Passive acoustic monitoring provides predictable and reliable underestimates of population size and longevity in wild Savannah Sparrows
Bibliographic record
Abstract
Abstract Many breeding birds produce conspicuous sounds, providing tremendous opportunities to study free-living birds through acoustic recordings. Traditional methods for studying population size and demographic features depend on labor-intensive field research. Passive acoustic monitoring provides an alternative method for quantifying population size and demographic parameters, but this approach requires careful validation. To determine the accuracy of passive acoustic monitoring for estimating population size and demographic parameters, we used autonomous recorders to sample an island-living population of Savannah Sparrows (Passerculus sandwichensis) over a 6-year period. Using the individually distinctive songs of males, we estimated the male population size as the number of unique songs detected in the recordings. We analyzed songs across 6 years to estimate birth year, death year, and longevity. We then compared the estimates with field data in a blind analysis. Estimates of male population size through passive acoustic monitoring were, on average, 72% of the true male population size, with higher accuracy in lower-density years. Estimates of demographic rates were lower than true values by 29% for birth year, 23% for death year, and 29% for longevity. This is the first investigation to estimate longevity with passive acoustic monitoring and adds to a growing number of studies that have used passive acoustic monitoring to estimate population size. Although passive acoustic monitoring underestimated true population parameters, likely due to the high similarity among many male songs, our findings suggest that autonomous recorders can provide reliable estimates of population size and longevity in a wild songbird.
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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".