The role of dispersal, selection, and timing of sampling on the false discovery rate of loci under selection during geographic range expansion
Bibliographic record
Abstract
Identifying adaptive loci is important to understand the evolutionary potential of species undergoing range expansion. However, in expanding populations, spatial demographic processes such as allele surfing can create spatial patterns of neutral genetic variation that appear similar to those generated through adaptive processes. As a result, the false discovery rate of adaptive loci may be inflated in landscape genomic analyses. Here, we take a simulation modelling approach to investigate how range expansion affects our ability to correctly distinguish between neutral and adaptive genetic variation, using the mountain pine beetle outbreak system as a motivating example. We simulated the demographic and population genetic dynamics of populations undergoing range expansion using an individual-based genetic model CDMetaPOP. We investigated how the false discovery rate of adaptive loci is affected by (i) dispersal capacity, (ii) timing of sampling, and (iii) the strength of selection on an adaptive reference locus. We found that a combination of weak dispersal, weak selection, and early sampling presents the greatest risk of misidentifying loci under selection. Expanding populations present unique challenges to the reliable identification of adaptive loci. We demonstrate that there is a need for further methodological development to account for directional demographic processes in landscape genomics.
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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 | 0.086 | 0.238 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".