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Record W2090257147 · doi:10.1242/jeb.066282

BLACKBIRDS ADAPT SONGS TO HUMAN NOISE

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

Bibliographic record

VenueJournal of Experimental Biology · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAnimal Vocal Communication and Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsMarshWindsorEcologyRumbleEngineeringBiologyWetland

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.063
GPT teacher head0.351
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2011
Admission routes1
Has abstractyes

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