Springing forward: Migrating songbirds catch up with the start of spring in North America
Bibliographic record
Abstract
In temperate regions, the annual pattern of spring onset can be envisioned as a 'green wave' of emerging vegetation that moves across continents from low to high latitudes, signifying increasing food availability for consumers. Many herbivorous migrants 'surf' such resource waves, timing their movements to exploit peak vegetation resources in early spring. Although less well studied at the individual level, secondary consumers such as insectivorous songbirds can track vegetation phenology during migration as well. We hypothesized that four species of ground-foraging songbirds in eastern North America-two warblers and two thrushes-time their spring migrations to coincide with later phases of vegetation phenology, corresponding to increased arthropod prey, and predicted they would match their migration rate to the green wave but trail behind it rather than surfing its leading edge. We further hypothesized that the rate at which spring onset progresses across the continent influences bird migration rates, such that individuals adjust migration timing within North America to phenological conditions they experience en route. To test our hypotheses, we used a continent-wide automated radio telemetry network to track individual songbirds on spring migration between the U.S. Gulf Coast region and northern locations closer to their breeding grounds. We measured vegetation phenology using two metrics of spring onset, the spring index first leaf date and the normalized difference vegetation index (NDVI), then calculated the rate and timing of spring onset relative to bird detections. All individuals arrived in the southeastern United States well after local spring onset. Counter to our expectations, we found that songbirds exhibited a 'catching up' pattern: Individuals migrated faster than the green wave of spring onset, effectively closing in on the start of spring as they approached breeding areas. While surfing of resource waves is a well-documented migration strategy for herbivorous waterfowl and ungulates, individual songbirds in our study migrated faster than the green wave and increasingly caught up to its leading edge en route. Consequently, songbirds experience a range of vegetation phenophases while migrating through North America, suggesting flexibility in their capacity to exploit variable resources in spring.
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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".