Usefulness and limitations of thermal performance curves in predicting ectotherm development under climatic variability
Bibliographic record
Abstract
Thermal performance curves (TPCs) have been estimated in multiple ectotherm species to understand their thermal plasticity and adaptation and to predict the effect of global warming. However, TPCs are typically assessed under constant temperature regimes, so their reliability for predicting thermal responses in the wild where temperature fluctuates diurnally and seasonally remains poorly documented. Here, we use distant latitudinal populations of five species of sepsid flies (Diptera: Sepsidae) from the temperate region (Europe, North Africa, North America) to compare estimates derived from constant TPCs with observed development rate under fluctuating temperatures in laboratory and field conditions. TPCs changed across gradients in that flies originating from higher latitudes showed accelerated development at higher temperatures, an adaptive response. TPCs were then used to predict development rates observed under fluctuating temperatures; these predictions were relatively accurate in the laboratory but not the field. Interestingly, the precision of TPC predictions depended not only on the resolution of temperature data, with daily and overall temperature summing performing better than hourly temperature summing, but also on the frequency of temperatures falling below the estimated critical minimum temperature. Hourly temperature resolution most strongly underestimated actual development rates, because flies apparently either did not stop growing when temperatures dropped below this threshold, or they sped up their growth when the temperature rose again, thus most severely reflecting this error. We conclude that when flies do not encounter cold temperatures, TPC predictions based on constant temperatures can accurately reflect performance under fluctuating temperatures if adequately adjusted for nonlinearities, but when encountering cold temperatures, this method is more error-prone. Our study emphasizes the importance of the resolution of temperature data and cold temperatures in shaping thermal reaction norms.
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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) | 0.000 | 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".