Whole-organism responses to constant temperatures do not predict responses to variable temperatures in the ecosystem engineer <i>Mytilus trossulus</i>
Bibliographic record
Abstract
Understanding and predicting responses of ectothermic animals to temperature are essential for decision-making and management. The thermal performance curve (TPC), which quantifies the thermal sensitivity of traits such as metabolism, growth and feeding rates in laboratory conditions, is often used to predict responses of wild populations. However, central assumptions of this approach are that TPCs are relatively static between populations and that curves measured under stable temperature conditions can predict performance under variable conditions. We test these assumptions using two latitudinally matched populations of the ecosystem engineer Mytilus trossulus that differ in their experienced temperature variability regime. We acclimated each population in a range of constant or fluctuating temperatures for six weeks and measured a series of both short term (feeding rate, byssal thread production) and long-term (growth, survival) metrics to test the hypothesis that performance in fluctuating temperatures can be predicted from constant temperatures. We find that this was not true for any metric, and that there were important interactions with the population of origin. Our results emphasize that responses to fluctuating conditions are still poorly understood and suggest caution must be taken in the use of TPCs generated under constant temperature conditions for the prediction of wild population responses.
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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 | 0.000 | 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".