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Enregistrement W4379388848 · doi:10.1242/jeb.245008

Batting around a new idea: attracting insect buffets for endangered bats

2023· article· en· W4379388848 sur OpenAlexaboutno aff
Kristina A. Muise

Notice bibliographique

RevueJournal of Experimental Biology · 2023
Typearticle
Langueen
DomaineAgricultural and Biological Sciences
ThématiqueBat Biology and Ecology Studies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMyotis lucifugusEndangered speciesHibernation (computing)CaveBiologyEcologyWhite (mutation)ZoologyCaptive breedingGeographyHabitat

Résumé

récupéré en direct d'OpenAlex

During the autumn months, many bat species face challenges to find sufficient food and generate the large fat stores they require to survive winter. However, North American bats face an additional challenge: a fungal disease called white-nose syndrome. The disease causes bats to arouse too frequently during hibernation, eventually starving to death before spring. Twelve bat species are currently affected, and some are listed as Endangered in either the USA or Canada. Winifred Frick, from Bat Conservation International and the University of California, Santa Cruz, USA, with a team of collaborators from various US and Canadian institutions, set out to test a new conservation approach that could protect bats by increasing the number of flying insects in an area, allowing the animals to gorge before and after hibernation.To test their approach, the team chose caves in the Upper Peninsula of Michigan, USA, which were known to have ∼100 remaining hibernating little brown bats (Myotis lucifugus) after declines from white-nose syndrome. They then monitored five caves in the autumn of 2019 (1 September–4 October), when the bats were fattening for hibernation, and four caves in the spring of 2021 (19 April–25 May), when bats that survived white-nose syndrome were emerging from hibernation. To attract flying insects, Frick and colleagues placed a single UV light 250 m from each cave entrance, 3 m above the ground. In the autumn, the team alternated nights when the UV lights were on, to assess changes in the number of insects they attracted. However, in the spring, to determine whether the bats could learn to respond to changes in the numbers of insects available to dine on, some of the caves were provided with a UV light on each night, while others had no UV lights on.The team also measured insect abundance every night during the autumn but only once a week during the spring, using a funnel and bucket to trap insects that were then brought back to the lab to be weighted and identified. To monitor bat foraging activity at the caves, the researchers used bat detectors placed at the sites to record the bats’ ultrasonic calls while flying from sunset to sunrise each night during the autumn and spring. Back in the lab, the recordings were reviewed to identify and count the number of ‘feeding buzzes’ – the echolocation calls that bats make when successfully catching insects – to determine the bats’ foraging success rates.The researchers found that when the UV lights were on during the autumn fattening period, the bats successfully captured and ate three times more insects than on nights when the UV lights were off. Additionally, the team collected a far greater mass of insects (16.7 times more) on the UV illuminated nights. Additionally, in the spring, the hunting bats were 8.5 times more successful in capturing prey, and the mass of insects collected by the team was 26.1 times greater at the locations where the UV lights were on than at the locations where the UV lights were off. These results show that UV lights in the autumn and spring increase the insect abundance for bats to eat, and that bats learn to forage more when there is increased food availability.Overall, Frick and colleagues have successively shown a novel approach for artificially attracting greater numbers of insects for bats to feast on during the critical spring and autumn periods. The results from the study could aid in the recovery of bat populations from white-nose syndrome and inspire wider conservation approaches for critically endangered hibernating bats in North America.

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)0,002
score de la tête « metaresearch » (Gemma)0,003
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,006
Score d'incertitude au seuil0,021

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0000,000
Études des sciences et des technologies0,0020,001
Communication savante0,0020,001
Science ouverte0,0010,001
Intégrité de la recherche0,0020,002
Charge utile insuffisante (le modèle a refusé de juger)0,0060,002

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 Opus0,093
Tête enseignante GPT0,321
Écart entre enseignants0,228 · 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'étudeExpérimental (laboratoire)
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
Publié2023
Routes d'admission1
Résumé présentoui

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Même revueJournal of Experimental Biology→Même sujetBat Biology and Ecology Studies→Travaux en français237 207→