Local cold adaption increases the thermal window of temperate mussels in the Arctic
Bibliographic record
Abstract
Abstract Species expand towards higher latitudes in response to climate warming, but the pace of this expansion is related to the physiological capacity to resist cold stress. However, few studies exist that have quantified the level of inter-population local adaptation in marine species freeze tolerance, especially in the Arctic. We investigated the importance of cold adaptation and thermal window width towards high latitudes from the temperate to the Arctic region. We measured upper and lower lethal air temperatures (i.e. LT and LT50) in temperate and Arctic populations of blue mussels (Mytilus edulis), and analysed weather data and membrane fatty acid compositions, following emersion simulations. Both populations had similar upper LT (~38 °C), but Arctic mussels survived 4°C colder air temperatures than temperate mussels (−13 vs. −9°C, respectively), corresponding to an 8% increase in their thermal window. There were strong latitudinal relationships between thermal window width and local air temperatures, indicating Arctic mussels are highly adapted to the Arctic environment where the seasonal temperature span exceeds 60°C. Local adaptation and local habitat heterogeneity thus allow leading-edge M. edulis to inhabit high Arctic intertidal zones. This intraspecific pattern provides insight into the importance of accounting for cold adaptation in climate change, conservation and biogeographic studies.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".