Evolution of an Amazonian Fish Is Driven by Allopatric Divergence Rather Than Ecological Divergence
Bibliographic record
Abstract
Lowland central Amazonia is characterized by heterogeneous riverscapes dominated by two chemically divergent water types: black (ion-poor, rich in dissolved organic carbonate and acidic) and white (rich in nutrient and turbid) waters. Recent phylogeographic and genomic studies have associated the ecotone formed by these environments to an ecologically driven genetic divergence between fish present in both water types. With the objective of better understanding the evolutionary forces behind the central 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 divergence at the junction of these ecotones. Results obtained through our genetic assessment based on 41,268 single nucleotide polymorphism (SNPs) contrast with previous findings and support a low influence of diverging water physicochemical characteristics on the genetic structuration of M. festivus populations. Conversely, we detected patterns of isolation by downstream water current and evidence of past events of vicariance potentially linked to the Amazon River formation. Using a combination of population genetics, phylogeographic analysis and environmental association models, we decomposed the spatial and environmental genetic variances to assess which evolutionary forces shaped inter-population differences in M. festivus ’ genome. Our sampling design, comprising three confluences of black and white water rivers, supports the main role of evolution by allopatry. While an ecologically driven evolution admittedly played a role in Amazonian fish diversification, we argue that neutral evolutionary processes explain most of the divergence between M. festivus populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".