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Record W4416784048 · doi:10.1242/jeb.250817

Rivers and roads, silence and songs: female crickets respond similarly to conspecific male song in natural and anthropogenic soundscapes

2025· article· en· W4416784048 on OpenAlexafffund
Erik A. Etzler, Hannah M. ter Hofstede, John M. Ratcliffe

Bibliographic record

VenueJournal of Experimental Biology · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAnimal Vocal Communication and Behavior
Canadian institutionsUniversity of WindsorAmgen (Canada)University of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Toronto
KeywordsSoundscapeNatural soundsPredationAnimal communicationSilenceNoise (video)PerceptionNatural (archaeology)

Abstract

fetched live from OpenAlex

Many studies have demonstrated that anthropogenic noise affects animals' auditory perception of salient stimuli. Few have tested whether these effects are different from those experienced in nature. We tested the ability of female field crickets, Teleogryllus oceanicus, to phonotactically locate a speaker playing conspecific male song in four acoustic backgrounds: silence, road noise, river noise and heterospecific song. Crickets approaching conspecific song paused more frequently in river noise and heterospecific song treatments compared with silence or road noise. We also recorded auditory interneuron (AN1 and AN2) activity under the first three acoustic background treatments to construct and compare treatment-specific audiograms and interneuron responses to conspecific song. We found little difference in activity, other than that AN2 thresholds for 6 kHz sounds (the tested frequency closest to male song) were highest in river noise, while heterospecific song increased baseline AN2 activity and reduced AN2 activity to conspecific song onset. Our results suggest road noise is not necessarily a greater disturbance than river noise.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.134
Threshold uncertainty score0.369

Codex and Gemma teacher scores by category

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

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 head0.017
GPT teacher head0.330
Teacher spread0.313 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations1
Published2025
Admission routes2
Has abstractyes

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