Cross‐scale environmental influences on migratory stopover behaviour
Bibliographic record
Abstract
Abstract Migratory species may be especially sensitive to climate change because their lifecycles are affected by dynamic ecological processes operating at global spatial scales. Insight into environmental effects upon en route decisions by migrants can therefore be a critical first step toward assessing their vulnerability to future climatic shifts. We extracted behaviour‐related parameters from a recent formulation of multistate mark–recapture models, and used them to evaluate the importance of variation in local weather and broad‐scale climate to decisions made by autumn passerine migrants at a coastal stopover site in Atlantic Canada. We found dramatic interannual fluctuations in the proportion of migrants that were transient (departing within a day of arrival), with annual average values ranging from 24% to 96% over the years 1996–2007. However, the relationships of stopover behaviour (transience and departure probability) to local weather (wind and precipitation) and to regional climatic fluctuations were similar across three distinct species groups and for birds of different fuel‐loads, indicating the potential for both immediate (day‐to‐day) and long‐term environmental influences (spanning several seasons). This cross‐scale approach contributes valuable information toward the conservation of migrants in the face of a changing climate by (i) quantifying environmental influences on stopover behaviour in broad temporal and geographic contexts, and (ii) demonstrating largely parallel interannual variation in stopover site‐use among birds with contrasting migratory timing and strategies.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".