Notice bibliographique
Résumé
On average, the world is getting warmer, and heatwaves are becoming more common and unpredictable – especially in areas closer to the Earth's poles. Warming near the poles may be bad news for animals, such as brook trout, living in these areas. Luckily, many animals have evolved ways to deal with heat, which could give them a buffer against severe weather. Some animals can adjust how their bodies work when the environment gets warmer, which can help them better handle brief periods of extreme heat. But these internal adjustments take time and may depend on what temperatures the animal has experienced recently. Predicting how animals will handle temperature changes is hard, because the ability to modify body function varies across the animal kingdom, and often we do not know how long it takes to make these adjustments. Erin Stewart and Graham Raby at Trent University, Canada, and Chris Wilson and Vince Frasca at the Ontario Ministry of Natural Resources and Forestry, Canada, wanted to know whether living in warmer conditions helps brook trout to better tolerate extreme temperatures and, if so, how long it takes for their tolerance to develop.To answer these questions, Stewart and colleagues used aquarium heaters and a slow, steady flow of water from a nearby creek to create three naturally fluctuating temperatures: unheated, warmed (+3°C) and warmer still (+6°C). All the water temperatures used in the experiment were within the 10–20°C range that brook trout typically survive best in. To find the hottest temperature that brook trout from each water temperature could stand before losing control of their body movements, Stewart slowly heated up an insulated tank of water containing a small number of trout until the fish reached the point where they began floating on their sides. After this, the trout were placed in a cool tank of water and monitored until they recovered. Almost all the trout survived this brush with extreme heat. To figure out whether the length of time a trout spent living in each creek water temperature (unheated or heated) influenced its heat tolerance, Stewart measured the heat tolerance of different fish from each group after 1, 4, 8, 16 and 30 days.The team found that living in warmer water improved the ability of the brook trout to handle hotter temperatures, but that it took time for the fish to adjust. Trout from the warmest treatment (+6°C) showed a marked increase in heat tolerance after only a single day of living in these warmer conditions. In contrast, trout from the warm group (+3°C) took about 8 days before they showed an improvement in survival over fish living in unheated creek water. Stewart and colleagues also found that the heat tolerance of both warmed groups continued to improve throughout the 30 days of living in the different creek water temperatures.These results suggest that brook trout can adjust to warming environments, within reasonable limits. More importantly, this study highlights an often-overlooked factor that applies to the study of heat tolerance for all animals, not just fish: how long these adjustments take. With climates changing faster than ever in history, many scientists are turning their attention to studying the heat sensitivity of animals. But, if we, as researchers and conservationists, want to use our finite time and resources wisely, we need to account for the fact that biological adjustments to warming temperatures take time.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».