Local weather and regional climate influence breeding dynamics of Mountain Bluebirds (<i>Sialia</i> <i>currucoides</i>) and Tree Swallows (<i>Tachycineta</i> <i>bicolor</i>): a 35-year study
Bibliographic record
Abstract
Many songbirds are under increasing pressure owing to habitat loss, land-use changes, and rapidly changing climatic conditions. Using citizen science data collected from 1980 to 2014, we asked how local weather and regional climate influenced the breeding dynamics of Mountain Bluebirds (Sialia currucoides (Bechstein, 1798)) and Tree Swallows (Tachycineta bicolor (Vieillot, 1808)). Mountain Bluebird reproduction was strongly associated with local weather: number of nestlings and fledglings both decreased in years of high rainfall. Clutch size and number of fledglings also declined over the study period. Abundance of Mountain Bluebirds was higher in years of lower early-season snowfall and warmer local temperatures, as well as more negative Southern Oscillation Index (SOI) values, indicating a positive influence of El Niño conditions. Tree Swallow reproduction (clutch size, number of nestlings, and number of fledglings) was negatively associated with SOI values, and the number of Tree Swallow nestlings decreased in years of higher rainfall and warmer temperatures. Tree Swallows also showed a marked decline in abundance over the period of the study, consistent with recent range-wide declines. Together, our results demonstrate that local weather and regional climate differentially affect the reproductive dynamics of Mountain Bluebirds and Tree Swallows and highlight the importance of long-term citizen science data sets.
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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".