Snowmelt and laying date impact the parental care strategy of a high-Arctic shorebird
Bibliographic record
Abstract
Why parental care strategies can vary from uniparental to biparental care across taxa remains unclear, likely because various sets of ecological conditions are at-play. Here we tested ten possible hypotheses to decipher the direct and indirect impacts of critical factors likely to influence the parental care strategy during incubation in Sanderlings (Calidris alba), one of the few species that uses both types of care during that critical time of the breeding cycle. We examined three ecological factors (timing of local snowmelt, regional temperatures, and the North Atlantic Oscillation experienced just before breeding), one trophic factor (predation pressure), and two social factors (relative abundance of Sanderlings and their laying dates). Using long-term data from Greenland (2011-2023), path analyses revealed that laying date and snowmelt influence parental care strategies during incubation, with indirect climatic effects during migration and on breeding grounds. We observed a greater proportion of uniparental nests in years with delayed laying dates, and the reverse in years with delayed snowmelt. These findings underscore the complex interplay between environmental parameters and parental care strategies, offering insights into how these strategies are likely to respond to rapidly changing Arctic ecological conditions driven by climate change.
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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 | 0.000 |
| 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".