Acoustic monitoring of nocturnally migrating birds accurately assesses the timing and magnitude of migration through the Great Lakes
Bibliographic record
Abstract
Tracking the movements of migratory songbirds poses many challenges because much of their journey takes place at night. One promising technique for studying migratory birds relies on microphones to record the nocturnal flight calls produced by birds on the wing. We compared recordings of night flight calls with bird-banding data in a southern Great Lakes ecosystem. We collected >6,200 hr of nocturnal recordings at 7 locations around Lake Erie. We detected >60,000 flight calls from migratory birds and classified 45,775 calls to species level or to a bioacoustic category comprising several species with similar calls. We compared these acoustic data with records of 5,624 birds captured in mist nets. We found that acoustic recordings accurately quantified the magnitude of migration; comparison with mist-net data revealed significant positive correlations between the number of acoustic detections and the number of mist-net detections across species. We also found that acoustic recordings accurately quantified the timing of migration; we found significant positive correlations between the date of passage of the 10th, 50th, and 90th percentiles of the populations of up to 25 groups of passage migrant species in the acoustic data and mist-net data. A careful examination of 6 species with distinctive flight calls revealed only subtle seasonal differences between peak detections via acoustic monitoring and mist netting, at both daily and weekly timescales. This research enhances our understanding of the role that acoustic sampling can play in monitoring migratory birds, providing important empirical support for the validity of night-flight-call monitoring.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".