Comparative population genomics provide new insight into the evolutionary history and adaptive potential of World Ocean krill
Bibliographic record
Abstract
Abstract Genetic variation is instrumental for adaptation to new or changing environments but it is poorly understood how it is structured and contributes to adaptation in pelagic species without clear barriers to gene flow. Here we use extensive transcriptome datasets from 20 krill species collected across the Atlantic, Indian, Pacific and Southern Oceans and compare genetic variation both within and between species across thousands of genes. We resolve phylogenetic interrelationships and uncover genomic evidence in support of elevating the cryptic Euphausia similis var. armata into species. We estimate levels of genetic variation and rates of adaptive protein evolution among species and find that these are comparably low in large Southern Ocean species endemic to cold environments, including the Antarctic krill Euphausia superba , suggesting their adaptive potential to rapid climate change may also be low. We uncover hundreds of candidate loci with signatures of adaptive divergence between krill native to cold and warm waters and identify candidates for cold-adaptation that have also been detected in Antarctic fish, including genes that govern thermal reception such as TrpA1 . Our results suggest parallel genetic responses to similar selection pressures across Antarctic taxa and provide new insights into the adaptive potential of important zooplankton that are already strongly affected by 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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".