MétaCan
Menu
← Back to cohort
Record W2081596331 · doi:10.1242/jeb.086231

SALMON TOLERANCE TO HEAT AND LOW OXYGEN

2013· letter· en· W2081596331 on OpenAlexaboutno aff
Nicola Stead

Bibliographic record

VenueJournal of Experimental Biology · 2013
Typeletter
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsnot available
Fundersnot available
KeywordsAdaptation (eye)Fish <Actinopterygii>AcclimatizationBiologyForagingPopulationAquacultureEcologyFisheryZoologyDemography

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.016
GPT teacher head0.251
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Experimental Biology→Same topicPhysiological and biochemical adaptations→French-language works237,207→