MétaCan
Menu
← Retour à la cohorte
Enregistrement W4311165645 · doi:10.1101/2022.11.30.518597

Costs and benefits of maternal nest choice: tradeoffs between brood survival and thermal stress for small carpenter bees

2022· preprint· en· W4311165645 sur OpenAlexafffund
JL deHaan, Jesse Maretzki, Adonis Skandalis, Glenn J. Tattersall, MH Richards

Notice bibliographique

RevuebioRxiv (Cold Spring Harbor Laboratory) · 2022
Typepreprint
Langueen
DomaineAgricultural and Biological Sciences
ThématiquePlant and animal studies
Établissements canadiensBrock University
Organismes subventionnairesNatural Sciences and Engineering Research Council of CanadaBrock University
Mots-clésBroodNest (protein structural motif)BiologyJuvenileOffspringEcologyForagingPredationPregnancy

Résumé

récupéré en direct d'OpenAlex

Abstract Nest site selection is a crucial decision for bees because where mothers construct their nests influences the developmental environment of their offspring. Small carpenter bees ( Ceratina calcarata ) nest in sun or shade, suggesting that maternal decisions about nest sites are influenced by thermal conditions that influence juvenile growth and survival. We investigated the costs and benefits to mothers and their offspring of warmer or cooler nest sites using a field experiment in which mothers and newly founded nests were placed in sunny or shady habitats. Maternal costs and benefits in sunny and shady treatments were quantified by comparing brood provisioning behaviour, nest size, number of brood cells, and offspring survival rates. Juvenile costs and benefits were quantified as body size, high temperature tolerance (CT max ), metabolic rate, and pupal duration. The major maternal benefit of nesting in sun was significantly lower rates of total nest failure (caused by predation, parasitism or abandonment), which led to sun mothers producing 3.2 brood on average, while shade mothers produced only 2.9. However, sun nesting entailed costs to brood, which were significantly smaller, less likely to survive to adulthood and had significantly elevated CT max . This suggests that juvenile bees in sun nests bees experienced thermal stress during development, causing them to shunt resources from growth to thermoprotection, at the cost of smaller size and higher mortality. Pupae raised in a thermal-gradient “BeeCR” machine developed significantly faster at warmer average temperatures, which may be an additional benefit of sun nesting. Overall, our results highlight a tradeoff between maternal benefits and offspring costs when mothers choose nest sites, in which maternal fitness is enhanced by nesting in sun, despite significant physiological costs to offspring, due to the necessity for thermoprotective responses. Thinking through pandemic research The first lockdowns of the COVID-19 pandemic began as we prepared to enter the second field season of this study in 2020. Student research halted overnight. Lab access and travel were restricted. With limited access to field sites and no access to lab equipment, we brainstormed alternative approaches that would repeat, if not replicate, our main experiments of 2019 and fulfill degree requirements for JL de Haan’s MSc in a satisfying way. Our 2019 results had provided convincing evidence developmental temperature has long-term impacts on C. calcarata physiology, so we thought about which physiological measurements would be feasible outside the lab. Authors MH Richards and GJ Tattersall suggested collecting more measurements of CT max : the Peltier plate device required running water, but a portable water pump and a bucket allowed the apparatus to be set up anywhere. No calibration of instruments was required, and the only maintenance was to change the water in the bucket after a few hours of use. Thus, a student’s home basement became a laboratory. To investigate how temperatures affect developmental rate, we needed to raise bees in controlled environments, but incubators were not available. Author A Skandalis suggested repurposing a gradient PCR unit as a portable insect incubator (“The BeeCR”). The idea was tested successfully at home in 20202, so a larger study was done by J Maretzki in 2021 when undergraduate lab access was permitted again. Two outcomes of our pandemic pivot produced long-term benefits for our research. The BeeCR is a flexible, inexpensive, easy-to-use incubator perfectly suited for raising small insects at multiple simultaneous sets of variable temperatures. And the ease with which “field” sites could be established in our backyards demonstrates how amenable small carpenter bees are to field manipulations, suggesting this is a model species for addressing a variety of ecological and physiological questions.

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)0,000
score de la tête « metaresearch » (Gemma)0,001
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: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,002
Score d'incertitude au seuil0,005

Scores du classifieur distillé par catégorie (deux têtes)

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

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 Opus0,045
Tête enseignante GPT0,214
Écart entre enseignants0,169 · 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

Citations4
Publié2022
Routes d'admission2
Résumé présentoui

Explorer davantage

Même revuebioRxiv (Cold Spring Harbor Laboratory)→Même sujetPlant and animal studies→Travaux en français237 207→