Population Genetic Structure of Savannah Elephants in Kenya: Conservation and Management Implications
Bibliographic record
Abstract
We investigated population genetic structure and regional differentiation among African savannah elephants in Kenya using mitochondrial and microsatellite markers. We observed mitochondrial DNA (mtDNA) nucleotide diversity of 1.68% and microsatellite variation in terms of average number of alleles, expected and observed heterozygosities in the total study population of 10.20, 0.75, and 0.69, respectively. Hierarchical analysis of molecular variance of mtDNA variation revealed significant differentiation among the 3 geographical regions studied (F(CT) = 0.264; P < 0.05) and a relatively lower differentiation among populations within regions (F(SC) = 0.218; P < 0.0001). Microsatellite variation significantly differentiated among populations within regions (F(SC) = 0.019; P < 0.0001) but not at the regional levels (F(CT) = 0.000; P > 0.500). We attribute the high differentiation at the mitochondrial genome to the matrilineal social structure of elephant populations, female natal philopatry, and probably ancient vicariance. Lack of significant regional differentiation at the nuclear loci vis-a-vis strong differences at mtDNA loci between regions is likely the effect of subsequent homogenization through male-mediated gene flow. Our results depicting 3 broad regional mtDNA groups and the observed population genetic differentiation as well as connectivity patterns should be incorporated in the planning of future management activities such as translocations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".