Stronger spatial genetic structure in recolonized areas than in refugia in the <scp>E</scp>uropean beech
Bibliographic record
Abstract
Extant rear-edge populations located in former glacial refugia remain understudied despite their high conservation value. These populations should have experienced strong genetic drift due to their small size and long isolation. Moreover, the prolonged action of isolation by distance in refugial areas should result in stronger regional spatial genetic structure (SGS) than in recolonized areas, but empirical tests of this prediction are scarce. To fill this gap, we first used a set of 16 microsatellite markers to investigate the genetic structure of European beech in France in 65 populations from three refugial areas and one control recolonized (nonrefugial) area. Then, using the same approach, we reanalysed published isozyme data from 375 populations distributed across the entire species range. We found stronger genetic differentiation among populations in refugia than in recolonized areas. However, contrary to expectations, regional SGS was lower within refugia than within recolonized areas. Published studies presenting similar analyses suggest that our results could have generality across different biogeographical settings and types of organisms. Strong and prolonged genetic drift in refugial areas could have erased the signature of range expansions that is still visible in recolonized areas. Our results therefore suggest that Pleistocene population isolation has played a key role in increasing the genetic complexity of extant rear-edge populations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".