Variable effect of playback of chickadee mobbing calls on detection probability of boreal forest birds
Bibliographic record
Abstract
Modification of the point count survey method to include playback of songbird mobbing calls in an attempt to increase detection probabilities has met with mixed success. We compared detection probabilities for boreal forest songbirds using traditional point count methods and counts using broadcasts of the mobbing calls of Black-capped Chickadees (Poecile atricapillus) in an attempt to increase detection probability. We conducted 594 point counts during the 2010 breeding season in Newfoundland, Canada. Each point count consisted of an 8-min silent observation period followed by an 8-min broadcast of Black-capped Chickadee mobbing calls. Occupancy model results showed that response to playback broadcast varied across species, with detection probabilities higher for seven of 17 species during the silent portions of point counts and three species more likely to be detected during playback intervals. For all species, the number of visual detections increased during periods of playback and, averaged across species, individuals were >6 times more likely to be seen during the playback period than during the silent period. Differences in detection probability among observers were apparent during both silent and playback periods. We suggest that using playback of chickadee mobbing calls during point count surveys of common boreal forest songbird species may be most beneficial when visual detection is important. However, playback may also be useful for species-specific surveys during periods when birds are less likely to be vocal or for studies of less common species with chronically low detection probabilities. A combined silent and playback approach could also be useful, although observer and species differences should be accounted for if comparing data across species or studies.
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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 | 0.001 | 0.005 |
| 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 | 0.001 | 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".