Modelling adaptation in two genetically-correlated traits under antagonistic selection in an intertidal snail (Littorina subrotundata) population
Bibliographic record
Abstract
Adaptive trait evolution in natural populations can be difficult to predict because knowledge of selection gradients, and trait variances and covariances are incomplete. This is evident in an experimental system on Vancouver Island. Empirical increases in shell thickness for an intertidal gastropod in response to transplanted crab predators are ~4× smaller than previous univariate model predictions. I hypothesized that a multivariate model might better represent field conditions. I tested this using an additive, individual-based model that assumed shell size and thickness had a positive genetic correlation and were under antagonistic selection. The multivariate model demonstrated that adaptive evolution became constrained with increasing genetic correlation strength and population extinction risk was sensitive to intense selection. Adaptive increases in shell thickness were ~1.5× smaller than previous univariate predictions and better aligned to published field measurements. Overall, better predictions of quantitative trait evolution were accomplished by considering correlated traits and multivariate selection gradients.
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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".