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Enregistrement W2090257147 · doi:10.1242/jeb.066282

BLACKBIRDS ADAPT SONGS TO HUMAN NOISE

2011· article· en· W2090257147 sur OpenAlexaboutno aff
Kathryn Knight

Notice bibliographique

RevueJournal of Experimental Biology · 2011
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueAnimal Vocal Communication and Behavior
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMarshWindsorEcologyRumbleEngineeringBiologyWetland

Résumé

récupéré en direct d'OpenAlex

It's hard to find peace and quiet these days. No matter where you are, you can usually hear the rumble of passing cars or an aeroplane overhead. Humans tackle noisy environments by raising their voices, but how has our continual racket affected the calls of other species? This is the question that puzzled University of Ottawa honours student Dalal Hanna when she struck up a collaboration with David Wilson from the University of Windsor. ‘We were both doing fieldwork at the Queen's University Biological Station. My expertise is in animal communication and her interest was in conservation biology, so we joined forces,’ recalls Wilson (p. 3549).Drawing on the experience of Gabriel Blouin-Demers and staff at the Research Station, Hanna and Wilson decided to find out how anthropogenic noise might affect the calls of red-winged blackbirds by comparing the songs of populations living in marshes adjacent to Canadian Provincial Highway no. 15 to the songs of red-winged blackbirds from pristine marshes on the biology station.Heading into the marshes in the early morning, Hanna and Wilson recorded the calls of birds adjacent to the highway, before the traffic – and noise – levels became too high, and in the relative peace of the wilderness. ‘Working in the marshes was a bit of a challenge,’ admits Wilson, ‘you can't walk into or through them so you are confined to the perimeter and even then it can be very soggy and difficult to move through to record different birds around the edges of the marshes.’After successfully recording 436 songs from over 60 birds, Wilson teamed up with Daniel Mennill to analyse and compare the birds' calls. ‘The challenge was separating the signal from the background noise to make very accurate measurements of the songs,’ recalls Wilson. However, they eventually found that the final harsh trill of the song produced by the highway population had become deeper and more whistle-like than the wilderness birds' songs: in other words, the song had become more tonal, allowing the birds to be heard above the road noise. Also, instead of gaining low frequency components, the highway birds had lost the higher frequencies found in their rural cousin's songs, leaving the low frequency sounds that travel further for communication.Next, Hanna and Wilson wondered how the wilderness red-winged blackbirds would respond if they suddenly encountered noise levels that the highway population endure constantly. Could they adapt and, if so, would they use the same strategy as Highway no. 15's neighbours?Playing white noise and silence to the wilderness birds and recording their songs, Wilson and Hanna successfully extracted the noise from the recordings and compared the songs. Again, the bird's songs had become more whistle-like as they competed with the noise. So, even though the birds had never experienced traffic noise, they were able to adjust their calls in exactly the same way as birds that had been living with human noise for generations.But how could these song changes affect the birds' lifestyles? Wilson says that it would be interesting to find out whether the alteration affects mate selection by females and how males defend their territories. ‘Ultimately, we could use this information to identify the real costs of anthropogenic noise in terms of survival and reproduction in birds and use that as a model for gauging the effects on other species, as well as ones that are more endangered,’ says Wilson.

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 enseignants0,012
Score d'incertitude au seuil0,023

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 technologies0,0000,001
Communication savante0,0010,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0020,001

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 Opus0,063
Tête enseignante GPT0,351
Écart entre enseignants0,288 · 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
Publié2011
Routes d'admission1
Résumé présentoui

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