Experimental evidence suggests that broadcasting rail calls overnight can influence broad-scale site selection by migrating rails
Bibliographic record
Abstract
Broadcasting conspecific calls during migration is known to attract some focal species. Our goal was to determine if broadcasting rail calls overnight during spring migration could influence site selection by Virginia Rails (<em>Rallus limicola</em>), Soras (<em>Porzana carolina</em>), Yellow Rails (<em>Coturnicops noveboracensis</em>), and King Rails (<em>Rallus elegans</em>); if so, we sought to identify the scale at which this effect occurred. We completed in-person marsh bird surveys in 2021 (the ‘control’ year) and in 2022, when we experimentally broadcast vocalizations of focal species overnight in central Michigan at study sites that ranged from lower-quality to higher-quality habitat. In 2022, we also used autonomous recording units (ARUs) to document arrival dates and presence. Soras and Virginia Rails were the only focal species detected during in-person surveys, though a King Rail was opportunistically detected during the experimental year. We detected a mean of 0.86 ± 0.38 [SE] more rails per survey point (a 218% increase) in the experimental year compared to the control year within the same wetland units. This effect was most pronounced in higher-quality habitat. Citizen science (eBird) data indicated increased rail abundance and occurrence at our study sites during our experimental and post-experimental years, but not our control year, relative to the eight years before field work began. We did not find differences in rail abundance between locations that varied in proximity to overnight playback. ARU data provided preliminary evidence that, in lower-quality habitat, focal species tended to arrive ≥2 weeks earlier in the immediate vicinity of playback stations. Our results suggest that broadcasting overnight playback of the calls of focal species could influence site selection by rails, especially in concert with habitat restoration initiatives, though effects may extend beyond the immediate vicinity where playback is broadcast.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".