Using video monitoring to assess the accuracy of nest fate and nest productivity estimates by field observation
Bibliographic record
Abstract
Nest fate and nest productivity are key demographic parameters for understanding songbird population dynamics, yet little consideration has been paid to assessing and improving the accuracy of these estimates in the field. We considered the magnitude and sources of error in field estimates of nest fate and productivity for 13 species of boreal forest songbirds, the implications of this error when estimating rates of nest survival and population growth, and the utility of common field cues used to assess fate. Using video from 127 nests, we found that observers correctly identified 85% of nest fates but overestimated nest productivity by up to 35%. This resulted in population growth rates being overestimated by 6%. Field estimates were less accurate when nestling age approached the estimated fledge date and when the nest was depredated. Accuracy of field estimates can be improved by focusing on nest condition and the presence of fecal droppings outside the nest. Spending additional time searching for family groups would be prudent when nests are deemed successful on the basis of nestling age alone. Nest predators force fledged one or more nestlings from 14% of nests. The fate of force-fledged young is unknown. Our measures of error declined as increasingly younger force-fledged individuals were considered successful. Resolving this uncertainty would further improve the accuracy of field-based estimates. We encourage the use of video to quantify and improve the accuracy of field estimates and to evaluate the potential for differential bias in error within variables of interest.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".