Impact of fluctuating developmental temperatures on phenotypic traits in reptiles: a meta-analysis
Bibliographic record
Abstract
During the vulnerable stages of early life, most ectothermic animals experience hourly and diel fluctuations in temperature as air temperatures change. While we know a great deal about how different constant temperatures impact the phenotypes of developing ectotherms, we know remarkably little about the impacts of temperature fluctuations on the development of ectotherms. In this study, we used a meta-analytic approach to compare the mean and variance of phenotypic outcomes from constant and fluctuating incubation temperatures across reptile species. We found that fluctuating temperatures provided a small benefit (higher hatching success and shorter incubation durations) at cool mean temperatures compared with constant temperatures, but had a negative effect at warm mean temperatures. In addition, more extreme temperature fluctuations led to greater reductions in embryonic survival compared with moderate temperature fluctuations. Within the limited data available from species with temperature-dependent sex determination, embryos had a higher chance of developing as female when developing in fluctuating temperatures compared with those developing in constant temperatures. With our meta-analytic approach, we identified average mean nest temperatures across all taxa where reptiles switch from receiving benefits to incurring costs when incubation temperatures fluctuate. More broadly, our study indicates that the impact of fluctuating developmental temperature on some phenotypes in ectothermic taxa are likely to be predictable via integration of developmental temperature profiles with thermal performance curves.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.015 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".