Two hundred and five newly assembled mitogenomes provide mixed evidence for rivers as drivers of speciation for Amazonian primates
Bibliographic record
Abstract
Mitochondrial DNA remains a cornerstone for molecular ecology, especially for study species from which high-quality tissue samples cannot be easily obtained. Methods using mitochondrial markers are usually reliant on reference databases, but these are often incomplete. Furthermore, available mitochondrial genomes often lack crucial metadata, such as sampling location, limiting their utility for many analyses. Here, we assembled 205 new mitochondrial genomes for platyrrhine primates, most from the Amazon and with known sampling locations. We present a dated mitogenomic phylogeny based on these samples along with additional published platyrrhine mitogenomes, and use this to assess support for the long-standing riverine barrier hypothesis (RBH), which proposes that river formation was a major driver of speciation in Amazonian primates. Along the Amazon, Negro, and Madeira rivers, we found mixed support for the RBH. While we identified divergences that coincide with a river barrier, only some occur synchronously and also overlap with the proposed dates of river formation. The most compelling evidence is for the Amazon river potentially driving speciation within bearded saki monkeys (Chiropotes spp.) and within the smallest extant platyrrhines, the marmosets and tamarins. However, we also found that even large rivers do not appear to be barriers for some primates, including howler monkeys (Alouatta spp.), uakaris (Cacajao spp.), sakis (Pithecia spp.), and robust capuchins (Sapajus spp.). Our results support a more nuanced, clade-specific effect of riverine barriers and suggest that other evolutionary mechanisms, besides the RBH and allopatric speciation, may have played an important role in the diversification of platyrrhines.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".