Food Provisioning and Parental Status in Songbirds: Can Occupancy Models Be Used to Estimate Nesting Performance?
Bibliographic record
Abstract
Indirect methods to estimate parental status, such as the observation of parental provisioning, have been problematic due to potential biases associated with imperfect detection. We developed a method to evaluate parental status based on a novel combination of parental provisioning observations and hierarchical modeling. In the summers of 2009 to 2011, we surveyed 393 sites, each on three to four consecutive days at Forêt Montmorency, Québec, Canada. We assessed parental status of 2331 adult songbirds based on parental food provisioning. To account for imperfect detection of parental status, we applied MacKenzie et al.'s (2002) two-state hierarchical model to obtain unbiased estimates of the proportion of sites with successfully nesting birds, and the proportion of adults with offspring. To obtain an independent evaluation of detection probability, we monitored 16 active nests in 2010 and conducted parental provisioning observations away from them. The probability of detecting food provisioning was 0.31 when using nest monitoring, a value within the 0.11 to 0.38 range that was estimated by two-state models. The proportion of adults or sites with broods approached 0.90 and varied depending on date during the sampling season and year, exemplifying the role of eastern boreal forests as highly productive nesting grounds for songbirds. This study offers a simple and effective sampling design for studying avian reproductive performance that could be implemented in national surveys such as breeding bird atlases.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".