Accurate prediction of olive‐sided flycatcher breeding status using song rate measured with autonomous recording units
Bibliographic record
Abstract
Abstract Autonomous recording units (ARUs) are recognized for their use in detecting vocalizing bird species to assess presence, occupancy, and density, but their potential to monitor reproductive status of individuals and reproductive rates is not well known. We investigated whether song rates derived from ARU data, when combined with the known date, can be used to predict the proportion of male songbirds in 3 breeding status classes (single, paired, and feeding young). We monitored breeding status with weekly field visits and collected daily ARU recordings at 46 olive‐sided flycatcher ( Contopus cooperi ) breeding territories in northwestern Canada in 2016–2017. We tested 4 variations of a hierarchical multinomial regression model that used time of day, day of year, and song rate derived from 2‐minute recordings to predict breeding status, and evaluated models using a novel, likelihood‐based approach. We found the top model correctly estimated 79% of the observed proportions of birds in each breeding status across the length of the breeding season. Although date was the primary predictor of breeding status, singing rate reduced some of the uncertainty and provided more accurate estimates for a given time. A major challenge to prediction accuracy and data interpretation was accounting for bird movement and the associated impact on detection, which we partly addressed by limiting our study to individuals who were detected on at least 30% of ARU sampling days. We demonstrate that ARUs can be used to assess breeding status in a cryptic, low‐density species at risk such as the olive‐sided flycatcher, suggesting this method could be applied to a wider range of species to better understand demographics and population dynamics, and inform management decisions, for bird species of concern.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".