Developmental temperature affects phenotypic means and variability: A meta‐analysis of fish data
Bibliographic record
Abstract
Abstract Fishes are sensitive to their thermal environment and face an uncertain future in a warming world. Theoretically, populations in novel environments might express greater levels of phenotypic variability to increase the chance of surviving—and eventually thriving—in the new conditions. Most research on the effect of the early thermal environment in fish species focuses on average phenotypic effects rather than phenotypic variability, but to understand how fishes will respond to rising temperatures we need to consider both the average response of the population, as well as the breadth of individual responses. Here we present the first meta‐analysis on the effects of developmental temperature in fishes. Using data from 43 species and over 6,000 individual fish, we show that a change in developmental temperature induces a significant change in phenotypic means and variability, but differently depending on whether the temperature is increased or decreased. Decreases in temperature (cool environments) showed a significant decrease in phenotypic means and no change in phenotypic variability. Increases in temperature (warm environments) showed a non‐significant increase in phenotypic means and a marginally significant increase in phenotypic variability. Larger increases in temperature saw greater increases in phenotypic variability, but no increase in the mean phenotypic response. Together, our results suggest that fishes exhibit both directed and stochastic developmental plasticity in response to warming temperatures, which could facilitate or accelerate adaptation to a changing environment.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.016 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".