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 nonlinear viability selection gradients on morphological traits of 12-day 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 nonlinear 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 nonlinear 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 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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".