Cyclic thermal fluctuations can be burden or relief for an ectotherm depending on fluctuations’ average and amplitude
Bibliographic record
Abstract
Abstract Predicting the implications of ongoing ocean climate warming demands a better understanding of how short‐term thermal variability impacts marine ectotherms, particularly at beyond‐optimal average conditions during summer heatwaves. Using a globally important model species, the blue mussel Mytilus , in a 5‐week‐long experiment, we (a) assessed growth performance traits under 12 scenarios, consisting of four thermal averages (18.5, 21, 23.5 and 26℃) imposed as constant or daily fluctuating regimes with amplitudes of 2 or 4℃. Additionally, we conducted a short‐term assay using different mussel individuals to (b) test for the species capacity for suppression and recovery of metabolic performance traits (feeding and aerobic respiration) when exposed to a 1‐day thermal fluctuation regime (16.8–30.5℃). Using this high‐resolution data, we (c) generated short‐term thermal metabolic performance curves to predict and explain growth responses observed in the long‐term experiment. We found that daily high‐amplitude thermal cycles (4℃) improved mussel growth when fluctuations were imposed around an extreme average temperature of 26℃, representing end‐of‐century heatwaves. In contrast, thermal cycles negatively affected mussel growth at a less extreme average temperature of 23.5℃, resembling current peak summer temperature scenarios. These results suggest that fluctuations ameliorate heat stress impacts only at critically high average temperatures. The short‐term assay demonstrated that during the warming phase, animals stopped feeding between 24 and 30℃ while gradually suppressing respiration. In the subsequent cooling phase, feeding and respiration partially and fully recovered to pre‐heating rates respectively. Furthermore, nonlinear averaging of short‐term feeding responses (upscaling) well‐predicted longer term growth responses to fluctuations. Our findings suggest that fluctuations can be beneficial to or detrimental for the long‐term performance of ectothermic animals, depending on the fluctuations' average and amplitude. Furthermore, the observed effects can be linked to fluctuation‐mediated metabolic suppression and recovery. In a general framework, we propose various hypothetical scenarios of fluctuation impacts on ectotherm performance considering inter‐ or intra‐species variability in heat sensitivity. Our research highlights the need for studying metabolic performance in relation to cyclic abiotic fluctuations to advance the understanding of climate change impacts on aquatic systems. A free Plain Language Summary can be found within the Supporting Information of this article.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.007 | 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 teacher head, 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".