Freshening increases the susceptibility to heat stress in intertidal mussels (<i>Mytilus edulis</i>) from the Arctic
Bibliographic record
Abstract
Temperatures in the Arctic are increasing at a faster pace than at lower latitudes resulting in range expansion of boreal species. In Greenland, the warming also drives accelerating melt of the Greenland Ice Sheet resulting in more meltwater entering Greenland fjords in summer. Our aim was to determine if increasing summer temperatures combined with lower salinity can induce the expression of stress-related proteins, for example, heat shock protein, in boreal intertidal mussels in Greenland, and whether low salinity reduces the upper thermal limit at which mortality occurs. We conducted a mortality experiment, using 12 different combinations of salinity and air temperature treatments during a simulated tidal regime, and quantified the change in mRNA levels of five stress-related genes (hsp24, hsp70, hsp90, sod and p38) in surviving mussels to discern the level of sublethal stress. Heat-induced mortality occurred in mussels exposed to an air temperature of 30°C and mortality was higher in treatments with lowered salinity (5 and 15‰), which confirms that low habitat salinity decreases the upper thermal limit of Mytilus edulis. The gene expression analysis supported the mortality results, with the highest gene expression found at combinations of high temperature and low salinity. Combined with seasonal measurements of intertidal temperatures in Greenland, we suggest heat stress occurs in low salinity intertidal area, and that further lowered salinity in coastal water due to increased run-off can make intertidal bivalves more susceptible to summer heat stress. This study thus provides an example of how different impacts of climate warming can work synergistically to stress marine organisms.
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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".