that allowed implementation of realistic seasonal temperature shifts peaking midpoint. Warming decreased sea stars' (Asterias rubens) energy uptake (Mytilus edulis consumption) and overall growth. Variability around the warming scenario imposed additional stress onto Asterias leading to an earlier collapse in feeding under sinusoidal fluctuations. High-peak temperatures prevented feeding, which was not compensated during phases of stress release (low-temperature peaks). In contrast, increased temperatures increased feeding on Mytilus but not growth rates of the recent invader Hemigrapsus takanoi, irrespective of the scale at which temperature variability was imposed. This study highlights species-specific impacts of warming and identifies temperature variability at the scale of days to weeks/months as important driver of thermal responses. When species' thermal limits are exceeded, temperature variability represents an additional source of stress as seen from future warming scenarios.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".