Breeding dispersal of Northern Flickers<i>Colaptes auratus</i>in relation to natural nest predation and experimentally increased perception of predation risk
Bibliographic record
Abstract
After nest predation, breeding dispersal can be an effective strategy to avoid local nest predators. Furthermore, encounters with predators at a nest during the pre‐laying stage may be used by parents to judge future risk, such that they may abandon a nest when a nest predator has been encountered. We studied whether the between‐ and within‐year breeding dispersal of Northern Flickers Colaptes auratus was dependent upon the outcome of the previous nesting attempt. We also tested whether pairs presented with a model predator prior to egg‐laying were more likely to abandon their nests than were pairs presented with a control model. Between years, males moved significantly further after having their nest depredated than did successful males, and females showed the same trend. However, these movements did not result in greater reproductive success. More pairs switched sites within years after having their nest depredated, but those that remained and those that moved had equal subsequent nest success. Stressful encounters with predators involving nest defence may trigger dispersal both between and within years, although reproductive benefits are unclear. The proportion of pairs abandoning nests did not differ between parents presented with control or predator models, suggesting that a single encounter with a predator is not a sufficient deterrent against continued use of a particular nest.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".