The effects of nesting success and mate fidelity on breeding dispersal in burrowing owls
Bibliographic record
Abstract
Understanding and describing the factors that affect avian breeding dispersal are critical for modeling population dynamics and designing conservation strategies. We investigated the hypothesis that dispersal probability and dispersal distance are affected by nesting success and mate fidelity with band–resight data (1998–2003) from burrowing owls (Athene cunicularia (Molina, 1782)) nesting in southern California. Most owls (167 of 253, 66%) remained near their initial nest (<100 m), and those that moved >100 m dispersed 472 ± 65 m (mean ± 1 SE; n = 86). Both female and male owls whose nests failed were more likely to disperse and dispersed longer distances than owls with successful nests. Failed nesting attempts were also associated with an increased probability of divorce, and divorce was related to increased dispersal probabilities and distances. Moreover, female and male owls tended to be more likely to disperse and to disperse greater distances following the death of a mate than those that remained paired. Although dispersal was related to mate fidelity, nesting success remained an important factor affecting dispersal even after controlling for the effects of mate loss. Our results suggest that nesting failure was the primary factor associated with dispersal probability and dispersal distance in burrowing owls in our population.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| 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".