Genetic connectivity across marginal habitats: the elephants of the Namib Desert
Bibliographic record
Abstract
Locally isolated populations in marginal habitats may be genetically distinctive and of heightened conservation concern. Elephants inhabiting the Namib Desert have been reported to show distinctive behavioral and phenotypic adaptations in that severely arid environment. The genetic distinctiveness of Namibian desert elephants relative to other African savanna elephant (Loxodonta africana) populations has not been established. To investigate the genetic structure of elephants in Namibia, we determined the mitochondrial (mt) DNA control region sequences and genotyped 17 microsatellite loci in desert elephants (n = 8) from the Hoanib River catchment and the Hoarusib River catchment. We compared these to the genotypes of elephants (n = 77) from other localities in Namibia. The mtDNA haplotype sequences and frequencies among desert elephants were similar to those of elephants in Etosha National Park, the Huab River catchment, the Ugab River catchment, and central Kunene, although the geographically distant Caprivi Strip had different mtDNA haplotypes. Likewise, analysis of the microsatellite genotypes of desert-dwelling elephants revealed that they were not genetically distinctive from Etosha elephants, and there was no evidence for isolation by distance across the Etosha region. These results, and a review of the historical record, suggest that a high learning capacity and long-distance migrations allowed Namibian elephants to regularly shift their ranges to survive in the face of high variability in climate and in hunting pressure.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".