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Wind Farms Alter Amphibian Community Diversity and Chorusing Behavior

2022· article· en· W4229456105 on OpenAlexaff
Cory M. Trowbridge, Jacqueline D. Litzgus

Bibliographic record

VenueHerpetologica · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAnimal Vocal Communication and Behavior
Canadian institutionsLaurentian University
Fundersnot available
KeywordsEcologyTransectWetlandHabitatBiodiversityThreatened speciesEnvironmental scienceGeographyBiology

Abstract

fetched live from OpenAlex

Anurans exhibit altered chorusing behaviors in response to anthropogenic noise, yet no studies have considered the effects of wind farm presence on anuran chorusing behaviors. We studied amphibian communities in a wind farm situated in a landscape that includes relatively pristine wetlands and forests. We measured amphibian diversity in habitats adjacent to wetlands using transect surveys, and we quantified anuran chorus and call characteristics (diversity, frequency, and duration) using nightly audio recordings in replicated turbine sites (<0.5 km from turbines) and control sites (>1.5 km from turbines). If wind farms present a source of disturbance, then we expected wetlands near turbines to have lower species diversity, lower chorus intensity, and altered call characteristics. We found significantly lower chorus diversity in turbine-site recordings, but no differences in biodiversity between turbine and control sites based on animals captured during transect surveys. Call characteristics did not differ between control and turbine sites; however, frogs calling in the wind farm displayed call characteristics similar to those of frogs calling near noisy roads within control sites, and some anuran species were notably absent from turbine sites. Identification of new threats, including those resulting from putatively green energy alternatives, is essential to mitigating global amphibian decline.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.193
Threshold uncertainty score0.999

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 studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.004
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 head0.062
GPT teacher head0.290
Teacher spread0.228 · 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.

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

Citations7
Published2022
Admission routes1
Has abstractyes

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