Crossing the Rift valley: using complete mitogenomes to infer the diversification and biogeographic history of ethiopian highlands Ptychadena (anura: Ptychadenidae)
Bibliographic record
Abstract
The Ethiopian Highlands are considered a biodiversity hotspot, harboring a high number of endemic species. Some of the endemic species probably diversified in situ; this is, for example, the case of a monophyletic clade containing 12 known species of grass frogs of the genus Ptychadena. The different species occur at elevations ranging from 1,500 to above 3,400 m and constitute excellent models to study the process of diversification in the highlands as well as adaptations to high elevations. In this study, we sampled 294 specimens across the distribution of this clade and used complete mitogenomes and genome-wide SNP data to better understand how landscape features influenced the population structure and dispersal of these grass frogs across time and space. Using phylogenetic inference, population structure analyses, and biogeographic reconstructions, we found that the species complex probably first diversified on the south-east side of the Great Rift Valley. Later on, species dispersed to the north-west side, where more recent diversification occurred. We further demonstrate that Ptychadena species have dispersed across the Great Rift Valley at different times. Our analyses allowed for a more complete understanding of the contribution of geological events, biogeographic barriers and climatic changes as drivers of species diversification and adaptation in this important biogeographic region.
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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 | 0.001 | 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) | 0.001 | 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".