Amazonian rivers are leaky barriers to gene flow in forest understory birds
Bibliographic record
Abstract
Ever since Alfred Russel Wallace's nineteenth-century observation that related terrestrial species are often separated on opposing riverbanks, major Amazonian rivers have been recognized as key drivers of speciation. However, rivers are dynamic entities whose widths and courses may vary through time. It thus remains unknown how effective rivers are at reducing gene flow and promoting speciation over long timescales. We fit demographic models to genomic sequences to reconstruct the history of gene flow in three pairs of avian taxa fully separated by different Amazonian rivers, and whose geographic ranges do not make contact in headwater regions where rivers may cease to be barriers. Models with gene flow were best fit but still supported an initial period without any gene flow, which ranged from 187 000 to over 959 000 years, suggesting that rivers are capable of initiating speciation through long stretches of allopatric divergence. Allopatry was followed by either bursts or prolonged episodes of gene flow that retarded genomic differentiation but did not fully homogenize populations. Our results support Amazonian rivers as key barriers that promoted speciation and the build-up of species richness, but they also suggest that river barriers are often leaky, with genomic divergence accumulating slowly owing to episodes of substantial gene flow.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".