Addressing issues of experimental design, ecological realism and local adaptation for applications of ectotherm upper thermal limits
Bibliographic record
Abstract
Upper thermal limits of ectotherms are widely used to understand and predict species' thermal responses and sensitivity to warming. These limits are often defined for species using experiments with rapid ramping temperatures that test critical thermal maxima (CTmax). However, there are issues that arise with relying on these experimental results including (1) the influence of experimental design on thermal maxima, (2) the lack of ecological realism and (3) the potential for population-level local adaptation of upper thermal limits. We addressed these issues by comparing the CTmax approach with an ecologically realistic design using slower incremental temperature ramping with diel fluctuations (ITDmax) and by applying both to evaluate local adaptation of juvenile coho salmon (Oncorhynchus kisutch). We compared populations from thermal regimes spanning 7° latitude and coastal to inland systems by testing three populations, combining results with a fourth population from a prior ITDmax study, and comparing with other studies that used CTmax experiments to test thermal maxima of juvenile coho salmon. Most notably, we found that unlike CTmax experiments, ITDmax results were not influenced by acclimation temperature. This stemmed from acclimation during the ITDmax trials, likely representing more ecologically relevant responses to longer term warming. Furthermore, local adaptation of thermal maxima, as measured by both CTmax and ITDmax, was not evident for juvenile coho salmon, with no influence of population across the nine included in the cross-study examination. The results suggest the ability to use ITDmax-based upper thermal limits across species' extents and with differing prior environmental exposure, providing a more accurate representation of responses and sensitivity to long-term warming.
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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 | 0.171 | 0.229 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".