Wintering bird communities are tracking climate change faster than breeding communities
Bibliographic record
Abstract
Global climate change is driving species' distributions towards the poles and mountain tops during both non-breeding and breeding seasons, leading to changes in the composition of natural communities. However, the degree of season differences in climate-driven community shifts has not been thoroughly investigated at large spatial scales. We compared the rates of change in the community composition during both winter (non-breeding season) and summer (breeding) and their relation to temperature changes. Based on continental-scale data from Europe and North America, we examined changes in bird community composition using the community temperature index (CTI) approach and compared the changes with observed regional temperature changes during 1980-2016. CTI increased faster in winter than in summer. This seasonal discrepancy is probably because individuals are less site-faithful in winter, and can more readily shift their wintering sites in response to weather in comparison to the breeding season. Regional long-term changes in community composition were positively associated with regional temperature changes during both seasons, but the pattern was only significant during summer due to high annual variability in winter communities. Annual changes in community composition were positively associated with the annual temperature changes during both seasons. Our results were broadly consistent across continents, suggesting some climate-driven restructuring in both European and North American avian communities. Because community composition has changed much faster during the winter than during the breeding season, it is important to increase our knowledge about climate-driven impacts during the less-studied non-breeding season.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".