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Enregistrement W4386255487 · doi:10.1242/jeb.245025

A clashing colour combination with deadly consequences

2023· article· en· W4386255487 sur OpenAlexaff
Giulia S. Rossi

Notice bibliographique

RevueJournal of Experimental Biology · 2023
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueImpact of Light on Environment and Health
Établissements canadiensMcMaster University
Organismes subventionnairesnon disponible
Mots-clésLight pollutionDuskShoreSkyOceanographyGeographyFisheryEcologyBiologyMeteorologyGeologyAstronomy

Résumé

récupéré en direct d'OpenAlex

Humans have a powerful ability to illuminate the night sky. We leave streetlights on from dusk until dawn, operate businesses through the night and light our homes well after sunset. In a world where natural light cycles govern the rhythm of life, we have created a major disruptor known as artificial light at night. In some cases, this light pollution we create is considered ‘diffuse’, such that there are many light sources originating from multiple directions and therefore weak shadows are cast in the environment (imagine an illuminated soccer pitch). In other cases, we create ‘direct’ sources of light that cast many dark shadows in the environment (imagine a lighthouse). In a fascinating new study, Kathryn Bullough and her team of researchers from the University of Exeter, UK, investigated the impacts of both diffuse and direct light pollution on the sea roach (Ligia oceanica), a marine crustacean that changes colour to blend in with its surroundings and avoid the prying eyes of predators. The researchers discovered that illuminated nights trigger an intriguing clash between sea roach behaviour and their colour-changing tactics at night.Sea roaches live near the shore, where light from major coastal cities illuminates the underwater world. To understand how brighter nights impact these crustaceans, the team first collected dozens of sea roaches from the rocky shoreline of Swanpool Beach, UK. Afterwards, the team placed the animals into a pitch-black box to activate their colour-changing superpowers, encouraging them to turn as dark as possible. The researchers then transferred the sea roaches into buckets in which half of the bottom was lined with black gravel and half with white gravel. Some buckets were exposed to a direct source of light that projected strong shadows among the textured gravel floor, whereas others were exposed to diffuse light that obliterated any shadows. Over a 15-min observation period, the crustaceans chose to spend more time on black gravel. Moreover, when shadows were available, sea roaches actively chose to be in shadowy regions of the bucket, suggesting they had a clear preference for a dark background. This is where things got interesting. When the sea roaches experienced direct lighting, they generally stayed darker in colouration – sometimes becoming even darker than before to better match their coveted black and shadowy background. In contrast, sea roaches experiencing diffuse lighting became lighter in colour, even when on a dark background. Thus, the diffuse light pollution causes sea roaches to mix up which colour they should become – a mishap that leaves them especially visible and vulnerable to predators.The team decided to take the study one step further and explore the movement of sea roaches in their buckets. The team predicted that sea roaches would move quickly but erratically when they clashed with their background to draw less attention to themselves. Bullough and colleagues hypothesized that if the crustaceans camouflage well with their environment, they would have no reason to move in such a stealthy manner. Indeed, when faced with diffuse lighting – where shadowy hideouts on both white and dark backgrounds were few and far between – the sea roaches were clever enough to make fast and irregular movements on white backgrounds, but if camouflaged, they hardly moved.In the end, this study sheds light on the effects that our artificially brightened nights have on underwater animals. The team showed that artificially brightening our nights leads to colour-changing confusion in sea roaches, which can have major consequences on their ability to survive. Let's all turn down our lights at night and give sea roaches a chance to shine – just not too brightly.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,156
Score d'incertitude au seuil0,970

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
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)0,0010,000

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 Opus0,027
Tête enseignante GPT0,308
Écart entre enseignants0,281 · 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 tête enseignante, 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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