The Effects of Weather on Avian Growth and Implications for Adaptation to Climate Change
Bibliographic record
Abstract
Climate change is forecasted to generate a range of evolutionary changes and plastic responses. One important aspect of avian responses to climate change is how weather conditions may change nestling growth and development. Early life growth is sensitive to environmental effects and can potentially have long-lasting effects on adult phenotypes and fitness. A detailed understanding of both how and when weather conditions affect the entire growth trajectory of a nestling may help predict population changes in phenotypes and demography under climate change. This review covers three main topics on the impacts of weather variation (air temperature, rainfall, wind speed, solar radiation) on nestling growth. Firstly, we highlight why understanding the effects of weather on nestling growth might be important in understanding adaptation to, and population persistence in, environments altered by climate change. Secondly, we review the documented effects of weather variation on nestling growth curves. We investigate both altricial and precocial species, but we find a limited number of studies on precocial species in the wild. Increasing temperatures and rainfall have mixed effects on nestling growth, while increasing windspeeds tend to have negative impacts on the growth rate of open cup nesting species. Thirdly, we discuss how weather variation might affect the evolution of nestling growth traits and suggest that more estimates of the inheritance of and selection acting on growth traits in natural settings are needed to make evolutionary predictions. We suggest that predictions will be improved by considering concurrently changing selection pressures like urbanization. The importance of adaptive plastic or evolutionary changes in growth may depend on where a species or population is located geographically and the species’ life-history. Detailed characterization of the effects of weather on growth patterns will help answer whether variation in avian growth frequently plays a role in adaption to climate change.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".