Permanent and temporary mate-switching in a long-lived seabird: Insights from a 64-year study
Bibliographic record
Abstract
In long-lived monogamous animals, pair bonds play a crucial role in breeding success. In many predominantly monogamous animals, however, there is often some degree of mate-switching. Mate-switching may represent an opportunity to acquire a higher quality partner or breeding site following breeding failure. Using a 64-year dataset, we investigated the dynamics of mate-switching in Leach’s storm-petrels (Hydrobates leucorhous). We observed that, on average, 4.3 % of pairs permanently switched mates each year (i.e., permanent mate switch), with an increasing rate in recent years. A small proportion (1.1 % annual average) of pairs switched mates but reunited in a later year (i.e., temporary mate switch). As expected from previous studies, breeding failure was a significant predictor of permanent mate-switching. But temporary mate-switching was unrelated to breeding failure, suggesting these two kinds of mate-switching are caused by different decision-making processes. Rising global mean temperatures (GMT) was associated with increases in both temporary and permanent mate switching rates, raising the possibility that ongoing climate change will destabilize future population dynamics in this declining seabird species.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".