A Population-level Analysis of Morning Song: Exploring the Implications for Point Counts
Bibliographic record
Abstract
Point counts are widely used for conducting ecological surveys of wild birds. Vocal output of birds varies with time of day, and therefore the results of ecological surveys should also vary with time of day. We modeled how males’ singing rates change over the morning. We calculated song rates in 3 min sampling periods (the standard sampling period used by the North American Breeding Bird Survey) and compared how detection rates vary as sampling period increases. We recorded singing activity in 15 neighbourhoods of breeding Black-capped Chickadees (Poecile atricapillus) with 16-element microphone arrays that recorded every song from every male in every neighbourhood. We calculated the proportion of males that produced one or more songs during 3, 5, and 10 min count periods between nautical twilight and late morning. our results show a strong peak in singing activity just before sunrise followed by a steady decline in singing activity over the course of the morning. We found that longer sampling periods yielded significantly higher detection rates at all times after sunrise. After sunrise, detection rates never exceeded 60%, even with 10 min sampling periods. We found that unpaired males had significantly higher detection rates than paired males but we found no difference between paired males with fertile versus incubating mates. our results provide strong evidence that, for black-capped Chickadees, surveys during the dawn chorus provide the most comprehensive assessment of the number of birds present, and that longer sampling periods yield significantly better estimates of population sizes at all times after sunrise.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".