Age-dependent carry-over effects in a long-distance migratory bird
Bibliographic record
Abstract
Migratory birds usually respond to climate change by modifying breeding and/or wintering areas, as well as by reproducing earlier. In addition, changes in winter habitat use or breeding phenology could have important carry-over effects on subsequent breeding success. Here, we studied age- and sex-dependent carry-over effects from wintering to the breeding stage of a small aerial insectivorous long-distance migratory bird, the barn swallows (Hirundo rustica) breeding in Denmark during 1984-2013. First, we used stable isotope analyses combined with ringing recoveries to identify wintering areas. Second, we found that environmental conditions as inferred by Normalized Differential Vegetation Index (NDVI) have deteriorated at the wintering grounds. Third, we used confirmatory path analysis to quantify the indirect effect of winter conditions on subsequent breeding success. Females advanced onset of breeding, laid more eggs and raised more fledglings in the first brood when ecological conditions during the previous winter improved. This response was age dependent, since yearlings did not respond to this environmental cue but the response was increasingly stronger as females aged. Males showed a similar response to winter conditions, although not statistically significant. These results highlight the importance of studying carry-over effects within the context of climate change, especially in relation to age of individuals.
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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 | 0.002 |
| 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".