Data from: Physiological and genomic signatures of evolutionary thermal adaptation in redband trout from extreme climates
Bibliographic record
Abstract
AbstractTemperature is a master environmental factor that limits the geographical distribution of species, especially in ectotherms. To address challenges in biodiversity conservation under ongoing climate change, it is essential to characterize relevant functional limitations and adaptive genomic content at population and species levels. Here, we present evidence for adaptive divergence in cardiac function and genomic regions in redband trout (Oncorhynchus mykiss gairdneri) populations from desert and montane streams. Cardiac phenotypes of individual fish were measured in the field with a custom-built electrocardiogram apparatus. Maximum heart rate and its rate limiting temperature during acute warming were significantly higher in fish that have evolved in the extreme of a desert climate compared to a montane climate. Association mapping with 526,301 single nucleotide polymorphisms (SNPs) across the genome revealed signatures of thermal selection both within and among ecotypes. Among desert and montane populations, 413 SNPs were identified as putative outliers under natural selection and 20 of these loci showed significant association with average summer water temperatures among populations. Phenotypes for cardiac performance were variable within each ecotype and 207 genomic regions were strongly associated with either maximum heart rate or rate limiting temperatures among individuals. Annotation of significant loci provided candidate genes that underlie thermal adaptation, including pathways associated with cardiac function (IRX5, CASQ1, CAC1D and TITIN), neuroendocrine system (GPR17 and NOS) and stress response (SERPH). By integrating comparative physiology and population genomics, results here advance our knowledge on evolutionary processes of thermal adaptation in aquatic ectotherms.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".