Natal experience and pre‐breeding environmental conditions affect lay date plasticity in Savannah Sparrows
Bibliographic record
Abstract
Phenotypic plasticity allows organisms to adjust the timing of life-history events in response to environmental and demographic conditions. Shifts by individuals in the timing of breeding with respect to variation in age and temperature are well documented in nature, and these changes are known to scale to affect population dynamics. However, relatively little is known about how organisms alter phenology in response to other demographic and environmental factors. We investigated how pre-breeding temperature, breeding population density, age, and rainfall in the first month of life influenced the timing and plasticity of lay date in a population of Savannah Sparrows (Passerculus sandwichensis) monitored over 33 yr (1987-2019). Females that experienced warmer pre-breeding temperatures tended to lay eggs earlier, as did older females, but breeding population density had no effect on lay date. Natal precipitation interacted with age to influence lay date plasticity, with females that experienced high precipitation levels as nestlings advancing lay dates more strongly over the course of their lives. We also found evidence for varied pace of life; females that experienced high natal precipitation had shorter lifespans and reduced fecundity, but more nesting attempts over their lifetimes. Rainfall during the nestling period increased through time, while population density and fecundity declined, suggesting that increased precipitation on the breeding grounds may be detrimental to breeding females and ultimately the viability of the population as a whole. Our results suggest that females adjust their laying date in response to pre-breeding temperature, and as they age, while presenting new evidence that environmental conditions during the natal period can affect phenological plasticity and generate downstream, population-level effects.
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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".