The Influence of Migration Timing and Local Conditions on Reproductive Timing in Arctic‐Breeding Birds
Bibliographic record
Abstract
For birds breeding in the Arctic, nest success is affected by the timing of nest initiation, which is partially determined by local conditions such as snow cover. However, conditions during the non-breeding season can carry over to affect the timing of breeding. We used tracking and breeding data from 248 individuals of 8 species and subspecies of Arctic-breeding shorebirds to estimate how the timing of nest initiation is related to local conditions like snowmelt phenology versus prior conditions, measured by the timing and speed of migration. Using path analysis, our global model showed that local and prior conditions have similar effect sizes (Standardised Path Coefficients ± SE of 0.44 ± 0.07 and 0.43 ± 0.07 for snowmelt and arrival timing, respectively), suggesting that both influence the timing of breeding and therefore potentially reproductive output. However, the importance of each variable varied across species. Individuals that arrived later to the breeding grounds did not leave the wintering grounds later, but instead took longer to migrate, potentially reflecting differences in flight speed or time spent at stopover sites. We hypothesise that this may be due to reduced habitat quality at some stopover sites or an inability to adjust their departure timing or migration speed to match the advancing spring phenology in the North. Individuals that migrated longer distances also arrived and nested later. Our results highlight the benefits and potential conservation implications of using a full annual cycle approach to assess the factors influencing reproductive timing of birds.
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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".