Genetic variability in reaction norms in fishes
Bibliographic record
Abstract
The ability of populations to adapt to environmental change and the spatial scale at which this adaptation occurs are fundamentally important issues in evolutionary biology, and ones that may benefit greatly from the study of genetic variability in reaction norms, which represent the plasticity of phenotypic traits across an environmental gradient. Therefore variable reaction norms can reflect genetic differences in the ability of individuals, families, populations, and species to respond to natural and anthropogenic environmental change. Fishes are ideal organisms in which to study plasticity because of their remarkable intraspecific morphological, physiological, behavioural, and life history variation. Here, we review studies demonstrating genetic variability in reaction norms in fishes. Genetic variability in plasticity among full- and half-sib families suggests potential for some populations to develop an adaptive norm of reaction (recalling that plasticity need not be adaptive). Reaction norm variability among populations suggests that adaptive genetic divergence can occur rapidly when selection pressures are strong and that the spatial scale of adaptation is much smaller than previously believed for some species with high dispersal capabilities. These studies demonstrate the potential of using reaction norms to study the evolution of novel phenotypes and the influence of temporal environmental variability and gene flow on the evolution of phenotypic plasticity, which can then be used to predict how populations will respond to directional environmental change. To promote future research into genetic variability in reaction norms, we propose questions that would benefit from such an approach and discuss some important considerations for designing experiments to investigate questions related to genetic variation in plasticity and phenotypic evolution.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 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".