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Enregistrement W2081596331 · doi:10.1242/jeb.086231

SALMON TOLERANCE TO HEAT AND LOW OXYGEN

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

Notice bibliographique

RevueJournal of Experimental Biology · 2013
Typeletter
Langueen
DomaineEnvironmental Science
ThématiquePhysiological and biochemical adaptations
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésAdaptation (eye)Fish <Actinopterygii>AcclimatizationBiologyForagingPopulationAquacultureEcologyFisheryZoologyDemography

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,010
Score d'incertitude au seuil0,020

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,016
Tête enseignante GPT0,251
Écart entre enseignants0,236 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2013
Routes d'admission1
Résumé présentoui

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