Elephants inhabiting two forested sites in western Uganda exhibit contrasting patterns of species identity, density, and history of hybridization
Bibliographic record
Abstract
Abstract Elephant populations across much of Africa face severe rates of decline due to poaching and habitat loss. The recent decision by the International Union for the Conservation of Nature (IUCN) to separately list African forest ( Loxodonta cyclotis ) and savanna ( L. africana ) elephants on the IUCN Red List both highlights the different threats of extinction faced by these two species and emphasizes the need for genetic data to classify taxonomically undefined populations across the continent. This includes western Uganda – a region that harbors the largest known modern hybrid zone between the two species. We combined a new high-throughput amplicon sequencing (HTAS) approach with fecal DNA-based Capture Mark Recapture (CMR) analysis to infer the population sizes and species compositions of elephants living in two forests. We demonstrate that Kibale National Park hosts a relatively large elephant population (573 individuals, 95% CI: 410 to 916; 0.72 elephants/km 2 ) composed primarily of hybrids (81.5%) and savanna elephants (17.7%), while Bwindi Impenetrable National Park hosts a smaller population (96 individuals, 95% CI: 64 to 145; 0.29 elephants/km 2 ) composed of forest elephants (86.8%) and hybrids (13.2%). We then sequenced maternally inherited (mtDNA) and paternally inherited (AMELY) genetic markers and found that the two parks’ populations exhibit different patterns of sex-linked genetic variation. The contrasting patterns of species identity and genetic variation between these parks demonstrate different histories of hybridization and highlight the importance of site-specific monitoring where elephants are taxonomically undefined.
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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 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".