Phylogeography of an Amazonian Cichlid supports strong structuration by water current and past evolution by vicariance associated to the Amazon's formation.
Bibliographic record
Abstract
Amazonia is characterized by very heterogeneous riverscapes dominated by two drastically divergent water types: black (ion-poor, dissolved organic carbonate rich and acidic) and white (nutrient rich and turbid) waters. Recent phylogeographic and genomic studies have associated the ecotone formed by these environments to ecologically driven speciation in fish species. With the objective of better understanding the evolutionary forces behind the Amazonian Teleostean diversification, we sampled 240 Mesonauta festivus from 12 sites on a wide area of the Amazonian basin. These sites included three confluences of black and white water environments to seek for repeated evidences of ecological speciation at these ecotones. Our genetic dataset of 41,268 SNPs is contrasting with previous results and supports a low structuring power of water types. Conversely, we detected a strong pattern of isolation by unidirectional downstream water current and evidence of past events of vicariance potentially linked to the Amazon River formation and salt-water incursions that occurred 2.5 Mya. Using a combination of population genetic, phylogeographic analysis and environmental association models, we decomposed the spatial variance from the environmental genetic variance specifically to assess which evolutive forces have shaped inter-population differences in M. festivus’ genome. Our sampling design comprising four major Amazonian rivers and three confluences of black and white water rivers supports the possibility that past studies potentially confounded ecological speciation with a site effect unrepresentative of the full Amazonian watershed. While ecological speciation admittedly played a role in Amazonian fish species diversification, we argue that neutral evolutionary processes explain most of the divergence between M. festivus populations.
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 | 0.001 |
| 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 | 0.000 |
| 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".