Impacts of environmental heterogeneity on natural selection in a wild bird population
Bibliographic record
Abstract
Natural selection has been studied for several decades, resulting in the computation of thousands of selection estimates. Although the importance of environmental conditions on selection has often been suggested, published estimates rarely take into account the effects of environmental heterogeneity on selection patterns. Here, we estimated linear and non‐linear viability selection gradients on morphological traits of 12 days‐old nestlings in a wild population of tree swallows (Tachycineta bicolor) across a large‐scale heterogeneous study system in southern Québec, Canada. We assessed the environmental drivers of nestling survival and evaluated their effects on strength and direction of selection gradients. Separate analyses of environmental variables showed that high temperatures and heavy rainfall caused stronger positive linear selection on morphological traits. Weaker linear selection was also measured in more extensively cultivated areas. Both strength and shape of non‐linear quadratic and correlational components of selection were modified by environmental variables. Considering all environmental variables revealed that precipitation since hatching affected patterns of linear selection on traits, while temperatures since hatching shaped nonlinear selection patterns. Our study underlines the importance of quantifying linear and non‐linear natural selection under various environmental conditions and how the evolutionary response of traits may be affected by ongoing human‐induced environmental changes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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; both teacher heads agree on what is shown here.
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".