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Enregistrement W4407342088 · doi:10.1242/jeb.249930

Feeling the burn: young fish struggle in acidic water

2025· article· en· W4407342088 sur OpenAlexaffabout
Alexandra N. Schoen

Notice bibliographique

RevueJournal of Experimental Biology · 2025
Typearticle
Langueen
DomaineEarth and Planetary Sciences
ThématiqueOcean Acidification Effects and Responses
Établissements canadiensUniversity of Winnipeg
Organismes subventionnairesnon disponible
Mots-clésFish <Actinopterygii>FeelingPsychologyFisheryBiologySocial psychology

Résumé

récupéré en direct d'OpenAlex

Climate change has many effects on aquatic environments, including changes in temperature and chemicals in the water. One chemical, carbon dioxide (CO2), is discussed a lot among scientists because it has a big impact on how acidic the water is (pH, or the number of hydrogen molecules in the water). More CO2 in water makes the water more acidic, which can cause problems for aquatic animals and plants. Many adult animals can handle changes in water acidity, but earlier stages of life (e.g. eggs, larvae) in animals such as fish might not tolerate even small increases in acidity. This is because fish eggs and larvae do not have the fully developed body parts that are resistant to changes in acidity and may not be able to escape higher acidity areas as well as adults can. Knowing this, Grace Wallace, Rosemary Minns and Caleb Hasler of the University of Winnipeg, Canada, were particularly interested in how small increases in acidity might affect the eggs and larvae of Japanese medaka (Oryzias latipes), a fast-growing freshwater fish that is often raised and studied in laboratories.To study this, Wallace's team bred medaka and raised their offspring for 9 days. At 3 days old, the medaka inside the eggs had developed beating hearts, so Wallace decided to place the eggs into acidic water for 24 h at this time, to see whether it changed their heartbeats. Different groups of eggs were placed into five different levels of acidic water from normal (pH 7.1) to more acidic (pH 6.4, 6.1 and 5.8), to the most acidic (pH 5.7). The researchers found that the fish's heartbeats slowed more every time the water became more acidic. Wallace and her colleagues suggested that the more acidic water (and higher CO2) might have affected the communication systems in the heart, causing the heart muscle to beat slower. Wallace also recorded how often the medaka moved inside the eggs: while they moved more when the water was slightly acidic (pH 6.4), movement did not increase further when the water became even more acidic (pH 6.1, 5.8 and 5.7).After the acidic water treatment, the scientists tracked how many eggs survived for 4 days. Like the heartbeat results, Wallace found that eggs survived better in less acidic water, possibly because the fish's hearts beat better in less acidic water. When the medaka hatched, Wallace placed a group of 9 day old larvae in the same levels of acidic water for 24 h. This time, the researchers were interested in the swimming behaviour of the larvae, as lower pH has been shown to change different behaviours of larval fish of other species. Wallace placed the larvae into a circular tank and recorded how much time each fish spent in the centre or close to the walls of the tank over the course of 10 min. She also measured how far and fast the larvae swam during this time.Interestingly, there was no change in how far or fast the larvae swam. However, in some of the acidic waters (pH 6.4 and 5.8), larvae spent more time in the inside zone of the circular tank. Wallace found this strange because their behaviour did not change like their heartbeat across acidic water levels, showing that some body systems in medaka may respond more to acidity than others.Although Wallace noted that medaka raised in the laboratory might act differently than in the wild, more acidic water still caused the heartbeats of these fish to slow down. However, their swimming behaviour might be more resilient to increases in acidity. This suggests that medaka, and potentially other freshwater fish, may not survive if climate change causes aquatic environments to become more acidic.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut 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 enseignants0,221
Score d'incertitude au seuil0,725

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
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 ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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 Opus0,012
Tête enseignante GPT0,267
Écart entre enseignants0,255 · 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 tête enseignante, 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
Publié2025
Routes d'admission2
Résumé présentoui

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Même revueJournal of Experimental BiologyMême sujetOcean Acidification Effects and ResponsesTravaux en français237 207