Small‐scale spatial genetic structure in the Central African rainforest tree species <i>Aucoumea klaineana</i>: a stepwise approach to infer the impact of limited gene dispersal, population history and habitat fragmentation
Bibliographic record
Abstract
Under the isolation-by-distance model, the strength of spatial genetic structure (SGS) depends on seed and pollen dispersal and genetic drift, which in turn depends on local demographic structure. SGS can also be influenced by historical events such as admixture of differentiated gene pools. We analysed the fine-scale SGS in six populations of a pioneer tree species endemic to Central Africa, Aucoumea klaineana. To infer the impacts of limited gene dispersal, population history and habitat fragmentation on isolation by distance, we followed a stepwise approach consisting of a Bayesian clustering method to detect differentiated gene pools followed by the analysis of kinship-distance curves. Interestingly, despite considerable variation in density, the five populations situated under continuous forest cover displayed very similar extent of SGS. This is likely due to an increase in dispersal distance with decreased tree density. Admixture between two gene pools was detected in one of these five populations creating a distinctive pattern of SGS. In the last population sampled in open habitat, the genetic diversity was in the same range as in the other populations despite a recent habitat fragmentation. This result may due to the increase of gene dispersal compensating the effect of the disturbance as suggested by the reduced extent of SGS estimated in this population. Thus, in A. klaineana, the balance between drift and dispersal may facilitate the maintenance of genetic diversity. Finally, from the strength of the SGS and population density, an indirect estimate of gene dispersal distances was obtained for one site: the quadratic mean parent-offspring distance, sigma(g), ranged between 210 m and 570 m.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".