AFLP utility for population assignment studies: analytical investigation and empirical comparison with microsatellites
Bibliographic record
Abstract
Individual-based population assignment tests have thus far mainly relied on the use of microsatellite loci. However, the logistic difficulty of screening large numbers of loci required to reach sufficient statistical power hampers the usefulness of microsatellites in situations of weak population structuring. Amplified fragment length polymorphisms (AFLP) represents an alternative for overcoming this logistical issue as the technique allows the user to characterize a much larger number of loci with a comparable analytical effort. In this study, an assignment test based on maximum likelihood for dominant markers was used to investigate the potential usefulness of AFLP for population assignment. We also compared assignment success achieved with AFLP with that obtained using microsatellites in a case study of low population differentiation involving whitefish (Coregonus clupeaformis) sympatric ecotypes. The analytical investigation showed that the minimum number of AFLP loci required to reach an assignment success of 95% stood within values that are easily achievable in many situations. This also showed how assignment success varied according to the number of AFLP loci used, their absolute frequency and their frequency differential and sampling errors, as well as the number of putative source populations. The case study showed that given a comparable analytical effort in the laboratory, AFLP were much more efficient than the microsatellite loci in discriminating the source of an individual among putative populations. AFLP resulted in higher assignment success at all levels of stringency and the log-likelihood differences between populations obtained with AFLP for each individual were much larger than those obtained with microsatellites. These results indicate that research involving individual-based population assignment methods should benefit importantly from the use of AFLP markers, especially in systems characterized by weak population structuring.
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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 | 0.040 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".