Effects of severe weather on reproduction for sympatric songbirds in an alpine environment: Interactions of climate extremes influence nesting success
Bibliographic record
Abstract
We examined the impact of daily and severe multiday weather events on nest survival of Horned Larks (Eremophila alpestris) and Savannah Sparrows (Passerculus sandwichensis) breeding sympatrically in alpine habitat. The two species' thermal regimes varied. The breeding season of Horned Larks was ∼2°C colder and had more precipitation and more storms than that of Savannah Sparrows, which initiated laying 2 wk later. The breeding season of Savannah Sparrows was, on average, 27% shorter than that of Horned Larks. Overall daily nest survival (DNS) was similar for the two species, but Savannah Sparrows had more failure due to abandonment (33% of nests) than Horned Larks (10%). Using Program MARK and Akaike's Information Criterion model selection to evaluate effects of daily and cumulative temperature and precipitation on DNS, we found no direct effect of daily temperature on nest survival, but nest survival declined in colder years for both species. For Horned Larks, the top nest survival models included a decline in DNS with increasing nest age and number of storm events, and a temperature × storm interaction. Daily nest mortality (DNM) increased by 8–9× over background failure levels during cold storms (average = 5°C), but there was little change in DNM during warmer storms (8°C). For Savannah Sparrows, the top nest survival models included a negative influence of cumulative precipitation. The top model's predicted DNM was ∼4.6× higher after ≥2 days of precipitation than following days without rain. Both species coped well with the range of daily temperatures and single days of precipitation typical of alpine habitats, but the earlier-breeding Horned Larks were more susceptible to storm events, whereas extended precipitation events most strongly affected Savannah Sparrow nest survival. The ability of these songbirds to persist in alpine habitats may depend partly on the proportion of “cold” and “warm” storm events in future alpine climates.
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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 | 0.000 | 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".