Carry‐over effects of weather and decision‐making on nest success of a migratory shorebird
Bibliographic record
Abstract
Abstract Weather conditions experienced by birds can influence their migration decision‐making and strategy both within and across seasons. Additionally, decision‐making during migration may influence subsequent fitness (reproductive success and/or survival). Examining the effects of fine‐scale weather variables on individuals throughout the year could help identify stages of the annual cycle when species may be most affected by weather. In this study, we captured 24 black‐bellied plovers (gray plovers; Pluvialis squatarola ) on nonbreeding areas along the western Gulf of Mexico coast and tracked their locations once every 2 h through their breeding season in the Alaskan and Canadian Arctic. We quantified migration strategies and weather conditions experienced by each individual throughout the nonbreeding, northward migration, and breeding seasons. We used a Bayesian hierarchical model which connected regressions linking weather with migration metrics, and migration metrics and breeding season weather with reproductive success. We found strong negative relationships between two migration metrics (migration duration and number of stopovers) and reproductive success, but no substantial relationships between breeding season weather variables and reproductive success. We found negative relationships between nonbreeding season temperature, migration temperature, and migration NDVI and both migration duration and number of stopovers, in addition to positive relationships between the number of stopovers and storms during migration, migration duration, and nonbreeding season precipitation. These results suggest that reproductive success is influenced by weather throughout the annual cycle and migration strategy is a key mechanism through which these effects operate. Our findings suggest that environmental factors throughout the year influence shorebird fitness, and, because black‐bellied plovers are often associated with mixed‐species flocks, many species likely experience similar constraints.
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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".