Global adaptation confounds the search for local adaptation
Bibliographic record
Abstract
Abstract Spatially varying selection promotes variance in allele frequencies, increasing genetic differentiation between the demes of a metapopulation. For that reason, outliers in the genome wide distribution of summary statistics measuring genetic differentiation, such as F ST , are often interpreted as evidence for alleles which contribute to local adaptation. However, in spatially structured populations, the spread of beneficial mutations with spatially uniform effects can also induce transient genetic differentiation and numerous theoretical studies have suggested that species-wide, or global, adaptation makes a substantial contribution to molecular evolution. In this study, we ask whether such global adaptation affects the genome-wide distribution of F ST and generates statistical outliers which could be mistaken for local adaptation. Using forward-in-time population genetic simulations assuming parameters for the rate and strength of beneficial mutations similar to those that have been estimated for natural populations, we show the spread of globally beneficial in parapatric populations can readily generate F ST outliers, which may be misinterpreted as evidence for local adaptation. The spread of beneficial mutations causes selective sweeps at flanking sites, so the effects of global versus local adaptation may be distinguished by examining patterns of nucleotide diversity along with F ST . Our study suggests that global adaptation should be considered in the interpretation of genome-scan results and the design of future studies aimed at understanding the genetic basis of local adaptation.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".