Breeding experience and population density affect the ability of a songbird to respond to future climate variation
Bibliographic record
Abstract
Predicting how populations respond to climate change requires an understanding of whether individuals or cohorts within populations vary in their response to climate variation. We used mixed-effects models on a song sparrow (Melospiza melodia) population in British Columbia, Canada, to examine differences among females and cohorts in their average breeding date and breeding date plasticity in response to the El Niño Southern Oscillation. Climatic variables, age and population density were strong predictors of timing of breeding, but we also found considerable variation among individual females and cohorts. Within cohorts, females differed markedly in their breeding date and cohorts also differed in their average breeding date and breeding date plasticity. The plasticity of a cohort appeared to be due primarily to an interaction between the environmental conditions (climate and density) experienced at different ages rather than innate inter-cohort differences. Cohorts that expressed higher plasticity in breeding date experienced warmer El Niño springs in their second or third breeding season, suggesting that prior experience affects how well individuals responded to abnormal climatic conditions. Cohorts born into lower density populations also expressed higher plasticity in breeding date. Interactions between age, experience and environmental conditions have been reported previously for long-lived taxa. Our current results indicate that similar effects operate in a short-lived, temperate songbird.
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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 | 0.001 | 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".