Genetic Diversity and Population Structure of a Threatened African Tree Species,<i>Milicia excelsa,</i>Using Nuclear Microsatellites DNA Markers
Bibliographic record
Abstract
To accurately estimate the genetic diversity and population structure for improved conservation planning ofMilicia excelsatree, 212 individuals from twelve population samples covering the species' range in Benin were surveyed at seven specific microsatellite DNA loci. All loci were variable, with the mean number of alleles per locus ranging from 5.86 to 7.69. Considerable genetic variability was detected for all populations at the seven loci ( AR=4.60 ; HE=0.811 ). Moderate but statistically significant genetic differentiation was found among populations considering both FST (0.112) and RST (0.342). All of the populations showed heterozygosity deficits in test of Hardy-Weinberg Equilibrium and significantly positive FIS values due to inbreeding occurring in the species. Pairwise FST values were positively and significantly correlated with geographical distances ( r=0.432 ; P=.007 , Mantel's test) indicating that populations are differentiated by “isolation by distance.” Bayesian analysis of population structure showed division of the genetic variation into four clusters revealing the existence of heterogeneity in population genetic structure. Altogether, these results indicate that genetic variation inMilicia excelsais geographically structured. Information gained from this study also emphasized the need for in situ conservation of the relict populations and establishment of gene flow corridors through agroforestry systems for interconnecting these remnant 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 | 0.000 |
| 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) | 0.001 | 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".