Probing parentage in parasitic birds: an evaluation of methods to detect conspecific brood parasitism using goldeneyes <i>Bucephala islandica</i> and <i>Bl. clangula</i> as a test case
Bibliographic record
Abstract
Conspecific brood parasitism (CBP) occurs in over 200 species of birds. Efforts to detect CBP have relied on either observational criteria, or more recently, on molecular methods. While molecular approaches are powerful, they are expensive, time consuming and may prove prohibitive for studies requiring estimates of CBP over large spatial and temporal scales involving hundreds of nests. We evaluated a series of observational methods that have been applied in previous studies to detect CBP, using two species of cavity‐nesting ducks, the Barrow's goldeneye Bucephala islandica and common goldeneye B. clangula , as test species. We first describe a method based on differences in egg morphology and find it to be a reliable method to detect CBP in both species in British Columbia, Canada. The application of recursive partitioning analysis was especially effective in classifying parasitized and non‐parasitized nests using differences in egg morphology. We then evaluated five additional observational criteria that have been used previously in several studies to detect CBP in birds. We show that considerable redundancy exists among all criteria, as expected, but no single method is effective at detecting all suspected cases of CBP. Subsets of criteria (2 or more eggs/d, eggs laid 2 or more days after incubation, and clutch sizes exceeding 12 eggs) were successful, in combination, in detecting 75% of parasitized nests for goldeneyes. Finally, we suggest that ecological and evolutionary analyses of the dynamics of CBP will require estimates of the frequency of the parasitic tactic in the population (rather than just the proportion of parasitized nests) and we provide a simple method to obtain such an estimate. Although our data are specific to goldeneyes, the techniques we used should have broad application to other studies of CBP.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".