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Enregistrement W2088562598 · doi:10.1242/jeb.077800

GROW FAST, DIE YOUNG

2013· article· en· W2088562598 sur OpenAlexaff
Constance M. O’Connor

Notice bibliographique

RevueJournal of Experimental Biology · 2013
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueMarine and fisheries research
Établissements canadiensMcMaster University
Organismes subventionnairesnon disponible
Mots-clésLife history theoryJuvenileEcologyGasterosteusLife historyBiologyReproductionPremiseReproductive successFish <Actinopterygii>DemographyFisherySociologyPopulation

Résumé

récupéré en direct d'OpenAlex

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 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)0,001
score de la tête « metaresearch » (Gemma)0,002
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 enseignants0,071
Score d'incertitude au seuil0,236

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0020,001
Communication savante0,0020,002
Science ouverte0,0000,002
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0710,048

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.

Tête enseignante Opus0,016
Tête enseignante GPT0,282
Écart entre enseignants0,265 · 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
Publié2013
Routes d'admission1
Résumé présentoui

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