Low genetic diversity and complex population structure in black piranha ( Serrasalmus rhombeus ), a key Amazonian predator
Bibliographic record
Abstract
The black piranha ( Serrasalmus rhombeus ), a widely spread species in the rivers of the Amazon basin, plays a vital role as both key predator and important prey. Despite its essential contribution to ecosystem stability, there is a lack of information regarding its genetic diversity and population dynamics in the central Amazon region. As the Amazon continues to undergo environmental changes in the context of growing anthropogenic threats, such knowledge is fundamental for assist in the conservation of this species. This study is the first to analyze the genetic diversity and population structure of S. rhombeus in the central Amazon region using high-resolution genomic data. We employed a Genotyping-by-Sequencing approach with 248 samples across 14 study sites from various tributaries, encompassing diverse water types (black, white and clear water) and characterized by 34 physiochemical parameters. The data reveals low diversity accompanied by pronounced signs of inbreeding in half of the sites and robust genetic differentiation and variation among sites and within-sites. Surprisingly, we also found evidence of higher dispersal capacity than previously recognized. Our analysis exposed a complex and high population structure with genetic groups exclusive to some sites. Gene flow was low and some groups presented ambiguous genealogical divergence index (gdi) signals, suggesting the occurrence of potential cryptic species. Moreover, our results suggest that the the population structure of black piranha appears more influenced by historical events than contemporary factors. These results underscore the need to give greater attention to this keystone species, for which no regulatory framework or conservation strategies is presently in effect.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".