Wind Farms Alter Amphibian Community Diversity and Chorusing Behavior
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".