Phylogenomics of piranhas and pacus (Serrasalmidae) uncovers how convergent diets obfuscate traditional morphological taxonomy
Bibliographic record
Abstract
ABSTRACT The Amazon and neighboring South American river basins harbor the world’s most diverse assemblages of freshwater fishes. One of the most prominent South American fish families are the Serrasalmidae (pacus and piranhas), found in nearly every continental basin. Serrasalmids are keystone ecological taxa, being some of the top riverine predators as well as the primary seed dispersers in the flooded forest. Despite their widespread occurrence and notable ecologies, serrasalmid evolutionary history and systematics are controversial. For example, the sister taxon to serrasalmids is contentious, the relationships of major clades within the family are obfuscated by different methodologies, and half of the extant serrasalmid genera are suggested to be non-monophyletic. We used exon capture to explore the evolutionary relationships among 64 (of 99) species across all 16 serrasalmid genera and their nearest outgroups, including multiple individuals per species in order to account for cryptic lineages. To reconstruct the timeline of serrasalmid diversification, we time-calibrated this phylogeny using two different fossil-calibration schemes to account for uncertainty in taxonomy with respect to fossil teeth. Finally, we analyzed diet evolution across the family and comment on associated changes in dentition, highlighting the ecomorphological diversity within serrasalmids. We document widespread non-monophyly within Myleinae, as well as between Serrasalmus and Pristobrycon , and propose that reliance on traits like teeth to distinguish among genera is confounded by ecological convergence, especially among herbivorous and omnivorous taxa. We clarify the relationships among all serrasalmid genera, propose new subfamily affiliations, and support hemiodontids as the sister taxon to Serrasalmidae.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".