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Enregistrement W2276254040 · doi:10.1242/jeb.123836

Seminal plugs cost red-sided garter snakes dear

2015· article· en· W2276254040 sur OpenAlexaboutno aff
Kathryn Knight

Notice bibliographique

RevueJournal of Experimental Biology · 2015
Typearticle
Langueen
DomaineAgricultural and Biological Sciences
ThématiqueAnimal Behavior and Reproduction
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésReproductionHibernation (computing)SpermMatingBiologyZoologyEcologyBotany

Résumé

récupéré en direct d'OpenAlex

Bubbling out of their hibernation burrows as the temperature begins to rise, male red-sided garter snakes only have one thing on their mind: mating. And with females in short supply, the pressure is on. But how much effort do these males invest in reproduction? The expense is clear for females, but how costly is seminal fluid production for males? Christopher Friesen from the University of Sydney, Australia, explains that male red-sided garter snakes are clearly exerting themselves as the seminal plugs left inside the females after copulation – to avoid sperm leakage and prevent the female from mating with other males – are massive. Also, the males’ blood lactate levels soar, suggesting that seminal fluid production could be costly. Knowing that males produce and store their sperm in late summer, while the majority of the seminal fluid components are produced in spring, Friesen and his thesis advisor Robert Mason from Oregon State University, USA, realised that they could tease apart the males’ investment in seminal fluid production from the cost of sperm production to begin understanding how costly reproduction is for red-sided garter snake males.Collecting large and small males as they emerged from their Manitoba hibernation chamber, the duo then provided the males with a continual supply of fresh females, allowing half of the males to court and mate enthusiastically, while the attempts of the other group were thwarted by tape placed over the females’ cloacae. Then they measured the snakes’ energy consumption over the course of 9 days and found that it was around 50% higher (7.33 kJ day−1) than that of males outside of the mating season.Next, they calculated the energy consumption (per unit mass) for each of the snakes as they courted and mated with females and although they could see that the largest males invested little energy in seminal fluid production, the smallest snakes invested up to eight times more energy. And when the team tracked the snakes’ mass loss relative to the number of times that they mated – the males do not feed during the mating season – the most successful males (that mated 5 times) lost as much as 8 g, while the least successful lovers (that only mated once) lost 4–6 g. In addition, Donald Powers and Paige Copenhaver measured the metabolic rates of males that had successfully mated and the males that had just lost out and found that the metabolic rates of the largest courting males were barely raised at all. However, the metabolic rates of the smallest males rose by approximately 30% during courting and rocketed by almost 50% when they mated successfully.Finally, the team calculated the net cost of producing the seminal fluid's plug components, and they were impressed that the males were investing as much as 18% of their daily energy expenditure per ejaculation. They were also surprised that the resting metabolic rates of males after seminal fluid production (V̇O2=0.0025 ml g−1 min−1) were similar to the metabolic rates of pregnant female garter snakes (V̇O2=0.0023 ml g−1 min−1). However, the team were intrigued that sperm-free plugs produced by vasectomised males were 26% more energy dense than the plugs produced by fertile males, suggesting that sperm contain less energy than other seminal fluid components.Reflecting on the smaller males’ greater exertions, the team suspects that they throw everything they can into each mating opportunity as they may not survive the next harsh Manitoba winter to take advantage of the lower mating costs when older and larger.

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: Empirique
Score de désaccord entre enseignants0,015
Score d'incertitude au seuil0,052

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,000
Études des sciences et des technologies0,0010,001
Communication savante0,0010,001
Science ouverte0,0000,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0150,002

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 Opus0,058
Tête enseignante GPT0,306
Écart entre enseignants0,248 · 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
Publié2015
Routes d'admission1
Résumé présentoui

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