Individual quality overwrites carry‐over effects across the annual cycle of a long‐distance migrant
Bibliographic record
Abstract
In seasonal environments, the fitness of animals depends upon the successful integration of life-history stages throughout their annual cycle. Failing to do so can lead to negative carry-over effects where individuals are transitioning into the next season in different states, consequently affecting their future performance. However, carry-over effects can be masked by individual quality when individuals vary in their efficiency at acquiring resources year after year (i.e. 'quality'), leading to cross-seasonal consistency in individual performance. Here we investigated the relative importance of carry-over effects and individual quality in determining cross-seasonal interactions and consequences for breeding success over the full annual cycle of a migratory seabird (black-legged kittiwake Rissa tridactyla). We monitored the reproduction and annual movement of kittiwakes over 13 years using geolocators to estimate their breeding success, distribution and winter energy expenditure. We combined this with an experimental approach (clutch removal experiment, 2 years) to manipulate the reproductive effort irrespective of individual quality. Piecewise path analyses showed that successful breeders reproduced earlier and were more likely to breed successfully again the following year. This positive interaction among consecutive breeding stages disappeared after controlling for individual quality, suggesting that quality was dominant in determining seasonal interactions. Moreover, controlling experimentally for individual quality revealed underlying carry-over effects that were otherwise masked by quality, with breeding costs paid in higher energy expenditure and delayed onset of reproduction. We highlight the need to combine an experimental approach along with long-term data while assessing apparent carry-over effects in wild animals, and their potential impact on fitness and population demography.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".