Evolution of salinity tolerance from transcriptome to physiological system
Bibliographic record
Abstract
The relationship between microevolution and macroevolution is a topic of fundamental importance in evolutionary biology. The increasing accessibility of genomic tools is making the hunt for genes that underlie evolutionary divergence more tractable and, when combined with physiological approaches, provides exceptional power to elucidate the causal mechanisms of the relationship. In this issue of Molecular Ecology, Whitehead et al. (2013) employ this strategy to show that common physiological and genomic mechanisms lead to divergence in salinity tolerance across micro- and macroevolutionary timescales. They compare two killifish species from the genus Fundulus, F. majalis, which inhabits primarily marine and brackish environments and represents the ancestral state of the genus, and F. heteroclitus, which has derived an osmotic niche that expands into freshwater. Corresponding to the differences in osmotic niche, the species differ strikingly in how the structure of the ion-transporting epithelium and the transcriptome of the gills respond to osmotic challenge. These inter-specific differences were similar to but more pronounced than the differences associated with the more subtle intra-specific variation in osmotic niche within each species. It appears that a progression of the same functional adjustments first allowed expansion of the osmotic niche of F. heteroclitus into freshwater and then further expanded the niche of select F. heteroclitus populations towards more dilute freshwater environments. The work of Whitehead et al. therefore emphasizes how the mechanisms of adaptive divergence between populations can be expanded over time to produce the more complex differences that can exist between species.
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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".