Varied diversification patterns and distinct demographic trajectories in Ethiopian montane forest bird (Aves: Passeriformes) populations separated by the Great Rift Valley
Bibliographic record
Abstract
Taxon-specific characteristics and extrinsic climatic and geological forces may both shape population differentiation and speciation. In geographically and taxonomically focused investigations, differentiation may occur synchronously as species respond to the same external conditions. Conversely, when evolution is investigated in taxa with largely varying traits, population differentiation and speciation is complex and shaped by interactions of Earth’s template and species-specific traits. As such, it is important to characterize evolutionary histories broadly across the tree of life, especially in geographic regions that are exceptionally diverse and under pressures from human activities such as in biodiversity hotspots. Here, using whole-genome sequencing data, we characterize genomic variation in populations of six Ethiopian Highlands forest bird species separated by a lowland biogeographic barrier, the Great Rift Valley (GRV). In all six species, populations on either side of the GRV exhibited significant but varying levels of genetic differentiation. Species’ dispersal ability was negatively correlated with levels of population differentiation. Isolation with migration models indicated varied patterns of population differentiation and connectivity among populations of the focal species. We found that demographic histories—estimated for each individual—varied by both species and population but were consistent between individuals of the same species and sampling region. We found that genomic diversity varied by half an order of magnitude across species, and that this variation could largely be explained by the harmonic mean of effective population size over the past 200,000 years. Overall, we found that even in highly dispersive species like birds, the GRV acts as a substantial biogeographic barrier.
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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".