Bibliographic record
Abstract
Our climate is changing; however, as you sweat out the next heat wave, spare a thought for fish, which also have to cope with these warmer climes. As temperatures increase so do their metabolic rates, which in turn increases their need for oxygen. This problem is compounded by the fact that oxygen supplies become quickly depleted in warmer water by the accelerated growth of other waterborne organisms like algae. If fish are to survive long-term they need to evolve to cope with these challenges. However, evolution can only occur if there is already a significant amount of heritable variation within a population. Moreover, as low oxygen and high temperature are linked, tolerance for either condition needs to be correlated, as Patricia Schulte from the University of British Columbia, Canada, explains: ‘If you're trying to select for individuals that do well in both, if everyone who does well in one does poorly in the other, it's a non-starter.’ So, is there hope for fish? Is there enough heritable variation for evolution to act? Schulte and her colleagues turned to Atlantic salmon to investigate (p. 1183).Teaming up with a large aquaculture firm, the team reared 41 salmon families by crossing 41 females each with one of 29 males, with some males fathering up to three families. Two postdocs, Katja Anttila and Rashpal Dhillon, then had the mammoth task of testing over 800 offspring for their tolerance to high temperatures and low oxygen. After acclimatization to the experimental tank, the duo slowly raised the water temperature, carefully monitoring the fish for signs of wooziness, which occurs when the fish has reached its upper limit of heat tolerance. At 23°C some fish were already feeling faint and flopped over, whilst others stuck it out to a toasty 27.5°C. The results were just what they'd hoped for, as Anttila recalls: ‘We were thrilled when we started to see that there is huge variability in the temperature tolerance and that the closely related fish (full siblings and half-siblings) resembled each other so much.’ This similarity amongst fish fathered by the same male was the essential clue that they'd been looking for that tolerance was heritable.Next, the team tested how long it would take for dizziness to set in when the salmon was placed in poorly oxygenated water. The team saw a wide variation in tolerance, with wooziness beginning within 22.9–120 min. Again, they found tolerance levels were similar amongst related fish and, moreover, these tolerant families were the same families that had been tolerant to higher temperatures.So, it seems that salmon meet all the right criteria for evolution to work, but what exactly were the traits that conferred tolerance to these stressors? Schulte reasoned that ‘variation would be in the weakest link in the chain, strengthen this and then the whole chain is stronger’. They suspected the heart was the weak link as, in salmon, a large portion of the heart has no direct supply of oxygenated blood and instead scrounges for leftover oxygen in the blood as it passes through the heart. Therefore any variation in oxygen levels could stop the heart working efficiently. Indeed, they found that fish with higher tolerance for heat had larger ventricles. At the protein level, they found more tolerant fish had higher levels of myoglobin, which can act as storage for oxygen. However, variation in these traits doesn't explain all the variability in heat tolerance the team sees, so the investigation for more traits to explain heat tolerance continues. For now though, we can rest more easily knowing that there is at least some hope for salmon's future.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".