Continent‐Wide Patterns of Climate and Mast Seeding Entrain Boreal Bird Irruptions
Bibliographic record
Abstract
Avian irruptions are facultative, often periodic, migrations of thousands of birds outside of their resident range. Irruptive movements produce regional anomalies of abundance that oscillate over time, forming ecological dipoles (geographically disjunct regions of low and high abundance) at continental scales. Potential drivers of irruptions include climate and food variability, but these relationships are rarely tested over broad geographic scales. We used community science data on winter bird abundance (1989-2021) to identify spatiotemporal patterns of irruption for nine boreal birds across the United States and Canada and compared them to time series of winter climate and annual tree seed production. We hypothesized that, during irruption, bird abundance would decrease in regions experiencing colder winter climates (climate variability hypothesis) or low seed production resulting from the boom-and-bust of widespread mast-seeding patterns (resource variability hypothesis). Across all species, we detected latitudinal or longitudinal irruption modes, or both, demonstrating north-south and east-west migration dynamics across the northern United States and southern Canada. Seven of nine species displayed associations consistent with the climate variability hypothesis and six with the resource variability hypothesis. While irruption dynamics are likely entrained by multiple environmental drivers, future climate change could alter the spatial and temporal characteristics of avian irruption.
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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 | 0.000 |
| 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".