Supplementary Tables S3-S5 detailing reduced biological function GO terms from REVIGO of differentially expressed transcripts between Winter Skate Populations from Adaptive phenotypic response to climate enabled by epigenetics in a K-strategy species, the fish <i>Leucoraja ocellata</i> (Rajidae)
Bibliographic record
Abstract
The relative importance of genetic versus epigenetic changes in adaptive evolution is a hotly debated topic, with studies showing that some species appear to be able to adapt rapidly without significant genetic change. Epigenetic mechanisms may be particularly important for the evolutionary potential of species with long maturation times and low reproductive potential ('K-strategists'), particularly when faced with rapidly changing environmental conditions. Here we study the transcriptome of two populations of the winter skate (Leucoraja ocellata), a typical 'K-strategist', in Atlantic Canada; an endemic population in southern Gulf of St. Lawrence and a large population on the Scotian Shelf. The endemic population has been able to adapt to a 10 degrees Celsius higher water temperatures over short evolutionary time (7000 years), dramatically reducing its body size (by 45%) significantly below the minimum maturation size of Scotian Shelf and other populations of winter skate, as well as exhibiting other adaptations in life history and physiology. We demonstrate that the adaptive response to selection has an epigenetic basis, cataloguing 3653 changes in gene expression that may have enabled this species to rapidly respond to the novel environment. We argue that the epigenetic augmentation of species evolutionary potential (its regulation though gene expression) can enable K-strategists to survive and adapt to different environments, and this mechanism may be particularly important for the persistence of sharks, skates and rays in the light of future climate change.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.318 | 0.049 |
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".