THE EFFECTS OF ENVIRONMENTAL HETEROGENEITY ON MULTIVARIATE SELECTION ON REPRODUCTIVE TRAITS IN FEMALE GREAT TITS
Bibliographic record
Abstract
Describing natural selection on phenotypic traits under varying environmental conditions is essential for a quantitative assessment of the scale at which adaptation might occur and of the impact of environmental variability on evolution. Here we analyzed patterns of multivariate selection via fecundity and viability on three reproductive traits (laying date, clutch size, and egg weight) in a population of great tits (Parus major). We quantified selection under different environmental conditions using (1) local variation in breeding density and (2) distinct areas of the population's habitat. We found that selection gradients were generally stronger for fecundity than for viability selection. We also found correlational selection acting on the combination of laying date and clutch size; this is the first documented evidence of such selection acting on these two traits in a passerine bird. Our analyses showed that both local breeding density and habitat significantly influenced selection patterns, hence favoring different patterns of reproductive investment at a small-scale relative to typical dispersal distances in this species. Canonical rotation of the nonlinear selection matrices yielded similar conclusions as traditional nonlinear selection analyses, and also showed that the main axes of selection and fitness surfaces varied over space within the population. Our results emphasize the importance of quantifying different forms of selection, and of including variation in environmental conditions at small scales to gain a better understanding of potential evolutionary dynamics in wild populations. This study suggests that the fitness landscape for this species is relatively rugged at scales relevant to the life histories of individual birds and their close relatives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".