<b>Life‐history tradeoffs revealed by seasonal declines in reproductive traits of Arctic‐breeding shorebirds</b>
Bibliographic record
Abstract
Seasonal declines in breeding performance are widespread in wild animals, resulting from temporal changes in environmental conditions or from individual variation. Seasonal declines might drive selection for early breeding, with implications for other stages of the annual cycle. Alternatively, selection on the phenology of nonbreeding stages could constrain timing of the breeding season and lead to seasonal changes in reproductive performance. We studied 25 taxa of migratory shorebirds (including five subspecies) at 16 arctic sites in Russia, Alaska, and Canada. We investigated seasonal changes in four reproductive traits, and developed a novel Bayesian risk‐partitioning model of daily nest survival to examine seasonal trends in two causes of nest failure. We found strong seasonal declines in reproductive traits for a subset of species. The probability of laying a full four‐egg clutch declined by 8–78% in 12 of 25 taxa tested, daily nest survival rates declined by 1–12% in eight of 22 taxa, incubation duration declined by 2.0–2.5% in two of seven taxa, and mean egg volume declined by 5% in one of 15 taxa. Temporal changes were not fully explained by individual variation. Across all species, the proportion of failed nests that were depredated declined over the season from 0.98 to 0.60, while the proportion abandoned increased from 0.01 to 0.35 and drove the seasonal declines in nest survival. An increase in abandonment of late nests is consistent with a life‐history tradeoff whereby either adult mortality increased or adults deserted the breeding attempt to maximize adult survival. In turn, seasonal declines in clutch size and incubation duration might be adaptive to hasten hatching of later nests. In other species of shorebirds, we found no seasonal patterns in breeding performance, suggesting that some species are not subject to selective pressure for early breeding.
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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 | 0.001 |
| 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 | 0.000 | 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".