Phenotypic plasticity and divergence in gene expression
Bibliographic record
Abstract
The extent to which phenotypic plasticity, or the ability of a single genotype to produce different phenotypes in different environments, impedes or promotes genetic divergence has been a matter of debate within evolutionary biology for many decades (see, for example, Ghalambor et al. ; Pfennig et al. ). Similarly, the role of evolution in shaping phenotypic plasticity remains poorly understood (Pigliucci ). In this issue of Molecular Ecology, Dayan et al. () provide empirical data relevant to these questions by assessing the extent of plasticity and divergence in the expression levels of 2272 genes in muscle tissue from killifish (genus Fundulus) exposed to different temperatures. F. heteroclitus (Fig. A) and F. grandis are minnows that inhabit estuarine marshes (Fig. B) along the coasts of the Atlantic Ocean and Gulf of Mexico in North America. These habitats undergo large variations in temperature both daily and seasonally, and these fish are known to demonstrate substantial phenotypic plasticity in response to temperature change (e.g. Fangue et al. ). Furthermore, the range of F. heteroclitus spans a large latitudinal gradient of temperatures, such that northern populations experience temperatures that are on average ~10°C colder than do southern populations (Schulte ). By comparing gene expression patterns between populations of these fish from different thermal habitats held in the laboratory at three different temperatures, Dayan et al. () address two important questions regarding the interacting effects of plasticity and evolution: (i) How does phenotypic plasticity affect adaptive divergence? and (ii) How does adaptive divergence affect plasticity?
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".