Community‐science reveals delayed fall migration of waterfowl and spatiotemporal effects of a changing climate
Bibliographic record
Abstract
Climate change has well-documented, yet variable, influences on the annual movements of migratory birds. The effects of climate change on fall migration remains understudied compared with spring but appears to be less consistent among species, regions and years. Changes in the pattern and timing of waterfowl migration in particular may result in cascading effects on ecosystem function, and socio-economic and cultural outcomes. We investigated changes in the migration of 15 waterfowl species along a major flyway corridor of continental importance in northeastern North America using 43 years of community-science data. We built spatially- and temporally explicit hierarchical generative additive models for each species and demonstrated that climate, specifically the interaction between minimum temperature and precipitation, significantly influences migration phenology for most species. Certain species' migratory movements responded to specific temperature thresholds (climate migrants) and others reacted more to the interaction of temperature and precipitation (extreme event migrants). There are already significant changes in the fall migration phenology of common waterfowl species with high ecological and economic importance, which may simply increase in the context of a changing climate. If not addressed, climate change could induce mismatches in management, regulations and population surveys which would negatively impact the hunting industry. Our findings highlight the importance of considering species-specific spatiotemporal scales of effect on climate on migration and our methods can be widely adapted to quantify and forecast climate-driven changes in wildlife migration.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".