Atmospheric pressure predicts probability of departure for migratory songbirds
Bibliographic record
Abstract
BACKGROUND: Weather can have both delayed and immediate impacts on animal populations, and species have evolved behavioral adaptions to respond to weather conditions. Weather has long been hypothesized to affect the timing and intensity of avian migration, and radar studies have demonstrated strong correlations between weather and broad-scale migration patterns. How weather affects individual decisions about the initiation of migratory flights, particularly at the beginning of migration, remains uncertain. METHODS: Here, we combine automated radio telemetry data from four species of songbirds collected at five breeding and wintering sites in North America with hourly weather data from a global weather model. We use these data to determine how wind profit, atmospheric pressure, precipitation, and cloud cover affect probability of departure from breeding and wintering sites. RESULTS: We found that the probability of departure was related to changes in atmospheric pressure, almost completely regardless of species, season, or location. Individuals were more likely to depart on nights when atmospheric pressure had been rising over the past 24 h, which is predictive of fair weather over the next several days. By contrast, wind profit, precipitation, and cloud cover were each only informative predictors of departure probability in a single species. CONCLUSIONS: Our results suggest that individual birds actively use weather information to inform decision-making regarding the initiation of departure from the breeding and wintering grounds. We propose that birds likely choose which date to depart on migration in a hierarchical fashion with weather not influencing decision-making until after the departure window has already been narrowed down by other ultimate and proximate factors.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".