One species but two patterns: Populations of the Hudsonian Godwit (<i>Limosa haemastica</i>) differ in spring migration timing
Bibliographic record
Abstract
Climate change can cause mismatches between the breeding phenology and peak abundance of food resources of migratory species. Moreover, asynchronously changing climate regimes across their ranges may constrain the ability of migratory species to adapt to all the regimes they encounter. To understand the potential effect of asynchronous changes, I examined the influences of both large- and local-scale weather and climate on the timing of arrival of two disjunct breeding populations of Hudsonian Godwits (Limosa haemastica). I used arrival data from two study sites—Beluga River, Alaska, and Churchill, Manitoba—combined with 37 years of weather and climate data from both winter and stopover sites and the breeding grounds. The Alaskan population now arrives ∼9 days earlier than it did in the early 1970s, and the Churchill population arrives >10 days later. A model-selection process using linear regression models suggested that these divergent trends result from different suites of environmental factors affecting the timing of migration for the two populations. The cues used by the Alaskan population have remained reliable indicators of the timing of the onset of spring on their breeding grounds, but this is not the case for the Churchill population. Conflicting warming regimes in midcontinental North America cause the Churchill population to arrive later to their breeding grounds and limit their ability to properly time their breeding efforts. These results suggest that ecological and phenological limitations, not just evolutionary constraints, are critical to determining how populations respond to 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".