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Enregistrement W4388046855 · doi:10.1242/jeb.245032

Should I stay (in hot water) or should I go?

2023· article· en· W4388046855 sur OpenAlexaff
Angelina Dichiera

Notice bibliographique

RevueJournal of Experimental Biology · 2023
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueFish Ecology and Management Studies
Établissements canadiensUniversity of British Columbia
Organismes subventionnairesnon disponible
Mots-clésTroutHabitatTributaryFisheryPopulationFish migrationGlobal warmingFish <Actinopterygii>Climate changeWildlifeEnvironmental scienceGeographyEcologyBiology

Résumé

récupéré en direct d'OpenAlex

In an increasingly warm world, many scientists believe that animals only have a few options: they cope with their (hotter) environment or find a better habitat to live in. But often these choices aren't so simple. Many river habitats across the world are broken up by human-made dams, leaving some fish without the option to turn fin and escape warming waters. This is the case for some of the redband trout (Oncorhynchus mykiss newberrii) in the Klamath River Basin, USA. One population resides in Upper Klamath Lake, a shallow body of water prone to seasonal warming, but which also has cooler tributaries (∼11°C) for escape. Just south, the population of ‘Keno trout’ is trapped between two dams, a habitat which can reach above 25°C in the summertime. Because of their habitat differences, these neighbours might choose to use different coping mechanisms (stay or move away) to deal with warming. This led Nick Halhbeck and a team of scientists from Oregon State University, Oregon Department of Fish and Wildlife, the Wild Salmon Center and the University of California Santa Barbara, USA to predict that if there is a place to hide from the heat, fish will choose to change their behaviour and swim away; but if there's nowhere to go, fish are forced to change how their body works – at a greater cost.The team spent three summers researching the different populations’ behaviour, tracking where fish move when water warms. Unsurprisingly, Keno trout do not move much, but Upper Klamath Lake trout take full advantage of their escape routes, keeping cool during summer by moving to tributaries. To see if these two populations’ ability to handle intense exercise changed with warming, and figure out the maximum temperature they can handle, the team created a mini-lab in the field and tested the fish stream side. As predicted, Keno trout are better at maintaining their metabolism and recovering from exercise faster when it's hot, tolerating temperatures 2.4°C higher than Upper Klamath Lake trout and setting the record for the highest temperature that any rainbow trout studied could tolerate (at 31.3°C!). This means that these ‘trapped’ Keno trout are not only surviving but thriving in their hot habitat. But what is causing these differences between the two populations?The researchers examined genetic markers that might explain any differences in these populations’ heat tolerance, but genes don't seem to be the deciding factor for better (or worse) tolerance. But the team still expected that the process of adjusting their body to tolerate heat would be more taxing for Keno trout than the act of simply swimming away would be for Upper Klamath Lake trout. So, they took measurements of how much energy stores the fish had and how fast they're being used. Surprisingly, the data showed that moving to a new area was more energetically costly than tolerating the heat. Although all fish lost some of their energy stores over the summer due to the heat, the Upper Klamath Lake fish lost twice as much as the Keno trout. In fact, the Keno trout looked like they were recovering these stores by the end of the summer.Halhbeck and colleagues have shed new light onto how landscape determines how adaptable fish can be. With four of the six dams soon to be removed from the Klamath River Basin, what does this mean for fish that have adopted coping mechanisms based on this landscape for the last century? For now, Keno trout have a fin up on the Upper Klamath Lake trout, but we have much to learn about what choices these populations will make in a new barrier-free world.

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)0,002
score de la tête « metaresearch » (Gemma)0,010
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,033
Score d'incertitude au seuil0,111

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,010
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0000,000
Études des sciences et des technologies0,0040,004
Communication savante0,0040,006
Science ouverte0,0010,002
Intégrité de la recherche0,0040,008
Charge utile insuffisante (le modèle a refusé de juger)0,0330,023

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 Opus0,057
Tête enseignante GPT0,325
Écart entre enseignants0,268 · 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'étudeObservationnel
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
Publié2023
Routes d'admission1
Résumé présentoui

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