Notice bibliographique
Résumé
Anyone who has seen a middle-aged rock star understands that certain lifestyles take more of a toll than others. Live fast, die young! This rock star mantra is also the premise of life-history theory, one of the central tenants of ecological research. Life-history traits are all of those characteristics related to an individual's lifestyle, such as growth rate, reproduction and lifespan. Life-history theory states that not all life-history traits can be maximized simultaneously, and individuals will need to make trade-offs among competing functions.Theoretically, one of the key life-history trade-offs may be between growth rate and total lifespan. However, it is difficult to manipulate growth without manipulating other confounding factors, like nutrient availability. Who-Seung Lee, Pat Monaghan and Neil Metcalfe from the University of Glasgow addressed this long-standing question by taking advantage of the biology of juvenile three-spined sticklebacks (Gasterosteus aculeatus). Like all fish, sticklebacks are cold-blooded, and their metabolism and hence growth can be sped up or slowed down by changing water temperature. Furthermore, sticklebacks reproduce in the spring, and as reproductive success is related to body size, they are motivated to attain a large size prior to their first spring.To examine the effect of growth rate on total lifespan, the scientists used water temperature and photoperiod to manipulate both how much the fish would need to grow and how fast they would have to grow prior to their first spring. The researchers predicted that higher growth rates would come at a cost to overall lifespan. Using juvenile fish captured in November, they experimentally manipulated the period available for growth; half the fish were kept under a normal photoperiod, while the other half were exposed to a delayed photoperiod, so that the fish perceived that they had an extra month before spring. Fish from both groups were then subjected to a ‘cold snap’ (6°C) or a ‘warm spell’ (14°C), or kept at a constant 10°C for 4 weeks. While all fish were fed the same diet, their metabolism, and therefore growth rate, was influenced by temperature. Thus, fish in the cold snap group grew more slowly than those in the other groups. After 4 weeks, all the fish were returned to 10°C for the rest of their lives.When the fish were returned to 10°C, they faced a resource allocation decision. The fish stunted by the cold snap were much smaller than their counterparts, and to attain reproductive size they would need to grow quickly by directing all of their resources towards growth. Conversely, fish exposed to a warm spell were already larger and could afford to grow at a more leisurely pace. These warm spell fish could use some resources to fuel other processes that are important for overall lifespan, such as the immune system. There was even less pressure on fish that had been subjected to a delayed photoperiod – they had an extra month for growth. But did different growth rates influence overall lifespan?Excitingly, the results matched the researchers' predictions. All of the fish had similar final sizes, but fish exposed to the cold snap directed more resources towards growth and grew more quickly once returned to 10°C, and consequently had the shortest lives. By replicating the experiment with fish captured in January, with mere weeks before spring, the researchers found that lifespans were shortened still further by the shortened growth time frame. This paper elegantly provides the first experimental evidence that growth rates are directly linked to total lifespan, and this indeed represents a key life-history trade-off. There is now empirical evidence that if you grow fast, you die young.
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 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,000 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,071 | 0,048 |
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 ».