Can singing rate be used to predict male breeding status of forest songbirds? A comparison of three calibration models
Bibliographic record
Abstract
Abstract For male songbirds, song rate varies throughout the breeding season and is correlated with breeding cycle stages. Although these patterns have been well documented, this relationship has not been used to predict a bird's breeding status from acoustic monitoring. This challenge of using a response (i.e., behavior) to indirectly measure an underlying biological state is common in ecology, but correctly addressing the associated statistical challenge of calibration is rare. The objective of this study was to determine whether variation in song rate can be used to predict the breeding status of the Olive‐sided Flycatcher (Contopus cooperi). In 2016, song rates from 28 male Olive‐sided Flycatchers were collected from human observers (n = 545 five‐minute counts) and breeding status (i.e., single, paired, and feeding young) was monitored throughout the breeding season. The predictive ability of three modeling approaches—regression, hierarchical, and a classification tree—was evaluated using sensitivity and specificity to determine the best modeling approach. The hierarchical model was the best at predicting all three breeding status classes, with a mean sensitivity of 69%, compared with 54% and 50% from the regression and machine learning models, respectively. Our results suggest that song rate can be used as an indirect measurement of breeding status in the Olive‐sided Flycatcher when using a hierarchical modeling approach to calibrate the breeding status–song rate relationship. This novel modeling approach provides a cost‐effective tool to collect much needed demographic information over large spatial extents and inform species status assessments, recovery strategies, and management plans for species of conservation concern.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".